From 4e72f54c239c84fdcbaed8ee8c637acc54d1ce6d Mon Sep 17 00:00:00 2001 From: Laura Marshall Date: Mon, 27 Nov 2023 10:37:14 +0000 Subject: [PATCH 01/11] Remove variance vignette Closes #94 --- DESCRIPTION | 2 +- vignettes/mrds-var.bib | 9868 ---------------------------------------- vignettes/mrds.Rmd | 126 - 3 files changed, 1 insertion(+), 9995 deletions(-) delete mode 100644 vignettes/mrds-var.bib delete mode 100644 vignettes/mrds.Rmd diff --git a/DESCRIPTION b/DESCRIPTION index 4ba6e5f..9f4ea51 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,7 @@ Description: Animal abundance estimation via conventional, multiple covariate fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator. -Version: 2.2.9 +Version: 2.2.9.9000 URL: https://github.com/DistanceDevelopment/mrds/ BugReports: https://github.com/DistanceDevelopment/mrds/issues Depends: diff --git a/vignettes/mrds-var.bib b/vignettes/mrds-var.bib deleted file mode 100644 index d335639..0000000 --- a/vignettes/mrds-var.bib +++ /dev/null @@ -1,9868 +0,0 @@ - -@article{aarts_comparative_2012, - title = {Comparative Interpretation of Count, Presence-Absence and Point Methods for Species Distribution Models: {{Species}} Distribution as Spatial Point Process}, - shorttitle = {Comparative Interpretation of Count, Presence-Absence and Point Methods for Species Distribution Models}, - author = {Aarts, Geert and Fieberg, John and Matthiopoulos, Jason}, - year = {2012}, - month = feb, - journal = {Methods in Ecology and Evolution}, - volume = {3}, - number = {1}, - pages = {177--187}, - issn = {2041210X}, - doi = {10.1111/j.2041-210X.2011.00141.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/WQAI8KY4/Aarts et al. - 2012 - Comparative interpretation of count, presence-abse.pdf} -} - -@article{aitkin_mixture_1980, - title = {Mixture {{Models}}, {{Outliers}}, and the {{EM Algorithm}}}, - author = {Aitkin, Murray and Wilson, Granville Tunnicliffe}, - year = {1980}, - month = aug, - journal = {Technometrics}, - volume = {22}, - number = {3}, - pages = {325}, - issn = {00401706}, - doi = {10.2307/1268316}, - file = {/Users/dill/Zotero/storage/U8N9IL7Z/Technometrics 1980 Aitkin.pdf} -} - -@techreport{amante_etopo1_2009, - title = {{{ETOPO1}} 1 Arc-Minute Global Relief Model: {{Procedures}}, Data Sources and Analysis}, - author = {Amante, C and Eakins, Barry E}, - year = {2009}, - number = {NESDIS NGDC-24}, - pages = {25}, - address = {{Boulder, CO: National Geophysical Data Center}}, - langid = {english}, - file = {/Users/dill/Zotero/storage/GBT3EKJ7/Eakins - Mention of a commercial company or product does no.pdf} -} - -@article{armstrong_method_1936, - title = {A Method of Reducing Disturbances in Radio Signaling by a System of Frequency Modulation}, - author = {Armstrong, Edwin H.}, - year = {1936}, - journal = {proceedings of the Institute of Radio Engineers}, - volume = {24}, - number = {5}, - pages = {689--740}, - file = {/Users/dill/Zotero/storage/8QXWPYMJ/Radio Engineers Proceedings of the Institute of 1936 Armstrong.pdf} -} - -@article{augiron_winter_2015, - title = {Winter Spatial Distribution of Threatened Acridivorous Avian Predators: {{Implications}} for Their Conservation in a Changing Landscape}, - shorttitle = {Winter Spatial Distribution of Threatened Acridivorous Avian Predators}, - author = {Augiron, Steve and Gangloff, Benoit and Brodier, Salomon and Chevreux, Fabrice and Blanc, Jean-Fran{\c c}ois and Pilard, Philippe and Coly, Adrien and Sonko, Abba and Schlaich, Almut and Bretagnolle, Vincent and Villers, Alexandre}, - year = {2015}, - month = feb, - journal = {Journal of Arid Environments}, - volume = {113}, - pages = {145--153}, - issn = {01401963}, - doi = {10.1016/j.jaridenv.2014.10.001}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QNQT9Z3M/Journal of Arid Environments 2015 Augiron.pdf} -} - -@article{augustin_modeling_2009, - title = {Modeling {{Spatiotemporal Forest Health Monitoring Data}}}, - author = {Augustin, Nicole H. and Musio, Monica and {von Wilpert}, Klaus and Kublin, Edgar and Wood, Simon N. and Schumacher, Martin}, - year = {2009}, - month = sep, - journal = {Journal of the American Statistical Association}, - volume = {104}, - number = {487}, - pages = {899--911}, - issn = {0162-1459, 1537-274X}, - doi = {10.1198/jasa.2009.ap07058}, - langid = {english}, - file = {/Users/dill/Zotero/storage/5MYRX8XE/Journal of the American Statistical Association 2009 Augustin.pdf} -} - -@article{augustin_quantile_2012, - title = {On Quantile Quantile Plots for Generalized Linear Models}, - author = {Augustin, Nicole H. and Sauleau, Erik-Andr{\'e} and Wood, Simon N.}, - year = {2012}, - month = aug, - journal = {Computational Statistics \& Data Analysis}, - volume = {56}, - number = {8}, - pages = {2404--2409}, - issn = {01679473}, - doi = {10.1016/j.csda.2012.01.026}, - langid = {english}, - file = {/Users/dill/Zotero/storage/72HJW3AA/Computational Statistics and Data Analysis 2012 Augustin.pdf} -} - -@article{augustin_space-time_2013, - title = {Space-Time Modelling of Blue Ling for Fisheries Stock Management: Space-Time Modelling of Blue Ling}, - shorttitle = {Space-Time Modelling of Blue Ling for Fisheries Stock Management}, - author = {Augustin, Nicole H. and Trenkel, Verena M. and Wood, Simon N. and Lorance, Pascal}, - year = {2013}, - month = mar, - journal = {Environmetrics}, - volume = {24}, - number = {2}, - pages = {109--119}, - issn = {11804009}, - doi = {10.1002/env.2196}, - langid = {english}, - file = {/Users/dill/Zotero/storage/BY45P7MF/Environmetrics 2013 Augustin.pdf} -} - -@article{augustin_spatiotemporal_1998, - title = {Spatiotemporal Modelling for the Annual Egg Production Method of Stock Assessment Using Generalized Additive Models}, - author = {Augustin, N. H. and Borchers, D. L. and Clarke, E. D. and Buckland, S. T. and Walsh, M.}, - year = {1998}, - journal = {Canadian Journal of Fisheries and Aquatic Sciences}, - volume = {55}, - number = {12}, - pages = {2608--2621}, - file = {/Users/dill/Zotero/storage/S5KPSLW6/Can. J. Fish. Aquat. Sci. 1998 Augustin.pdf} -} - -@inproceedings{avidan_seam_2007, - title = {Seam Carving for Content-Aware Image Resizing}, - booktitle = {{{ACM Transactions}} on Graphics ({{TOG}})}, - author = {Avidan, Shai and Shamir, Ariel}, - year = {2007}, - volume = {26}, - pages = {10}, - publisher = {{ACM}}, - file = {/Users/dill/Zotero/storage/BW2VQEBP/ACM SIGGRAPH 2007 papers on - SIGGRAPH '07 2007 Avidan.pdf} -} - -@article{awasthi_effect_2016, - title = {Effect of Human Use, Season and Habitat on Ungulate Density in {{Kanha Tiger Reserve}}, {{Madhya Pradesh}}, {{India}}}, - author = {Awasthi, Neha and Kumar, Ujjwal and Qureshi, Q. and Pradhan, Anup and Chauhan, J. S. and Jhala, Y. V.}, - year = {2016}, - month = aug, - journal = {Regional Environmental Change}, - volume = {16}, - number = {S1}, - pages = {31--41}, - issn = {1436-3798, 1436-378X}, - doi = {10.1007/s10113-016-0953-z}, - langid = {english}, - file = {/Users/dill/Zotero/storage/RAX2K5AL/Regional Environmental Change 2016 Awasthi.pdf} -} - -@article{baayen_autocorrelated_2018, - title = {Autocorrelated Errors in Experimental Data in the Language Sciences: {{Some}} Solutions Offered by {{Generalized Additive Mixed Models}}}, - shorttitle = {Autocorrelated Errors in Experimental Data in the Language Sciences}, - author = {Baayen, R. Harald and {van Rij}, Jacolien and {de Cat}, Cecile and Wood, Simon}, - year = {2018}, - journal = {Mixed-Effects Regression Models in Linguistics}, - pages = {49--69}, - file = {/Users/dill/Zotero/storage/49M72HZE/arXiv 2016 Baayen.pdf} -} - -@article{bachl_inlabru_2019, - title = {Inlabru: An {{R}} Package for {{Bayesian}} Spatial Modelling from Ecological Survey Data}, - shorttitle = {Inlabru}, - author = {Bachl, Fabian E. and Lindgren, Finn and Borchers, David L. and Illian, Janine B.}, - editor = {Freckleton, Robert}, - year = {2019}, - month = mar, - journal = {Methods in Ecology and Evolution}, - issn = {2041-210X, 2041-210X}, - doi = {10.1111/2041-210X.13168}, - langid = {english}, - file = {/Users/dill/Zotero/storage/637PEC8P/Bachl et al. - 2019 - inlabru an R package for Bayesian spatial modelli.pdf} -} - -@article{baddeley_practical_2000, - title = {Practical {{Maximum Pseudolikelihood}} for {{Spatial Point Patterns}}}, - author = {Baddeley, Adrian and Turner, Rolf}, - year = {2000}, - journal = {Australian \& New Zealand Journal of Statistics}, - volume = {42}, - number = {3}, - pages = {283--322}, - abstract = {This paper describes a technique for computing approximate maximum pseudolikelihood estimates of the parameters of a spatial point process. The method is an extension of Berman \& Turner's (1992) device for maximizing the likelihoods of inhomogeneous spatial Poisson processes. For a very wide class of spatial point process models the likelihood is intractable, while the pseudolikelihood is known explicitly, except for the computation of an integral over the sampling region. Approximation of this integral by a finite sum in a special way yields an approximate pseudolikelihood which is formally equivalent to the (weighted) likelihood of a loglinear model with Poisson responses. This can be maximized using standard statistical software for generalized linear or additive models, provided the conditional intensity of the process takes an `exponential family' form. Using this approach a wide variety of spatial point process models of Gibbs type can be fitted rapidly, incorporating spatial trends, interaction between points, dependence on spatial covariates, and mark information.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/AHW9H755/BADDELEY and TURNER - 2000 - PRACTICAL MAXIMUM PSEUDOLIKELIHOOD FOR SPATIAL POI.pdf} -} - -@article{bahn_can_2007, - title = {Can Niche-Based Distribution Models Outperform Spatial Interpolation?}, - author = {Bahn, Volker and McGill, Brian J.}, - year = {2007}, - month = nov, - journal = {Global Ecology and Biogeography}, - volume = {16}, - number = {6}, - pages = {733--742}, - issn = {1466-822X, 1466-8238}, - doi = {10.1111/j.1466-8238.2007.00331.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/TFZMHXKC/Global Ecol Biogeography 2007 Bahn.pdf} -} - -@article{bahn_testing_2013, - title = {Testing the Predictive Performance of Distribution Models}, - author = {Bahn, Volker and McGill, Brian J.}, - year = {2013}, - month = mar, - journal = {Oikos}, - volume = {122}, - number = {3}, - pages = {321--331}, - issn = {00301299}, - doi = {10.1111/j.1600-0706.2012.00299.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QPYW2ZKI/Oikos 2012 Bahn.pdf} -} - -@article{bailey_nonlinear_2013, - title = {A {{Nonlinear Model}} for {{Predicting Interannual Changes}} in {{Calanus}} Finmarchicus {{Abundance}} in the {{Gulf}} of {{Maine}}}, - author = {Bailey, Barbara A. and Pershing, Andrew J.}, - year = {2013}, - month = jun, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {18}, - number = {2}, - pages = {234--249}, - issn = {1085-7117, 1537-2693}, - doi = {10.1007/s13253-013-0133-2}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ZDSL47BN/JABES 2013 Bailey.pdf} -} - -@article{bakka_how_2018, - title = {How to Solve the Stochastic Partial Differential Equation That Gives a {{Mat}}\textbackslash 'ern Random Field Using the Finite Element Method}, - author = {Bakka, Haakon}, - year = {2018}, - month = mar, - journal = {arXiv:1803.03765 [stat]}, - eprint = {1803.03765}, - eprinttype = {arxiv}, - primaryclass = {stat}, - abstract = {This tutorial teaches parts of the finite element method (FEM), and solves a stochastic partial differential equation (SPDE). The contents herein are considered ``known'' in the numerics literature, but for statisticians it is very difficult to find a resource for learning these ideas in a timely manner (without doing a year's worth of courses in numerics).}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Statistics - Computation}, - file = {/Users/dill/Zotero/storage/W8DQJMFU/Bakka - 2018 - How to solve the stochastic partial differential e.pdf} -} - -@article{bakka_non-stationary_2016, - title = {Non-Stationary {{Gaussian}} Models with Physical Barriers}, - author = {Bakka, Haakon and Vanhatalo, Jarno and Illian, Janine and Simpson, Daniel and Rue, H{\aa}vard}, - year = {2016}, - month = aug, - journal = {arXiv:1608.03787 [stat]}, - eprint = {1608.03787}, - eprinttype = {arxiv}, - primaryclass = {stat}, - abstract = {The classical tools in spatial statistics are stationary models, like the Mat\textasciiacute ern field. However, in some applications there are boundaries, holes, or physical barriers in the study area, e.g. a coastline, and stationary models will inappropriately smooth over these features, requiring the use of a non-stationary model.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Statistics - Applications,Statistics - Methodology}, - file = {/Users/dill/Zotero/storage/VYKMP9ZR/Bakka et al. - 2016 - Non-stationary Gaussian models with physical barri.pdf} -} - -@article{bakka_spatial_2018, - title = {Spatial Modelling with {{R-INLA}}: {{A}} Review}, - shorttitle = {Spatial Modelling with {{R-INLA}}}, - author = {Bakka, Haakon and Rue, H{\aa}vard and Fuglstad, Geir-Arne and Riebler, Andrea and Bolin, David and Krainski, Elias and Simpson, Daniel and Lindgren, Finn}, - year = {2018}, - month = feb, - journal = {arXiv:1802.06350 [stat]}, - eprint = {1802.06350}, - eprinttype = {arxiv}, - primaryclass = {stat}, - abstract = {Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realisticallysized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R-INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Statistics - Computation,Statistics - Methodology}, - file = {/Users/dill/Zotero/storage/UBY4N44P/Bakka et al. - 2018 - Spatial modelling with R-INLA A review.pdf} -} - -@article{bakka_spatial_2018-1, - title = {Spatial Modeling with {{R-INLA}}: {{A}} Review}, - shorttitle = {Spatial Modeling with {{R-INLA}}}, - author = {Bakka, Haakon and Rue, H{\aa}vard and Fuglstad, Geir-Arne and Riebler, Andrea and Bolin, David and Illian, Janine and Krainski, Elias and Simpson, Daniel and Lindgren, Finn}, - year = {2018}, - month = jul, - journal = {Wiley Interdisciplinary Reviews: Computational Statistics}, - pages = {e1443}, - issn = {19395108}, - doi = {10.1002/wics.1443}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ERTF5HUQ/Bakka et al. - 2018 - Spatial modeling with R-INLA A review.pdf} -} - -@article{ballard_coexistence_2012, - title = {Coexistence of Mesopredators in an Intact Polar Ocean Ecosystem: {{The}} Basis for Defining a {{Ross Sea}} Marine Protected Area}, - shorttitle = {Coexistence of Mesopredators in an Intact Polar Ocean Ecosystem}, - author = {Ballard, Grant and Jongsomjit, Dennis and Veloz, Samuel D. and Ainley, David G.}, - year = {2012}, - month = nov, - journal = {Biological Conservation}, - volume = {156}, - pages = {72--82}, - issn = {00063207}, - doi = {10.1016/j.biocon.2011.11.017}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Y7XCUN9I/Biological Conservation 2012 Ballard.pdf} -} - -@book{banerjee2003hierarchical, - title = {Hierarchical Modeling and Analysis for Spatial Data}, - author = {Banerjee, S. and Carlin, B.P. and Gelfand, A.E.}, - year = {2003}, - series = {Chapman \& {{Hall}}/{{CRC}} Monographs on Statistics \& Applied Probability}, - publisher = {{Taylor \& Francis}}, - isbn = {978-1-58488-410-1}, - lccn = {2003062652}, - file = {/Users/dill/Zotero/storage/Z87D3JN3/[Chapman & Hall_CRC Monographs on Statistics & Applied Probability] Banerjee, Sudipto_ Carlin, Bradley P._ Gelfand, Alan E - Hierarchical Modeling and Analysis for Spatial Data, Second Edition (2015, CRC Press).pdf} -} - -@inproceedings{barbulescu_fitting_2012, - title = {Fitting Precipitation Variability in {{Dobrudja}} Region}, - booktitle = {Proceeding {{ACACOS}}'12 {{Proceedings}} of the 11th {{WSEAS}} International Conference on {{Applied Computer}} and {{Applied Computational Science}}, {{World Scientific}} and {{Engineering Academy}} and {{Society}} ({{WSEAS}}) {{Stevens Point}}, {{Wisconsin}}, {{USA}}}, - author = {B{\u a}rbulescu, Alina and Deguenon, Judicael}, - year = {2012}, - pages = {30--34}, - file = {/Users/dill/Zotero/storage/VZGIGHK3/Journal of the Royal Statistical Society Series B ( … 1985 Silverman.pdf} -} - -@article{barker_reliability_2017, - title = {On the Reliability of {{N-mixture}} Models for Count Data: {{On}} the {{Reliability}} of {{N-Mixture Models}}}, - shorttitle = {On the Reliability of {{N-mixture}} Models for Count Data}, - author = {Barker, Richard J. and Schofield, Matthew R. and Link, William A. and Sauer, John R.}, - year = {2017}, - month = jul, - journal = {Biometrics}, - issn = {0006341X}, - doi = {10.1111/biom.12734}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9D3TZ5RW/biom12734.pdf} -} - -@article{barlow_abundance_1995, - title = {The Abundance of Cetaceans in {{California}} Waters. {{Part I}}: {{Ship}} Surveys in Summer and Fall of 1991}, - author = {Barlow, Jay}, - year = {1995}, - journal = {Fisheries Bulletin}, - number = {93}, - pages = {1--14}, - langid = {english}, - file = {/Users/dill/Zotero/storage/R2R9R3XP/Barlow - The abundance of cetaceans in California waters..pdf} -} - -@article{barlow_abundance_2007, - title = {Abundance and Population Density of Cetaceans in the {{California Current}} Ecosystem}, - author = {Barlow, Jay and Forney, Karin A.}, - year = {2007}, - journal = {Fishery Bulletin}, - number = {105}, - pages = {509--526}, - file = {/Users/dill/Zotero/storage/R5IFMQS3/Barlow&Forney2007-FishBull.pdf} -} - -@techreport{barlow_effective_2011, - title = {Effective Strip Widths for Ship-Based Line-Transect Surveys of Cetaceans}, - author = {Barlow, Jay and Ballance, L. T. and Forney, Karin}, - year = {2011}, - number = {NOAA-TM-NMFS-SWFSC-484}, - abstract = {Effective strip width is a key parameter in estimating abundance and density from line- transect surveys. Here we estimate effective strip widths for 58 categories (genera, species, subspecies, stocks, or other groups) of cetaceans based on 13,500 sightings from 32 line-transect surveys conducted in the eastern Pacific Ocean by the Southwest Fisheries Science Center from 1991 to 2008. Generalized linear models (GLMs) are used to first identify factors that are important in determining the perpendicular sighting distances using stepwise model selection based on AIC. Six species groups of similar taxa are created and modeled separately. Important factors for most species groups include Beaufort sea state, swell height, visibility, group size, species, and a survey-specific categorical variable (Cruise\#). Interactions between species and the other factors generally do not improve GLM fits, indicating that the effects of those factors are relatively consistent for species within a species group. Factors selected for the best-fit GLMs are included as potential covariates in a line-transect model fit to a subset of the same data, again using stepwise model selection based on AIC. The best-fit line-transect models do not include Cruise\# and are generally simpler than the GLMs, likely because distant sightings were eliminated by truncation. Species-specific differences in ESW are seen within the species groups indicating that species of cetacean do differ in the distances at which they can be detected, even after accounting for the effects of group size and other covariates that affect sighting distances. Results from this analysis of multiple surveys can be used to improve estimates of effective strip widths for any survey using the same methods and similar vessels. This is especially true for seldom-seen species whose abundance is difficult to estimate from a single survey.}, - file = {/Users/dill/Zotero/storage/XT6QBNSL/Barlow et al 2011 EffectiveStripWidths TM-484.pdf} -} - -@article{barlow_inferring_2015, - title = {Inferring Trackline Detection Probabilities, g(0), for Cetaceans from Apparent Densities in Different Survey Conditions}, - author = {Barlow, Jay}, - year = {2015}, - month = jul, - journal = {Marine Mammal Science}, - volume = {31}, - number = {3}, - pages = {923--943}, - issn = {08240469}, - doi = {10.1111/mms.12205}, - abstract = {Visual line-transect surveys are commonly used to estimate cetacean abundance. A key parameter in such studies is g(0), the probability of detecting an animal that is directly on the transect line. This is typically considered to be constant for a species across survey conditions. A method is developed to estimate the relative values of g(0) in different survey conditions (Beaufort state) by comparing Beaufort-specific density estimates. The approach is based on fitting generalized additive models, with the presence of a sighting on a survey segment as the dependent variable, Beaufort state as the key explanatory variable, and year, latitude, and longitude as nuisance variables to control for real differences in density over time and space. Values of relative g(0) are estimated for 20 cetacean taxa using 175,000 km of line-transect survey data from the eastern and central Pacific Ocean from 1986 to 2010. Results show that g(0) decreases as Beaufort state increases, even for visually conspicuous species. This effect is greatest for the least conspicuous species (rough-toothed dolphins, beaked whales, minke whales, and dwarf and pygmy sperm whales). Ignoring these large effects results in a nontrivial bias in cetacean abundance estimates.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/BNQ6AGG8/Barlow - 2015 - Inferring trackline detection probabilities, g(0),.pdf} -} - -@techreport{barlow_predictive_2009, - title = {Predictive Modeling of Cetacean Densities in the Eastern \{\vphantom\}{{P}}\vphantom\{\}acific Ocean}, - author = {Barlow, Jay and Ferguson, Megan C. and Becker, Elizabeth and Redfern, Jessica and Vilchis, Ignacio L. and Fiedler, Paul and Gerrodette, Tim and Ballance, L. T.}, - year = {2009}, - number = {NOAA-TM-NMFS-SWFSC-444}, - pages = {229}, - langid = {english}, - file = {/Users/dill/Zotero/storage/TTL5FJGX/Barlow - Final Technical Report.pdf} -} - -@article{barry_distance_2001, - title = {Distance Sampling Methodology}, - author = {Barry, Simon C. and Welsh, A. H.}, - year = {2001}, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {63}, - number = {1}, - pages = {23--31}, - file = {/Users/dill/Zotero/storage/DNPTUVIH/Journal of the Royal Statistical Society Series B (Statistical Methodology) 2001 Barry.pdf} -} - -@article{barry_generalized_2002, - title = {Generalized Additive Modelling and Zero Inflated Count Data}, - author = {Barry, Simon C. and Welsh, Alan H.}, - year = {2002}, - journal = {Ecological Modelling}, - volume = {157}, - number = {2-3}, - pages = {179--188}, - file = {/Users/dill/Zotero/storage/FVM3XY9X/Ecological Modelling 2002 Barry.pdf} -} - -@article{barwell_can_2014, - title = {Can Coarse-Grain Patterns in Insect Atlas Data Predict Local Occupancy?}, - author = {Barwell, Louise J. and Azaele, Sandro and Kunin, William E. and Isaac, Nick J. B.}, - editor = {Richardson, David}, - year = {2014}, - month = aug, - journal = {Diversity and Distributions}, - volume = {20}, - number = {8}, - pages = {895--907}, - issn = {13669516}, - doi = {10.1111/ddi.12203}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QZJA6NDG/Diversity Distrib. 2014 Barwell.pdf} -} - -@article{bates_fitting_2015, - title = {Fitting {{Linear Mixed-Effects Models Using}} {\textbf{Lme4}}}, - author = {Bates, Douglas and M{\"a}chler, Martin and Bolker, Ben and Walker, Steve}, - year = {2015}, - journal = {Journal of Statistical Software}, - volume = {67}, - number = {1}, - issn = {1548-7660}, - doi = {10.18637/jss.v067.i01}, - langid = {english}, - file = {/Users/dill/Zotero/storage/KIX7B676/v67i01.pdf} -} - -@article{bates_generalized_nodate, - title = {Generalized Linear Models}, - author = {Bates, Douglas}, - pages = {9}, - langid = {english}, - file = {/Users/dill/Zotero/storage/BL35UC74/Bates - Generalized linear models.pdf} -} - -@article{beale_incorporating_2012, - title = {Incorporating Uncertainty in Predictive Species Distribution Modelling}, - author = {Beale, C. M. and Lennon, J. J.}, - year = {2012}, - month = jan, - journal = {Philosophical Transactions of the Royal Society B: Biological Sciences}, - volume = {367}, - number = {1586}, - pages = {247--258}, - issn = {0962-8436, 1471-2970}, - doi = {10.1098/rstb.2011.0178}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Q92KK8UA/Philosophical Transactions of the Royal Society B Biological Sciences 2011 Beale.pdf} -} - -@article{beale_new_2014, - title = {A New Statistical Framework for the Quantification of Covariate Associations with Species Distributions}, - author = {Beale, Colin M. and Brewer, Mark J. and Lennon, Jack J.}, - editor = {Kriticos, Darren}, - year = {2014}, - month = may, - journal = {Methods in Ecology and Evolution}, - volume = {5}, - number = {5}, - pages = {421--432}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12174}, - langid = {english}, - file = {/Users/dill/Zotero/storage/TM2WRTRT/Methods in Ecology and Evolution 2014 Beale.pdf} -} - -@article{beavers_detectability_1998, - title = {Detectability {{Analysis}} in {{Transect Surveys}}}, - author = {Beavers, Sallie C. and Ramsey, Fred L.}, - year = {1998}, - month = jul, - journal = {The Journal of Wildlife Management}, - volume = {62}, - number = {3}, - pages = {948}, - issn = {0022541X}, - doi = {10.2307/3802547}, - file = {/Users/dill/Zotero/storage/3HVGAF2W/The Journal of Wildlife Management 1998 Beavers.pdf} -} - -@article{becker_comparing_2010, - title = {Comparing {{California Current}} Cetacean\textendash Habitat Models Developed Using in Situ and Remotely Sensed Sea Surface Temperature Data}, - author = {Becker, Ea and Forney, Ka and Ferguson, Mc and Foley, Dg and Smith, Rc and Barlow, J and Redfern, Jv}, - year = {2010}, - month = aug, - journal = {Marine Ecology Progress Series}, - volume = {413}, - pages = {163--183}, - issn = {0171-8630, 1616-1599}, - doi = {10.3354/meps08696}, - langid = {english}, - file = {/Users/dill/Zotero/storage/RM27EHSB/Mar. Ecol. Prog. Ser. 2010 Becker.pdf} -} - -@article{becker_dynamic_2022, - title = {Dynamic {{Habitat Models Reflect Interannual Movement}} of {{Cetaceans Within}} the {{California Current Ecosystem}}}, - author = {Becker, Elizabeth A. and Forney, Karin A. and Miller, David L. and Barlow, Jay and {Rojas-Bracho}, Lorenzo and Urb{\'a}n R, Jorge and Moore, Jeff E.}, - year = {2022}, - month = may, - journal = {Frontiers in Marine Science}, - volume = {9}, - pages = {829523}, - issn = {2296-7745}, - doi = {10.3389/fmars.2022.829523}, - abstract = {The distribution of wide-ranging cetacean species often cross national or jurisdictional boundaries, which creates challenges for monitoring populations and managing anthropogenic impacts, especially if data are only available for a portion of the species' range. Many species found off the U.S. West Coast are known to have continuous distributions into Mexican waters, with highly variable abundance within the U.S. portion of their range. This has contributed to annual variability in design-based abundance estimates from systematic shipboard surveys off the U.S. West Coast, particularly for the abundance of warm temperate species such as striped dolphin, Stenella coeruleoalba , which increases off California during warm-water conditions and decreases during cool-water conditions. Species distribution models (SDMs) can accurately describe shifts in cetacean distribution caused by changing environmental conditions, and are increasingly used for marine species management. However, until recently, data from waters off the Baja California peninsula, M\'exico, have not been available for modeling species ranges that span from Baja California to the U.S. West Coast. In this study, we combined data from 1992\textendash 2018 shipboard surveys to develop SDMs off the Pacific Coast of Baja California for ten taxonomically diverse cetaceans. We used a Generalized Additive Modeling framework to develop SDMs based on line-transect surveys and dynamic habitat variables from the Hybrid Coordinate Ocean Model (HYCOM). Models were developed for ten species: long- and short-beaked common dolphins ( Delphinus delphis delphis and D. d. bairdii ), Risso's dolphin ( Grampus griseus ), Pacific white-sided dolphin ( Lagenorhynchus obliquidens ), striped dolphin, common bottlenose dolphin ( Tursiops truncatus ), sperm whale ( Physeter macrocephalus ), blue whale ( Balaenoptera musculus ), fin whale ( B. physalus ), and humpback whale ( Megaptera novaeangliae ). The SDMs provide the first fine-scale (approximately 9 x 9~km grid) estimates of average species density and abundance, including spatially-explicit measures of uncertainty, for waters off the Baja California peninsula. Results provide novel insights into cetacean ecology in this region as well as quantitative spatial data for the assessment and mitigation of anthropogenic impacts.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/P2WE78DT/Becker et al. - 2022 - Dynamic Habitat Models Reflect Interannual Movemen.pdf} -} - -@article{becker_estimating_2021, - title = {Estimating Brown Bear Abundance and Harvest Rate on the Southern {{Alaska Peninsula}}}, - author = {Becker, Earl F. and Crowley, David W.}, - editor = {Mousseau, Tim A.}, - year = {2021}, - month = jan, - journal = {PLOS ONE}, - volume = {16}, - number = {1}, - pages = {e0245367}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0245367}, - abstract = {Abundance estimation of hunted brown bear populations should occur on the same geographic scale as harvest data analyses for estimation of harvest rate. Estimated harvest rates are an important statistic for managing hunted bear populations. In Alaska, harvest data is collected over large geographic units, called Game Management Units (GMUs) and sub-GMUs. These sub GMUs often exceed 10,000 km2. In the spring of 2002, we conducted an aerial survey of GMU 9D (12,600 km2) and GMU 10 (4,070 km2) using distance sampling with mark-resight data. We used a mark-resight distance sampling method with a two-piece normal detection function to estimate brown bear abundance as 1,682.9 (SE = 174.29) and 316.9 (SE = 48.25) for GMU 9D and GMU 10, respectively. We used reported hunter harvest to estimate harvest rates of 4.35\% (SE = 0.45\%) and 3.06\% (SE = 0.47\%) for GMU 9D and GMU 10, respectively. Management objective for these units support sustained, high quality hunting opportunity which harvest data indicate are met with an annual harvest rate of approximately 5\textendash 6\% or less.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/IMT5P76A/Becker and Crowley - 2021 - Estimating brown bear abundance and harvest rate o.pdf} -} - -@article{becker_gamma-shaped_2009, - title = {A Gamma-Shaped Detection Function for Line-Transect Surveys with Mark-Recapture and Covariate Data}, - author = {Becker, E. F. and Quang, P. X.}, - year = {2009}, - month = jun, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {14}, - number = {2}, - pages = {207--223}, - issn = {1085-7117, 1537-2693}, - doi = {10.1198/jabes.2009.0013}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QH2R4MD6/JABES 2009 Becker.pdf} -} - -@article{becker_habitat-based_2017, - title = {Habitat-{{Based Density Models}} for {{Three Cetacean Species}} off {{Southern California Illustrate Pronounced Seasonal Differences}}}, - author = {Becker, Elizabeth A. and Forney, Karin A. and Thayre, Bruce J. and Debich, Amanda J. and Campbell, Gregory S. and Whitaker, Katherine and Douglas, Annie B. and Gilles, Anita and Hoopes, Ryan and Hildebrand, John A.}, - year = {2017}, - month = may, - journal = {Frontiers in Marine Science}, - volume = {4}, - issn = {2296-7745}, - doi = {10.3389/fmars.2017.00121}, - abstract = {Results provide the first fine scale (10 km) density predictions for these species during the cool seasons and reveal distribution patterns that are markedly different from summer/fall, thus providing novel insights into species ecology and quantitative data for the seasonal assessment of potential anthropogenic impacts.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LFYJ34U4/Becker et al. - 2017 - Habitat-Based Density Models for Three Cetacean Sp.pdf} -} - -@techreport{becker_habitat-based_2020, - type = {{{NOAA Technical Memorandum}}}, - title = {Habitat-Based Density Estimates for Cetaceans in the {{California Current Ecosystem}} Based on 1991\textendash 2018 Survey Data}, - author = {Becker, Elizabeth A and Forney, Karin A and Miller, David L and Fiedler, Paul C and Barlow, Jay and Moore, Jeff E}, - year = {2020}, - number = {NMFS-SWFSC-638}, - pages = {78}, - institution = {{NOAA SWFSC}}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JCNC3VLH/Becker et al. - 2020 - Habitat-based density estimates for cetaceans in t.pdf} -} - -@article{becker_moving_2016, - title = {Moving {{Towards Dynamic Ocean Management}}: {{How Well Do Modeled Ocean Products Predict Species Distributions}}?}, - shorttitle = {Moving {{Towards Dynamic Ocean Management}}}, - author = {Becker, Elizabeth and Forney, Karin and Fiedler, Paul and Barlow, Jay and Chivers, Susan and Edwards, Christopher and Moore, Andrew and Redfern, Jessica}, - year = {2016}, - month = feb, - journal = {Remote Sensing}, - volume = {8}, - number = {2}, - pages = {149}, - issn = {2072-4292}, - doi = {10.3390/rs8020149}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9Y342A8Y/Remote Sensing 2016 Becker.pdf} -} - -@article{becker_predicting_2014, - title = {Predicting Seasonal Density Patterns of {{California}} Cetaceans Based on Habitat Models}, - author = {Becker, Ea and Forney, Ka and Foley, Dg and Smith, Rc and Moore, Tj and Barlow, J}, - year = {2014}, - month = jan, - journal = {Endangered Species Research}, - volume = {23}, - number = {1}, - pages = {1--22}, - issn = {1863-5407, 1613-4796}, - doi = {10.3354/esr00548}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JA9TPUL8/Endang. Species. Res. 2014 Becker.pdf} -} - -@article{becker_predicting_2019, - title = {Predicting Cetacean Abundance and Distribution in a Changing Climate}, - author = {Becker, Elizabeth A. and Forney, Karin A. and Redfern, Jessica V. and Barlow, Jay and Jacox, Michael G. and Roberts, Jason J. and Palacios, Daniel M.}, - editor = {Beger, Maria}, - year = {2019}, - month = apr, - journal = {Diversity and Distributions}, - volume = {25}, - number = {4}, - pages = {626--643}, - issn = {1366-9516, 1472-4642}, - doi = {10.1111/ddi.12867}, - abstract = {Aim: Changes in abundance and shifts in distribution as a result of a warming climate have been documented for many marine species, but opportunities to test our ability to forecast such changes have been limited. This study evaluates the ability of habitat-based density models to accurately forecast cetacean abundance and distribution during a novel year with unprecedented warm ocean temperatures caused by a sustained marine heatwave.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/5CGA8CNP/Becker et al. - 2019 - Predicting cetacean abundance and distribution in .pdf} -} - -@techreport{becker_predictive_2012, - title = {Predictive {{Modeling}} of {{Cetacean Densities}} in the {{California Current Ecosystem Based}} on {{Summer}}/{{Fall Ship Surveys}} in 1991\textendash 2008}, - author = {Becker, E. A. and Forney, K. A. and Ferguson, Megan C. and Barlow, Jay and Redfern, Jessica V.}, - year = {2012}, - number = {NMFS-SWFSC-499}, - address = {{La Jolla, CA}}, - file = {/Users/dill/Zotero/storage/M7GDGXMN/Becker et al 2012 TM499_UpdatedCCEModels.pdf} -} - -@article{becker_rejection_2019, - title = {Rejection of {{Schmidt}} et al.'s Estimators for Bear Population Size}, - author = {Becker, Earl and Christ, Aaron}, - year = {2019}, - month = may, - journal = {Ecology and Evolution}, - volume = {9}, - number = {10}, - pages = {6157--6164}, - issn = {2045-7758, 2045-7758}, - doi = {10.1002/ece3.5134}, - abstract = {Aerial distance sampling of bears to estimate population size has been used throughout many parts of Alaska. The distance sampling models are complex since they need to account for undetected bears and differences in detection probabilities. This will require covariates and mark-recapture data. The models proposed by Schmidt et al. do not use covariates or mark-recapture data and are inappropriate for these surveys.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/HKAZ24U4/Becker and Christ - 2019 - Rejection of Schmidt et al.'s estimators for bear .pdf} -} - -@article{becker_unimodal_2015, - title = {A {{Unimodal Model}} for {{Double Observer Distance Sampling Surveys}}}, - author = {Becker, Earl F. and Christ, Aaron M.}, - editor = {Reed, Aaron W.}, - year = {2015}, - month = aug, - journal = {PLOS ONE}, - volume = {10}, - number = {8}, - pages = {e0136403}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0136403}, - langid = {english}, - file = {/Users/dill/Zotero/storage/F587JWMN/journal.pone.0136403.pdf;/Users/dill/Zotero/storage/HDPRMV7Y/PLoS ONE 2015 Becker.pdf} -} - -@article{beissinger_incorporating_2016, - title = {Incorporating {{Imperfect Detection}} into {{Joint Models}} of {{Communities}}: {{A}} Response to {{Warton}} et Al.}, - shorttitle = {Incorporating {{Imperfect Detection}} into {{Joint Models}} of {{Communities}}}, - author = {Beissinger, Steven R. and Iknayan, Kelly J. and {Guillera-Arroita}, Gurutzeta and Zipkin, Elise F. and Dorazio, Robert M. and Royle, J. Andrew and K{\'e}ry, Marc}, - year = {2016}, - month = oct, - journal = {Trends in Ecology \& Evolution}, - volume = {31}, - number = {10}, - pages = {736--737}, - issn = {01695347}, - doi = {10.1016/j.tree.2016.07.009}, - langid = {english}, - file = {/Users/dill/Zotero/storage/GQU6PJ6I/beissinger2016.pdf} -} - -@article{belitz_simultaneous_2008, - title = {Simultaneous Selection of Variables and Smoothing Parameters in Structured Additive Regression Models}, - author = {Belitz, Christiane and Lang, Stefan}, - year = {2008}, - month = sep, - journal = {Computational Statistics \& Data Analysis}, - volume = {53}, - number = {1}, - pages = {61--81}, - issn = {01679473}, - doi = {10.1016/j.csda.2008.05.032}, - langid = {english}, - file = {/Users/dill/Zotero/storage/N3HP2NUM/Computational Statistics and Data Analysis 2008 Belitz.pdf} -} - -@article{bellier_reducing_2013, - title = {Reducing the Uncertainty of Wildlife Population Abundance: Model-Based versus Design-Based Estimates: {{POPULATION ABUNDANCE ESTIMATES IN SPACE}}}, - shorttitle = {Reducing the Uncertainty of Wildlife Population Abundance}, - author = {Bellier, Edwige and Monestiez, Pascal and Certain, Gr{\'e}goire and Chad{\oe}uf, Joel and Bretagnolle, Vincent}, - year = {2013}, - month = nov, - journal = {Environmetrics}, - volume = {24}, - number = {7}, - pages = {476--488}, - issn = {11804009}, - doi = {10.1002/env.2240}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UKSM7JBG/Environmetrics 2013 Bellier.pdf} -} - -@article{ben_quantilequantile_2004, - title = {Quantile\textendash{{Quantile Plot}} for {{Deviance Residuals}} in the {{Generalized Linear Model}}}, - author = {Ben, Marta Garc{\'i}a and Yohai, V{\'i}ctor J}, - year = {2004}, - month = mar, - journal = {Journal of Computational and Graphical Statistics}, - volume = {13}, - number = {1}, - pages = {36--47}, - issn = {1061-8600, 1537-2715}, - doi = {10.1198/1061860042949_a}, - langid = {english}, - file = {/Users/dill/Zotero/storage/C73QWD78/Journal of Computational and Graphical Statistics 2004 Ben.pdf} -} - -@article{bennema_fish_2015, - title = {Fish Abundance, Fisheries, Fish Trade and Consumption in Sixteenth-Century {{Netherlands}} as Described by {{Adriaen Coenen}}}, - author = {Bennema, Floris P. and Rijnsdorp, Adriaan D.}, - year = {2015}, - month = jan, - journal = {Fisheries Research}, - volume = {161}, - pages = {384--399}, - issn = {01657836}, - doi = {10.1016/j.fishres.2014.09.001}, - langid = {english}, - file = {/Users/dill/Zotero/storage/P42PFCY3/Fisheries Research 2015 Bennema.pdf} -} - -@article{berman_approximating_1992, - title = {Approximating {{Point Process Likelihoods}} with {{GLIM}}}, - author = {Berman, Mark and Turner, T. Rolf}, - year = {1992}, - journal = {Applied Statistics}, - volume = {41}, - number = {1}, - pages = {31}, - issn = {00359254}, - doi = {10.2307/2347614}, - file = {/Users/dill/Zotero/storage/VZMUKVSN/2347614.pdf} -} - -@article{besag_conditional_1995, - title = {On {{Conditional}} and {{Intrinsic Autoregression}}}, - author = {Besag, Julian and Kooperberg, Charles}, - year = {1995}, - month = dec, - journal = {Biometrika}, - volume = {82}, - number = {4}, - pages = {733}, - issn = {00063444}, - doi = {10.2307/2337341}, - abstract = {Gaussian conditional autoregressions have been widely used in spatial statistics and Bayesian image analysis, where they are intended to describe interactions between random variables at fixed sites in Euclidean space. The main appeal of these distributions is in the Markovian interpretation of their full conditionals. Intrinsic autoregressions are limiting forms that retain the Markov property. Despite being improper, they can have advantages over the standard autoregressions, both conceptually and in practice. For example, they often avoid difficulties in parameter estimation, without apparent loss, or exhibit appealing invariances, as in texture analysis. However, on small arrays and in nonlattice applications, both forms of autoregression can lead to undesirable second-order characteristics, either in the variables themselves or in contrasts among them. This paper discusses standard and intrinsic autoregressions and describes how the problems that arise can be alleviated using Dempster's (1972) algorithm or an appropriate modification. The approach represents a partial synthesis of standard geostatistical and Gaussian Markov random field formulations. Some nonspatial applications are also mentioned.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6CRXBLHV/Besag and Kooperberg - 1995 - On Conditional and Intrinsic Autoregression.pdf} -} - -@article{bessone_drawn_2020, - title = {Drawn out of the Shadows: {{Surveying}} Secretive Forest Species with Camera Trap Distance Sampling}, - shorttitle = {Drawn out of the Shadows}, - author = {Bessone, Mattia and K{\"u}hl, Hjalmar S. and Hohmann, Gottfried and Herbinger, Ilka and N'Goran, Kouame Paul and Asanzi, Papy and Da Costa, Pedro B. and D{\'e}rozier, Violette and Fotsing, Ernest D. B. and Beka, Bernard Ikembelo and Iyomi, Mpongo D. and Iyatshi, Iyomi B. and Kafando, Pierre and Kambere, Mbangi A. and Moundzoho, Dissondet B. and Wanzalire, Musubaho L. K. and Fruth, Barbara}, - editor = {Michalski, Fernanda}, - year = {2020}, - month = may, - journal = {Journal of Applied Ecology}, - volume = {57}, - number = {5}, - pages = {963--974}, - issn = {0021-8901, 1365-2664}, - doi = {10.1111/1365-2664.13602}, - langid = {english}, - file = {/Users/dill/Zotero/storage/CXRLJHM3/Bessone et al. - 2020 - Drawn out of the shadows Surveying secretive fore.pdf} -} - -@article{biernacki_choosing_2003, - title = {Choosing Starting Values for the {{EM}} Algorithm for Getting the Highest Likelihood in Multivariate {{Gaussian}} Mixture Models}, - author = {Biernacki, Christophe and Celeux, Gilles and Govaert, G{\'e}rard}, - year = {2003}, - month = jan, - journal = {Computational Statistics \& Data Analysis}, - volume = {41}, - number = {3-4}, - pages = {561--575}, - issn = {01679473}, - doi = {10.1016/S0167-9473(02)00163-9}, - abstract = {Simple methods to choose sensible starting values for the EM algorithm to get maximum likelihood parameter estimation in mixture models are compared. They are based on random initialization, using a classi\"ycation EM algorithm (CEM), a Stochastic EM algorithm (SEM) or previous short runs of EM itself. Those initializations are included in a search=run=select strategy which can be compounded by repeating the three steps. They are compared in the context of multivariate Gaussian mixtures on the basis of numerical experiments on both simulated and real data sets in a target number of iterations. The main conclusions of those numerical experiments are the following. The simple random initialization which is probably the most employed way of initiating EM is often outperformed by strategies using CEM, SEM or shorts runs of EM before running EM. Also, it appears that compounding is generally pro\"ytable since using a single run of EM can often lead to suboptimal solutions. Otherwise, none of the experimental strategies can be regarded as the best one and it is di cult to characterize situations where a particular strategy can be expected to outperform the other ones. However, the strategy initiating EM with short runs of EM can be recommended. This strategy, which as far as we know was not used before the present study, has some advantages. It is simple, performs well in a lot of situations presupposing no particular form of the mixture to be \"ytted to the data and seems little sensitive to noisy data. c 2002 Elsevier Science B.V. All rights reserved.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/WRWAN7IU/Biernacki et al. - 2003 - Choosing starting values for the EM algorithm for .pdf} -} - -@article{bishop_utility_2013, - title = {The Utility of Distribution Data in Predicting Phenology}, - author = {Bishop, Tom R. and Botham, Marc S. and Fox, Richard and Leather, Simon R. and Chapman, Daniel S. and Oliver, Tom H.}, - editor = {Freckleton, Robert}, - year = {2013}, - month = nov, - journal = {Methods in Ecology and Evolution}, - volume = {4}, - number = {11}, - pages = {1024--1032}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12112}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Z6XC4AXW/Methods in Ecology and Evolution 2013 Bishop.pdf} -} - -@article{bispo_modeling_2013, - title = {Modeling Carcass Removal Time for Avian Mortality Assessment in Wind Farms Using Survival Analysis}, - author = {Bispo, Regina and Bernardino, Joana and Marques, Tiago A. and Pestana, Dinis}, - year = {2013}, - month = mar, - journal = {Environmental and Ecological Statistics}, - volume = {20}, - number = {1}, - pages = {147--165}, - issn = {1352-8505, 1573-3009}, - doi = {10.1007/s10651-012-0212-5}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DIQ5MKCB/Environ Ecol Stat 2012 Bispo.pdf} -} - -@book{bivand_applied_2008, - title = {Applied {{Spatial Data Analysis}} with {{R}}}, - author = {Bivand, R.S. and Pebesma, E.J. and {G{\'o}mez-Rubio}, V.}, - year = {2008}, - series = {Use {{R}}!}, - publisher = {{Springer New York}}, - isbn = {978-0-387-78171-6}, - lccn = {2008931196} -} - -@book{bivand_applied_2008-1, - title = {Applied {{Spatial Data Analysis}} with {{R}}}, - author = {Bivand, R.S. and Pebesma, E.J. and {G{\'o}mez-Rubio}, V.}, - year = {2008}, - series = {Use {{R}}!}, - publisher = {{Springer New York}}, - isbn = {978-0-387-78171-6}, - lccn = {2008931196} -} - -@book{blangiardo_spatial_2015, - title = {Spatial and {{Spatio-temporal Bayesian Models}} with {{R}} - {{INLA}}}, - author = {Blangiardo, M. and Cameletti, M.}, - year = {2015}, - publisher = {{Wiley}}, - isbn = {978-1-118-32655-8}, - lccn = {2015000696} -} - -@article{bograd_phenology_2009, - title = {Phenology of Coastal Upwelling in the {{California Current}}}, - author = {Bograd, Steven J. and Schroeder, Isaac and Sarkar, Nandita and Qiu, Xuemei and Sydeman, William J. and Schwing, Franklin B.}, - year = {2009}, - month = jan, - journal = {Geophysical Research Letters}, - volume = {36}, - number = {1}, - issn = {0094-8276}, - doi = {10.1029/2008GL035933}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9BDUX6TE/Bograd et al. - 2009 - Phenology of coastal upwelling in the California C.pdf} -} - -@article{boisvert_kriging_2009, - title = {Kriging in the {{Presence}} of {{Locally Varying Anisotropy Using Non-Euclidean Distances}}}, - author = {Boisvert, J. B. and Manchuk, J. G. and Deutsch, C. V.}, - year = {2009}, - month = jul, - journal = {Mathematical Geosciences}, - volume = {41}, - number = {5}, - pages = {585--601}, - issn = {1874-8961, 1874-8953}, - doi = {10.1007/s11004-009-9229-1}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2B2RNY7J/Math Geosci 2009 Boisvert.pdf} -} - -@article{boj_projection_2009, - title = {Projection Error Term in {{Gower}}'s Interpolation}, - author = {Boj, Eva and Merc{\`e} Claramunt, M. and Gran{\'e}, Aurea and Fortiana, Josep}, - year = {2009}, - month = jun, - journal = {Journal of Statistical Planning and Inference}, - volume = {139}, - number = {6}, - pages = {1867--1878}, - issn = {03783758}, - doi = {10.1016/j.jspi.2008.07.021}, - langid = {english}, - file = {/Users/dill/Zotero/storage/B2GHICU3/Journal of Statistical Planning and Inference 2009 Boj.pdf} -} - -@article{bolker_generalized_2009, - title = {Generalized Linear Mixed Models: A Practical Guide for Ecology and Evolution}, - shorttitle = {Generalized Linear Mixed Models}, - author = {Bolker, Benjamin M. and Brooks, Mollie E. and Clark, Connie J. and Geange, Shane W. and Poulsen, John R. and Stevens, M. Henry H. and White, Jada-Simone S.}, - year = {2009}, - month = mar, - journal = {Trends in Ecology \& Evolution}, - volume = {24}, - number = {3}, - pages = {127--135}, - issn = {01695347}, - doi = {10.1016/j.tree.2008.10.008}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FAJXQ57B/Trends in Ecology & Evolution 2009 Bolker.pdf} -} - -@techreport{bolker_multimodel_nodate, - title = {Multimodel Approaches Are Not the Best Way to Understand Multifactorial Systems}, - author = {Bolker, Ben}, - pages = {10}, - abstract = {Information-theoretic (IT) and multi-model averaging (MMA) statistical approaches are widely used but suboptimal tools for pursuing a multifactorial approach (also known as the method of multiple working hypotheses) in ecology. (1) Conceptually, IT encourages ecologists to perform tests on sets of straw-man models. (2) MMA improves on IT model comparison by implementing a simple form of shrinkage estimation (a way to make accurate predictions from a model with many parameters, by ``shrinking'' parameter estimates toward zero). However, newer approaches to shrinkage estimation are faster and have more rigorous statistical underpinnings. (3) It is likely that correct confidence intervals (CIs) for MMA (and other shrinkage estimates) are no narrower than those from the original (full) model. If researchers want to accurately estimate the strength of multiple competing ecological processes along with reliable confidence intervals, their best hope is to use full (maximal) statistical models after making principled, a priori decisions about which predictors to include.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/KH46RUJR/Bolker - Multimodel approaches are not the best way to unde.pdf} -} - -@article{borchers_accommodating_2006, - title = {Accommodating {{Unmodeled Heterogeneity}} in {{Double-Observer Distance Sampling Surveys}}}, - author = {Borchers, D. L. and Laake, J. L. and Southwell, C. and Paxton, C. G. M.}, - year = {2006}, - month = jun, - journal = {Biometrics}, - volume = {62}, - number = {2}, - pages = {372--378}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2005.00493.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/8WDV7BD3/Biom 2005 Borchers.pdf} -} - -@article{borchers_double-observer_2015, - title = {Double-Observer Line Transect Surveys with {{Markov-modulated Poisson}} Process Models for Animal Availability: {{Availability Modeling}} on {{Double-Observer Surveys}}}, - shorttitle = {Double-Observer Line Transect Surveys with {{Markov-modulated Poisson}} Process Models for Animal Availability}, - author = {Borchers, D. L. and Langrock, R.}, - year = {2015}, - month = dec, - journal = {Biometrics}, - volume = {71}, - number = {4}, - pages = {1060--1069}, - issn = {0006341X}, - doi = {10.1111/biom.12341}, - langid = {english}, - file = {/Users/dill/Zotero/storage/TKJIU68H/biom12341.pdf} -} - -@book{borchers_estimating_2002, - title = {Estimating {{Animal Abundance}}: {{Closed Populations}}}, - author = {Borchers, D.L. and Buckland, S.T. and Zucchini, W.}, - year = {2002}, - series = {Statistics for {{Biology}} and {{Health}}}, - publisher = {{Springer London}}, - isbn = {978-1-84996-885-0} -} - -@article{borchers_estimating_2010, - title = {Estimating {{Distance Sampling Detection Functions When Distances Are Measured With Errors}}}, - author = {Borchers, David and Marques, Tiago and Gunnlaugsson, Thorvaldur and Jupp, Peter}, - year = {2010}, - month = sep, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {15}, - number = {3}, - pages = {346--361}, - issn = {1085-7117, 1537-2693}, - doi = {10.1007/s13253-010-0021-y}, - langid = {english}, - file = {/Users/dill/Zotero/storage/XHJQ3ED3/JABES 2010 Marques.pdf} -} - -@article{borchers_horvitz-thompson_1998, - title = {Horvitz-{{Thompson Estimators}} for {{Double-Platform Line Transect Surveys}}}, - author = {Borchers, David L. and Buckland, Stephen T. and Goedhart, Paul W. and Clarke, Elizabeth D. and Hedley, Sharon L.}, - year = {1998}, - month = dec, - journal = {Biometrics}, - volume = {54}, - number = {4}, - pages = {1221}, - issn = {0006341X}, - doi = {10.2307/2533652}, - file = {/Users/dill/Zotero/storage/U6MJRDLW/Biom 1998 Borchers.pdf} -} - -@article{borchers_latent_2020, - title = {A Latent Capture History Model for Digital Aerial Surveys}, - author = {Borchers, David L. and Nightingale, Peter and Stevenson, Ben C. and Fewster, Rachel M.}, - year = {2020}, - month = dec, - journal = {Biometrics}, - pages = {biom.13403}, - issn = {0006-341X, 1541-0420}, - doi = {10.1111/biom.13403}, - abstract = {We anticipate that unmanned aerial vehicles will become popular wildlife survey platforms. Because detecting animals from the air is imperfect, we develop a mark-recapture line-transect method using two digital cameras, possibly mounted on one aircraft, which cover the same area with a short time delay between them. Animal movement between the passage of the cameras introduces uncertainty in individual identity, so individual capture histories are unobservable and are treated as latent variables. We obtain the likelihood for mark-recapture line transects without capture histories by automatically enumerating all possibilities within segments of the transect that contain ambiguous identities, instead of attempting to decide identities in a prior step. We call this method `Latent Capture-history Enumeration' (LCE). We include an availability model for species that are periodically unavailable for detection, such as cetaceans that are undetectable while diving. External data are needed to estimate the availability cycle length, but not the mean availability rate, if the full availability model is employed.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ISEDQLU9/Borchers et al. - 2020 - A latent capture history model for digital aerial .pdf} -} - -@article{borchers_mark-recapture_1998, - title = {Mark-Recapture Models for Line Transect Surveys}, - author = {Borchers, David L. and Zucchini, Walter and Fewster, Rachel M.}, - year = {1998}, - journal = {Biometrics}, - pages = {1207--1220}, - file = {/Users/dill/Zotero/storage/QA67PPS8/Biom 1998 Borchers-1.pdf} -} - -@article{borchers_spatial_2016, - title = {Spatial Capture\textendash Recapture Models}, - author = {Borchers, David and Fewster, Rachel}, - year = {2016}, - journal = {Statistical Science}, - volume = {31}, - number = {2}, - pages = {219--232}, - file = {/Users/dill/Zotero/storage/UUHXVWMI/Statist. Sci. 2016 Borchers.pdf} -} - -@article{borchers_spatially_2008, - title = {Spatially {{Explicit Maximum Likelihood Methods}} for {{Capture-Recapture Studies}}}, - author = {Borchers, D. L. and Efford, M. G.}, - year = {2008}, - month = jun, - journal = {Biometrics}, - volume = {64}, - number = {2}, - pages = {377--385}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2007.00927.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/TLDYVPQ8/Biom 2008 Borchers.pdf} -} - -@article{borchers_unifying_2015, - title = {A {{Unifying Model}} for {{Capture}}\textendash{{Recapture}} and {{Distance Sampling Surveys}} of {{Wildlife Populations}}}, - author = {Borchers, D. L. and Stevenson, B. C. and Kidney, D. and Thomas, L. and Marques, T. A.}, - year = {2015}, - month = jan, - journal = {Journal of the American Statistical Association}, - volume = {110}, - number = {509}, - pages = {195--204}, - issn = {0162-1459, 1537-274X}, - doi = {10.1080/01621459.2014.893884}, - langid = {english}, - file = {/Users/dill/Zotero/storage/X95JNCZ4/Journal of the American Statistical Association 2015 Borchers.pdf} -} - -@article{borchers_using_2013, - title = {Using {{Hidden Markov Models}} to {{Deal}} with {{Availability Bias}} on {{Line Transect Surveys}}: {{Line Transect Availability Bias}}}, - shorttitle = {Using {{Hidden Markov Models}} to {{Deal}} with {{Availability Bias}} on {{Line Transect Surveys}}}, - author = {Borchers, D. L. and Zucchini, W. and {Heide-J{\o}rgensen}, M. P. and Ca{\~n}adas, A. and Langrock, R.}, - year = {2013}, - month = sep, - journal = {Biometrics}, - volume = {69}, - number = {3}, - pages = {703--713}, - issn = {0006341X}, - doi = {10.1111/biom.12049}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LX8KZAQX/Biom 2013 Borchers.pdf} -} - -@article{borkin_surveying_2012, - title = {Surveying Abundance and Stand Type Associations of {{Formica}} Aquilonia and {{F}}. Lugubris ({{Hymenoptera}}: {{Formicidae}}) Nest Mounds over an Extensive Area: {{Trialing}} a Novel Method}, - author = {Borkin, Kerry M. and Summers, Ron W. and Thomas, Len}, - year = {2012}, - journal = {European Journal of Entomology}, - volume = {109}, - pages = {47--53}, - file = {/Users/dill/Zotero/storage/C5TXBS3K/European Journal of Entomology 2012 Borkin.pdf} -} - -@article{borland_rainbow_2007, - title = {Rainbow Color Map (Still) Considered Harmful}, - author = {Borland, David and Ii, Russell M. Taylor}, - year = {2007}, - journal = {IEEE computer graphics and applications}, - volume = {27}, - number = {2}, - file = {/Users/dill/Zotero/storage/7G7XAQCX/IEEE Comput Graph Appl 2007 Borland.pdf} -} - -@article{bortolotto_humpback_2016, - title = {Humpback Whale {{Megaptera}} Novaeangliae ({{Cetartiodactyla}}: {{Balaenopteridae}}) Group Sizes in Line Transect Ship Surveys: {{An}} Evaluation of Observer Errors}, - shorttitle = {Humpback Whale {{Megaptera}} Novaeangliae ({{Cetartiodactyla}}}, - author = {Bortolotto, Guilherme A. and Danilewicz, Daniel and Andriolo, Artur and Zerbini, Alexandre N.}, - year = {2016}, - journal = {Zoologia (Curitiba)}, - volume = {33}, - number = {2}, - issn = {1984-4689}, - doi = {10.1590/S1984-4689zool-20150133}, - langid = {english}, - file = {/Users/dill/Zotero/storage/BZHMFLS2/Zoologia (Curitiba) 2016 Bortolotto.pdf} -} - -@article{bortolotto_whale_2017, - title = {Whale Distribution in a Breeding Area: Spatial Models of Habitat Use and Abundance of Western {{South Atlantic}} Humpback Whales}, - shorttitle = {Whale Distribution in a Breeding Area}, - author = {Bortolotto, Ga and Danilewicz, D and Hammond, Ps and Thomas, L and Zerbini, An}, - year = {2017}, - month = dec, - journal = {Marine Ecology Progress Series}, - volume = {585}, - pages = {213--227}, - issn = {0171-8630, 1616-1599}, - doi = {10.3354/meps12393}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YKV8AW37/Bortolotto_et_al_2017_MEPS_m585p213.pdf} -} - -@article{bouchet_dsmextra_2020, - title = {Dsmextra: {{Extrapolation}} Assessment Tools for Density Surface Models}, - shorttitle = {Dsmextra}, - author = {Bouchet, Phil J. and Miller, David L. and Roberts, Jason J. and Mannocci, Laura and Harris, Catriona M. and Thomas, Len}, - editor = {Graham, Laura}, - year = {2020}, - month = nov, - journal = {Methods in Ecology and Evolution}, - volume = {11}, - number = {11}, - pages = {1464--1469}, - issn = {2041-210X, 2041-210X}, - doi = {10.1111/2041-210X.13469}, - langid = {english}, - file = {/Users/dill/Zotero/storage/94346FYF/Bouchet et al. - 2020 - dsmextra Extrapolation assessment tools for densi.pdf} -} - -@techreport{bouchet_here_2019, - title = {From Here and Now to There and Then: Practical Recommendations for Extrapolating Cetacean Density Surface Models to Novel Conditions}, - author = {Bouchet, Phil J and Miller, Dave L and Roberts, Jason J and Mannocci, Laura and Harris, Catriona M and Thomas, Len}, - year = {2019}, - number = {2019-1}, - pages = {59}, - institution = {{University of St Andrews}}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2WWCQS36/Bouchet et al. - From here and now to there and then.pdf} -} - -@article{boyd_bayesian_2019, - title = {Bayesian Estimation of Group Sizes for a Coastal Cetacean Using Aerial Survey Data}, - author = {Boyd, Charlotte and Hobbs, Roderick C. and Punt, Andr{\'e} E. and Shelden, Kim E. W. and Sims, Christy L. and Wade, Paul R.}, - year = {2019}, - month = oct, - journal = {Marine Mammal Science}, - volume = {35}, - number = {4}, - pages = {1322--1346}, - issn = {0824-0469, 1748-7692}, - doi = {10.1111/mms.12592}, - abstract = {Many small cetacean, sirenian, and pinniped species aggregate in groups of large or variable size. Accurate estimation of group sizes is essential for estimating the abundance and distribution of these species, but is challenging as individuals are highly mobile and only partially visible. We developed a Bayesian approach for estimating group sizes using wide-angle aerial photographic or video imagery. Our approach accounts for both availability and perception bias, including a new method (analogous to distance sampling) for estimating perception bias due to small image size in wide-angle images. We demonstrate our approach through an application to aerial survey data for an endangered population of beluga whales (Delphinapterus leucas) in Cook Inlet, Alaska. Our results strengthen understanding of variation in group size estimates and allow for probabilistic statements about the size of detected groups. Aerial surveys are a standard tool for estimating the abundance and distribution of various marine mammal species. The role of aerial photographic and video data in wildlife assessment is expected to increase substantially with the widespread uptake of unmanned aerial vehicle technology. Key aspects of our approach are relevant to group size estimation for a broad range of marine mammal, seabird, other waterfowl, and terrestrial ungulate species.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FYDSCPPB/Boyd et al. - 2019 - Bayesian estimation of group sizes for a coastal c.pdf} -} - -@book{boyd_convex_2004, - title = {Convex Optimization}, - author = {Boyd, Stephen P. and Vandenberghe, Lieven}, - year = {2004}, - publisher = {{Cambridge University Press}}, - address = {{Cambridge, UK ; New York}}, - isbn = {978-0-521-83378-3}, - lccn = {QA402.5 .B69 2004}, - keywords = {Convex functions,Mathematical optimization}, - file = {/Users/dill/Zotero/storage/DIGZA3TG/Journal of Statistical Software 2014 Nash.pdf} -} - -@article{boyd_estimation_2018, - title = {Estimation of Population Size and Trends for Highly Mobile Species with Dynamic Spatial Distributions}, - author = {Boyd, Charlotte and Barlow, Jay and Becker, Elizabeth A. and Forney, Karin A. and Gerrodette, Tim and Moore, Jeffrey E. and Punt, Andr{\'e} E.}, - editor = {Regan, Helen}, - year = {2018}, - month = jan, - journal = {Diversity and Distributions}, - volume = {24}, - number = {1}, - pages = {1--12}, - issn = {13669516}, - doi = {10.1111/ddi.12663}, - abstract = {Aim: To develop a more ecologically realistic approach for estimating the population size of cetaceans and other highly mobile species with dynamic spatial distributions. Location: California Current Ecosystem, USA. Methods: Conventional spatial density models assume a constant relationship between densities and habitat covariates over some time period, typically a survey season. The estimated population size must change whenever total habitat availability changes. For highly mobile long-\-lived species, however, density\textendash habitat relationships likely adjust more rapidly than population size. We developed an integrated population-\- redistribution model based on a more ecologically plausible alternative hypothesis: (1) population size is effectively constant over each survey season; (2) if habitat availability changes, then the population redistributes itself following an ideal free distribution process. Thus, the estimated relationship between densities and habitat covariates adjusts rather than population size. We constructed Bayesian hierarchical models corresponding to the conventional and alternative hypotheses and applied them to distance sampling data for Dall's porpoise (Phocoenoides dalli), a highly mobile cetacean with distribution patterns closely tied to cool sea-\-surface temperatures.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ZB9JKDXS/Boyd et al. - 2018 - Estimation of population size and trends for highl.pdf} -} - -@article{bradbury_mapping_2014, - title = {Mapping {{Seabird Sensitivity}} to {{Offshore Wind Farms}}}, - author = {Bradbury, Gareth and Trinder, Mark and Furness, Bob and Banks, Alex N. and Caldow, Richard W. G. and Hume, Duncan}, - editor = {Magar, Vanesa}, - year = {2014}, - month = sep, - journal = {PLoS ONE}, - volume = {9}, - number = {9}, - pages = {e106366}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0106366}, - langid = {english}, - file = {/Users/dill/Zotero/storage/RVBZH9N5/PLoS ONE 2014 Bradbury.pdf} -} - -@article{bradford_accounting_2014, - title = {Accounting for {{Subgroup Structure}} in {{Line-Transect Abundance Estimates}} of {{False Killer Whales}} ({{Pseudorca}} Crassidens) in {{Hawaiian Waters}}}, - author = {Bradford, Amanda L. and Forney, Karin A. and Oleson, Erin M. and Barlow, Jay}, - editor = {{Ropert-Coudert}, Yan}, - year = {2014}, - month = feb, - journal = {PLoS ONE}, - volume = {9}, - number = {2}, - pages = {e90464}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0090464}, - abstract = {For biological populations that form aggregations (or clusters) of individuals, cluster size is an important parameter in linetransect abundance estimation and should be accurately measured. Cluster size in cetaceans has traditionally been represented as the total number of individuals in a group, but group size may be underestimated if group members are spatially diffuse. Groups of false killer whales (Pseudorca crassidens) can comprise numerous subgroups that are dispersed over tens of kilometers, leading to a spatial mismatch between a detected group and the theoretical framework of linetransect analysis. Three stocks of false killer whales are found within the U.S. Exclusive Economic Zone of the Hawaiian Islands (Hawaiian EEZ): an insular main Hawaiian Islands stock, a pelagic stock, and a Northwestern Hawaiian Islands (NWHI) stock. A ship-based line-transect survey of the Hawaiian EEZ was conducted in the summer and fall of 2010, resulting in six systematic-effort visual sightings of pelagic (n = 5) and NWHI (n = 1) false killer whale groups. The maximum number and spatial extent of subgroups per sighting was 18 subgroups and 35 km, respectively. These sightings were combined with data from similar previous surveys and analyzed within the conventional line-transect estimation framework. The detection function, mean cluster size, and encounter rate were estimated separately to appropriately incorporate data collected using different methods. Unlike previous line-transect analyses of cetaceans, subgroups were treated as the analytical cluster instead of groups because subgroups better conform to the specifications of line-transect theory. Bootstrap values (n = 5,000) of the line-transect parameters were randomly combined to estimate the variance of stock-specific abundance estimates. Hawai'i pelagic and NWHI false killer whales were estimated to number 1,552 (CV = 0.66; 95\% CI = 479\textendash 5,030) and 552 (CV = 1.09; 95\% CI = 97\textendash 3,123) individuals, respectively. Subgroup structure is an important factor to consider in linetransect analyses of false killer whales and other species with complex grouping patterns.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Z23TATLC/Bradford et al. - 2014 - Accounting for Subgroup Structure in Line-Transect.pdf} -} - -@article{branch_abundance_2007, - title = {Abundance of {{Antarctic}} Blue Whales South of 60 {{S}} from Three Complete Circumpolar Sets of Surveys}, - author = {Branch, Trevor A.}, - year = {2007}, - file = {/Users/dill/Zotero/storage/FH49M4KC/Branch 2007 Abundance of Antarctic blue whales south of 60S.pdf} -} - -@article{branch_southern_2001, - title = {Southern {{Hemisphere}} Minke Whales: Standardised Abundance Estimates from the 1978/79 to 1997/98 {{IDCR-SOWER}} Surveys}, - shorttitle = {Southern {{Hemisphere}} Minke Whales}, - author = {Branch, T. A. and Butterworth, D. S.}, - year = {2001}, - journal = {Journal of Cetacean Research and Management}, - volume = {3}, - number = {2}, - pages = {143--174}, - file = {/Users/dill/Zotero/storage/RHY5BXJ4/2001 Branch.pdf} -} - -@article{branch_southern_2001-1, - title = {Southern {{Hemisphere}} Minke Whales: Standardised Abundance Estimates from the 1978/79 to 1997/98 {{IDCR-SOWER}} Surveys}, - author = {Branch, T A and Butterworth, D S}, - year = {2001}, - pages = {32}, - abstract = {Minke whale abundance estimates, standardised by the use of consistent methodology throughout, are presented from the IWC/IDCR and SOWER Antarctic circumpolar sightings surveys for three circumpolar sets of cruises: 1978/79\textendash 1983/84, 1985/86\textendash 1990/91 and 1991/92\textendash 1997/98 (*still incomplete). The database estimation package DESS is used to obtain these standardised estimates. Two survey modes (closing and IO) are used in the surveys; IO mode is considered to provide less biased estimates. An updated estimate for the conversion factor from closing to `pseudo-passing' mode of R = 0.826 (CV = 0.089) is obtained. IO and `pseudo-passing' estimates are then combined using inverse-variance weighting to give estimates of 608,000 (CV = 0.130), 766,000 (CV = 0.091) and 268,000* (CV = 0.093) for the three circumpolar sets of cruises. These cruises have covered approximately 65\%, 81\% and 68\% of the ice-free area south of 60\textdegree S. As estimates of abundance for Southern Hemisphere minke whales, these are negatively biased because some areas inside the pack ice cannot be surveyed, not all whales migrate into the area south of 60\textdegree S, the assumption is made that all whales on the trackline are sighted, and minke whale sightings for which species identification is uncertain (`like minkes') are omitted. The three circumpolar estimates are extrapolated simply to account for the different areas covered in the sets of surveys, and also for the increasing proportion of `like-minke' sightings over time. The results suggest that for comparable areas the abundance estimates for the third circumpolar set of cruises are 55\% (closing mode only) and 45\% (IO mode only) of those for the second set, but that the first and second set estimates are within 15\% of each other. The decrease in abundance between the second and third sets is statistically significant at the 5\% level. Possible reasons for this estimated decline are discussed, related both to factors that might render the estimates non-comparable, and to population dynamics effects that could have led to a real decline. Further attention should be given, in particular, to the most appropriate method for estimation of mean school size for these surveys.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/B3TLI3M6/Branch and Butterworth - 2001 - Southern Hemisphere minke whales standardised abu.pdf} -} - -@article{bravington_absolute_2016, - title = {Absolute Abundance of Southern Bluefin Tuna Estimated by Close-Kin Mark-Recapture}, - author = {Bravington, Mark V. and Grewe, Peter M. and Davies, Campbell R.}, - year = {2016}, - month = nov, - journal = {Nature Communications}, - volume = {7}, - pages = {13162}, - issn = {2041-1723}, - doi = {10.1038/ncomms13162}, - file = {/Users/dill/Zotero/storage/63BCG48Z/ncomms13162.pdf} -} - -@techreport{bravington_antarctic_2009, - title = {Antarctic Minke Whale Abundance Estimates from the Second and Third Circumpolar {{IDCR}}/{{SOWER}} Surveys Using the {{SPLINTR}} Model}, - author = {Bravington, MARK V. and Hedley, SHARON L.}, - year = {2009}, - institution = {{Paper SC/61/IA14 presented to the IWC Scientific Committee}}, - file = {/Users/dill/Zotero/storage/HJHGEZ6F/2009 Bravington.pdf} -} - -@techreport{bravington_antarctic_2010, - title = {Antarctic Minke Whale Abundance from the {{SPLINTR}} Model: Some'reference'dataset Results and'preferred'estimates from the Second and Third Circumpolar {{IDCR}}/{{SOWER}} Surveys}, - shorttitle = {Antarctic Minke Whale Abundance from the {{SPLINTR}} Model}, - author = {Bravington, MARK V. and Hedley, SHARON L.}, - year = {2010}, - institution = {{Paper SC/62/IA12 presented the IWC Scientific Committee, 2010 (unpublished)}}, - file = {/Users/dill/Zotero/storage/3VUEZH2G/2010 Hedley.pdf} -} - -@article{bravington_antarctic_2014, - title = {Antarctic {{Blue Whale}} Surveys: Augmenting via Genetics for Close-Kin and Ordinal Age}, - shorttitle = {Antarctic {{Blue Whale}} Surveys}, - author = {Bravington, Mark V. and Jarman, Simon N. and Skaug, Hans J.}, - year = {2014}, - journal = {International Whaling Commission Background Paper}, - file = {/Users/dill/Zotero/storage/8U62XER9/SC-65b-SH17.pdf} -} - -@article{bravington_close-kin_2016, - title = {Close-Kin Mark-Recapture Methods}, - author = {Bravington, Mark V and Skaug, Hans J and Anderson, Eric C}, - year = {2016}, - pages = {22}, - abstract = {Mark-recapture (MR) methods are commonly used to study wildlife populations. Taking advantage of modern genetics one can generalize from ``recapture of self'' to ``recapture of closely-related kin''. Abundance and other demographic parameters of adults can then be estimated using, if necessary, only samples from dead animals (liverelease is optional). This greatly widens the scope of MR, e.g. to commercial fisheries where large-scale tagging is impractical, and enhances the power of conventional MR studies where live release and tissue sampling is possible. We give explicit formulae for kinship (i.e. recapture) probabilities in general and specific cases. These yield a pseudo likelihood based on pairwise comparisons of individuals in the sample. It is shown that the pseudo likelihood approximates the full likelihood under sparse sampling of large populations. Experimental design is addressed via the principle of maximizing the Fisher information for parameters of interest. Finally, we discuss challenges related to kinship determination from genetic data, focusing on current limitations and future possibilities.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/4K84PP7X/16-STS552.pdf;/Users/dill/Zotero/storage/UU27YNWD/Bravington et al. - 2016 - Close-kin mark-recapture methods.pdf} -} - -@article{bravington_variance_2021, - title = {Variance {{Propagation}} for {{Density Surface Models}}}, - author = {Bravington, Mark V. and Miller, David L. and Hedley, Sharon L.}, - year = {2021}, - month = feb, - journal = {Journal of Agricultural, Biological and Environmental Statistics}, - issn = {1085-7117, 1537-2693}, - doi = {10.1007/s13253-021-00438-2}, - abstract = {Abstract Spatially explicit estimates of population density, together with appropriate estimates of uncertainty, are required in many management contexts. Density surface models (DSMs) are a two-stage approach for estimating spatially varying density from distance sampling data. First, detection probabilities\textemdash perhaps depending on covariates\textemdash are estimated based on details of individual encounters; next, local densities are estimated using a GAM, by fitting local encounter rates to location and/or spatially varying covariates while allowing for the estimated detectabilities. One criticism of DSMs has been that uncertainty from the two stages is not usually propagated correctly into the final variance estimates. We show how to reformulate a DSM so that the uncertainty in detection probability from the distance sampling stage (regardless of its complexity) is captured as an extra random effect in the GAM stage. In effect, we refit an approximation to the detection function model at the same time as fitting the spatial model. This allows straightforward computation of the overall variance via exactly the same software already needed to fit the GAM. A further extension allows for spatial variation in group size, which can be an important covariate for detectability as well as directly affecting abundance. We illustrate these models using point transect survey data of Island Scrub-Jays on Santa Cruz Island, CA, and harbour porpoise from the SCANS-II line transect survey of European waters. Supplementary materials accompanying this paper appear on-line.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/4TN66NT9/Bravington - Variance Propagation for Density Surface Models.pdf} -} - -@article{breiman_statistical_2001, - title = {Statistical Modeling: {{The}} Two Cultures (with Comments and a Rejoinder by the Author)}, - shorttitle = {Statistical Modeling}, - author = {Breiman, Leo}, - year = {2001}, - journal = {Statistical science}, - volume = {16}, - number = {3}, - pages = {199--231}, - file = {/Users/dill/Zotero/storage/NEJYHGPJ/Statist. Sci. 2001 Breiman.pdf} -} - -@article{breslow_approximate_1993, - title = {Approximate {{Inference}} in {{Generalized Linear Mixed Models}}}, - author = {Breslow, N.E. and Clayton, D.G.}, - year = {1993}, - journal = {Journal of the American Statistical Association}, - volume = {88}, - number = {421}, - pages = {9--25}, - file = {/Users/dill/Zotero/storage/KQEKUX77/breslow-clayton-1993.pdf} -} - -@article{brewer_relative_2016, - title = {The Relative Performance of {{AIC}}, {{AIC}} {{{\textsubscript{C}}}} and {{BIC}} in the Presence of Unobserved Heterogeneity}, - author = {Brewer, Mark J. and Butler, Adam and Cooksley, Susan L.}, - editor = {Freckleton, Robert}, - year = {2016}, - month = jun, - journal = {Methods in Ecology and Evolution}, - volume = {7}, - number = {6}, - pages = {679--692}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12541}, - langid = {english}, - file = {/Users/dill/Zotero/storage/CM6SPD9R/Methods in Ecology and Evolution 2016 Brewer.pdf} -} - -@article{brezger_bayesx_2005, - title = {{{BayesX}} : {{Analyzing Bayesian Structured Additive Regression Models}}}, - shorttitle = {{{{\emph{BayesX}}}}}, - author = {Brezger, Andreas and Kneib, Thomas and Lang, Stefan}, - year = {2005}, - journal = {Journal of Statistical Software}, - volume = {14}, - number = {11}, - issn = {1548-7660}, - doi = {10.18637/jss.v014.i11}, - abstract = {There has been much recent interest in Bayesian inference for generalized additive and related models. The increasing popularity of Bayesian methods for these and other model classes is mainly caused by the introduction of Markov chain Monte Carlo (MCMC) simulation techniques which allow realistic modeling of complex problems. This paper describes the capabilities of the free software package BayesX for estimating regression models with structured additive predictor based on MCMC inference. The program extends the capabilities of existing software for semiparametric regression included in S-PLUS, SAS, R or Stata. Many model classes well known from the literature are special cases of the models supported by BayesX. Examples are generalized additive (mixed) models, dynamic models, varying coefficient models, geoadditive models, geographically weighted regression and models for space-time regression. BayesX supports the most common distributions for the response variable. For univariate responses these are Gaussian, Binomial, Poisson, Gamma, negative Binomial, zero inflated Poisson and zero inflated negative binomial. For multicategorical responses, both multinomial logit and probit models for unordered categories of the response as well as cumulative threshold models for ordered categories can be estimated. Moreover, BayesX allows the estimation of complex continuous time survival and hazard rate models.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/3VH8CRMM/Brezger et al. - 2005 - iBayesXi Analyzing Bayesian Structured Addi.pdf} -} - -@article{broderick_automatic_2021, - title = {An {{Automatic Finite-Sample Robustness Metric}}: {{Can Dropping}} a {{Little Data Change Conclusions}}?}, - shorttitle = {An {{Automatic Finite-Sample Robustness Metric}}}, - author = {Broderick, Tamara and Giordano, Ryan and Meager, Rachael}, - year = {2021}, - month = apr, - journal = {arXiv:2011.14999 [econ, stat]}, - eprint = {2011.14999}, - eprinttype = {arxiv}, - primaryclass = {econ, stat}, - abstract = {We propose a method to assess the sensitivity of econometric analyses to the removal of a small fraction of the sample. Analyzing all possible data subsets of a certain size is computationally prohibitive, so we provide a finite-sample metric to approximately compute the number (or fraction) of observations that has the greatest influence on a given result when dropped. We call our resulting metric the Approximate Maximum Influence Perturbation. Our approximation is automatically computable and works for common estimators (including OLS, IV, GMM, MLE, and variational Bayes). We provide explicit finite-sample error bounds on our approximation for linear and instrumental variables regressions. At minimal computational cost, our metric provides an exact finite-sample lower bound on sensitivity for any estimator, so any non-robustness our metric finds is conclusive. We demonstrate that the Approximate Maximum Influence Perturbation is driven by a low signal-to-noise ratio in the inference problem, is not reflected in standard errors, does not disappear asymptotically, and is not a product of misspecification. Several empirical applications show that even 2-parameter linear regression analyses of randomized trials can be highly sensitive. While we find some applications are robust, in others the sign of a treatment effect can be changed by dropping less than 1\% of the sample even when standard errors are small.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Economics - Econometrics,Statistics - Methodology}, - file = {/Users/dill/Zotero/storage/MJ9R55QV/Broderick et al. - 2021 - An Automatic Finite-Sample Robustness Metric Can .pdf} -} - -@article{broker_comparison_2019, - title = {A Comparison of Image and Observer Based Aerial Surveys of Narwhal}, - author = {Br{\"o}ker, Koen C. A. and Hansen, Rikke G. and Leonard, Kathleen E. and Koski, William R. and Heide-J{\o}rgensen, Mads Peter}, - year = {2019}, - month = oct, - journal = {Marine Mammal Science}, - volume = {35}, - number = {4}, - pages = {1253--1279}, - issn = {0824-0469, 1748-7692}, - doi = {10.1111/mms.12586}, - abstract = {From 25 to 30 August 2014 a double-observer line-transect survey was conducted over Melville Bay, home to one of two summering populations of narwhal (Monodon monoceros) off West Greenland. A total of 1,932 linear kilometers was surveyed along 33 transects. In addition to using observers, the aircraft was equipped with two oblique cameras to capture a comparable data set. Analysts reviewed the images for narwhal sightings, which were then matched to the observer sightings. The objectives of the study were to determine advantages and disadvantages of the detection capabilities of both methodologies, and to conduct a comparative analysis of population abundance estimates. Correcting for the truncated detection distance of the images (500 m), the image analysts recorded more sightings (62) and a lower mean group size (2.2) compared to aerial observers (36 and 3.5, respectively), resulting in comparable numbers of individuals detected by both platforms (135 vs. 126). The abundance estimate based on the image sightings was 2,536 (CV = 0.51, 95\% CI: 1,003\textendash 6,406), which was not significantly different from the aerial observers estimate of 2,596 individuals (CV = 0.51; 95\% CI: 961\textendash 7,008). This study supports the potential of using UAS for marine mammal abundance studies.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/I4X67W6N/Bröker et al. - 2019 - A comparison of image and observer based aerial su.pdf} -} - -@book{bronshtein_handbook_2015, - title = {Handbook of {{Mathematics}}}, - author = {Bronshtein, I.N. and Semendyayev, K.A. and Musiol, G. and M{\"u}hlig, H.}, - year = {2015}, - publisher = {{Springer Berlin Heidelberg}}, - isbn = {978-3-662-46221-8} -} - -@article{brooks_automatic_1994, - title = {Automatic Starting Point Selection for Function Optimization}, - author = {Brooks, Stephen P. and Morgan, Byron JT}, - year = {1994}, - journal = {Statistics and Computing}, - volume = {4}, - number = {3}, - pages = {173--177}, - file = {/Users/dill/Zotero/storage/3LHX276U/art%3A10.1007%2FBF00142569.pdf} -} - -@book{buckland_advanced_2004, - title = {Advanced {{Distance Sampling}}: {{Estimating}} Abundance of Biological Populations}, - author = {Buckland, S.T. and Anderson, D.R. and Burnham, K.P. and Laake, J.L. and Borchers, D.L. and Thomas, L.}, - year = {2004}, - publisher = {{OUP Oxford}}, - isbn = {978-0-19-850783-3}, - lccn = {2004303414} -} - -@article{buckland_aerial_2012, - title = {Aerial Surveys of Seabirds: The Advent of Digital Methods: {{{\emph{Assessing}}}}{\emph{ the Effects of Offshore Wind Farms}}}, - shorttitle = {Aerial Surveys of Seabirds}, - author = {Buckland, Stephen T. and Burt, M. Louise and Rexstad, Eric A. and Mellor, Matt and Williams, Adrian E. and Woodward, Rebecca}, - year = {2012}, - month = aug, - journal = {Journal of Applied Ecology}, - volume = {49}, - number = {4}, - pages = {960--967}, - issn = {00218901}, - doi = {10.1111/j.1365-2664.2012.02150.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/GMGJZVSN/Journal of Applied Ecology 2012 Buckland.pdf} -} - -@article{buckland_algorithm_1992, - title = {Algorithm {{AS}} 270: {{Maximum Likelihood Fitting}} of {{Hermite}} and {{Simple Polynomial Densities}}}, - shorttitle = {Algorithm {{AS}} 270}, - author = {Buckland, S. T.}, - year = {1992}, - journal = {Applied Statistics}, - volume = {41}, - number = {1}, - pages = {241}, - issn = {00359254}, - doi = {10.2307/2347650}, - file = {/Users/dill/Zotero/storage/BYDCGPKN/Applied Statistics 1992 Buckland-1.pdf} -} - -@article{buckland_analyzing_2009, - title = {Analyzing Designed Experiments in Distance Sampling}, - author = {Buckland, Stephen T. and Russell, Robin E. and Dickson, Brett G. and Saab, Victoria A. and Gorman, Donal N. and Block, William M.}, - year = {2009}, - month = dec, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {14}, - number = {4}, - pages = {432--442}, - issn = {1085-7117, 1537-2693}, - doi = {10.1198/jabes.2009.08030}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YK4PSVZ4/JABES 2009 Buckland.pdf} -} - -@book{buckland_distance_2015, - title = {Distance {{Sampling}}: {{Methods}} and {{Applications}}}, - author = {Buckland, S.T. and Rexstad, E.A. and Marques, T.A. and Oedekoven, C.S.}, - year = {2015}, - series = {Methods in {{Statistical Ecology}}}, - publisher = {{Springer International Publishing}}, - isbn = {978-3-319-19219-2} -} - -@article{buckland_double-observer_2010, - title = {Double-{{Observer Line Transect Methods}}: {{Levels}} of {{Independence}}}, - shorttitle = {Double-{{Observer Line Transect Methods}}}, - author = {Buckland, Stephen T. and Laake, Jeffrey L. and Borchers, David L.}, - year = {2010}, - month = mar, - journal = {Biometrics}, - volume = {66}, - number = {1}, - pages = {169--177}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2009.01239.x}, - abstract = {Double-observer line transect methods are becoming increasingly widespread, especially for the estimation of marine mammal abundance from aerial and shipboard surveys when detection of animals on the line is uncertain. The resulting data supplement conventional distance sampling data with two-sample mark\textendash recapture data. Like conventional mark\textendash recapture data, these have inherent problems for estimating abundance in the presence of heterogeneity. Unlike conventional mark\textendash recapture methods, line transect methods use knowledge of the distribution of a covariate, which affects detection probability (namely, distance from the transect line) in inference. This knowledge can be used to diagnose unmodeled heterogeneity in the mark\textendash recapture component of the data. By modeling the covariance in detection probabilities with distance, we show how the estimation problem can be formulated in terms of different levels of independence. At one extreme, full independence is assumed, as in the Petersen estimator (which does not use distance data); at the other extreme, independence only occurs in the limit as detection probability tends to one. Between the two extremes, there is a range of models, including those currently in common use, which have intermediate levels of independence. We show how this framework can be used to provide more reliable analysis of double-observer line transect data. We test the methods by simulation, and by analysis of a dataset for which true abundance is known. We illustrate the approach through analysis of minke whale sightings data from the North Sea and adjacent waters.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/C2NMC8TI/Buckland et al. - 2010 - Double-Observer Line Transect Methods Levels of I.pdf} -} - -@article{buckland_estimated_1993, - title = {{{ESTIMATED POPULATION SIZE OF THE CALIFORNIA GRAY WHALE}}}, - author = {Buckland, S. T. and Breiwick, J. M. and Cattanach, K. L. and Laake, J. L.}, - year = {1993}, - month = jul, - journal = {Marine Mammal Science}, - volume = {9}, - number = {3}, - pages = {235--249}, - issn = {0824-0469, 1748-7692}, - doi = {10.1111/j.1748-7692.1993.tb00452.x}, - abstract = {The 1987-1988 counts of gray whales passing Monterey are reanalyzed to provide a revised population size estimate. The double count data are modeled using iterative logistic regression to allow for the effects of various covariates on probability of detection, and a correction factor is introduced for night rate of travel. The revised absolute population size estimate is 20,869 animals, with CV = 4.37\% and 95\% confidence interval (19,200, 22,700). In addition the series of relative population size estimates from 1967-1968 to 1987-1988 is scaled to pass through this estimate and modeled to provide variance estimates from interannual variation in population size estimates. This method yields an alternative population size estimate for 1987-1988 of 2 1,296 animals, with CV = 6.05\% and 95\% confidence interval (18,900,24,000). The average annual rate of increase between 1967-1968 and 1987-1988 was estimated to be 3.29\% with standard error 0.44\%.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/4ERQ2PRF/Buckland et al. - 1993 - ESTIMATED POPULATION SIZE OF THE CALIFORNIA GRAY W.pdf} -} - -@article{buckland_fitting_1992, - title = {Fitting {{Density Functions}} with {{Polynomials}}}, - author = {Buckland, Stephen T.}, - year = {1992}, - journal = {Applied Statistics}, - volume = {41}, - number = {1}, - pages = {63}, - issn = {00359254}, - doi = {10.2307/2347618}, - file = {/Users/dill/Zotero/storage/4FGQD5KL/Applied Statistics 1992 Buckland.pdf} -} - -@book{buckland_introduction_2001, - title = {Introduction to {{Distance Sampling}}: {{Estimating Abundance}} of {{Biological Populations}}}, - author = {Buckland, S.T. and Anderson, D.R. and Burnham, K. and Laake, J.L. and Borchers, D.L. and Thomas, L.}, - year = {2001}, - series = {Introduction to {{Distance Sampling}}: {{Estimating Abundance}} of {{Biological Populations}}}, - publisher = {{Oxford University Press}}, - isbn = {978-0-19-850649-2}, - lccn = {2001033232} -} - -@article{buckland_line_2007, - title = {Line {{Transect Methods}} for {{Plant Surveys}}}, - author = {Buckland, S. T. and Borchers, D. L. and Johnston, A. and Henrys, P. A. and Marques, T. A.}, - year = {2007}, - month = dec, - journal = {Biometrics}, - volume = {63}, - number = {4}, - pages = {989--998}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2007.00798.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/GMX59MC7/Biom 2007 Buckland.pdf} -} - -@article{buckland_model_1997, - title = {Model {{Selection}}: {{An Integral Part}} of {{Inference}}}, - shorttitle = {Model {{Selection}}}, - author = {Buckland, S. T. and Burnham, K. P. and Augustin, N. H.}, - year = {1997}, - month = jun, - journal = {Biometrics}, - volume = {53}, - number = {2}, - pages = {603}, - issn = {0006341X}, - doi = {10.2307/2533961}, - file = {/Users/dill/Zotero/storage/CAB7HTLV/Buckland et. al. 1997.pdf} -} - -@article{buckland_model-based_2016, - title = {Model-{{Based Distance Sampling}}}, - author = {Buckland, S. T. and Oedekoven, C. S. and Borchers, D. L.}, - year = {2016}, - month = mar, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {21}, - number = {1}, - pages = {58--75}, - issn = {1085-7117, 1537-2693}, - doi = {10.1007/s13253-015-0220-7}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UUT48V9Q/art%3A10.1007%2Fs13253-015-0220-7.pdf} -} - -@article{buckland_monte_1983, - title = {Monte {{Carlo Methods For Confidence Interval Estimation Using The Bootstrap Technique}}}, - author = {Buckland, Stephen T}, - year = {1983}, - journal = {Journal of Applied Statistics}, - volume = {10}, - number = {2}, - pages = {194--212}, - abstract = {An extension of Monte Carlo methods to confidence interval estimation, using the bootstrap technique, is investigated. The approach may have considerable potential for parameters that have estimators with complicated analytic properties but with probability distribution that can be simulated. Potential fields of application include ratio estimation, compound distribution and estimation of probabilities.}, - file = {/Users/dill/Zotero/storage/W6DE99X3/buckland1983.pdf} -} - -@article{buckland_perpendicular_1985, - title = {Perpendicular Distance Models for Line Transect Sampling}, - author = {Buckland, S. T.}, - year = {1985}, - journal = {Biometrics}, - pages = {177--195}, - file = {/Users/dill/Zotero/storage/78IB6C8A/Biom 1985 Buckland.pdf} -} - -@article{buckland_point-transect_2006, - title = {Point-Transect Surveys for Songbirds: Robust Methodologies}, - shorttitle = {Point-Transect Surveys for Songbirds}, - author = {Buckland, Stephen T.}, - year = {2006}, - journal = {The Auk}, - volume = {123}, - number = {2}, - pages = {345--357}, - file = {/Users/dill/Zotero/storage/9R5SK9PZ/3d6f4ba742396b8d451e491d7735ab88d397.pdf} -} - -@article{buckland_state-space_2004, - title = {State-Space Models for the Dynamics of Wild Animal Populations}, - author = {Buckland, S.T. and Newman, K.B. and Thomas, L. and Koesters, N.B.}, - year = {2004}, - month = jan, - journal = {Ecological Modelling}, - volume = {171}, - number = {1-2}, - pages = {157--175}, - issn = {03043800}, - doi = {10.1016/j.ecolmodel.2003.08.002}, - langid = {english}, - file = {/Users/dill/Zotero/storage/RB67ENQU/Ecological Modelling 2004 Buckland.pdf} -} - -@article{bucklin_comparing_2015, - title = {Comparing Species Distribution Models Constructed with Different Subsets of Environmental Predictors}, - author = {Bucklin, David N. and Basille, Mathieu and Benscoter, Allison M. and Brandt, Laura A. and Mazzotti, Frank J. and Roma{\~n}ach, Stephanie S. and Speroterra, Carolina and Watling, James I.}, - editor = {Thuiller, Wilfried}, - year = {2015}, - month = jan, - journal = {Diversity and Distributions}, - volume = {21}, - number = {1}, - pages = {23--35}, - issn = {13669516}, - doi = {10.1111/ddi.12247}, - langid = {english}, - file = {/Users/dill/Zotero/storage/XYSZJ2NQ/Diversity Distrib. 2014 Bucklin.pdf} -} - -@article{buja_statistical_2009, - title = {Statistical Inference for Exploratory Data Analysis and Model Diagnostics}, - author = {Buja, A. and Cook, D. and Hofmann, H. and Lawrence, M. and Lee, E.-K. and Swayne, D. F. and Wickham, H.}, - year = {2009}, - month = nov, - journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences}, - volume = {367}, - number = {1906}, - pages = {4361--4383}, - issn = {1364-503X, 1471-2962}, - doi = {10.1098/rsta.2009.0120}, - langid = {english}, - file = {/Users/dill/Zotero/storage/BNHVJY9E/Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences 2009 Buja.pdf} -} - -@article{burkner_brms_2017, - title = {Brms : {{An R Package}} for {{Bayesian Multilevel Models Using Stan}}}, - shorttitle = {{\textbf{Brms}}}, - author = {B\{{\textbackslash}:u\}rkner, Paul-Christian}, - year = {2017}, - journal = {Journal of Statistical Software}, - volume = {80}, - number = {1}, - issn = {1548-7660}, - doi = {10.18637/jss.v080.i01}, - abstract = {The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan. A wide range of distributions and link functions are supported, allowing users to fit \textendash{} among others \textendash{} linear, robust linear, binomial, Poisson, survival, ordinal, zero-inflated, hurdle, and even non-linear models all in a multilevel context. Further modeling options include autocorrelation of the response variable, user defined covariance structures, censored data, as well as meta-analytic standard errors. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. In addition, model fit can easily be assessed and compared with the Watanabe-Akaike information criterion and leave-one-out cross-validation.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QAFKXWEP/Bürkner - 2017 - bbrmsb An iRi Package for Bayesian Mul.pdf} -} - -@article{burt_appendix_nodate, - title = {{{APPENDIX S1}}: {{Introduction}} to Mark-Recapture Distance Sampling Methods}, - author = {Burt, M L and Borchers, D L and Jenkins, K J and Marques, T A}, - pages = {7}, - abstract = {This supporting information is provided as a brief introduction for those with little, or no, knowledge of distance sampling or mark-recapture methods. The reader is referred to Buckland et al. (2001; 2004) for a more comprehensive assessment of the subject. Examples of running a mark-recapture distance sampling analysis in Distance (Thomas et al. 2010) and R (R Core Team 2013) using the mrds package (Laake et al. 2012) are provided in Appendix S2.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/RSXHBIRQ/Burt et al. - APPENDIX S1 Introduction to mark-recapture distan.pdf} -} - -@techreport{burt_review_2006, - title = {Review of Density Surface Modelling Applied to {{JARPA}} Survey Data}, - author = {Burt, M. L. and Paxton, C. G. M.}, - year = {2006}, - institution = {{Paper SC}}, - file = {/Users/dill/Zotero/storage/7VRSTSK3/2006 Burt.pdf} -} - -@article{burt_using_2014, - title = {Using Mark-Recapture Distance Sampling Methods on Line Transect Surveys}, - author = {Burt, Mary Louise and Borchers, David L. and Jenkins, Kurt J. and Marques, Tiago A.}, - editor = {Isaac, Nick}, - year = {2014}, - month = nov, - journal = {Methods in Ecology and Evolution}, - volume = {5}, - number = {11}, - pages = {1180--1191}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12294}, - langid = {english}, - file = {/Users/dill/Zotero/storage/3SI4QSJE/mee312294.pdf;/Users/dill/Zotero/storage/UKB83IK9/Methods in Ecology and Evolution 2014 Burt.pdf} -} - -@article{caffo_case_2007, - title = {A Case Study in Pharmacologic Imaging Using Principal Curves in {{Single Photon Emission Computed Tomography}}}, - author = {Caffo, Brian S. and Crainiceanu, Ciprian M. and Deng, Lijuan and Hendrix, Craig W.}, - year = {2007}, - file = {/Users/dill/Zotero/storage/4MEC7C45/Journal of the American Statistical Association 2008 Caffo.pdf} -} - -@phdthesis{camp_improved_2020, - title = {Improved Methods for Estimating Spatial and Temporal Trends from Point Transect Survey Data}, - author = {Camp, Richard J.}, - year = {2020}, - school = {University of St Andrews}, - file = {/Users/dill/Zotero/storage/8VEWGFNL/160027053-Final.pdf} -} - -@article{camp_using_2020, - title = {Using Density Surface Models to Estimate Spatio-Temporal Changes in Population Densities and Trend}, - author = {Camp, Richard J. and Miller, David L. and Thomas, Len and Buckland, Stephen T. and Kendall, Steve J.}, - year = {2020}, - month = apr, - journal = {Ecography}, - issn = {09067590}, - doi = {10.1111/ecog.04859}, - langid = {english}, - file = {/Users/dill/Zotero/storage/32YR9MQ9/Camp et al. - 2020 - Using density surface models to estimate spatio-te.pdf} -} - -@article{campbell_survey_1977, - title = {A {{Survey}} of {{Statistical Work}} on the {{Mackenzie River Series}} of {{Annual Canadian Lynx Trappings}} for the {{Years}} 1821-1934 and a {{New Analysis}}}, - author = {Campbell, M. J. and Walker, A. M.}, - year = {1977}, - journal = {Journal of the Royal Statistical Society. Series A (General)}, - volume = {140}, - number = {4}, - pages = {411}, - issn = {00359238}, - doi = {10.2307/2345277}, - abstract = {This paper falls into two main parts. The first reviews past statistical analyses of a very well-known time series consisting of annual lynx trappings in a region of North-west Canada for 114 consecutive years. The second describes a new analysis carried out recently by the authors, producing a model in which, after a logarithmic transformation, the series is generated by the superposition of a pure sine wave and a second-order autoregressive process.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/V2MFCJ3T/Campbell and Walker - 1977 - A Survey of Statistical Work on the Mackenzie Rive.pdf} -} - -@techreport{camphuysen_towards_2004, - title = {Towards Standardised Seabirds at Sea Census Techniques in Connection with Environmental Impact Assessments for Offshore Wind Farms in the {{U}}.{{K}}.}, - author = {Camphuysen, C.J. and Fox, A.D. and Leopold, M.F. and Petersen, I.K.}, - year = {2004}, - number = {COWRIE BAM-02-2002}, - pages = {39}, - institution = {{Royal Netherlands Institute for Sea Research}}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2V5BS8ME/Towards standardised seabirds at sea census techni.pdf} -} - -@article{canadas_abundance_2008, - title = {Abundance and Habitat Preferences of the Short-Beaked Common Dolphin {{Delphinus}} Delphis in the Southwestern {{Mediterranean}}: Implications for Conservation}, - shorttitle = {Abundance and Habitat Preferences of the Short-Beaked Common Dolphin {{Delphinus}} Delphis in the Southwestern {{Mediterranean}}}, - author = {Ca{\~n}adas, A and Hammond, Ps}, - year = {2008}, - month = may, - journal = {Endangered Species Research}, - volume = {4}, - pages = {309--331}, - issn = {1863-5407, 1613-4796}, - doi = {10.3354/esr00073}, - abstract = {The Mediterranean sub-population of short-beaked common dolphin is believed to have suffered a steep decline in the Mediterranean in recent years, and in 2003 it was listed as endangered in the IUCN Red List of Threatened Species. Effective conservation will depend critically on our understanding of the relationship between the species and its habitats. The Albor\'an Sea is believed to be the most important remaining Mediterranean habitat for this species, and thus constitutes a vital source of information for the development of conservation measures. We used spatial modelling to estimate the abundance and explore the habitat use of common dolphins in this area, examining regional, seasonal and interannual variations, as well as the influence of biological factors such as presence of calves, interspecific relationships and behaviour. From 1992 to 2004, 37 385 km of non-systematic line transects generated 738 sightings in a 19 189 km2 study area. The point estimate of abundance was 19 428 (95\% CI = 15 277 to 22 804) dolphins. Seasonal and geographical variations in abundance were detected, with higher average density in summer than in winter, and in the Western Albor\'an Sea than in the east Gulf of Vera, which has different physical/environmental characteristics. No overall trend in abundance was observed in the Albor\'an area. However, a decline was observed in the Gulf of Vera, with a summer density 3-fold lower in the period from 1996 to 2004 than in 1992 to 1995. A potential link of this decline with prey depletion due to the exponential growth of aquaculture in the area is discussed. Clear differences in habitat use were also found when examining the influence of biological factors. In particular, groups with calves and groups that were feeding preferred more coastal waters. This result could have important implications for the development of conservation measures for this species in the Mediterranean.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9XDZI572/Cañadas and Hammond - 2008 - Abundance and habitat preferences of the short-bea.pdf} -} - -@article{candy_modelling_2004, - title = {Modelling Catch and Effort Data Using Generalised Linear Models, the {{Tweedie}} Distribution, Random Vessel Effects and Random Stratum-by-Year Effects}, - author = {Candy, S. G.}, - year = {2004}, - journal = {Ccamlr Science}, - volume = {11}, - pages = {59--80}, - file = {/Users/dill/Zotero/storage/YKH6ZA7E/Ccamlr Science 2004 Candy.pdf} -} - -@article{carlin_approaches_1990, - title = {Approaches for {{Empirical Bayes Confidence Intervals}}}, - author = {Carlin, Bradley P and Gelfand, Alan E}, - year = {1990}, - pages = {11}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DW64KEQE/Carlin and Gelfand - 2020 - Approaches for Empirical Bayes Confidence Interval.pdf} -} - -@article{carlin_sample_1991, - title = {A Sample Reuse Method for Accurate Parametric Empirical {{Bayes}} Confidence Intervals}, - author = {Carlin, Bradley P. and Gelfand, Alan E.}, - year = {1991}, - journal = {Journal of the Royal Statistical Society. Series B (Methodological)}, - pages = {189--200}, - file = {/Users/dill/Zotero/storage/CUICHI24/Journal of the Royal Statistical Society Series B ( … 1991 Carlin.pdf} -} - -@article{caron_population_2007, - title = {Population Trends and Habitat Use of {{Harlequin Ducks}} in {{Rhode Island}}}, - author = {Caron, Christine M. and Paton, Peter W. C.}, - year = {2007}, - month = sep, - journal = {Journal of Field Ornithology}, - volume = {78}, - number = {3}, - pages = {254--262}, - issn = {0273-8570, 1557-9263}, - doi = {10.1111/j.1557-9263.2007.00110.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/8SHIGFVU/J Field Ornithology 2007 Caron.pdf} -} - -@article{carpenter_stan_2017, - title = {Stan: {{A Probabilistic Programming Language}}}, - author = {Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen}, - year = {2017}, - journal = {Journal of Statistical Software, Articles}, - volume = {76}, - number = {1}, - pages = {1--32}, - issn = {1548-7660}, - doi = {10.18637/jss.v076.i01}, - abstract = {Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.}, - keywords = {algorithmic differentiation,Bayesian inference,probabilistic programming,Stan} -} - -@article{casella_introduction_1985, - title = {An {{Introduction}} to {{Empirical Bayes Data Analysis}}}, - author = {Casella, George}, - year = {1985}, - month = may, - journal = {The American Statistician}, - volume = {39}, - number = {2}, - pages = {83}, - issn = {00031305}, - doi = {10.2307/2682801}, - file = {/Users/dill/Zotero/storage/BEA3PXAZ/The American Statistician 1985 Casella.pdf} -} - -@article{cassey_assessment_1999, - title = {An Assessment of Distance Sampling Techniques for Estimating Animal Abundance}, - author = {Cassey, Phillip and McArdle, Brian H.}, - year = {1999}, - journal = {Environmetrics}, - volume = {10}, - number = {3}, - pages = {261--278}, - file = {/Users/dill/Zotero/storage/DGNKNMTG/Environmetrics 1999 Cassey.pdf} -} - -@article{chandler_inference_2011, - title = {Inference about Density and Temporary Emigration in Unmarked Populations}, - author = {Chandler, Richard B. and Royle, J. Andrew and King, David I.}, - year = {2011}, - journal = {Ecology}, - volume = {92}, - number = {7}, - pages = {1429--1435}, - file = {/Users/dill/Zotero/storage/INT2TT2S/Ecology 2011 Chandler.pdf} -} - -@article{chandler_spatially-explicit_nodate, - title = {Spatially-Explicit Models for Inference about Density in Unmarked Populations}, - author = {Chandler, Richard B and Royle, J Andrew}, - pages = {19}, - abstract = {Recently-developed spatial capture-recapture (SCR) methods represent a major advance over traditional capture-capture methods because they yield explicit estimates of animal density instead of population size within an unknown area, and they account for heterogeneity in capture probability arising from the juxtaposition of individuals and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR methods to be applied in studies of unmarked or partially-marked populations. The data required for our model are spatially-referenced counts made on one or more sample occasions at a collection of closely-spaced sample units such that individuals can be encountered at multiple locations. Our approach utilizes the spatial correlation in counts as information about the location of individual activity centers, which enables estimation of density and distance-related heterogeneity in detection. Cameratraps, hair snares, track plates, sound recordings, and even point counts can yield spatially-correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior distribution of abundance or density is strongly skewed in small samples, the posterior mode is an accurate point estimator as long as the trap spacing is not too large relative to scale parameter ({$\sigma$}) of the detection function. Marking a subset of the population can lead to substantial reductions in posterior skew and increased posterior precision. We also fit the model to point count data collected on the northern parula (Parula americana), and obtained a density estimate (posterior mode) of 0.38 (95\% CI: 0.19, 1.64) birds/ha. Our paper challenges sampling and analytical conventions by demonstrating that neither spatial independence nor individual recognition is needed to estimate population density \textemdash{} rather, spatial dependence induced by design can be informative about individual distribution and density.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/5DB8QCUX/Chandler and Royle - Spatially-explicit models for inference about dens.pdf} -} - -@article{chandler_use_2005, - title = {On the Use of Generalized Linear Models for Interpreting Climate Variability}, - author = {Chandler, Richard E.}, - year = {2005}, - month = nov, - journal = {Environmetrics}, - volume = {16}, - number = {7}, - pages = {699--715}, - issn = {1180-4009, 1099-095X}, - doi = {10.1002/env.731}, - langid = {english}, - file = {/Users/dill/Zotero/storage/NWMCFPA3/Environmetrics 2005 Chandler.pdf} -} - -@article{chavez-rosales_environmental_2019, - title = {Environmental Predictors of Habitat Suitability and Occurrence of Cetaceans in the Western {{North Atlantic Ocean}}}, - author = {{Chavez-Rosales}, Samuel and Palka, Debra L. and Garrison, Lance P. and Josephson, Elizabeth A.}, - year = {2019}, - month = dec, - journal = {Scientific Reports}, - volume = {9}, - number = {1}, - issn = {2045-2322}, - doi = {10.1038/s41598-019-42288-6}, - langid = {english}, - file = {/Users/dill/Zotero/storage/AGKCHSA9/Chavez-Rosales et al. - 2019 - Environmental predictors of habitat suitability an.pdf} -} - -@article{chelgren_using_2011, - title = {Using Spatiotemporal Models and Distance Sampling to Map the Space Use and Abundance of Newly Metamorphosed Western Toads ({{Anaxyrus}} Boreas)}, - author = {Chelgren, Nathan D. and Samora, Barbara and Adams, Michael J. and McCreary, Brome}, - year = {2011}, - journal = {Herpetological Conservation and Biology}, - volume = {6}, - number = {2}, - pages = {175--190}, - file = {/Users/dill/Zotero/storage/4SXIY5IQ/Herpetological … 2011 Chelgren.pdf} -} - -@article{cheng_polynomial_2018, - title = {Polynomial {{Regression As}} an {{Alternative}} to {{Neural Nets}}}, - author = {Cheng, Xi and Khomtchouk, Bohdan and Matloff, Norman and Mohanty, Pete}, - year = {2018}, - month = jun, - journal = {arXiv:1806.06850 [cs, stat]}, - eprint = {1806.06850}, - eprinttype = {arxiv}, - primaryclass = {cs, stat}, - abstract = {Despite the success of neural networks (NNs), there is still a concern among many over their ``black box'' nature. Why do they work? Here we present a simple analytic argument that NNs are in fact essentially polynomial regression models. This view will have various implications for NNs, e.g. providing an explanation for why convergence problems arise in NNs, and it gives rough guidance on avoiding overfitting. In addition, we use this phenomenon to predict and confirm a multicollinearity property of NNs not previously reported in the literature. Most importantly, given this loose correspondence, one may choose to routinely use polynomial models instead of NNs, thus avoiding some major problems of the latter, such as having to set many tuning parameters and dealing with convergence issues. We present a number of empirical results; in each case, the accuracy of the polynomial approach matches or exceeds that of NN approaches. A many-featured, open-source software package, polyreg, is available.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}, - file = {/Users/dill/Zotero/storage/RE98MIAG/Cheng et al. - 2018 - Polynomial Regression As an Alternative to Neural .pdf} -} - -@article{clapham_are_2015, - title = {Are Social Aggregation and Temporary Immigration Driving High Rates of Increase in Some {{Southern Hemisphere}} Humpback Whale Populations?}, - author = {Clapham, Phillip J. and Zerbini, Alexandre N.}, - year = {2015}, - month = mar, - journal = {Marine Biology}, - volume = {162}, - number = {3}, - pages = {625--634}, - issn = {0025-3162, 1432-1793}, - doi = {10.1007/s00227-015-2610-3}, - langid = {english}, - file = {/Users/dill/Zotero/storage/HDII828J/Mar Biol 2015 Clapham.pdf} -} - -@techreport{clarke_distribution_2020, - title = {Distribution and {{Relative Abundance}} of {{Marine Mammals}} in the {{Eastern Chukchi Sea}}, {{Eastern}} and {{Western Beaufort Sea}}, and {{Amundsen Gulf}}, 2019 {{Annual Report}}}, - author = {Clarke, T., Janet and Brower, A., Amelia and Ferguson, C., Megan and Willoughby, L., Amy and Rotrock, D., Audrey}, - year = {2020}, - address = {{Seattle, WA}}, - institution = {{United States. National Marine Fisheries Service.}}, - abstract = {This report describes field activities of the Aerial Surveys of Arctic Marine Mammals (ASAMM) project conducted during summer and fall (1 July\textendash 31 October) 2019, and data and analyses used to summarize field activities. Surveys were based in Utqia\.gvik, and Deadhorse, Alaska, USA, and targeted the eastern Chukchi Sea and western Beaufort Sea, between 67\textdegree N and 72\textdegree N latitude, 140\textdegree W and 169\textdegree W longitude, referred to as the ASAMM study area. Field activities also included surveys conducted in August 2019 in the eastern Beaufort Sea and Amundsen Gulf, between 67\textdegree N and 73\textdegree N latitude, 118\textdegree W and 140\textdegree W longitude, in support of the ASAMM Bowhead Abundance (ABA) project, to collect aerial survey data specific to estimating abundance of the Western Arctic (also known as the Bering-Chukchi-Beaufort Seas) bowhead whale population. Surveys in the ABA study area were based in Inuvik and Ulukhaktok, Northwest Territories, Canada.}, - keywords = {Aerial surveys in wildlife management,Estimates,Marine mammal populations}, - file = {/Users/dill/Zotero/storage/3FU9HHPX/Clarke et al. - 2020 - Distribution and Relative Abundance of Marine Mamm.pdf} -} - -@article{cleguer_novel_2021, - title = {A {{Novel Method}} for {{Using Small Unoccupied Aerial Vehicles}} to {{Survey Wildlife Species}} and {{Model Their Density Distribution}}}, - author = {Cleguer, Christophe and Kelly, Natalie and Tyne, Julian and Wieser, Martin and Peel, David and Hodgson, Amanda}, - year = {2021}, - month = may, - journal = {Frontiers in Marine Science}, - volume = {8}, - pages = {640338}, - issn = {2296-7745}, - doi = {10.3389/fmars.2021.640338}, - abstract = {There is growing interest from research and conservation groups in the potential for using small unoccupied aerial vehicles (UAVs; {$<$}2 kg) to conduct wildlife surveys because they are affordable, easy to use, readily available and reliable. However, limitations such as short flight endurance, and in many situations, aviation regulations, have constrained the use of small UAVs in survey applications. Thus, there is a need to refine survey methods adapted to small UAVs that conform to standard operations within aviation law. We developed a novel survey approach based on a grid sampling design using two multirotor UAVs (Phantom 4 Pros) flying simultaneously, within visual line of sight, from our vessel base-station. We used this approach to assess the finescale distribution and abundance of dugongs (Dugong dugon) in the remote waters of the Pilbara, Western Australia during three field seasons across 2 years. We surveyed 64 non-overlapping survey cells in random order one or more times and obtained complete image coverage of each surveyed cell of our 31 km2 survey area. Our sampling design maximizes sampling effort while limiting survey time by surveying four cells, two at a time, from one location. Overall, we conducted 240 flights with up to 17 flights per day (mean = 14 flights per day) and could obtain complete coverage of up to 11.36 km2per day. A total of 149 dugongs were sighted within the 50,482 images which we manually reviewed. Spatially-explicit models of dugong density distribution (corrected for availability and perception bias) were produced using general additive models to identify areas more or less used by dugongs (range of corrected dugong densities across all field season = 0.002\textendash 1.79 dugongs per 0.04 km2). Dugong abundance estimates ranged from 47 individuals in June 2019 (CV = 0.17) to 103 individuals in May 2018 (CV = 0.36). Our method, which proved convincing in a real-word application by its feasibility, ease of implementation, and achievable surface coverage has the potential to be used in a wide range of applications from community-based local-scale surveys, to long-term repeated/intensive surveys, and impact assessments and environmental monitoring studies.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/L35BHMVB/Cleguer et al. - 2021 - A Novel Method for Using Small Unoccupied Aerial V.pdf} -} - -@article{cleveland_locally_1988, - title = {Locally {{Weighted Regression}}: {{An Approach}} to {{Regression Analysis}} by {{Local Fitting}}}, - shorttitle = {Locally {{Weighted Regression}}}, - author = {Cleveland, William S. and Devlin, Susan J.}, - year = {1988}, - month = sep, - journal = {Journal of the American Statistical Association}, - volume = {83}, - number = {403}, - pages = {596}, - issn = {01621459}, - doi = {10.2307/2289282}, - file = {/Users/dill/Zotero/storage/2PM3LFB3/Journal of the American Statistical Association 1988 Cleveland.pdf} -} - -@article{cleveland_robust_1979, - title = {Robust {{Locally Weighted Regression}} and {{Smoothing Scatterplots}}}, - author = {Cleveland, William S.}, - year = {1979}, - month = dec, - journal = {Journal of the American Statistical Association}, - volume = {74}, - number = {368}, - pages = {829}, - issn = {01621459}, - doi = {10.2307/2286407}, - file = {/Users/dill/Zotero/storage/G5VKDXDM/Journal of the American Statistical Association 1979 Cleveland.pdf} -} - -@article{collier_modeling_2013, - title = {Modeling Spatially Explicit Densities of Endangered Avian Species in a Heterogeneous Landscape}, - author = {Collier, Bret A. and Farrell, Shannon L. and Long, Ashley M. and Campomizzi, Andrew J. and Hays, K. Brian and Laake, Jeffrey L. and Morrison, Michael L. and Wilkins, R. Neal}, - year = {2013}, - month = oct, - journal = {The Auk}, - volume = {130}, - number = {4}, - pages = {666--676}, - issn = {00048038, 19384254}, - doi = {10.1525/auk.2013.13017}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LILFV3NW/The Auk 2013 Collier.pdf} -} - -@article{conlisk_impossibility_2007, - title = {The {{Impossibility}} of {{Estimating}} a {{Negative Binomial Clustering Parameter}} from {{Presence}}-{{Absence Data}}: {{A Comment}} on {{He}} and {{Gaston}}}, - shorttitle = {The {{Impossibility}} of {{Estimating}} a {{Negative Binomial Clustering Parameter}} from {{Presence}}-{{Absence Data}}}, - author = {Conlisk, Erin and Conlisk, John and Harte, John}, - year = {2007}, - month = oct, - journal = {The American Naturalist}, - volume = {170}, - number = {4}, - pages = {651--654}, - issn = {0003-0147, 1537-5323}, - doi = {10.1086/521339}, - langid = {english}, - file = {/Users/dill/Zotero/storage/A2H9G32Q/Am Nat 2007 Conlisk.pdf} -} - -@article{conn_accommodating_2013, - title = {Accommodating Species Identification Errors in Transect Surveys}, - author = {Conn, Paul B. and McClintock, Brett T. and Cameron, Michael F. and Johnson, Devin S. and Moreland, Erin E. and Boveng, Peter L.}, - year = {2013}, - month = nov, - journal = {Ecology}, - volume = {94}, - number = {11}, - pages = {2607--2618}, - issn = {0012-9658}, - doi = {10.1890/12-2124.1}, - abstract = {Ecologists often use transect surveys to estimate the density and abundance of animal populations. Errors in species classification are often evident in such surveys, yet few statistical methods exist to properly account for them. In this paper, we examine biases that result from species misidentification when ignored, and we develop statistical models to provide unbiased estimates of density in the face of such errors. Our approach treats true species identity as a latent variable and requires auxiliary information on the misclassification process (such as informative priors, experiments using known species, or a double-observer protocol). We illustrate our approach with simulated census data and with double-observer survey data for ice-associated seals in the Bering Sea. For the seal analysis, we integrated misclassification into a model-based framework for distance-sampling data. The simulated data analysis demonstrated reliable estimation of animal density when there are experimental data to inform misclassification rates; double-observer protocols provided robust inference when there were "unknown" species observations but no outright misclassification, or when misclassification probabilities were symmetric and a symmetry constraint was imposed during estimation. Under our modeling framework, we obtained reasonable apparent densities of seal species even under considerable imprecision in species identification. We obtained more reliable inferences when modeling variation in density among transects. We argue that ecologists should often use spatially explicit models to account for differences in species distributions when trying to account for species misidentification. Our results support using double-observer sampling protocols that guard against species misclassification (i.e., by recording uncertain observations as "unknown").}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FFCU2Q5H/Conn et al. - 2013 - Accommodating species identification errors in tra.pdf} -} - -@article{conn_aerial_2021, - title = {Aerial Survey Estimates of Polar Bears and Their Tracks in the {{Chukchi Sea}}}, - author = {Conn, Paul B. and Chernook, Vladimir I. and Moreland, Erin E. and Trukhanova, Irina S. and Regehr, Eric V. and Vasiliev, Alexander N. and Wilson, Ryan R. and Belikov, Stanislav E. and Boveng, Peter L.}, - editor = {Chiaradia, Andr{\'e}}, - year = {2021}, - month = may, - journal = {PLOS ONE}, - volume = {16}, - number = {5}, - pages = {e0251130}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0251130}, - abstract = {Polar bears are of international conservation concern due to climate change but are difficult to study because of low densities and an expansive, circumpolar distribution. In a collaborative U.S.-Russian effort in spring of 2016, we used aerial surveys to detect and estimate the abundance of polar bears on sea ice in the Chukchi Sea. Our surveys used a combination of thermal imagery, digital photography, and human observations. Using spatio-temporal statistical models that related bear and track densities to physiographic and biological covariates (e.g., sea ice extent, resource selection functions derived from satellite tags), we predicted abundance and spatial distribution throughout our study area. Estimates of 2016 abundance ( N \^ * ) ranged from 3,435 (95\% CI: 2,300-5,131) to 5,444 (95\% CI: 3,636-8,152) depending on the proportion of bears assumed to be missed on the transect line during Russian surveys ( g (0)). Our point estimates are larger than, but of similar magnitude to, a recent estimate for the period 2008-2016 ( N \^ * = 2 , 937 ; 95\% CI 1,522-5,944) derived from an integrated population model applied to a slightly smaller area. Although a number of factors (e.g., equipment issues, differing platforms, low sample sizes, size of the study area relative to sampling effort) required us to make a number of assumptions to generate estimates, it establishes a useful lower bound for abundance, and suggests high spring polar bear densities on sea ice in Russian waters south of Wrangell Island. With future improvements, we suggest that springtime aerial surveys may represent a plausible avenue for studying abundance and distribution of polar bears and their prey over large, remote areas.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/WA6K9PBR/Conn et al. - 2021 - Aerial survey estimates of polar bears and their t.pdf} -} - -@article{conn_estimating_2014, - title = {Estimating Multispecies Abundance Using Automated Detection Systems: Ice-Associated Seals in the {{Bering Sea}}}, - shorttitle = {Estimating Multispecies Abundance Using Automated Detection Systems}, - author = {Conn, Paul~B. and Ver Hoef, Jay~M. and McClintock, Brett~T. and Moreland, Erin~E. and London, Josh~M. and Cameron, Michael~F. and Dahle, Shawn~P. and Boveng, Peter~L.}, - editor = {Francis, Charles}, - year = {2014}, - month = dec, - journal = {Methods in Ecology and Evolution}, - volume = {5}, - number = {12}, - pages = {1280--1293}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12127}, - langid = {english}, - file = {/Users/dill/Zotero/storage/H936VL58/Conn et al. - 2014 - Estimating multispecies abundance using automated .pdf} -} - -@article{conn_extrapolating_2015, - title = {On Extrapolating Past the Range of Observed Data When Making Statistical Predictions in Ecology}, - author = {Conn, Paul B. and Johnson, Devin S. and Boveng, Peter L.}, - year = {2015}, - journal = {PloS one}, - volume = {10}, - number = {10}, - pages = {e0141416}, - file = {/Users/dill/Zotero/storage/9WBLP2LG/PLoS ONE 2015 Conn.pdf} -} - -@article{conn_hierarchical_2012, - title = {A Hierarchical Modeling Framework for Multiple Observer Transect Surveys}, - author = {Conn, Paul B. and Laake, Jeffrey L. and Johnson, Devin S.}, - year = {2012}, - journal = {PLoS One}, - volume = {7}, - number = {8}, - pages = {e42294}, - file = {/Users/dill/Zotero/storage/34563NRL/PLoS ONE 2012 Conn.pdf} -} - -@article{cook_influential_1979, - title = {Influential {{Observations}} in {{Linear Regression}}}, - author = {Cook, R. Dennis}, - year = {1979}, - month = mar, - journal = {Journal of the American Statistical Association}, - volume = {74}, - number = {365}, - pages = {169}, - issn = {01621459}, - doi = {10.2307/2286747}, - file = {/Users/dill/Zotero/storage/N5PJC2D7/Journal of the American Statistical Association 1979 Cook.pdf} -} - -@article{cowling_spatial_1998, - title = {Spatial {{Methods}} for {{Line Transect Surveys}}}, - author = {Cowling, Ann}, - year = {1998}, - month = sep, - journal = {Biometrics}, - volume = {54}, - number = {3}, - pages = {828}, - issn = {0006341X}, - doi = {10.2307/2533837}, - file = {/Users/dill/Zotero/storage/PHRIBNMW/Biom 1998 Cowling.pdf} -} - -@article{cox_applied_2007, - title = {Applied Statistics: {{A}} Review}, - shorttitle = {Applied Statistics}, - author = {Cox, D. R.}, - year = {2007}, - month = jun, - journal = {The Annals of Applied Statistics}, - volume = {1}, - number = {1}, - pages = {1--16}, - issn = {1932-6157}, - doi = {10.1214/07-AOAS113}, - langid = {english}, - file = {/Users/dill/Zotero/storage/76M33USI/Ann. Appl. Stat. 2007 Cox.pdf} -} - -@article{cox_estimating_2011, - title = {Estimating the Density of {{Antarctic}} Krill ({{Euphausia}} Superba) from Multi-Beam Echo-Sounder Observations Using Distance Sampling Methods}, - author = {Cox, Martin J. and Borchers, David L. and Demer, David A. and Cutter, George R. and Brierley, Andrew S.}, - year = {2011}, - journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}, - volume = {60}, - number = {2}, - pages = {301--316}, - file = {/Users/dill/Zotero/storage/NTT6MUZX/Journal of the Royal Statistical Society Series C (Applied Statistics) 2011 Cox.pdf} -} - -@book{cox_point_1980, - title = {Point {{Processes}}}, - author = {Cox, D.R. and Isham, V.}, - year = {1980}, - series = {Chapman \& {{Hall}}/{{CRC Monographs}} on {{Statistics}} \& {{Applied Probability}}}, - publisher = {{Taylor \& Francis}}, - isbn = {978-0-412-21910-8}, - lccn = {81188647} -} - -@article{crase_incorporating_2014, - title = {Incorporating Spatial Autocorrelation into Species Distribution Models Alters Forecasts of Climate-Mediated Range Shifts}, - author = {Crase, Beth and Liedloff, Adam and Vesk, Peter A. and Fukuda, Yusuke and Wintle, Brendan A.}, - year = {2014}, - month = aug, - journal = {Global Change Biology}, - volume = {20}, - number = {8}, - pages = {2566--2579}, - issn = {13541013}, - doi = {10.1111/gcb.12598}, - langid = {english}, - file = {/Users/dill/Zotero/storage/AXJMB5SS/Glob Change Biol 2014 Crase.pdf} -} - -@article{craven_smoothing_1978, - title = {Smoothing Noisy Data with Spline Functions}, - author = {Craven, Peter and Wahba, Grace}, - year = {1978}, - journal = {Numerische mathematik}, - volume = {31}, - number = {4}, - pages = {377--403}, - file = {/Users/dill/Zotero/storage/BVLG2CRN/10.1007_BF01404567.pdf} -} - -@article{davis_exploring_2020, - title = {Exploring Movement Patterns and Changing Distributions of Baleen Whales in the Western {{North Atlantic}} Using a Decade of Passive Acoustic Data}, - author = {Davis, Genevieve E. and Baumgartner, Mark F. and Corkeron, Peter J. and Bell, Joel and Berchok, Catherine and Bonnell, Julianne M. and Bort Thornton, Jacqueline and Brault, Solange and Buchanan, Gary A. and Cholewiak, Danielle M. and Clark, Christopher W. and Delarue, Julien and Hatch, Leila T. and Klinck, Holger and Kraus, Scott D. and Martin, Bruce and Mellinger, David K. and Moors-Murphy, Hilary and Nieukirk, Sharon and Nowacek, Douglas P. and Parks, Susan E. and Parry, Dawn and Pegg, Nicole and Read, Andrew J. and Rice, Aaron N. and Risch, Denise and Scott, Alyssa and Soldevilla, Melissa S. and Stafford, Kathleen M. and Stanistreet, Joy E. and Summers, Erin and Todd, Sean and Van Parijs, Sofie M.}, - year = {2020}, - month = sep, - journal = {Global Change Biology}, - volume = {26}, - number = {9}, - pages = {4812--4840}, - issn = {1354-1013, 1365-2486}, - doi = {10.1111/gcb.15191}, - abstract = {Six baleen whale species are found in the temperate western North Atlantic Ocean, with limited information existing on the distribution and movement patterns for most. There is mounting evidence of distributional shifts in many species, including marine mammals, likely because of climate-driven changes in ocean temperature and circulation. Previous acoustic studies examined the occurrence of minke (Balaenoptera acutorostrata) and North Atlantic right whales (NARW; Eubalaena glacialis). This study assesses the acoustic presence of humpback (Megaptera novaeangliae), sei (B. borealis), fin (B. physalus), and blue whales (B. musculus) over a decade, based on daily detections of their vocalizations. Data collected from 2004 to 2014 on 281 bottommounted recorders, totaling 35,033 days, were processed using automated detection software and screened for each species' presence. A published study on NARW acoustics revealed significant changes in occurrence patterns between the periods of 2004\textendash 2010 and 2011\textendash 2014; therefore, these same time periods were examined here. All four species were present from the Southeast United States to Greenland; humpback whales were also present in the Caribbean. All species occurred throughout all regions in the winter, suggesting that baleen whales are widely distributed during these months. Each of the species showed significant changes in acoustic occurrence after 2010. Similar to NARWs, sei whales had higher acoustic occurrence in mid-Atlantic regions after 2010. Fin, blue, and sei whales were more frequently detected in the northern latitudes of the study area after 2010. Despite this general northward shift, all four species were detected less on the Scotian Shelf area after 2010, matching documented shifts in prey availability in this region. A decade of acoustic observations have shown important distributional changes over the range of baleen whales, mirroring known climatic shifts and identifying new habitats that will require further protection from anthropogenic threats like fixed fishing gear, shipping, and noise pollution.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2KJCGY5P/Davis et al. - 2020 - Exploring movement patterns and changing distribut.pdf} -} - -@article{davison_deviance_1989, - title = {Deviance Residuals and Normal Scores Plots}, - author = {Davison, A. C. and Gigli and {A}}, - year = {1989}, - journal = {Biometrika}, - volume = {76}, - number = {2}, - pages = {211--221}, - file = {/Users/dill/Zotero/storage/XCAPNGTM/davison1989.pdf} -} - -@article{davison_efficient_1986, - title = {Efficient {{Bootstrap Simulation}}}, - author = {Davison, A C and Hinkley, D. V. and Schechtman, E}, - year = {1986}, - journal = {Biometrika}, - volume = {73}, - number = {3}, - pages = {555--566}, - abstract = {Bootstrap methods are simulation methods for assessing sampling properties of statistical estimates. We discuss two ideas for making the simulation more efficient. The first idea is to balance the simulated samples, and the second idea is to make explicit use of approximations which do not require simulation. Both ideas are illustrated with three examples.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JHHH2VEP/Davison - Efficient Bootstrap Simulation.pdf} -} - -@article{dawson_design_2008, - title = {Design and Field Methods for Sighting Surveys of Cetaceans in Coastal and Riverine Habitats}, - author = {Dawson, Steve and Wade, Paul and Slooten, Elisabeth and Barlow, Jay}, - year = {2008}, - journal = {Mammal Review}, - volume = {38}, - number = {1}, - pages = {19--49}, - file = {/Users/dill/Zotero/storage/IAV6MSER/Mammal Review 2008 Dawson.pdf} -} - -@techreport{de_silva_sparse_2004, - title = {Sparse Multidimensional Scaling Using Landmark Points}, - author = {De Silva, Vin and Tenenbaum, Joshua B.}, - year = {2004}, - institution = {{Technical report, Stanford University}}, - file = {/Users/dill/Zotero/storage/IVB564I3/2004 De Silva.pdf} -} - -@misc{de_valpine_nimble_2020, - title = {{{NIMBLE}}: {{MCMC}}, {{Particle Filtering}}, and {{Programmable Hierarchical Modeling}}}, - author = {{de Valpine}, Perry and Paciorek, Christopher J. and Turek, Daniel and Michaud, N and {Anderson-Bergman}, C and Obermeyer, F and Wehrhahn Cortes, C and Rodr{\`i}guez, A and Temple Lang, D and Paganin, S}, - year = {2020} -} - -@article{de_valpine_programming_2017, - title = {Programming with Models: Writing Statistical Algorithms for General Model Structures with {{NIMBLE}}}, - shorttitle = {Programming with Models}, - author = {{de Valpine}, Perry and Turek, Daniel and Paciorek, Christopher J. and {Anderson-Bergman}, Clifford and Lang, Duncan Temple and Bodik, Rastislav}, - year = {2017}, - month = apr, - journal = {Journal of Computational and Graphical Statistics}, - volume = {26}, - number = {2}, - eprint = {1505.05093}, - eprinttype = {arxiv}, - pages = {403--413}, - issn = {1061-8600, 1537-2715}, - doi = {10.1080/10618600.2016.1172487}, - abstract = {We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simulations, and query the relationships among variables. For algorithm programming, NIMBLE provides functions that operate with model objects using two stages of evaluation. The first stage allows specialization of a function to a particular model and/or nodes, such as creating a Metropolis-Hastings sampler for a particular block of nodes. The second stage allows repeated execution of computations using the results of the first stage. To achieve efficient second-stage computation, NIMBLE compiles models and functions via C++, using the Eigen library for linear algebra, and provides the user with an interface to compiled objects. The NIMBLE language represents a compilable domain-specific language (DSL) embedded within R. This paper provides an overview of the design and rationale for NIMBLE along with illustrative examples including importance sampling, Markov chain Monte Carlo (MCMC) and Monte Carlo expectation maximization (MCEM).}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Statistics - Computation}, - file = {/Users/dill/Zotero/storage/WEK74HKL/de Valpine et al. - 2017 - Programming with models writing statistical algor.pdf} -} - -@book{deboor_practical_1978, - title = {A {{Practical Guide}} to {{Splines}}}, - author = {DeBoor, C.}, - year = {1978}, - publisher = {{Springer New York}}, - isbn = {978-0-387-98922-8}, - lccn = {99042676} -} - -@article{dellabianca_spatial_2016, - title = {Spatial {{Models}} of {{Abundance}} and {{Habitat Preferences}} of {{Commerson}}'s and {{Peale}}'s {{Dolphin}} in {{Southern Patagonian Waters}}}, - author = {Dellabianca, Natalia A. and Pierce, Graham J. and Raya Rey, Andrea and Scioscia, Gabriela and Miller, David L. and Torres, M{\'o}nica A. and Paso Viola, M. Natalia and Goodall, R. Natalie P. and Schiavini, Adri{\'a}n C. M.}, - editor = {Rosenfeld, Cheryl S.}, - year = {2016}, - month = oct, - journal = {PLOS ONE}, - volume = {11}, - number = {10}, - pages = {e0163441}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0163441}, - langid = {english}, - file = {/Users/dill/Zotero/storage/MCX7U4EG/PLoS ONE 2016 Dellabianca.PDF} -} - -@article{dempster_covariance_1972, - title = {Covariance {{Selection}}}, - author = {Dempster, A P}, - year = {1972}, - journal = {Biometrics}, - volume = {28}, - number = {1}, - pages = {157--175}, - abstract = {The covariance structure of a multivariate normal population can be simplified by setting elements of the inverse of the covariance matrix to zero. Reasons for adopting such a model and a rule for estimating its parameters are given in section 2. It is also proposed to select the zeros in the inverse from sample data. A numerical illustration of the proposed technique is given in section 3. Appendix A sketches the general theory of exponential families which underlies the special results of section 2, and Appendix B describes two approaches to computation of the proposed estimator.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ALGU4IG3/Dempster - Covariance Selection.pdf} -} - -@article{dennis_computational_2015, - title = {Computational Aspects of {{N-mixture}} Models: {{Computational Aspects}} of {{N-Mixture Models}}}, - shorttitle = {Computational Aspects of {{N-mixture}} Models}, - author = {Dennis, Emily B. and Morgan, Byron J.T. and Ridout, Martin S.}, - year = {2015}, - month = mar, - journal = {Biometrics}, - volume = {71}, - number = {1}, - pages = {237--246}, - issn = {0006341X}, - doi = {10.1111/biom.12246}, - langid = {english}, - file = {/Users/dill/Zotero/storage/79YVVAEN/biom12246.pdf} -} - -@article{diaconis_horseshoes_2008, - title = {Horseshoes in Multidimensional Scaling and Local Kernel Methods}, - author = {Diaconis, Persi and Goel, Sharad and Holmes, Susan}, - year = {2008}, - month = sep, - journal = {The Annals of Applied Statistics}, - volume = {2}, - number = {3}, - pages = {777--807}, - issn = {1932-6157}, - doi = {10.1214/08-AOAS165}, - langid = {english}, - file = {/Users/dill/Zotero/storage/8VRYWXJM/Ann. Appl. Stat. 2008 Diaconis.pdf} -} - -@article{dias_conservation_2016, - title = {Conservation Zones Promote Oak Regeneration and Shrub Diversity in Certified {{Mediterranean}} Oak Woodlands}, - author = {Dias, Filipe S. and Miller, David L. and Marques, Tiago A. and Marcelino, Joana and Caldeira, Maria C. and Orestes Cerdeira, J. and Bugalho, Miguel N.}, - year = {2016}, - month = mar, - journal = {Biological Conservation}, - volume = {195}, - pages = {226--234}, - issn = {00063207}, - doi = {10.1016/j.biocon.2016.01.009}, - langid = {english}, - file = {/Users/dill/Zotero/storage/M9TXT3Z2/Biological Conservation 2016 Dias.pdf} -} - -@article{dias_density_2019, - title = {Density and Distribution of Western Chimpanzees around a Bauxite Deposit in the {{Bo\'e Sector}}, {{Guinea}}-{{Bissau}}}, - author = {Dias, Filipe S. and Wenceslau, Jos{\'e} F. C. and Marques, Tiago A. and Miller, David L.}, - year = {2019}, - month = sep, - journal = {American Journal of Primatology}, - volume = {81}, - number = {9}, - issn = {0275-2565, 1098-2345}, - doi = {10.1002/ajp.23047}, - abstract = {The Bo\'e sector in southeast Guinea-Bissau harbors a population of western chimpanzees (Pan troglodytes verus) that inhabits a mosaic of forest and savanna. The Bo\'e sector contains a substantial bauxite deposit in a region called Ronde Hill, and there are plans for the construction of a mine, which may endanger the chimpanzee population. In 1-week survey in May 2013, we used the standing crop nest counts method to obtain the number of chimpanzee nests and from that estimate the density and abundance of chimpanzees. We carried out five 1-km line transects that covered the bauxite deposit and surrounding valleys. We used density surface modeling to analyze habitat preferences, then predicted chimpanzee nest density and distribution based on environmental variables. We found the projected location of the mine partially coincides with an area of high predicted abundances of chimpanzee nests and is surrounded by highly suitable areas for chimpanzees (northeast and southwest). We conclude the mine could have significant direct and indirect effects on this population of chimpanzees whose impacts must be carefully considered and properly mitigated if the mine is built.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JGTWNA8Y/Dias et al. - 2019 - Density and distribution of western chimpanzees ar.pdf} -} - -@article{diggle_geostatistical_2010, - title = {Geostatistical Inference under Preferential Sampling}, - author = {Diggle, Peter J. and Menezes, Raquel and Su, Ting-li}, - year = {2010}, - month = mar, - journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}, - volume = {59}, - number = {2}, - pages = {191--232}, - issn = {00359254, 14679876}, - doi = {10.1111/j.1467-9876.2009.00701.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FWZXGR9Z/Diggle et al. - 2010 - Geostatistical inference under preferential sampli.pdf} -} - -@article{diggle_model-based_2002, - title = {Model-Based Geostatistics}, - author = {Diggle, P. J. and Tawn, J. A. and Moyeed, R. A.}, - year = {2002}, - month = jan, - journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}, - volume = {47}, - number = {3}, - pages = {299--350}, - issn = {00359254, 14679876}, - doi = {10.1111/1467-9876.00113}, - abstract = {Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S…x) based on observations Yi ˆ S…xi † ‡ Zi at sampling locations xi , where the Zi are mutually independent, zero-mean Gaussian random variables. We describe two spatial applications for which Gaussian distributional assumptions are clearly inappropriate. The \textregistered rst concerns the assessment of residual contamination from nuclear weapons testing on a South Paci\textregistered c island, in which the sampling method generates spatially indexed Poisson counts conditional on an unobserved spatially varying intensity of radioactivity; we conclude that a conventional geostatistical analysis oversmooths the data and underestimates the spatial extremes of the intensity. The second application provides a description of spatial variation in the risk of campylobacter infections relative to other enteric infections in part of north Lancashire and south Cumbria. For this application, we treat the data as binomial counts at unit postcode locations, conditionally on an unobserved relative risk surface which we estimate. The theoretical framework for our extension of geostatistical methods is that, conditionally on the unobserved process S…x†, observations at sample locations xi form a generalized linear model with the corresponding values of S…xi † appearing as an offset term in the linear predictor. We use a Bayesian inferential framework, implemented via the Markov chain Monte Carlo method, to solve the prediction problem for non-linear functionals of S…x†, making a proper allowance for the uncertainty in the estimation of any model parameters.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/TFTQABFT/Diggle et al. - 2002 - Model-based geostatistics.pdf} -} - -@article{dinsdale_methods_2019, - title = {Methods for Preferential Sampling in Geostatistics}, - author = {Dinsdale, Daniel and Salibian-Barrera, Matias}, - year = {2019}, - month = jan, - journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}, - volume = {68}, - number = {1}, - pages = {181--198}, - issn = {0035-9254, 1467-9876}, - doi = {10.1111/rssc.12286}, - abstract = {Preferential sampling in geostatistics occurs when the locations at which observations are made may depend on the spatial process that underlines the correlation structure of the measurements. We show that previously proposed Monte Carlo estimates for the likelihood function may not be approximating the desired function. Furthermore, we argue that, for preferential sampling of moderate complexity, alternative and widely available numerical methods to approximate the likelihood function produce better results than Monte Carlo methods. We illustrate our findings on the Galicia data set analysed previously in the literature.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QNGEJDYC/Dinsdale and Salibian‐Barrera - 2019 - Methods for preferential sampling in geostatistics.pdf} -} - -@incollection{dold_splines_1977, - title = {Splines Minimizing Rotation-Invariant Semi-Norms in {{Sobolev}} Spaces}, - booktitle = {Constructive {{Theory}} of {{Functions}} of {{Several Variables}}}, - author = {Duchon, Jean}, - editor = {Dold, A. and Eckmann, B. and Schempp, Walter and Zeller, Karl}, - year = {1977}, - volume = {571}, - pages = {85--100}, - publisher = {{Springer Berlin Heidelberg}}, - address = {{Berlin, Heidelberg}}, - doi = {10.1007/BFb0086566}, - isbn = {978-3-540-08069-5 978-3-540-37496-1}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JCCTDELW/Duchon - 1977 - Splines minimizing rotation-invariant semi-norms i.pdf;/Users/dill/Zotero/storage/YWMSN2RT/paper.pdf} -} - -@article{dong_pseudo-splines_2007, - title = {Pseudo-Splines, Wavelets and Framelets}, - author = {Dong, Bin and Shen, Zuowei}, - year = {2007}, - journal = {Applied and Computational Harmonic Analysis}, - volume = {22}, - number = {1}, - pages = {78--104}, - file = {/Users/dill/Zotero/storage/2GEMM92H/10.1.1.63.1688.pdf} -} - -@techreport{doniol-valcroze_abundance_2015, - title = {Abundance Estimate of the {{Eastern Canada}} \textendash{} {{West Greenland}} Bowhead Whale Population Based on the 2013 {{High Arctic Cetacean Survey}}}, - author = {{Doniol-Valcroze}, T}, - year = {2015}, - number = {2015/058}, - pages = {32}, - langid = {english}, - file = {/Users/dill/Zotero/storage/A4Y95KXA/Doniol-Valcroze - Abundance estimate of the Eastern Canada – West Gr.pdf} -} - -@article{dorazio_mixture_2003, - title = {Mixture Models for Estimating the Size of a Closed Population When Capture Rates Vary among Individuals}, - author = {Dorazio, Robert M. and Andrew Royle, J.}, - year = {2003}, - journal = {Biometrics}, - volume = {59}, - number = {2}, - pages = {351--364}, - file = {/Users/dill/Zotero/storage/H3ABNFHG/Biom 2003 Dorazio.pdf;/Users/dill/Zotero/storage/QH4YNBAE/Biom 2003 Dorazio-1.pdf} -} - -@article{dormann_promising_2007, - title = {Promising the Future? {{Global}} Change Projections of Species Distributions}, - shorttitle = {Promising the Future?}, - author = {Dormann, Carsten F.}, - year = {2007}, - month = sep, - journal = {Basic and Applied Ecology}, - volume = {8}, - number = {5}, - pages = {387--397}, - issn = {14391791}, - doi = {10.1016/j.baae.2006.11.001}, - abstract = {Projections of species' distribution under global change (climatic and environmental) are of great scientific and societal relevance. They rely on a proper understanding of how environmental drivers determine species occurrence patterns. This understanding is usually derived from an analysis of the species' present distribution by statistical means (species distribution models). Projections based on species distribution models make several assumptions (such as constancy of limiting factors, no evolutionary adaptation to drivers, global dispersal), some of which are ecologically untenable. Also, methodological issues muddy the waters (e.g. spatial autocorrelation, collinearity of drivers). Here, I review the main shortcomings of species distribution models and species distribution projections, identify limits to their use and open a perspective on how to overcome some current obstacles. As a consequence, I caution biogeographers against making projections too lightheartedly and conservation ecologists and policy makers to be aware that there are several unresolved problems.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/4YDICRNU/Dormann - 2007 - Promising the future Global change projections of.pdf} -} - -@article{downs_spatially_2008, - title = {Spatially Modelling Pathways of Migratory Birds for Nature Reserve Site Selection}, - author = {Downs, J. A. and Horner, M. W.}, - year = {2008}, - month = jun, - journal = {International Journal of Geographical Information Science}, - volume = {22}, - number = {6}, - pages = {687--702}, - issn = {1365-8816, 1362-3087}, - doi = {10.1080/13658810701674962}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DUQAKQ5X/Int. J. of Geographical Info. Sc. 2008 Downs.pdf} -} - -@article{drake_early_2010, - title = {Early Warning Signals of Extinction in Deteriorating Environments}, - author = {Drake, John M. and Griffen, Blaine D.}, - year = {2010}, - month = sep, - journal = {Nature}, - volume = {467}, - number = {7314}, - pages = {456--459}, - issn = {0028-0836, 1476-4687}, - doi = {10.1038/nature09389}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DAWFJ5ML/Nature 2010 Drake.pdf} -} - -@article{dunn_randomized_1996, - title = {Randomized {{Quantile Residuals}}}, - author = {Dunn, Peter K. and Smyth, Gordon K.}, - year = {1996}, - journal = {Journal of Computational and Graphical Statistics}, - volume = {5}, - number = {3}, - pages = {236--244}, - issn = {10618600}, - abstract = {In this article we give a general definition of residuals for regression models with independent responses. Our definition produces residuals that are exactly normal, apart from sampling variability in the estimated parameters, by inverting the fitted distribution function for each response value and finding the equivalent standard normal quantile. Our definition includes some randomization to achieve continuous residuals when the response variable is discrete. Quantile residuals are easily computed in computer packages such as SAS, S-Plus, GLIM, or LispStat, and allow residual analyses to be carried out in many commonly occurring situations in which the customary definitions of residuals fail. Quantile residuals are applied in this article to three example data sets.}, - file = {/Users/dill/Zotero/storage/4PBZ7JUM/Dunn and Smyth - Randomized Quantile Residuals.pdf} -} - -@article{dunn_series_2005, - title = {Series Evaluation of {{Tweedie}} Exponential Dispersion Model Densities}, - author = {Dunn, Peter K. and Smyth, Gordon K.}, - year = {2005}, - journal = {Statistics and Computing}, - volume = {15}, - number = {4}, - pages = {267--280}, - file = {/Users/dill/Zotero/storage/DZR32XAW/Statistics and Computing 2005 Dunn.pdf} -} - -@inproceedings{dunn_tweedie_2001, - title = {Tweedie Family Densities: Methods of Evaluation}, - shorttitle = {Tweedie Family Densities}, - booktitle = {Proceedings of the 16th {{International Workshop}} on {{Statistical Modelling}}, {{Odense}}, {{Denmark}}}, - author = {Dunn, Peter K. and Smyth, Gordon K.}, - year = {2001}, - pages = {2--6}, - file = {/Users/dill/Zotero/storage/CDWLXKGJ/2001 Dunn.pdf} -} - -@article{dunn_tweedie_nodate, - title = {Tweedie {{Family Densities}}: {{Methods}} of {{Evaluation}}}, - author = {Dunn, Peter K and Smyth, Gordon K}, - pages = {8}, - abstract = {Two numerical evaluation methods are considered for Tweedie family densities which are then used to assess the accuracy of the saddlepoint approximation. This has implications for the distribution of the residual deviance in generalized linear models. Other applications include residual analysis through quantile residuals.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/WUL9CUJP/Dunn and Smyth - Tweedie Family Densities Methods of Evaluation.pdf} -} - -@article{dupont_spatial_2020, - title = {Spatial+: A Novel Approach to Spatial Confounding}, - shorttitle = {Spatial+}, - author = {Dupont, Emiko and Wood, Simon N. and Augustin, Nicole}, - year = {2020}, - month = sep, - journal = {arXiv:2009.09420 [math, stat]}, - eprint = {2009.09420}, - eprinttype = {arxiv}, - primaryclass = {math, stat}, - abstract = {In spatial regression models, collinearity between covariates and spatial effects can lead to significant bias in effect estimates. This problem, known as spatial confounding, is encountered modelling forestry data to assess the effect of temperature on tree health. Reliable inference is difficult as results depend on whether or not spatial effects are included in the model. The mechanism behind spatial confounding is poorly understood and methods for dealing with it are limited. We propose a novel approach, spatial+, in which collinearity is reduced by replacing the covariates in the spatial model by their residuals after spatial dependence has been regressed away. Using a thin plate spline model formulation, we recognise spatial confounding as a smoothing-induced bias identified by Rice (1986), and through asymptotic analysis of the effect estimates, we show that spatial+ avoids the bias problems of the spatial model. This is also demonstrated in a simulation study. Spatial+ is straight-forward to implement using existing software and, as the response variable is the same as that of the spatial model, standard model selection criteria can be used for comparisons. A major advantage of the method is also that it extends to models with non-Gaussian response distributions. Finally, while our results are derived in a thin plate spline setting, the spatial+ methodology transfers easily to other spatial model formulations.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Mathematics - Statistics Theory,Statistics - Applications,Statistics - Methodology}, - file = {/Users/dill/Zotero/storage/RM5Q6E7A/Dupont et al. - 2020 - Spatial+ a novel approach to spatial confounding.pdf} -} - -@article{duran_misfortunes_2014, - title = {The {{Misfortunes}} of a {{Trio}} of {{Mathematicians Using Computer Algebra Systems}}. {{Can We Trust}} in {{Them}}?}, - author = {Dur{\'a}n, Antonio J. and P{\'e}rez, Mario and Varona, Juan L.}, - year = {2014}, - month = nov, - journal = {Notices of the American Mathematical Society}, - volume = {61}, - number = {10}, - pages = {1249}, - issn = {0002-9920, 1088-9477}, - doi = {10.1090/noti1173}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FHGYG7GA/Notices Amer. Math. Soc. 2014 Durán.pdf} -} - -@article{ebden_gaussian_2008, - title = {Gaussian {{Processes}} for {{Regression}}: {{A Quick Introduction}}}, - author = {Ebden, M}, - year = {2008}, - journal = {arXiv preprint arXiv:1505.02965v2}, - eprint = {1505.02965v2}, - eprinttype = {arxiv}, - pages = {11}, - archiveprefix = {arXiv}, - langid = {english}, - file = {/Users/dill/Zotero/storage/IGZ88XTW/Ebden - Gaussian Processes for Regression A Quick Introdu.pdf} -} - -@incollection{efford_density_2009, - title = {Density Estimation by Spatially Explicit Capture\textendash Recapture: Likelihood-Based Methods}, - shorttitle = {Density Estimation by Spatially Explicit Capture\textendash Recapture}, - booktitle = {Modeling Demographic Processes in Marked Populations}, - author = {Efford, Murray G. and Borchers, David L. and Byrom, Andrea E.}, - year = {2009}, - pages = {255--269}, - publisher = {{Springer}}, - file = {/Users/dill/Zotero/storage/R5CY4NQW/Modeling Demographic Processes In Marked Populations 2009 Efford.pdf} -} - -@article{efford_population_2009, - title = {Population Density Estimated from Locations of Individuals on a Passive Detector Array}, - author = {Efford, Murray G. and Dawson, Deanna K. and Borchers, David L.}, - year = {2009}, - journal = {Ecology}, - volume = {90}, - number = {10}, - pages = {2676--2682}, - file = {/Users/dill/Zotero/storage/T5W8QB5W/Ecology 2009 Efford.pdf} -} - -@article{efron_least_2004, - title = {Least Angle Regression}, - author = {Efron, Bradley and Hastie, Trevor and Johnstone, Iain and Tibshirani, Robert}, - year = {2004}, - journal = {The Annals of statistics}, - volume = {32}, - number = {2}, - pages = {407--499}, - file = {/Users/dill/Zotero/storage/HKAQ5T7K/arXiv 2004 Efron.pdf} -} - -@article{efron_ra_1998, - title = {{{RA Fisher}} in the 21st Century}, - author = {Efron, Bradley}, - year = {1998}, - journal = {Statistical Science}, - pages = {95--114}, - file = {/Users/dill/Zotero/storage/KKZYM6YC/Statist. Sci. 1998 Efron.pdf} -} - -@article{eidous_improving_2005, - title = {On {{Improving Kernel Estimators Using Line Transect Sampling}}}, - author = {Eidous, Omar M.}, - year = {2005}, - month = apr, - journal = {Communications in Statistics - Theory and Methods}, - volume = {34}, - number = {4}, - pages = {931--941}, - issn = {0361-0926, 1532-415X}, - doi = {10.1081/STA-200054439}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UIL95XHK/Communications in Statistics - Theory and Methods 2005 Eidous.pdf} -} - -@article{eilers_flexible_1996, - title = {Flexible Smoothing with {{B-splines}} and Penalties}, - author = {Eilers, Paul HC and Marx, Brian D.}, - year = {1996}, - journal = {Statistical science}, - volume = {11}, - number = {2}, - pages = {89--102}, - file = {/Users/dill/Zotero/storage/DY34QEE3/Statist. Sci. 1996 Eilers.pdf} -} - -@article{eilers_twenty_2015, - title = {Twenty Years of {{P-splines}}}, - author = {Eilers, Paul HC and Marx, Brian D. and Durb{\'a}n, Maria}, - year = {2015}, - journal = {SORT-Statistics and Operations Research Transactions}, - volume = {39}, - number = {2}, - pages = {149--186}, - file = {/Users/dill/Zotero/storage/S7TS33HA/39.2.1.eilers-etal.pdf} -} - -@book{el-shaarawi_encyclopedia_2002, - title = {Encyclopedia of Environmetrics}, - editor = {{El-Shaarawi}, A. H. and Piegorsch, Walter W.}, - year = {2002}, - publisher = {{Wiley}}, - address = {{Chichester ; New York}}, - isbn = {978-0-471-89997-6}, - lccn = {GE45.S73 E53 2002}, - keywords = {Environmental sciences,Methodology,Statistical methods}, - file = {/Users/dill/Zotero/storage/DLWWS58K/2002 Thomas.pdf} -} - -@article{elith_species_2009, - title = {Species {{Distribution Models}}: {{Ecological Explanation}} and {{Prediction Across Space}} and {{Time}}}, - shorttitle = {Species {{Distribution Models}}}, - author = {Elith, Jane and Leathwick, John R.}, - year = {2009}, - month = dec, - journal = {Annual Review of Ecology, Evolution, and Systematics}, - volume = {40}, - number = {1}, - pages = {677--697}, - issn = {1543-592X, 1545-2069}, - doi = {10.1146/annurev.ecolsys.110308.120159}, - langid = {english}, - file = {/Users/dill/Zotero/storage/AIUHZL88/Annu. Rev. Ecol. Evol. Syst. 2009 Elith.pdf} -} - -@article{elith_statistical_2011, - title = {A Statistical Explanation of {{MaxEnt}} for Ecologists: {{Statistical}} Explanation of {{MaxEnt}}}, - shorttitle = {A Statistical Explanation of {{MaxEnt}} for Ecologists}, - author = {Elith, Jane and Phillips, Steven J. and Hastie, Trevor and Dud{\'i}k, Miroslav and Chee, Yung En and Yates, Colin J.}, - year = {2011}, - month = jan, - journal = {Diversity and Distributions}, - volume = {17}, - number = {1}, - pages = {43--57}, - issn = {13669516}, - doi = {10.1111/j.1472-4642.2010.00725.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6GQWR6N3/j.1472-4642.2010.00725.x.pdf;/Users/dill/Zotero/storage/QSIRYHFQ/Diversity Distrib. 2010 Elith.pdf} -} - -@article{elston_adapting_1997, - title = {Adapting Regression Equations to Minimize the Mean Squared Error of Predictions Made Using Covariate Data from a {{GIS}}}, - author = {Elston, D. A. and Jayasinghe, G. and Buckland, S. T. and Macmillan, D. C. and Aspinall, R. J.}, - year = {1997}, - month = apr, - journal = {International Journal of Geographical Information Science}, - volume = {11}, - number = {3}, - pages = {265--280}, - issn = {1365-8816, 1362-3087}, - doi = {10.1080/136588197242392}, - abstract = {Regression equations between a response variable and candidate explanatory variables are often estimated using a training set of data from closely observed locations but are then applied using covariate data held in a GIS to predict the response variable at locations throughout a region. When the regression assumptions hold and the GIS data are free from error, this procedure gives unbiased estimates of the response variable and minimizes the prediction mean squared error. However, when the explanatory variables in the GIS are recorded with substantially greater errors than were present in the training set, this procedure does not minimize the prediction mean squared error. A theoretical argument leads to the proposal of an adaptation for regression equations to minimize the prediction mean squared error. The e\"Aectiveness of this adaptation is demonstrated by a simulation study and by its application to an equation for tree growth rate.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/T9B8RKNP/Elston et al. - 1997 - Adapting regression equations to minimize the mean.pdf} -} - -@article{elton_ten-year_1942, - title = {The {{Ten-Year Cycle}} in {{Numbers}} of the {{Lynx}} in {{Canada}}}, - author = {Elton, Charles and Nicholson, Mary}, - year = {1942}, - month = nov, - journal = {The Journal of Animal Ecology}, - volume = {11}, - number = {2}, - pages = {215}, - issn = {00218790}, - doi = {10.2307/1358}, - langid = {english}, - file = {/Users/dill/Zotero/storage/VDF8HSAH/Elton and Nicholson - 1942 - The Ten-Year Cycle in Numbers of the Lynx in Canad.pdf} -} - -@article{evans_modelling_2012, - title = {Modelling Ecological Systems in a Changing World}, - author = {Evans, M. R.}, - year = {2012}, - month = jan, - journal = {Philosophical Transactions of the Royal Society B: Biological Sciences}, - volume = {367}, - number = {1586}, - pages = {181--190}, - issn = {0962-8436, 1471-2970}, - doi = {10.1098/rstb.2011.0172}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YMFC4YEF/Philosophical Transactions of the Royal Society B Biological Sciences 2011 Evans.pdf} -} - -@article{fahrmeir_bayesian_2001, - title = {Bayesian Inference for Generalized Additive Mixed Models Based on {{Markov}} Random Field Priors}, - author = {Fahrmeir, Ludwig and Lang, Stefan}, - year = {2001}, - journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}, - volume = {50}, - number = {2}, - pages = {201--220}, - abstract = {Most regression problems in practice require flexible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a unified approach for Bayesian inference via Markov chain Monte Carlo simulation in generalized additive and semiparametric mixed models. Different types of covariates, such as the usual covariates with mixed effects, metrical covariates with non-linear effects, unstructured random effects, trend and seasonal components in longitudinal data and spatial covariates, are all treated within the same general framework by assigning appro- priate Markov random field priors with different forms and degrees of smoothness. We applied the approach in several case-studies and consulting cases, showing that the methods are also computationally feasible in problems with many covariates and large data sets. In this paper, we choose two typical applications.}, - file = {/Users/dill/Zotero/storage/JBP4SGL7/05f17ac8a0d6d859ffa4af70fd2a8aa73352.pdf} -} - -@article{fahrmeir_bayesian_2010, - title = {Bayesian Regularisation in Structured Additive Regression: A Unifying Perspective on Shrinkage, Smoothing and Predictor Selection}, - shorttitle = {Bayesian Regularisation in Structured Additive Regression}, - author = {Fahrmeir, Ludwig and Kneib, Thomas and Konrath, Susanne}, - year = {2010}, - month = apr, - journal = {Statistics and Computing}, - volume = {20}, - number = {2}, - pages = {203--219}, - issn = {0960-3174, 1573-1375}, - doi = {10.1007/s11222-009-9158-3}, - langid = {english}, - file = {/Users/dill/Zotero/storage/5NYENBMR/bayesian_reg_psplines.pdf} -} - -@incollection{fahrmeir_identification_2008, - title = {On the Identification of Trend and Correlation in Temporal and Spatial Regression}, - booktitle = {Recent Advances in Linear Models and Related Areas}, - author = {Fahrmeir, Ludwig and Kneib, Thomas}, - year = {2008}, - pages = {1--27}, - publisher = {{Springer}}, - file = {/Users/dill/Zotero/storage/ZBUS3IUW/Recent advances in linear models and related areas 2008 Fahrmeir.pdf} -} - -@article{fahrmeir_penalized_2004, - title = {Penalized {{Structured Additive Regression}} for {{Space-Time Data}}: A {{Bayesian Perspective}}}, - author = {Fahrmeir, Ludwig and Kneib, Thomas and Lang, Stefan}, - year = {2004}, - journal = {Statistica Sinica}, - volume = {14}, - pages = {731--761}, - abstract = {We propose extensions of penalized spline generalized additive models for analyzing space-time regression data and study them from a Bayesian perspective. Non-linear effects of continuous covariates and time trends are modelled through Bayesian versions of penalized splines, while correlated spatial effects follow a Markov random field prior. This allows to treat all functions and effects within a unified general framework by assigning appropriate priors with different forms and degrees of smoothness. Inference can be performed either with full (FB) or empirical Bayes (EB) posterior analysis. FB inference using MCMC techniques is a slight extension of previous work. For EB inference, a computationally efficient solution is developed on the basis of a generalized linear mixed model representation. The second approach can be viewed as posterior mode estimation and is closely related to penalized likelihood estimation in a frequentist setting. Variance components, corresponding to inverse smoothing parameters, are then estimated by marginal likelihood. We carefully compare both inferential procedures in simulation studies and illustrate them through data applications. The methodology is available in the open domain statistical package BayesX and as an S-plus/R function.}, - file = {/Users/dill/Zotero/storage/D9IFEUNR/Fahrmeir et al. - PENALIZED STRUCTURED ADDITIVE REGRESSION FOR SPACE.pdf} -} - -@book{fahrmeir_regression_2013, - title = {Regression: {{Models}}, {{Methods}} and {{Applications}}}, - author = {Fahrmeir, L. and Kneib, T. and Lang, S. and Marx, B.}, - year = {2013}, - publisher = {{Springer Berlin Heidelberg}}, - isbn = {978-3-642-34333-9} -} - -@article{faraway_backscoring_2012, - title = {Backscoring in {{Principal Coordinates Analysis}}}, - author = {Faraway, Julian J.}, - year = {2012}, - month = apr, - journal = {Journal of Computational and Graphical Statistics}, - volume = {21}, - number = {2}, - pages = {394--412}, - issn = {1061-8600, 1537-2715}, - doi = {10.1080/10618600.2012.672097}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UY67FLX7/Journal of Computational and Graphical Statistics 2012 Faraway.pdf} -} - -@article{feinstein_its_2015, - title = {It's a {{Trap}}: {{Emperor Palpatine}}'s {{Poison Pill}}}, - shorttitle = {It's a {{Trap}}}, - author = {Feinstein, Zachary}, - year = {2015}, - journal = {arXiv preprint arXiv:1511.09054}, - eprint = {1511.09054}, - eprinttype = {arxiv}, - archiveprefix = {arXiv}, - file = {/Users/dill/Zotero/storage/989RDMY2/arXiv 2015 Feinstein.pdf} -} - -@techreport{ferguson_bering-chukchi-beaufort_2020, - title = {Bering-{{Chukchi-Beaufort Seas}} Bowhead Whale ({{Balaena}} Mysticetus) Abundance Estimate from the 2019 Aerial Line- Transect Survey}, - author = {Ferguson, Megan C}, - year = {2020}, - number = {SC/68B/ASI/09}, - pages = {48}, - institution = {{International Whaling Commission}}, - abstract = {We estimated the abundance of the Bering-Chukchi-Beaufort Seas stock of bowhead whales in 2019 to be 14,531 whales (CV = 0.540; bootstrap 95\% CI [7,968, 29,376]) based on aerial line-transect surveys conducted over the whales' summer range in the Beaufort Sea shelf and Amundsen Gulf. A geographically stratified analysis, incorporating correction factors for trackline detection probability and availability bias, was used to estimate bowhead whale abundance in three regions. The regional abundance estimate for Amundsen Gulf was 275 whales (CV = 0.550; bootstrap 95\% CI [83, 654]), the eastern Beaufort Sea was 13,207 whales (CV = 0.570; bootstrap 95\% CI [7,108, 27,522]), and the western Beaufort Sea was 1,049 whales (CV = 0.538; bootstrap 95\% CI [252, 2,132]). A bootstrap sensitivity analysis suggested that the largest contributors to the uncertainty in the overall abundance estimate were the trackline detection probability and variability among the line-transect survey sample units. Increasing the sample size of imagery (i.e., the ``independent observer'' in the mark-recapture distancesampling analysis used to estimate trackline detection probability) would likely reduce CV(\dbend\dbend{\^\dbend}\dbend\dbend\dbend ). Efficient and accurate auto-detection algorithms for large cetaceans would help streamline the photo analysis process. Furthermore, spatially explicit density modeling techniques could likely reduce CV(\dbend\dbend{\^\dbend}\dbend\dbend\dbend ) by accounting for the unexplained variability among samples in the geographically stratified analysis.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/E3ACI7HJ/Ferguson - Bering-Chukchi-Beaufort Seas bowhead whale (Balaen.pdf} -} - -@article{ferguson_spatial_2006, - title = {Spatial Models of Delphinid (Family {{Delphinidae}}) Encounter Rate and Group Size in the Eastern Tropical {{Pacific Ocean}}}, - author = {Ferguson, Megan C. and Barlow, Jay and Fiedler, Paul and Reilly, Stephen B. and Gerrodette, Tim}, - year = {2006}, - month = mar, - journal = {Ecological Modelling}, - volume = {193}, - number = {3-4}, - pages = {645--662}, - issn = {03043800}, - doi = {10.1016/j.ecolmodel.2005.10.034}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Y94GXZEP/Ecological Modelling 2006 Ferguson.pdf} -} - -@article{ferland_integer-valued_2006, - title = {Integer-{{Valued GARCH Process}}}, - author = {Ferland, Ren{\'e} and Latour, Alain and Oraichi, Driss}, - year = {2006}, - month = nov, - journal = {Journal of Time Series Analysis}, - volume = {27}, - number = {6}, - pages = {923--942}, - issn = {0143-9782, 1467-9892}, - doi = {10.1111/j.1467-9892.2006.00496.x}, - abstract = {An integer-valued analogue of the classical generalized autoregressive conditional heteroskedastic (GARCH) (p,q) model with Poisson deviates is proposed and a condition for the existence of such a process is given. For the case p {$\frac{1}{4}$} 1, q {$\frac{1}{4}$} 1, it is explicitly shown that an integer-valued GARCH process is a standard autoregressive moving average (1, 1) process. The problem of maximum likelihood estimation of parameters is treated. An application of the model to a real time series with a numerical example is given.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/I6LBAJ7Z/Ferland et al. - 2006 - Integer-Valued GARCH Process.pdf} -} - -@book{ferraty_nonparametric_2006, - title = {Nonparametric Functional Data Analysis: Theory and Practice}, - shorttitle = {Nonparametric Functional Data Analysis}, - author = {Ferraty, Fr{\'e}d{\'e}ric and Vieu, Philippe}, - year = {2006}, - series = {Springer Series in Statistics}, - publisher = {{Springer}}, - address = {{New York}}, - isbn = {978-0-387-30369-7}, - lccn = {QA278.8 .F47 2006}, - keywords = {Multivariate analysis,Nonparametric statistics}, - annotation = {OCLC: ocm70261207}, - file = {/Users/dill/Zotero/storage/IH678NUR/2006 Ferraty.pdf} -} - -@article{fewster_estimating_2009, - title = {Estimating the {{Encounter Rate Variance}} in {{Distance Sampling}}}, - author = {Fewster, Rachel M. and Buckland, Stephen T. and Burnham, Kenneth P. and Borchers, David L. and Jupp, Peter E. and Laake, Jeffrey L. and Thomas, Len}, - year = {2009}, - month = mar, - journal = {Biometrics}, - volume = {65}, - number = {1}, - pages = {225--236}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2008.01018.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9ZRX4K4L/Biometrics 2009 Fewster.pdf} -} - -@article{fewster_information_2013, - title = {Information on Parameters of Interest Decreases under Transformations}, - author = {Fewster, R. M. and Jupp, Peter E.}, - year = {2013}, - journal = {Journal of Multivariate Analysis}, - volume = {120}, - pages = {34--39}, - file = {/Users/dill/Zotero/storage/5QSMDXT4/Fewster_Jupp_Infgain.pdf} -} - -@article{fewster_trace-contrast_2016, - title = {Trace-{{Contrast Models}} for {{Capture}}\textendash{{Recapture Without Capture Histories}}}, - author = {Fewster, R. M. and Stevenson, B. C. and Borchers, D. L.}, - year = {2016}, - month = may, - journal = {Statistical Science}, - volume = {31}, - number = {2}, - pages = {245--258}, - issn = {0883-4237}, - doi = {10.1214/16-STS551}, - abstract = {Capture-recapture studies increasingly rely upon natural tags that allow animals to be identified by features such as coat markings, DNA profiles, acoustic profiles, or spatial locations. These innovations greatly increase the number of capture samples achievable and enable capture-recapture estimation for many inaccessible and elusive species. However, natural features are invariably imperfect as indicators of identity. Drawing on the recently-developed Palm likelihood approach to parameter estimation in clustered point processes, we propose a new estimation framework based on comparing pairs of detections, which we term the trace-contrast framework. Importantly, no reconstruction of capture histories is needed. We show that we can achieve accurate, precise, and computationally fast inference. We illustrate the methods with a camera-trap study of a partially-marked population of ship rats (Rattus rattus) in New Zealand.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JLP4ZIS4/Fewster et al. - 2016 - Trace-Contrast Models for Capture–Recapture Withou.pdf} -} - -@article{fewster_variance_2011, - title = {Variance {{Estimation}} for {{Systematic Designs}} in {{Spatial Surveys}}}, - author = {Fewster, R. M.}, - year = {2011}, - month = dec, - journal = {Biometrics}, - volume = {67}, - number = {4}, - pages = {1518--1531}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2011.01604.x}, - abstract = {In spatial surveys for estimating the density of objects in a survey region, systematic designs will generally yield lower variance than random designs. However, estimating the systematic variance is well known to be a difficult problem. Existing methods tend to overestimate the variance, so although the variance is genuinely reduced, it is over-reported, and the gain from the more efficient design is lost. The current approaches to estimating a systematic variance for spatial surveys are to approximate the systematic design by a random design, or approximate it by a stratified design. Previous work has shown that approximation by a random design can perform very poorly, while approximation by a stratified design is an improvement but can still be severely biased in some situations. We develop a new estimator based on modeling the encounter process over space. The new ``striplet'' estimator has negligible bias and excellent precision in a wide range of simulation scenarios, including strip-sampling, distance-sampling, and quadrat-sampling surveys, and including populations that are highly trended or have strong aggregation of objects. We apply the new estimator to survey data for the spotted hyena (Crocuta crocuta) in the Serengeti National Park, Tanzania, and find that the reported coefficient of variation for estimated density is 20\% using approximation by a random design, 17\% using approximation by a stratified design, and 11\% using the new striplet estimator. This large reduction in reported variance is verified by simulation.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/K8LNSTN2/Fewster - 2011 - Variance Estimation for Systematic Designs in Spat.pdf} -} - -@article{fieberg_correlation_2010, - title = {Correlation and Studies of Habitat Selection: Problem, Red Herring or Opportunity?}, - shorttitle = {Correlation and Studies of Habitat Selection}, - author = {Fieberg, J. and Matthiopoulos, J. and Hebblewhite, M. and Boyce, M. S. and Frair, J. L.}, - year = {2010}, - month = jul, - journal = {Philosophical Transactions of the Royal Society B: Biological Sciences}, - volume = {365}, - number = {1550}, - pages = {2233--2244}, - issn = {0962-8436, 1471-2970}, - doi = {10.1098/rstb.2010.0079}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FGBQ6TND/Philosophical Transactions of the Royal Society B Biological Sciences 2010 Fieberg.pdf} -} - -@article{fifield_employing_2017, - title = {Employing {{Predictive Spatial Models}} to {{Inform Conservation Planning}} for {{Seabirds}} in the {{Labrador Sea}}}, - author = {Fifield, David A. and Hedd, April and {Avery-Gomm}, Stephanie and Robertson, Gregory J. and Gjerdrum, Carina and McFarlane Tranquilla, Laura}, - year = {2017}, - month = may, - journal = {Frontiers in Marine Science}, - volume = {4}, - pages = {149}, - issn = {2296-7745}, - doi = {10.3389/fmars.2017.00149}, - abstract = {Seabirds are vulnerable to incidental harm from human activities in the ocean, and knowledge of their seasonal distribution is required to assess risk and effectively inform marine conservation planning. Significant hydrocarbon discoveries and exploration licenses in the Labrador Sea underscore the need for quantitative information on seabird seasonal distribution and abundance, as this region is known to provide important habitat for seabirds year-round. We explore the utility of density surface modeling (DSM) to improve seabird information available for regional conservation and management decision making. We, (1) develop seasonal density surface models for seabirds in the Labrador Sea using data from vessel-based surveys (2006\textendash 2014; 13,783 linear km of surveys), (2) present measures of uncertainty in model predictions, (3) discuss how density surface models can inform conservation and management decision making, and 4) explore challenges and potential pitfalls associated with using these modeling procedures. Models predicted large areas of high seabird density in fall over continental shelf waters (max. {$\sim$}80 birds{$\cdot$}km-2) driven largely by the southward migration of murres (Uria spp.) and dovekies (Alle alle) from Arctic breeding colonies. The continental shelf break was also highlighted as an important habitat feature, with predictions of high seabird densities particularly during summer (max. {$\sim$}70 birds{$\cdot$}km-2). Notable concentrations of seabirds overlapped with several significant hydrocarbon discoveries on the continental shelf and large areas in the vicinity of the southern shelf break, which are in the early stages of exploration. Some, but not all, areas of high seabird density were within current Ecologically and Biologically Significant Area (EBSA) boundaries. Building predictive spatial models required knowledge of Distance Sampling and GAMs, and significant investments of time and computational power\textemdash resource needs that are becoming more common in ecological modeling. Visualization of predictions and their uncertainty needed to be considered for appropriate interpretation by end users. Model uncertainty tended to be greater where survey effort was limited or where predictor covariates exceeded the range of those observed. Predictive spatial models proved useful in generating defensible estimates of seabird densities in many areas of interest to the oil and gas industry in the Labrador Sea, and will have continued use in marine risk assessments and spatial planning activities in the region and beyond.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ISE5PH8I/Fifield et al. - 2017 - Employing Predictive Spatial Models to Inform Cons.pdf} -} - -@article{fiske_unmarked_2011, - title = {Unmarked: An {{R}} Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance}, - shorttitle = {Unmarked}, - author = {Fiske, Ian and Chandler, Richard}, - year = {2011}, - journal = {Journal of Statistical Software}, - volume = {43}, - number = {10}, - pages = {1--23}, - file = {/Users/dill/Zotero/storage/ASIV4WDE/Journal of Statistical Software 2011 Fiske.pdf} -} - -@article{fithian_bias_2015, - title = {Bias Correction in Species Distribution Models: Pooling Survey and Collection Data for Multiple Species}, - shorttitle = {Bias Correction in Species Distribution Models}, - author = {Fithian, William and Elith, Jane and Hastie, Trevor and Keith, David A.}, - editor = {O'Hara, Robert B.}, - year = {2015}, - month = apr, - journal = {Methods in Ecology and Evolution}, - volume = {6}, - number = {4}, - pages = {424--438}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12242}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ALQ8RQH8/Methods in Ecology and Evolution 2014 Fithian.pdf} -} - -@article{flaxman_poisson_2017, - title = {Poisson Intensity Estimation with Reproducing Kernels}, - author = {Flaxman, Seth and Teh, Yee Whye and Sejdinovic, Dino}, - year = {2017}, - journal = {Proceedings of the 20th International Conference on Artifi- cial Intelligence and Statistics (AISTATS) 2017, Fort Laud- erdale, Florida, USA.}, - volume = {54}, - keywords = {Integral equations,Numerical solutions}, - file = {/Users/dill/Zotero/storage/2FG7D5ZD/1610.08623.pdf} -} - -@article{fletcher_modelling_2012, - title = {Modelling Data from Different Sites, Times or Studies: Weighted vs. Unweighted Regression: {{Combining}} Information}, - shorttitle = {Modelling Data from Different Sites, Times or Studies}, - author = {Fletcher, David and Dixon, Philip M.}, - year = {2012}, - month = feb, - journal = {Methods in Ecology and Evolution}, - volume = {3}, - number = {1}, - pages = {168--176}, - issn = {2041210X}, - doi = {10.1111/j.2041-210X.2011.00140.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/VUIKVKCT/Methods in Ecology and Evolution 2011 Fletcher.pdf} -} - -@article{fornberg_observations_2002, - title = {Observations on the Behavior of Radial Basis Function Approximations near Boundaries}, - author = {Fornberg, Bengt and Driscoll, Tobin A. and Wright, Grady and Charles, Richard}, - year = {2002}, - journal = {Computers \& Mathematics with Applications}, - volume = {43}, - number = {3-5}, - pages = {473--490}, - file = {/Users/dill/Zotero/storage/RPJ9SP77/Computers & Mathematics with Applications 2002 Fornberg.pdf} -} - -@article{forney_environmental_2000, - title = {Environmental {{Models}} of {{Cetacean Abundance}}: {{Reducing Uncertainty}} in {{Population Trends}}}, - shorttitle = {Environmental {{Models}} of {{Cetacean Abundance}}}, - author = {Forney, Karin A.}, - year = {2000}, - month = oct, - journal = {Conservation Biology}, - volume = {14}, - number = {5}, - pages = {1271--1286}, - issn = {0888-8892, 1523-1739}, - doi = {10.1046/j.1523-1739.2000.99412.x}, - abstract = {Trends in population abundance are often used to monitor species affected by human activities. For highly mobile species in dynamic environments, however, such as cetaceans in the marine realm, natural variability can confound attempts to detect and interpret trends in abundance. Environmental variability can cause dramatic shifts in the distribution of cetaceans, and thus abundance estimates for a fixed region may be based on a different proportion of the population each time. This adds variability, decreasing statistical power to detect trends and introducing uncertainty whether apparent trends represent true changes in population size or merely reflect natural changes in the distribution of cetaceans. To minimize these problems, surveys ideally would be based on species-specific design criteria that optimize sampling within all relevant habitat throughout a species' range. Our knowledge of cetacean habitats is limited, however, and financial and logistic constraints generally force those surveying cetacean abundance to include all species within a limited geographic region. Alternately, it may be possible to account for environmental variability analytically by including models of species-environment patterns in trend analyses, but this will be successful only if such models have interannual predictive power. I developed and evaluated generalized additive models of cetacean sighting rates in relation to environmental variables. I used data from shipboard surveys of Dall's porpoise ( Phocoenoides dalli) and short-beaked common dolphins ( Delphinus delphis) conducted in 1991, 1993, and 1996 off California. Sighting rates for these two species are variable and can be partially accounted for by environmental models, but additional surveys are needed to model species-environment relationships adequately. If patterns are consistent across years, generalized additive models may represent an effective tool for reducing uncertainty caused by environmental variability and for improving our ability to detect and interpret trends in abundance.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FJJT2H9Z/Forney - 2000 - Environmental Models of Cetacean Abundance Reduci.pdf} -} - -@article{forney_habitat-based_2012, - title = {Habitat-Based Spatial Models of Cetacean Density in the Eastern {{Pacific Ocean}}}, - author = {Forney, Ka and Ferguson, Mc and Becker, Ea and Fiedler, Pc and Redfern, Jv and Barlow, J and Vilchis, Il and Ballance, Lt}, - year = {2012}, - month = feb, - journal = {Endangered Species Research}, - volume = {16}, - number = {2}, - pages = {113--133}, - issn = {1863-5407, 1613-4796}, - doi = {10.3354/esr00393}, - abstract = {Many users of the marine environment (e.g. military, seismic researchers, fisheries) conduct activities that can potentially harm cetaceans. In the USA, Environmental Assessments or Environmental Impact Statements evaluating potential impacts are required, and these must include information on the expected number of cetaceans in specific areas where activities will occur. Typically, however, such information is only available for broad geographic regions, e.g. the entire West Coast of the United States. We present species-habitat models that estimate finer scale cetacean densities within the eastern Pacific Ocean. The models were developed and validated for 22 species or species groups, based on 15 large-scale shipboard cetacean and ecosystem assessment surveys conducted in the temperate and tropical eastern Pacific during the period from 1986 to 2006. Model development included consideration of different modeling frameworks, spatial and temporal resolutions of input variables, and spatial interpolation techniques. For the final models, expected group encounter rate and group size were modeled separately, using generalized additive models, as functions of environmental predictors, including bathymetry, distance to shore or isobaths, sea surface temperature (SST), variance in SST, salinity, chlorophyll, and mixed-layer depth. Model selection was performed using cross-validation on novel data. Smoothed maps of species density (and variance therein) were created from the final models for the California Current Ecosystem and eastern tropical Pacific Ocean. Model results were integrated into a web-interface that allows end-users to estimate densities for specified areas and provides fine-scale information for marine mammal assessments, monitoring, and mitigation.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Z2HZCE8E/Forney et al. - 2012 - Habitat-based spatial models of cetacean density i.pdf} -} - -@article{forney_habitat-based_2015, - title = {Habitat-Based Models of Cetacean Density and Distribution in the Central {{North Pacific}}}, - author = {Forney, Ka and Becker, Ea and Foley, Dg and Barlow, J and Oleson, Em}, - year = {2015}, - month = feb, - journal = {Endangered Species Research}, - volume = {27}, - number = {1}, - pages = {1--20}, - issn = {1863-5407, 1613-4796}, - doi = {10.3354/esr00632}, - abstract = {The central North Pacific Ocean includes diverse temperate and tropical pelagic habitats. Studies of the abundance and distribution of cetaceans within these dynamic marine ecosystems have generally been patchy or conducted at coarse spatial and temporal scales, limiting their utility for pelagic conservation planning. Habitat-based density models provide a tool for identifying pelagic areas of importance to cetaceans, because model predictions are spatially explicit. In this study, we present habitat-based models of cetacean density that were developed and validated for the central North Pacific. Spatial predictions of cetacean densities and measures of uncertainty were derived based on data collected during 15 large-scale shipboard cetacean and ecosystem assessment surveys conducted from 1997 to 2012. We developed generalized additive models using static and remotely sensed dynamic habitat variables, including distance to land, sea-surface temperature (SST), standard deviation of SST, surface chlorophyll concentration, seasurface height (SSH), and SSH root-mean-square variation. The resulting models, developed using new grid-based prediction methods, provide finer scale information on the distribution and density of cetaceans than previously available. Habitat-based abundance estimates around Hawaii are similar to those derived from standard line-transect analyses of the same data and provide enhanced spatial resolution to inform management and conservation of pelagic cetacean species.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/TUYAECRS/Forney et al. - 2015 - Habitat-based models of cetacean density and distr.pdf} -} - -@article{foster_poissongamma_2013, - title = {A {{Poisson}}\textendash{{Gamma}} Model for Analysis of Ecological Non-Negative Continuous Data}, - author = {Foster, Scott D. and Bravington, Mark V.}, - year = {2013}, - month = dec, - journal = {Environmental and Ecological Statistics}, - volume = {20}, - number = {4}, - pages = {533--552}, - issn = {1352-8505, 1573-3009}, - doi = {10.1007/s10651-012-0233-0}, - langid = {english}, - file = {/Users/dill/Zotero/storage/7LVJWADF/Environ Ecol Stat 2012 Foster.pdf} -} - -@article{foster_uncertainty_2012, - title = {Uncertainty in Spatially Predicted Covariates: Is It Ignorable?}, - shorttitle = {Uncertainty in Spatially Predicted Covariates}, - author = {Foster, Scott D. and Shimadzu, Hideyasu and Darnell, Ross}, - year = {2012}, - journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}, - volume = {61}, - number = {4}, - pages = {637--652}, - file = {/Users/dill/Zotero/storage/6U92RU3D/Journal of the Royal Statistical Society Series C (Applied Statistics) 2012 Foster.pdf} -} - -@article{fourcade_paintings_2018, - title = {Paintings Predict the Distribution of Species, or the Challenge of Selecting Environmental Predictors and Evaluation Statistics}, - author = {Fourcade, Yoan and Besnard, Aur{\'e}lien G. and Secondi, Jean}, - year = {2018}, - month = feb, - journal = {Global Ecology and Biogeography}, - volume = {27}, - number = {2}, - pages = {245--256}, - issn = {1466822X}, - doi = {10.1111/geb.12684}, - langid = {english}, - file = {/Users/dill/Zotero/storage/SEIW4DDD/geb12684.pdf} -} - -@article{fournier_ad_2012, - title = {{{AD Model Builder}}: Using Automatic Differentiation for Statistical Inference of Highly Parameterized Complex Nonlinear Models}, - shorttitle = {{{AD Model Builder}}}, - author = {Fournier, David A. and Skaug, Hans J. and Ancheta, Johnoel and Ianelli, James and Magnusson, Arni and Maunder, Mark N. and Nielsen, Anders and Sibert, John}, - year = {2012}, - month = apr, - journal = {Optimization Methods and Software}, - volume = {27}, - number = {2}, - pages = {233--249}, - issn = {1055-6788, 1029-4937}, - doi = {10.1080/10556788.2011.597854}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JBGW756P/10556788%2E2011%2E597854.pdf} -} - -@article{fournier_volunteer_2015, - title = {Volunteer Field Technicians Are Bad for Wildlife Ecology: {{Volunteer Field Technicians Are Bad}} for {{Wildlife Ecology}}}, - shorttitle = {Volunteer Field Technicians Are Bad for Wildlife Ecology}, - author = {Fournier, Auriel M. V. and Bond, Alexander L.}, - year = {2015}, - month = dec, - journal = {Wildlife Society Bulletin}, - volume = {39}, - number = {4}, - pages = {819--821}, - issn = {19385463}, - doi = {10.1002/wsb.603}, - langid = {english}, - file = {/Users/dill/Zotero/storage/3XYJJ6DR/Fournier, Bond - 2015 - Wildlife Society Bulletin.pdf} -} - -@article{fox_generalized_nodate, - title = {Generalized {{Collinearity Diagnostics}}}, - author = {Fox, John and Monette, Georges}, - pages = {7}, - langid = {english}, - file = {/Users/dill/Zotero/storage/MQXUM6Z6/Fox and Monette - Generalized Collinearity Diagnostics.pdf} -} - -@article{freedman_note_1983, - title = {A {{Note}} on {{Screening Regression Equations}}}, - author = {Freedman, David A.}, - year = {1983}, - month = may, - journal = {The American Statistician}, - volume = {37}, - number = {2}, - pages = {152}, - issn = {00031305}, - doi = {10.2307/2685877}, - file = {/Users/dill/Zotero/storage/D7RTAHYZ/The American Statistician 1983 Freedman.pdf} -} - -@article{fretwell_whales_2014, - title = {Whales from Space: Counting Southern Right Whales by Satellite}, - shorttitle = {Whales from Space}, - author = {Fretwell, Peter T. and Staniland, Iain J. and Forcada, Jaume}, - year = {2014}, - journal = {PLoS One}, - volume = {9}, - number = {2}, - pages = {e88655}, - file = {/Users/dill/Zotero/storage/AGY53KN4/PLoS ONE 2014 Fretwell.pdf} -} - -@article{friedman_changes_nodate, - title = {Changes in {{R}}}, - author = {Friedman, Kruskal-Wallis and Mood, Quade}, - file = {/Users/dill/Zotero/storage/EULKJ5VX/2001 Wood.pdf} -} - -@article{friedman_regularization_2010, - title = {Regularization Paths for Generalized Linear Models via Coordinate Descent}, - author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Rob}, - year = {2010}, - journal = {Journal of statistical software}, - volume = {33}, - number = {1}, - pages = {1}, - file = {/Users/dill/Zotero/storage/YFX3MA73/Journal of Statistical Software 2010 Friedman.pdf} -} - -@article{fuglstad_does_2015, - title = {Does Non-Stationary Spatial Data Always Require Non-Stationary Random Fields?}, - author = {Fuglstad, Geir-Arne and Simpson, Daniel and Lindgren, Finn and Rue, H{\aa}vard}, - year = {2015}, - journal = {Spatial Statistics}, - volume = {14}, - pages = {505--531}, - issn = {2211-6753}, - doi = {10.1016/j.spasta.2015.10.001}, - abstract = {A stationary spatial model is an idealization and we expect that the true dependence structures of physical phenomena are spatially varying, but how should we handle this non-stationarity in practice? We study the challenges involved in applying a flexible non-stationary model to a dataset of annual precipitation in the conterminous US, where exploratory data analysis shows strong evidence of a non-stationary covariance structure. The aim of this paper is to investigate the modelling pipeline once non-stationarity has been detected in spatial data. We show that there is a real danger of over-fitting the model and that careful modelling is necessary in order to properly account for varying second-order structure. In fact, the example shows that sometimes non-stationary Gaussian random fields are not necessary to model non-stationary spatial data.}, - keywords = {Annual precipitation,Gaussian Markov random fields,Gaussian random fields,Non-stationary spatial modelling,Penalized maximum likelihood,Stochastic partial differential equations}, - file = {/Users/dill/Zotero/storage/MAPZ7MC2/Fuglstad et al. - 2015 - Does non-stationary spatial data always require no.pdf} -} - -@article{furrer_framework_2007, - title = {A Framework to Understand the Asymptotic Properties of {{Kriging}} and Splines}, - author = {Furrer, Eva M and Nychka, Douglas W}, - year = {2007}, - journal = {Journal of the Korean Statistical Society}, - volume = {36}, - pages = {57--76}, - abstract = {Kriging is a nonparametric regression method used in geostatistics for estimating curves and surfaces for spatial data. It may come as a surprise that the Kriging estimator, normally derived as the best linear unbiased estimator, is also the solution of a particular variational problem. Thus, Kriging estimators can also be interpreted as generalized smoothing splines where the roughness penalty is determined by the covariance function of a spatial process. We build off the early work by Silverman (1982, 1984) and the analysis by Cox (1983, 1984), Messer (1991), Messer and Goldstein (1993) and others and develop an equivalent kernel interpretation of geostatistical estimators. Given this connection we show how a given covariance function influences the bias and variance of the Kriging estimate as well as the mean squared prediction error. Some specific asymptotic results are given for one dimensional corresponding Mat\textasciiacute ern covariances that have as their limit cubic smoothing splines.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/BQIRXSKS/Furrer and Nychka - A framework to understand the asymptotic propertie.pdf} -} - -@article{gabry_visualization_2019, - title = {Visualization in {{Bayesian}} Workflow}, - author = {Gabry, Jonah and Simpson, Daniel and Vehtari, Aki and Betancourt, Michael and Gelman, Andrew}, - year = {2019}, - month = feb, - journal = {Journal of the Royal Statistical Society: Series A (Statistics in Society)}, - volume = {182}, - number = {2}, - pages = {389--402}, - issn = {09641998}, - doi = {10.1111/rssa.12378}, - abstract = {Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/E6KAY44X/Gabry et al. - 2019 - Visualization in Bayesian workflow.pdf} -} - -@article{garcia-rosello_using_2014, - title = {Using Modestr to Download, Import and Clean Species Distribution Records}, - shorttitle = {Using}, - author = {{Garc{\'i}a-Rosell{\'o}}, Emilio and Guisande, C{\'a}stor and Heine, Juergen and {Pelayo-Villamil}, Patricia and {Manjarr{\'e}s-Hern{\'a}ndez}, Ana and Gonz{\'a}lez Vilas, Luis and {Gonz{\'a}lez-Dacosta}, Jacinto and Vaamonde, Antonio and {Granado-Lorencio}, Carlos}, - editor = {Orme, David}, - year = {2014}, - month = jul, - journal = {Methods in Ecology and Evolution}, - volume = {5}, - number = {7}, - pages = {708--713}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12209}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DSQDSHZ7/Methods in Ecology and Evolution 2014 García-Roselló.pdf} -} - -@article{garthwaite_generating_1992, - title = {Generating {{Monte Carlo Confidence Intervals}} by the {{Robbins-Monro Process}}}, - author = {Garthwaite, Paul H. and Buckland, Stephen T.}, - year = {1992}, - journal = {Applied Statistics}, - volume = {41}, - number = {1}, - pages = {159}, - issn = {00359254}, - doi = {10.2307/2347625}, - abstract = {A new use of the Robbins-Monro search process to generate Monte Carlo confidence intervals for a single-parameter density function is described. When the optimal value of a 'step length constant' is known, asymptotically the process gives exact confidence intervals and is fully efficient. We modify the process for the case where the optimal step length constant is unknown and find that it has low bias and typically achieves an efficiency above 75\% for 90\% and 95\% confidence intervals and above 65\% for 99\% intervals. Multiple-sample mark-recapture data are used to illustrate the method.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DFUXRQP7/Garthwaite and Buckland - 1992 - Generating Monte Carlo Confidence Intervals by the.pdf} -} - -@article{geenens_curse_2011, - title = {Curse of Dimensionality and Related Issues in Nonparametric Functional Regression}, - author = {Geenens, Gery}, - year = {2011}, - journal = {Statistics Surveys}, - volume = {5}, - number = {0}, - pages = {30--43}, - issn = {1935-7516}, - doi = {10.1214/09-SS049}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6II7BF5N/Statist. Surv. 2011 Geenens.pdf} -} - -@article{gelfand_sampling-based_1990, - title = {Sampling-Based Approaches to Calculating Marginal Densities}, - author = {Gelfand, Alan E. and Smith, Adrian FM}, - year = {1990}, - journal = {Journal of the American statistical association}, - volume = {85}, - number = {410}, - pages = {398--409}, - file = {/Users/dill/Zotero/storage/7HB4SL3I/GelfandSmith90.pdf} -} - -@article{gelman_all_1999, - title = {All Maps of Parameter Estimates Are Misleading}, - author = {Gelman, Andrew and Price, Phillip N.}, - year = {1999}, - journal = {Statistics in medicine}, - volume = {18}, - number = {23}, - pages = {3221--3234}, - file = {/Users/dill/Zotero/storage/5KMLX2F6/allmaps.pdf} -} - -@article{gelman_analysis_2005, - title = {Analysis of Variance --- Why It Is More Important than Ever}, - shorttitle = {Analysis of Variance?}, - author = {Gelman, Andrew}, - year = {2005}, - month = feb, - journal = {The Annals of Statistics}, - volume = {33}, - number = {1}, - pages = {1--53}, - issn = {0090-5364}, - doi = {10.1214/009053604000001048}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ZJC7NKTI/Gelman - 2005 - Analysis of variancewhy it is more important than.pdf} -} - -@article{gelman_exploratory_2004, - title = {Exploratory {{Data Analysis}} for {{Complex Models}}}, - author = {Gelman, Andrew}, - year = {2004}, - month = dec, - journal = {Journal of Computational and Graphical Statistics}, - volume = {13}, - number = {4}, - pages = {755--779}, - issn = {1061-8600, 1537-2715}, - doi = {10.1198/106186004X11435}, - langid = {english}, - file = {/Users/dill/Zotero/storage/NPW6V6EY/Journal of Computational and Graphical Statistics 2004 Gelman.pdf} -} - -@article{gelman_multilevel_2006, - title = {Multilevel ({{Hierarchical}}) {{Modeling}}: {{What It Can}} and {{Cannot Do}}}, - shorttitle = {Multilevel ({{Hierarchical}}) {{Modeling}}}, - author = {Gelman, Andrew}, - year = {2006}, - month = aug, - journal = {Technometrics}, - volume = {48}, - number = {3}, - pages = {432--435}, - issn = {0040-1706, 1537-2723}, - doi = {10.1198/004017005000000661}, - langid = {english}, - file = {/Users/dill/Zotero/storage/4AABFMVJ/Technometrics 2006 Gelman.pdf} -} - -@article{gelman_prior_2006, - title = {Prior Distributions for Variance Parameters in Hierarchical Models (Comment on Article by {{Browne}} and {{Draper}})}, - author = {Gelman, Andrew}, - year = {2006}, - month = sep, - journal = {Bayesian Analysis}, - volume = {1}, - number = {3}, - pages = {515--534}, - issn = {1936-0975}, - doi = {10.1214/06-BA117A}, - abstract = {Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new folded-noncentral-t family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative priors in this family. We use an example to illustrate serious problems with the inverse-gamma family of ``noninformative'' prior distributions. We suggest instead to use a uniform prior on the hierarchical standard deviation, using the half-t family when the number of groups is small and in other settings where a weakly informative prior is desired. We also illustrate the use of the half-t family for hierarchical modeling of multiple variance parameters such as arise in the analysis of variance.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FZ5RBRXC/Gelman - 2006 - Prior distributions for variance parameters in hie.pdf} -} - -@article{gelman_splitting_2009, - title = {Splitting a Predictor at the Upper Quarter or Third and the Lower Quarter or Third}, - author = {Gelman, Andrew and Park, David K.}, - year = {2009}, - journal = {The American Statistician}, - volume = {63}, - number = {1}, - pages = {1--8}, - file = {/Users/dill/Zotero/storage/AH3574JZ/The American Statistician 2009 Gelman.pdf} -} - -@article{gelman_we_2010, - title = {Do We Need an Integrated {{Bayesian}}/Likelihood Inference}, - author = {Gelman, Andrew and Robert, Christian P. and Rousseau, Judith}, - year = {2010}, - journal = {arXiv preprint arXiv:1012.2184}, - eprint = {1012.2184}, - eprinttype = {arxiv}, - archiveprefix = {arXiv}, - file = {/Users/dill/Zotero/storage/E6W2RFQA/arXiv 2010 Gelman.pdf} -} - -@article{gelman_why_2011, - title = {Why {{Tables Are Really Much Better Than Graphs}}}, - author = {Gelman, Andrew}, - year = {2011}, - month = jan, - journal = {Journal of Computational and Graphical Statistics}, - volume = {20}, - number = {1}, - pages = {3--7}, - issn = {1061-8600, 1537-2715}, - doi = {10.1198/jcgs.2011.09166}, - langid = {english}, - file = {/Users/dill/Zotero/storage/BD3MN9UF/Journal of Computational and Graphical Statistics 2011 Gelman.pdf} -} - -@book{gelman2013bayesian, - title = {Bayesian Data Analysis}, - author = {Gelman, A. and Carlin, J.B. and Stern, H.S. and Dunson, D.B. and Vehtari, A. and Rubin, D.B.}, - year = {2013}, - series = {Chapman \& {{Hall}}/{{CRC}} Texts in Statistical Science}, - edition = {Third}, - publisher = {{Taylor \& Francis}}, - isbn = {978-1-4398-4095-5}, - lccn = {2013039507} -} - -@article{geoffrey_print_2009, - title = {Print: 50 {{Years}} of {{Stupid Grammar Advice}} - {{ChronicleReview}}.Com}, - author = {Geoffrey, PULLUM}, - year = {2009}, - month = apr, - pages = {3}, - langid = {english}, - file = {/Users/dill/Zotero/storage/WT6KDMUA/Geoffrey - Print 50 Years of Stupid Grammar Advice - Chronic.pdf} -} - -@article{gerrodette_non-recovery_2005, - title = {Non-Recovery of Two Spotted and Spinner Dolphin Populations in the Eastern Tropical {{Pacific Ocean}}}, - author = {Gerrodette, T and Forcada, J}, - year = {2005}, - journal = {Marine Ecology Progress Series}, - volume = {291}, - pages = {1--21}, - issn = {0171-8630, 1616-1599}, - doi = {10.3354/meps291001}, - abstract = {Populations of northeastern offshore spotted dolphins Stenella attenuata attenuata and eastern spinner dolphins S. longirostris orientalis have been reduced because the dolphins are bycatch in the purse-seine fishery for yellowfin tuna in the eastern tropical Pacific Ocean (the `tuna\textendash dolphin issue'). Abundance and trends of these dolphin stocks were assessed from 12 large-scale pelagic surveys carried out between 1979 and 2000. Estimates of abundance were based on a multivariate linetransect analysis, using covariates to model the detection process and group size. Current estimates of abundance are about 640 000 northeastern offshore spotted dolphins (CV = 0.17) and 450 000 eastern spinner dolphins (CV = 0.23). For the whole period from 1979 to 2000, annual estimates of abundance ranged from 494 000 to 954 000 for northeastern offshore spotted dolphins and from 271 000 to 734 000 for eastern spinner dolphins. Management actions by USA and international fishing agencies over 3 decades have successfully reduced dolphin bycatch by 2 orders of magnitude, yet neither stock is showing clear signs of recovery. Possible reasons include underreporting of dolphin bycatch, effects of chase and encirclement on dolphin survival and reproduction, longterm changes in the ecosystem, and effects of other species on spotted and spinner dolphin population dynamics.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/H4PGW7WW/Gerrodette and Forcada - 2005 - Non-recovery of two spotted and spinner dolphin po.pdf} -} - -@article{giammarino_estimating_2014, - title = {On Estimating {{Hooded}} Crow Density from Line Transect Data through Exponential Mixture Models}, - author = {Giammarino, Mauro and Quatto, Piero}, - year = {2014}, - month = dec, - journal = {Environmental and Ecological Statistics}, - volume = {21}, - number = {4}, - pages = {689--696}, - issn = {1352-8505, 1573-3009}, - doi = {10.1007/s10651-014-0275-6}, - langid = {english}, - file = {/Users/dill/Zotero/storage/NVYVVGXY/Environ Ecol Stat 2014 Giammarino.pdf} -} - -@article{gilles_seasonal_2016, - title = {Seasonal Habitat-based Density Models for a Marine Top Predator, the Harbor Porpoise, in a Dynamic Environment}, - author = {Gilles, A. and Viquerat, S. and Becker, E. A. and Forney, K. A. and Geelhoed, S. C. V. and Haelters, J. and Nabe-Nielsen, J. and Scheidat, M. and Siebert, U. and Sveegaard, S. and Beest, F. M. and Bemmelen, R. and Aarts, G.}, - year = {2016}, - month = jun, - journal = {Ecosphere}, - volume = {7}, - number = {6}, - issn = {2150-8925, 2150-8925}, - doi = {10.1002/ecs2.1367}, - abstract = {Effective species conservation and management requires information on species distribution patterns, which is challenging for highly mobile and cryptic species that may be subject to multiple anthropogenic stressors across international boundaries. Understanding species\textendash h\- abitat relationships can improve the assessment of trends and distribution by explicitly allowing high-\-resolution data on habitats to inform abundance estimation and the identification of protected areas. In this study, we aggregated an unprecedented set of survey data of a marine top predator, the harbor porpoise (Phocoena phocoena), collected in the UK (SCANS II, Dogger Bank), Belgium, the Netherlands, Germany, and Denmark, to develop seasonal habitat-\-based density models for the central and southern North Sea. Visual survey data were collected over 9 yr (2005\textendash 2013) by means of dedicated line-\-transect surveys, taking into account the proportion of missed sightings. Generalized additive models of porpoise density were fitted to 156,630 km of on-e\- ffort survey data with 14,356 sightings of porpoise groups. Selected predictors included static and dynamic variables, such as depth, distance to shore and to sandeel (Ammodytes spp.) grounds, sea surface temperature (SST), proxies for fronts, and day length. Day length and the spatial distribution of daily SST proved to be good proxies for ``season,'' allowing predictions in both space and time. The density models captured seasonal distribution shifts of porpoises across international boundaries. By combining the large-\-scale international SCANS II survey with the more frequent, small-s\- cale national surveys, it has been possible to provide seasonal maps that will be used to assist the EU Habitats and Marine Strategy Framework Directives in effectively assessing the conservation status of harbor porpoises. Moreover, our results can facilitate the identification of regions where human activities and disturbances are likely to impact the population and are especially relevant for marine spatial planning, which requires accurate fine-\-scale maps of species distribution to assess risks of increasing human activities at sea.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6NDVR4CP/Gilles et al. - 2016 - Seasonal habitat‐based density models for a marine.pdf} -} - -@article{gillies_application_2006, - title = {Application of Random Effects to the Study of Resource Selection by Animals: {{Random}} Effects in Resource Selection}, - shorttitle = {Application of Random Effects to the Study of Resource Selection by Animals}, - author = {Gillies, Cameron S. and Hebblewhite, Mark and Nielsen, Scott E. and Krawchuk, Meg A. and Aldridge, Cameron L. and Frair, Jacqueline L. and Saher, D. Joanne and Stevens, Cameron E. and Jerde, Christopher L.}, - year = {2006}, - month = jun, - journal = {Journal of Animal Ecology}, - volume = {75}, - number = {4}, - pages = {887--898}, - issn = {00218790}, - doi = {10.1111/j.1365-2656.2006.01106.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/CCLNB5NV/Journal of Animal Ecology 2006 GILLIES.pdf} -} - -@techreport{gjerdrum_eastern_2012, - title = {Eastern {{Canada Seabirds}} at {{Sea}} ({{ECSAS}}) Standardized Protocol for Pelagic Seabird Surveys from Moving and Stationary Platforms}, - author = {Gjerdrum, Carina and Fifield, David A and Wilhelm, Sabina I}, - year = {2012}, - number = {Technical Report Series Number 515}, - pages = {44}, - langid = {english}, - file = {/Users/dill/Zotero/storage/4RF9YADP/Gjerdrum et al. - Eastern Canada Seabirds at Sea (ECSAS) standardize.pdf} -} - -@article{glenn_spotted_2004, - title = {Spotted Owl Home-Range and Habitat Use in Young Forests of Western {{Oregon}}}, - author = {Glenn, Elizabeth M. and Hansen, Michael C. and Anthony, Robert G.}, - year = {2004}, - journal = {Journal of Wildlife Management}, - volume = {68}, - number = {1}, - pages = {33--50}, - file = {/Users/dill/Zotero/storage/Y9NCZCBH/Journal of Wildlife Management 2004 Glenn.pdf} -} - -@article{glennie_effect_2015, - title = {The {{Effect}} of {{Animal Movement}} on {{Line Transect Estimates}} of {{Abundance}}}, - author = {Glennie, Richard and Buckland, Stephen T. and Thomas, Len}, - editor = {{Festa-Bianchet}, Marco}, - year = {2015}, - month = mar, - journal = {PLOS ONE}, - volume = {10}, - number = {3}, - pages = {e0121333}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0121333}, - langid = {english}, - file = {/Users/dill/Zotero/storage/98RGUGA6/journal.pone.0121333.pdf;/Users/dill/Zotero/storage/D8ZL696P/PLoS ONE 2015 Glennie.pdf} -} - -@article{golding_fast_2016, - title = {Fast and Flexible {{Bayesian}} Species Distribution Modelling Using {{Gaussian}} Processes}, - author = {Golding, Nick and Purse, Bethan V.}, - editor = {Warton, David}, - year = {2016}, - month = may, - journal = {Methods in Ecology and Evolution}, - volume = {7}, - number = {5}, - pages = {598--608}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12523}, - langid = {english}, - file = {/Users/dill/Zotero/storage/TZRV5Q8X/Methods in Ecology and Evolution 2016 Golding.pdf} -} - -@article{golding_greta_2019, - title = {Greta: Simple and Scalable Statistical Modelling in {{R}}}, - shorttitle = {Greta}, - author = {Golding, Nick}, - year = {2019}, - month = aug, - journal = {Journal of Open Source Software}, - volume = {4}, - number = {40}, - pages = {1601}, - issn = {2475-9066}, - doi = {10.21105/joss.01601}, - abstract = {Statistical modelling is used throughout the sciences. Often, statistical analyses require custom models that cannot be fitted using off-the shelf statistical software. These models can be specified in a statistical syntax and can then be automatically fit to data using methods such as Markov Chain monte Carlo (MCMC) and maximum likelihood. This lets users focus on the statistical nature of the model, rather than implementation details and inference procedures. Since the development of the widely successful WinBUGS (later developed as OpenBUGS; Spiegelhalter, Thomas, Best, \& Lunn (2014)) a number of alternative software packages for custom statistical modelling have been introduced, including JAGS, Stan, and NIMBLE (Carpenter et al., 2017; de Valpine et al., 2017; Plummer \& others, 2003). In these software packages, users typically write out models in a domain-specific language, which is then compiled into computational code. Though see the Python packages PyMC and Edward (Salvatier, Wiecki, \& Fonnesbeck, 2016; Tran et al., 2016) in which models are specified in Python code.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/HINUHCJG/Golding - 2019 - greta simple and scalable statistical modelling i.pdf} -} - -@article{good_nonparametric_1971, - title = {Nonparametric {{Roughness Penalties}} for {{Probability Densities}}}, - author = {Good, I.J and Gaskins, R.A.}, - year = {1971}, - journal = {Biometrika}, - volume = {58}, - number = {2}, - pages = {255--277}, - abstract = {Given a number of observations xl, ..., xN, a nonparametric method is suggested for estimating the entire probability density curve. The method is to subtract a roughness penalty from the log likelihood, where the roughness penalty is a certain functional of the assumed density function f. Those used are linear combinations off y'2dx andf y"2dx, where y = If. The method appears to be consistent under wide conditions, although consistent methods can be rough. Numerical examples are given and show that for certain values of the coefficients in this linear expression the density function turns out to be very smooth even when N is small. Multivariate extensions are proposed, including one to distributions having some continuous and some discrete components, but numerical examples of these have not been tried. Some of the techniques are borrowed from quantum mechanics and tensor calculus.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DACNCP9M/Nonparametric Roughness Penalties for Probability .pdf} -} - -@article{goodman_exact_1960, - title = {On the {{Exact Variance}} of {{Products}}}, - author = {Goodman, Leo A.}, - year = {1960}, - month = dec, - journal = {Journal of the American Statistical Association}, - volume = {55}, - number = {292}, - pages = {708}, - issn = {01621459}, - doi = {10.2307/2281592}, - file = {/Users/dill/Zotero/storage/U2254FXD/Journal of the American Statistical Association 1960 Goodman.pdf} -} - -@article{gowan_wintering_2014, - title = {Wintering {{Habitat Model}} for the {{North Atlantic Right Whale}} ({{Eubalaena}} Glacialis) in the {{Southeastern United States}}}, - author = {Gowan, Timothy A. and {Ortega-Ortiz}, Joel G.}, - editor = {Deng, Z. Daniel}, - year = {2014}, - month = apr, - journal = {PLoS ONE}, - volume = {9}, - number = {4}, - pages = {e95126}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0095126}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9STP6FQ6/PLoS ONE 2014 Gowan.pdf} -} - -@article{gower_adding_1968, - title = {Adding a Point to Vector Diagrams in Multivariate Analysis}, - author = {Gower, John Clifford}, - year = {1968}, - journal = {Biometrika}, - volume = {55}, - number = {3}, - pages = {582--585}, - file = {/Users/dill/Zotero/storage/YCNLVAKA/Biometrika 1968 Gower.pdf} -} - -@article{gower_general_1971, - title = {A General Coefficient of Similarity and Some of Its Properties}, - author = {Gower, John C.}, - year = {1971}, - journal = {Biometrics}, - pages = {857--871}, - file = {/Users/dill/Zotero/storage/YYTQCBZY/Biom 1971 Gower.pdf} -} - -@article{grace_wahba_bayesian_1983, - title = {Bayesian "{{Confidence Intervals}}" for the {{Cross-Validated Smoothing Spline}}}, - author = {Grace Wahba}, - year = {1983}, - journal = {Journal of the Royal Statistical Society. Series B (Methodological)}, - volume = {45}, - number = {1}, - pages = {133--150}, - abstract = {We consider the model Y(t) =g(ti) + ei, i = 17 2, . . ., n, where g(t), t [0, 1] is a smooth function and the \{ei) are independent N(0, a2 ) errors with G2 unknown. The cross-validated smoothing spline can be used to estimate g non-parametrically from observations on Y(ti), i = 1, 2, . . ., n, and the purpose of this paper is to study confidence intervals for this estimate. Properties of smoothing splines as Bayes estimates are used to derive confidence intervals based on the posterior covariance function of the estimate. A small Monte Carlo study with the cubic smoothing spline is carried out to suggest by example to what extent the resulting 95 per cent confidence intervals can be expected to cover about 95 per cent of the true (but in practice unknown) values of g(ti), i = 1, 2,. . ., n. The method was also applied to one example of a twodimensional thin plate smoothing spline. An asymptotic theoretical argument is presented to explain why the method can be expected to work on fixed smooth functions (like those tried), which are "smoother" than the sample functions from the prior distributions on which the confidence interval theory is based.\vphantom\}}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JQ2TQP38/Grace Wahba - Bayesian Confidence Intervals for the Cross-Vali.pdf} -} - -@article{graham_incorporating_2019, - title = {Incorporating Fine-scale Environmental Heterogeneity into Broad-extent Models}, - author = {Graham, Laura J. and Spake, Rebecca and Gillings, Simon and Watts, Kevin and Eigenbrod, Felix}, - editor = {Isaac, Nick}, - year = {2019}, - month = jun, - journal = {Methods in Ecology and Evolution}, - volume = {10}, - number = {6}, - pages = {767--778}, - issn = {2041-210X, 2041-210X}, - doi = {10.1111/2041-210X.13177}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FRI5T2SB/Graham et al. - 2019 - Incorporating fine‐scale environmental heterogenei.pdf} -} - -@article{gregoire_regression_nodate, - title = {Regression {{Estimation Following}} the {{Square-Root Transformation}} of the {{Response}}}, - author = {Gregoire, Timothy G and Lin, Qi Feng and Boudreau, Johnathan and Nelson, Ross}, - pages = {10}, - abstract = {In a variety of regression situations, there is interest in predicting the value of Y2, yet it is useful to model it using a square root transformation, such that Y rather than Y2 is regressed on one or more covariates. The back-transformation bias of the square root transformation of the response variable of interest is presented taihngeadsieentsaetisltt.himaAtanotofurnstwbaioaresbedidaesreeisvdteimedsataitmnoaraltyiostricps:arelEl\textasciicircum sybe[naYtn2ed͉dx:*v]Ee\textasciicircum r[ϭiYfi2e␮͉\textasciicircum xdy2*͉x]b*yϭϩm␮\textasciicircum ␴e\textasciicircum y2a͉axn*nsdϩofE\textasciicircum ␴p\textasciicircum a[YϪsi2m͉xV\textasciicircum *u(]l␮a\textasciicircum ϭty2i͉ox*n␮\textasciicircum ).y2s͉xItu*ts.dTyp.ehreBfoofritrmhstabntiwacseoeidms eocsmotmiemnpatastroeordsf have lower mean square errors than the unbiased estimator. An example wherein aboveground biomass is the response variable is presented for illustration. FOR. SCI. 54(6):597\textendash{} 606.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/E6XNYUSU/Gregoire et al. - Regression Estimation Following the Square-Root Tr.pdf} -} - -@article{greven_behaviour_2010, - title = {On the Behaviour of Marginal and Conditional {{AIC}} in Linear Mixed Models}, - author = {Greven, S. and Kneib, T.}, - year = {2010}, - month = dec, - journal = {Biometrika}, - volume = {97}, - number = {4}, - pages = {773--789}, - issn = {0006-3444, 1464-3510}, - doi = {10.1093/biomet/asq042}, - abstract = {In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion, AIC, have been used, based either on the marginal or on the conditional distribution. We show that the marginal AIC is not an asymptotically unbiased estimator of the Akaike information, and favours smaller models without random effects. For the conditional AIC, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that can lead to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional AIC, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package (R Development Core Team, 2010) is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LWF7LHVS/Greven and Kneib - 2010 - On the behaviour of marginal and conditional AIC i.pdf} -} - -@article{grimes_viability_2016, - title = {On the {{Viability}} of {{Conspiratorial Beliefs}}}, - author = {Grimes, David Robert}, - editor = {Bauch, Chris T.}, - year = {2016}, - month = jan, - journal = {PLOS ONE}, - volume = {11}, - number = {1}, - pages = {e0147905}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0147905}, - langid = {english}, - file = {/Users/dill/Zotero/storage/T3AQVJC4/PLoS ONE 2016 Grimes.PDF} -} - -@article{grolemund_dates_2011, - title = {Dates and Times Made Easy with Lubridate}, - author = {Grolemund, Garrett and Wickham, Hadley}, - year = {2011}, - journal = {Journal of Statistical Software}, - volume = {40}, - number = {3}, - pages = {1--25}, - file = {/Users/dill/Zotero/storage/64USWERN/Journal of Statistical Software 2011 Grolemund.pdf} -} - -@article{gu_minimizing_1991, - title = {Minimizing {{GCV}}/{{GML Scores}} with {{Multiple Smoothing Parameters}} via the {{Newton Method}}}, - author = {Gu, Chong and Wahba, Grace}, - year = {1991}, - month = mar, - journal = {SIAM Journal on Scientific and Statistical Computing}, - volume = {12}, - number = {2}, - pages = {383--398}, - issn = {0196-5204, 2168-3417}, - doi = {10.1137/0912021}, - abstract = {The (modified) Newton method is adapted to optimize generalized cross validation (GCV) and generalized maximum likelihood (GML) scores with multiple smoothing parameters. The main concerns in solving the optimization problem are the speed and the reliability of the algorithm, as well as the invariance of the algorithm under transformations under which the problem itself is invariant. The proposed algorithm is believed to be highly efficient for the problem, though it is still rather expensive for large data sets, since its operational counts are (2/3)kn + O(n2), with k the number of smoothing parameters and n the number of observations. Sensible procedures for computing good starting values are also proposed, which should help in keeping the execution load to the minimum possible. The algorithm is implemented in Rkpack [RKPACK and its applications: Fitting smoothing spline models, Tech. Report 857, Department of Statistics, University of Wisconsin, Madison, WI, 1989] and illustrated by examples of fitting additive and interaction spline models. It is noted that the algorithm can also be applied to the maximum likelihood (ML) and the restricted maximum likelihood (REML) estimation of the variance component models.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/87L64AES/Gu and Wahba - 1991 - Minimizing GCVGML Scores with Multiple Smoothing .pdf} -} - -@article{guillera-arroita_models_2012, - title = {Models for Species-Detection Data Collected along Transects in the Presence of Abundance-Induced Heterogeneity and Clustering in the Detection Process: {{Abundance}} and Clustered Detections}, - shorttitle = {Models for Species-Detection Data Collected along Transects in the Presence of Abundance-Induced Heterogeneity and Clustering in the Detection Process}, - author = {{Guillera-Arroita}, Gurutzeta and Ridout, Martin S. and Morgan, Byron J. T. and Linkie, Matthew}, - year = {2012}, - month = apr, - journal = {Methods in Ecology and Evolution}, - volume = {3}, - number = {2}, - pages = {358--367}, - issn = {2041210X}, - doi = {10.1111/j.2041-210X.2011.00159.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/4FQUVZIX/Methods in Ecology and Evolution 2011 Guillera-Arroita.pdf} -} - -@article{guillera-arroita_species_2011, - title = {Species {{Occupancy Modeling}} for {{Detection Data Collected Along}} a {{Transect}}}, - author = {{Guillera-Arroita}, Gurutzeta and Morgan, Byron J. T. and Ridout, Martin S. and Linkie, Matthew}, - year = {2011}, - month = sep, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {16}, - number = {3}, - pages = {301--317}, - issn = {1085-7117, 1537-2693}, - doi = {10.1007/s13253-010-0053-3}, - langid = {english}, - file = {/Users/dill/Zotero/storage/EK4Q4VI6/JABES 2011 Guillera-Arroita.pdf} -} - -@techreport{gunnlaugsson_estimate_2003, - title = {An Estimate of the Abundance of Minke Whales ({{Balaenoptera}} Acutorostrata) from the {{NASS-2001}} Shipboard Survey}, - author = {Gunnlaugsson, Thorvaldur and Pike, Daniel G. and V{\'i}kingsson, G{\'i}sli A. and Desportes, Genevi{\`e}ve and Mikkelson, Bjarni}, - year = {2003}, - institution = {{NAMMCO SC/11/AE/6}}, - file = {/Users/dill/Zotero/storage/A9RWYGSB/PLoS ONE 2003 Pike.pdf} -} - -@article{halpin_obis-seamap_2009, - title = {{{OBIS-SEAMAP}}: {{The World Data Center}} for {{Marine Mammal}}, {{Sea Bird}}, and {{Sea Turtle Distributions}}}, - shorttitle = {{{OBIS-SEAMAP}}}, - author = {Halpin, Patrick and Read, Andrew and Fujioka, Ei and Best, Ben and Donnelly, Ben and Hazen, Lucie and Kot, Connie and Urian, Kim and LaBrecque, Erin and Dimatteo, Andrew and Cleary, Jesse and Good, Caroline and Crowder, Larry and Hyrenbach, K. David}, - year = {2009}, - month = jun, - journal = {Oceanography}, - volume = {22}, - number = {2}, - pages = {104--115}, - issn = {10428275}, - doi = {10.5670/oceanog.2009.42}, - file = {/Users/dill/Zotero/storage/L88BGX3Z/Oceanog. 2009 Halpin.pdf} -} - -@article{hamilton_accounting_2018, - title = {Accounting for Uncertainty in Duplicate Identification and Group Size Judgements in Mark\textendash Recapture Distance Sampling}, - author = {Hamilton, Olivia N. P. and Kincaid, Sophie E. and Constantine, Rochelle and Kozmian-Ledward, Lily and Walker, Cameron G. and Fewster, Rachel M.}, - editor = {Yoccoz, Nigel}, - year = {2018}, - month = feb, - journal = {Methods in Ecology and Evolution}, - volume = {9}, - number = {2}, - pages = {354--362}, - issn = {2041-210X, 2041-210X}, - doi = {10.1111/2041-210X.12895}, - langid = {english}, - file = {/Users/dill/Zotero/storage/IV2FNS55/Hamilton et al. - 2018 - Accounting for uncertainty in duplicate identifica.pdf} -} - -@article{hammond_cetacean_2013, - title = {Cetacean Abundance and Distribution in {{European Atlantic}} Shelf Waters to Inform Conservation and Management}, - author = {Hammond, Philip S. and Macleod, Kelly and Berggren, Per and Borchers, David L. and Burt, Louise and Ca{\~n}adas, Ana and Desportes, Genevi{\`e}ve and Donovan, Greg P. and Gilles, Anita and Gillespie, Douglas and Gordon, Jonathan and Hiby, Lex and Kuklik, Iwona and Leaper, Russell and Lehnert, Kristina and Leopold, Mardik and Lovell, Phil and {\O}ien, Nils and Paxton, Charles G.M. and Ridoux, Vincent and Rogan, Emer and Samarra, Filipa and Scheidat, Meike and Sequeira, Marina and Siebert, Ursula and Skov, Henrik and Swift, Ren{\'e} and Tasker, Mark L. and Teilmann, Jonas and Van Canneyt, Olivier and V{\'a}zquez, Jos{\'e} Antonio}, - year = {2013}, - month = aug, - journal = {Biological Conservation}, - volume = {164}, - pages = {107--122}, - issn = {00063207}, - doi = {10.1016/j.biocon.2013.04.010}, - langid = {english}, - file = {/Users/dill/Zotero/storage/86H2T6WP/1-s2.0-S0006320713001055-mmc1.pdf;/Users/dill/Zotero/storage/C8D5S8SK/Biological Conservation 2013 Hammond.pdf} -} - -@book{hankin_step-by-step_2007, - title = {A Step-by-Step Guide to Writing a Simple Package That Uses {{S4}} Methods: A ``Hello World'' Example}, - shorttitle = {A Step-by-Step Guide to Writing a Simple Package That Uses {{S4}} Methods}, - author = {Hankin, Robin KS}, - year = {2007}, - file = {/Users/dill/Zotero/storage/FKYQWTA4/2011 Hankin.pdf} -} - -@article{hardle_how_1988, - title = {How {{Far Are Automatically Chosen Regression Smoothing Parameters From Their Optimum}}?}, - author = {Hardle, Wolfgang and Hall, Peter and Marron, J. S.}, - year = {1988}, - month = mar, - journal = {Journal of the American Statistical Association}, - volume = {83}, - number = {401}, - pages = {86}, - issn = {01621459}, - doi = {10.2307/2288922}, - file = {/Users/dill/Zotero/storage/VZLVFA7D/Journal of the American Statistical Association 1988 Hardle.pdf} -} - -@article{harihar_identifying_2014, - title = {Identifying Realistic Recovery Targets and Conservation Actions for Tigers in a Human-Dominated Landscape Using Spatially Explicit Densities of Wild Prey and Their Determinants}, - author = {Harihar, Abishek and Pandav, Bivash and MacMillan, Douglas C.}, - editor = {Robertson, Mark}, - year = {2014}, - month = may, - journal = {Diversity and Distributions}, - volume = {20}, - number = {5}, - pages = {567--578}, - issn = {13669516}, - doi = {10.1111/ddi.12174}, - langid = {english}, - file = {/Users/dill/Zotero/storage/G9V59UIU/Diversity Distrib. 2014 Harihar.pdf} -} - -@article{harris_building_2014, - title = {Building Realistic Assemblages with a Joint Species Distribution Model}, - author = {Harris, David J.}, - year = {2014}, - journal = {bioRxiv}, - pages = {003947}, - file = {/Users/dill/Zotero/storage/LX3X7P7G/2014 Harris.pdf} -} - -@article{harris_generating_2015, - title = {Generating Realistic Assemblages with a Joint Species Distribution Model}, - author = {Harris, David J.}, - editor = {Warton, David}, - year = {2015}, - month = apr, - journal = {Methods in Ecology and Evolution}, - volume = {6}, - number = {4}, - pages = {465--473}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12332}, - langid = {english}, - file = {/Users/dill/Zotero/storage/XEY24UFP/Methods in Ecology and Evolution 2015 Harris.pdf} -} - -@article{harrison_getting_2011, - title = {Getting Started with Meta-Analysis: {{{\emph{Getting}}}}{\emph{ Started with Meta-Analysis}}}, - shorttitle = {Getting Started with Meta-Analysis}, - author = {Harrison, Freya}, - year = {2011}, - month = jan, - journal = {Methods in Ecology and Evolution}, - volume = {2}, - number = {1}, - pages = {1--10}, - issn = {2041210X}, - doi = {10.1111/j.2041-210X.2010.00056.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ALAEG5MZ/Methods in Ecology and Evolution 2010 Harrison.pdf} -} - -@article{harrison_using_2014, - title = {Using Observation-Level Random Effects to Model Overdispersion in Count Data in Ecology and Evolution}, - author = {Harrison, Xavier A.}, - year = {2014}, - month = oct, - journal = {PeerJ}, - volume = {2}, - pages = {e616}, - issn = {2167-8359}, - doi = {10.7717/peerj.616}, - langid = {english}, - file = {/Users/dill/Zotero/storage/WG3SS448/PeerJ 2014 Harrison.pdf} -} - -@article{harville_bayesian_1974, - title = {Bayesian {{Inference}} for {{Variance Components Using Only Error Contrasts}}}, - author = {Harville, David A.}, - year = {1974}, - month = aug, - journal = {Biometrika}, - volume = {61}, - number = {2}, - pages = {383}, - issn = {00063444}, - doi = {10.2307/2334370}, - abstract = {A closed form for the variance of the unbiased estimator of the proportion defective in a population, derived by inverse binomial sampling, is given. Comparisons are made with the maximum likelihood estimator which indicate that the unbiased estimator has greater efficiency and smaller mean squared error.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FKHS3ZN4/Harville - 1974 - Bayesian Inference for Variance Components Using O.pdf} -} - -@article{harville_maximum_1977, - title = {Maximum {{Likelihood Approaches}} to {{Variance Component Estimation}} and to {{Related Problems}}}, - author = {Harville, David A.}, - year = {1977}, - month = jun, - journal = {Journal of the American Statistical Association}, - volume = {72}, - number = {358}, - pages = {320}, - issn = {01621459}, - doi = {10.2307/2286796}, - file = {/Users/dill/Zotero/storage/EICWF9DD/Journal of the American Statistical Association 1977 Harville.pdf} -} - -@article{hasan_simple_2010, - title = {A Simple {{Poisson}}\textendash Gamma Model for Modelling Rainfall Occurrence and Amount Simultaneously}, - author = {Hasan, Md Masud and Dunn, Peter K.}, - year = {2010}, - month = sep, - journal = {Agricultural and Forest Meteorology}, - volume = {150}, - number = {10}, - pages = {1319--1330}, - issn = {01681923}, - doi = {10.1016/j.agrformet.2010.06.002}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ZHX7P43S/Agricultural and Forest Meteorology 2010 Hasan.pdf} -} - -@article{hasan_two_2011, - title = {Two {{Tweedie}} Distributions That Are Near-Optimal for Modelling Monthly Rainfall in {{Australia}}}, - author = {Hasan, Md Masud and Dunn, Peter K.}, - year = {2011}, - month = jul, - journal = {International Journal of Climatology}, - volume = {31}, - number = {9}, - pages = {1389--1397}, - issn = {08998418}, - doi = {10.1002/joc.2162}, - langid = {english}, - file = {/Users/dill/Zotero/storage/HZ9GDFYN/Int. J. Climatol. 2010 Hasan.pdf} -} - -@article{hastie_bayesian_2000, - title = {Bayesian Backfitting}, - author = {Hastie, Trevor J. and Tibshirani, Robert}, - year = {2000}, - journal = {Statistical Science}, - volume = {15}, - number = {3}, - pages = {196--223}, - file = {/Users/dill/Zotero/storage/YD3RC25T/euclid.ss.1009212815.pdf} -} - -@book{hastie_elements_2009, - title = {The {{Elements}} of {{Statistical Learning}}}, - author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome}, - year = {2009}, - edition = {Second}, - publisher = {{Springer series in statistics New York}}, - file = {/Users/dill/Zotero/storage/UJVIQSBB/ESLII.pdf} -} - -@article{hastie_generalized_1986, - title = {Generalized {{Additive Models}}}, - author = {Hastie, Trevor and Tibshirani, Robert}, - year = {1986}, - journal = {Statistical Science}, - volume = {1}, - number = {3}, - pages = {297--318}, - file = {/Users/dill/Zotero/storage/3IZKQSA2/Statist. Sci. 1986 Hastie.pdf} -} - -@book{hastie_generalized_1990, - title = {Generalized {{Additive Models}}}, - author = {Hastie, Trevor and Tibshirani, Robert}, - year = {1990}, - series = {Monographs on {{Statistics}} and {{Applied Probability}}}, - number = {43}, - publisher = {{Chapman and Hall}} -} - -@article{hastie_inference_2013, - title = {Inference from Presence-Only Data; the Ongoing Controversy}, - author = {Hastie, Trevor and Fithian, Will}, - year = {2013}, - month = aug, - journal = {Ecography}, - volume = {36}, - number = {8}, - pages = {864--867}, - issn = {09067590}, - doi = {10.1111/j.1600-0587.2013.00321.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/8PF9FWHV/Ecography 2013 Hastie.pdf} -} - -@article{hastie_regression_1989, - title = {Regression with an Ordered Categorical Response}, - author = {Hastie, T. J. and Botha, J. L. and Schnitzler, C. M.}, - year = {1989}, - month = jul, - journal = {Statistics in Medicine}, - volume = {8}, - number = {7}, - pages = {785--794}, - issn = {02776715, 10970258}, - doi = {10.1002/sim.4780080703}, - abstract = {A survey on Mselenijoint disease in South Africa involved the scoring of pelvic X-rays of women to measure osteoporosis. The scores were ordinal by construction and ranged from 0 to 12. It is standard practice to use ordinary regression techniques with an ordinal response that has that many categories. We give evidence for these data that the constraints on the response result in a misleading regression analysis. McCullagh's" proportional-odds model is designed specificallyfor the regression analysis of ordinal data. We demonstrate the technique on these data, and show how it fills the gap between ordinary regressionand logistic regression (for discrete data with two categories).In addition, we demonstrate non-parametric versions of these models that do not make any linearity assumptions about the regression function.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/A59Y9Z8T/Hastie et al. - 1989 - Regression with an ordered categorical response.pdf} -} - -@article{hauenstein_computing_2016, - title = {Computing {{AIC}} for Black-Box Models Using {{Generalised Degrees}} of {{Freedom}}: A Comparison with Cross-Validation}, - shorttitle = {Computing {{AIC}} for Black-Box Models Using {{Generalised Degrees}} of {{Freedom}}}, - author = {Hauenstein, Severin and Dormann, Carsten F. and Wood, Simon N.}, - year = {2016}, - month = mar, - journal = {arXiv:1603.02743 [stat]}, - eprint = {1603.02743}, - eprinttype = {arxiv}, - primaryclass = {stat}, - abstract = {Generalised Degrees of Freedom (GDF), as defined by Ye (1998 JASA 93:120-131), represent the sensitivity of model fits to perturbations of the data. As such they can be computed for any statistical model, making it possible, in principle, to derive the number of parameters in machine-learning approaches. Defined originally for normally distributed data only, we here investigate the potential of this approach for Bernoulli-data. GDF-values for models of simulated and real data are compared to model complexity-estimates from cross-validation. Similarly, we computed GDF-based AICc for randomForest, neural networks and boosted regression trees and demonstrated its similarity to cross-validation. GDF-estimates for binary data were unstable and inconsistently sensitive to the number of data points perturbed simultaneously, while at the same time being extremely computer-intensive in their calculation. Repeated 10-fold cross-validation was more robust, based on fewer assumptions and faster to compute. Our findings suggest that the GDF-approach does not readily transfer to Bernoulli data and a wider range of regression approaches.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Statistics - Machine Learning}, - file = {/Users/dill/Zotero/storage/B26AXFPW/Hauenstein et al. - 2016 - Computing AIC for black-box models using Generalis.pdf} -} - -@article{hawkins_eight_2012, - title = {Eight (and a Half) Deadly Sins of Spatial Analysis: {{Spatial}} Analysis}, - shorttitle = {Eight (and a Half) Deadly Sins of Spatial Analysis}, - author = {Hawkins, Bradford A.}, - year = {2012}, - month = jan, - journal = {Journal of Biogeography}, - volume = {39}, - number = {1}, - pages = {1--9}, - issn = {03050270}, - doi = {10.1111/j.1365-2699.2011.02637.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/I2WYBH2B/J. Biogeogr. 2011 Hawkins.pdf} -} - -@article{hayes_severe_2013, - title = {Severe Uncertainty and Info-Gap Decision Theory}, - author = {Hayes, Keith R. and Barry, Simon C. and Hosack, Geoffrey R. and Peters, Gareth W.}, - editor = {Freckleton, Robert}, - year = {2013}, - month = jul, - journal = {Methods in Ecology and Evolution}, - volume = {4}, - number = {7}, - pages = {601--611}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12046}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DH2VP9NI/Methods in Ecology and Evolution 2013 Hayes.pdf} -} - -@article{hazen_whalewatch_2017, - title = {{{WhaleWatch}}: A Dynamic Management Tool for Predicting Blue Whale Density in the {{California Current}}}, - shorttitle = {{{WhaleWatch}}}, - author = {Hazen, Elliott L. and Palacios, Daniel M. and Forney, Karin A. and Howell, Evan A. and Becker, Elizabeth and Hoover, Aimee L. and Irvine, Ladd and DeAngelis, Monica and Bograd, Steven J. and Mate, Bruce R. and Bailey, Helen}, - editor = {Singh, Navinder}, - year = {2017}, - month = oct, - journal = {Journal of Applied Ecology}, - volume = {54}, - number = {5}, - pages = {1415--1428}, - issn = {00218901}, - doi = {10.1111/1365-2664.12820}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2Q4SMU66/Hazen et al. - 2017 - WhaleWatch a dynamic management tool for predicti.pdf} -} - -@article{he_deep_2015, - title = {Deep {{Residual Learning}} for {{Image Recognition}}}, - author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, - year = {2015}, - month = dec, - journal = {arXiv:1512.03385 [cs]}, - eprint = {1512.03385}, - eprinttype = {arxiv}, - primaryclass = {cs}, - abstract = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers\textemdash 8\texttimes{} deeper than VGG nets [41] but still having lower complexity. An ensemble of these residual nets achieves 3.57\% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Computer Science - Computer Vision and Pattern Recognition}, - file = {/Users/dill/Zotero/storage/EDF26E7N/He et al. - 2015 - Deep Residual Learning for Image Recognition.pdf} -} - -@article{heaton_case_2018, - title = {A {{Case Study Competition Among Methods}} for {{Analyzing Large Spatial Data}}}, - author = {Heaton, Matthew J. and Datta, Abhirup and Finley, Andrew O. and Furrer, Reinhard and Guinness, Joseph and Guhaniyogi, Rajarshi and Gerber, Florian and Gramacy, Robert B. and Hammerling, Dorit and Katzfuss, Matthias and Lindgren, Finn and Nychka, Douglas W. and Sun, Furong and {Zammit-Mangion}, Andrew}, - year = {2018}, - month = dec, - journal = {Journal of Agricultural, Biological and Environmental Statistics}, - issn = {1085-7117, 1537-2693}, - doi = {10.1007/s13253-018-00348-w}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UX2ISZ9R/Heaton et al. - 2018 - A Case Study Competition Among Methods for Analyzi.pdf} -} - -@article{hedley_design-based_nodate, - title = {Design-Based Abundance Estimation}, - author = {Hedley, Sharon and Bravington, Mark}, - file = {/Users/dill/Zotero/storage/YY8WFWDQ/2014 Hedley.pdf} -} - -@article{hedley_spatial_2004, - title = {Spatial Models for Line Transect Sampling}, - author = {Hedley, Sharon L. and Buckland, Stephen T.}, - year = {2004}, - month = jun, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {9}, - number = {2}, - pages = {181--199}, - issn = {1085-7117, 1537-2693}, - doi = {10.1198/1085711043578}, - langid = {english}, - file = {/Users/dill/Zotero/storage/G95JGQ3H/JABES 2004 Hedley.pdf} -} - -@article{hedley_spatial_nodate, - title = {Spatial {{Modelling}} from {{Line Transect Data}}}, - author = {Hedley, Sharon L and Buckland, Stephen T and Borchers, David L}, - pages = {25}, - abstract = {In this paper, two new methods are presented that enable spatial models to be fitted from line transect data. Building on preliminary work by Cumberworth et al. (1996) and Hedley et al. (1997), the first method is based on a count model and involves cutting up the survey effort into small segments then modelling the number of schools in each segment. In contrast, the second method uses a model based on the intervals between detections. Its formulation is derived in detail to obtain the likelihood function for the distances between detections, conditional on an estimated detection function.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2ES9Y9P9/Hedley et al. - Spatial Modelling from Line Transect Data.pdf} -} - -@article{hefley_basis_2017, - title = {The Basis Function Approach for Modeling Autocorrelation in Ecological Data}, - author = {Hefley, Trevor J. and Broms, Kristin M. and Brost, Brian M. and Buderman, Frances E. and Kay, Shannon L. and Scharf, Henry R. and Tipton, John R. and Williams, Perry J. and Hooten, Mevin B.}, - year = {2017}, - month = mar, - journal = {Ecology}, - volume = {98}, - number = {3}, - pages = {632--646}, - issn = {00129658}, - doi = {10.1002/ecy.1674}, - abstract = {Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many of the statistical methods used to account for autocorrelation can be viewed as regression models that include basis functions. Understanding the concept of basis functions enables ecologists to modify commonly used ecological models to account for autocorrelation, which can improve inference and predictive accuracy. Understanding the properties of basis functions is essential for evaluating the fit of spatial or time-series models, detecting a hidden form of multicollinearity, and analyzing large data sets. We present important concepts and properties related to basis functions and illustrate several tools and techniques ecologists can use when modeling autocorrelation in ecological data.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/XTPFEP2A/Hefley et al. - 2017 - The basis function approach for modeling autocorre.pdf} -} - -@article{hefley_hierarchical_2016, - title = {Hierarchical {{Species Distribution Models}}}, - author = {Hefley, Trevor J. and Hooten, Mevin B.}, - year = {2016}, - month = jun, - journal = {Current Landscape Ecology Reports}, - volume = {1}, - number = {2}, - pages = {87--97}, - issn = {2364-494X}, - doi = {10.1007/s40823-016-0008-7}, - langid = {english}, - file = {/Users/dill/Zotero/storage/RW5N5L28/Hefley and Hooten - 2016 - Hierarchical Species Distribution Models.pdf} -} - -@article{hefley_when_2016, - title = {When Can the Cause of a Population Decline Be Determined?}, - author = {Hefley, Trevor J. and Hooten, Mevin B. and Drake, John M. and Russell, Robin E. and Walsh, Daniel P.}, - editor = {Bradshaw, Corey}, - year = {2016}, - month = nov, - journal = {Ecology Letters}, - volume = {19}, - number = {11}, - pages = {1353--1362}, - issn = {1461023X}, - doi = {10.1111/ele.12671}, - langid = {english}, - file = {/Users/dill/Zotero/storage/NFLT7D8V/Ecol Letters 2016 Hefley.pdf} -} - -@article{heide-jorgensen_fully_2010, - title = {Fully Corrected Estimates of Common Minke Whale Abundance in {{West Greenland}} in 2007}, - author = {{Heide-J{\o}rgensen}, M P and Witting, L and Laidre, K L and Hansen, R G and Rasmussen, M}, - year = {2010}, - journal = {Journal of Cetacean Research and Management}, - volume = {11}, - number = {2}, - pages = {75--82}, - abstract = {A visual aerial line transect survey for common minke whales (Balaenoptera acutorostrata) was conducted off West Greenland in August and September 2007. A total of 8,670km of survey effort covered 11 strata in sea states {$<$}5 with a total stratum area of 213,807km2. The 27 sightings of common minke whales were all within a strip width of 300m and the average time from first detection to when the sighting passed abeam was 1.7 sec. Due to the uniform and narrow distribution of the detections, strip census methods were used to analyse the survey. Two methods were deployed to correct the strip census estimates for whales missed by the observers and whales that were submerged during the passage of the plane. Method 1 included all detections of common minke whales (n = 27) and correction for an instantaneous availability that included submergence of whales. Using data from sea states {$<$}3 (n = 22) the `at surface' abundance of common minke whales was 1,866 (CV = 0.30) whales. A correction for whales missed by the observers with a simple mark-recapture estimator resulted in a corrected abundance of 1,904 (CV = 0.30) whales. Adjusting for the availability bias resulted in a fully corrected estimate of 16,609 (95\% CI 7,172\textendash 38,461) common minke whales. Method 2 used only detections of common minke whales that were observed to break the surface (n = 19). Applying this method to effort data at sea state {$<$}3 (n = 14) resulted in an `at surface' abundance of 1,174 (CV = 0.39) whales. A correction for whales missed by the observers increased the abundance to 1,198 (0.39) whales. Adjusting for the availability bias resulted in a fully corrected estimate of 22,952 (95\% CI 7,815\textendash 67,403) common minke whales.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Q5Q5K473/Heide-Jørgensen et al. - 2010 - Fully corrected estimates of common minke whale ab.pdf} -} - -@article{hengl_about_2007, - title = {About Regression-Kriging: {{From}} Equations to Case Studies}, - shorttitle = {About Regression-Kriging}, - author = {Hengl, Tomislav and Heuvelink, Gerard B.M. and Rossiter, David G.}, - year = {2007}, - month = oct, - journal = {Computers \& Geosciences}, - volume = {33}, - number = {10}, - pages = {1301--1315}, - issn = {00983004}, - doi = {10.1016/j.cageo.2007.05.001}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ICZK5CB9/Computers & Geosciences 2007 Hengl.pdf} -} - -@article{herr_seals_2009, - title = {Seals at Sea: Modelling Seal Distribution in the {{German}} Bight Based on Aerial Survey Data}, - shorttitle = {Seals at Sea}, - author = {Herr, Helena and Scheidat, M. and Lehnert, K. and Siebert, U.}, - year = {2009}, - month = apr, - journal = {Marine Biology}, - volume = {156}, - number = {5}, - pages = {811--820}, - issn = {0025-3162, 1432-1793}, - doi = {10.1007/s00227-008-1105-x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/WNP4PBB5/Mar Biol 2009 Herr.pdf} -} - -@incollection{hiby_objective_1999, - title = {The Objective Identification of Duplicate Sightings in Aerial Survey for Porpoise.}, - booktitle = {Marine {{Mammal Survey}} and {{Assessment Methods}}}, - author = {Hiby, Lex}, - editor = {Garner, Gerald W. and Amstrup, Steven C. and Laake, Jeffrey L. and Manly, Bryan F.J. and McDonald, Lyman L. and Robertson, Donna G.}, - year = {1999}, - pages = {179--189}, - publisher = {{Balkema}}, - address = {{Rotterdam}} -} - -@book{higham_matrix_1988, - title = {Matrix Nearness Problems and Applications}, - author = {Higham, Nicholas J.}, - year = {1988}, - publisher = {{University of Manchester. Department of Mathematics}}, - file = {/Users/dill/Zotero/storage/2HRTJ4V3/1988 Higham.pdf} -} - -@article{hildebrand_national_nodate, - title = {National {{Academy}} of {{Sciences}} ({{NAS}}) {{Award}} \#200006419}, - author = {Hildebrand, John A and Frasier, Kaitlin E and Soldevilla, Melissa S and Garrison, Lance P}, - pages = {13}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2WTAWGNA/Hildebrand et al. - National Academy of Sciences (NAS) Award #20000641.pdf} -} - -@article{hodges_adding_2010, - title = {Adding {{Spatially-Correlated Errors Can Mess Up}} the {{Fixed Effect You Love}}}, - author = {Hodges, James S. and Reich, Brian J.}, - year = {2010}, - month = nov, - journal = {The American Statistician}, - volume = {64}, - number = {4}, - pages = {325--334}, - issn = {0003-1305, 1537-2731}, - doi = {10.1198/tast.2010.10052}, - langid = {english}, - file = {/Users/dill/Zotero/storage/P2MQA5TW/The American Statistician 2010 Hodges.pdf} -} - -@techreport{hodges_random_2011, - type = {Technical {{Report}}}, - title = {Random Effects Old and New}, - author = {Hodges, James S and Clayton, Murray K}, - year = {2011}, - pages = {22}, - institution = {{University of Minnesota; Minneapolis, MN}}, - abstract = {The term ``random effect'' is now used much more broadly than it was, say, 50 years ago. At that time, a random effect was an effect having (in analysis-of-variance jargon) levels that were draws from a population, and the draws were not of interest in themselves but only as samples from the larger population (e.g., Scheff\textasciiacute e 1959, p. 238). By contrast, new-style random effects have levels that are not draws from any population, or that are the entire population, or that may be a sample but a new draw from the random effect could not conceivably be drawn; and the levels themselves are usually of interest. All such new-style random effects can be understood as formal devices to facilitate smoothing or shrinkage, interpreting those terms broadly. The distinction between oldand new-style random effects is not a mere nicety but has practical consequences for inference and prediction, simulation experiments for evaluating statistical methods, and interpretation of analytical artifacts. Therefore, this distinction should be developed beyond the catalog of examples and consequences given here and incorporated into statistical theory and practice.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FAXG63RL/Hodges and Clayton - Random Effects Old and New.pdf} -} - -@article{hodgson_unmanned_2013, - title = {Unmanned {{Aerial Vehicles}} ({{UAVs}}) for {{Surveying Marine Fauna}}: {{A Dugong Case Study}}}, - shorttitle = {Unmanned {{Aerial Vehicles}} ({{UAVs}}) for {{Surveying Marine Fauna}}}, - author = {Hodgson, Amanda and Kelly, Natalie and Peel, David}, - editor = {Fenton, Brock}, - year = {2013}, - month = nov, - journal = {PLoS ONE}, - volume = {8}, - number = {11}, - pages = {e79556}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0079556}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YSX2XAIA/journal.pone.0079556.PDF} -} - -@article{hodgson_unmanned_2017, - title = {Unmanned Aerial Vehicles for Surveying Marine Fauna: Assessing Detection Probability}, - shorttitle = {Unmanned Aerial Vehicles for Surveying Marine Fauna}, - author = {Hodgson, Amanda and Peel, David and Kelly, Natalie}, - year = {2017}, - journal = {Ecological Applications}, - volume = {27}, - number = {4}, - pages = {1253--1267}, - file = {/Users/dill/Zotero/storage/5SG973QL/eap1519.pdf;/Users/dill/Zotero/storage/JHQKUCFD/eap1519-sup-0008-appendixs8.pdf} -} - -@techreport{hodgson_using_2010, - title = {Using {{Unmanned Aerial Vehicles}} for Surveys of Marine Mammals in {{Australia}}: Test of Concept}, - author = {Hodgson, Amanda and Noad, Michael and Marsh, Helene and Lanyon, Janet and Kniest, Eric}, - year = {2010}, - pages = {76}, - address = {{Final Report to the Australian Marine Mammal Centre}}, - langid = {english}, - file = {/Users/dill/Zotero/storage/3Q7YZMAZ/Hodgson - Using Unmanned Aerial Vehicles for surveys of mari.pdf} -} - -@article{hoef_relationship_2017, - title = {On the {{Relationship}} between {{Conditional}} ({{CAR}}) and {{Simultaneous}} ({{SAR}}) {{Autoregressive Models}}}, - author = {Hoef, Jay M. Ver and Hanks, Ephraim M. and Hooten, Mevin B.}, - year = {2017}, - month = oct, - journal = {arXiv:1710.07000 [math, stat]}, - eprint = {1710.07000}, - eprinttype = {arxiv}, - primaryclass = {math, stat}, - abstract = {We clarify relationships between conditional (CAR) and simultaneous (SAR) autoregressive models. We review the literature on this topic and find that it is mostly incomplete. Our main result is that a SAR model can be written as a unique CAR model, and while a CAR model can be written as a SAR model, it is not unique. In fact, we show how any multivariate Gaussian distribution on a finite set of points with a positive-definite covariance matrix can be written as either a CAR or a SAR model. We illustrate how to obtain any number of SAR covariance matrices from a single CAR covariance matrix by using Givens rotation matrices on a simulated example. We also discuss sparseness in the original CAR construction, and for the resulting SAR weights matrix. For a real example, we use crime data in 49 neighborhoods from Columbus, Ohio, and show that a geostatistical model optimizes the likelihood much better than typical first-order CAR models. We then use the implied weights from the geostatistical model to estimate CAR model parameters that provides the best overall optimization.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Mathematics - Statistics Theory}, - file = {/Users/dill/Zotero/storage/WPDL8F6A/Hoef et al. - 2017 - On the Relationship between Conditional (CAR) and .pdf} -} - -@article{hoekman_line_2011, - title = {{{LINE TRANSECT SAMPLING FOR MURRELETS}}: {{ACCOUNTING FOR INCOMPLETE DETECTION AND IDENTIFICATION}}}, - author = {HOEKMAN, STEVEN T and MOYNAHAN, BRENDAN J and LINDBERG, MARK S and SHARMAN, LEWIS C and JOHNSON, WILLIAM F}, - year = {2011}, - pages = {10}, - abstract = {HOEKMAN, S.T., MOYNAHAN, B.J., LINDBERG, M.S., SHARMAN, L.C. \& JOHNSON, W.F. 2011. Line transect surveys for murrelets: accounting for incomplete detection and identification. Marine Ornithology 39: 35-44.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9XQIPMSV/HOEKMAN et al. - 2011 - LINE TRANSECT SAMPLING FOR MURRELETS ACCOUNTING F.pdf} -} - -@article{hofmeester_simple_2017, - title = {A Simple Method for Estimating the Effective Detection Distance of Camera Traps}, - author = {Hofmeester, Tim R. and Rowcliffe, J. Marcus and Jansen, Patrick A.}, - editor = {Williams, Rob and Kelly, Natalie}, - year = {2017}, - month = jun, - journal = {Remote Sensing in Ecology and Conservation}, - volume = {3}, - number = {2}, - pages = {81--89}, - issn = {20563485}, - doi = {10.1002/rse2.25}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UB2334FI/Remote Sens Ecol Conserv 2016 Hofmeester.pdf} -} - -@article{hogmander_random_1991, - title = {A Random Field Approach to Transect Counts of Wildlife Populations}, - author = {H{\"o}gmander, H.}, - year = {1991}, - journal = {Biometrical Journal}, - volume = {33}, - number = {8}, - pages = {1013--1023}, - file = {/Users/dill/Zotero/storage/FTKICMQA/Biometrical Journal 1991 Högmander.pdf} -} - -@book{hooten_animal_2017, - title = {Animal {{Movement}}: {{Statistical Models}} for {{Telemetry Data}}}, - author = {Hooten, Mevin B. and Johnson, Devin S. and McClintock, Brett T. and Morales, Juan M.}, - year = {2017}, - publisher = {{CRC Press}} -} - -@article{hooten_guide_2015, - title = {A Guide to {{Bayesian}} Model Selection for Ecologists}, - author = {Hooten, Mevin B. and Hobbs, N. T.}, - year = {2015}, - journal = {Ecological Monographs}, - volume = {85}, - number = {1}, - pages = {3--28}, - file = {/Users/dill/Zotero/storage/H98YI7IQ/Ecological Monographs 2015 Hooten.pdf} -} - -@article{hooten_running_2018, - title = {Running on Empty: Recharge Dynamics from Animal Movement Data}, - shorttitle = {Running on Empty}, - author = {Hooten, Mevin B. and Scharf, Henry R. and Morales, Juan M.}, - editor = {Nathan, Ran}, - year = {2018}, - month = dec, - journal = {Ecology Letters}, - issn = {1461-023X, 1461-0248}, - doi = {10.1111/ele.13198}, - abstract = {Vital rates such as survival and recruitment have always been important in the study of population and community ecology. At the individual level, physiological processes such as energetics are critical in understanding biomechanics and movement ecology and also scale up to influence food webs and trophic cascades. Although vital rates and population-level characteristics are tied with individual-level animal movement, most statistical models for telemetry data are not equipped to provide inference about these relationships because they lack the explicit, mechanistic connection to physiological dynamics. We present a framework for modelling telemetry data that explicitly includes an aggregated physiological process associated with decision making and movement in heterogeneous environments. Our framework accommodates a wide range of movement and physiological process specifications. We illustrate a specific model formulation in continuous-time to provide direct inference about gains and losses associated with physiological processes based on movement. Our approach can also be extended to accommodate auxiliary data when available. We demonstrate our model to infer mountain lion (Puma concolor; in Colorado, USA) and African buffalo (Syncerus caffer; in Kruger National Park, South Africa) recharge dynamics.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/HLZJ52E3/Hooten et al. - 2018 - Running on empty recharge dynamics from animal mo.pdf} -} - -@article{horvitz_generalization_1952, - title = {A {{Generalization}} of {{Sampling Without Replacement From}} a {{Finite Universe}}}, - author = {Horvitz, D. G. and Thompson, D. J.}, - year = {1952}, - month = dec, - journal = {Journal of the American Statistical Association}, - volume = {47}, - number = {260}, - pages = {663}, - issn = {01621459}, - doi = {10.2307/2280784}, - file = {/Users/dill/Zotero/storage/BMKT97WU/Horvitz-Thompson-1952-jasa.pdf;/Users/dill/Zotero/storage/ZGPY4W2Z/2347625.pdf} -} - -@article{hoschle_potential_2021, - title = {The {{Potential}} of {{Satellite Imagery}} for {{Surveying Whales}}}, - author = {H{\"o}schle, Caroline and Cubaynes, Hannah C. and Clarke, Penny J. and Humphries, Grant and Borowicz, Alex}, - year = {2021}, - month = feb, - journal = {Sensors}, - volume = {21}, - number = {3}, - pages = {963}, - issn = {1424-8220}, - doi = {10.3390/s21030963}, - abstract = {The emergence of very high-resolution (VHR) satellite imagery (less than 1 m spatial resolution) is creating new opportunities within the fields of ecology and conservation biology. The advancement of sub-meter resolution imagery has provided greater confidence in the detection and identification of features on the ground, broadening the realm of possible research questions. To date, VHR imagery studies have largely focused on terrestrial environments; however, there has been incremental progress in the last two decades for using this technology to detect cetaceans. With advances in computational power and sensor resolution, the feasibility of broad-scale VHR ocean surveys using VHR satellite imagery with automated detection and classification processes has increased. Initial attempts at automated surveys are showing promising results, but further development is necessary to ensure reliability. Here we discuss the future directions in which VHR satellite imagery might be used to address urgent questions in whale conservation. We highlight the current challenges to automated detection and to extending the use of this technology to all oceans and various whale species. To achieve basin-scale marine surveys, currently not feasible with any traditional surveying methods (including boat-based and aerial surveys), future research requires a collaborative effort between biology, computation science, and engineering to overcome the present challenges to this platform's use.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LQKFNX2A/Höschle et al. - 2021 - The Potential of Satellite Imagery for Surveying W.pdf} -} - -@article{howard_improving_2014, - title = {Improving Species Distribution Models: The Value of Data on Abundance}, - shorttitle = {Improving Species Distribution Models}, - author = {Howard, Christine and Stephens, Philip A. and {Pearce-Higgins}, James W. and Gregory, Richard D. and Willis, Stephen G.}, - editor = {McPherson, Jana}, - year = {2014}, - month = jun, - journal = {Methods in Ecology and Evolution}, - volume = {5}, - number = {6}, - pages = {506--513}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12184}, - langid = {english}, - file = {/Users/dill/Zotero/storage/KRFWAJ3B/Methods in Ecology and Evolution 2014 Howard.pdf} -} - -@article{howe_model_2019, - title = {Model Selection with Overdispersed Distance Sampling Data}, - author = {Howe, Eric J. and Buckland, Stephen T. and Despr{\'e}s-Einspenner, Marie-Lyne and K{\"u}hl, Hjalmar S.}, - editor = {Matthiopoulos, Jason}, - year = {2019}, - month = jan, - journal = {Methods in Ecology and Evolution}, - volume = {10}, - number = {1}, - pages = {38--47}, - issn = {2041-210X, 2041-210X}, - doi = {10.1111/2041-210X.13082}, - langid = {english}, - file = {/Users/dill/Zotero/storage/MDQ54GR3/Howe et al. - 2019 - Model selection with overdispersed distance sampli.pdf} -} - -@article{hurlbert_spatiotemporal_2012, - title = {Spatiotemporal {{Variation}} in {{Avian Migration Phenology}}: {{Citizen Science Reveals Effects}} of {{Climate Change}}}, - shorttitle = {Spatiotemporal {{Variation}} in {{Avian Migration Phenology}}}, - author = {Hurlbert, Allen H. and Liang, Zhongfei}, - editor = {Korb, Judith}, - year = {2012}, - month = feb, - journal = {PLoS ONE}, - volume = {7}, - number = {2}, - pages = {e31662}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0031662}, - langid = {english}, - file = {/Users/dill/Zotero/storage/VEVATMWE/PLoS ONE 2012 Hurlbert.pdf} -} - -@article{hurley_modelling_1992, - title = {Modelling Bedload Transport Events Using an Inhomogeneous Gamma Process}, - author = {Hurley, Margaret Anne}, - year = {1992}, - journal = {Journal of Hydrology}, - volume = {138}, - number = {3-4}, - pages = {529--541}, - file = {/Users/dill/Zotero/storage/EPFR65LM/hurley1992.pdf} -} - -@book{illian_statistical_2008, - title = {Statistical {{Analysis}} and {{Modelling}} of {{Spatial Point Patterns}}}, - author = {Illian, J. and Penttinen, P.A. and Stoyan, H. and Stoyan, D.}, - year = {2008}, - series = {Statistics in {{Practice}}}, - publisher = {{Wiley}}, - isbn = {978-0-470-72515-3} -} - -@article{illian_toolbox_2012, - title = {A Toolbox for Fitting Complex Spatial Point Process Models Using Integrated Nested {{Laplace}} Approximation ({{INLA}})}, - author = {Illian, Janine B. and S{\o}rbye, Sigrunn H. and Rue, H{\aa}vard}, - year = {2012}, - month = dec, - journal = {The Annals of Applied Statistics}, - volume = {6}, - number = {4}, - pages = {1499--1530}, - issn = {1932-6157}, - doi = {10.1214/11-AOAS530}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UDYA3T84/Ann. Appl. Stat. 2012 Illian.pdf} -} - -@article{innes_surveys_2002, - title = {Surveys of Belugas and Narwhals in the {{Canadian}} High {{Arctic}} in 1996}, - author = {Innes, Stuart and {Heide-J{\o}rgensen}, M. P. and Laake, Jeff L. and Laidre, Kristin L. and Cleator, Holly J. and Richard, Pierre and Stewart, Robert EA}, - year = {2002}, - journal = {NAMMCO Scientific Publications}, - volume = {4}, - pages = {169--190}, - file = {/Users/dill/Zotero/storage/DV3I95LC/NAMMCO Scientific Publications 2002 Innes.pdf} -} - -@article{international_whaling_commission_requirements_2012, - title = {Requirements and {{Guidelines}} for {{Conducting Surveys}} and {{Analysing Data}} within the {{Revised Management Scheme}}}, - author = {International Whaling Commission}, - year = {2012}, - journal = {Journal of Cetacean Research and Management}, - volume = {13}, - pages = {509--517}, - file = {/Users/dill/Zotero/storage/U4W54QEY/RMP_Guidelines_Abundance_RS297_13Supp507_518_ReqGuidSurv.pdf} -} - -@article{ivashchenko_distribution_2014, - title = {Distribution of {{Soviet}} Catches of Sperm Whales {{Physeter}} Macrocephalus in the {{North Pacific}}}, - author = {Ivashchenko, Yv and Brownell, Rl and Clapham, Pj}, - year = {2014}, - month = oct, - journal = {Endangered Species Research}, - volume = {25}, - number = {3}, - pages = {249--263}, - issn = {1863-5407, 1613-4796}, - doi = {10.3354/esr00641}, - langid = {english}, - file = {/Users/dill/Zotero/storage/X8I6UD5X/Endang. Species. Res. 2014 Ivashchenko.pdf} -} - -@article{janson_effective_2015, - title = {Effective Degrees of Freedom: A Flawed Metaphor}, - shorttitle = {Effective Degrees of Freedom}, - author = {Janson, Lucas and Fithian, William and Hastie, Trevor J.}, - year = {2015}, - month = jun, - journal = {Biometrika}, - volume = {102}, - number = {2}, - pages = {479--485}, - issn = {0006-3444, 1464-3510}, - doi = {10.1093/biomet/asv019}, - abstract = {To most applied statisticians, a fitting procedure's degrees of freedom is synonymous with its model complexity, or its capacity for overfitting to data. In particular, it is often used to parameterize the bias-variance tradeoff in model selection. We argue that, contrary to folk intuition, model complexity and degrees of freedom are not synonymous and may correspond very poorly. We exhibit various examples of fitting procedures for which degrees of freedom is not monotonic in the model complexity parameter, and can exceed the total dimension of the largest model. Even in very simple settings, the degrees of freedom can exceed the dimension of the ambient space by an arbitrarily large amount.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LCDTYLDR/Janson et al. - 2015 - Effective degrees of freedom a flawed metaphor.pdf} -} - -@article{jaramillo-legorreta_passive_2017, - title = {Passive Acoustic Monitoring of the Decline of {{Mexico}}'s Critically Endangered Vaquita: {{Decline}} of {{Vaquita}}}, - shorttitle = {Passive Acoustic Monitoring of the Decline of {{Mexico}}'s Critically Endangered Vaquita}, - author = {{Jaramillo-Legorreta}, Armando and {Cardenas-Hinojosa}, Gustavo and {Nieto-Garcia}, Edwyna and {Rojas-Bracho}, Lorenzo and Ver Hoef, Jay and Moore, Jeffrey and Tregenza, Nicholas and Barlow, Jay and Gerrodette, Tim and Thomas, Len and Taylor, Barbara}, - year = {2017}, - month = feb, - journal = {Conservation Biology}, - volume = {31}, - number = {1}, - pages = {183--191}, - issn = {08888892}, - doi = {10.1111/cobi.12789}, - langid = {english}, - file = {/Users/dill/Zotero/storage/85E52QVT/Conservation Biology 2016 Jaramillo-Legorreta.pdf} -} - -@article{jensen_landscape-based_2006, - title = {Landscape-Based Geostatistics: A Case Study of the Distribution of Blue Crab in {{Chesapeake Bay}}}, - shorttitle = {Landscape-Based Geostatistics}, - author = {Jensen, Olaf P. and Christman, Mary C. and Miller, Thomas J.}, - year = {2006}, - month = sep, - journal = {Environmetrics}, - volume = {17}, - number = {6}, - pages = {605--621}, - issn = {1180-4009, 1099-095X}, - doi = {10.1002/env.767}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9T5ZMPUN/Environmetrics 2006 Jensen.pdf} -} - -@article{jensen_winter_2005, - title = {Winter Distribution of Blue Crab {{Callinectes}} Sapidus in {{Chesapeake Bay}}: Application and Cross-Validation of a Two-Stage Generalized Additive Model}, - shorttitle = {Winter Distribution of Blue Crab {{Callinectes}} Sapidus in {{Chesapeake Bay}}}, - author = {Jensen, Olaf P. and Seppelt, Ralf and Miller, Thomas J. and Bauer, Laurie J.}, - year = {2005}, - journal = {Marine Ecology Progress Series}, - volume = {299}, - pages = {239--255}, - file = {/Users/dill/Zotero/storage/EFQM2DZT/Mar. Ecol. Prog. Ser. 2005 Jensen.pdf} -} - -@article{jing_correspondence_2017, - title = {On the Correspondence of Deviances and Maximum Likelihood and Interval Estimates from Log-Linear to Logistic Regression Modelling}, - author = {Jing, Wei and Papathomas, Michail}, - year = {2017}, - month = nov, - journal = {arXiv:1711.10440 [stat]}, - eprint = {1711.10440}, - eprinttype = {arxiv}, - primaryclass = {stat}, - abstract = {Consider a set of categorical variables \$\textbackslash mathcal\{P\}\$ where at least one, denoted by \$Y\$, is binary. The log-linear model that describes the counts in the resulting contingency table implies a specific logistic regression model, with the binary variable as the outcome. Extending results in Christensen (1997), by also considering the case where factors present in the contingency table disappear from the logistic regression model, we prove that the Maximum Likelihood Estimate (MLE) for the parameters of the logistic regression equals the MLE for the corresponding parameters of the log-linear model. We prove that, asymptotically, standard errors for the two sets of parameters are also equal. Subsequently, Wald confidence intervals are asymptotically equal. These results demonstrate the extent to which inferences from the log-linear framework can be translated to inferences within the logistic regression framework, on the magnitude of main effects and interactions. Finally, we prove that the deviance of the log-linear model is equal to the deviance of the corresponding logistic regression, provided that the latter is fitted to a dataset where no cell observations are merged when one or more factors in \$\textbackslash mathcal\{P\} \textbackslash setminus \textbackslash\{ Y \textbackslash\}\$ become obsolete. We illustrate the derived results with the analysis of a real dataset.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Statistics - Methodology}, - file = {/Users/dill/Zotero/storage/CKVSGGAK/Jing and Papathomas - 2017 - On the correspondence of deviances and maximum lik.pdf} -} - -@article{johnson_model-based_2010, - title = {A {{Model-Based Approach}} for {{Making Ecological Inference}} from {{Distance Sampling Data}}}, - author = {Johnson, Devin S. and Laake, Jeffrey L. and Ver Hoef, Jay M.}, - year = {2010}, - month = mar, - journal = {Biometrics}, - volume = {66}, - number = {1}, - pages = {310--318}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2009.01265.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/XRG7LBG8/Biom 2009 Johnson.pdf} -} - -@article{johnston_comparing_2017, - title = {Comparing Occupied and Unoccupied Aircraft Surveys of Wildlife Populations: {{Assessing}} the Gray Seal ({{{\emph{Halichoerus}}}}{\emph{ Grypus}}) Breeding Colony on {{Muskeget Island}}, {{USA}}}, - shorttitle = {Comparing Occupied and Unoccupied Aircraft Surveys of Wildlife Populations}, - author = {Johnston, David William and Dale, Julian and Murray, Kimberly and Josephson, Elizabeth and Newton, Everette and Wood, Stephanie}, - year = {2017}, - month = sep, - journal = {Journal of Unmanned Vehicle Systems}, - pages = {juvs-2017-0012}, - issn = {2291-3467}, - doi = {10.1139/juvs-2017-0012}, - abstract = {Unoccupied aircraft systems (UAS) are now frequently used in wildlife research, including studies of marine species. Researchers are turning to UAS platforms for population assessment purposes because they may provide flexible, safe, and low-cost data collection. In these cases, it is important that the accuracy and precision of UAS-based approaches are evaluated to ensure data quality and comparability with legacy data. The present study compares image quality and survey performance of two small UAS with that of an occupied aircraft as applied to a population survey and molt-stage assessment of gray seals (Halichoerus grypus) in the northeastern United States. Population surveys using fixedwing UAS and occupied aircraft provided similar quality imagery with only minor deviations in counts of both adult seals ({$<$}1\% difference) and pups (3.7\% difference). The multicopter UAS proved especially useful for molt-stage assessment when compared to both fixed-wing UAS and occupied aircraft surveys. The results of this study clearly illustrate that small UAS are reliable tools for conducting population assessments of pinnipeds and establishing life history stages of animals. These new tools provide flexibility in operations and may reduce costs and human risk in some cases.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/J8T3H67F/Johnston et al. - 2017 - Comparing occupied and unoccupied aircraft surveys.pdf} -} - -@article{johnston_modelling_2015, - title = {Modelling the Abundance and Distribution of Marine Birds Accounting for Uncertain Species Identification}, - author = {Johnston, Alison and Thaxter, Chris B. and Austin, Graham E. and Cook, Aonghais S.C.P. and Humphreys, Elizabeth M. and Still, David A. and Mackay, Alastair and Irvine, Ryan and Webb, Andy and Burton, Niall H.K.}, - editor = {Votier, Steve}, - year = {2015}, - month = feb, - journal = {Journal of Applied Ecology}, - volume = {52}, - number = {1}, - pages = {150--160}, - issn = {00218901}, - doi = {10.1111/1365-2664.12364}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LQAEAEKW/Johnston et al. - 2015 - Modelling the abundance and distribution of marine.pdf} -} - -@article{johnston_unoccupied_2019, - title = {Unoccupied {{Aircraft Systems}} in {{Marine Science}} and {{Conservation}}}, - author = {Johnston, David W.}, - year = {2019}, - month = jan, - journal = {Annual Review of Marine Science}, - volume = {11}, - number = {1}, - pages = {439--463}, - issn = {1941-1405, 1941-0611}, - doi = {10.1146/annurev-marine-010318-095323}, - abstract = {The use of unoccupied aircraft systems (UASs, also known as drones) in science is growing rapidly. Recent advances in microelectronics and battery technology have resulted in the rapid development of low-cost UASs that are transforming many industries. Drones are poised to revolutionize marine science and conservation, as they provide essentially on-demand remote sensing capabilities at low cost and with reduced human risk. A variety of multirotor, fixed-wing, and transitional UAS platforms are capable of carrying various optical and physical sampling payloads and are being employed in almost every subdiscipline of marine science and conservation. This article provides an overview of the UAS platforms and sensors used in marine science and conservation missions along with example physical, biological, and natural resource management applications and typical analytical workflows. It concludes with details on potential effects of UASs on marine wildlife and a look to the future of UASs in marine science and conservation.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/VBASRDEI/Johnston - 2019 - Unoccupied Aircraft Systems in Marine Science and .pdf} -} - -@article{jones_novel_2016, - title = {Novel Application of a Quantitative Spatial Comparison Tool to Species Distribution Data}, - author = {Jones, Esther L. and Rendell, Luke and Pirotta, Enrico and Long, Jed A.}, - year = {2016}, - month = nov, - journal = {Ecological Indicators}, - volume = {70}, - pages = {67--76}, - issn = {1470160X}, - doi = {10.1016/j.ecolind.2016.05.051}, - langid = {english}, - file = {/Users/dill/Zotero/storage/W6CXEN8B/Ecological Indicators 2016 Jones.pdf} -} - -@article{jonsen_movement_2018, - title = {Movement Behaviour Responses to Environment: Fast Inference of Individual Variation with a Mixed Effects Model}, - shorttitle = {Movement Behaviour Responses to Environment}, - author = {Jonsen, Ian and McMahon, Clive and Patterson, Toby and {Auger-Methe}, Marie and Harcourt, Robert and Hindell, Mark and Bestley, Sophie}, - year = {2018}, - month = may, - doi = {10.1101/314690}, - abstract = {Telemetry data provide a rich source of information on animals' use of space, habitat preferences and movement behaviour. Yet habitat models fit to these data are blind to the underlying behavioural context. Conversely, behavioural models accounting for individual variability are too slow for meaningful analysis of large telemetry datasets. Applying new fast-estimation tools, we show how a model incorporating mixed effects within a flexible random walk movement process rapidly infers among-individual variability in environment-movement behaviour relationships. We demonstrate our approach using southern elephant seal ( ) telemetry data. Seals consistently reduced speed and directionality (move persistence) with increasing sea ice coverage, had variable responses to chlorophyll concentration and consistently reduced move persistence in regions where circumpolar deep water shoaled. Our new modelling framework is extensible and substantively advances analysis of telemetry data by allowing fast and flexible mixed effects estimation of potential drivers of movement behaviour processes.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/MPLD83YM/Jonsen et al. - 2018 - Movement behaviour responses to environment fast .pdf} -} - -@article{kahle_ggmap_2013, - title = {Ggmap: {{Spatial Visualization}} with Ggplot2.}, - shorttitle = {Ggmap}, - author = {Kahle, David and Wickham, Hadley}, - year = {2013}, - journal = {R Journal}, - volume = {5}, - number = {1}, - file = {/Users/dill/Zotero/storage/6UU7ED8B/The R Journal 2013 Kahle.pdf} -} - -@article{kammann_geoadditive_2003, - title = {Geoadditive Models}, - author = {Kammann, E. E. and Wand, M. P.}, - year = {2003}, - month = jan, - journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}, - volume = {52}, - number = {1}, - pages = {1--18}, - issn = {0035-9254, 1467-9876}, - doi = {10.1111/1467-9876.00385}, - abstract = {A study into geographical variability of reproductive health outcomes (e.g. birth weight) in Upper Cape Cod, Massachusetts, USA, benefits from geostatistical mapping or kriging. However, also observed are some continuous covariates (e.g. maternal age) that exhibit pronounced non-linear relationships with the response variable. To account for such effects properly we merge kriging with additive models to obtain what we call geoadditive models. The merging becomes effortless by expressing both as linear mixed models. The resulting mixed model representation for the geoadditive model allows for fitting and diagnosis using standard methodology and software.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/VGFMNN9K/Kammann and Wand - 2003 - Geoadditive models.pdf} -} - -@article{kaschner_global_2012, - title = {Global {{Coverage}} of {{Cetacean Line-Transect Surveys}}: {{Status Quo}}, {{Data Gaps}} and {{Future Challenges}}}, - shorttitle = {Global {{Coverage}} of {{Cetacean Line-Transect Surveys}}}, - author = {Kaschner, Kristin and Quick, Nicola J. and Jewell, Rebecca and Williams, Rob and Harris, Catriona M.}, - editor = {Bograd, Steven J.}, - year = {2012}, - month = sep, - journal = {PLoS ONE}, - volume = {7}, - number = {9}, - pages = {e44075}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0044075}, - langid = {english}, - file = {/Users/dill/Zotero/storage/VAY4TGDQ/PLoS ONE 2012 Kaschner.pdf;/Users/dill/Zotero/storage/VPVUW4LP/PLoS ONE 2012 Kaschner-1.PDF} -} - -@article{kaschner_global_2012-1, - title = {Global {{Coverage}} of {{Cetacean Line-Transect Surveys}}: {{Status Quo}}, {{Data Gaps}} and {{Future Challenges}}}, - shorttitle = {Global {{Coverage}} of {{Cetacean Line-Transect Surveys}}}, - author = {Kaschner, Kristin and Quick, Nicola J. and Jewell, Rebecca and Williams, Rob and Harris, Catriona M.}, - editor = {Bograd, Steven J.}, - year = {2012}, - month = sep, - journal = {PLoS ONE}, - volume = {7}, - number = {9}, - pages = {e44075}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0044075}, - abstract = {Knowledge of abundance, trends and distribution of cetacean populations is needed to inform marine conservation efforts, ecosystem models and spatial planning. We compiled a geo-spatial database of published data on cetacean abundance from dedicated visual line-transect surveys and encoded .1100 abundance estimates for 47 species from 430 surveys conducted worldwide from 1975\textendash 2005. Our subsequent analyses revealed large spatial, temporal and taxonomic variability and gaps in survey coverage. With the exception of Antarctic waters, survey coverage was biased toward the northern hemisphere, especially US and northern European waters. Overall, ,25\% of the world's ocean surface was surveyed and only 6\% had been covered frequently enough (\$5 times) to allow trend estimation. Almost half the global survey effort, defined as total area (km2) covered by all survey study areas across time, was concentrated in the Eastern Tropical Pacific (ETP). Neither the number of surveys conducted nor the survey effort had increased in recent years. Across species, an average of 10\% of a species' predicted range had been covered by at least one survey, but there was considerable variation among species. With the exception of three delphinid species, ,1\% of all species' ranges had been covered frequently enough for trend analysis. Sperm whales emerged from our analyses as a relatively data-rich species. This is a notoriously difficult species to survey visually, and we use this as an example to illustrate the challenges of using available data from line-transect surveys for the detection of trends or for spatial planning. We propose field and analytical methods to fill in data gaps to improve cetacean conservation efforts.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/B997MG7V/Kaschner et al. - 2012 - Global Coverage of Cetacean Line-Transect Surveys.pdf} -} - -@article{kaschner_mapping_2006, - title = {Mapping World-Wide Distributions of Marine Mammal Species Using a Relative Environmental Suitability ({{RES}}) Model}, - author = {Kaschner, K and Watson, R and Trites, Aw and Pauly, D}, - year = {2006}, - month = jul, - journal = {Marine Ecology Progress Series}, - volume = {316}, - pages = {285--310}, - issn = {0171-8630, 1616-1599}, - doi = {10.3354/meps316285}, - abstract = {The lack of comprehensive sighting data sets precludes the application of standard habitat suitability modeling approaches to predict distributions of the majority of marine mammal species on very large scales. As an alternative, we developed an ecological niche model to map global distributions of 115 cetacean and pinniped species living in the marine environment using more readily available expert knowledge about habitat usage. We started by assigning each species to broad-scale niche categories with respect to depth, sea-surface temperature, and ice edge association based on synopses of published information. Within a global information system framework and a global grid of 0.5\textdegree{} latitude/longitude cell dimensions, we then generated an index of the relative environmental suitability (RES) of each cell for a given species by relating known habitat usage to local environmental conditions. RES predictions closely matched published maximum ranges for most species, thus representing useful, more objective alternatives to existing sketched distributional outlines. In addition, raster-based predictions provided detailed information about heterogeneous patterns of potentially suitable habitat for species throughout their range. We tested RES model outputs for 11 species (northern fur seal, harbor porpoise, sperm whale, killer whale, hourglass dolphin, fin whale, humpback whale, blue whale, Antarctic minke, and dwarf minke whales) from a broad taxonomic and geographic range, using data from dedicated surveys. Observed encounter rates and species-specific predicted environmental suitability were significantly and positively correlated for all but 1 species. In comparison, encounter rates were correlated with {$<$}1\% of 1000 simulated random data sets for all but 2 species. Mapping of large-scale marine mammal distributions using this environmental envelope model is helpful for evaluating current assumptions and knowledge about species' occurrences, especially for data-poor species. Moreover, RES modeling can help to focus research efforts on smaller geographic scales and usefully supplement other, statistical, habitat suitability models.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9UR7ME7S/Kaschner et al. - 2006 - Mapping world-wide distributions of marine mammal .pdf} -} - -@article{kass_approximate_1989, - title = {Approximate {{Bayesian Inference}} in {{Conditionally Independent Hierarchical Models}} ({{Parametric Empirical Bayes Models}})}, - author = {Kass, Robert E. and Steffey, Duane}, - year = {1989}, - month = sep, - journal = {Journal of the American Statistical Association}, - volume = {84}, - number = {407}, - pages = {717--726}, - issn = {0162-1459, 1537-274X}, - doi = {10.1080/01621459.1989.10478825}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YSWZVEEQ/Journal of the American Statistical Association 1989 Kass.pdf} -} - -@article{kass_approximate_1989-1, - title = {Approximate {{Bayesian Inference}} in {{Conditionally Independent Hierarchical Models}} ({{Parametric Empirical Bayes Models}})}, - author = {Kass, Robert E. and Steffey, Duane}, - year = {1989}, - month = sep, - journal = {Journal of the American Statistical Association}, - volume = {84}, - number = {407}, - pages = {717}, - issn = {01621459}, - doi = {10.2307/2289653}, - file = {/Users/dill/Zotero/storage/JMNB6S4B/Kass and Steffey - 1989 - Approximate Bayesian Inference in Conditionally In.pdf} -} - -@article{kass_statistical_2011, - title = {Statistical Inference: {{The}} Big Picture}, - shorttitle = {Statistical Inference}, - author = {Kass, Robert E.}, - year = {2011}, - journal = {Statistical science: a review journal of the Institute of Mathematical Statistics}, - volume = {26}, - number = {1}, - pages = {1}, - file = {/Users/dill/Zotero/storage/29VTS6LA/Statist. Sci. 2011 Kass.pdf} -} - -@article{kaul_trial_2010, - title = {Trial and {{Error}}}, - author = {Kaul, Sanjay and Diamond, George A.}, - year = {2010}, - month = feb, - journal = {Journal of the American College of Cardiology}, - volume = {55}, - number = {5}, - pages = {415--427}, - issn = {07351097}, - doi = {10.1016/j.jacc.2009.06.065}, - langid = {english}, - file = {/Users/dill/Zotero/storage/XWWXWSG2/Kaul and Diamond - 2010 - Trial and Error.pdf} -} - -@article{kelly_analysis_2011, - title = {{{ANALYSIS OF MINKE WHALE SIGHTING DATA FROM AERIAL SURVEYS OVER PACK ICE IN EAST ANTARCTICA}}}, - author = {Kelly, Natalie and Peel, David and Bravington, M. V. and Gales, Nick}, - year = {2011}, - journal = {Scientific Committee of the International Whaling Commission, Troms\o, Norway}, - file = {/Users/dill/Zotero/storage/3FMBLPU7/2011 Bravington.pdf} -} - -@article{kennedy_know_2020, - title = {Know Your Population and Know Your Model: {{Using}} Model-Based Regression and Poststratification to Generalize Findings beyond the Observed Sample}, - shorttitle = {Know Your Population and Know Your Model}, - author = {Kennedy, Lauren and Gelman, Andrew}, - year = {2020}, - month = apr, - journal = {arXiv:1906.11323 [stat]}, - eprint = {1906.11323}, - eprinttype = {arxiv}, - primaryclass = {stat}, - abstract = {Psychology research focuses on interactions, and this has deep implications for inference from non-representative samples. For the goal of estimating average treatment effects, we propose to fit a model allowing treatment to interact with background variables and then average over the distribution of these variables in the population. This can be seen as an extension of multilevel regression and poststratification (MRP), a method used in political science and other areas of survey research, where researchers wish to generalize from a sparse and possibly non-representative sample to the general population. In this paper, we discuss areas where this method can be used in the psychological sciences. We use our method to estimate the norming distribution for the Big Five Personality Scale using open source data. We argue that large open data sources like this and other collaborative data sources can be combined with MRP to help resolve current challenges of generalizability and replication in psychology.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Statistics - Applications}, - file = {/Users/dill/Zotero/storage/6BN79QGV/Kennedy and Gelman - 2020 - Know your population and know your model Using mo.pdf} -} - -@incollection{kent_link_1994, - title = {The Link between Kriging and Thin-Plate Splines}, - booktitle = {Probability, {{Statistics}} and {{Optimization}}}, - author = {Kent, J.T. and Mardia, K.V.}, - year = {1994}, - pages = {325--339}, - publisher = {{New York: Wiley}}, - file = {/Users/dill/Zotero/storage/9IA7WLNF/Kent and Mardia - 1994 - The link between kriging and thin-plate splines.pdf} -} - -@article{kidney_efficient_2016, - title = {An Efficient Acoustic Density Estimation Method with Human Detectors Applied to Gibbons in {{Cambodia}}}, - author = {Kidney, Darren and Rawson, Benjamin M. and Borchers, David L. and Stevenson, Ben C. and Marques, Tiago A. and Thomas, Len}, - year = {2016}, - journal = {PloS one}, - volume = {11}, - number = {5}, - pages = {e0155066}, - file = {/Users/dill/Zotero/storage/6NE5H5CE/PLoS ONE 2016 Kidney.PDF;/Users/dill/Zotero/storage/RD3QSJDJ/S1%20Appendix.PDF} -} - -@article{kim_smoothing_2004, - title = {Smoothing Spline {{Gaussian}} Regression: More Scalable Computation via Efficient Approximation}, - shorttitle = {Smoothing Spline {{Gaussian}} Regression}, - author = {Kim, Young-Ju and Gu, Chong}, - year = {2004}, - month = may, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {66}, - number = {2}, - pages = {337--356}, - issn = {1369-7412, 1467-9868}, - doi = {10.1046/j.1369-7412.2003.05316.x}, - abstract = {Smoothing splines via the penalized least squares method provide versatile and effective nonparametric models for regression with Gaussian responses. The computation of smoothing splines is generally of the order O.n3/, n being the sample size, which severely limits its practical applicability. We study more scalable computation of smoothing spline regression via certain low dimensional approximations that are asymptotically as efficient. A simple algorithm is presented and the Bayes model that is associated with the approximations is derived, with the latter guiding the porting of Bayesian confidence intervals. The practical choice of the dimension of the approximating space is determined through simulation studies, and empirical comparisons of the approximations with the exact solution are presented. Also evaluated is a simple modification of the generalized cross-validation method for smoothing parameter selection, which to a large extent fixes the occasional undersmoothing problem that is suffered by generalized cross-validation.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6BQWPCQJ/Kim and Gu - 2004 - Smoothing spline Gaussian regression more scalabl.pdf} -} - -@article{kimeldorf_george_s._correspondence_1970, - title = {A {{Correspondence Between Bayesian Estimation}} on {{Stochastic Processes}} and {{Smoothing}} by {{Splines}}}, - author = {Kimeldorf, George S. and Wahba, Grace}, - year = {1970}, - journal = {The Annals of Mathematical Statistics}, - volume = {41}, - number = {2}, - pages = {495--502}, - file = {/Users/dill/Zotero/storage/U9B5PKBY/The Annals of Mathematical Statistics 1970 Kimeldorf.pdf} -} - -@article{kimeldorf_results_1971, - title = {Some Results on {{Tchebycheffian}} Spline Functions}, - author = {Kimeldorf, George S and Wahba, G.}, - year = {1971}, - journal = {Journal of Mathematical Analysis and Applications}, - volume = {33}, - pages = {82--95}, - file = {/Users/dill/Zotero/storage/L5XA4883/1-s2.0-0022247X71901843-main.pdf} -} - -@article{kimeldorf_spline_nodate, - title = {Spline {{Functions}} and {{Stochastic Processes}}}, - author = {Kimeldorf, George S and Wahba, Grace}, - pages = {9}, - abstract = {This paper explores relationships between certain prediction problems for stochastic processes and the theory of spline functions. Given a linear differential operator L, a class of stochastic processes is constructed for which the minimum variance unbiased linear prediction is an JS-spline.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QCXHGDDW/Kimeldorf and Wahba - Spline Functions and Stochastic Processes.pdf} -} - -@techreport{kinzey_marine_2000, - title = {Marine {{Mammal Data Collection Procedures}} on {{Research Ship Line-Transect Surveys}} by the {{Southwest Fisheries Science Center}}}, - author = {Kinzey, Douglas and Olson, Paula and Gerrodette, Tim}, - year = {2000}, - number = {LJ-00-08}, - address = {{La Jolla, CA}}, - institution = {{National Marine Fisheries Service}}, - langid = {english}, - file = {/Users/dill/Zotero/storage/76KU7TN5/Kinzey et al. - Marine Mammal Data Collection Procedures on Resear.pdf} -} - -@article{kiran_efficient_nodate, - title = {An {{Efficient Approach}} for {{Filling Incomplete Data}}}, - author = {Kiran, P. M. and Rao, A. Prakash and Ratnamala, B.}, - file = {/Users/dill/Zotero/storage/6J3E2AWP/Journal of the royal statistical society … 1977 Dempster.pdf} -} - -@article{kleiber_visualizing_2016, - title = {Visualizing {{Count Data Regressions Using Rootograms}}}, - author = {Kleiber, Christian and Zeileis, Achim}, - year = {2016}, - month = jul, - journal = {The American Statistician}, - volume = {70}, - number = {3}, - pages = {296--303}, - issn = {0003-1305, 1537-2731}, - doi = {10.1080/00031305.2016.1173590}, - langid = {english}, - file = {/Users/dill/Zotero/storage/AAFB9BWG/The American Statistician 2016 Kleiber.pdf} -} - -@article{klein_scale-dependent_2016, - title = {Scale-{{Dependent Priors}} for {{Variance Parameters}} in {{Structured Additive Distributional Regression}}}, - author = {Klein, Nadja and Kneib, Thomas}, - year = {2016}, - month = dec, - journal = {Bayesian Analysis}, - volume = {11}, - number = {4}, - pages = {1071--1106}, - issn = {1936-0975}, - doi = {10.1214/15-BA983}, - abstract = {The selection of appropriate hyperpriors for variance parameters is an important and sensible topic in all kinds of Bayesian regression models involving the specification of (conditionally) Gaussian prior structures where the variance parameters determine a data-driven, adaptive amount of prior variability or precision. We consider the special case of structured additive distributional regression where Gaussian priors are used to enforce specific properties such as smoothness or shrinkage on various effect types combined in predictors for multiple parameters related to the distribution of the response. Relying on a recently proposed class of penalised complexity priors motivated from a general set of construction principles, we derive a hyperprior structure where prior elicitation is facilitated by assumptions on the scaling of the different effect types. The posterior distribution is assessed with an adaptive Markov chain Monte Carlo scheme and conditions for its propriety are studied theoretically. We investigate the new type of scaledependent priors in simulations and two challenging applications, in particular in comparison to the standard inverse gamma priors but also alternatives such as half-normal, half-Cauchy and proper uniform priors for standard deviations.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DHKFV6MA/Klein and Kneib - 2016 - Scale-Dependent Priors for Variance Parameters in .pdf} -} - -@article{kneib_modular_nodate, - title = {Modular Regression - a {{Lego}} System for Building Structured Additive Distributional Regression Models with Tensor Product Interactions}, - author = {Kneib, Thomas}, - pages = {39}, - abstract = {Semiparametric regression models offer considerable flexibility concerning the specification of additive regression predictors including effects as diverse as nonlinear effects of continuous covariates, spatial effects, random effects, or varying coefficients. Recently, such flexible model predictors have been combined with the possibility to go beyond pure mean-based analyses by specifying regression predictors on potentially all parameters of the response distribution in a distributional regression framework. In this paper, we discuss a generic concept for defining interaction effects in such semiparametric distributional regression models based on tensor products of main effects. These interactions can be assigned anisotropic penalties, i.e. different amounts of smoothness will be associated with the interacting covariates. We investigate identifiability and the decomposition of interactions into main effects and pure interaction effects (similar as in a smoothing spline analysis of variance) to facilitate a modular model building process. The decomposition is based on orthogonality in function spaces which allows for considerable flexibility in setting up the effect decomposition. Inference is based on Markov chain Monte Carlo simulations with iteratively weighted least squares proposals under constraints to ensure identifiability and effect decomposition. One important aspect is therefore to maintain sparse matrix structures of the tensor product also in identifiable, decomposed model formulations. The performance of modular regression is verified in a simulation on decomposed interaction surfaces of two continuous covariates and two applications on the construction of spatio-temporal interactions for the analysis of precipitation on the one hand and functional random effects for analysing house prices on the other hand.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/EUD9T88T/Kneib - Modular regression - a Lego system for building st.pdf} -} - -@book{kneib_statistical_2010, - title = {Statistical {{Modelling}} and {{Regression Structures}}}, - editor = {Kneib, Thomas and Tutz, Gerhard}, - year = {2010}, - publisher = {{Physica-Verlag HD}}, - address = {{Heidelberg}}, - doi = {10.1007/978-3-7908-2413-1}, - isbn = {978-3-7908-2412-4 978-3-7908-2413-1}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Y67UIFQB/Kneib and Tutz - 2010 - Statistical Modelling and Regression Structures.pdf} -} - -@article{knezevic_overlapping_2008, - title = {Overlapping Confidence Intervals and Statistical Significance}, - author = {Knezevic, Andrea}, - year = {2008}, - journal = {StatNews: Cornell University Statistical Consulting Unit}, - volume = {73}, - number = {1}, - file = {/Users/dill/Zotero/storage/H28BLAHU/StatNews Cornell University Statistical Consulting … 2008 Knezevic.pdf} -} - -@article{koenker_convex_2014, - title = {Convex Optimization in {{R}}}, - author = {Koenker, Roger and Mizera, Ivan}, - year = {2014}, - journal = {Journal of Statistical Software}, - volume = {60}, - number = {5}, - pages = {1--23}, - file = {/Users/dill/Zotero/storage/5J9VXAZP/Journal of Statistical Software 2013 Koenker.pdf} -} - -@article{koopman_spline_2008, - title = {Spline {{Smoothing}} over {{Difficult Regions}}}, - author = {Koopman, Siem Jan and Wong, Soon Y.}, - year = {2008}, - file = {/Users/dill/Zotero/storage/SJH3HLZ4/2008 wdriesse.pdf} -} - -@book{krainski_advanced_2019, - title = {Advanced {{Spatial Modeling}} with {{Stochastic Partial Differential Equations Using R}} and {{INLA}}}, - author = {Krainski, Elias and {G{\'o}mez-Rubio}, Virgilio and Bakka, Haakon and Lenzi, Amanda and {Castro-Camilo}, Daniela and Simpson, Daniel and Lindgren, Finn and Rue, H{\aa}vard}, - year = {2019}, - publisher = {{CRC Press/Taylor and Francis Group}} -} - -@article{krivobokova_note_2007, - title = {A {{Note}} on {{Penalized Spline Smoothing With Correlated Errors}}}, - author = {Krivobokova, Tatyana and Kauermann, G{\"o}ran}, - year = {2007}, - month = dec, - journal = {Journal of the American Statistical Association}, - volume = {102}, - number = {480}, - pages = {1328--1337}, - issn = {0162-1459, 1537-274X}, - doi = {10.1198/016214507000000978}, - langid = {english}, - file = {/Users/dill/Zotero/storage/MVPH9LCA/Krivobokova and Kauermann - 2007 - A Note on Penalized Spline Smoothing With Correlat.pdf} -} - -@article{kuhn_less_2012, - title = {Less than Eight (and a Half) Misconceptions of Spatial Analysis}, - author = {K{\"u}hn, Ingolf and Dormann, Carsten F.}, - year = {2012}, - journal = {Journal of Biogeography}, - volume = {39}, - number = {5}, - pages = {995--998}, - file = {/Users/dill/Zotero/storage/HM58NC8G/J. Biogeogr. 2012 Kühn.pdf} -} - -@misc{laake_mrds_2018, - title = {Mrds: {{Mark-Recapture Distance Sampling}}}, - author = {Laake, Jeffrey L. and Borchers, David L and Thomas, Len and Miller, David L and Bishop, Jon R.B.}, - year = {2018} -} - -@article{laake_point-based_2011, - title = {Point-{{Based Mark-Recapture Distance Sampling}}}, - author = {Laake, J. L. and Collier, B. A. and Morrison, M. L. and Wilkins, R. N.}, - year = {2011}, - month = sep, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {16}, - number = {3}, - pages = {389--408}, - issn = {1085-7117, 1537-2693}, - doi = {10.1007/s13253-011-0059-5}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ACYCZGN5/JABES 2011 Laake.pdf} -} - -@article{laake_probability_1997, - title = {Probability of Detecting Harbor Porpoise from Aerial Surveys: Estimating g (0)}, - shorttitle = {Probability of Detecting Harbor Porpoise from Aerial Surveys}, - author = {Laake, Jeffrey L. and Calambokidis, John and Osmek, Steven D. and Rugh, David J.}, - year = {1997}, - journal = {The Journal of wildlife management}, - pages = {63--75}, - file = {/Users/dill/Zotero/storage/TNDHEFBR/Laake et al. - 1997 - Probability of detecting harbor porpoise from aeri.pdf} -} - -@article{laake_visibility_2008, - title = {Visibility Bias in Aerial Survey: Mark\textendash Recapture, Line-Transect or Both?}, - shorttitle = {Visibility Bias in Aerial Survey}, - author = {Laake, Jeff and Dawson, Michelle J. and Hone, Jim}, - year = {2008}, - journal = {Wildlife Research}, - volume = {35}, - number = {4}, - pages = {299}, - issn = {1035-3712}, - doi = {10.1071/WR07034}, - langid = {english}, - file = {/Users/dill/Zotero/storage/64QWR226/Wildl. Res. 2008 Laake.pdf} -} - -@article{lafortuna_locomotor_2003, - title = {Locomotor Behaviours and Respiratory Pattern of the {{Mediterranean}} Fin Whale ( {{Balaenoptera}} Physalus )}, - author = {Lafortuna, Claudio L. and Jahoda, Maddalena and Azzellino, Arianna and Saibene, Franco and Colombini, Angelo}, - year = {2003}, - month = oct, - journal = {European Journal of Applied Physiology}, - volume = {90}, - number = {3-4}, - pages = {387--395}, - issn = {1439-6319, 1439-6327}, - doi = {10.1007/s00421-003-0887-2}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LQTEGHUH/Lafortuna et al. - 2003 - Locomotor behaviours and respiratory pattern of th.pdf} -} - -@article{lahoz-monfort_imperfect_2014, - title = {Imperfect Detection Impacts the Performance of Species Distribution Models: {{Imperfect}} Detection Impacts Species Distribution Models}, - shorttitle = {Imperfect Detection Impacts the Performance of Species Distribution Models}, - author = {{Lahoz-Monfort}, Jos{\'e} J. and {Guillera-Arroita}, Gurutzeta and Wintle, Brendan A.}, - year = {2014}, - month = apr, - journal = {Global Ecology and Biogeography}, - volume = {23}, - number = {4}, - pages = {504--515}, - issn = {1466822X}, - doi = {10.1111/geb.12138}, - langid = {english}, - file = {/Users/dill/Zotero/storage/M3YDRIRB/geb12138.pdf;/Users/dill/Zotero/storage/S799Z8S3/Global Ecol Biogeography 2013 Lahoz-Monfort.pdf} -} - -@article{laird_empirical_1987, - title = {Empirical {{Bayes Confidence Intervals Based}} on {{Bootstrap Samples}}}, - author = {Laird, Nan M. and Louis, Thomas A.}, - year = {1987}, - month = sep, - journal = {Journal of the American Statistical Association}, - volume = {82}, - number = {399}, - pages = {739--750}, - issn = {0162-1459, 1537-274X}, - doi = {10.1080/01621459.1987.10478490}, - langid = {english}, - file = {/Users/dill/Zotero/storage/5JYSZ5RH/Journal of the American Statistical Association 1987 Laird.pdf} -} - -@book{lancaster_curve_1986, - title = {Curve and {{Surface Fitting}}: {{An Introduction}}}, - author = {Lancaster, Peter and {\v S}alkauskas, K.}, - year = {1986}, - series = {Computational Mathematics and Applications}, - publisher = {{Academic Press}}, - isbn = {978-0-12-436061-7}, - lccn = {85014681} -} - -@article{lang_bayesian_2004, - title = {Bayesian {{P-Splines}}}, - author = {Lang, Stefan and Brezger, Andreas}, - year = {2004}, - month = mar, - journal = {Journal of Computational and Graphical Statistics}, - volume = {13}, - number = {1}, - pages = {183--212}, - issn = {1061-8600, 1537-2715}, - doi = {10.1198/1061860043010}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FPD3SH22/Lang and Brezger - 2004 - Bayesian P-Splines.pdf} -} - -@article{lang_enhancing_2014, - title = {Enhancing {{R}} with Advanced Compilation Tools and Methods}, - author = {Lang, Duncan Temple}, - year = {2014}, - journal = {Statistical Science}, - pages = {181--200}, - file = {/Users/dill/Zotero/storage/U2W5GIWQ/arXiv 2014 Lang.pdf} -} - -@article{langrock_markov-modulated_2013, - title = {Markov-{{Modulated Nonhomogeneous Poisson Processes}} for {{Modeling Detections}} in {{Surveys}} of {{Marine Mammal Abundance}}}, - author = {Langrock, Roland and Borchers, David L. and Skaug, Hans J.}, - year = {2013}, - month = sep, - journal = {Journal of the American Statistical Association}, - volume = {108}, - number = {503}, - pages = {840--851}, - issn = {0162-1459, 1537-274X}, - doi = {10.1080/01621459.2013.797356}, - langid = {english}, - file = {/Users/dill/Zotero/storage/RQVUQ5RU/Langrock+al MMPPLTM JASA 2013.pdf} -} - -@article{langrock_markov-switching_2017, - title = {Markov-Switching Generalized Additive Models}, - author = {Langrock, Roland and Kneib, Thomas and Glennie, Richard and Michelot, Th{\'e}o}, - year = {2017}, - month = jan, - journal = {Statistics and Computing}, - volume = {27}, - number = {1}, - pages = {259--270}, - issn = {0960-3174, 1573-1375}, - doi = {10.1007/s11222-015-9620-3}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FHCBZVJM/art%3A10.1007%2Fs11222-015-9620-3.pdf} -} - -@article{langrock_nonparametric_2013, - title = {Nonparametric Inference in Hidden {{Markov}} Models via Penalized Likelihood Methods}, - author = {Langrock, Roland and Kneib, Thomas and Sohn, Alexander}, - year = {2013}, - journal = {arXiv preprint arXiv:1309.0423}, - eprint = {1309.0423}, - eprinttype = {arxiv}, - archiveprefix = {arXiv}, - file = {/Users/dill/Zotero/storage/NUQGFZBS/1309.0423v1.pdf} -} - -@article{langrock_nonparametric_2015, - title = {Nonparametric Inference in Hidden {{Markov}} Models Using {{P-splines}}: {{Nonparametric Inference}} in {{Hidden Markov Models}}}, - shorttitle = {Nonparametric Inference in Hidden {{Markov}} Models Using {{P-splines}}}, - author = {Langrock, Roland and Kneib, Thomas and Sohn, Alexander and DeRuiter, Stacy L.}, - year = {2015}, - month = jun, - journal = {Biometrics}, - volume = {71}, - number = {2}, - pages = {520--528}, - issn = {0006341X}, - doi = {10.1111/biom.12282}, - langid = {english}, - file = {/Users/dill/Zotero/storage/NZV9RXK5/biom12282.pdf} -} - -@article{laslett_kriging_1994, - title = {Kriging and {{Splines}}: {{An Empirical Comparison}} of {{Their Predictive Performance}} in {{Some Applications}}}, - shorttitle = {Kriging and {{Splines}}}, - author = {Laslett, Geoffrey M.}, - year = {1994}, - month = jun, - journal = {Journal of the American Statistical Association}, - volume = {89}, - number = {426}, - pages = {391}, - issn = {01621459}, - doi = {10.2307/2290837}, - file = {/Users/dill/Zotero/storage/P3FNZQUU/Journal of the American Statistical Association 1994 Laslett.pdf} -} - -@article{laub_information_2006, - title = {On the Information and Representation of Non-{{Euclidean}} Pairwise Data}, - author = {Laub, Julian and Roth, Volker and Buhmann, Joachim M. and M{\"u}ller, Klaus-Robert}, - year = {2006}, - month = oct, - journal = {Pattern Recognition}, - volume = {39}, - number = {10}, - pages = {1815--1826}, - issn = {00313203}, - doi = {10.1016/j.patcog.2006.04.016}, - langid = {english}, - file = {/Users/dill/Zotero/storage/VBINFYCF/Pattern Recognition 2006 Laub.pdf} -} - -@article{lawless_negative_1987, - title = {Negative Binomial and Mixed Poisson Regression}, - author = {Lawless, Jerald F.}, - year = {1987}, - month = sep, - journal = {Canadian Journal of Statistics}, - volume = {15}, - number = {3}, - pages = {209--225}, - issn = {03195724, 1708945X}, - doi = {10.2307/3314912}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UYV8QHKA/Canadian Journal of Statistics 1987 Lawless.pdf} -} - -@article{leemis_univariate_2008, - title = {Univariate {{Distribution Relationships}}}, - author = {Leemis, Lawrence M and McQueston, Jacquelyn T}, - year = {2008}, - month = feb, - journal = {The American Statistician}, - volume = {62}, - number = {1}, - pages = {45--53}, - issn = {0003-1305, 1537-2731}, - doi = {10.1198/000313008X270448}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2TKNP3NH/The American Statistician 2008 Leemis-1.pdf;/Users/dill/Zotero/storage/9BHI3FEM/The American Statistician 2008 Leemis.pdf} -} - -@article{lehtomaki_methods_2013, - title = {Methods and Workflow for Spatial Conservation Prioritization Using {{Zonation}}}, - author = {Lehtom{\"a}ki, Joona and Moilanen, Atte}, - year = {2013}, - month = sep, - journal = {Environmental Modelling \& Software}, - volume = {47}, - pages = {128--137}, - issn = {13648152}, - doi = {10.1016/j.envsoft.2013.05.001}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9CPAQEMY/Environmental Modelling and Software 2013 Lehtomäki.pdf} -} - -@article{lele_data_2007, - title = {Data Cloning: Easy Maximum Likelihood Estimation for Complex Ecological Models Using {{Bayesian Markov}} Chain {{Monte Carlo}} Methods}, - shorttitle = {Data Cloning}, - author = {Lele, Subhash R. and Dennis, Brian and Lutscher, Frithjof}, - year = {2007}, - month = jul, - journal = {Ecology Letters}, - volume = {10}, - number = {7}, - pages = {551--563}, - issn = {1461-023X, 1461-0248}, - doi = {10.1111/j.1461-0248.2007.01047.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/4ZUGKEID/Ecol Letters 2007 Lele.pdf} -} - -@article{lele_estimability_2010, - title = {Estimability and {{Likelihood Inference}} for {{Generalized Linear Mixed Models Using Data Cloning}}}, - author = {Lele, Subhash R. and Nadeem, Khurram and Schmuland, Byron}, - year = {2010}, - month = dec, - journal = {Journal of the American Statistical Association}, - volume = {105}, - number = {492}, - pages = {1617--1625}, - issn = {0162-1459, 1537-274X}, - doi = {10.1198/jasa.2010.tm09757}, - langid = {english}, - file = {/Users/dill/Zotero/storage/HTCHHGN2/Journal of the American Statistical Association 2010 Lele.pdf} -} - -@article{li_decreasing_2011, - title = {Decreasing Uncertainty in Catch Rate Analyses Using {{Delta-AdaBoost}}: {{An}} Alternative Approach in Catch and Bycatch Analyses with High Percentage of Zeros}, - shorttitle = {Decreasing Uncertainty in Catch Rate Analyses Using {{Delta-AdaBoost}}}, - author = {Li, Yan and Jiao, Yan and He, Qing}, - year = {2011}, - month = jan, - journal = {Fisheries Research}, - volume = {107}, - number = {1-3}, - pages = {261--271}, - issn = {01657836}, - doi = {10.1016/j.fishres.2010.11.008}, - langid = {english}, - file = {/Users/dill/Zotero/storage/CW5KKNVC/Fisheries Research 2011 Li.pdf} -} - -@article{lim_case_2005, - title = {The Case of the Disappearing Teaspoons: Longitudinal Cohort Study of the Displacement of Teaspoons in an {{Australian}} Research Institute}, - shorttitle = {The Case of the Disappearing Teaspoons}, - author = {Lim, M. S C}, - year = {2005}, - month = dec, - journal = {BMJ}, - volume = {331}, - number = {7531}, - pages = {1498--1500}, - issn = {0959-8138, 1468-5833}, - doi = {10.1136/bmj.331.7531.1498}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6SM8FNVY/BMJ 2005 Lim.pdf} -} - -@article{lin_why_2017, - title = {Why Does Deep and Cheap Learning Work so Well?}, - author = {Lin, Henry W. and Tegmark, Max and Rolnick, David}, - year = {2017}, - journal = {Journal of Statistical Physics}, - volume = {168}, - number = {6}, - pages = {1223--1247}, - file = {/Users/dill/Zotero/storage/TSSS3L3L/arXiv 2016 Lin.pdf} -} - -@article{lindgren_bayesian_2015, - title = {Bayesian {{Spatial Modelling}} with {{R}} - {{INLA}}}, - author = {Lindgren, Finn and Rue, H{\aa}vard}, - year = {2015}, - journal = {Journal of Statistical Software}, - volume = {63}, - number = {19}, - issn = {1548-7660}, - doi = {10.18637/jss.v063.i19}, - abstract = {The principles behind the interface to continuous domain spatial models in the RINLA software package for R are described. The Integrated Nested Laplace Approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009) is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized) linear mixed to spatial and spatio-temporal models. Combined with the Stochastic Partial Differential Equation approach (SPDE, Lindgren and Lindgren 2011), one can accommodate all kinds of geographically referenced data, including areal and geostatistical ones, as well as spatial point process data. The implementation interface covers stationary spatial models, non-stationary spatial models, and also spatio-temporal models, and is applicable in epidemiology, ecology, environmental risk assessment, as well as general geostatistics.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/24ZBQ98C/Lindgren and Rue - 2015 - Bayesian Spatial Modelling with iRi - bINLA.pdf} -} - -@article{lindgren_explicit_2011, - title = {An Explicit Link between {{Gaussian}} Fields and {{Gaussian Markov}} Random Fields: The Stochastic Partial Differential Equation Approach}, - shorttitle = {An Explicit Link between {{Gaussian}} Fields and {{Gaussian Markov}} Random Fields}, - author = {Lindgren, Finn and Rue, H{\aa}vard and Lindstr{\"o}m, Johan}, - year = {2011}, - month = sep, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {73}, - number = {4}, - pages = {423--498}, - issn = {13697412}, - doi = {10.1111/j.1467-9868.2011.00777.x}, - abstract = {Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical modelling and geostatistics. The specification through the covariance function gives an intuitive interpretation of the field properties. On the computational side, GFs are hampered with the big n problem, since the cost of factorizing dense matrices is cubic in the dimension. Although computational power today is at an all time high, this fact seems still to be a computational bottleneck in many applications. Along with GFs, there is the class of Gaussian Markov random fields (GMRFs) which are discretely indexed. The Markov property makes the precision matrix involved sparse, which enables the use of numerical algorithms for sparse matrices, that for fields in R2 only use the square root of the time required by general algorithms. The specification of a GMRF is through its full conditional distributions but its marginal properties are not transparent in such a parameterization. We show that, using an approximate stochastic weak solution to (linear) stochastic partial differential equations, we can, for some GFs in the Mat\'ern class, provide an explicit link , for any triangulation of Rd , between GFs and GMRFs, formulated as a basis function representation. The consequence is that we can take the best from the two worlds and do the modelling by using GFs but do the computations by using GMRFs. Perhaps more importantly, our approach generalizes to other covariance functions generated by SPDEs, including oscillating and non-stationary GFs, as well as GFs on manifolds. We illustrate our approach by analysing global temperature data with a non-stationary model defined on a sphere.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/S8C9XDR8/Lindgren et al. - 2011 - An explicit link between Gaussian fields and Gauss.pdf} -} - -@misc{lindgren_spde2_2013, - title = {{{SPDE2}}: Precision Structure Model Adapted to Simple {{SPDEs}}}, - author = {Lindgren, Finn}, - year = {2013}, - file = {/Users/dill/Zotero/storage/8FX3CYTC/spde2.pdf} -} - -@article{link_nonidentifiability_2003, - title = {Nonidentifiability of Population Size from Capture-Recapture Data with Heterogeneous Detection Probabilities}, - author = {Link, William A.}, - year = {2003}, - journal = {Biometrics}, - volume = {59}, - number = {4}, - pages = {1123--1130}, - file = {/Users/dill/Zotero/storage/RFJD8LRK/Biom 2003 Link.pdf} -} - -@article{loland_spatial_2003, - title = {Spatial Covariance Modelling in a Complex Coastal Domain by Multidimensional Scaling: {{SPATIAL COVARIANCE MODELLING IN A COMPLEX COASTAL DOMAIN}}}, - shorttitle = {Spatial Covariance Modelling in a Complex Coastal Domain by Multidimensional Scaling}, - author = {L{\o}land, Anders and H{\o}st, Gudmund}, - year = {2003}, - month = may, - journal = {Environmetrics}, - volume = {14}, - number = {3}, - pages = {307--321}, - issn = {11804009}, - doi = {10.1002/env.588}, - abstract = {In aquatic studies, spatial interactions may be both easier to interpret and to quantify by using water distance than by using geographic distance. The water distance is the shortest path between those two sites that may be traversed entirely over water. One problem is that water distances may be non-Euclidean, and thus covariance and variogram functions are not necessarily valid when using the water distance as a distance metric. Another problem is that the computation of water distances for a large set of spatial locations is computationally expensive. Our alternative is a computationally efficient method for calculation of a Euclidean approximation to water distances. The first step of the method is to define a triangular grid covering the complex domain of interest. Using this triangular grid, we pre-compute approximate water distances using a graph search algorithm. These water distances are then approximated by multidimensional scaling, giving a Euclidean space. Finally, we use linear interpolation to move the data locations into the new Euclidean space. By using this method, subsequent computations of water distances between any locations can be done very fast and the method leads to a theoretically valid spatial covariance model. We apply our method to herring data from the Vestfjord system in Northern Norway. Copyright \# 2003 John Wiley \& Sons, Ltd.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/8MSHPARB/Løland and Høst - 2003 - Spatial covariance modelling in a complex coastal .pdf} -} - -@article{lusseau_effects_2003, - title = {Effects of Tour Boats on the Behavior of Bottlenose Dolphins: Using {{Markov}} Chains to Model Anthropogenic Impacts}, - shorttitle = {Effects of Tour Boats on the Behavior of Bottlenose Dolphins}, - author = {Lusseau, David}, - year = {2003}, - journal = {Conservation Biology}, - volume = {17}, - number = {6}, - pages = {1785--1793}, - file = {/Users/dill/Zotero/storage/LSQDNWB5/Conservation Biology 2003 LUSSEAU.pdf} -} - -@article{mack_kernel_1998, - title = {Kernel Methods in Line and Point Transect Sampling}, - author = {Mack, Y. P. and Quang, Pham X.}, - year = {1998}, - journal = {Biometrics}, - pages = {606--619}, - file = {/Users/dill/Zotero/storage/JR86FTVG/Biom 1998 Mack.pdf} -} - -@article{mackay_introduction_1998, - title = {Introduction to {{Gaussian}} Processes}, - author = {Mackay, David J C}, - year = {1998}, - journal = {NATO ASI Series F Computer and Systems Sciences}, - volume = {168}, - pages = {133--166}, - abstract = {Feedforward neural networks such as multilayer perceptrons are popular tools for nonlinear regression and classi cation problems. From a Bayesian perspective, a choice of a neural network model can be viewed as de ning a prior probability distribution over non-linear functions, and the neural network's learning process can be interpreted in terms of the posterior probability distribution over the unknown function. (Some learning algorithms search for the function with maximum posterior probability and other Monte Carlo methods draw samples from this posterior probability). In the limit of large but otherwise standard networks, Neal (1996) has shown that the prior distribution over non-linear functions implied by the Bayesian neural network falls in a class of probability distributions known as Gaussian processes. The hyperparameters of the neural network model determine the characteristic lengthscales of the Gaussian process. Neal's observation motivates the idea of discarding parameterized networks and working directly with Gaussian processes. Computations in which the parameters of the network are optimized are then replaced by simple matrix operations using the covariance matrix of the Gaussian process.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9LZEQQET/Mackay - INTRODUCTION TO GAUSSIAN PROCESSES.pdf} -} - -@article{mackenzie_designing_2005, - title = {Designing Occupancy Studies: General Advice and Allocating Survey Effort: {{{\emph{Designing}}}}{\emph{ Occupancy Studies}}}, - shorttitle = {Designing Occupancy Studies}, - author = {Mackenzie, Darryl I. and Royle, J. Andrew}, - year = {2005}, - month = dec, - journal = {Journal of Applied Ecology}, - volume = {42}, - number = {6}, - pages = {1105--1114}, - issn = {00218901}, - doi = {10.1111/j.1365-2664.2005.01098.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/CAWYHCHS/Journal of Applied Ecology 2005 Royle.pdf} -} - -@article{mackenzie_estimating_2002, - title = {Estimating Site Occupancy Rates When Detection Probabilities Are Less than One}, - author = {MacKenzie, Darryl I. and Nichols, James D. and Lachman, Gideon B. and Droege, Sam and Andrew Royle, J. and Langtimm, Catherine A.}, - year = {2002}, - journal = {Ecology}, - volume = {83}, - number = {8}, - pages = {2248--2255}, - file = {/Users/dill/Zotero/storage/BGJPKWN7/Ecology 2002 MacKenzie.pdf} -} - -@article{mackenzie_statistical_2013, - title = {Statistical {{Modelling}} of {{Seabird}} and {{Cetacean}} Data: {{Guidance Document}}}, - shorttitle = {Statistical {{Modelling}} of {{Seabird}} and {{Cetacean}} Data}, - author = {Mackenzie, M. L. and {Scott-Hayward}, L. A. and Oedekoven, C. S. and Skov, H. and Humphreys, E. and Rexstad, E.}, - year = {2013}, - journal = {Report SB9 (CR/2012/05), Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews}, - file = {/Users/dill/Zotero/storage/EXWLKVGK/Report SB9 (CR2012 … 2013 Mackenzie.pdf} -} - -@article{maggini_are_2011, - title = {Are {{Swiss}} Birds Tracking Climate Change?}, - author = {Maggini, Ramona and Lehmann, Anthony and K{\'e}ry, Marc and Schmid, Hans and Beniston, Martin and Jenni, Lukas and Zbinden, Niklaus}, - year = {2011}, - month = jan, - journal = {Ecological Modelling}, - volume = {222}, - number = {1}, - pages = {21--32}, - issn = {03043800}, - doi = {10.1016/j.ecolmodel.2010.09.010}, - langid = {english}, - file = {/Users/dill/Zotero/storage/AWN63PNQ/Ecological Modelling 2011 Maggini-1.pdf;/Users/dill/Zotero/storage/CN3ZVJTG/Ecological Modelling 2011 Maggini.pdf} -} - -@article{magnusson_measuring_2013, - title = {Measuring Uncertainty in Fisheries Stock Assessment: The Delta Method, Bootstrap, and {{MCMC}}: {{Uncertainty}} in Stock Assessment}, - shorttitle = {Measuring Uncertainty in Fisheries Stock Assessment}, - author = {Magnusson, Arni and Punt, Andr{\'e} E and Hilborn, Ray}, - year = {2013}, - month = sep, - journal = {Fish and Fisheries}, - volume = {14}, - number = {3}, - pages = {325--342}, - issn = {14672960}, - doi = {10.1111/j.1467-2979.2012.00473.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/SDZZVYQF/Fish Fish 2012 Magnusson.pdf} -} - -@article{mannocci_extrapolating_2015, - title = {Extrapolating Cetacean Densities beyond Surveyed Regions: Habitat-Based Predictions in the Circumtropical Belt}, - shorttitle = {Extrapolating Cetacean Densities beyond Surveyed Regions}, - author = {Mannocci, Laura and Monestiez, Pascal and Spitz, J{\'e}r{\^o}me and Ridoux, Vincent}, - year = {2015}, - month = jul, - journal = {Journal of Biogeography}, - volume = {42}, - number = {7}, - pages = {1267--1280}, - issn = {03050270}, - doi = {10.1111/jbi.12530}, - langid = {english}, - file = {/Users/dill/Zotero/storage/V5XLVMBH/J. Biogeogr. 2015 Mannocci.pdf} -} - -@article{mannocci_extrapolating_2017, - title = {Extrapolating Cetacean Densities to Quantitatively Assess Human Impacts on Populations in the High Seas: {{Cetacean Densities}} in the {{High Seas}}}, - shorttitle = {Extrapolating Cetacean Densities to Quantitatively Assess Human Impacts on Populations in the High Seas}, - author = {Mannocci, Laura and Roberts, Jason J. and Miller, David L. and Halpin, Patrick N.}, - year = {2017}, - month = jun, - journal = {Conservation Biology}, - volume = {31}, - number = {3}, - pages = {601--614}, - issn = {08888892}, - doi = {10.1111/cobi.12856}, - langid = {english}, - file = {/Users/dill/Zotero/storage/8TWDILEF/cobi12856-sup-0003-text.pdf;/Users/dill/Zotero/storage/G82Q6BRI/cobi12856-sup-0001-text.pdf;/Users/dill/Zotero/storage/HYX2LRCG/cobi12856-sup-0002-text.pdf;/Users/dill/Zotero/storage/Y5NU3RGB/Conservation Biology 2016 Mannocci.pdf} -} - -@article{mannocci_geographical_2020, - title = {Geographical Differences in Habitat Relationships of Cetaceans across an Ocean Basin}, - author = {Mannocci, Laura and Roberts, Jason J. and Pedersen, Eric J. and Halpin, Patrick N.}, - year = {2020}, - month = aug, - journal = {Ecography}, - volume = {43}, - number = {8}, - pages = {1250--1259}, - issn = {0906-7590, 1600-0587}, - doi = {10.1111/ecog.04979}, - langid = {english}, - file = {/Users/dill/Zotero/storage/L32TZUHQ/Mannocci et al. - 2020 - Geographical differences in habitat relationships .pdf} -} - -@article{mannocci_temporal_2017, - title = {Temporal Resolutions in Species Distribution Models of Highly Mobile Marine Animals: {{Recommendations}} for Ecologists and Managers}, - shorttitle = {Temporal Resolutions in Species Distribution Models of Highly Mobile Marine Animals}, - author = {Mannocci, Laura and Boustany, Andre M. and Roberts, Jason J. and Palacios, Daniel M. and Dunn, Daniel C. and Halpin, Patrick N. and Viehman, Shay and Moxley, Jerry and Cleary, Jesse and Bailey, Helen and Bograd, Steven J. and Becker, Elizabeth A. and Gardner, Beth and Hartog, Jason R. and Hazen, Elliott L. and Ferguson, Megan C. and Forney, Karin A. and Kinlan, Brian P. and Oliver, Matthew J. and Perretti, Charles T. and Ridoux, Vincent and Teo, Steven L. H. and Winship, Arliss J.}, - editor = {Beger, Maria}, - year = {2017}, - month = oct, - journal = {Diversity and Distributions}, - volume = {23}, - number = {10}, - pages = {1098--1109}, - issn = {13669516}, - doi = {10.1111/ddi.12609}, - langid = {english}, - file = {/Users/dill/Zotero/storage/IV72LVC9/Mannocci et al. - 2017 - Temporal resolutions in species distribution model.pdf} -} - -@article{marques_accounting_2013, - title = {Accounting for Animal Density Gradients Using Independent Information in Distance Sampling Surveys}, - author = {Marques, Tiago A. and Buckland, Stephen T. and Bispo, Regina and Howland, Brett}, - year = {2013}, - month = mar, - journal = {Statistical Methods \& Applications}, - volume = {22}, - number = {1}, - pages = {67--80}, - issn = {1618-2510, 1613-981X}, - doi = {10.1007/s10260-012-0223-2}, - langid = {english}, - file = {/Users/dill/Zotero/storage/TZXCFRII/Stat Methods Appl 2012 Marques.pdf} -} - -@incollection{marques_covariate_2004, - title = {Covariate Models for the Detection Function}, - booktitle = {Advanced {{Distance Sampling}}}, - author = {Marques, Fernanda F. C. and Buckland, Stephen T}, - year = {2004}, - pages = {31--47}, - publisher = {{Oxford University Press}} -} - -@article{marques_estimating_2011, - title = {Estimating {{North Pacific}} Right Whale {{Eubalaena}} Japonica Density Using Passive Acoustic Cue Counting}, - author = {Marques, Ta and Munger, L and Thomas, L and Wiggins, S and Hildebrand, Ja}, - year = {2011}, - month = mar, - journal = {Endangered Species Research}, - volume = {13}, - number = {3}, - pages = {163--172}, - issn = {1863-5407, 1613-4796}, - doi = {10.3354/esr00325}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6BTNTUV5/n013p163.pdf;/Users/dill/Zotero/storage/QCJBFQFG/Endangered Species … 2011 Marques.pdf} -} - -@article{marques_estimating_2013, - title = {Estimating Animal Population Density Using Passive Acoustics: {{Passive}} Acoustic Density Estimation}, - shorttitle = {Estimating Animal Population Density Using Passive Acoustics}, - author = {Marques, Tiago A. and Thomas, Len and Martin, Stephen W. and Mellinger, David K. and Ward, Jessica A. and Moretti, David J. and Harris, Danielle and Tyack, Peter L.}, - year = {2013}, - month = may, - journal = {Biological Reviews}, - volume = {88}, - number = {2}, - pages = {287--309}, - issn = {14647931}, - doi = {10.1111/brv.12001}, - langid = {english}, - file = {/Users/dill/Zotero/storage/K6M4Z9EY/Biol Rev 2012 Marques.pdf} -} - -@article{marques_improving_2007, - title = {Improving Estimates of Bird Density Using Multiple-Covariate Distance Sampling}, - author = {Marques, Tiago A. and Thomas, Len and Fancy, Steven G. and Buckland, Stephen T.}, - year = {2007}, - journal = {The Auk}, - volume = {124}, - number = {4}, - pages = {1229--1243}, - file = {/Users/dill/Zotero/storage/HHFDI9F9/The Auk 2007 Marques.pdf} -} - -@article{marques_incorporating_2003, - title = {Incorporating {{Covariates}} into {{Standard Line Transect Analyses}}}, - author = {Marques, Fernanda F. C. and Buckland, Stephen T.}, - year = {2003}, - month = dec, - journal = {Biometrics}, - volume = {59}, - number = {4}, - pages = {924--935}, - issn = {0006-341X, 1541-0420}, - doi = {10.1111/j.0006-341X.2003.00107.x}, - abstract = {An implicit assumption of standard line transect methodology is that detection probabilities depend solely on the perpendicular distance of detected objects to the transect line. Heterogeneity in detection probabilities is commonly minimized using stratification, but this may be precluded by small sample sizes. We develop a general methodology which allows the effects of multiple covariates to be directly incorporated into the estimation procedure using a conditional likelihood approach. Small sample size properties of estimators are examined via simulations. As an example the method is applied to eastern tropical Pacific dolphin sightings data.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QFZZ62R5/Marques and Buckland - 2003 - Incorporating Covariates into Standard Line Transe.pdf} -} - -@phdthesis{marques_incorporating_2007, - title = {Incorporating Measurement Error and Density Gradients in Distance Sampling Surveys}, - author = {Marques, Tiago Andre Lamas Oliveira}, - year = {2007}, - school = {University of St Andrews}, - file = {/Users/dill/Zotero/storage/LWMCNXFQ/2007 Marques.pdf} -} - -@article{marques_model-based_2017, - title = {Model-Based Approaches to Deal with Detectability: A Comment on {{Hutto}} (2016)}, - shorttitle = {Model-Based Approaches to Deal with Detectability}, - author = {Marques, Tiago A. and Thomas, Len and K{\'e}ry, Marc and Buckland, Steve T. and Borchers, David L. and Rexstad, Eric and Fewster, Rachel M. and MacKenzie, Darryl I. and Royle, J. Andrew and {Guillera-Arroita}, Gurutzeta}, - year = {2017}, - journal = {Ecological Applications}, - file = {/Users/dill/Zotero/storage/LSWKHGN6/eap1553_002.pdf} -} - -@article{marques_point_2010, - title = {Point {{Transect Sampling Along Linear Features}}}, - author = {Marques, T. A. and Buckland, S. T. and Borchers, D. L. and Tosh, D. and McDonald, R. A.}, - year = {2010}, - month = dec, - journal = {Biometrics}, - volume = {66}, - number = {4}, - pages = {1247--1255}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2009.01381.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DAGKHLID/Biometrics 2010 Marques.pdf} -} - -@article{marques_predicting_2004, - title = {Predicting and Correcting Bias Caused by Measurement Error in Line Transect Sampling Using Multiplicative Error Models}, - author = {Marques, Tiago A.}, - year = {2004}, - journal = {Biometrics}, - volume = {60}, - number = {3}, - pages = {757--763}, - file = {/Users/dill/Zotero/storage/VWN2KV6A/Biom 2004 Marques.pdf} -} - -@article{marques_update_2012, - title = {An Update to the Methods in {{Endangered Species Research}} 2011 Paper" {{Estimating North Pacific}} Right Whale {{Eubalaena}} Japonica Density Using Passive Acoustic Cue Counting"}, - author = {Marques, Tiago A. and Munger, Lisa and Thomas, Len and Wiggins, Sean and Hildebrand, John}, - year = {2012}, - file = {/Users/dill/Zotero/storage/Y6IA7NUY/2012 Marques.pdf} -} - -@article{marra_coverage_2012, - title = {Coverage {{Properties}} of {{Confidence Intervals}} for {{Generalized Additive Model Components}}: {{Coverage}} Properties of {{GAM}} Intervals}, - shorttitle = {Coverage {{Properties}} of {{Confidence Intervals}} for {{Generalized Additive Model Components}}}, - author = {Marra, Giampiero and Wood, Simon N.}, - year = {2012}, - month = mar, - journal = {Scandinavian Journal of Statistics}, - volume = {39}, - number = {1}, - pages = {53--74}, - issn = {03036898}, - doi = {10.1111/j.1467-9469.2011.00760.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/WC5N3BWU/Scandinavian Journal of Statistics 2012 Marra.pdf} -} - -@article{marra_flexible_2011, - title = {A Flexible Instrumental Variable Approach}, - author = {Marra, Giampiero and Radice, Rosalba}, - year = {2011}, - month = dec, - journal = {Statistical Modelling: An International Journal}, - volume = {11}, - number = {6}, - pages = {581--603}, - issn = {1471-082X, 1477-0342}, - doi = {10.1177/1471082X1001100607}, - abstract = {Classical regression model literature has generally assumed that measured and unmeasured covariates are statistically independent. For many applications, this assumption is clearly tenuous. When unobservables are associated with included regressors and have an impact on the response, standard estimation methods will not be valid. This means that estimation results from observational studies, whose aim is to evaluate the impact of a treatment of interest on a response variable, will be biased and inconsistent in the presence of unmeasured confounders. One method for obtaining consistent estimates of treatment effects when dealing with linear models is the instrumental variable (IV) approach. Linear models have been extended to generalized linear models (GLMs) and generalized additive models (GAMs), and although IV methods have been proposed to deal with GLMs, fitting methods to carry out IV analysis within the GAM context have not been developed. We propose a simple but effective two-stage procedure for IV estimation when dealing with GAMs represented using any penalized regression spline approach and a correction procedure for confidence intervals. We explain under which conditions the proposed method works and illustrate its empirical validity through an extensive simulation experiment and a health study where unmeasured confounding is suspected to be present.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/F6IYWU9A/Marra and Radice - 2011 - A flexible instrumental variable approach.pdf} -} - -@article{marra_modelling_2012, - title = {Modelling the Spatiotemporal Distribution of the Incidence of Resident Foreign Population: {{Spatiotemporal Smoothing}} of {{Resident Foreign Population}}}, - shorttitle = {Modelling the Spatiotemporal Distribution of the Incidence of Resident Foreign Population}, - author = {Marra, Giampiero and Miller, David L. and Zanin, Luca}, - year = {2012}, - month = may, - journal = {Statistica Neerlandica}, - volume = {66}, - number = {2}, - pages = {133--160}, - issn = {00390402}, - doi = {10.1111/j.1467-9574.2011.00500.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/NNRQ2DVL/Statistica Neerlandica 2011 Marra.pdf} -} - -@article{marra_penalised_2010, - title = {Penalised Regression Splines: Theory and Application to Medical Research}, - shorttitle = {Penalised Regression Splines}, - author = {Marra, Giampiero and Radice, Rosalba}, - year = {2010}, - month = apr, - journal = {Statistical Methods in Medical Research}, - volume = {19}, - number = {2}, - pages = {107--125}, - issn = {0962-2802, 1477-0334}, - doi = {10.1177/0962280208096688}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YW76ZJ4T/Statistical Methods in Medical Research 2010 Marra.pdf} -} - -@article{marra_practical_2011, - title = {Practical Variable Selection for Generalized Additive Models}, - author = {Marra, Giampiero and Wood, Simon N.}, - year = {2011}, - month = jul, - journal = {Computational Statistics \& Data Analysis}, - volume = {55}, - number = {7}, - pages = {2372--2387}, - issn = {01679473}, - doi = {10.1016/j.csda.2011.02.004}, - langid = {english}, - file = {/Users/dill/Zotero/storage/J55875KN/Computational Statistics and Data Analysis 2011 Marra.pdf} -} - -@article{marsh_correcting_1989, - title = {Correcting for Visibility Bias in Strip Transect Aerial Surveys of Aquatic Fauna}, - author = {Marsh, Helene and Sinclair, Dennis F.}, - year = {1989}, - journal = {The Journal of Wildlife Management}, - pages = {1017--1024}, - file = {/Users/dill/Zotero/storage/U2JU2V3C/The Journal of Wildlife Management 1989 Marsh.pdf} -} - -@techreport{marshall_dealing_nodate, - title = {Dealing with {{Unidentified Sightings}}}, - author = {Marshall, L H and Thomas, L}, - number = {2011-1}, - pages = {22}, - address = {{St Andrews, Scotland}}, - institution = {{University of St Andrews}}, - langid = {english}, - file = {/Users/dill/Zotero/storage/XKBI7YH6/Marshall and Thomas - Dealing with Unidentified Sightings.pdf} -} - -@incollection{marx_p-spline_2010, - title = {P-Spline Varying Coefficient Models for Complex Data}, - booktitle = {Statistical {{Modelling}} and {{Regression Structures}}: {{Festschrift}} in {{Honour}} of {{Ludwig Fahrmeir}}}, - author = {Marx, Brian D.}, - editor = {Kneib, Thomas and Tutz, Gerhard}, - year = {2010}, - pages = {19--45}, - publisher = {{Physica-Verlag}} -} - -@article{mason_jay-zs_2011, - title = {Jay-{{Z}}'s 99 {{Problems}}, {{Verse}} 2: {{A Close Reading}} with {{Fourth Amendment Guidance}} for {{Cops}} and {{Perps}}}, - shorttitle = {Jay-{{Z}}'s 99 {{Problems}}, {{Verse}} 2}, - author = {Mason, Caleb}, - year = {2011}, - journal = {. Louis ULJ}, - volume = {56}, - pages = {567}, - file = {/Users/dill/Zotero/storage/AZVXJXUY/Louis ULJ 2011 Mason.pdf} -} - -@article{mazerolle_estimating_2015, - title = {Estimating {{Detectability}} and {{Biological Parameters}} of {{Interest}} with the {{Use}} of the {{R Environment}}}, - author = {Mazerolle, Marc J.}, - year = {2015}, - month = dec, - journal = {Journal of Herpetology}, - volume = {49}, - number = {4}, - pages = {541--559}, - issn = {0022-1511}, - doi = {10.1670/14-075}, - langid = {english}, - file = {/Users/dill/Zotero/storage/GN2UVVQ7/document(2).pdf} -} - -@article{mcardle_variance_2004, - title = {Variance Heterogeneity, Transformations, and Models of Species Abundance: A Cautionary Tale}, - shorttitle = {Variance Heterogeneity, Transformations, and Models of Species Abundance}, - author = {McArdle, Brian H and Anderson, Marti J}, - year = {2004}, - month = jul, - journal = {Canadian Journal of Fisheries and Aquatic Sciences}, - volume = {61}, - number = {7}, - pages = {1294--1302}, - issn = {0706-652X, 1205-7533}, - doi = {10.1139/f04-051}, - langid = {english}, - file = {/Users/dill/Zotero/storage/A444LEA4/Can. J. Fish. Aquat. Sci. 2004 McArdle.pdf} -} - -@article{mcclenachan_documenting_2009, - title = {Documenting {{Loss}} of {{Large Trophy Fish}} from the {{Florida Keys}} with {{Historical Photographs}}}, - author = {McCLENACHAN, Loren}, - year = {2009}, - month = jun, - journal = {Conservation Biology}, - volume = {23}, - number = {3}, - pages = {636--643}, - issn = {08888892, 15231739}, - doi = {10.1111/j.1523-1739.2008.01152.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/3GAVPKNC/Conservation Biology 2009 McClenachan.pdf} -} - -@article{mccormick_given_2021, - title = {The "given Data" Paradigm Undermines Both Cultures}, - author = {McCormick, Tyler}, - year = {2021}, - month = may, - journal = {arXiv:2105.12478 [cs, stat]}, - eprint = {2105.12478}, - eprinttype = {arxiv}, - primaryclass = {cs, stat}, - abstract = {Breiman organizes Statistical modeling: The two cultures around a simple visual. Data, to the far right, are compelled into a ``black box'' with an arrow and then catapulted left by a second arrow, having been transformed into an output. Breiman then posits two interpretations of this visual as encapsulating a distinction between two cultures in statistics. The divide, he argues is about what happens in the ``black box.'' In this comment, I argue for a broader perspective on statistics and, in doing so, elevate questions from ``before'' and ``after'' the box as fruitful areas for statistical innovation and practice.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}, - file = {/Users/dill/Zotero/storage/TXPZ5FAL/McCormick - 2021 - The given data paradigm undermines both cultures.pdf} -} - -@article{mcgill_variations_1978, - title = {Variations of {{Box Plots}}}, - author = {McGill, Robert and Tukey, John W. and Larsen, Wayne A.}, - year = {1978}, - month = feb, - journal = {The American Statistician}, - volume = {32}, - number = {1}, - pages = {12}, - issn = {00031305}, - doi = {10.2307/2683468}, - file = {/Users/dill/Zotero/storage/V9CGKM6J/The American Statistician 1978 McGill.pdf} -} - -@article{mcinerny_fine-scale_2011, - title = {Fine-Scale Environmental Variation in Species Distribution Modelling: Regression Dilution, Latent Variables and Neighbourly Advice: {{Regression}} Dilution in Species Distribution Models}, - shorttitle = {Fine-Scale Environmental Variation in Species Distribution Modelling}, - author = {McInerny, Greg J. and Purves, Drew W.}, - year = {2011}, - month = jun, - journal = {Methods in Ecology and Evolution}, - volume = {2}, - number = {3}, - pages = {248--257}, - issn = {2041210X}, - doi = {10.1111/j.2041-210X.2010.00077.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FFJ9FIMB/Methods in Ecology and Evolution 2011 McInerny.pdf} -} - -@techreport{mclachlan_em_2004, - title = {The {{EM}} Algorithm}, - author = {McLachlan, Geoffrey J. and Krishnan, Thriyambakam and Ng, See Ket}, - year = {2004}, - institution = {{Papers/Humboldt-Universit\"at Berlin, Center for Applied Statistics and Economics (CASE)}}, - file = {/Users/dill/Zotero/storage/7U9SR7B7/IEEE Trans Neural Netw 2004 Ng.pdf} -} - -@article{measey_counting_2017, - title = {Counting Chirps: Acoustic Monitoring of Cryptic Frogs}, - shorttitle = {Counting Chirps}, - author = {Measey, G. John and Stevenson, Ben C. and Scott, Tanya and Altwegg, Res and Borchers, David L.}, - editor = {Bellard, C{\'e}line}, - year = {2017}, - month = jun, - journal = {Journal of Applied Ecology}, - volume = {54}, - number = {3}, - pages = {894--902}, - issn = {00218901}, - doi = {10.1111/1365-2664.12810}, - langid = {english}, - file = {/Users/dill/Zotero/storage/3JGDHU5H/Journal of Applied Ecology 2016 Measey.pdf;/Users/dill/Zotero/storage/85MYA3BI/jpe12810.pdf} -} - -@article{melville_model-based_2014, - title = {Model-Based {{Prediction In Ecological Surveys Including Those}} with {{Incomplete Detection}}}, - author = {Melville, Gavin J. and Welsh, Alan H.}, - year = {2014}, - month = sep, - journal = {Australian \& New Zealand Journal of Statistics}, - volume = {56}, - number = {3}, - pages = {257--281}, - issn = {13691473}, - doi = {10.1111/anzs.12084}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DKLKCZS5/Aust. N. Z. J. Stat. 2014 Melville.pdf} -} - -@article{merow_practical_2013, - title = {A Practical Guide to {{MaxEnt}} for Modeling Species' Distributions: What It Does, and Why Inputs and Settings Matter}, - shorttitle = {A Practical Guide to {{MaxEnt}} for Modeling Species' Distributions}, - author = {Merow, Cory and Smith, Matthew J. and Silander, John A.}, - year = {2013}, - month = oct, - journal = {Ecography}, - volume = {36}, - number = {10}, - pages = {1058--1069}, - issn = {09067590}, - doi = {10.1111/j.1600-0587.2013.07872.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6QNRDS9L/j.1600-0587.2013.07872.x.pdf} -} - -@article{michelot_linking_2018, - title = {Linking Resource Selection and Step Selection Models for Habitat Preferences in Animals}, - author = {Michelot, Th{\'e}o and Blackwell, Paul G. and Matthiopoulos, Jason}, - year = {2018}, - month = jul, - journal = {Ecology}, - issn = {00129658}, - doi = {10.1002/ecy.2452}, - langid = {english}, - file = {/Users/dill/Zotero/storage/MPBE2QP2/Michelot et al. - 2018 - Linking resource selection and step selection mode.pdf} -} - -@article{michelot_maximum_2016, - title = {Maximum Penalized Likelihood Estimation in Semiparametric Mark-Recapture-Recovery Models: {{Maximum}} Penalized Likelihood Estimation in Semiparametric {{MRR}} Models}, - shorttitle = {Maximum Penalized Likelihood Estimation in Semiparametric Mark-Recapture-Recovery Models}, - author = {Michelot, Th{\'e}o and Langrock, Roland and Kneib, Thomas and King, Ruth}, - year = {2016}, - month = jan, - journal = {Biometrical Journal}, - volume = {58}, - number = {1}, - pages = {222--239}, - issn = {03233847}, - doi = {10.1002/bimj.201400222}, - langid = {english}, - file = {/Users/dill/Zotero/storage/K6L5F3VT/bimj1624.pdf} -} - -@article{miller_bayesian_2019, - title = {Bayesian Views of Generalized Additive Modelling}, - author = {Miller, David L.}, - year = {2019}, - month = feb, - journal = {arXiv:1902.01330 [stat]}, - eprint = {1902.01330}, - eprinttype = {arxiv}, - primaryclass = {stat}, - abstract = {Links between frequentist and Bayesian approaches to smoothing were highlighted early on in the smoothing literature, and power much of the machinery that underlies the modern generalized additive modelling framework (implemented in software such as the R package mgcv), but they tend to be unknown or under appreciated. This article aims to highlight useful links between Bayesian and frequentist approaches to smoothing, and their practical applications (with a somewhat mgcvcentric viewpoint).}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Statistics - Methodology}, - file = {/Users/dill/Zotero/storage/WGLUP8KR/Miller - 2019 - Bayesian views of generalized additive modelling.pdf} -} - -@article{miller_bayesian_2021, - title = {Bayesian Views of Generalized Additive Modelling}, - author = {Miller, David L.}, - year = {2021}, - month = oct, - journal = {arXiv:1902.01330 [stat]}, - eprint = {1902.01330}, - eprinttype = {arxiv}, - primaryclass = {stat}, - abstract = {Generalized additive models (GAMs) are a commonly used, flexible framework applied to many problems in statistical ecology. GAMs are often considered to be a purely frequentist framework (`generalized linear models with wiggly bits'), however links between frequentist and Bayesian approaches to these models were highlighted early on in the literature. Bayesian thinking underlies many parts of the implementation in the popular R package mgcv as well as in GAM theory more generally. This article aims to highlight useful links (and differences) between Bayesian and frequentist approaches to smoothing, and their practical applications in ecology (with an mgcvcentric viewpoint). Here I give some background for these results then move onto two important topics for quantitative ecologists: term/model selection and uncertainty estimation.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Statistics - Methodology}, - file = {/Users/dill/Zotero/storage/WTB3MTED/Miller - 2021 - Bayesian views of generalized additive modelling.pdf} -} - -@article{miller_distance_2019, - title = {Distance {{Sampling}} in {{R}}}, - author = {Miller, David L. and Rexstad, Eric and Thomas, Len and Marshall, Laura and Laake, Jeffrey L.}, - year = {2019}, - journal = {Journal of Statistical Software}, - volume = {89}, - number = {1}, - issn = {1548-7660}, - doi = {10.18637/jss.v089.i01}, - abstract = {Estimating the abundance and spatial distribution of animal and plant populations is essential for conservation and management. We introduce the R package Distance that implements distance sampling methods to estimate abundance. We describe how users can obtain estimates of abundance (and density) using the package as well as documenting the links it provides with other more specialized R packages. We also demonstrate how Distance provides a migration pathway from previous software, thereby allowing us to deliver cutting-edge methods to the users more quickly.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UVGZKVQ4/Miller et al. - 2019 - Distance Sampling in iRi.pdf} -} - -@article{miller_extending_2021, - title = {Extending Density Surface Models to Include Multiple and Double-Observer Survey Data}, - author = {Miller, David L and Fifield, David A and Wakefield, ED and Sigourney, Douglas B.}, - year = {2021}, - journal = {PeerJ}, - volume = {9}, - number = {e12113}, - pages = {18}, - abstract = {Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2BSVVP98/Miller - 2021 - Extending density surface models to include multip.pdf} -} - -@article{miller_finite_2014, - title = {Finite Area Smoothing with Generalized Distance Splines}, - author = {Miller, David L. and Wood, Simon N.}, - year = {2014}, - month = dec, - journal = {Environmental and Ecological Statistics}, - volume = {21}, - number = {4}, - pages = {715--731}, - issn = {1352-8505, 1573-3009}, - doi = {10.1007/s10651-014-0277-4}, - abstract = {Most conventional spatial smoothers smooth with respect to the Euclidean distance between observations, even though this distance may not be a meaningful measure of spatial proximity, especially when boundary features are present. When domains have complicated boundaries leakage (the inappropriate linking of parts of the domain which are separated by physical barriers) can occur. To overcome this problem, we develop a method of smoothing with respect to generalized distances, such as within domain distances. We obtain the generalized distances between our points and then use multidimensional scaling to find a configuration of our observations in a Euclidean space of 2 or more dimensions, such that the Euclidian distances between points in that space closely approximate the generalized distances between the points. Smoothing is performed over this new point configuration, using a conventional smoother. To mitigate the problems associated with smoothing in high dimensions we use a generalization of thin plate spline smoothers proposed by Duchon (Constructive theory of functions of several variables, pp 85\textendash 100, 1977). This general method for smoothing with respect to generalized distances improves on the performance of previous within-domain distance spatial smoothers, and often provides a more natural model than the soap film approach of Wood et al. (J R Stat Soc Ser B Stat Methodol 70(5):931\textendash 955, 2008). The smoothers are of the linear basis with quadratic penalty type easily incorporated into a range of statistical models.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/X9VTZX99/Miller and Wood - 2014 - Finite area smoothing with generalized distance sp.pdf} -} - -@article{miller_mixture_2015, - title = {Mixture Models for Distance Sampling Detection Functions}, - author = {Miller, David L. and Thomas, Len}, - year = {2015}, - journal = {PloS one}, - volume = {10}, - number = {3}, - pages = {e0118726}, - file = {/Users/dill/Zotero/storage/ABU2AB3V/PLoS ONE 2015 Miller.pdf} -} - -@phdthesis{miller_smooth_2012, - title = {On Smooth Models for Complex Domains and Distances}, - author = {Miller, David}, - year = {2012}, - school = {University of Bath}, - file = {/Users/dill/Zotero/storage/DZZTTHUR/2012 Miller.pdf;/Users/dill/Zotero/storage/NDQMRB9W/UnivBath_PhD_2012_D_L_Miller.pdf} -} - -@article{miller_spatial_2013, - title = {Spatial Models for Distance Sampling Data: Recent Developments and Future Directions}, - shorttitle = {Spatial Models for Distance Sampling Data}, - author = {Miller, David L. and Burt, M. Louise and Rexstad, Eric A. and Thomas, Len}, - editor = {Gimenez, Olivier}, - year = {2013}, - month = nov, - journal = {Methods in Ecology and Evolution}, - volume = {4}, - number = {11}, - pages = {1001--1010}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12105}, - langid = {english}, - file = {/Users/dill/Zotero/storage/HJPNM9RM/Methods in Ecology and Evolution 2013 Miller.pdf} -} - -@article{miller_understanding_2019, - title = {Understanding the {{Stochastic Partial Differential Equation Approach}} to {{Smoothing}}}, - author = {Miller, David L. and Glennie, Richard and Seaton, Andrew E.}, - year = {2019}, - month = sep, - journal = {Journal of Agricultural, Biological and Environmental Statistics}, - issn = {1085-7117, 1537-2693}, - doi = {10.1007/s13253-019-00377-z}, - langid = {english}, - file = {/Users/dill/Zotero/storage/SF9NBTAZ/Miller et al. - 2019 - Understanding the Stochastic Partial Differential .pdf} -} - -@article{moilanen_administrative_2011, - title = {Administrative Regions in Conservation: {{Balancing}} Local Priorities with Regional to Global Preferences in Spatial Planning}, - shorttitle = {Administrative Regions in Conservation}, - author = {Moilanen, Atte and Arponen, Anni}, - year = {2011}, - month = may, - journal = {Biological Conservation}, - volume = {144}, - number = {5}, - pages = {1719--1725}, - issn = {00063207}, - doi = {10.1016/j.biocon.2011.03.007}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YUWUVM5K/Biological Conservation 2011 Moilanen.pdf} -} - -@article{moilanen_prioritizing_2005, - title = {Prioritizing Multiple-Use Landscapes for Conservation: Methods for Large Multi-Species Planning Problems}, - shorttitle = {Prioritizing Multiple-Use Landscapes for Conservation}, - author = {Moilanen, A. and Franco, A. M.A and Early, R. I and Fox, R. and Wintle, B. and Thomas, C. D}, - year = {2005}, - month = sep, - journal = {Proceedings of the Royal Society B: Biological Sciences}, - volume = {272}, - number = {1575}, - pages = {1885--1891}, - issn = {0962-8452, 1471-2954}, - doi = {10.1098/rspb.2005.3164}, - langid = {english}, - file = {/Users/dill/Zotero/storage/M68XQJM4/Proceedings of the Royal Society B Biological Sciences 2005 Moilanen.pdf} -} - -@article{monnahan_faster_2017, - title = {Faster Estimation of {{Bayesian}} Models in Ecology Using {{Hamiltonian Monte Carlo}}}, - author = {Monnahan, Cole C. and Thorson, James T. and Branch, Trevor A.}, - editor = {O'Hara, Robert B.}, - year = {2017}, - month = mar, - journal = {Methods in Ecology and Evolution}, - volume = {8}, - number = {3}, - pages = {339--348}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12681}, - langid = {english}, - file = {/Users/dill/Zotero/storage/CD8XGPMD/Methods in Ecology and Evolution 2016 Monnahan.pdf} -} - -@article{moore_bayesian_2011, - title = {Bayesian State-Space Model of Fin Whale Abundance Trends from a 1991-2008 Time Series of Line-Transect Surveys in the {{California Current}}: {{Bayesian}} Trend Analysis from Line-Transect Data}, - shorttitle = {Bayesian State-Space Model of Fin Whale Abundance Trends from a 1991-2008 Time Series of Line-Transect Surveys in the {{California Current}}}, - author = {Moore, Jeffrey E. and Barlow, Jay}, - year = {2011}, - month = oct, - journal = {Journal of Applied Ecology}, - volume = {48}, - number = {5}, - pages = {1195--1205}, - issn = {00218901}, - doi = {10.1111/j.1365-2664.2011.02018.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/VLDA4AWZ/Journal of Applied Ecology 2011 Moore.pdf;/Users/dill/Zotero/storage/WI769C8A/jpe_2018_sm_apps4a.pdf} -} - -@article{moore_regional_2011, - title = {The {{Regional Ocean Modeling System}} ({{ROMS}}) 4-Dimensional Variational Data Assimilation Systems}, - author = {Moore, Andrew M. and Arango, Hernan G. and Broquet, Gregoire and Edwards, Chris and Veneziani, Milena and Powell, Brian and Foley, Dave and Doyle, James D. and Costa, Dan and Robinson, Patrick}, - year = {2011}, - month = oct, - journal = {Progress in Oceanography}, - volume = {91}, - number = {1}, - pages = {50--73}, - issn = {00796611}, - doi = {10.1016/j.pocean.2011.05.003}, - abstract = {The Regional Ocean Modeling System (ROMS) 4-dimensional variational (4D-Var) data assimilation systems have been systematically applied to the mesoscale circulation environment of the California Current to demonstrate the performance and practical utility of the various components of ROMS 4D-Var. In particular, we present a comparison of three approaches to 4D-Var, namely: the primal formulation of the incremental strong constraint approach; the dual formulation ``physical-space statistical analysis system''; and the dual formulation indirect representer approach. In agreement with theoretical considerations all three approaches converge to the same ocean circulation estimate when using the same observations and prior information. However, the rate of convergence of the dual formulation was found to be inferior to that of the primal formulation. Other aspects of the 4D-Var performance that relate to the use of multiple outer-loops, preconditioning, and the weak constraint are also explored. A systematic evaluation of the impact of the various components of the 4D-Var control vector (i.e. the initial conditions, surface forcing and open boundary conditions) is also presented. It is shown that correcting for uncertainties in the model initial conditions exerts the largest influence on the ability of the model to fit the available observations. Various important diagnostics of 4D-Var are also examined, including estimates of the posterior error, the information content of the observation array, and innovation-based consistency checks on the prior error assumptions. Using these diagnostic tools, we find that more than 90\% of the observations assimilated into the model provide redundant information. This is a symptom of the large percentage of satellite data that are used and to some extent the nature of the data processing employed. This is the second in a series of three papers describing the ROMS 4D-Var systems.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/CCBQPGZQ/Moore et al. - 2011 - The Regional Ocean Modeling System (ROMS) 4-dimens.pdf} -} - -@article{moretti_risk_2014, - title = {A {{Risk Function}} for {{Behavioral Disruption}} of {{Blainville}}'s {{Beaked Whales}} ({{Mesoplodon}} Densirostris) from {{Mid-Frequency Active Sonar}}}, - author = {Moretti, David and Thomas, Len and Marques, Tiago and Harwood, John and Dilley, Ashley and Neales, Bert and Shaffer, Jessica and McCarthy, Elena and New, Leslie and Jarvis, Susan and Morrissey, Ronald}, - editor = {Fahlman, Andreas}, - year = {2014}, - month = jan, - journal = {PLoS ONE}, - volume = {9}, - number = {1}, - pages = {e85064}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0085064}, - abstract = {There is increasing concern about the potential effects of noise pollution on marine life in the world's oceans. For marine mammals, anthropogenic sounds may cause behavioral disruption, and this can be quantified using a risk function that relates sound exposure to a measured behavioral response. Beaked whales are a taxon of deep diving whales that may be particularly susceptible to naval sonar as the species has been associated with sonar-related mass stranding events. Here we derive the first empirical risk function for Blainville's beaked whales (Mesoplodon densirostris) by combining in situ data from passive acoustic monitoring of animal vocalizations and navy sonar operations with precise ship tracks and sound field modeling. The hydrophone array at the Atlantic Undersea Test and Evaluation Center, Bahamas, was used to locate vocalizing groups of Blainville's beaked whales and identify sonar transmissions before, during, and after Mid-Frequency Active (MFA) sonar operations. Sonar transmission times and source levels were combined with ship tracks using a sound propagation model to estimate the received level (RL) at each hydrophone. A generalized additive model was fitted to data to model the presence or absence of the start of foraging dives in 30-minute periods as a function of the corresponding sonar RL at the hydrophone closest to the center of each group. This model was then used to construct a risk function that can be used to estimate the probability of a behavioral change (cessation of foraging) the individual members of a Blainville's beaked whale population might experience as a function of sonar RL. The function predicts a 0.5 probability of disturbance at a RL of 150dBrms re mPa (CI: 144 to 155) This is 15dB lower than the level used historically by the US Navy in their risk assessments but 10 dB higher than the current 140 dB step-function.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/IAYSMD7H/Moretti et al. - 2014 - A Risk Function for Behavioral Disruption of Blain.pdf} -} - -@article{morgan_new_2008, - title = {A New Mixture Model for Capture Heterogeneity}, - author = {Morgan, Byron JT and Ridout, Martin S.}, - year = {2008}, - journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}, - volume = {57}, - number = {4}, - pages = {433--446}, - file = {/Users/dill/Zotero/storage/MF45RDPW/Journal of the Royal Statistical Society Series C (Applied Statistics) 2008 Morgan.pdf} -} - -@article{morisette_tracking_2009, - title = {Tracking the Rhythm of the Seasons in the Face of Global Change: Phenological Research in the 21st Century}, - shorttitle = {Tracking the Rhythm of the Seasons in the Face of Global Change}, - author = {Morisette, Jeffrey T and Richardson, Andrew D and Knapp, Alan K and Fisher, Jeremy I and Graham, Eric A and Abatzoglou, John and Wilson, Bruce E and Breshears, David D and Henebry, Geoffrey M and Hanes, Jonathan M and Liang, Liang}, - year = {2009}, - month = jun, - journal = {Frontiers in Ecology and the Environment}, - volume = {7}, - number = {5}, - pages = {253--260}, - issn = {1540-9295}, - doi = {10.1890/070217}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9WCR9ZD4/Frontiers in Ecology and the Environment 2009 Morisette.pdf} -} - -@article{mullen_continuous_2014, - title = {Continuous Global Optimization in {{R}}}, - author = {Mullen, Katharine M.}, - year = {2014}, - journal = {Journal of Statistical Software}, - volume = {60}, - number = {6}, - pages = {1--45}, - file = {/Users/dill/Zotero/storage/JCT684DP/Journal of Statistical … 2014 Mullen.pdf} -} - -@article{muller_functional_2008, - title = {Functional {{Additive Models}}}, - author = {M{\"u}ller, Hans-Georg and Yao, Fang}, - year = {2008}, - month = dec, - journal = {Journal of the American Statistical Association}, - volume = {103}, - number = {484}, - pages = {1534--1544}, - issn = {0162-1459, 1537-274X}, - doi = {10.1198/016214508000000751}, - langid = {english}, - file = {/Users/dill/Zotero/storage/43RJU4BH/Journal of the American Statistical Association 2008 Müller.pdf} -} - -@article{munger_north_2011, - title = {North {{Pacific}} Right Whale Up-Call Source Levels and Propagation Distance on the Southeastern {{Bering Sea}} Shelf}, - author = {Munger, Lisa M. and Wiggins, Sean M. and Hildebrand, John A.}, - year = {2011}, - journal = {The Journal of the Acoustical Society of America}, - volume = {129}, - number = {6}, - pages = {4047--4054}, - file = {/Users/dill/Zotero/storage/IT8DSV2W/J. Acoust. Soc. Am. 2011 Munger.pdf} -} - -@article{murtaugh_simplicity_2007, - title = {Simplicity and Complexity in Ecological Data Analysis}, - author = {Murtaugh, Paul A.}, - year = {2007}, - journal = {Ecology}, - volume = {88}, - number = {1}, - pages = {56--62}, - file = {/Users/dill/Zotero/storage/3A4TLUT2/Ecology 2007 Murtaugh.pdf} -} - -@article{myers_spatial_1994, - title = {Spatial Interpolation: An Overview}, - shorttitle = {Spatial Interpolation}, - author = {Myers, Donald E.}, - year = {1994}, - journal = {Geoderma}, - volume = {62}, - number = {1-3}, - pages = {17--28}, - file = {/Users/dill/Zotero/storage/96ZPM9XC/Geoderma 1994 Myers.pdf} -} - -@article{nadeem_integrating_2016, - title = {Integrating Population Dynamics Models and Distance Sampling Data: A Spatial Hierarchical State-Space Approach}, - shorttitle = {Integrating Population Dynamics Models and Distance Sampling Data}, - author = {Nadeem, Khurram and Moore, Jeffrey E. and Zhang, Ying and Chipman, Hugh}, - year = {2016}, - month = jul, - journal = {Ecology}, - volume = {97}, - number = {7}, - pages = {1735--1745}, - issn = {00129658}, - doi = {10.1890/15-1406.1}, - abstract = {Stochastic versions of Gompertz, Ricker, and various other dynamics models play a fundamental role in quantifying strength of density dependence and studying long-\- term dynamics of wildlife populations. These models are frequently estimated using time series of abundance estimates that are inevitably subject to observation error and missing data. This issue can be addressed with a state-\-space modeling framework that jointly estimates the observed data model and the underlying stochastic population dynamics (SPD) model. In cases where abundance data are from multiple locations with a smaller spatial resolution (e.g., from mark\textendash recapture and distance sampling studies), models are conventionally fitted to spatially pooled estimates of yearly abundances. Here, we demonstrate that a spatial version of SPD models can be directly estimated from short time series of spatially referenced distance sampling data in a unified hierarchical state-s\-pace modeling framework that also allows for spatial variance (covariance) in population growth. We also show that a full range of likelihood based inference, including estimability diagnostics and model s\-election, is feasible in this class of models using a data cloning algorithm. We further show through simulation experiments that the hierarchical state-s\-pace framework introduced herein efficiently captures the underlying dynamical parameters and spatial abundance distribution. We apply our methodology by analyzing a time series of line-t\-ransect distance sampling data for fin whales (Balaenoptera physalus) off the U.S. west coast. Although there were only seven surveys conducted during the study time frame, 1991\textendash 2014, our analysis detected presence of strong density regulation and provided reliable estimates of fin whale densities. In summary, we show that the integrative framework developed herein allows ecologists to better infer key population characteristics such as presence of density regulation and spatial variability in a population's intrinsic growth potential.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2FK9QELG/Nadeem et al. - 2016 - Integrating population dynamics models and distanc.pdf;/Users/dill/Zotero/storage/H489XJF6/Nadeem_etal_2016_Supporting_ecy1403-sup-0005-tables1-s5.pdf} -} - -@article{neal_fast_2015, - title = {Fast Exact Summation Using Small and Large Superaccumulators}, - author = {Neal, Radford M.}, - year = {2015}, - journal = {arXiv preprint arXiv:1505.05571}, - eprint = {1505.05571}, - eprinttype = {arxiv}, - archiveprefix = {arXiv}, - file = {/Users/dill/Zotero/storage/DMY9DWMR/arXiv 2015 Neal.pdf} -} - -@article{nelder_generalized_1972, - title = {Generalized {{Linear Models}}}, - author = {Nelder, J. A. and Wedderburn, R. W. M.}, - year = {1972}, - journal = {Journal of the Royal Statistical Society. Series A (General)}, - volume = {135}, - number = {3}, - pages = {370}, - issn = {00359238}, - doi = {10.2307/2344614}, - file = {/Users/dill/Zotero/storage/7ARGHQPK/Journal of the Royal Statistical Society. Series A (General) 1972 Nelder.pdf} -} - -@article{newman_monte_2009, - title = {Monte {{Carlo Inference}} for {{State-Space Models}} of {{Wild Animal Populations}}}, - author = {Newman, Ken B. and Fern{\'a}ndez, Carmen and Thomas, Len and Buckland, Stephen T.}, - year = {2009}, - month = jun, - journal = {Biometrics}, - volume = {65}, - number = {2}, - pages = {572--583}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2008.01073.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/KC7CEHGE/Biom 2008 Newman.pdf} -} - -@inproceedings{nguyen_deep_2015, - title = {Deep Neural Networks Are Easily Fooled: {{High}} Confidence Predictions for Unrecognizable Images}, - shorttitle = {Deep Neural Networks Are Easily Fooled}, - booktitle = {Proceedings of the {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}}}, - author = {Nguyen, Anh and Yosinski, Jason and Clune, Jeff}, - year = {2015}, - pages = {427--436}, - file = {/Users/dill/Zotero/storage/VZVC7929/1412.1897v4.pdf} -} - -@article{niemi_bayesian_2010, - title = {Bayesian {{Spatial Point Process Modeling}} of {{Line Transect Data}}}, - author = {Niemi, Aki and Fern{\'a}ndez, Carmen}, - year = {2010}, - month = sep, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {15}, - number = {3}, - pages = {327--345}, - issn = {1085-7117, 1537-2693}, - doi = {10.1007/s13253-010-0024-8}, - langid = {english}, - file = {/Users/dill/Zotero/storage/P4YSM7WQ/JABES 2010 Niemi.pdf} -} - -@misc{noauthor_barlow_etal_2009_serdp_noaa-tm-nmfs-swfsc-444-1.pdf_nodate, - title = {Barlow\_etal\_2009\_{{SERDP}}\_{{NOAA-TM-NMFS-SWFSC-444-1}}.Pdf} -} - -@article{noauthor_joint_1915, - title = {Joint {{Committee}} on {{Standards}} for {{Graphic Presentation}}}, - year = {1915}, - month = dec, - journal = {Publications of the American Statistical Association}, - volume = {14}, - number = {112}, - pages = {790}, - issn = {15225437}, - doi = {10.2307/2965153}, - file = {/Users/dill/Zotero/storage/8M3TUM56/Publications of the American Statistical Association 1915.pdf} -} - -@article{noauthor_letters_1990, - title = {Letters to the {{Editor Author}}(s): {{Brian Schott}}, {{R}}. {{W}}. {{Farebrother}}, {{Victor R}}. {{Prybutok}}, {{Steve M}}. {{Bajgier}}, {{MaryAnne Atkinson}}, {{Grace Wahba}}, {{Noel Cressie}}, {{Richard Goldstein}}, {{Richard A}}. {{Lockhart}}, {{John J}}. {{Spinelli}}, {{Robert Kinnison}}, {{A}}. {{S}}. {{C}}. {{Ehrenberg}}, {{James D}}. {{Knoke}}, {{Roderick J}}. {{A}}. {{Little}}, {{W}}. {{E}}. {{Sharp}}, {{Haim Shore}}, {{Robert M}}. {{Norton}}, {{P}}. {{A}}. {{V}}. {{B}}. {{Swamy}}, {{Sastry G}}. {{Pantula}}, {{Marcia Gumpertz}}, {{Jay H}}. {{Beder}}, {{T}}. {{D}}. {{Stanley}}, {{Clint Cummins}} and {{H}}. {{D}}. {{Vinod}}}, - year = {1990}, - journal = {The American Statistician}, - volume = {44}, - number = {3}, - pages = {254--264}, - langid = {english}, - file = {/Users/dill/Zotero/storage/X3QSI84R/1990 - Letters to the Editor Author(s) Brian Schott, R. .pdf} -} - -@article{noauthor_modeling_2006, - title = {Modeling the {{Probability}} of {{Resource Use}}: {{The Effect}} of, and {{Dealing}} with, {{Detecting}} a {{Species Imperfectly Author}}(s): {{Darryl I}}. {{MacKenzie Reviewed}} Work(s):}, - year = {2006}, - journal = {The Journal of Wildlife Management}, - volume = {70}, - number = {2}, - pages = {367--374}, - langid = {english}, - file = {/Users/dill/Zotero/storage/WQRXJMAL/2006 - Modeling the Probability of Resource Use The Effe.pdf} -} - -@article{nobis_kissmig_2014, - title = {{{KISSMig}} - a Simple Model for {{R}} to Account for Limited Migration in Analyses of Species Distributions}, - author = {Nobis, Michael P. and Normand, Signe}, - year = {2014}, - month = dec, - journal = {Ecography}, - volume = {37}, - number = {12}, - pages = {1282--1287}, - issn = {09067590}, - doi = {10.1111/ecog.00930}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FRZZFRDT/Ecography 2014 Nobis.pdf} -} - -@article{nychka_bayesian_1988, - title = {Bayesian {{Confidence Intervals}} for {{Smoothing Splines}}}, - author = {Nychka, Douglas}, - year = {1988}, - month = dec, - journal = {Journal of the American Statistical Association}, - volume = {83}, - number = {404}, - pages = {1134}, - issn = {01621459}, - doi = {10.2307/2290146}, - file = {/Users/dill/Zotero/storage/GVAUN7IX/Journal of the American Statistical Association 1988 Nychka.pdf} -} - -@article{obrien_use_2012, - title = {Use of Kernel Density Estimation and Maximum Curvature to Set {{Marine Protected Area}} Boundaries: {{Identifying}} a {{Special Protection Area}} for Wintering Red-Throated Divers in the {{UK}}}, - shorttitle = {Use of Kernel Density Estimation and Maximum Curvature to Set {{Marine Protected Area}} Boundaries}, - author = {O'Brien, Susan H. and Webb, Andrew and Brewer, Mark J. and Reid, James B.}, - year = {2012}, - month = nov, - journal = {Biological Conservation}, - volume = {156}, - pages = {15--21}, - issn = {00063207}, - doi = {10.1016/j.biocon.2011.12.033}, - langid = {english}, - file = {/Users/dill/Zotero/storage/HGJADK4T/Biological Conservation 2012 O'Brien.pdf} -} - -@article{oedekoven_improving_2013, - title = {Improving Distance Sampling: Accounting for Covariates and Non-Independency between Sampled Sites}, - shorttitle = {Improving Distance Sampling}, - author = {Oedekoven, Cornelia S. and Buckland, Stephen T. and Mackenzie, Monique L. and Evans, Kristine O. and Burger, Loren W.}, - editor = {Devictor, Vincent}, - year = {2013}, - month = jun, - journal = {Journal of Applied Ecology}, - volume = {50}, - number = {3}, - pages = {786--793}, - issn = {00218901}, - doi = {10.1111/1365-2664.12065}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LI4E2HN4/Journal of Applied Ecology 2013 Oedekoven.pdf;/Users/dill/Zotero/storage/UFPZ9RR5/jpe12065.pdf} -} - -@article{oedekoven_mixed_nodate, - title = {{{MIXED EFFECT MODELS IN DISTANCE SAMPLING}}}, - author = {Oedekoven, Cornelia Sabrina}, - pages = {179}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FVN8UP5S/Oedekoven - MIXED EFFECT MODELS IN DISTANCE SAMPLING.pdf} -} - -@article{oehlert_note_1992, - title = {A {{Note}} on the {{Delta Method}}}, - author = {Oehlert, Gary W.}, - year = {1992}, - month = feb, - journal = {The American Statistician}, - volume = {46}, - number = {1}, - pages = {27}, - issn = {00031305}, - doi = {10.2307/2684406}, - file = {/Users/dill/Zotero/storage/7AYJDHX9/The American Statistician 1992 Oehlert.pdf} -} - -@article{okamura_abundance_2003, - title = {Abundance {{Estimation}} of {{Diving Animals}} by the {{Double-Platform Line Transect Method}}}, - author = {Okamura, Hiroshi and Kitakado, Toshihide and Hiramatsu, Kazuhiko and Mori, Mitsuyo}, - year = {2003}, - journal = {Biometrics}, - volume = {59}, - number = {3}, - pages = {512--520}, - file = {/Users/dill/Zotero/storage/2PVSQZPY/Okamaura et al. 2003.pdf} -} - -@article{okamura_abundance_2012, - title = {Abundance {{Estimation}} of {{Long-Diving Animals Using Line Transect Methods}}}, - author = {Okamura, Hiroshi and Minamikawa, Shingo and Skaug, Hans J. and Kishiro, Toshiya}, - year = {2012}, - month = jun, - journal = {Biometrics}, - volume = {68}, - number = {2}, - pages = {504--513}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2011.01689.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YTM7RT39/Biom 2011 Okamura.pdf} -} - -@article{oppel_comparison_2012, - title = {Comparison of Five Modelling Techniques to Predict the Spatial Distribution and Abundance of Seabirds}, - author = {Oppel, Steffen and Meirinho, Ana and Ram{\'i}rez, Iv{\'a}n and Gardner, Beth and O'Connell, Allan F. and Miller, Peter I. and Louzao, Maite}, - year = {2012}, - month = nov, - journal = {Biological Conservation}, - volume = {156}, - pages = {94--104}, - issn = {00063207}, - doi = {10.1016/j.biocon.2011.11.013}, - langid = {english}, - file = {/Users/dill/Zotero/storage/3GWP29HE/Biological Conservation 2012 Oppel.pdf;/Users/dill/Zotero/storage/ERWUR9KI/Biological Conservation 2011 Oppel.pdf} -} - -@article{opsomer_non-parametric_2008, - title = {Non-Parametric Small Area Estimation Using Penalized Spline Regression}, - author = {Opsomer, Jean D. and Claeskens, Gerda and Ranalli, Maria Giovanna and Kauermann, Goeran and Breidt, F. J.}, - year = {2008}, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {70}, - number = {1}, - pages = {265--286}, - file = {/Users/dill/Zotero/storage/CW4VHYGC/Journal of the Royal Statistical Society Series B (Statistical Methodology) 2008 Opsomer.pdf} -} - -@article{orzack_philosophy_2012, - title = {The Philosophy of Modelling or Does the Philosophy of Biology Have Any Use?}, - author = {Orzack, S. H.}, - year = {2012}, - month = jan, - journal = {Philosophical Transactions of the Royal Society B: Biological Sciences}, - volume = {367}, - number = {1586}, - pages = {170--180}, - issn = {0962-8436, 1471-2970}, - doi = {10.1098/rstb.2011.0265}, - langid = {english}, - file = {/Users/dill/Zotero/storage/XNAFI9AQ/Philosophical Transactions of the Royal Society B Biological Sciences 2011 Orzack.pdf} -} - -@article{otto_size_1990, - title = {Size {{Bias}} in {{Line Transect Sampling}}: {{A Field Test}}}, - shorttitle = {Size {{Bias}} in {{Line Transect Sampling}}}, - author = {Otto, Mark C. and Pollock, Kenneth H.}, - year = {1990}, - month = mar, - journal = {Biometrics}, - volume = {46}, - number = {1}, - pages = {239}, - issn = {0006341X}, - doi = {10.2307/2531648}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Q5FXSNUU/Otto and Pollock - 1990 - Size Bias in Line Transect Sampling A Field Test.pdf} -} - -@article{p._whittle_smoothing_1958, - title = {On the {{Smoothing}} of {{Probability Density Functions}}}, - author = {P. Whittle}, - year = {1958}, - journal = {Journal of the Royal Statistical Society. Series B (Methodological)}, - volume = {20}, - number = {2}, - pages = {334--343}, - file = {/Users/dill/Zotero/storage/9WAPA5U7/P. Whittle - On the Smoothing of Probability Density Functions.pdf} -} - -@article{pace_state-space_2017, - title = {State-Space Mark-Recapture Estimates Reveal a Recent Decline in Abundance of {{North Atlantic}} Right Whales}, - author = {Pace, Richard M. and Corkeron, Peter J. and Kraus, Scott D.}, - year = {2017}, - month = nov, - journal = {Ecology and Evolution}, - volume = {7}, - number = {21}, - pages = {8730--8741}, - issn = {20457758}, - doi = {10.1002/ece3.3406}, - abstract = {North Atlantic right whales (Eubalaena glacialis M\"uller 1776) present an interesting problem for abundance and trend estimation in marine wildlife conservation. They are long lived, individually identifiable, highly mobile, and one of the rarest of cetaceans. Individuals are annually resighted at different rates, primarily due to varying stay durations among several principal habitats within a large geographic range. To date, characterizations of abundance have been produced that use simple accounting procedures with differing assumptions about mortality. To better characterize changing abundance of North Atlantic right whales between 1990 and 2015, we adapted a state\textendash space formulation with Jolly-\-Seber assumptions about population entry (birth and immigration) to individual resighting histories and fit it using empirical Bayes methodology. This hierarchical model included accommodation for the effect of the substantial individual capture heterogeneity. Estimates from this approach were only slightly higher than published accounting procedures, except for the most recent years (when recapture rates had declined substantially). North Atlantic right whales' abundance increased at about 2.8\% per annum from median point estimates of 270 individuals in 1990 to 483 in 2010, and then declined to 2015, when the final estimate was 458 individuals (95\% credible intervals 444\textendash 471). The probability that the population's trajectory post-\-2010 was a decline was estimated at 99.99\%. Of special concern was the finding that reduced survival rates of adult females relative to adult males have produced diverging abundance trends between sexes. Despite constraints in recent years, both biological (whales' distribution changing) and logistical (fewer resources available to collect individual photo-\-identifications), it is still possible to detect this relatively recent, small change in the population's trajectory. This is thanks to the massive dataset of individual North Atlantic right whale identifications accrued over the past three decades. Photo-\-identification data provide biological information that allows more informed inference on the status of this species.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/K2WSZUTV/Pace et al. - 2017 - State-space mark-recapture estimates reveal a rece.pdf} -} - -@article{paciorek_importance_2010, - title = {The {{Importance}} of {{Scale}} for {{Spatial-Confounding Bias}} and {{Precision}} of {{Spatial Regression Estimators}}}, - author = {Paciorek, Christopher J.}, - year = {2010}, - month = feb, - journal = {Statistical Science}, - volume = {25}, - number = {1}, - pages = {107--125}, - issn = {0883-4237}, - doi = {10.1214/10-STS326}, - langid = {english}, - file = {/Users/dill/Zotero/storage/MAID9EJ2/Statist. Sci. 2010 Paciorek.pdf} -} - -@techreport{palka_atlantic_2017, - title = {Atlantic {{Marine Assessment Program}} for {{Protected Species}}: 2010-2014}, - author = {Palka, Debra L. and {Chavez-Rosales}, Sam and Josephson, Beth and Cholewiak, Dani and Haas, Heather L and Garrison, Lance P and Jones, M and Sigourney, Douglas and Waring, G and Jech, M and Broughton, E and Soldevilla, Melissa S and Davis, G and DeAngelis, A and Sasso, C.R. and Winton, M.V. and Smolowitz, R.J. and Fay, G and LeBreque, E and Leiness, J.B. and Dettloff and Warden, M and Murray, K and Orphanides, C}, - year = {2017}, - number = {BOEM 2017-071}, - pages = {211}, - address = {{Washington DC}}, - institution = {{US Dept. of the Interior, Bureau of Ocean Energy Management}}, - file = {/Users/dill/Zotero/storage/LVEZHZMI/5638.pdf} -} - -@article{palmer_small-scale_1990, - title = {Small-Scale Environmental Heterogeneity and the Analysis of Species Distributions along Gradients}, - author = {Palmer, Michael W. and Dixon, Philip M.}, - year = {1990}, - journal = {Journal of Vegetation Science}, - volume = {1}, - number = {1}, - pages = {57--65}, - file = {/Users/dill/Zotero/storage/J6HTGYTW/Journal of Vegetation Science 1990 Palmer.pdf} -} - -@article{papathomas_correspondence_2018, - title = {On the Correspondence from {{Bayesian}} Log-Linear Modelling to Logistic Regression Modelling with g-Priors}, - author = {Papathomas, Michail}, - year = {2018}, - month = mar, - journal = {TEST}, - volume = {27}, - number = {1}, - pages = {197--220}, - issn = {1133-0686, 1863-8260}, - doi = {10.1007/s11749-017-0540-8}, - abstract = {Consider a set of categorical variables where at least one of them is binary. The log-linear model that describes the counts in the resulting contingency table implies a specific logistic regression model, with the binary variable as the outcome. Within the Bayesian framework, the g-prior and mixtures of g-priors are commonly assigned to the parameters of a generalized linear model. We prove that assigning a g-prior (or a mixture of g-priors) to the parameters of a certain log-linear model designates a g-prior (or a mixture of g-priors) on the parameters of the corresponding logistic regression. By deriving an asymptotic result, and with numerical illustrations, we demonstrate that when a g-prior is adopted, this correspondence extends to the posterior distribution of the model parameters. Thus, it is valid to translate inferences from fitting a log-linear model to inferences within the logistic regression framework, with regard to the presence of main effects and interaction terms.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/5HTW5FM8/Papathomas - 2018 - On the correspondence from Bayesian log-linear mod.pdf} -} - -@article{parks_sound_2011, - title = {Sound Production Behavior of Individual {{North Atlantic}} Right Whales: Implications for Passive Acoustic Monitoring}, - shorttitle = {Sound Production Behavior of Individual {{North Atlantic}} Right Whales}, - author = {Parks, Se and Searby, A and C{\'e}l{\'e}rier, A and Johnson, Mp and Nowacek, Dp and Tyack, Pl}, - year = {2011}, - month = oct, - journal = {Endangered Species Research}, - volume = {15}, - number = {1}, - pages = {63--76}, - issn = {1863-5407, 1613-4796}, - doi = {10.3354/esr00368}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QG4UAGDT/Endang. Species. Res. 2011 Parks.pdf} -} - -@article{paxton_density_2009, - title = {Density Surface Fitting to Estimate the Abundance of Humpback Whales Based on the {{NASS-95}} and {{NASS-2001}} Aerial and Shipboard Surveys}, - author = {Paxton, Charles GM and Burt, M. Louise and Hedley, Sharon L. and V{\'i}kingsson, G{\'i}sli A. and Gunnlaugsson, Thorvaldur and Desportes, Genevi{\`e}ve}, - year = {2009}, - journal = {NAMMCO Scientific Publications}, - volume = {7}, - pages = {143--160}, - file = {/Users/dill/Zotero/storage/ANP9MREW/ch09_web.pdf} -} - -@article{paxton_unleashing_2016, - title = {Unleashing the {{Kraken}}: On the Maximum Length in Giant Squid ( {{{\emph{Architeuthis}}}} Sp.)}, - shorttitle = {Unleashing the {{Kraken}}}, - author = {Paxton, C. G. M.}, - year = {2016}, - month = oct, - journal = {Journal of Zoology}, - volume = {300}, - number = {2}, - pages = {82--88}, - issn = {09528369}, - doi = {10.1111/jzo.12347}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6TH6VVR9/Journal of Zoology 2016 Paxton.pdf} -} - -@article{pearce-higgins_greater_2012, - title = {Greater Impacts of Wind Farms on Bird Populations during Construction than Subsequent Operation: Results of a Multi-Site and Multi-Species Analysis: {{{\emph{Changes}}}}{\emph{ in Bird Populations on Wind Farms}}}, - shorttitle = {Greater Impacts of Wind Farms on Bird Populations during Construction than Subsequent Operation}, - author = {{Pearce-Higgins}, James W. and Stephen, Leigh and Douse, Andy and Langston, Rowena H. W.}, - year = {2012}, - month = apr, - journal = {Journal of Applied Ecology}, - volume = {49}, - number = {2}, - pages = {386--394}, - issn = {00218901}, - doi = {10.1111/j.1365-2664.2012.02110.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QCG8JZGK/Journal of Applied Ecology 2012 Pearce-Higgins.pdf} -} - -@article{pedersen_hierarchical_2019, - title = {Hierarchical Generalized Additive Models in Ecology: An Introduction with Mgcv}, - shorttitle = {Hierarchical Generalized Additive Models in Ecology}, - author = {Pedersen, Eric J. and Miller, David L. and Simpson, Gavin L. and Ross, Noam}, - year = {2019}, - month = may, - journal = {PeerJ}, - volume = {7}, - pages = {e6876}, - issn = {2167-8359}, - doi = {10.7717/peerj.6876}, - abstract = {In this paper, we discuss an extension to two popular approaches to modeling complex structures in ecological data: the generalized additive model (GAM) and the hierarchical model (HGLM). The hierarchical GAM (HGAM), allows modeling of nonlinear functional relationships between covariates and outcomes where the shape of the function itself varies between different grouping levels. We describe the theoretical connection between HGAMs, HGLMs, and GAMs, explain how to model different assumptions about the degree of intergroup variability in functional response, and show how HGAMs can be readily fitted using existing GAM software, the mgcv package in R. We also discuss computational and statistical issues with fitting these models, and demonstrate how to fit HGAMs on example data. All code and data used to generate this paper are available at: github.com/eric-pedersen/ mixed-effect-gams.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/F8R7RXIW/Pedersen et al. - 2019 - Hierarchical generalized additive models in ecolog.pdf} -} - -@article{peel_designing_2015, - title = {Designing an Effective Mark\textendash Recapture Study of {{Antarctic}} Blue Whales}, - author = {Peel, David and Bravington, Mark and Kelly, Natalie and Double, Michael C.}, - year = {2015}, - journal = {Ecological applications}, - volume = {25}, - number = {4}, - pages = {1003--1015}, - file = {/Users/dill/Zotero/storage/LWG88VNX/Ecol Appl 2015 Peel.pdf} -} - -@article{peel_model-based_2013, - title = {A {{Model-Based Approach}} to {{Designing}} a {{Fishery-Independent Survey}}}, - author = {Peel, D. and Bravington, M. V. and Kelly, N. and Wood, S. N. and Knuckey, I.}, - year = {2013}, - month = mar, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {18}, - number = {1}, - pages = {1--21}, - issn = {1085-7117, 1537-2693}, - doi = {10.1007/s13253-012-0114-x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JGR9ZBR7/JABES 2012 Peel.pdf} -} - -@book{perrin_encyclopedia_2002, - title = {Encyclopedia of {{Marine Mammals}}}, - author = {Perrin, W.F. and Perrin, W.F. and {ed}, B.W. and W{\"u}rsig, B. and Thewissen, J.G.M. and Gale (Firm), Thomson}, - year = {2002}, - series = {Gale Virtual Reference Library}, - publisher = {{Elsevier Science}}, - isbn = {978-0-12-551340-1}, - lccn = {2001087128} -} - -@incollection{pfahringer_automating_2015, - title = {Automating {{Marine Mammal Detection}} in {{Aerial Images Captured During Wildlife Surveys}}: {{A Deep Learning Approach}}}, - shorttitle = {Automating {{Marine Mammal Detection}} in {{Aerial Images Captured During Wildlife Surveys}}}, - booktitle = {{{AI}} 2015: {{Advances}} in {{Artificial Intelligence}}}, - author = {Maire, Frederic and Alvarez, Luis Mejias and Hodgson, Amanda}, - editor = {Pfahringer, Bernhard and Renz, Jochen}, - year = {2015}, - volume = {9457}, - pages = {379--385}, - publisher = {{Springer International Publishing}}, - address = {{Cham}}, - doi = {10.1007/978-3-319-26350-2_33}, - abstract = {Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80 \% and improve precision to 27 \% by using DCNNs as the core approach.}, - isbn = {978-3-319-26349-6 978-3-319-26350-2}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UL3SXEQN/Maire et al. - 2015 - Automating Marine Mammal Detection in Aerial Image.pdf} -} - -@article{phillips_maximum_2006, - title = {Maximum Entropy Modeling of Species Geographic Distributions}, - author = {Phillips, Steven J. and Anderson, Robert P. and Schapire, Robert E.}, - year = {2006}, - month = jan, - journal = {Ecological Modelling}, - volume = {190}, - number = {3-4}, - pages = {231--259}, - issn = {03043800}, - doi = {10.1016/j.ecolmodel.2005.03.026}, - langid = {english}, - file = {/Users/dill/Zotero/storage/E55Z4CM6/Ecological Modelling 2006 Phillips.pdf} -} - -@article{pledger_performance_2005, - title = {The Performance of Mixture Models in Heterogeneous Closed Population Capture\textendash Recapture}, - author = {Pledger, Shirley}, - year = {2005}, - journal = {Biometrics}, - volume = {61}, - number = {3}, - pages = {868--873}, - file = {/Users/dill/Zotero/storage/N2H9YETE/Biom 2005 Pledger.pdf} -} - -@inproceedings{plummer_jags_2003, - title = {{{JAGS}}: {{A}} Program for Analysis of {{Bayesian}} Graphical Models Using {{Gibbs}} Sampling}, - booktitle = {Proceedings of the 3rd {{International Workshop}} on {{Distributed Statistical Computing}} ({{DSC}} 2003)}, - author = {Plummer, Martyn}, - year = {2003}, - pages = {8}, - address = {{Vienna, Austria}}, - abstract = {JAGS is a program for Bayesian Graphical modelling which aims for compatibility with Classic BUGS. The program could eventually be developed as an R package. This article explains the motivations for this program, briefly describes the architecture and then discusses some ideas for a vectorized form of the BUGS language.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/RFSGBPF3/Plummer - 2003 - JAGS A program for analysis of Bayesian graphical.pdf} -} - -@article{pohle_selecting_2017, - title = {Selecting the {{Number}} of {{States}} in {{Hidden Markov Models-Pitfalls}}, {{Practical Challenges}} and {{Pragmatic Solutions}}}, - author = {Pohle, Jennifer and Langrock, Roland and {van Beest}, Floris and Schmidt, Niels Martin}, - year = {2017}, - journal = {arXiv preprint arXiv:1701.08673}, - eprint = {1701.08673}, - eprinttype = {arxiv}, - archiveprefix = {arXiv}, - file = {/Users/dill/Zotero/storage/RGXQHVNK/arXiv 2017 Pohle.pdf} -} - -@article{pollock_estimating_2006, - title = {Estimating {{Animal Abundance}} in {{Heterogeneous Environments}}: {{An Application}} To ...}, - author = {Pollock, Kenneth H and Marsh, Helene D and Lawler, Ivan R and Alldredge, Mathew W}, - year = {2006}, - journal = {Journal of Wildlife Management}, - volume = {70}, - number = {1}, - pages = {255}, - langid = {english}, - file = {/Users/dill/Zotero/storage/GKCU6RVU/Pollock et al. - Estimating Animal Abundance in Heterogeneous Envir.pdf} -} - -@article{pollock_understanding_2014, - title = {Understanding Co-Occurrence by Modelling Species Simultaneously with a {{Joint Species Distribution Model}} ({{JSDM}})}, - author = {Pollock, Laura J. and Tingley, Reid and Morris, William K. and Golding, Nick and O'Hara, Robert B. and Parris, Kirsten M. and Vesk, Peter A. and McCarthy, Michael A.}, - editor = {McPherson, Jana}, - year = {2014}, - month = may, - journal = {Methods in Ecology and Evolution}, - volume = {5}, - number = {5}, - pages = {397--406}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12180}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9Q8GCHLM/Methods in Ecology and Evolution 2014 Pollock.pdf} -} - -@article{porter_communities_2009, - title = {Communities in Networks}, - author = {Porter, Mason A. and Onnela, Jukka-Pekka and Mucha, Peter J.}, - year = {2009}, - journal = {Notices of the AMS}, - volume = {56}, - number = {9}, - pages = {1082--1097}, - file = {/Users/dill/Zotero/storage/5MW4YI2L/arXiv 2009 Porter.pdf} -} - -@article{powell_approximating_2007, - title = {Approximating Variance of Demographic Parameters Using the Delta Method: A Reference for Avian Biologists}, - author = {Powell, Larkin A.}, - year = {2007}, - journal = {The Condor}, - volume = {109}, - pages = {949--954}, - file = {/Users/dill/Zotero/storage/TAQ534NG/Powell2007.pdf} -} - -@article{prendergast_correcting_1993, - title = {Correcting for {{Variation}} in {{Recording Effort}} in {{Analyses}} of {{Diversity Hotspots}}}, - author = {Prendergast, J. R. and Wood, S. N. and Lawton, J. H. and Eversham, B. C.}, - year = {1993}, - month = mar, - journal = {Biodiversity Letters}, - volume = {1}, - number = {2}, - pages = {39}, - issn = {09679952}, - doi = {10.2307/2999649}, - file = {/Users/dill/Zotero/storage/SZ3IDCRX/Biodiversity Letters 1993 Prendergast.pdf} -} - -@article{prietogonzalez_estimation_2017, - title = {Estimation Bias under Model Selection for Distance Sampling Detection Functions}, - author = {Prieto~Gonzalez, Rocio and Thomas, Len and Marques, Tiago A.}, - year = {2017}, - month = sep, - journal = {Environmental and Ecological Statistics}, - volume = {24}, - number = {3}, - pages = {399--414}, - issn = {1352-8505, 1573-3009}, - doi = {10.1007/s10651-017-0376-0}, - langid = {english}, - file = {/Users/dill/Zotero/storage/IYPWJEXA/art%3A10.1007%2Fs10651-017-0376-0.pdf} -} - -@article{pya_note_2016, - title = {A Note on Basis Dimension Selection in Generalized Additive Modelling}, - author = {Pya, Natalya and Wood, Simon N.}, - year = {2016}, - journal = {arXiv preprint arXiv:1602.06696}, - eprint = {1602.06696}, - eprinttype = {arxiv}, - archiveprefix = {arXiv}, - file = {/Users/dill/Zotero/storage/4PTIH4K3/1602.06696v1.pdf} -} - -@article{pya_shape_2015, - title = {Shape Constrained Additive Models}, - author = {Pya, Natalya and Wood, Simon N.}, - year = {2015}, - month = may, - journal = {Statistics and Computing}, - volume = {25}, - number = {3}, - pages = {543--559}, - issn = {0960-3174, 1573-1375}, - doi = {10.1007/s11222-013-9448-7}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Z8FL42BX/art%3A10.1007%2Fs11222-013-9448-7.pdf} -} - -@article{quintana_nonparametric_2004, - title = {Nonparametric {{Bayesian Assessment}} of the {{Order}} of {{Dependence}} for {{Binary Sequences}}}, - author = {Quintana, Fernando A and M{\"u}ller, Peter}, - year = {2004}, - month = mar, - journal = {Journal of Computational and Graphical Statistics}, - volume = {13}, - number = {1}, - pages = {213--231}, - issn = {1061-8600, 1537-2715}, - doi = {10.1198/1061860042949}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UXRETZCC/Journal of Computational and Graphical Statistics 2004 Quintana.pdf} -} - -@inproceedings{ramsay_spatial_2011, - title = {Spatial Spline Regression Models for Data Distributed over Irregularly Shaped Regions}, - booktitle = {Proceedings of {{S}}. {{Co}}. 2011 {{Conference}}. {{Available}} at {{http://sco2011.}} Stat. Unipd. It}, - author = {Ramsay, James O. and Ramsay, Timothy O. and Sangalli, Laura M.}, - year = {2011}, - file = {/Users/dill/Zotero/storage/PIQ56R5A/2011 Ramsay.pdf} -} - -@book{ramsay2013functional, - title = {Functional Data Analysis}, - author = {Ramsay, J. and Silverman, B.W.}, - year = {2013}, - series = {Springer Series in Statistics}, - publisher = {{Springer New York}}, - isbn = {978-1-4757-7107-7}, - lccn = {96054729} -} - -@article{rapacciuolo_temporal_2014, - title = {Temporal Validation Plots: Quantifying How Well Correlative Species Distribution Models Predict Species' Range Changes over Time}, - shorttitle = {Temporal Validation Plots}, - author = {Rapacciuolo, Giovanni and Roy, David B. and Gillings, Simon and Purvis, Andy}, - editor = {McPherson, Jana}, - year = {2014}, - month = may, - journal = {Methods in Ecology and Evolution}, - volume = {5}, - number = {5}, - pages = {407--420}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12181}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6S6E86IZ/Methods in Ecology and Evolution 2014 Rapacciuolo.pdf} -} - -@book{rasmussen_gaussian_2006, - title = {Gaussian {{Processes}} for {{Machine Learning}}}, - author = {Rasmussen, Carl Edward and Williams, Christopher K. I.}, - year = {2006}, - publisher = {{MIT Press}}, - file = {/Users/dill/Zotero/storage/MILI3HWQ/Rasmussen and Williams - 2006 - Gaussian Processes for Machine Learning.pdf} -} - -@article{redfern_absence_2008, - title = {Absence of Scale Dependence in Dolphin-Habitat Models for the Eastern Tropical {{Pacific Ocean}}}, - author = {Redfern, Jv and Barlow, J and Ballance, Lt and Gerrodette, T and Becker, Ea}, - year = {2008}, - month = jul, - journal = {Marine Ecology Progress Series}, - volume = {363}, - pages = {1--14}, - issn = {0171-8630, 1616-1599}, - doi = {10.3354/meps07495}, - langid = {english}, - file = {/Users/dill/Zotero/storage/C8EGLGEP/Mar. Ecol. Prog. Ser. 2008 Redfern.pdf} -} - -@article{redfern_assessing_2013, - title = {Assessing the {{Risk}} of {{Ships Striking Large Whales}} in {{Marine Spatial Planning}}: {{Assessing Ship-Strike Risk}}}, - shorttitle = {Assessing the {{Risk}} of {{Ships Striking Large Whales}} in {{Marine Spatial Planning}}}, - author = {Redfern, J. V. and Mckenna, M. F. and Moore, T. J. and Calambokidis, J. and Deangelis, M. L. and Becker, E. A. and Barlow, J. and Forney, K. A. and Fiedler, P. C. and Chivers, S. J.}, - year = {2013}, - month = apr, - journal = {Conservation Biology}, - volume = {27}, - number = {2}, - pages = {292--302}, - issn = {08888892}, - doi = {10.1111/cobi.12029}, - abstract = {Marine spatial planning provides a comprehensive framework for managing multiple uses of the marine environment and has the potential to minimize environmental impacts and reduce conflicts among users. Spatially explicit assessments of the risks to key marine species from human activities are a requirement of marine spatial planning. We assessed the risk of ships striking humpback (Megaptera novaeangliae), blue (Balaenoptera musculus), and fin (Balaenoptera physalus) whales in alternative shipping routes derived from patterns of shipping traffic off Southern California (U.S.A.). Specifically, we developed whale-habitat models and assumed ship-strike risk for the alternative shipping routes was proportional to the number of whales predicted by the models to occur within each route. This definition of risk assumes all ships travel within a single route. We also calculated risk assuming ships travel via multiple routes. We estimated the potential for conflict between shipping and other uses (military training and fishing) due to overlap with the routes. We also estimated the overlap between shipping routes and protected areas. The route with the lowest risk for humpback whales had the highest risk for fin whales and vice versa. Risk to both species may be ameliorated by creating a new route south of the northern Channel Islands and spreading traffic between this new route and the existing route in the Santa Barbara Channel. Creating a longer route may reduce the overlap between shipping and other uses by concentrating shipping traffic. Blue whales are distributed more evenly across our study area than humpback and fin whales; thus, risk could not be ameliorated by concentrating shipping traffic in any of the routes we considered. Reducing ship-strike risk for blue whales may be necessary because our estimate of the potential number of strikes suggests that they are likely to exceed allowable levels of anthropogenic impacts established under U.S. laws.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/AB8ZECES/Redfern et al. - 2013 - Assessing the Risk of Ships Striking Large Whales .pdf} -} - -@article{redfern_evaluating_2019, - title = {Evaluating Stakeholder-derived Strategies to Reduce the Risk of Ships Striking Whales}, - author = {Redfern, Jessica V. and Moore, Thomas J. and Becker, Elizabeth A. and Calambokidis, John and Hastings, Sean P. and Irvine, Ladd M. and Mate, Bruce R. and Palacios, Daniel M.}, - editor = {Hawkes, Lucy}, - year = {2019}, - month = oct, - journal = {Diversity and Distributions}, - volume = {25}, - number = {10}, - pages = {1575--1585}, - issn = {1366-9516, 1472-4642}, - doi = {10.1111/ddi.12958}, - abstract = {Aim: Ship strikes are one of the largest sources of human-caused mortality for baleen whales on the West Coast of the United States. Reducing ship-strike risk in this region is complicated by changes in ship traffic that resulted from air pollution regulations and economic factors. A diverse group of stakeholders was convened to develop strategies to reduce ship-strike risk in the Southern California Bight. Strategies proposed by some stakeholders included: (a) adding a shipping route; (b) expanding the existing area to be avoided (ATBA); and (c) reducing ship speeds.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JHL7U9FM/Redfern et al. - 2019 - Evaluating stakeholder‐derived strategies to reduc.pdf} -} - -@article{redfern_techniques_2006, - title = {Techniques for Cetacean\textendash Habitat Modeling}, - author = {Redfern, J. V. and Ferguson, M. C. and Becker, E. A. and Hyrenbach, K. D. and Good, C. and Barlow, Jay and Kaschner, K. and Baumgartner, Mark F. and Forney, K. A. and Ballance, L. T.}, - year = {2006}, - journal = {Marine Ecology Progress Series}, - volume = {310}, - pages = {271--295}, - file = {/Users/dill/Zotero/storage/AS47WV6E/Mar. Ecol. Prog. Ser. 2006 Redfern.pdf} -} - -@article{reimherr_comments_2019, - title = {Comments on: {{Modular}} Regression\textemdash a {{Lego}} System for Building Structured Additive Distributional Regression Models with Tensor Product Interactions}, - shorttitle = {Comments On}, - author = {Reimherr, Matthew}, - year = {2019}, - month = mar, - journal = {TEST}, - volume = {28}, - number = {1}, - pages = {43--45}, - issn = {1133-0686, 1863-8260}, - doi = {10.1007/s11749-019-00635-9}, - abstract = {This note considers several technical points concerning the work of Kneib, Klein, Lang, and Umlauf. These points are related to the use of basis functions versus function spaces, types of covariates, and model identifiability.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/L3MTT7W9/Reimherr - 2019 - Comments on Modular regression—a Lego system for .pdf} -} - -@article{reinsch_smoothing_1967, - title = {Smoothing by Spline Functions}, - author = {Reinsch, Christian H.}, - year = {1967}, - month = oct, - journal = {Numerische Mathematik}, - volume = {10}, - number = {3}, - pages = {177--183}, - issn = {0029-599X, 0945-3245}, - doi = {10.1007/BF02162161}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QC2MC2ZJ/Reinsch - 1967 - Smoothing by spline functions.pdf} -} - -@article{reiss_penalized_2017, - title = {Penalized {{Nonparametric Scalar-on-Function Regression}} via {{Principal Coordinates}}}, - author = {Reiss, Philip T. and Miller, David L. and Wu, Pei-Shien and Hua, Wen-Yu}, - year = {2017}, - month = jul, - journal = {Journal of Computational and Graphical Statistics}, - volume = {26}, - number = {3}, - pages = {569--578}, - issn = {1061-8600, 1537-2715}, - doi = {10.1080/10618600.2016.1217227}, - langid = {english}, - file = {/Users/dill/Zotero/storage/HXQCGIU2/Journal of Computational and Graphical Statistics 2016 Reiss.pdf} -} - -@article{reiss_smoothing_2009, - title = {Smoothing Parameter Selection for a Class of Semiparametric Linear Models}, - author = {Reiss, Philip T. and Ogden, Todd R.}, - year = {2009}, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {71}, - number = {2}, - pages = {505--523}, - file = {/Users/dill/Zotero/storage/Z7FGUBQQ/Journal of the Royal Statistical Society Series B (Statistical Methodology) 2009 Reiss.pdf} -} - -@article{renner_equivalence_2013, - title = {Equivalence of {{MAXENT}} and {{Poisson Point Process Models}} for {{Species Distribution Modeling}} in {{Ecology}}}, - shorttitle = {Equivalence of {{MAXENT}} and {{Poisson Point Process Models}} for {{Species Distribution Modeling}} in {{Ecology}}}, - author = {Renner, Ian W. and Warton, David I.}, - year = {2013}, - month = mar, - journal = {Biometrics}, - volume = {69}, - number = {1}, - pages = {274--281}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2012.01824.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/I9RKN9E6/Biom 2013 Renner.pdf} -} - -@article{rexstad_non-uniform_2007, - title = {Non-Uniform Coverage Estimators for Distance Sampling.}, - author = {Rexstad, Eric}, - year = {2007}, - file = {/Users/dill/Zotero/storage/DIT2TMTC/1154520.pdf} -} - -@article{rexstad_questionable_1988, - title = {Questionable {{Multivariate Statistical Inference}} in {{Wildlife Habitat}} and {{Community Studies}}}, - author = {Rexstad, Eric A. and Miller, Dirk D. and Flather, Curtis H. and Anderson, Eric M. and Hupp, Jerry W. and Anderson, David R.}, - year = {1988}, - month = oct, - journal = {The Journal of Wildlife Management}, - volume = {52}, - number = {4}, - pages = {794}, - issn = {0022541X}, - doi = {10.2307/3800948}, - abstract = {We analyzed a data set constructedfrom functionally unrelated, easily collected observations (e.g., meat, stock, and liquor prices) around Fort Collins, Colorado, using principal components analysis (PCA),canonicalcorrelationanalysis(CC), and discriminantfunction analysis(DFA). Each producedseemingly significantresultsand suggestedstrongrelationshipsbetween the variablesmeasured.We suggestthat multivariatetechniquescan provideinvalid inferenceswhen used with data containingno relationships.We questionthe use of these techniquesin studiesof wildlife habitat.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/PLS5YJU9/Rexstad et al. - 1988 - Questionable Multivariate Statistical Inference in.pdf} -} - -@article{rexstad_questionable_1990, - title = {Questionable {{Multivariate Statistical Inference}} in {{Wildlife Habitat}} and {{Community Studies}}: {{A Reply}}}, - shorttitle = {Questionable {{Multivariate Statistical Inference}} in {{Wildlife Habitat}} and {{Community Studies}}}, - author = {Rexstad, Eric A. and Miller, Dirk D. and Flather, Curtis H. and Anderson, Eric M. and Hupp, Jerry W. and Anderson, David R.}, - year = {1990}, - month = jan, - journal = {The Journal of Wildlife Management}, - volume = {54}, - number = {1}, - pages = {189}, - issn = {0022541X}, - doi = {10.2307/3808921}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QLETUEGZ/Rexstad et al. - 1990 - Questionable Multivariate Statistical Inference in.pdf} -} - -@article{ridgway_line_2010, - title = {Line Transect Distance Sampling in Aerial Surveys for Double-Crested Cormorants in Coastal Regions of {{Lake Huron}}}, - author = {Ridgway, Mark S.}, - year = {2010}, - month = sep, - journal = {Journal of Great Lakes Research}, - volume = {36}, - number = {3}, - pages = {403--410}, - issn = {03801330}, - doi = {10.1016/j.jglr.2010.06.003}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6FXLZ9GT/Journal of Great Lakes Research 2010 Ridgway.pdf} -} - -@article{rigby_generalized_2005, - title = {Generalized Additive Models for Location, Scale and Shape}, - author = {Rigby, R. A. and Stasinopoulos, D. M.}, - year = {2005}, - journal = {Applied Statistics}, - volume = {54}, - number = {3}, - pages = {507--554}, - file = {/Users/dill/Zotero/storage/HIKE94HT/Journal of the Royal Statistical Society Series C (Applied Statistics) 2005 Rigby.pdf} -} - -@article{robert_metropolishastings_nodate, - title = {The {{Metropolis}}\textendash{{Hastings}} Algorithm}, - author = {Robert, Christian P.}, - file = {/Users/dill/Zotero/storage/ENLPNBQF/arXiv 2015 Robert.pdf} -} - -@article{roberts_cross-validation_2017, - title = {Cross-Validation Strategies for Data with Temporal, Spatial, Hierarchical, or Phylogenetic Structure}, - author = {Roberts, David R. and Bahn, Volker and Ciuti, Simone and Boyce, Mark S. and Elith, Jane and {Guillera-Arroita}, Gurutzeta and Hauenstein, Severin and {Lahoz-Monfort}, Jos{\'e} J. and Schr{\"o}der, Boris and Thuiller, Wilfried and Warton, David I. and Wintle, Brendan A. and Hartig, Florian and Dormann, Carsten F.}, - year = {2017}, - month = aug, - journal = {Ecography}, - volume = {40}, - number = {8}, - pages = {913--929}, - issn = {09067590}, - doi = {10.1111/ecog.02881}, - langid = {english}, - file = {/Users/dill/Zotero/storage/6QFITKAB/ecog2881.pdf} -} - -@article{roberts_habitat-based_2016, - title = {Habitat-Based Cetacean Density Models for the {{U}}.{{S}}. {{Atlantic}} and {{Gulf}} of {{Mexico}}}, - author = {Roberts, Jason J. and Best, Benjamin D. and Mannocci, Laura and Fujioka, Ei and Halpin, Patrick N. and Palka, Debra L. and Garrison, Lance P. and Mullin, Keith D. and Cole, Timothy V. N. and Khan, Christin B. and McLellan, William A. and Pabst, D. Ann and Lockhart, Gwen G.}, - year = {2016}, - month = sep, - journal = {Scientific Reports}, - volume = {6}, - number = {1}, - issn = {2045-2322}, - doi = {10.1038/srep22615}, - langid = {english}, - file = {/Users/dill/Zotero/storage/K9IFF4Y7/Sci. Rep. 2016 Roberts.pdf} -} - -@article{robertson_behavioral_2016, - title = {Behavioral Responses Affect Distribution Analyses of Bowhead Whales in the Vicinity of Seismic Operations}, - author = {Robertson, Fc and Koski, Wr and Trites, Aw}, - year = {2016}, - month = may, - journal = {Marine Ecology Progress Series}, - volume = {549}, - pages = {243--262}, - issn = {0171-8630, 1616-1599}, - doi = {10.3354/meps11665}, - langid = {english}, - file = {/Users/dill/Zotero/storage/SW4RXF2T/Mar. Ecol. Prog. Ser. 2016 Robertson.pdf} -} - -@article{robertson_correction_2015, - title = {Correction Factors Account for the Availability of Bowhead Whales Exposed to Seismic Operations in the {{Beaufort Sea}}}, - author = {Robertson, Frances C and Koski, William R and Brandon, John R and Thomas, Tannis A and Trites, Andrew W}, - year = {2015}, - pages = {10}, - abstract = {The accuracy of estimates of cetacean density from line-transect survey data depends in large part on how visible the target species is to the observer. Behavioural data (i.e. surface and dive times) from government- and industry-funded aerial observation programmes (1980\textendash 2000) were used to calculate availability correction factors needed to estimate the number of bowhead whales (Balaena mysticetus) from aerial survey sighting data. Correction factors were calculated for bowhead whales exposed and not exposed to seismic operations. Travelling non-calf whales were found to be less likely to be available for detection than other whales, and their availability further declined in the presence of seismic operations. Noncalves were also less available to observers during autumn when exposed to seismic operations than when not exposed, regardless of activity (travelling or otherwise). Such differences in availability appear to reflect behavioural responses to the sound of seismic operations that alters the surfacing and diving patterns of bowhead whales. Localised abundance estimated from aerial surveys may range from 3\% to as much as 63\% higher in areas ensonified by seismic operations if correction factors are applied to account for differences in availability associated with the presence of seismic operations, compared to abundance estimates derived from assessments that only account for changes in availability of undisturbed whales. These results provide the first empirical estimates of availability for bowhead whales exposed to seismic operations and highlight the implications of not correcting for disturbance-related availability in density assessments in the vicinity of seismic operations.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/IM2UEAMP/Robertson et al. - 2015 - Correction factors account for the availability of.pdf} -} - -@article{robertson_effects_2014, - title = {Effects of Seismic Operations on Bowhead Whale Behaviour: {{Implications}} for Distribution and Abundance Assessments}, - shorttitle = {Effects of Seismic Operations on Bowhead Whale Behaviour}, - author = {Robertson, Frances Charlotte}, - year = {2014}, - file = {/Users/dill/Zotero/storage/25DD2LK4/ubc_2014_september_robertson_frances.pdf} -} - -@article{robinson_that_1991, - title = {That {{BLUP}} Is a Good Thing: The Estimation of Random Effects}, - author = {Robinson, G. K.}, - year = {1991}, - journal = {Statistical Science}, - volume = {6}, - number = {1}, - pages = {15--51}, - file = {/Users/dill/Zotero/storage/AEJUN4F5/euclid.ss.1177011926.pdf} -} - -@article{rocha_jr._emptying_2015, - title = {Emptying the {{Oceans}}: {{A Summary}} of {{Industrial Whaling Catches}} in the 20th {{Century}}}, - shorttitle = {Emptying the {{Oceans}}}, - author = {Rocha, Jr., Robert C. and Clapham, Phillip J. and Ivashchenko, Yulia}, - year = {2015}, - month = mar, - journal = {Marine Fisheries Review}, - volume = {76}, - number = {4}, - pages = {37--48}, - issn = {00901830}, - doi = {10.7755/MFR.76.4.3}, - file = {/Users/dill/Zotero/storage/MDQHXUJI/MFR 2015 Rocha Jr.pdf} -} - -@article{romer_spurious_1986, - title = {Spurious Volatility in Historical Unemployment Data}, - author = {Romer, Christina}, - year = {1986}, - journal = {Journal of Political Economy}, - volume = {94}, - number = {1}, - pages = {1--37}, - file = {/Users/dill/Zotero/storage/B3JPDSWZ/Journal of Political Economy 1986 Romer.pdf} -} - -@article{rone_using_2012, - title = {Using Air-Deployed Passive Sonobuoys to Detect and Locate Critically Endangered {{North Pacific}} Right Whales}, - author = {Rone, Brenda K. and Berchok, Catherine L. and Crance, Jessica L. and Clapham, Phillip J.}, - year = {2012}, - month = oct, - journal = {Marine Mammal Science}, - volume = {28}, - number = {4}, - pages = {E528-E538}, - issn = {08240469}, - doi = {10.1111/j.1748-7692.2012.00573.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/49L6Z7EW/Marine Mammal Science 2012 Rone.pdf} -} - -@article{royle_modeling_2004, - title = {Modeling Abundance Effects in Distance Sampling}, - author = {Royle, J. Andrew and Dawson, Deanna K. and Bates, Scott}, - year = {2004}, - journal = {Ecology}, - volume = {85}, - number = {6}, - pages = {1591--1597}, - file = {/Users/dill/Zotero/storage/FC5TT36Q/Ecology 2004 Royle.pdf} -} - -@book{royle2008hierarchical, - title = {Hierarchical Modeling and Inference in Ecology: {{The}} Analysis of Data from Populations, Metapopulations and Communities}, - author = {Royle, J.A. and Dorazio, R.M.}, - year = {2008}, - publisher = {{Elsevier Science}}, - isbn = {978-0-08-055925-4} -} - -@article{rubin_bayesian_1981, - title = {The {{Bayesian Bootstrap}}}, - author = {Rubin, D}, - year = {1981}, - journal = {The Annals of Statistics}, - volume = {9}, - number = {1}, - pages = {130--134}, - file = {/Users/dill/Zotero/storage/E9N7D7HV/euclid.aos.1176345338.pdf} -} - -@article{rubolini_intraspecific_2007, - title = {Intraspecific Consistency and Geographic Variability in Temporal Trends of Spring Migration Phenology among {{European}} Bird Species}, - author = {Rubolini, D and M{\o}ller, Ap and Rainio, K and Lehikoinen, E}, - year = {2007}, - month = dec, - journal = {Climate Research}, - volume = {35}, - pages = {135--146}, - issn = {0936-577X, 1616-1572}, - doi = {10.3354/cr00720}, - langid = {english}, - file = {/Users/dill/Zotero/storage/62J2AZTI/Clim. Res. 2007 Rubolini.pdf} -} - -@article{rue_approximate_2009, - title = {Approximate {{Bayesian}} Inference for Latent {{Gaussian}} Models by Using Integrated Nested {{Laplace}} Approximations}, - author = {Rue, H{\aa}vard and Martino, Sara and Chopin, Nicolas}, - year = {2009}, - month = apr, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {71}, - number = {2}, - pages = {319--392}, - issn = {13697412, 14679868}, - doi = {10.1111/j.1467-9868.2008.00700.x}, - abstract = {Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/S3V2K68H/Rue et al. - 2009 - Approximate Bayesian inference for latent Gaussian.pdf} -} - -@article{rue_fitting_2002, - title = {Fitting {{Gaussian Markov Random Fields}} to {{Gaussian Fields}}}, - author = {Rue, Havard and Tjelmeland, Hakon}, - year = {2002}, - month = mar, - journal = {Scandinavian Journal of Statistics}, - volume = {29}, - number = {1}, - pages = {31--49}, - issn = {0303-6898, 1467-9469}, - doi = {10.1111/1467-9469.00058}, - abstract = {This paper discusses the following task often encountered in building Bayesian spatial models: construct a homogeneous Gaussian Markov random \textregistered eld (GMRF) on a lattice with correlation properties either as present in some observed data, or consistent with prior knowledge. The Markov property is essential in designing computationally ef\textregistered cient Markov chain Monte Carlo algorithms to analyse such models. We argue that we can restate both tasks as that of \textregistered tting a GMRF to a prescribed stationary Gaussian \textregistered eld on a lattice when both local and global properties are important. We demonstrate that using the Kullback{$\pm$}Leibler discrepancy often fails for this task, giving severely undesirable behaviour of the correlation function for lags outside the neighbourhood. We propose a new criterion that resolves this dif\textregistered culty, and demonstrate that GMRFs with small neighbourhoods can approximate Gaussian \textregistered elds surprisingly well even with long correlation lengths. Finally, we discuss implications of our \textregistered ndings for likelihood based inference for general Markov random \textregistered elds when global properties are also important.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/J3A8VCQN/Rue and Tjelmeland - 2002 - Fitting Gaussian Markov Random Fields to Gaussian .pdf} -} - -@book{rue_gaussian_2005, - title = {Gaussian {{Markov Random Fields}}: {{Theory}} and {{Applications}}}, - author = {Rue, H. and Held, L.}, - year = {2005}, - series = {Monographs on {{Statistics}} and {{Applied Probability}}}, - volume = {104}, - publisher = {{Chapman \& Hall}}, - address = {{London}} -} - -@book{ruppert_semiparametric_2003, - title = {Semiparametric {{Regression}}}, - author = {Ruppert, D. and Wand, M.P. and Carroll, R.J.}, - year = {2003}, - series = {Cambridge {{Series}} in {{Statistical}} and {{Probabilistic Mathematics}}}, - publisher = {{Cambridge University Press}}, - isbn = {978-0-521-78516-7}, - lccn = {2002041460} -} - -@article{ruppert_semiparametric_2009, - title = {Semiparametric Regression during 2003\textendash 2007}, - author = {Ruppert, David and Wand, M.P. and Carroll, Raymond J.}, - year = {2009}, - journal = {Electronic Journal of Statistics}, - volume = {3}, - number = {0}, - pages = {1193--1256}, - issn = {1935-7524}, - doi = {10.1214/09-EJS525}, - langid = {english}, - file = {/Users/dill/Zotero/storage/N4YXGUCR/Electron. J. Statist. 2009 Ruppert.pdf} -} - -@article{russell_avoidance_2016, - title = {Avoidance of Wind Farms by Harbour Seals Is Limited to Pile Driving Activities}, - author = {Russell, Debbie J.F. and Hastie, Gordon D. and Thompson, David and Janik, Vincent M. and Hammond, Philip S. and {Scott-Hayward}, Lindesay A.S. and Matthiopoulos, Jason and Jones, Esther L. and McConnell, Bernie J.}, - editor = {Votier, Steve}, - year = {2016}, - month = dec, - journal = {Journal of Applied Ecology}, - volume = {53}, - number = {6}, - pages = {1642--1652}, - issn = {00218901}, - doi = {10.1111/1365-2664.12678}, - langid = {english}, - file = {/Users/dill/Zotero/storage/E9VVETJD/Russell et al. - 2016 - Avoidance of wind farms by harbour seals is limite.pdf} -} - -@article{sampson_nonparametric_1992, - title = {Nonparametric {{Estimation}} of {{Nonstationary Spatial Covariance Structure}}}, - author = {Sampson, Paul D. and Guttorp, Peter}, - year = {1992}, - month = mar, - journal = {Journal of the American Statistical Association}, - volume = {87}, - number = {417}, - pages = {108}, - issn = {01621459}, - doi = {10.2307/2290458}, - file = {/Users/dill/Zotero/storage/MED54RVC/Journal of the American Statistical Association 1992 Sampson.pdf} -} - -@article{sangalli_spatial_2013, - title = {Spatial Spline Regression Models}, - author = {Sangalli, Laura M. and Ramsay, James O. and Ramsay, Timothy O.}, - year = {2013}, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {75}, - number = {4}, - pages = {681--703}, - file = {/Users/dill/Zotero/storage/CC62EV3Y/old.mate.polimi.it Sangalli.pdf} -} - -@article{sasse_job-related_2003, - title = {Job-Related Mortality of Wildlife Workers in the {{Untied States}}, 1937--2000}, - author = {Sasse, D. Blake}, - year = {2003}, - journal = {Wildlife Society Bulletin}, - volume = {31}, - number = {4}, - pages = {1000--1003}, - file = {/Users/dill/Zotero/storage/L98KTI4P/Sasse_2003_Job-related mortality of wildlife workers.pdf} -} - -@article{scheffer_catastrophic_2001, - title = {Catastrophic Shifts in Ecosystems}, - author = {Scheffer, Marten and Carpenter, Steve and Foley, Jonathan A. and Folke, Carl and Walker, Brian}, - year = {2001}, - month = oct, - journal = {Nature}, - volume = {413}, - number = {6856}, - pages = {591--596}, - issn = {0028-0836}, - doi = {10.1038/35098000}, - langid = {english}, - file = {/Users/dill/Zotero/storage/B6ZW6L3Y/Scheffer et al. - 2001 - Catastrophic shifts in ecosystems.pdf} -} - -@article{scheipl_generalized_2016, - title = {Generalized Functional Additive Mixed Models}, - author = {Scheipl, Fabian and Gertheiss, Jan and Greven, Sonja}, - year = {2016}, - journal = {Electronic Journal of Statistics}, - volume = {10}, - number = {1}, - pages = {1455--1492}, - file = {/Users/dill/Zotero/storage/GHXYMHIT/arXiv 2015 Scheipl.pdf} -} - -@incollection{schimek_spatial-process_2012, - title = {Spatial-{{Process Estimates}} as {{Smoothers}}}, - booktitle = {Smoothing and Regression: Approaches, Computation, and Application}, - author = {Nychka, Douglas W.}, - editor = {Schimek, Michael G.}, - year = {2012}, - series = {Wiley {{Series}} in {{Probability}} and {{Statistics}}}, - pages = {393--424}, - publisher = {{John Wiley \& Sons, Inc.}}, - address = {{Hoboken, NJ, USA}}, - doi = {10.1002/9781118150658.ch13}, - isbn = {978-1-118-15065-8 978-0-471-17946-7}, - file = {/Users/dill/Zotero/storage/QAFX6KP8/Nychka - 2012 - Spatial-Process Estimates as Smoothers.pdf} -} - -@article{schittkowski_robust_2011, - title = {A Robust Implementation of a Sequential Quadratic Programming Algorithm with Successive Error Restoration}, - author = {Schittkowski, K.}, - year = {2011}, - month = may, - journal = {Optimization Letters}, - volume = {5}, - number = {2}, - pages = {283--296}, - issn = {1862-4472, 1862-4480}, - doi = {10.1007/s11590-010-0207-9}, - langid = {english}, - file = {/Users/dill/Zotero/storage/58N44XIU/art%3A10.1007%2Fs11590-010-0207-9.pdf} -} - -@article{schmidt_using_2012, - title = {Using Distance Sampling and Hierarchical Models to Improve Estimates of {{Dall}}'s Sheep Abundance}, - author = {Schmidt, Joshua H. and Rattenbury, Kumi L. and Lawler, James P. and Maccluskie, Margaret C.}, - year = {2012}, - month = feb, - journal = {The Journal of Wildlife Management}, - volume = {76}, - number = {2}, - pages = {317--327}, - issn = {0022541X}, - doi = {10.1002/jwmg.216}, - langid = {english}, - file = {/Users/dill/Zotero/storage/F6FNN9IM/The Journal of Wildlife Management 2011 Schmidt.pdf} -} - -@article{schofield_full_2011, - title = {Full Open Population Capture\textendash Recapture Models with Individual Covariates}, - author = {Schofield, Matthew R. and Barker, Richard J.}, - year = {2011}, - journal = {Journal of agricultural, biological, and environmental statistics}, - volume = {16}, - number = {2}, - pages = {253--268}, - file = {/Users/dill/Zotero/storage/8UBFDIPN/Journal of Agricultural 2011 Schofield.pdf} -} - -@article{schroeder_spatial_2014, - title = {Spatial and {{Seasonal Dynamic}} of {{Abundance}} and {{Distribution}} of {{Guanaco}} and {{Livestock}}: {{Insights}} from {{Using Density Surface}} and {{Null Models}}}, - shorttitle = {Spatial and {{Seasonal Dynamic}} of {{Abundance}} and {{Distribution}} of {{Guanaco}} and {{Livestock}}}, - author = {Schroeder, Natalia M. and Matteucci, Silvia D. and Moreno, Pablo G. and Gregorio, Pablo and Ovejero, Ramiro and Taraborelli, Paula and Carmanchahi, Pablo D.}, - editor = {{Festa-Bianchet}, Marco}, - year = {2014}, - month = jan, - journal = {PLoS ONE}, - volume = {9}, - number = {1}, - pages = {e85960}, - issn = {1932-6203}, - doi = {10.1371/journal.pone.0085960}, - langid = {english}, - file = {/Users/dill/Zotero/storage/XSLJBGQ6/PLoS ONE 2014 Schroeder.pdf} -} - -@article{scott-hayward_complex_2014, - title = {Complex {{Region Spatial Smoother}} ({{CReSS}})}, - author = {{Scott-Hayward}, L. A. S. and Mackenzie, M. L. and Donovan, C. R. and Walker, C. G. and Ashe, E.}, - year = {2014}, - month = apr, - journal = {Journal of Computational and Graphical Statistics}, - volume = {23}, - number = {2}, - pages = {340--360}, - issn = {1061-8600, 1537-2715}, - doi = {10.1080/10618600.2012.762920}, - langid = {english}, - file = {/Users/dill/Zotero/storage/783C3KNX/Journal of Computational and Graphical Statistics 2014 Scott-Hayward.pdf} -} - -@article{scott-hayward_modelling_2015, - title = {Modelling {{Killer Whale Feeding Behaviour Using}} a {{Spatially Adaptive Complex Region Spatial Smoother}} ({{CReSS}}) and {{Generalised Estimating Equations}} ({{GEEs}})}, - author = {{Scott-Hayward}, Lindesay A. S. and Mackenzie, Monique L. and Ashe, Erin and Williams, Rob}, - year = {2015}, - month = sep, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {20}, - number = {3}, - pages = {305--322}, - issn = {1085-7117, 1537-2693}, - doi = {10.1007/s13253-015-0209-2}, - langid = {english}, - file = {/Users/dill/Zotero/storage/MQ7AJSQK/art%3A10.1007%2Fs13253-015-0209-2.pdf} -} - -@techreport{scott-hayward_use_2014, - title = {Use of {{Zero}} and {{One-Inflated Beta Regression}} to {{Model Availability}} of {{Loggerhead Turtles}} off the {{East Coast}} of the {{United States}}}, - author = {{Scott-Hayward}, Lindesay A. S. and Borchers, David L and Burt, M. Louise and Barco, S and Haas, Heather L and Sasso, C.R. and Smolowitz, R.J.}, - year = {2014}, - institution = {{Navfac}}, - file = {/Users/dill/Zotero/storage/IER3RLN4/CTO40 Availability final report_23 Oct 2014.pdf} -} - -@article{sculley_machine_nodate, - title = {Machine {{Learning}}: {{The High-Interest Credit Card}} of {{Technical Debt}}}, - author = {Sculley, D and Holt, Gary and Golovin, Daniel and Davydov, Eugene and Phillips, Todd and Ebner, Dietmar and Chaudhary, Vinay and Young, Michael}, - pages = {9}, - abstract = {Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/TV938U7P/Sculley et al. - Machine Learning The High-Interest Credit Card of.pdf} -} - -@book{seber_estimation_1987, - title = {Estimation of {{Animal Abundance}}}, - author = {Seber, G.A.}, - year = {1987}, - series = {Charles {{Griffin Book}}}, - publisher = {{Oxford University Press, Incorporated}}, - isbn = {978-0-19-520584-8} -} - -@article{sequeira_transferring_2018, - title = {Transferring Biodiversity Models for Conservation: {{Opportunities}} and Challenges}, - shorttitle = {Transferring Biodiversity Models for Conservation}, - author = {Sequeira, Ana M. M. and Bouchet, Phil J. and Yates, Katherine L. and Mengersen, Kerrie and Caley, M. Julian}, - editor = {McPherson, Jana}, - year = {2018}, - month = may, - journal = {Methods in Ecology and Evolution}, - volume = {9}, - number = {5}, - pages = {1250--1264}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12998}, - abstract = {1. After decades of extensive surveying, knowledge of the global distribution of species still remains inadequate for many purposes. In the short to medium term, such knowledge is unlikely to improve greatly given the often prohibitive costs of surveying and the typically limited resources available.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/IRCJHIM3/Sequeira et al. - 2018 - Transferring biodiversity models for conservation.pdf} -} - -@article{shade_computing_2015, - title = {Computing {{Workflows}} for {{Biologists}}: {{A Roadmap}}}, - shorttitle = {Computing {{Workflows}} for {{Biologists}}}, - author = {Shade, Ashley and Teal, Tracy K.}, - year = {2015}, - month = nov, - journal = {PLOS Biology}, - volume = {13}, - number = {11}, - pages = {e1002303}, - issn = {1545-7885}, - doi = {10.1371/journal.pbio.1002303}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UDKX4ASN/PLoS Biol 2015 Shade.PDF} -} - -@article{shono_application_2008, - title = {Application of the {{Tweedie}} Distribution to Zero-Catch Data in {{CPUE}} Analysis}, - author = {Shono, Hiroshi}, - year = {2008}, - month = sep, - journal = {Fisheries Research}, - volume = {93}, - number = {1-2}, - pages = {154--162}, - issn = {01657836}, - doi = {10.1016/j.fishres.2008.03.006}, - langid = {english}, - file = {/Users/dill/Zotero/storage/NUS9KNNV/Fisheries Research 2008 Shono.pdf} -} - -@article{sigourney_developing_2020, - title = {Developing and Assessing a Density Surface Model in a {{Bayesian}} Hierarchical Framework with a Focus on Uncertainty: Insights from Simulations and an Application to Fin Whales ( {{{\emph{Balaenoptera}}}}{\emph{ Physalus}} )}, - shorttitle = {Developing and Assessing a Density Surface Model in a {{Bayesian}} Hierarchical Framework with a Focus on Uncertainty}, - author = {Sigourney, Douglas B. and {Chavez-Rosales}, Samuel and Conn, Paul B. and Garrison, Lance and Josephson, Elizabeth and Palka, Debra}, - year = {2020}, - month = jan, - journal = {PeerJ}, - volume = {8}, - pages = {e8226}, - issn = {2167-8359}, - doi = {10.7717/peerj.8226}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9BB6UXVX/Sigourney et al. - 2020 - Developing and assessing a density surface model i.pdf} -} - -@article{sillero_what_2011, - title = {What Does Ecological Modelling Model? {{A}} Proposed Classification of Ecological Niche Models Based on Their Underlying Methods}, - shorttitle = {What Does Ecological Modelling Model?}, - author = {Sillero, Neftal{\'i}}, - year = {2011}, - month = apr, - journal = {Ecological Modelling}, - volume = {222}, - number = {8}, - pages = {1343--1346}, - issn = {03043800}, - doi = {10.1016/j.ecolmodel.2011.01.018}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Q4E26HY3/Ecological Modelling 2011 Sillero.pdf} -} - -@article{sillett_hierarchical_2012, - title = {Hierarchical Distance-Sampling Models to Estimate Population Size and Habitat-Specific Abundance of an Island Endemic}, - author = {Sillett, T. Scott and Chandler, Richard B. and Royle, J. Andrew and K{\'e}ry, Marc and Morrison, Scott A.}, - year = {2012}, - month = oct, - journal = {Ecological Applications}, - volume = {22}, - number = {7}, - pages = {1997--2006}, - issn = {1051-0761}, - doi = {10.1890/11-1400.1}, - abstract = {Population size and habitat-specific abundance estimates are essential for conservation management. A major impediment to obtaining such estimates is that few statistical models are able to simultaneously account for both spatial variation in abundance and heterogeneity in detection probability, and still be amenable to large-scale applications. The hierarchical distance-sampling model of J. A. Royle, D. K. Dawson, and S. Bates provides a practical solution. Here, we extend this model to estimate habitat-specific abundance and rangewide population size of a bird species of management concern, the Island Scrub-Jay (Aphelocoma insularis), which occurs solely on Santa Cruz Island, California, USA. We surveyed 307 randomly selected, 300 m diameter, point locations throughout the 250-km2 island during October 2008 and April 2009. Population size was estimated to be 2267 (95\% CI 1613\textendash 3007) and 1705 (1212\textendash 2369) during the fall and spring respectively, considerably lower than a previously published but statistically problematic estimate of 12 500. This large discrepancy emphasizes the importance of proper survey design and analysis for obtaining reliable information for management decisions. Jays were most abundant in low-elevation chaparral habitat; the detection function depended primarily on the percent cover of chaparral and forest within count circles. Vegetation change on the island has been dramatic in recent decades, due to release from herbivory following the eradication of feral sheep (Ovis aries) from the majority of the island in the mid-1980s. We applied best-fit fall and spring models of habitat-specific jay abundance to a vegetation map from 1985, and estimated the population size of A. insularis was 1400\textendash 1500 at that time. The 20\textendash 30\% increase in the jay population suggests that the species has benefited from the recovery of native vegetation since sheep removal. Nevertheless, this jay's tiny range and small population size make it vulnerable to natural disasters and to habitat alteration related to climate change. Our results demonstrate that hierarchical distance-sampling models hold promise for estimating population size and spatial density variation at large scales. Our statistical methods have been incorporated into the R package unmarked to facilitate their use by animal ecologists, and we provide annotated code in the Supplement.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FETPG56V/Sillett et al. - 2012 - Hierarchical distance-sampling models to estimate .pdf} -} - -@article{silverman_aspects_1985, - title = {Some {{Aspects}} of the {{Spline Smoothing Approach}} to {{Non-Parametric Regression Curve Fitting}}}, - author = {Silverman, Bernard W.}, - year = {1985}, - journal = {Journal of the Royal Statistical Society. Series B (Methodological)}, - volume = {47}, - number = {1}, - pages = {1--52}, - file = {/Users/dill/Zotero/storage/XR2IHTB2/Silverman-JRSSB85.pdf} -} - -@article{simpson_beyond_2015, - title = {Beyond the {{Valley}} of the {{Covariance Function}}}, - author = {Simpson, Daniel and Lindgren, Finn and Rue, H{\aa}vard}, - year = {2015}, - month = may, - journal = {Statistical Science}, - volume = {30}, - number = {2}, - eprint = {1507.08383}, - eprinttype = {arxiv}, - pages = {164--166}, - issn = {0883-4237}, - doi = {10.1214/15-STS515}, - abstract = {Discussion of "Cross-Covariance Functions for Multivariate Geostatistics" by Genton and Kleiber [arXiv:1507.08017].}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Statistics - Methodology}, - file = {/Users/dill/Zotero/storage/5G229JXN/Simpson et al. - 2015 - Beyond the Valley of the Covariance Function.pdf} -} - -@article{simpson_going_2016, - title = {Going off Grid: Computationally Efficient Inference for Log-{{Gaussian Cox}} Processes}, - shorttitle = {Going off Grid}, - author = {Simpson, D. and Illian, J. B. and Lindgren, F. and S{\o}rbye, S. H. and Rue, H.}, - year = {2016}, - month = mar, - journal = {Biometrika}, - volume = {103}, - number = {1}, - pages = {49--70}, - issn = {0006-3444, 1464-3510}, - doi = {10.1093/biomet/asv064}, - langid = {english}, - file = {/Users/dill/Zotero/storage/IWNJG8VE/Biometrika 2016 Simpson.pdf} -} - -@article{simpson_order_2012, - title = {In Order to Make Spatial Statistics Computationally Feasible, We Need to Forget about the Covariance Function}, - shorttitle = {In Order to Make Spatial Statistics Computationally Feasible, We Need to Forget about the Covariance Function}, - author = {Simpson, Daniel and Lindgren, Finn and Rue, H{\aa}vard}, - year = {2012}, - month = feb, - journal = {Environmetrics}, - volume = {23}, - number = {1}, - pages = {65--74}, - issn = {11804009}, - doi = {10.1002/env.1137}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Y32QLIBS/Simpson et al. - 2012 - In order to make spatial statistics computationall.pdf} -} - -@article{simpson_penalising_2017, - title = {Penalising {{Model Component Complexity}}: {{A Principled}}, {{Practical Approach}} to {{Constructing Priors}}}, - shorttitle = {Penalising {{Model Component Complexity}}}, - author = {Simpson, Daniel and Rue, H{\aa}vard and Riebler, Andrea and Martins, Thiago G. and S{\o}rbye, Sigrunn H.}, - year = {2017}, - month = feb, - journal = {Statistical Science}, - volume = {32}, - number = {1}, - pages = {1--28}, - issn = {0883-4237}, - doi = {10.1214/16-STS576}, - abstract = {In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base model. Proper priors are defined to penalise the complexity induced by deviating from the simpler base model and are formulated after the input of a user-defined scaling parameter for that model component, both in the univariate and the multivariate case. These priors are invariant to reparameterisations, have a natural connection to Jeffreys' priors, are designed to support Occam's razor and seem to have excellent robustness properties, all which are highly desirable and allow us to use this approach to define default prior distributions. Through examples and theoretical results, we demonstrate the appropriateness of this approach and how it can be applied in various situations.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/368PBA9M/Simpson et al. - 2017 - Penalising Model Component Complexity A Principle.pdf} -} - -@article{simpson_think_2012, - title = {Think Continuous: {{Markovian Gaussian}} Models in Spatial Statistics}, - shorttitle = {Think Continuous}, - author = {Simpson, Daniel and Lindgren, Finn and Rue, H{\aa}vard}, - year = {2012}, - month = may, - journal = {Spatial Statistics}, - volume = {1}, - pages = {16--29}, - issn = {22116753}, - doi = {10.1016/j.spasta.2012.02.003}, - abstract = {Gaussian Markov random fields (GMRFs) are frequently used as computationally efficient models in spatial statistics. Unfortunately, it has traditionally been difficult to link GMRFs with the more traditional Gaussian random field models, as the Markov property is difficult to deploy in continuous space. Following the pioneering work of Lindgren et al. (2011), we expound on the link between Markovian Gaussian random fields and GMRFs. In particular, we discuss the theoretical and practical aspects of fast computation with continuously specified Markovian Gaussian random fields, as well as the clear advantages they offer in terms of clear, parsimonious, and interpretable models of anisotropy and nonstationarity.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/37U8LGJQ/Simpson et al. - 2012 - Think continuous Markovian Gaussian models in spa.pdf} -} - -@article{skaug_automatic_2006, - title = {Automatic Approximation of the Marginal Likelihood in Non-{{Gaussian}} Hierarchical Models}, - author = {Skaug, Hans J. and Fournier, David A.}, - year = {2006}, - month = nov, - journal = {Computational Statistics \& Data Analysis}, - volume = {51}, - number = {2}, - pages = {699--709}, - issn = {01679473}, - doi = {10.1016/j.csda.2006.03.005}, - abstract = {Fitting of non-Gaussian hierarchical random effects models by approximate maximum likelihood can be made automatic to the same extent that Bayesian model fitting can be automated by the program BUGS. The word ``automatic'' means that the technical details of computation are made transparent to the user. This is achieved by combining a technique from computer science known as ``automatic differentiation'' with the Laplace approximation for calculating the marginal likelihood. Automatic differentiation, which should not be confused with symbolic differentiation, is mostly unknown to statisticians, and hence basic ideas and results are reviewed. The computational performance of the approach is compared to that of existing mixed-model software on a suite of datasets selected from the mixed-model literature.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/T7KJ5MDH/Skaug and Fournier - 2006 - Automatic approximation of the marginal likelihood.pdf} -} - -@article{skaug_hazard_1999, - title = {Hazard {{Models}} for {{Line Transect Surveys}} with {{Independent Observers}}}, - author = {Skaug, Hans J. and Schweder, Tore}, - year = {1999}, - month = mar, - journal = {Biometrics}, - volume = {55}, - number = {1}, - pages = {29--36}, - issn = {0006-341X, 1541-0420}, - doi = {10.1111/j.0006-341X.1999.00029.x}, - abstract = {The likelihood function for data from independent observer line transect surveys is derived, and a hazard model is proposed for the situation where animals are available for detection only at discrete time points. Under the assumption that the time points of availability follow a Poisson point process, we obtain an analytical expression for the detection function. We discuss different criteria for choosing the hazard function and consider in particular two different parametric families of hazard functions. Discrete and continuous hazard models are compared and the robustness of the discrete model is investigated. Finally, the methodology is applied to data from a survey for minke whales in the northeastern Atlantic.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FIHZJHIH/Skaug and Schweder - 1999 - Hazard Models for Line Transect Surveys with Indep.PDF} -} - -@article{skaug_markov_2006, - title = {Markov {{Modulated Poisson Processes}} for {{Clustered Line Transect Data}}}, - author = {Skaug, Hans J.}, - year = {2006}, - month = jun, - journal = {Environmental and Ecological Statistics}, - volume = {13}, - number = {2}, - pages = {199--211}, - issn = {1352-8505, 1573-3009}, - doi = {10.1007/s10651-005-0006-0}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DCELHPR5/Environ Ecol Stat 2006 Skaug.pdf} -} - -@article{soley-guardia_effect_2014, - title = {The Effect of Spatially Marginal Localities in Modelling Species Niches and Distributions}, - author = {{Soley-Guardia}, Mariano and Radosavljevic, Aleksandar and Rivera, Jhanine L. and Anderson, Robert P.}, - editor = {Ara{\'u}jo, Miguel}, - year = {2014}, - month = jul, - journal = {Journal of Biogeography}, - volume = {41}, - number = {7}, - pages = {1390--1401}, - issn = {03050270}, - doi = {10.1111/jbi.12297}, - langid = {english}, - file = {/Users/dill/Zotero/storage/XCI59K8J/J. Biogeogr. 2014 Soley-Guardia.pdf} -} - -@article{solymos_calibrating_2013, - title = {Calibrating Indices of Avian Density from Non-Standardized Survey Data: Making the Most of a Messy Situation}, - shorttitle = {Calibrating Indices of Avian Density from Non-Standardized Survey Data}, - author = {S{\'o}lymos, P{\'e}ter and Matsuoka, Steven M. and Bayne, Erin M. and Lele, Subhash R. and Fontaine, Patricia and Cumming, Steve G. and Stralberg, Diana and Schmiegelow, Fiona K. A. and Song, Samantha J.}, - editor = {O'Hara, Robert B.}, - year = {2013}, - month = nov, - journal = {Methods in Ecology and Evolution}, - volume = {4}, - number = {11}, - pages = {1047--1058}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12106}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DJZFCHBU/Methods in Ecology and Evolution 2013 Sólymos.pdf} -} - -@article{southwell_detectability_2008, - title = {Detectability of Penguins in Aerial Surveys over the Pack-Ice off {{Antarctica}}}, - author = {Southwell, Colin and Paxton, Charles G. M. and Borchers, David L.}, - year = {2008}, - journal = {Wildlife Research}, - volume = {35}, - number = {4}, - pages = {349}, - issn = {1035-3712}, - doi = {10.1071/WR07093}, - langid = {english}, - file = {/Users/dill/Zotero/storage/E2KH4JFS/Wildl. Res. 2008 Southwell.pdf} -} - -@article{southwell_estimation_2007, - title = {Estimation of Detection Probability in Aerial Surveys of Antarctic Pack-Ice Seals}, - author = {Southwell, Colin and Borchers, David and Paxton, Charles G. M. and Burt, Louise and Mare, William}, - year = {2007}, - month = mar, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {12}, - number = {1}, - pages = {41--54}, - issn = {1085-7117, 1537-2693}, - doi = {10.1198/108571107X162920}, - langid = {english}, - file = {/Users/dill/Zotero/storage/S5DCPESA/JABES 2007 Southwell.pdf} -} - -@article{speed_comment_1991, - title = {Comment on {{That BLUP}} Is a Good Thing: The Estimation of Random Effects (by {{G}}. {{K}}. {{Robinson}})}, - author = {Speed, Terry P}, - year = {1991}, - journal = {Statistical Science}, - volume = {6}, - number = {1}, - pages = {42--44}, - file = {/Users/dill/Zotero/storage/5AQFFQR2/euclid.ss.1177011930.pdf} -} - -@book{speelman_mixed-effects_2018, - title = {Mixed-{{Effects Regression Models}} in {{Linguistics}}}, - author = {Speelman, D. and Heylen, K. and Geeraerts, D.}, - year = {2018}, - series = {Quantitative {{Methods}} in the {{Humanities}} and {{Social Sciences}}}, - publisher = {{Springer International Publishing}}, - isbn = {978-3-319-69830-4} -} - -@article{spiegelhalter_visualizing_2011, - title = {Visualizing {{Uncertainty About}} the {{Future}}}, - author = {Spiegelhalter, D. and Pearson, M. and Short, I.}, - year = {2011}, - month = sep, - journal = {Science}, - volume = {333}, - number = {6048}, - pages = {1393--1400}, - issn = {0036-8075, 1095-9203}, - doi = {10.1126/science.1191181}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ESPPSI2N/Science 2011 Spiegelhalter.pdf} -} - -@article{stasinopoulos_comments_2019, - title = {Comments on: {{Modular}} Regression\textemdash a {{Lego}} System for Building Structured Additive Distributional Regression Models with Tensor Product Interactions}, - shorttitle = {Comments On}, - author = {Stasinopoulos, M. D. and Rigby, R. A. and Heller, G. Z. and De Bastiani, F.}, - year = {2019}, - month = mar, - journal = {TEST}, - volume = {28}, - number = {1}, - pages = {52--54}, - issn = {1133-0686, 1863-8260}, - doi = {10.1007/s11749-019-00634-w}, - langid = {english}, - file = {/Users/dill/Zotero/storage/J92SAHRY/Stasinopoulos et al. - 2019 - Comments on Modular regression—a Lego system for .pdf} -} - -@article{steel_applied_2013, - title = {Applied Statistics in Ecology: Common Pitfalls and Simple Solutions}, - shorttitle = {Applied Statistics in Ecology}, - author = {Steel, E. Ashley and Kennedy, Maureen C. and Cunningham, Patrick G. and Stanovick, John S.}, - year = {2013}, - month = sep, - journal = {Ecosphere}, - volume = {4}, - number = {9}, - pages = {art115}, - issn = {2150-8925}, - doi = {10.1890/ES13-00160.1}, - langid = {english}, - file = {/Users/dill/Zotero/storage/ZQFD8K7J/Ecosphere 2013 Steel.pdf} -} - -@inproceedings{stehman_estimating_1987, - title = {Estimating the Variance of the {{Horvitz-Thompson}} Estimator in Variable Probability Systematic Samples}, - booktitle = {Proceedings of the {{Section}} on {{Survey Research Methods}}}, - author = {Stehman, Stephen V. and Overton, W. Scott}, - year = {1987}, - pages = {743--748}, - file = {/Users/dill/Zotero/storage/VSCG5ZS5/1987_132.pdf} -} - -@article{steiker_taking_1995, - title = {Taking {{Text}} and {{Structurre Really Seriously}}: {{Constitutional Interpretation}} and the {{Crisis}} of {{Presidential Eligibility}}}, - shorttitle = {Taking {{Text}} and {{Structurre Really Seriously}}}, - author = {Steiker, Jordan and Levinson, Sanford and Balkin, J. M.}, - year = {1995}, - journal = {Tex. L. Rev.}, - volume = {74}, - pages = {237}, - file = {/Users/dill/Zotero/storage/X86Q5LIP/Tex L Rev 1995 Steiker.pdf} -} - -@book{stein_interpolation_1999, - title = {Interpolation of {{Spatial Data}}: {{Some Theory}} for {{Kriging}}}, - author = {Stein, M.L.}, - year = {1999}, - series = {Springer {{Series}} in {{Statistics}}}, - publisher = {{Springer New York}}, - lccn = {98044772}, - file = {/Users/dill/Zotero/storage/5NLR3DI2/Stein - 1999 - Interpolation of Spatial Data Some Theory for Kri.pdf} -} - -@article{stevenson_bottom_2019, - title = {Bottom Trawl Surveys in the Northern {{Bering Sea}} Indicate Recent Shifts in the Distribution of Marine Species}, - author = {Stevenson, Duane E. and Lauth, Robert R.}, - year = {2019}, - month = feb, - journal = {Polar Biology}, - volume = {42}, - number = {2}, - pages = {407--421}, - issn = {0722-4060, 1432-2056}, - doi = {10.1007/s00300-018-2431-1}, - abstract = {The climate regime in the eastern Bering Sea has recently been dominated by a pattern of multi-year stanzas, in which several successive years of minimal sea-ice formation and warm summer temperatures (e.g., 2002\textendash 2005, 2014\textendash 2017) alternate with several years of relatively extensive sea-ice formation and cold summer temperatures (e.g., 2006\textendash 2013). This emerging climate pattern may be forcing long-term changes in the spatial distributions of the Bering Sea's marine fauna. The National Marine Fisheries Service's Alaska Fisheries Science Center recently conducted two bottom trawl surveys covering the entire Bering Sea shelf from the Alaska Peninsula to the Bering Strait. The first, in the summer of 2010, was conducted during a cold year when the majority of the continental shelf was covered by a pool of cold ({$<$}\,2\,\textdegree C) water. The second, in the summer of 2017, was during a warmer year with water temperatures above the long-term survey mean. These two surveys recorded significantly different spatial distributions for populations of several commercially important fish species, including walleye pollock (Gadus chalcogrammus), Pacific cod (Gadus macrocephalus), and several flatfish species, as well as jellyfishes. Population shifts included latitudinal displacement as well as variable recruitment success. The large-scale distributional shifts reported here for high-biomass species raise questions about long-term ecosystem impacts, and highlight the need for continued monitoring. They also raise questions about our management strategies for these and other species in Alaska's large marine ecosystems.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Z9XY2TY8/Stevenson and Lauth - 2019 - Bottom trawl surveys in the northern Bering Sea in.pdf} -} - -@article{stevenson_general_2015, - title = {A General Framework for Animal Density Estimation from Acoustic Detections across a Fixed Microphone Array}, - author = {Stevenson, Ben C. and Borchers, David L. and Altwegg, Res and Swift, Ren{\'e} J. and Gillespie, Douglas M. and Measey, G. John}, - editor = {Freckleton, Robert}, - year = {2015}, - month = jan, - journal = {Methods in Ecology and Evolution}, - volume = {6}, - number = {1}, - pages = {38--48}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12291}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9GBPZHS3/Methods in Ecology and Evolution 2014 Stevenson.pdf} -} - -@article{stoklosa_climate_2015, - title = {A Climate of Uncertainty: Accounting for Error in Climate Variables for Species Distribution Models}, - shorttitle = {A Climate of Uncertainty}, - author = {Stoklosa, Jakub and Daly, Christopher and Foster, Scott D. and Ashcroft, Michael B. and Warton, David I.}, - editor = {O'Hara, Robert B.}, - year = {2015}, - month = apr, - journal = {Methods in Ecology and Evolution}, - volume = {6}, - number = {4}, - pages = {412--423}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12217}, - langid = {english}, - file = {/Users/dill/Zotero/storage/BLI5YB6I/Methods in Ecology and Evolution 2014 Stoklosa.pdf} -} - -@article{stone_asymptotic_1977, - title = {An Asymptotic Equivalence of Choice of Model by Cross-Validation and {{Akaike}}'s Criterion}, - author = {Stone, Mervyn}, - year = {1977}, - journal = {Journal of the Royal Statistical Society. Series B (Methodological)}, - pages = {44--47}, - file = {/Users/dill/Zotero/storage/YM8GWRHV/2984877.pdf} -} - -@article{strimbu_posteriori_2018, - title = {A Posteriori Bias Correction of Three Models Used for Environmental Reporting}, - author = {Strimbu, Bogdan M and Amarioarei, Alexandru and McTague, John Paul and Paun, Mihaela M}, - year = {2018}, - month = jan, - journal = {Forestry: An International Journal of Forest Research}, - volume = {91}, - number = {1}, - pages = {49--62}, - issn = {0015-752X, 1464-3626}, - doi = {10.1093/forestry/cpx032}, - langid = {english}, - file = {/Users/dill/Zotero/storage/8YZD7WGR/Strimbu et al. - 2018 - A posteriori bias correction of three models used .pdf} -} - -@incollection{strindberg_design_2004, - title = {Design of Distance Sampling Surveys and {{Geographic Information Systems}}}, - booktitle = {Advanced {{Distance Sampling}}}, - author = {Strindberg, Samantha and Buckland, Stephen T and Thomas, Len}, - year = {2004}, - pages = {190--259}, - publisher = {{Oxford University Press}} -} - -@article{strindberg_zigzag_2004, - title = {Zigzag Survey Designs in Line Transect Sampling}, - author = {Strindberg, Samantha and Buckland, Stephen T.}, - year = {2004}, - month = dec, - journal = {Journal of Agricultural, Biological, and Environmental Statistics}, - volume = {9}, - number = {4}, - pages = {443--461}, - issn = {1085-7117, 1537-2693}, - doi = {10.1198/108571104X15601}, - langid = {english}, - file = {/Users/dill/Zotero/storage/AUX5SK8I/JABES 2004 Strindberg.pdf} -} - -@article{strubell_energy_2019, - title = {Energy and {{Policy Considerations}} for {{Deep Learning}} in {{NLP}}}, - author = {Strubell, Emma and Ganesh, Ananya and McCallum, Andrew}, - year = {2019}, - month = jun, - journal = {arXiv:1906.02243 [cs]}, - eprint = {1906.02243}, - eprinttype = {arxiv}, - primaryclass = {cs}, - abstract = {Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Computer Science - Computation and Language}, - file = {/Users/dill/Zotero/storage/SYET34YJ/Strubell et al. - 2019 - Energy and Policy Considerations for Deep Learning.pdf} -} - -@article{suarez-seoane_scaling_2014, - title = {Scaling of Species Distribution Models across Spatial Resolutions and Extents along a Biogeographic Gradient. {{The}} Case of the {{Iberian}} Mole {{{\emph{Talpa}}}}{\emph{ Occidentalis}}}, - author = {{Su{\'a}rez-Seoane}, Susana and Virg{\'o}s, Emilio and Terroba, Olga and Pardavila, Xos{\'e} and {Barea-Azc{\'o}n}, Jose M.}, - year = {2014}, - month = mar, - journal = {Ecography}, - volume = {37}, - number = {3}, - pages = {279--292}, - issn = {09067590}, - doi = {10.1111/j.1600-0587.2013.00077.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/MEILPH3H/Ecography 2013 Suárez-Seoane.pdf} -} - -@article{sugihara_detecting_2012, - title = {Detecting {{Causality}} in {{Complex Ecosystems}}}, - author = {Sugihara, G. and May, R. and Ye, H. and Hsieh, C.-h. and Deyle, E. and Fogarty, M. and Munch, S.}, - year = {2012}, - month = oct, - journal = {Science}, - volume = {338}, - number = {6106}, - pages = {496--500}, - issn = {0036-8075, 1095-9203}, - doi = {10.1126/science.1227079}, - langid = {english}, - file = {/Users/dill/Zotero/storage/62Z2XZFM/Science 2012 Sugihara.pdf} -} - -@article{suryan_new_2012, - title = {New Approach for Using Remotely Sensed Chlorophyll a to Identify Seabird Hotspots}, - author = {Suryan, Rm and Santora, Ja and Sydeman, Wj}, - year = {2012}, - month = apr, - journal = {Marine Ecology Progress Series}, - volume = {451}, - pages = {213--225}, - issn = {0171-8630, 1616-1599}, - doi = {10.3354/meps09597}, - langid = {english}, - file = {/Users/dill/Zotero/storage/IKJ5HJVA/Mar. Ecol. Prog. Ser. 2012 Suryan.pdf} -} - -@article{swallow_bayesian_2016, - title = {Bayesian Hierarchical Modelling of Continuous Non-Negative Longitudinal Data with a Spike at Zero: {{An}} Application to a Study of Birds Visiting Gardens in Winter: {{Bayesian}} Hierarchical {{Tweedie}} Models}, - shorttitle = {Bayesian Hierarchical Modelling of Continuous Non-Negative Longitudinal Data with a Spike at Zero}, - author = {Swallow, Ben and Buckland, Stephen T. and King, Ruth and Toms, Mike P.}, - year = {2016}, - month = mar, - journal = {Biometrical Journal}, - volume = {58}, - number = {2}, - pages = {357--371}, - issn = {03233847}, - doi = {10.1002/bimj.201400081}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DCQ6YTZR/Swallow et al. - 2016 - Bayesian hierarchical modelling of continuous non-.pdf} -} - -@article{swallow_identifying_2016, - title = {Identifying Multispecies Synchrony in Response to Environmental Covariates}, - author = {Swallow, Ben and King, Ruth and Buckland, Stephen T. and Toms, Mike P.}, - year = {2016}, - month = dec, - journal = {Ecology and Evolution}, - volume = {6}, - number = {23}, - pages = {8515--8525}, - issn = {20457758}, - doi = {10.1002/ece3.2518}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Q6MXSJPT/Ecol Evol 2016 Swallow.pdf} -} - -@article{tasker_counting_1984, - title = {Counting Seabirds at Sea from Ships: A Review of Methods Employed and a Suggestion for a Standardized Approach}, - author = {Tasker, Mark L and Jones, Peter Hope and Dixon, Tim and Blake, Barry F}, - year = {1984}, - journal = {The Auk}, - volume = {101}, - pages = {567--577}, - langid = {english}, - file = {/Users/dill/Zotero/storage/BTMW2TRQ/Tasker et al. - COUNTING SEABIRDS AT SEA FROM SHIPS A REVIEW OF M.pdf} -} - -@article{tayleur_swedish_2015, - title = {Swedish Birds Are Tracking Temperature but Not Rainfall: Evidence from a Decade of Abundance Changes: {{Swedish}} Birds on the Move}, - shorttitle = {Swedish Birds Are Tracking Temperature but Not Rainfall}, - author = {Tayleur, Catherine and Caplat, Paul and Massimino, Dario and Johnston, Alison and Jonz{\'e}n, Niclas and Smith, Henrik G. and Lindstr{\"o}m, {\AA}ke}, - year = {2015}, - month = jul, - journal = {Global Ecology and Biogeography}, - volume = {24}, - number = {7}, - pages = {859--872}, - issn = {1466822X}, - doi = {10.1111/geb.12308}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UE27SNRF/Global Ecol Biogeography 2015 Tayleur.pdf} -} - -@article{taylor_incorporating_2000, - title = {Incorporating {{Uncertainty}} into {{Management Models}} for {{Marine Mammals}}}, - author = {Taylor, Barbara L. and Wade, Paul R. and De Master, Douglas P. and Barlow, Jay}, - year = {2000}, - month = oct, - journal = {Conservation Biology}, - volume = {14}, - number = {5}, - pages = {1243--1252}, - issn = {0888-8892, 1523-1739}, - doi = {10.1046/j.1523-1739.2000.99409.x}, - abstract = {Good management models and good models for understanding biology differ in basic philosophy. Management models must facilitate management decisions despite large amounts of uncertainty about the managed populations. Such models must be based on parameters that can be estimated readily, must explicitly account for uncertainty, and should be simple to understand and implement. In contrast, biological models are designed to elucidate the workings of biology and should not be constrained by management concerns. We illustrate the need to incorporate uncertainty in management models by reviewing the inadequacy of using standard biological models to manage marine mammals in the United States. Past management was based on a simple model that, although it may have represented population dynamics adequately, failed as a management tool because the parameter that triggered management action was extremely difficult to estimate for the majority of populations. Uncertainty in parameter estimation resulted in few conservation actions. We describe a recently adopted management scheme that incorporates uncertainty and its resulting implementation. The approach used in this simple management scheme, which was tested by using simulation models, incorporates uncertainty and mandates monitoring abundance and human-caused mortality. Although the entire scheme may be suitable for application to some terrestrial and marine problems, two features are broadly applicable: the incorporation of uncertainty through simulations of management and the use of quantitative management criteria to translate verbal objectives into levels of acceptable risk.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JHIU9VKV/Taylor et al. - 2000 - Incorporating Uncertainty into Management Models f.pdf} -} - -@article{taylor_lessons_2007, - title = {{{LESSONS FROM MONITORING TRENDS IN ABUNDANCE OF MARINE MAMMALS}}}, - author = {Taylor, Barbara L. and Martinez, Melissa and Gerrodette, Tim and Barlow, Jay and Hrovat, Yvana N.}, - year = {2007}, - month = jan, - journal = {Marine Mammal Science}, - volume = {23}, - number = {1}, - pages = {157--175}, - issn = {0824-0469, 1748-7692}, - doi = {10.1111/j.1748-7692.2006.00092.x}, - abstract = {We assessed scientists' ability to detect declines of marine mammal stocks based on recent levels of survey effort, when the actual decline is precipitous. We defined a precipitous decline as a 50\% decrease in abundance in 15 yr, at which point a stock could be legally classified as ``depleted'' under the U.S. Marine Mammal Protection Act. We assessed stocks for three categories of cetaceans: large whales (n = 23, most of which are listed as endangered), beaked whales (n = 11, potentially vulnerable to anthropogenic noise), and small whales/dolphins/porpoises (n = 69, bycatch in fisheries and important abundant predators), for two categories of pinnipeds with substantially different survey precision: counted on land (n = 13) and surveyed on ice (n = 5), and for a category containing polar bear and sea otter stocks (n = 6). The percentage of precipitous declines that would not be detected as declines was 72\% for large whales, 90\% for beaked whales, and 78\% for dolphins/porpoises, 5\% for pinnipeds on land, 100\% for pinnipeds on ice, and 55\% for polar bears/sea otters (based on a one-tailed t-test, \textvisiblespace{} = 0.05), given the frequency and precision of recent monitoring effort. We recommend alternatives to improve performance.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/E6D4I7NC/Taylor et al. - 2007 - LESSONS FROM MONITORING TRENDS IN ABUNDANCE OF MAR.pdf} -} - -@article{thibaud_measuring_2014, - title = {Measuring the Relative Effect of Factors Affecting Species Distribution Model Predictions}, - author = {Thibaud, Emeric and Petitpierre, Blaise and Broennimann, Olivier and Davison, Anthony C. and Guisan, Antoine}, - editor = {O{${'}$}Hara, Robert B.}, - year = {2014}, - month = sep, - journal = {Methods in Ecology and Evolution}, - volume = {5}, - number = {9}, - pages = {947--955}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12203}, - langid = {english}, - file = {/Users/dill/Zotero/storage/X2TM6FN7/Methods in Ecology and Evolution 2014 Thibaud.pdf} -} - -@article{thomas_designing_2007, - title = {Designing Line Transect Surveys for Complex Survey Regions}, - author = {Thomas, Len and Williams, Rob and Sandilands, Doug}, - year = {2007}, - journal = {Journal of Cetacean Research and Management}, - volume = {9}, - number = {1}, - pages = {1}, - file = {/Users/dill/Zotero/storage/48MGZ6SF/2007 Thomas.pdf} -} - -@article{thomas_distance_2010, - title = {Distance Software: Design and Analysis of Distance Sampling Surveys for Estimating Population Size}, - shorttitle = {Distance Software}, - author = {Thomas, Len and Buckland, Stephen T. and Rexstad, Eric A. and Laake, Jeff L. and Strindberg, Samantha and Hedley, Sharon L. and Bishop, Jon R.B. and Marques, Tiago A. and Burnham, Kenneth P.}, - year = {2010}, - month = feb, - journal = {Journal of Applied Ecology}, - volume = {47}, - number = {1}, - pages = {5--14}, - issn = {00218901, 13652664}, - doi = {10.1111/j.1365-2664.2009.01737.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/MXL3PRXE/Journal of Applied Ecology 2010 Thomas.pdf} -} - -@article{thomas_grey_2009, - title = {Grey Seals Red in Tooth and Claw: How {{Darwin}} Helps Model Their Population}, - shorttitle = {Grey Seals Red in Tooth and Claw}, - author = {Thomas, Len}, - year = {2009}, - journal = {Significance}, - volume = {6}, - number = {3}, - pages = {108--112}, - file = {/Users/dill/Zotero/storage/PG8HV62C/Significance 2009 Thomas.pdf} -} - -@article{thomson_heterogeneous_2012, - title = {Heterogeneous Patterns of Availability for Detection during Visual Surveys: Spatiotemporal Variation in Sea Turtle Dive-Surfacing Behaviour on a Feeding Ground: {{Diving-related}} Detection Heterogeneity}, - shorttitle = {Heterogeneous Patterns of Availability for Detection during Visual Surveys}, - author = {Thomson, Jordan A. and Cooper, Andrew B. and Burkholder, Derek A. and Heithaus, Michael R. and Dill, Lawrence M.}, - year = {2012}, - month = apr, - journal = {Methods in Ecology and Evolution}, - volume = {3}, - number = {2}, - pages = {378--387}, - issn = {2041210X}, - doi = {10.1111/j.2041-210X.2011.00163.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Q37XPY3U/Methods in Ecology and Evolution 2011 Thomson.pdf} -} - -@article{thorson_mixed_2015, - title = {Mixed Effects: A Unifying Framework for Statistical Modelling in Fisheries Biology}, - shorttitle = {Mixed Effects}, - author = {Thorson, James T. and Minto, C{\'o}il{\'i}n}, - year = {2015}, - month = jun, - journal = {ICES Journal of Marine Science}, - volume = {72}, - number = {5}, - pages = {1245--1256}, - issn = {1095-9289, 1054-3139}, - doi = {10.1093/icesjms/fsu213}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LZNMNMN2/Thorson and Minto - 2015 - Mixed effects a unifying framework for statistica.pdf} -} - -@article{thorson_spatial_2015, - title = {Spatial Delay-Difference Models for Estimating Spatiotemporal Variation in Juvenile Production and Population Abundance}, - author = {Thorson, James T. and Ianelli, James N. and Munch, Stephan B. and Ono, Kotaro and Spencer, Paul D.}, - editor = {Vinbrooke, Rolf}, - year = {2015}, - month = dec, - journal = {Canadian Journal of Fisheries and Aquatic Sciences}, - volume = {72}, - number = {12}, - pages = {1897--1915}, - issn = {0706-652X, 1205-7533}, - doi = {10.1139/cjfas-2014-0543}, - abstract = {Many important ecological questions require accounting for spatial variation in demographic rates (e.g., survival) and population variables (e.g., abundance per unit area). However, ecologists have few spatial modelling approaches that (i) fit directly to spatially referenced data, (ii) represent population dynamics explicitly and mechanistically, and (iii) estimate parameters using rigorous statistical methods. We therefore demonstrate a new and computationally efficient approach to spatial modelling that uses random fields in place of the random variables typically used in spatially aggregated models. We adapt this approach to delay-difference dynamics to estimate the impact of fishing and natural mortality, recruitment, and individual growth on spatial population dynamics for a fish population. In particular, we develop this approach to estimate spatial variation in average production of juvenile fishes (termed recruitment), as well as annual variation in the spatial distribution of recruitment. We first use a simulation experiment to demonstrate that the spatial delay-difference model can, in some cases, explain over 50\% of spatial variance in recruitment. We also apply the spatial delay-difference model to data for rex sole (Glyptocephalus zachirus) in the Gulf of Alaska and show that average recruitment (across all years) is greatest near Kodiak Island but that some years show greatest recruitment in Southeast Alaska or the western Gulf of Alaska. Using model developments and software advances presented here, we argue that future research can develop models to approximate adult movement, incorporate spatial covariates to explain annual variation in recruitment, and evaluate management procedures that use spatially explicit estimates of population abundance.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QAJFE5C2/Thorson et al. - 2015 - Spatial delay-difference models for estimating spa.PDF} -} - -@article{thorson_using_2017, - title = {Using Spatio-Temporal Models of Population Growth and Movement to Monitor Overlap between Human Impacts and Fish Populations}, - author = {Thorson, James T. and Jannot, Jason and Somers, Kayleigh}, - editor = {Punt, Andre}, - year = {2017}, - month = apr, - journal = {Journal of Applied Ecology}, - volume = {54}, - number = {2}, - pages = {577--587}, - issn = {00218901}, - doi = {10.1111/1365-2664.12664}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2IDZAHS9/Thorson et al. - 2017 - Using spatio-temporal models of population growth .pdf} -} - -@article{thorup_large-scale_2014, - title = {Large-Scale Spatial Analysis of Ringing and Re-Encounter Data to Infer Movement Patterns: {{A}} Review Including Methodological Perspectives}, - shorttitle = {Large-Scale Spatial Analysis of Ringing and Re-Encounter Data to Infer Movement Patterns}, - author = {Thorup, Kasper and {Korner-Nievergelt}, Fr{\"a}nzi and Cohen, Emily B. and Baillie, Stephen R.}, - editor = {Francis, Charles}, - year = {2014}, - month = dec, - journal = {Methods in Ecology and Evolution}, - volume = {5}, - number = {12}, - pages = {1337--1350}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12258}, - langid = {english}, - file = {/Users/dill/Zotero/storage/PV4P5YR6/Methods in Ecology and Evolution 2014 Thorup.pdf} -} - -@article{thyng_true_2016, - title = {True {{Colors}} of {{Oceanography}}: {{Guidelines}} for {{Effective}} and {{Accurate Colormap Selection}}}, - shorttitle = {True {{Colors}} of {{Oceanography}}}, - author = {Thyng, Kristen and Greene, Chad and Hetland, Robert and Zimmerle, Heather and DiMarco, Steven}, - year = {2016}, - month = sep, - journal = {Oceanography}, - volume = {29}, - number = {3}, - pages = {9--13}, - issn = {10428275}, - doi = {10.5670/oceanog.2016.66}, - file = {/Users/dill/Zotero/storage/SF69NKQY/Oceanog. 2016 Thyng.pdf} -} - -@article{tierney_accurate_2021, - title = {Accurate {{Approximations}} for {{Posterior Moments}} and {{Marginal Densities}}}, - author = {Tierney, Luke and Kadane, Joseph B}, - year = {2021}, - pages = {6}, - langid = {english}, - file = {/Users/dill/Zotero/storage/PHPQZWUE/Tierney and Kadane - 2021 - Accurate Approximations for Posterior Moments and .pdf} -} - -@article{tornqvist_how_1985, - title = {How {{Should Relative Changes Be Measured}}?}, - author = {Tornqvist, Leo and Vartia, Pentti and Vartia, Yrjo O.}, - year = {1985}, - month = feb, - journal = {The American Statistician}, - volume = {39}, - number = {1}, - pages = {43}, - issn = {00031305}, - doi = {10.2307/2683905}, - file = {/Users/dill/Zotero/storage/3HXH44ZB/The American Statistician 1985 Törnqvist.pdf} -} - -@article{torres_exploitation_2013, - title = {From Exploitation to Conservation: Habitat Models Using Whaling Data Predict Distribution Patterns and Threat Exposure of an Endangered Whale}, - shorttitle = {From Exploitation to Conservation}, - author = {Torres, Leigh G. and Smith, Tim D. and Sutton, Phil and MacDiarmid, Alison and Bannister, John and Miyashita, Tomio}, - editor = {Franklin, Janet}, - year = {2013}, - month = sep, - journal = {Diversity and Distributions}, - volume = {19}, - number = {9}, - pages = {1138--1152}, - issn = {13669516}, - doi = {10.1111/ddi.12069}, - langid = {english}, - file = {/Users/dill/Zotero/storage/V238DWH9/Diversity Distrib. 2013 Torres.pdf} -} - -@article{torres_fine-scale_2008, - title = {{{FINE-SCALE HABITAT MODELING OF A TOP MARINE PREDATOR}}: {{DO PREY DATA IMPROVE PREDICTIVE CAPACITY}}}, - shorttitle = {{{FINE-SCALE HABITAT MODELING OF A TOP MARINE PREDATOR}}}, - author = {Torres, Leigh G. and Read, Andrew J. and Halpin, Patrick}, - year = {2008}, - month = oct, - journal = {Ecological Applications}, - volume = {18}, - number = {7}, - pages = {1702--1717}, - issn = {1051-0761}, - doi = {10.1890/07-1455.1}, - abstract = {Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, USA, improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/XYNPUC88/Torres et al. - 2008 - FINE-SCALE HABITAT MODELING OF A TOP MARINE PREDAT.pdf} -} - -@article{trevor_hastie_varying-coefficient_1993, - title = {Varying-{{Coefficient Models}}}, - author = {Trevor Hastie and Robert Tibshirani}, - year = {1993}, - journal = {Journal of the Royal Statistical Society. Series B (Methodological)}, - volume = {55}, - number = {4}, - pages = {757--796}, - file = {/Users/dill/Zotero/storage/UBVGQK3Z/Trevor Hastie and Robert Tibshirani - Varying-Coefficient Models.pdf} -} - -@article{trosset_out--sample_2008, - title = {The Out-of-Sample Problem for Classical Multidimensional Scaling}, - author = {Trosset, Michael W. and Priebe, Carey E.}, - year = {2008}, - month = jun, - journal = {Computational Statistics \& Data Analysis}, - volume = {52}, - number = {10}, - pages = {4635--4642}, - issn = {01679473}, - doi = {10.1016/j.csda.2008.02.031}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YBP79DHG/Computational Statistics and Data Analysis 2008 Trosset.pdf} -} - -@techreport{u.s._department_of_the_navy_quantifying_2017, - title = {Quantifying {{Acoustic Impacts}} on {{Marine Mammals}} and {{Sea Turtles}}: {{Methods}} and {{Analytical Approach}} for {{Phase III Training}} and {{Testing}}}, - author = {{U.S. Department of the Navy}}, - year = {2017}, - address = {{Space and Naval Warfare Systems Center Pacific, San Diego and Naval Undersea Warfare Center, Newport}} -} - -@article{vakingsson_distribution_2015, - title = {Distribution, Abundance, and Feeding Ecology of Baleen Whales in {{Icelandic}} Waters: Have Recent Environmental Changes Had an Effect?}, - shorttitle = {Distribution, Abundance, and Feeding Ecology of Baleen Whales in {{Icelandic}} Waters}, - author = {V{\~A}\-kingsson, G{\~A}\-sli A. and Pike, Daniel G. and Valdimarsson, H{\~A}{\textcopyright}{\~A}{\textdegree}inn and Schleimer, Anna and Gunnlaugsson, Thorvaldur and Silva, Teresa and Elvarsson, Bjarki {\~A}{\v z}. and Mikkelsen, Bjarni and {\~A}{\texttildelow}ien, Nils and Desportes, Genevi{\~A}{\textasciidieresis}ve and Bogason, Valur and Hammond, Philip S.}, - year = {2015}, - month = feb, - journal = {Frontiers in Ecology and Evolution}, - volume = {3}, - issn = {2296-701X}, - doi = {10.3389/fevo.2015.00006}, - file = {/Users/dill/Zotero/storage/PY53C8KZ/Frontiers in Ecology … 2015 Víkingsson.pdf} -} - -@article{valavi_blockcv_2019, - title = {{{blockCV}}: {{An R}} Package for Generating Spatially or Environmentally Separated Folds for k-Fold Cross-Validation of Species Distribution Models}, - author = {Valavi, Roozbeh and Elith, Jane and {Lahoz-Monfort}, Jos{\'e} J. and {Guillera-Arroita}, Gurutzeta}, - editor = {Warton, David}, - year = {2019}, - month = feb, - journal = {Methods in Ecology and Evolution}, - volume = {10}, - number = {2}, - pages = {225--232}, - issn = {2041210X}, - doi = {10.1111/2041-210X.13107}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LCFMRCR8/Valavi et al. - 2019 - span style=font-variantsmall-caps\;blockspan.pdf} -} - -@article{valente_new_2016, - title = {A New Insight for Monitoring Ungulates: Density Surface Modelling of Roe Deer in a {{Mediterranean}} Habitat}, - shorttitle = {A New Insight for Monitoring Ungulates}, - author = {Valente, Ana M. and Marques, Tiago A. and Fonseca, Carlos and Torres, Rita Tinoco}, - year = {2016}, - month = oct, - journal = {European Journal of Wildlife Research}, - volume = {62}, - number = {5}, - pages = {577--587}, - issn = {1612-4642, 1439-0574}, - doi = {10.1007/s10344-016-1030-0}, - langid = {english}, - file = {/Users/dill/Zotero/storage/3T42H698/European Journal of Wildlife Research 2016 Valente.pdf} -} - -@article{van_de_schoot_bayesian_2021, - title = {Bayesian Statistics and Modelling}, - author = {{van de Schoot}, Rens and Depaoli, Sarah and King, Ruth and Kramer, Bianca and M{\"a}rtens, Kaspar and Tadesse, Mahlet G. and Vannucci, Marina and Gelman, Andrew and Veen, Duco and Willemsen, Joukje and Yau, Christopher}, - year = {2021}, - month = dec, - journal = {Nature Reviews Methods Primers}, - volume = {1}, - number = {1}, - pages = {1}, - issn = {2662-8449}, - doi = {10.1038/s43586-020-00001-2}, - abstract = {Bayesian statistics is an approach to data analysis based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. This Primer describes the stages involved in Bayesian analysis, from specifying the prior and data models to deriving inference, model checking and refinement. We discuss the importance of prior and posterior predictive checking, selecting a proper technique for sampling from a posterior distribution, variational inference and variable selection. Examples of successful applications of Bayesian analysis across various research fields are provided, including in social sciences, ecology, genetics, medicine and more. We propose strategies for reproducibility and reporting standards, outlining an updated WAMBS (when to Worry and how to Avoid the Misuse of Bayesian Statistics) checklist. Finally, we outline the impact of Bayesian analysis on artificial intelligence, a major goal in the next decade.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/E46PV9V9/van de Schoot et al. - 2021 - Bayesian statistics and modelling.pdf} -} - -@article{van_erp_shrinkage_2019, - title = {Shrinkage Priors for {{Bayesian}} Penalized Regression}, - author = {{van Erp}, Sara and Oberski, Daniel L and Mulder, Joris}, - year = {2019}, - journal = {Journal of Mathematical Psychology}, - pages = {20}, - abstract = {In linear regression problems with many predictors, penalized regression techniques are often used to guard against overfitting and to select variables relevant for predicting an outcome variable. Recently, Bayesian penalization is becoming increasingly popular in which the prior distribution performs a function similar to that of the penalty term in classical penalization. Specifically, the so-called shrinkage priors in Bayesian penalization aim to shrink small effects to zero while maintaining true large effects. Compared to classical penalization techniques, Bayesian penalization techniques perform similarly or sometimes even better, and they offer additional advantages such as readily available uncertainty estimates, automatic estimation of the penalty parameter, and more flexibility in terms of penalties that can be considered. However, many different shrinkage priors exist and the available, often quite technical, literature primarily focuses on presenting one shrinkage prior and often provides comparisons with only one or two other shrinkage priors. This can make it difficult for researchers to navigate through the many prior options and choose a shrinkage prior for the problem at hand. Therefore, the aim of this paper is to provide a comprehensive overview of the literature on Bayesian penalization. We provide a theoretical and conceptual comparison of nine different shrinkage priors and parametrize the priors, if possible, in terms of scale mixture of normal distributions to facilitate comparisons. We illustrate different characteristics and behaviors of the shrinkage priors and compare their performance in terms of prediction and variable selection in a simulation study. Additionally, we provide two empirical examples to illustrate the application of Bayesian penalization. Finally, an R package bayesreg is available online (https://github.com/sara-vanerp/bayesreg) which allows researchers to perform Bayesian penalized regression with novel shrinkage priors in an easy manner.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/I6DNAPWD/van Erp et al. - 2019 - Shrinkage priors for Bayesian penalized regression.pdf} -} - -@article{varadhan_numerical_2014, - title = {Numerical Optimization in {{R}}: {{Beyond}} Optim}, - shorttitle = {Numerical Optimization in r}, - author = {Varadhan, Ravi}, - year = {2014}, - journal = {Journal of Statistical Software}, - volume = {60}, - number = {1}, - pages = {1--3}, - file = {/Users/dill/Zotero/storage/237Z4HKK/Journal of Statistical Software 2014 Varadhan.pdf} -} - -@article{veech_intrinsic_2016, - title = {Intrinsic Heterogeneity in Detection Probability and Its Effect on {{{\emph{N}}}} -Mixture Models}, - author = {Veech, Joseph A. and Ott, James R. and Troy, Jeff R.}, - editor = {Murrell, David}, - year = {2016}, - month = sep, - journal = {Methods in Ecology and Evolution}, - volume = {7}, - number = {9}, - pages = {1019--1028}, - issn = {2041210X}, - doi = {10.1111/2041-210X.12566}, - langid = {english}, - file = {/Users/dill/Zotero/storage/WT66Y3QX/mee312566.pdf} -} - -@article{velasquez-tibata_using_2016, - title = {Using Measurement Error Models to Account for Georeferencing Error in Species Distribution Models}, - author = {{Vel{\'a}squez-Tibat{\'a}}, Jorge and Graham, Catherine H. and Munch, Stephan B.}, - year = {2016}, - month = mar, - journal = {Ecography}, - volume = {39}, - number = {3}, - pages = {305--316}, - issn = {09067590}, - doi = {10.1111/ecog.01205}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YUULBUTD/Ecography 2015 Velásquez-Tibatá.pdf} -} - -@article{ver_hoef_quasi-poisson_2007, - title = {Quasi-Poisson {{Vs}}. {{Negative Binomial Regression}}: {{How Should We Model Overdispersed Count Data}}?}, - shorttitle = {Quasi-Poisson {{Vs}}. {{Negative Binomial Regression}}}, - author = {Ver Hoef, Jay M. and Boveng, Peter L.}, - year = {2007}, - journal = {Ecology}, - volume = {88}, - number = {11}, - pages = {2766--2772}, - file = {/Users/dill/Zotero/storage/2WZ2YABU/Ecology 2007 Ver Hoef.pdf} -} - -@article{ver_hoef_relationship_2017, - title = {On the {{Relationship}} between {{Conditional}} ({{CAR}}) and {{Simultaneous}} ({{SAR}}) {{Autoregressive Models}}}, - author = {Ver Hoef, Jay M. and Hanks, Ephraim M. and Hooten, Mevin B.}, - year = {2017}, - month = oct, - journal = {arXiv:1710.07000 [math, stat]}, - eprint = {1710.07000}, - eprinttype = {arxiv}, - primaryclass = {math, stat}, - abstract = {We clarify relationships between conditional (CAR) and simultaneous (SAR) autoregressive models. We review the literature on this topic and find that it is mostly incomplete. Our main result is that a SAR model can be written as a unique CAR model, and while a CAR model can be written as a SAR model, it is not unique. In fact, we show how any multivariate Gaussian distribution on a finite set of points with a positive-definite covariance matrix can be written as either a CAR or a SAR model. We illustrate how to obtain any number of SAR covariance matrices from a single CAR covariance matrix by using Givens rotation matrices on a simulated example. We also discuss sparseness in the original CAR construction, and for the resulting SAR weights matrix. For a real example, we use crime data in 49 neighborhoods from Columbus, Ohio, and show that a geostatistical model optimizes the likelihood much better than typical first-order CAR models. We then use the implied weights from the geostatistical model to estimate CAR model parameters that provides the best overall optimization.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Mathematics - Statistics Theory}, - file = {/Users/dill/Zotero/storage/RK6CQQ8I/Hoef et al. - 2017 - On the Relationship between Conditional (CAR) and .pdf} -} - -@article{ver_hoef_spatial_2014, - title = {A Spatial Hierarchical Model for Abundance of Three Ice-Associated Seal Species in the Eastern {{Bering Sea}}}, - author = {Ver Hoef, Jay M. and Cameron, Michael F. and Boveng, Peter L. and London, Josh M. and Moreland, Erin E.}, - year = {2014}, - month = mar, - journal = {Statistical Methodology}, - volume = {17}, - pages = {46--66}, - issn = {15723127}, - doi = {10.1016/j.stamet.2013.03.001}, - langid = {english}, - file = {/Users/dill/Zotero/storage/PT2NVZTK/Statistical Methodology 2013 Ver Hoef.pdf} -} - -@article{ver_hoef_spatial_2018, - title = {Spatial Autoregressive Models for Statistical Inference from Ecological Data}, - author = {Ver Hoef, Jay M. and Peterson, Erin E. and Hooten, Mevin B. and Hanks, Ephraim M. and Fortin, Marie-Jos{\`e}e}, - year = {2018}, - month = feb, - journal = {Ecological Monographs}, - volume = {88}, - number = {1}, - pages = {36--59}, - issn = {00129615}, - doi = {10.1002/ecm.1283}, - abstract = {Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are network-based models (also known as graphical models) specifically designed to model spatially autocorrelated data based on neighborhood relationships. We identify and discuss six different types of practical ecological inference using CAR and SAR models, including: (1) model selection, (2) spatial regression, (3) estimation of autocorrelation, (4) estimation of other connectivity parameters, (5) spatial prediction, and (6) spatial smoothing. We compare CAR and SAR models, showing their development and connection to partial correlations. Special cases, such as the intrinsic autoregressive model (IAR), are described. Conditional autoregressive and SAR models depend on weight matrices, whose practical development uses neighborhood definition and row-standardization. Weight matrices can also include ecological covariates and connectivity structures, which we emphasize, but have been rarely used. Trends in harbor seals (Phoca vitulina) in southeastern Alaska from 463 polygons, some with missing data, are used to illustrate the six inference types. We develop a variety of weight matrices and CAR and SAR spatial regression models are fit using maximum likelihood and Bayesian methods. Profile likelihood graphs illustrate inference for covariance parameters. The same data set is used for both prediction and smoothing, and the relative merits of each are discussed. We show the nonstationary variances and correlations of a CAR model and demonstrate the effect of row-standardization. We include several take-home messages for CAR and SAR models, including (1) choosing between CAR and IAR models, (2) modeling ecological effects in the covariance matrix, (3) the appeal of spatial smoothing, and (4) how to handle isolated neighbors. We highlight several reasons why ecologists will want to make use of autoregressive models, both directly and in hierarchical models, and not only in explicit spatial settings, but also for more general connectivity models.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/7E5CY32B/Ver Hoef et al. - 2018 - Spatial autoregressive models for statistical infe.pdf;/Users/dill/Zotero/storage/8GLMLS8M/Ver Hoef et al. - 2018 - Spatial autoregressive models for statistical infe.pdf} -} - -@article{ver_hoef_who_2012, - title = {Who {{Invented}} the {{Delta Method}}?}, - author = {Ver Hoef, Jay M.}, - year = {2012}, - month = may, - journal = {The American Statistician}, - volume = {66}, - number = {2}, - pages = {124--127}, - issn = {0003-1305, 1537-2731}, - doi = {10.1080/00031305.2012.687494}, - langid = {english}, - file = {/Users/dill/Zotero/storage/T5SJNQSY/The American Statistician 2012 Ver Hoef.pdf} -} - -@article{verbyla_analysis_1999, - title = {The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines}, - author = {Verbyla, Ar{\~u}nas P. and Cullis, Brian R. and Kenward, Michael G. and Welham, Sue J.}, - year = {1999}, - journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}, - volume = {48}, - number = {3}, - pages = {269--311}, - file = {/Users/dill/Zotero/storage/FRLUNREH/1467-9876.00154.pdf} -} - -@article{viladomat_assessing_2014, - title = {Assessing the Significance of Global and Local Correlations under Spatial Autocorrelation: {{A}} Nonparametric Approach: {{Test}} for the {{Correlation When}} the {{Variables Are Smoothed}}}, - shorttitle = {Assessing the Significance of Global and Local Correlations under Spatial Autocorrelation}, - author = {Viladomat, J{\'u}lia and Mazumder, Rahul and McInturff, Alex and McCauley, Douglas J. and Hastie, Trevor}, - year = {2014}, - month = jun, - journal = {Biometrics}, - volume = {70}, - number = {2}, - pages = {409--418}, - issn = {0006341X}, - doi = {10.1111/biom.12139}, - langid = {english}, - file = {/Users/dill/Zotero/storage/7XQ4FX4R/Biom 2014 Viladomat.pdf} -} - -@book{vretblad_fourier_2003, - title = {Fourier {{Analysis}} and {{Its Applications}}}, - author = {Vretblad, A. and Axler, S. and Gehring, F.W. and Ribet, K.A.}, - year = {2003}, - series = {Graduate {{Texts}} in {{Mathematics}}}, - publisher = {{Springer}}, - isbn = {978-0-387-00836-3}, - lccn = {2002026664} -} - -@article{waagepetersen_convergence_2004, - title = {Convergence of Posteriors for Discretized Log {{Gaussian Cox}} Processes}, - author = {Waagepetersen, Rasmus}, - year = {2004}, - month = feb, - journal = {Statistics \& Probability Letters}, - volume = {66}, - number = {3}, - pages = {229--235}, - issn = {01677152}, - doi = {10.1016/j.spl.2003.10.014}, - abstract = {In Markov chain Monte Carlo posterior computation for log Gaussian Cox processes (LGCPs) a discretization of the continuously indexed Gaussian \"yeld is required. It is demonstrated that approximate posterior expectations computed from discretized LGCPs converge to the exact posterior expectations when the cell sizes of the discretization tends to zero. The e ect of discretization is studied in a data example.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/W7LY7IJQ/Waagepetersen - 2004 - Convergence of posteriors for discretized log Gaus.pdf} -} - -@article{waggitt_distribution_nodate, - title = {Distribution Maps of Cetacean and Seabird Populations in the {{North}}-{{East Atlantic}}}, - author = {Waggitt, James J and Evans, Peter G H and Andrade, Joana and Banks, Alex N and Boisseau, Oliver and Bolton, Mark and Bradbury, Gareth and Brereton, Tom and Camphuysen, Cornelis Jan and Durinck, Jan and Felce, Tom and Fijn, Ruben Christiaan and {Garcia-Baron}, Isabel and Garthe, Stefan and Geelhoed, Steve C V and Gilles, Anita and Goodall, Martin and Haelters, Jan and Hamilton, Sally and {Hartny-Mills}, Lauren and Hodgins, Nicola and James, Kathy and Jessopp, Mark and Kavanagh, Ailbhe S and Leopold, Mardik and Lohrengel, Katrin and Louzao, Maite and Markones, Nele and {Mart{\'i}nez-Cedeira}, Jose and Cadhla, Oliver {\'O} and Perry, Sarah L and Pierce, Graham J and Ridoux, Vincent and Robinson, Kevin P and Santos, M Bego{\~n}a and Saavedra, Camilo and Skov, Henrik and Stienen, Eric W M and Sveegaard, Signe and Thompson, Paul and Vanermen, Nicolas and Wall, Dave and Webb, Andy and Wilson, Jared and Wanless, Sarah and Hiddink, Jan Geert}, - pages = {17}, - langid = {english}, - file = {/Users/dill/Zotero/storage/4WASNV39/Waggitt et al. - Distribution maps of cetacean and seabird populati.pdf} -} - -@article{wahba_comparison_1985, - title = {A {{Comparison}} of {{GCV}} and {{GML}} for {{Choosing}} the {{Smoothing Parameter}} in the {{Generalized Spline Smoothing Problem}}}, - author = {Wahba, Grace}, - year = {1985}, - month = dec, - journal = {The Annals of Statistics}, - volume = {13}, - number = {4}, - pages = {1378--1402}, - doi = {10.1214/aos/1176349743}, - file = {/Users/dill/Zotero/storage/NF2YMSKR/euclid.aos.1176349743.pdf} -} - -@article{wahba_completely_1975, - title = {A Completely Automatic French Curve: Fitting Spline Functions by Cross Validation}, - shorttitle = {A Completely Automatic French Curve}, - author = {Wahba, G. and Wold, S.}, - year = {1975}, - month = jan, - journal = {Communications in Statistics}, - volume = {4}, - number = {1}, - pages = {1--17}, - issn = {0090-3272}, - doi = {10.1080/03610927508827223}, - langid = {english}, - file = {/Users/dill/Zotero/storage/SEFUAHA7/wahba1975.pdf} -} - -@article{wahba_improper_1978, - title = {Improper Priors, Spline Smoothing and the Problem of Guarding against Model Errors in Regression}, - author = {Wahba, Grace}, - year = {1978}, - journal = {Journal of the Royal Statistical Society. Series B (Methodological)}, - pages = {364--372}, - file = {/Users/dill/Zotero/storage/P3HHJ2CW/2984701.pdf} -} - -@book{wahba_spline_1990, - title = {Spline {{Models}} for {{Observational Data}}}, - author = {Wahba, Grace}, - year = {1990}, - publisher = {{Society for Industrial and Applied Mathematics}} -} - -@article{wakefield_summer_2021, - title = {The Summer Distribution, Habitat Associations and Abundance of Seabirds in the Sub-Polar Frontal Zone of the {{Northwest Atlantic}}}, - author = {Wakefield, Ewan D. and Miller, David L. and Bond, Sarah L. and {le Bouard}, Fabrice and Carvalho, Paloma C. and Catry, Paulo and Dilley, Ben J. and Fifield, David A. and Gjerdrum, Carina and {Gonz{\'a}lez-Sol{\'i}s}, Jacob and Hogan, Holly and Laptikhovsky, Vladimir and Merkel, Benjamin and Miller, Julie A.O. and Miller, Peter I. and Pinder, Simon J. and Pipa, T{\^a}nia and Ryan, Peter M. and Thompson, Laura A. and Thompson, Paul M. and Matthiopoulos, Jason}, - year = {2021}, - month = nov, - journal = {Progress in Oceanography}, - volume = {198}, - pages = {102657}, - issn = {00796611}, - doi = {10.1016/j.pocean.2021.102657}, - langid = {english}, - file = {/Users/dill/Zotero/storage/3X5YBSW3/Wakefield et al. - 2021 - The summer distribution, habitat associations and .pdf} -} - -@article{walker_salsa_2011, - title = {{{SALSA}} \textendash{} a Spatially Adaptive Local Smoothing Algorithm}, - author = {Walker, C. G. and Mackenzie, M. L. and Donovan, C. R. and O'Sullivan, M. J.}, - year = {2011}, - month = feb, - journal = {Journal of Statistical Computation and Simulation}, - volume = {81}, - number = {2}, - pages = {179--191}, - issn = {0094-9655, 1563-5163}, - doi = {10.1080/00949650903229041}, - langid = {english}, - file = {/Users/dill/Zotero/storage/AFSN99CS/walker2011.pdf} -} - -@article{wall_close_2004, - title = {A Close Look at the Spatial Structure Implied by the {{CAR}} and {{SAR}} Models}, - author = {Wall, Melanie M.}, - year = {2004}, - month = apr, - journal = {Journal of Statistical Planning and Inference}, - volume = {121}, - number = {2}, - pages = {311--324}, - issn = {03783758}, - doi = {10.1016/S0378-3758(03)00111-3}, - abstract = {Modeling spatial interactions that arise in spatially referenced data is commonly done by incorporating the spatial dependence into the covariance structure either explicitly or implicitly via an autoregressive model. In the case of lattice (regional summary) data, two common autoregressive models used are the conditional autoregressive model (CAR) and the simultaneously autoregressive model (SAR). Both of these models produce spatial dependence in the covariance structure as a function of a neighbor matrix W and often a \"yxed unknown spatial correlation parameter. This paper examines in detail the correlation structures implied by these models as applied to an irregular lattice in an attempt to demonstrate their many counterintuitive or impractical results. A data example is used for illustration where US statewide average SAT verbal scores are modeled and examined for spatial structure using di erent spatial models.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/BD6RGHU7/Wall - 2004 - A close look at the spatial structure implied by t.pdf} -} - -@article{wang_asymptotics_2011, - title = {On the Asymptotics of Penalized Spline Smoothing}, - author = {Wang, Xiao and Shen, Jinglai and Ruppert, David}, - year = {2011}, - journal = {Electronic Journal of Statistics}, - volume = {5}, - number = {0}, - pages = {1--17}, - issn = {1935-7524}, - doi = {10.1214/10-EJS593}, - langid = {english}, - file = {/Users/dill/Zotero/storage/D3SNSVK4/Electron. J. Statist. 2011 Wang.pdf} -} - -@article{wang_low-rank_2007, - title = {Low-{{Rank Smoothing Splines}} on {{Complicated Domains}}}, - author = {Wang, Haonan and Ranalli, M. Giovanna}, - year = {2007}, - month = mar, - journal = {Biometrics}, - volume = {63}, - number = {1}, - pages = {209--217}, - issn = {0006341X}, - doi = {10.1111/j.1541-0420.2006.00674.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/EYBWIQ8K/Biom 2006 Wang.pdf} -} - -@article{warton_many_2005, - title = {Many Zeros Does Not Mean Zero Inflation: Comparing the Goodness-of-Fit of Parametric Models to Multivariate Abundance Data}, - shorttitle = {Many Zeros Does Not Mean Zero Inflation}, - author = {Warton, David I.}, - year = {2005}, - month = may, - journal = {Environmetrics}, - volume = {16}, - number = {3}, - pages = {275--289}, - issn = {1180-4009, 1099-095X}, - doi = {10.1002/env.702}, - langid = {english}, - file = {/Users/dill/Zotero/storage/IFZ5ZEVY/Environmetrics 2005 Warton.pdf} -} - -@article{warton_poisson_2010, - title = {Poisson Point Process Models Solve the ``Pseudo-Absence Problem'' for Presence-Only Data in Ecology}, - author = {Warton, David I. and Shepherd, Leah C.}, - year = {2010}, - month = sep, - journal = {The Annals of Applied Statistics}, - volume = {4}, - number = {3}, - pages = {1383--1402}, - issn = {1932-6157}, - doi = {10.1214/10-AOAS331}, - langid = {english}, - file = {/Users/dill/Zotero/storage/4CH4KTCA/Ann. Appl. Stat. 2010 Warton.pdf} -} - -@book{wasserman_all_2004, - title = {All of {{Statistics}}: {{A Concise Course}} in {{Statistical Inference}}}, - author = {Wasserman, L.}, - year = {2004}, - series = {Springer {{Texts}} in {{Statistics}}}, - publisher = {{Springer}}, - isbn = {978-0-387-40272-7}, - lccn = {2003062209} -} - -@article{watson_general_2019, - title = {A General Theory for Preferential Sampling in Environmental Networks}, - author = {Watson, Joe and Zidek, James V. and Shaddick, Gavin}, - year = {2019}, - month = dec, - journal = {The Annals of Applied Statistics}, - volume = {13}, - number = {4}, - pages = {2662--2700}, - issn = {1932-6157}, - doi = {10.1214/19-AOAS1288}, - langid = {english}, - file = {/Users/dill/Zotero/storage/U3N2IVCR/Watson et al. - 2019 - A general theory for preferential sampling in envi.pdf} -} - -@article{watson_smoothing_1984, - title = {Smoothing and Interpolation by Kriging and with Splines}, - author = {Watson, G. S.}, - year = {1984}, - month = aug, - journal = {Journal of the International Association for Mathematical Geology}, - volume = {16}, - number = {6}, - pages = {601--615}, - issn = {0020-5958, 1573-8868}, - doi = {10.1007/BF01029320}, - abstract = {Let scalar measurements at distinct points x l , . . . , xn be Yl . . . . . Yn. We may look for a smooth function f(x) that goes through or near the points (xi, Yi).Kriging assumes f(x) is a random function with known (possibly estimable) covariance function (in the simplest case). Splines assume a definition o f the smoothness of a nonrandom function f(x). An elementary explanation is given o f the fact that spline approximations are speciat cases o f the solution of a kriging problem.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YHFKUSZ2/Watson - 1984 - Smoothing and interpolation by kriging and with sp.pdf} -} - -@article{watt_population_2020, - title = {Population Dynamics of the Threatened {{Cumberland Sound}} Beluga ( {{{\emph{Delphinapterus}}}}{\emph{ Leucas}} ) Population}, - author = {Watt, Cortney A. and Marcoux, Marianne and Ferguson, Steven H. and Hammill, Mike O. and Matthews, Cory J.D.}, - year = {2020}, - month = dec, - journal = {Arctic Science}, - pages = {1--22}, - issn = {2368-7460}, - doi = {10.1139/as-2019-0030}, - abstract = {Current scientific evidence indicates that the threatened Cumberland Sound beluga whale (Delphinapterus leucas (Pallas, 1776)) population is genetically differentiated and spatially segregated from other beluga whale populations. This population has been hunted for subsistence for centuries by Inuit who now live in the community of Pangnirtung, Nunavut, Canada, and was harvested commercially from 1860 until 1966. The commercial harvest removed at least 10 000 individuals from the population. Visual and photographic aerial surveys were flown during August 2014 and 2017 and produced beluga whale abundance estimates of 1151 (CV = 0.214; 95\% confidence interval (CI) = 760\textendash 1744) and 1381 (CV = 0.043; CI = 1270\textendash 1502), respectively. Long-term trends in abundance were examined by fitting a Bayesian surplus-production population model to a time series of abundance estimates (n = 5), flown between 1990 and 2017, taking into account reported subsistence harvests (1960\textendash 2017). The model suggests the population is declining. Engaged co-management of the Cumberland Sound beluga population and information on demographic parameters, such as reproductive rates, and age and sex composition of the harvest, are needed to restore the ecological integrity of the Cumberland Sound marine ecosystem.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/X9QYPEJ9/Watt et al. - 2020 - Population dynamics of the threatened Cumberland S.pdf} -} - -@article{watwood_deep-diving_2006, - title = {Deep-Diving Foraging Behaviour of Sperm Whales ({{Physeter}} Macrocephalus)}, - author = {Watwood, Stephanie L. and Miller, Patrick J. O. and Johnson, Mark and Madsen, Peter T. and Tyack, Peter L.}, - year = {2006}, - month = may, - journal = {Journal of Animal Ecology}, - volume = {75}, - number = {3}, - pages = {814--825}, - issn = {0021-8790, 1365-2656}, - doi = {10.1111/j.1365-2656.2006.01101.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/LUMJCHSQ/Watwood et al. - 2006 - Deep-diving foraging behaviour of sperm whales (Ph.pdf} -} - -@article{wegener_forecasting_2017, - title = {Forecasting in Nonlinear Univariate Time Series Using Penalized Splines}, - author = {Wegener, Michael and Kauermann, G{\"o}ran}, - year = {2017}, - month = sep, - journal = {Statistical Papers}, - volume = {58}, - number = {3}, - pages = {557--576}, - issn = {0932-5026, 1613-9798}, - doi = {10.1007/s00362-015-0711-1}, - langid = {english}, - file = {/Users/dill/Zotero/storage/WJ8X33CM/Statistical Papers 2015 Wegener.pdf} -} - -@article{weissgerber_beyond_2015, - title = {Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm}, - shorttitle = {Beyond Bar and Line Graphs}, - author = {Weissgerber, Tracey L. and Milic, Natasa M. and Winham, Stacey J. and Garovic, Vesna D.}, - year = {2015}, - journal = {PLoS biology}, - volume = {13}, - number = {4}, - pages = {e1002128}, - file = {/Users/dill/Zotero/storage/KJQPMPH9/PLoS Biol 2015 Weissgerber.pdf} -} - -@article{wenger_assessing_2012, - title = {Assessing Transferability of Ecological Models: An Underappreciated Aspect of Statistical Validation: {{Model}} Transferability}, - shorttitle = {Assessing Transferability of Ecological Models}, - author = {Wenger, Seth J. and Olden, Julian D.}, - year = {2012}, - month = apr, - journal = {Methods in Ecology and Evolution}, - volume = {3}, - number = {2}, - pages = {260--267}, - issn = {2041210X}, - doi = {10.1111/j.2041-210X.2011.00170.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/Y4YUQADC/Wenger_et_al-2012-Methods_in_Ecology_and_Evolution.pdf;/Users/dill/Zotero/storage/ZFN48MGK/Methods in Ecology and Evolution 2012 Wenger.pdf} -} - -@article{whittle_stationary_1954, - title = {On {{Stationary Processes}} in the {{Plane}}}, - author = {Whittle, P}, - year = {1954}, - journal = {Biometrika}, - volume = {41}, - number = {3}, - pages = {434--449}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2UY6RNXH/Whittle - On Stationary Processes in the Plane.pdf} -} - -@article{wickham_split-apply-combine_2011, - title = {The {{Split-Apply-Combine Strategy}} for {{Data Analysis}}}, - author = {Wickham, Hadley}, - year = {2011}, - journal = {Journal of Statistical Software}, - volume = {40}, - number = {1}, - issn = {1548-7660}, - doi = {10.18637/jss.v040.i01}, - abstract = {Many data analysis problems involve the application of a split-apply-combine strategy, where you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together. This insight gives rise to a new R package that allows you to smoothly apply this strategy, without having to worry about the type of structure in which your data is stored.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/V6323X8C/Wickham - 2011 - The Split-Apply-Combine Strategy for Data Analysis.pdf} -} - -@article{wickham_testthat_2011, - title = {Testthat: {{Get}} Started with Testing}, - shorttitle = {Testthat}, - author = {Wickham, Hadley}, - year = {2011}, - journal = {The R Journal}, - volume = {3}, - number = {1}, - pages = {5--10}, - file = {/Users/dill/Zotero/storage/S6ZFCGRH/The R Journal 2011 Wickham.pdf} -} - -@article{wiegand_dealing_2004, - title = {Dealing with Uncertainty in Spatially Explicit Population Models}, - author = {Wiegand, Thorsten and Revilla, Eloy and Knauer, Felix}, - year = {2004}, - journal = {Biodiversity \& Conservation}, - volume = {13}, - number = {1}, - pages = {53--78}, - file = {/Users/dill/Zotero/storage/H3928M5L/Biodiversity and Conservation 2004 Wiegand.pdf} -} - -@article{wikle_comparison_2019, - title = {Comparison of {{Deep Neural Networks}} and {{Deep Hierarchical Models}} for {{Spatio-Temporal Data}}}, - author = {Wikle, Christopher K}, - year = {2019}, - journal = {Journal of Agricultural, Biological and Environmental Statistics}, - volume = {24}, - number = {2}, - pages = {29}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YW6JNZYL/Wikle - Comparison of Deep Neural Networks and Deep Hierar.pdf} -} - -@article{wilkinson_adding_1970, - title = {Adding a {{Point}} to a {{Principal Coordinates Analysis}}}, - author = {Wilkinson, Christopher}, - year = {1970}, - month = sep, - journal = {Systematic Zoology}, - volume = {19}, - number = {3}, - pages = {258}, - issn = {00397989}, - doi = {10.2307/2412210}, - file = {/Users/dill/Zotero/storage/4ZB7N3B8/Syst. Zool. 1970 Wilkinson.pdf} -} - -@article{williams_behavioural_2006, - title = {Behavioural Responses of Killer Whales ({{Orcinus}} Orca) to Whale-Watching Boats: Opportunistic Observations and Experimental Approaches: {{Behavioural}} Responses of Killer Whales to Whale-Watching}, - shorttitle = {Behavioural Responses of Killer Whales ({{Orcinus}} Orca) to Whale-Watching Boats}, - author = {Williams, Rob and Trites, Andrew W. and Bain, David E.}, - year = {2006}, - month = feb, - journal = {Journal of Zoology}, - volume = {256}, - number = {2}, - pages = {255--270}, - issn = {09528369}, - doi = {10.1017/S0952836902000298}, - langid = {english}, - file = {/Users/dill/Zotero/storage/DUDSL65X/Journal of Zoology 2002 Williams.pdf} -} - -@article{williams_chilean_2011, - title = {Chilean {{Blue Whales}} as a {{Case Study}} to {{Illustrate Methods}} to {{Estimate Abundance}} and {{Evaluate Conservation Status}} of {{Rare Species}}: {{Estimating Abundance}} of {{Rare Species}}}, - shorttitle = {Chilean {{Blue Whales}} as a {{Case Study}} to {{Illustrate Methods}} to {{Estimate Abundance}} and {{Evaluate Conservation Status}} of {{Rare Species}}}, - author = {Williams, Rob and Hedley, Sharon L. and Branch, Trevor A. and Bravington, Mark V. and Zerbini, Alexandre N. and Findlay, Ken P.}, - year = {2011}, - month = jun, - journal = {Conservation Biology}, - volume = {25}, - number = {3}, - pages = {526--535}, - issn = {08888892}, - doi = {10.1111/j.1523-1739.2011.01656.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/SDN6CW8Z/Conservation Biology 2011 Williams.pdf} -} - -@article{williams_counting_2015, - title = {Counting Whales in a Challenging, Changing Environment}, - author = {Williams, R. and Kelly, N. and Boebel, O. and Friedlaender, A. S. and Herr, H. and Kock, K.-H. and Lehnert, L. S. and Maksym, T. and Roberts, J. and Scheidat, M. and Siebert, U. and Brierley, A. S.}, - year = {2015}, - month = may, - journal = {Scientific Reports}, - volume = {4}, - number = {1}, - issn = {2045-2322}, - doi = {10.1038/srep04170}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YD5Q7Z43/Sci. Rep. 2014 Williams.pdf} -} - -@article{williams_distribution_2007, - title = {Distribution and Abundance of Marine Mammals in the Coastal Waters of {{British Columbia}}, {{Canada}}}, - author = {Williams, Rob and Thomas, Len}, - year = {2007}, - journal = {Journal of Cetacean Research and Management}, - volume = {9}, - number = {1}, - pages = {15}, - file = {/Users/dill/Zotero/storage/NHRHRME3/J Cetacean Res Manage 2007 Williams.pdf} -} - -@article{williams_modeling_2006, - title = {Modeling Distribution and Abundance of {{Antarctic}} Baleen Whales Using Ships of Opportunity}, - author = {Williams, Rob and Hedley, Sharon L. and Hammond, Philip S.}, - year = {2006}, - journal = {Ecology and Society}, - volume = {11}, - number = {1}, - file = {/Users/dill/Zotero/storage/G77S2LQX/Ecology and … 2006 Williams.pdf} -} - -@article{williams_prioritizing_2014, - title = {Prioritizing Global Marine Mammal Habitats Using Density Maps in Place of Range Maps}, - author = {Williams, Rob and Grand, Joanna and Hooker, Sascha K. and Buckland, Stephen T. and Reeves, Randall R. and {Rojas-Bracho}, Lorenzo and Sandilands, Doug and Kaschner, Kristin}, - year = {2014}, - month = mar, - journal = {Ecography}, - volume = {37}, - number = {3}, - pages = {212--220}, - issn = {09067590}, - doi = {10.1111/j.1600-0587.2013.00479.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/T6EZV5XI/Ecography 2013 Williams.pdf} -} - -@article{winiarski_integrating_2014, - title = {Integrating Aerial and Ship Surveys of Marine Birds into a Combined Density Surface Model: {{A}} Case Study of Wintering {{Common Loons}}}, - shorttitle = {Integrating Aerial and Ship Surveys of Marine Birds into a Combined Density Surface Model}, - author = {Winiarski, Kristopher J. and Burt, M. Louise and Rexstad, Eric and Miller, David L. and Trocki, Carol L. and Paton, Peter W. C. and McWilliams, Scott R.}, - year = {2014}, - month = may, - journal = {The Condor}, - volume = {116}, - number = {2}, - pages = {149--161}, - issn = {0010-5422, 1938-5129}, - doi = {10.1650/CONDOR-13-085.1}, - langid = {english}, - file = {/Users/dill/Zotero/storage/38MKGLIU/The Condor 2014 Winiarski.pdf} -} - -@article{winiarski_spatial_2014, - title = {A Spatial Conservation Prioritization Approach for Protecting Marine Birds given Proposed Offshore Wind Energy Development}, - author = {Winiarski, Kristopher J. and Miller, David L. and Paton, Peter W.C. and McWilliams, Scott R.}, - year = {2014}, - month = jan, - journal = {Biological Conservation}, - volume = {169}, - pages = {79--88}, - issn = {00063207}, - doi = {10.1016/j.biocon.2013.11.004}, - langid = {english}, - file = {/Users/dill/Zotero/storage/J4VKXEJD/Biological Conservation 2014 Winiarski.pdf} -} - -@article{winiarski_spatially_2013, - title = {Spatially Explicit Model of Wintering Common Loons: Conservation Implications}, - shorttitle = {Spatially Explicit Model of Wintering Common Loons}, - author = {Winiarski, Kj and Miller, Dl and Paton, Pwc and McWilliams, Sr}, - year = {2013}, - month = oct, - journal = {Marine Ecology Progress Series}, - volume = {492}, - pages = {273--283}, - issn = {0171-8630, 1616-1599}, - doi = {10.3354/meps10492}, - langid = {english}, - file = {/Users/dill/Zotero/storage/N3KGNSE6/Mar. Ecol. Prog. Ser. 2013 Winiarski.pdf} -} - -@techreport{witting_abundance_2005, - title = {Abundance of Marine Mammals off {{West Greenland}}, 2002-2004}, - author = {Witting, Lars and Kingsley, Michael}, - year = {2005}, - number = {SC/57/AWMP/3}, - pages = {13}, - institution = {{International Whaling Commission}}, - abstract = {An aerial digital photo based strip-width survey for marine mammals off West Greenland was carried out over a total of 4.5 months in the fall of 2002 and 2004. The method was tested in the field against cue counting on Iceland in 2003 and found to be an efficient method for minke whale surveys. The total block area of the survey was 290 thousand km2, with 3.7\% of the area covered by images taken at sea state three or less. Sightings include two minke whales, three humpback whales, seven fin whales, and 1366 harp seals. Uncorrected estimates of animals at the surface are 46 (CV:0.74) minke whales, 100 (CV: 0.64) humpback whales, 250 (CV: 0.48) fin whales, and 33,000 (CV: 0.22) harp seals. Correcting the minke whale estimate for whales missed by observers and for animals not at the surface gives an estimate of 510 (CV: 0.75) whales, which is significantly smaller than a revised estimate of 6,390 (CV: 0.41) whales in 1993 (Larsen, 1995; Hedley et al., 1997). Correcting the fin whale estimate for animals not at the surface gives an estimate of 980 (CV: 0.48) whales, which is similar to an estimate of 1,100 (95\% CI: 520 - 2, 100) whales in 1987-88 (IWC, 1992). A rough correction of the humpback estimate for animals not at the surface suggests an abundance of approximately 400 whales, which is similar to a mark-recapture estimate of 359 (CV: 0.08) animals for 1988-1993 (Larsen \& Hammond, 2004).}, - langid = {english}, - file = {/Users/dill/Zotero/storage/92QSRNBE/Witting and Kingsley - Abundance of marine mammals off West Greenland, 200.pdf} -} - -@techreport{witting_applying_2005, - title = {Applying Aerial Digital Photo Based Strip-Width Surveys to Minke Whales}, - author = {Witting, Lars and Pike, Daniel G}, - year = {2005}, - number = {SC/57/AWMP/2}, - pages = {15}, - institution = {{International Whaling Commission}}, - abstract = {An aerial digital photo based strip-width survey for minke whales was experimentally tested against a cue-counting survey in Faxafl\textasciiacute oi Bay in September 2003. It was found that i) nearly all the minke whales that were surfacing within the photo-frame were photographed when pictures were taken every 2.6 sec. This percentage dropped linearly from 85\% to 21\% when pictures were taken every 5.2 sec. to every 13 sec. ii) The proportion of minke whales that were identified during a single reading of non-overlapping images was 0.85 (SE:0.01). iii) The distributions of perpendicular distances to whales identified on the images did not differ significantly from uniform (0.05 level), confirming that strip-width analysis can be applied to aerial photo surveys for minke whales. iv) The distribution of perpendicular distances for surfacing whales identified during the reading process did not differ significantly from the perpendicular distances for true distribution of surfacing minke whales, indicating that the image reading process does not induced a bias to the distribution of perpendicular distances. And v) the number of sightings expected from a photo based strip-width survey is comparable to the expected number from a corresponding cue-count survey.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QLGZBEKA/Witting and Pike - Applying aerial digital photo based strip-width su.pdf} -} - -@article{witting_distance_2009, - title = {Distance Estimation Experiment for Aerial Minke Whale Surveys}, - author = {Witting, Lars and Pike, Daniel G}, - year = {2009}, - month = sep, - journal = {NAMMCO Scientific Publications}, - volume = {7}, - pages = {111}, - issn = {2309-2491, 1560-2206}, - doi = {10.7557/3.2709}, - abstract = {A comparative study between aerial cue\textendash counting and digital photography surveys for minke whales conducted in Faxafl\'oi Bay in September 2003 is used to check the perpendicular distances estimated by the cue-counting observers. The study involved 2 aircraft with the photo plane at 1,700 feet flying above the cue\textendash counting plane at 750 feet. The observer\textendash based distance estimates were calculated from head angles estimated by angle-boards and declination angles estimated by declinometers. These distances were checked against image\textendash based estimates of the perpendicular distance to the same whale. The 2 independent distance estimates were obtained for 21 sightings of minke whale, and there was a good agreement between the 2 types of estimates. The relative absolute deviations between the 2 estimates were on average 23\% (se: 6\%), with the errors in the observer\textendash based distance estimates resembling that of a log-normal distribution. The linear regression of the observer\textendash based estimates (obs) on the image\textendash based estimates (img) was Obs=1.1Img (R2=0.85) with an intercept fixed at zero. There was no evidence of a distance estimation bias that could generate a positive bias in the absolute abundance estimated by cue\textendash counting.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/BXZRQKAK/Witting and Pike - 2009 - Distance estimation experiment for aerial minke wh.pdf} -} - -@article{wood_confidence_2006, - title = {On {{Confidence Intervals For Generalized Additive Models Based On Penalized Regression Splines}}}, - author = {Wood, Simon N.}, - year = {2006}, - month = dec, - journal = {Australian \& New Zealand Journal of Statistics}, - volume = {48}, - number = {4}, - pages = {445--464}, - issn = {1369-1473, 1467-842X}, - doi = {10.1111/j.1467-842X.2006.00450.x}, - langid = {english}, - file = {/Users/dill/Zotero/storage/4UEX3QQZ/Aust. N. Z. J. Stat. 2006 Wood.pdf} -} - -@article{wood_fast_2008, - title = {Fast Stable Direct Fitting and Smoothness Selection for Generalized Additive Models}, - author = {Wood, Simon N.}, - year = {2008}, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {70}, - number = {3}, - pages = {495--518}, - file = {/Users/dill/Zotero/storage/ZYFUYMCH/Journal of the Royal Statistical Society Series B (Statistical Methodology) 2008 Wood-1.pdf} -} - -@article{wood_fast_2011, - title = {Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models}, - author = {Wood, S. N.}, - year = {2011}, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {73}, - number = {1}, - pages = {3--36}, - file = {/Users/dill/Zotero/storage/9AS2HUHT/Journal of the Royal Statistical Society Series B (Statistical Methodology) 2011 Wood.pdf} -} - -@article{wood_gams_2002, - title = {{{GAMs}} with Integrated Model Selection Using Penalized Regression Splines and Applications to Environmental Modelling}, - author = {Wood, Simon N. and Augustin, Nicole H.}, - year = {2002}, - month = nov, - journal = {Ecological Modelling}, - volume = {157}, - number = {2-3}, - pages = {157--177}, - issn = {03043800}, - doi = {10.1016/S0304-3800(02)00193-X}, - abstract = {Generalized Additive Models (GAMs) have been popularized by the work of Hastie and Tibshirani (1990) and the availability of user friendly GAM software in Splus. However, whilst it is flexible and efficient, the GAM framework based on backfitting with linear smoothers presents some difficulties when it comes to model selection and inference. On the other hand, the mathematically elegant work of Wahba (1990) and co-workers on Generalized Spline Smoothing (GSS) provides a rigorous framework for model selection (Gu and Wahba, 1991) and inference with GAMs constructed from smoothing splines: but unfortunately these models are computationally very expensive with operations counts that are of cubic order in the number of data. A ``middle way'' between these approaches is to construct GAMs using penalized regression splines (see e.g. Wahba 1980, 1990; Eilers and Marx 1998, Wood 2000). In this paper we develop this idea and show how GAMs constructed using penalized regression splines can be used to get most of the practical benefits of GSS models, including well founded model selection and multi-dimensional smooth terms, with the ease of use and low computational cost of backfit GAMs. Inference with the resulting methods also requires slightly fewer approximations than are employed in the GAM modelling software provided in Splus. This paper presents the basic mathematical and numerical approach to GAMs implemented in the R package mgcv, and includes two environmental examples using the methods as implemented in the package.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/AKL6E6G6/Wood and Augustin - 2002 - GAMs with integrated model selection using penaliz.pdf} -} - -@article{wood_generalized_2015, - title = {Generalized Additive Models for Large Data Sets}, - author = {Wood, Simon N. and Goude, Yannig and Shaw, Simon}, - year = {2015}, - journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}, - volume = {64}, - number = {1}, - pages = {139--155}, - file = {/Users/dill/Zotero/storage/UE6AR9NB/Journal of the Royal Statistical Society Series C (Applied Statistics) 2015 Wood.pdf} -} - -@article{wood_generalized_2017, - title = {Generalized {{Additive Models}} for {{Gigadata}}: {{Modeling}} the {{U}}.{{K}}. {{Black Smoke Network Daily Data}}}, - shorttitle = {Generalized {{Additive Models}} for {{Gigadata}}}, - author = {Wood, Simon N. and Li, Zheyuan and Shaddick, Gavin and Augustin, Nicole H.}, - year = {2017}, - month = jul, - journal = {Journal of the American Statistical Association}, - volume = {112}, - number = {519}, - pages = {1199--1210}, - issn = {0162-1459, 1537-274X}, - doi = {10.1080/01621459.2016.1195744}, - langid = {english}, - file = {/Users/dill/Zotero/storage/MYJ2G6IM/Journal of the American Statistical Association 2016 Wood-1.pdf;/Users/dill/Zotero/storage/Q2D2D88A/Wood et al. - Supplementary material for Generalized additive m.pdf;/Users/dill/Zotero/storage/VBV8CPAD/gigam2-AppBC.pdf} -} - -@book{wood_generalized_2017-1, - title = {Generalized {{Additive Models}}. {{An Introduction}} with {{R}}}, - author = {Wood, S. N.}, - year = {2017}, - series = {Texts in {{Statistical Science}}}, - edition = {Second}, - publisher = {{CRC Press}} -} - -@article{wood_generalized_2017-2, - title = {A Generalized {{Fellner}}-{{Schall}} Method for Smoothing Parameter Optimization with Application to {{Tweedie}} Location, Scale and Shape Models}, - author = {Wood, Simon N. and Fasiolo, Matteo}, - year = {2017}, - month = dec, - journal = {Biometrics}, - volume = {73}, - number = {4}, - pages = {1071--1081}, - issn = {0006-341X, 1541-0420}, - doi = {10.1111/biom.12666}, - abstract = {We consider the optimization of smoothing parameters and variance components in models with a regular log likelihood subject to quadratic penalization of the model coefficients, via a generalization of the method of Fellner (1986) and Schall (1991). In particular: (i) we generalize the original method to the case of penalties that are linear in several smoothing parameters, thereby covering the important cases of tensor product and adaptive smoothers; (ii) we show why the method's steps increase the restricted marginal likelihood of the model, that it tends to converge faster than the EM algorithm, or obvious accelerations of this, and investigate its relation to Newton optimization; (iii) we generalize the method to any Fisher regular likelihood. The method represents a considerable simplification over existing methods of estimating smoothing parameters in the context of regular likelihoods, without sacrificing generality: for example, it is only necessary to compute with the same first and second derivatives of the log-likelihood required for coefficient estimation, and not with the third or fourth order derivatives required by alternative approaches. Examples are provided which would have been impossible or impractical with pre-existing Fellner-Schall methods, along with an example of a Tweedie location, scale and shape model which would be a challenge for alternative methods, and a sparse additive modeling example where the method facilitates computational efficiency gains of several orders of magnitude.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/FLYRP9DP/biom12666-sup-0001-suppdata.pdf;/Users/dill/Zotero/storage/KEYKBUAP/Wood and Fasiolo - 2017 - A generalized Fellner‐Schall method for smoothing .pdf} -} - -@article{wood_just_2016, - title = {Just {{Another Gibbs Additive Modeler}}: {{Interfacing JAGS}} and Mgcv}, - shorttitle = {Just {{Another Gibbs Additive Modeler}}}, - author = {Wood, Simon N.}, - year = {2016}, - journal = {Journal of Statistical Software}, - volume = {75}, - number = {7}, - issn = {1548-7660}, - doi = {10.18637/jss.v075.i07}, - langid = {english}, - file = {/Users/dill/Zotero/storage/PAJ5IHRF/v75i07.pdf} -} - -@article{wood_minimizing_2001, - title = {Minimizing Model Fitting Objectives That Contain Spurious Local Minima by Bootstrap Restarting}, - author = {Wood, Simon N.}, - year = {2001}, - journal = {Biometrics}, - volume = {57}, - number = {1}, - pages = {240--244}, - file = {/Users/dill/Zotero/storage/YGE3QRW5/Biom 2001 Wood.pdf} -} - -@article{wood_modelling_2000, - title = {Modelling and Smoothing Parameter Estimation with Multiple Quadratic Penalties}, - author = {Wood, Simon N.}, - year = {2000}, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {62}, - number = {2}, - pages = {413--428}, - file = {/Users/dill/Zotero/storage/QKYQ7WQG/Journal of the Royal Statistical Society Series B (Statistical Methodology) 2000 Wood.pdf} -} - -@article{wood_p-splines_2017, - title = {P-Splines with Derivative Based Penalties and Tensor Product Smoothing of Unevenly Distributed Data}, - author = {Wood, Simon N.}, - year = {2017}, - month = jul, - journal = {Statistics and Computing}, - volume = {27}, - number = {4}, - eprint = {1605.02446}, - eprinttype = {arxiv}, - pages = {985--989}, - issn = {0960-3174, 1573-1375}, - doi = {w}, - abstract = {The P-splines of Eilers and Marx (1996) combine a B-spline basis with a discrete quadratic penalty on the basis coefficients, to produce a reduced rank spline like smoother. P-splines have three properties that make them very popular as reduced rank smoothers: i) the basis and the penalty are sparse, enabling efficient computation, especially for Bayesian stochastic simulation; ii) it is possible to flexibly `mix-and-match' the order of B-spline basis and penalty, rather than the order of penalty controlling the order of the basis as in spline smoothing; iii) it is very easy to set up the Bspline basis functions and penalties. The discrete penalties are somewhat less interpretable in terms of function shape than the traditional derivative based spline penalties, but tend towards penalties proportional to traditional spline penalties in the limit of large basis size. However part of the point of P-splines is not to use a large basis size. In addition the spline basis functions arise from solving functional optimization problems involving derivative based penalties, so moving to discrete penalties for smoothing may not always be desirable. The purpose of this note is to point out that the three properties of basis-penalty sparsity, mix-and-match penalization and ease of setup are readily obtainable with B-splines subject to derivative based penalization. The penalty setup typically requires a few lines of code, rather than the two lines typically required for P-splines, but this one off disadvantage seems to be the only one associated with using derivative based penalties. As an example application, it is shown how basis-penalty sparsity enables efficient computation with tensor product smoothers of scattered data.}, - archiveprefix = {arXiv}, - langid = {english}, - keywords = {Statistics - Computation}, - file = {/Users/dill/Zotero/storage/798IBWJM/Wood - 2017 - P-splines with derivative based penalties and tens.pdf} -} - -@article{wood_p-values_2013, - title = {On P-Values for Smooth Components of an Extended Generalized Additive Model}, - author = {Wood, S. N.}, - year = {2013}, - month = mar, - journal = {Biometrika}, - volume = {100}, - number = {1}, - pages = {221--228}, - issn = {0006-3444, 1464-3510}, - doi = {10.1093/biomet/ass048}, - langid = {english}, - file = {/Users/dill/Zotero/storage/CMBZ4MGE/Biometrika 2013 Wood-1.pdf} -} - -@article{wood_simple_2013, - title = {A Simple Test for Random Effects in Regression Models}, - author = {Wood, S. N.}, - year = {2013}, - month = dec, - journal = {Biometrika}, - volume = {100}, - number = {4}, - pages = {1005--1010}, - issn = {0006-3444, 1464-3510}, - doi = {10.1093/biomet/ast038}, - langid = {english}, - file = {/Users/dill/Zotero/storage/KW8K3ULE/Biometrika 2013 Wood.pdf} -} - -@article{wood_simplified_2019, - title = {Simplified Integrated Nested {{Laplace}} Approximation}, - author = {Wood, Simon N}, - year = {2019}, - month = sep, - journal = {Biometrika}, - issn = {0006-3444, 1464-3510}, - doi = {10.1093/biomet/asz044}, - abstract = {Integrated nested Laplace approximation provides accurate and efficient approximations for marginal distributions in latent Gaussian random field models. Computational feasibility of the original Rue et al. (2009) methods relies on efficient approximation of Laplace approximations for the marginal distributions of the coefficients of the latent field, conditional on the data and hyperparameters. The computational efficiency of these approximations depends on the Gaussian field having a Markov structure. This note provides equivalent efficiency without requiring the Markov property, which allows for straightforward use of latent Gaussian fields without a sparse structure, such as reduced rank multi-dimensional smoothing splines. The method avoids the approximation for conditional modes used in Rue et al. (2009), and uses a log determinant approximation based on a simple quasi-Newton update. The latter has a desirable property not shared by the most commonly used variant of the original method.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9PK6VXGI/Wood - 2019 - Simplified integrated nested Laplace approximation.pdf} -} - -@article{wood_simplified_2019-1, - title = {Simplified Integrated Nested {{Laplace}} Approximation}, - author = {Wood, Simon N}, - year = {2019}, - month = sep, - journal = {Biometrika}, - pages = {asz044}, - issn = {0006-3444, 1464-3510}, - doi = {10.1093/biomet/asz044}, - abstract = {Summary Integrated nested Laplace approximation provides accurate and efficient approximations for marginal distributions in latent Gaussian random field models. Computational feasibility of the original Rue et al. (2009) methods relies on efficient approximation of Laplace approximations for the marginal distributions of the coefficients of the latent field, conditional on the data and hyperparameters. The computational efficiency of these approximations depends on the Gaussian field having a Markov structure. This note provides equivalent efficiency without requiring the Markov property, which allows for straightforward use of latent Gaussian fields without a sparse structure, such as reduced rank multi-dimensional smoothing splines. The method avoids the approximation for conditional modes used in Rue et al. (2009), and uses a log determinant approximation based on a simple quasi-Newton update. The latter has a desirable property not shared by the most commonly used variant of the original method.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/QPFCEUQS/Wood - 2019 - Simplified integrated nested Laplace approximation.pdf} -} - -@article{wood_smoothing_2016, - title = {Smoothing {{Parameter}} and {{Model Selection}} for {{General Smooth Models}}}, - author = {Wood, Simon N. and Pya, Natalya and S\{{\textbackslash}"a\}fken, Benjamin}, - year = {2016}, - month = oct, - journal = {Journal of the American Statistical Association}, - volume = {111}, - number = {516}, - pages = {1548--1563}, - issn = {0162-1459, 1537-274X}, - doi = {10.1080/01621459.2016.1180986}, - abstract = {This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to be quantified. The latter enables us to fix a well known problem with AIC for such models, thereby improving the range of model selection tools available. The smooth functions are represented by reduced rank spline like smoothers, with associated quadratic penalties measuring function smoothness. Model estimation is by penalized likelihood maximization, where the smoothing parameters controlling the extent of penalization are estimated by Laplace approximate marginal likelihood. The methods cover, for example, generalized additive models for nonexponential family responses (e.g., beta, ordered categorical, scaled t distribution, negative binomial and Tweedie distributions), generalized additive models for location scale and shape (e.g., two stage zero inflation models, and Gaussian location-scale models), Cox proportional hazards models and multivariate additive models. The framework reduces the implementation of new model classes to the coding of some standard derivatives of the log-likelihood. Supplementary materials for this article are available online.}, - langid = {english}, - annotation = {cites: wood\_smoothing\_2016}, - file = {/Users/dill/Zotero/storage/7ICS9Q9D/Wood et al. - 2016 - Smoothing Parameter and Model Selection for Genera.pdf} -} - -@article{wood_soap_2008, - title = {Soap Film Smoothing}, - author = {Wood, Simon N. and Bravington, Mark V. and Hedley, Sharon L.}, - year = {2008}, - month = nov, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {70}, - number = {5}, - pages = {931--955}, - issn = {13697412, 14679868}, - doi = {10.1111/j.1467-9868.2008.00665.x}, - abstract = {Conventional smoothing methods sometimes perform badly when used to smooth data over complex domains, by smoothing inappropriately across boundary features, such as peninsulas. Solutions to this smoothing problem tend to be computationally complex, and not to provide model smooth functions which are appropriate for incorporating as components of other models, such as generalized additive models, or mixed additive models. In this paper we propose a class of smoothers appropriate for smoothing over difficult regions of R2, which can be represented in terms of a low rank basis and one or two quadratic penalties. The key features of these smoothers are (i) that they do not `smooth across' boundary features; (ii) that their representation in terms of a basis and penalties allows straightforward incorporation as components of GAMs, mixed models and other non-standard models; (iii) that smoothness selection for these model components is straightforward to accomplish in a computationally efficient manner via GCV, AIC or REML, for example; (iv) that their low rank means that their use is computationally efficient.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/U7WQB7WY/Wood et al. - 2008 - Soap film smoothing.pdf} -} - -@article{wood_stable_2004, - title = {Stable and {{Efficient Multiple Smoothing Parameter Estimation}} for {{Generalized Additive Models}}}, - author = {Wood, Simon N}, - year = {2004}, - month = sep, - journal = {Journal of the American Statistical Association}, - volume = {99}, - number = {467}, - pages = {673--686}, - issn = {0162-1459, 1537-274X}, - doi = {10.1198/016214504000000980}, - langid = {english}, - file = {/Users/dill/Zotero/storage/UT79ZXHM/Journal of the American Statistical Association 2004 Wood.pdf} -} - -@article{wood_statistical_2010, - title = {Statistical Inference for Noisy Nonlinear Ecological Dynamic Systems}, - author = {Wood, Simon N.}, - year = {2010}, - month = aug, - journal = {Nature}, - volume = {466}, - number = {7310}, - pages = {1102--1104}, - issn = {0028-0836, 1476-4687}, - doi = {10.1038/nature09319}, - langid = {english}, - file = {/Users/dill/Zotero/storage/2XPLINHJ/41586_2010_BFnature09319_MOESM302_ESM.pdf;/Users/dill/Zotero/storage/9FI5ENHM/Wood - 2010 - Statistical inference for noisy nonlinear ecologic.pdf} -} - -@article{wood_straightforward_2013, - title = {Straightforward Intermediate Rank Tensor Product Smoothing in Mixed Models}, - author = {Wood, Simon N. and Scheipl, Fabian and Faraway, Julian J.}, - year = {2013}, - month = may, - journal = {Statistics and Computing}, - volume = {23}, - number = {3}, - pages = {341--360}, - issn = {0960-3174, 1573-1375}, - doi = {10.1007/s11222-012-9314-z}, - langid = {english}, - file = {/Users/dill/Zotero/storage/9ZUS87TC/Wood et al. - 2013 - Straightforward intermediate rank tensor product s.pdf} -} - -@article{wood_thin_2003, - title = {Thin Plate Regression Splines}, - author = {Wood, Simon N.}, - year = {2003}, - journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, - volume = {65}, - number = {1}, - pages = {95--114}, - file = {/Users/dill/Zotero/storage/38G6DVXY/Journal of the Royal Statistical Society Series B (Statistical Methodology) 2003 Wood.pdf} -} - -@article{yates_outstanding_2018, - title = {Outstanding {{Challenges}} in the {{Transferability}} of {{Ecological Models}}}, - author = {Yates, Katherine L. and Bouchet, Phil J. and Caley, M. Julian and Mengersen, Kerrie and Randin, Christophe F. and Parnell, Stephen and Fielding, Alan H. and Bamford, Andrew J. and Ban, Stephen and Barbosa, A. M{\'a}rcia and Dormann, Carsten F. and Elith, Jane and Embling, Clare B. and Ervin, Gary N. and Fisher, Rebecca and Gould, Susan and Graf, Roland F. and Gregr, Edward J. and Halpin, Patrick N. and Heikkinen, Risto K. and Hein{\"a}nen, Stefan and Jones, Alice R. and Krishnakumar, Periyadan K. and Lauria, Valentina and {Lozano-Montes}, Hector and Mannocci, Laura and Mellin, Camille and Mesgaran, Mohsen B. and {Moreno-Amat}, Elena and Mormede, Sophie and Novaczek, Emilie and Oppel, Steffen and Ortu{\~n}o Crespo, Guillermo and Peterson, A. Townsend and Rapacciuolo, Giovanni and Roberts, Jason J. and Ross, Rebecca E. and Scales, Kylie L. and Schoeman, David and Snelgrove, Paul and Sundblad, G{\"o}ran and Thuiller, Wilfried and Torres, Leigh G. and Verbruggen, Heroen and Wang, Lifei and Wenger, Seth and Whittingham, Mark J. and Zharikov, Yuri and Zurell, Damaris and Sequeira, Ana M.M.}, - year = {2018}, - month = oct, - journal = {Trends in Ecology \& Evolution}, - volume = {33}, - number = {10}, - pages = {790--802}, - issn = {01695347}, - doi = {10.1016/j.tree.2018.08.001}, - langid = {english}, - file = {/Users/dill/Zotero/storage/L5UCXR7B/Yates et al. - 2018 - Outstanding Challenges in the Transferability of E.pdf} -} - -@article{yuan_point_2017, - title = {Point Process Models for Spatio-Temporal Distance Sampling Data from a Large-Scale Survey of Blue Whales}, - author = {Yuan, Yuan and Bachl, Fabian E. and Lindgren, Finn and Borchers, David L. and Illian, Janine B. and Buckland, Stephen T. and avard Rue, H{\textbackslash}a and Gerrodette, Tim}, - year = {2017}, - journal = {The Annals of Applied Statistics}, - volume = {11}, - number = {4}, - pages = {2270--2297}, - file = {/Users/dill/Zotero/storage/NLV57HTF/euclid.aoas.1514430286.pdf;/Users/dill/Zotero/storage/YQLPF6MB/AOAS1609-010R2A0.pdf} -} - -@article{yue_bayesian_2014, - title = {Bayesian {{Adaptive Smoothing Splines Using Stochastic Differential Equations}}}, - author = {Yue, Yu Ryan and Simpson, Daniel and Lindgren, Finn and Rue, H{\aa}vard}, - year = {2014}, - month = jun, - journal = {Bayesian Analysis}, - volume = {9}, - number = {2}, - pages = {397--424}, - issn = {1936-0975}, - doi = {10.1214/13-BA866}, - abstract = {The smoothing spline is one of the most popular curve-fitting methods, partly because of empirical evidence supporting its effectiveness and partly because of its elegant mathematical formulation. However, there are two obstacles that restrict the use of the smoothing spline in practical statistical work. Firstly, it becomes computationally prohibitive for large data sets because the number of basis functions roughly equals the sample size. Secondly, its global smoothing parameter can only provide a constant amount of smoothing, which often results in poor performances when estimating inhomogeneous functions. In this work, we introduce a class of adaptive smoothing spline models that is derived by solving certain stochastic differential equations with finite element methods. The solution extends the smoothing parameter to a continuous data-driven function, which is able to capture the change of the smoothness of the underlying process. The new model is Markovian, which makes Bayesian computation fast. A simulation study and real data example are presented to demonstrate the effectiveness of our method.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/3DEHUJEY/Yue et al. - 2014 - Bayesian Adaptive Smoothing Splines Using Stochast.pdf} -} - -@article{yule_why_1926, - title = {Why Do We {{Sometimes}} Get {{Nonsense-Correlations}} between {{Time-Series}}?--{{A Study}} in {{Sampling}} and the {{Nature}} of {{Time-Series}}}, - shorttitle = {Why Do We {{Sometimes}} Get {{Nonsense-Correlations}} between {{Time-Series}}?}, - author = {Yule, G. Udny}, - year = {1926}, - month = jan, - journal = {Journal of the Royal Statistical Society}, - volume = {89}, - number = {1}, - pages = {1}, - issn = {09528385}, - doi = {10.2307/2341482}, - file = {/Users/dill/Zotero/storage/D3NAUVBR/biom12666.pdf;/Users/dill/Zotero/storage/TM8CNMYF/Yule - 1926 - Why do we Sometimes get Nonsense-Correlations betw.pdf} -} - -@phdthesis{zerbini_improving_2006, - title = {Improving {{Precision}} in {{Multiple Covariate Distance Sampling}}: {{A Case Study}} with {{Whales}} in {{Alaska}}}, - shorttitle = {Improving {{Precision}} in {{Multiple Covariate Distance Sampling}}}, - author = {Zerbini, Alexandre N.}, - year = {2006}, - school = {University of Washington}, - file = {/Users/dill/Zotero/storage/8T9EPNIW/2007 Zerbini.pdf} -} - -@article{zhang_inconsistent_2004, - title = {Inconsistent {{Estimation}} and {{Asymptotically Equal Interpolations}} in {{Model-Based Geostatistics}}}, - author = {Zhang, Hao}, - year = {2004}, - month = mar, - journal = {Journal of the American Statistical Association}, - volume = {99}, - number = {465}, - pages = {250--261}, - issn = {0162-1459, 1537-274X}, - doi = {10.1198/016214504000000241}, - langid = {english}, - file = {/Users/dill/Zotero/storage/67QNX49H/Zhang - 2004 - Inconsistent Estimation and Asymptotically Equal I.pdf} -} - -@article{zimmerman_deconfounding_2021, - title = {On {{Deconfounding Spatial Confounding}} in {{Linear Models}}}, - author = {Zimmerman, Dale L. and Ver Hoef, Jay M.}, - year = {2021}, - month = jul, - journal = {The American Statistician}, - pages = {1--9}, - issn = {0003-1305, 1537-2731}, - doi = {10.1080/00031305.2021.1946149}, - abstract = {Spatial confounding, that is, collinearity between fixed effects and random effects in a spatial generalized linear mixed model, can adversely affect estimates of the fixed effects. Restricted spatial regression methods have been proposed as a remedy for spatial confounding. Such methods replace inference for the fixed effects of the original model with inference for those effects under a model in which the random effects are restricted to a subspace orthogonal to the column space of the fixed effects model matrix; thus, they ``deconfound'' the two types of effects. We prove, however, that frequentist inference for the fixed effects of a deconfounded linear model is generally inferior to that for the fixed effects of the original spatial linear model; in fact, it is even inferior to inference for the corresponding nonspatial model. We show further that deconfounding also leads to inferior predictive inferences, though its impact on prediction appears to be relatively small in practice. Based on these results, we argue that deconfounding a spatial linear model is bad statistical practice and should be avoided.}, - langid = {english}, - file = {/Users/dill/Zotero/storage/JL3VXPB4/Zimmerman and Ver Hoef - 2021 - On Deconfounding Spatial Confounding in Linear Mod.pdf} -} - -@article{zipkin_fitting_2014, - title = {Fitting Statistical Distributions to Sea Duck Count Data: {{Implications}} for Survey Design and Abundance Estimation}, - shorttitle = {Fitting Statistical Distributions to Sea Duck Count Data}, - author = {Zipkin, Elise F. and Leirness, Jeffery B. and Kinlan, Brian P. and O'Connell, Allan F. and Silverman, Emily D.}, - year = {2014}, - month = mar, - journal = {Statistical Methodology}, - volume = {17}, - pages = {67--81}, - issn = {15723127}, - doi = {10.1016/j.stamet.2012.10.002}, - langid = {english}, - file = {/Users/dill/Zotero/storage/YGRICUWX/Statistical Methodology 2014 Zipkin.pdf} -} - - diff --git a/vignettes/mrds.Rmd b/vignettes/mrds.Rmd deleted file mode 100644 index 3156d50..0000000 --- a/vignettes/mrds.Rmd +++ /dev/null @@ -1,126 +0,0 @@ ---- -title: "Variance calculation differences between R and Distance for Windows" -author: "David L Miller and Len Thomas" -output: bookdown::html_document2 -bibliography: mrds-var.bib -vignette: > - %\VignetteIndexEntry{variance-opts} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - - -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>" -) -``` - -# Introduction - -As some general background for the following we first set out some notation. We want to estimate $\hat{N}$, the abundance in the study area and its variance $\text{Var}\hat{N}$. We calculate $\hat{N}$ using a Horvitz-Thompson estimator: -\begin{equation} -\hat{N}=\frac{A}{a}\sum_{i=1}^{n}\frac{s_{i}}{\hat{p}_{i}}, -(\#eq:HT) -\end{equation} -where $A$ is the area of the study area, $a$ is the covered area, $i$ indexes the observations, which have group size $s_{i}$ and probability of detection $\hat{p}_{i}$. For line transects $a=2wL$ where $L$ is the total line length and for point transects $a=2\pi wK$ where $K$ is the total number of transects. - -Following @buckland_introduction_2001 and @buckland_advanced_2004, we identify the following sources of uncertainty in \@ref(eq:HT): - -- $\hat{p}_{i}$ model uncertainty from calculating the probability of detection, -- $n/L$ or $n/K$ the encounter rate, -- $s_{i}$ the group size (if objects occur in groups). - -So, when we have objects occurring individually, we can obtain the total variance of $\hat{N}$ as: $$\text{Var}\hat{N}=\hat{N}\sqrt{\left[\text{CV}_{\boldsymbol{\theta}}\left(\hat{N}\right)\right]^{2}+\left[\text{CV}\left(n/L\right)\right]^{2}},$$ and when objects occur in groups: $$\text{Var}\hat{N}=\hat{N}\sqrt{\left[\text{CV}_{\boldsymbol{\theta}}\left(\hat{N}\right)\right]^{2}+\left[\text{CV}\left(n/L\right)\right]^{2}+\left\{ \text{CV}\left[\widehat{\mathbb{E}(s)}\right]\right\} ^{2}}$$ Each of these is addressed in-turn below. - -Before starting that, a few more definitions\... - -Per @buckland_introduction_2001 we call our detection function $g(x;\boldsymbol{\theta})$, where $x$are perpendicular distances from a line transect survey and $\boldsymbol{\theta}$ are parameters. For point transects we write $g(r;\boldsymbol{\theta})$, where $r$ are radial distances. From this we can define $\hat{p_{i}}$, which we write more fully as $\hat{p}(\mathbf{z}_{i};\boldsymbol{\theta})$ -- the probability of seeing an animal with covariate combination $\mathbf{z}_{i}$. We can calculate this as: -$$ -\hat{p}(\mathbf{z}_{i};\boldsymbol{\theta})=\int_{l}^{w}\pi(x)g(x;\boldsymbol{\theta})\text{d}x, -$$ -where $w$ and $l$ are the right and left truncation distances, respectively and $\pi(x)$ is the distribution of animals with respect to the sampler. For line transects $\pi(x)=1/w$ and for points $\pi(r)=2r/w^{2}$. - -# Variance of the detection function ($\hat{p_{i}}$) - -To obtain the variance of $\hat{p_{i}}$, we first note that from the maximum likelihood procedure used to fit the detection function, we obtain the Hessian (matrix of second derivatives of the likelihood with respect to the parameters), which we denote $\mathbf{H}_{\boldsymbol{\theta}}$[^1]. We can then note that $\mathbf{H}_{\boldsymbol{\theta}}^{-1}$ is the covariance matrix of the detection function parameters, $\mathbf{V}_{\hat{\boldsymbol{\theta}}}$. From this we can use the standard sandwich estimator technique and obtain expressions for the uncertainties for any derived quantities which are functions of $\boldsymbol{\theta}$. If our function of the parameters is $h(\boldsymbol{\theta})$, then we have: -$$ -\text{Var}_{\boldsymbol{\theta}}h=\left(\frac{\partial h(\boldsymbol{\theta)}}{\partial\boldsymbol{\theta}}\right)^{\intercal}\mathbf{V}_{\hat{\boldsymbol{\theta}}}\left(\frac{\partial h(\boldsymbol{\theta)}}{\partial\boldsymbol{\theta}}\right). -$$ - -So, for $\text{Var}\hat{N}$ we have [@buckland_introduction_2001 equation (3.25)]: -$$ -\text{Var}_{\boldsymbol{\theta}}\hat{N}=\left(\frac{\partial\hat{N}}{\partial\boldsymbol{\theta}}\right)^{\intercal}\mathbf{V}_{\hat{\boldsymbol{\theta}}}\left(\frac{\partial\hat{N}}{\partial\boldsymbol{\theta}}\right), -$$ -where $\hat{N}$ is implicitly a function of $\boldsymbol{\theta}$ and $\frac{\partial\hat{N}}{\partial\boldsymbol{\theta}}$ can be obtained by finite differencing in practice. - -# Variance of encounter rate ($n/L$ or $n/K$) - -Calculating the encounter rate variance is the most complicated part of the process and has a large number of possible estimators depending on the situation in question. - -## When $A=a$ (and objects are not in clusters) - -When we only wish to obtain an abundance estimate for the covered area and objects do not occur in clusters, we use the "binomial variance" estimator [@borchers_horvitz-thompson_1998 equation 13]: -$$ -\sum_{i=1}^{n}\frac{1-p(\mathbf{z}_{i};\boldsymbol{\theta})}{p(\mathbf{z}_{i};\boldsymbol{\theta})^{2}}. -$$ -In `mrds`, this behaviour can be obtained using `varflag=0`. - -## When there is only one sample - -In the case where there is only one transect, we make a "Poisson assumption" (that $\text{Var}x=x$), so the variance is "known." In this case the encounter rate variance is just the sum of the estimated abundances in the study area. - -## When size is not in the detection function {#buckland} - -Depending on whether group size is included as a covariate in the detection function we can use different estimators for the encounter rate and hence its variance. The "classic" @buckland_introduction_2001 estimator is the the encounter rate is $n/L$ or $n/K$. `mrds` documentation[^2] claims that using this estimator in variance calculations is not valid when group size is included. - -In `mrds`, this behaviour can be obtained using `varflag=1`. - -## When size is in the detection function {#innes} - -@innes_surveys_2002 propose using $\hat{N}/L$ or $\hat{N}/K$ for the estimator when group size is included as a covariate in the detection function.` mrds` documentation[^3] claims that this estimator is better when covariates are included in the detection function. - -In `mrds`, this behaviour can be obtained using `varflag=2`. - -## Fewster et al (2009) estimators - -Having decided whether to use $n/L$ or $n/K$, or $\hat{N}/L$ or $\hat{N}/K$, we can then plug them into the estimators offered by @fewster_estimating_2009[^4]. - -# Variance of group size ($s_{i}$) - -If we use the method of @innes_surveys_2002 outlined in Section \@ref(innes), group size variation is already accounted for, as $\hat{N}/L$ will include group size information. However, if we use the method of @buckland_introduction_2001 given in Section \@ref(buckland) then we need to include the empirical variance of the group sizes. - -# Stratification - -The above all applies when the total study area is used. When we wish to obtain stratified estimates, we sum over the appropriate indices (objects in the given stratum, line lengths in the given stratum, number of points in a given stratum etc). This will give us an estimate for each stratum. To obtain totals we need to sum coefficients of variation. - -# Constructing intervals - -To obtain confidence intervals around estimates of abundance, we can calculate $\left(\hat{N}/C,\hat{N}C\right)$ where -$$ -C=\exp\left[t_{m}(\alpha)\sqrt{\widehat{\text{Var}}(\log_{e}\hat{N})}\right],$$ and $$\widehat{\text{Var}}(\log_{e}\hat{N})=\log_{e}\left[1+\frac{\widehat{\text{Var}}\left(\hat{N}\right)}{\hat{N}^{2}}\right]=\log_{e}\left[1+\text{CV}\left(\hat{N}\right)^{2}\right]. -$$ -We find the degrees of freedom for the $t$-distribution, $m$, by the following formula [@buckland_introduction_2001 equation (3.75)]: -$$ -m=\frac{\left[\text{CV}\left(\hat{N}\right)\right]^{4}}{\sum_{k=1}^{K}\left(\text{CV}_{k}\right)^{4}/m_{k}}, -$$ -where $\text{CV}_{k}$ and $m_{k}$ are the CV and degrees of freedom for component $k$ (e.g., $k=1$ is the detection function, $k=2$ is the encounter rate, etc), respectively. $$$$ - -# Current `mrds` and `Distance` behaviour - -By default `mrds` and `Distance` will use the following options: - -- $\hat{N}/L$ or $\hat{N}/K$ used [@innes_surveys_2002] -- For line transects, estimator R2 is used from @fewster_estimating_2009. For point transects estimator P3 is used. - -# References - - -[^1]: Well, we actually use the product of the first partials to calculate this, per @buckland_introduction_2001 equation (3.24) - -[^2]: See `?mrds::dht.se.` - -[^3]: See `?mrds::dht.se.` - -[^4]: See `?mrds::varn` for more information on those options offered in `mrds`. From f41e3801d426c6eb43c195d1ea49f1f4efd3223c Mon Sep 17 00:00:00 2001 From: Laura Marshall Date: Mon, 27 Nov 2023 11:39:29 +0000 Subject: [PATCH 02/11] book.tee.data re-formatting Closes #91 --- DESCRIPTION | 2 +- R/mrds-package.R | 49 ++++++++++++++++++++++++++++++-------------- man/book.tee.data.Rd | 47 ++++++++++++++++++++++++++++-------------- 3 files changed, 67 insertions(+), 31 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 9f4ea51..0ae903e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,7 @@ Description: Animal abundance estimation via conventional, multiple covariate fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator. -Version: 2.2.9.9000 +Version: 2.2.9.9001 URL: https://github.com/DistanceDevelopment/mrds/ BugReports: https://github.com/DistanceDevelopment/mrds/issues Depends: diff --git a/R/mrds-package.R b/R/mrds-package.R index 82dcc52..d67f009 100644 --- a/R/mrds-package.R +++ b/R/mrds-package.R @@ -45,21 +45,40 @@ NULL #' #' @name book.tee.data #' @docType data -#' @format The format is: List of 4 $ book.tee.dataframe:'data.frame': 324 obs. -#' of 7 variables: ..$ object : num [1:324] 1 1 2 2 3 3 4 4 5 5 ... ..$ -#' observer: Factor w/ 2 levels "1","2": 1 2 1 2 1 2 1 2 1 2 ... ..$ -#' detected: num [1:324] 1 0 1 0 1 0 1 0 1 0 ... ..$ distance: num [1:324] -#' 2.68 2.68 3.33 3.33 0.34 0.34 2.53 2.53 1.46 1.46 ... ..$ size : num -#' [1:324] 2 2 2 2 1 1 2 2 2 2 ... ..$ sex : num [1:324] 1 1 1 1 0 0 1 1 1 1 -#' ... ..$ exposure: num [1:324] 1 1 0 0 0 0 1 1 0 0 ... $ book.tee.region -#' :'data.frame': 2 obs. of 2 variables: ..$ Region.Label: Factor w/ 2 levels -#' "1","2": 1 2 ..$ Area : num [1:2] 1040 640 $ book.tee.samples -#' :'data.frame': 11 obs. of 3 variables: ..$ Sample.Label: num [1:11] 1 2 3 -#' 4 5 6 7 8 9 10 ... ..$ Region.Label: Factor w/ 2 levels "1","2": 1 1 1 1 -#' 1 1 2 2 2 2 ... ..$ Effort : num [1:11] 10 30 30 27 21 12 23 23 15 12 ... -#' $ book.tee.obs :'data.frame': 162 obs. of 3 variables: ..$ object : int -#' [1:162] 1 2 3 21 22 23 24 59 60 61 ... ..$ Region.Label: int [1:162] 1 1 -#' 1 1 1 1 1 1 1 1 ... ..$ Sample.Label: int [1:162] 1 1 1 1 1 1 1 1 1 1 ... +#' @format +#' A list of 4 dataframes, with the list elements named: book.tee.dataframe, +#' book.tee.region, book.tee.samples and book.tee.obs. +#' +#' \describe{\strong{book.tee.dataframe} is the distance sampling data +#' dataframe. Used in the call to fit the detection function in \code{ddf}. +#' Contains the following columns: +#' \item{object}{numeric object id}\item{observer}{factor representing observer +#' 1 or 2}\item{detected}{numeric 1 if the animal was detected 0 otherwise} +#' \item{distance}{numeric value for the distance the animal was detected} +#' \item{size}{numeric value for the group size}\item{sex}{numeric value for +#' sex of animal}\item{exposure}{numeric value for exposure level 0 or 1}} +#' +#' \describe{\strong{book.tee.region}: is the region table dataframe. Used to +#' supply the strata areas to the \code{dht} function. Contains the following +#' columns: +#' \item{Region.Label}{factor giving the strata labels} +#' \item{Area}{numeric value giving the strata areas}} +#' +#' \describe{\strong{book.tee.samples} is the samples table dataframe to match +#' the transect ids to the region ids and supply the effort. Used in the +#' \code{dht} function. Contains the following columns: +#' \item{Sample.Label}{numeric giving the sample / transect labels} +#' \item{Region.Label}{factor giving the strata labels} +#' \item{Effort}{numeric value giving the sample / transect lengths}} +#' +#' +#' \describe{\strong{book.tee.obs} is the observations table dataframe to match +#' the object ids in the distance data to the transect labels. Used in the +#' \code{dht} function. Contains the following columns: +#' \item{object}{numeric value object id} +#' \item{Region.Label}{factor giving the strata labels} +#' \item{Sample.Label}{numeric giving the sample / transect labels}} +#' #' @keywords datasets NULL diff --git a/man/book.tee.data.Rd b/man/book.tee.data.Rd index 7532fde..cbe2910 100644 --- a/man/book.tee.data.Rd +++ b/man/book.tee.data.Rd @@ -5,21 +5,38 @@ \alias{book.tee.data} \title{Golf tee data used in chapter 6 of Advanced Distance Sampling examples} \format{ -The format is: List of 4 $ book.tee.dataframe:'data.frame': 324 obs. - of 7 variables: ..$ object : num [1:324] 1 1 2 2 3 3 4 4 5 5 ... ..$ - observer: Factor w/ 2 levels "1","2": 1 2 1 2 1 2 1 2 1 2 ... ..$ - detected: num [1:324] 1 0 1 0 1 0 1 0 1 0 ... ..$ distance: num [1:324] - 2.68 2.68 3.33 3.33 0.34 0.34 2.53 2.53 1.46 1.46 ... ..$ size : num - [1:324] 2 2 2 2 1 1 2 2 2 2 ... ..$ sex : num [1:324] 1 1 1 1 0 0 1 1 1 1 - ... ..$ exposure: num [1:324] 1 1 0 0 0 0 1 1 0 0 ... $ book.tee.region - :'data.frame': 2 obs. of 2 variables: ..$ Region.Label: Factor w/ 2 levels - "1","2": 1 2 ..$ Area : num [1:2] 1040 640 $ book.tee.samples - :'data.frame': 11 obs. of 3 variables: ..$ Sample.Label: num [1:11] 1 2 3 - 4 5 6 7 8 9 10 ... ..$ Region.Label: Factor w/ 2 levels "1","2": 1 1 1 1 - 1 1 2 2 2 2 ... ..$ Effort : num [1:11] 10 30 30 27 21 12 23 23 15 12 ... - $ book.tee.obs :'data.frame': 162 obs. of 3 variables: ..$ object : int - [1:162] 1 2 3 21 22 23 24 59 60 61 ... ..$ Region.Label: int [1:162] 1 1 - 1 1 1 1 1 1 1 1 ... ..$ Sample.Label: int [1:162] 1 1 1 1 1 1 1 1 1 1 ... +A list of 4 dataframes, with the list elements named: book.tee.dataframe, + book.tee.region, book.tee.samples and book.tee.obs. + + \describe{\strong{book.tee.dataframe} is the distance sampling data + dataframe. Used in the call to fit the detection function in \code{ddf}. + Contains the following columns: + \item{object}{numeric object id}\item{observer}{factor representing observer + 1 or 2}\item{detected}{numeric 1 if the animal was detected 0 otherwise} + \item{distance}{numeric value for the distance the animal was detected} + \item{size}{numeric value for the group size}\item{sex}{numeric value for + sex of animal}\item{exposure}{numeric value for exposure level 0 or 1}} + + \describe{\strong{book.tee.region}: is the region table dataframe. Used to + supply the strata areas to the \code{dht} function. Contains the following + columns: + \item{Region.Label}{factor giving the strata labels} + \item{Area}{numeric value giving the strata areas}} + + \describe{\strong{book.tee.samples} is the samples table dataframe to match + the transect ids to the region ids and supply the effort. Used in the + \code{dht} function. Contains the following columns: + \item{Sample.Label}{numeric giving the sample / transect labels} + \item{Region.Label}{factor giving the strata labels} + \item{Effort}{numeric value giving the sample / transect lengths}} + + + \describe{\strong{book.tee.obs} is the observations table dataframe to match + the object ids in the distance data to the transect labels. Used in the + \code{dht} function. Contains the following columns: + \item{object}{numeric value object id} + \item{Region.Label}{factor giving the strata labels} + \item{Sample.Label}{numeric giving the sample / transect labels}} } \description{ Double platform data collected in a line transect survey of golf tees by 2 From 9dc71fc7848b8b84858bda644b8e8545eebecf11 Mon Sep 17 00:00:00 2001 From: Laura Marshall Date: Mon, 27 Nov 2023 13:31:32 +0000 Subject: [PATCH 03/11] Change default ER var estimator to P2 rather than P3 Reference #65 This change is in line with the Distance for Windows software --- DESCRIPTION | 2 +- NEWS | 12 ++++++++++++ R/dht.R | 23 ++++++++++++----------- R/dht.se.R | 8 +++++--- R/varn.R | 2 +- man/dht.Rd | 9 +++++---- man/dht.se.Rd | 8 +++++--- man/varn.Rd | 2 +- tests/testthat/test_dht.R | 10 ++++++++-- 9 files changed, 50 insertions(+), 26 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 0ae903e..dd720bc 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,7 @@ Description: Animal abundance estimation via conventional, multiple covariate fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator. -Version: 2.2.9.9001 +Version: 2.2.9.9002 URL: https://github.com/DistanceDevelopment/mrds/ BugReports: https://github.com/DistanceDevelopment/mrds/issues Depends: diff --git a/NEWS b/NEWS index b0b0e2c..15f5b4c 100644 --- a/NEWS +++ b/NEWS @@ -1,3 +1,15 @@ +mrds 2.3.0 +---------- + +New Features + +* The 'P2' estimator is now the default for estimating the encouter rate variance for point transect surveys. + +Bug Fixes + +* Re-formatted the format section of the documentation for the book.tee.data +* + mrds 2.2.9 ---------- diff --git a/R/dht.R b/R/dht.R index a661a2c..89bcbd0 100644 --- a/R/dht.R +++ b/R/dht.R @@ -121,9 +121,10 @@ #' estimator forms given in Fewster et al (2009) for line transects: #' \code{"R2"}, \code{"R3"}, \code{"R4"}, \code{"S1"}, \code{"S2"}, #' \code{"O1"}, \code{"O2"} or \code{"O3"} by specifying the \code{ervar=} -#' option (default \code{"R2"}). For points estimator \code{"P3"} is the only -#' option. See \code{\link{varn}} and Fewster et al (2009) for further details -#' on these estimators. +#' option (default \code{"R2"}). For points, either the \code{"P2"} or +#' \code{"P3"} estimator can be selected (>=mrds 2.3.0 default \code{"P2"}, +#' <= mrds 2.2.9 default \code{"P3"}). See \code{\link{varn}} and Fewster +#' et al (2009) for further details on these estimators. #' #' @param model ddf model object #' @param region.table \code{data.frame} of region records. Two columns: @@ -182,7 +183,7 @@ #' length (Default: \code{1})} #' \item{\code{ervar}}{encounter rate variance type (see "Uncertainty" and #' \code{type} argument of \code{\link{varn}}). (Default: \code{"R2"} for -#' lines and \code{"P3"} for points)} +#' lines and \code{"P2"} for points)} #'} #' #' @author Jeff Laake, David L Miller @@ -433,7 +434,7 @@ dht <- function(model, region.table, sample.table, obs.table=NULL, subset=NULL, # Assign default values to options options <- assign.default.values(options, pdelta=0.001, varflag=2, convert.units=1, - ervar=ifelse(model$meta.data$point, "P3", + ervar=ifelse(model$meta.data$point, "P2", "R2"), areas.supplied=FALSE) @@ -483,16 +484,16 @@ dht <- function(model, region.table, sample.table, obs.table=NULL, subset=NULL, levels=levels(sample.table$Sample.Label)) - # P3 can only be used with points - if(options$ervar=="P3" & !model$meta.data$point){ - stop("Encounter rate variance estimator P3 may only be used with point transects, set with options=list(ervar=...)") + # P2 and P3 can only be used with points + if((options$ervar=="P3" || options$ervar=="P2") && !model$meta.data$point){ + stop("Encounter rate variance estimator P2 / P3 may only be used with point transects, set with options=list(ervar=...)") } - # switch to the P3 estimator if using points + # switch to the P2 estimator if using points if(model$meta.data$point){ if(!(options$ervar %in% c("P2", "P3"))){ - warning("Point transect encounter rate variance can only use estimators P2 or P3, switching to P3.") - options$ervar <- "P3" + warning("Point transect encounter rate variance can only use estimators P2 or P3, switching to P2.") + options$ervar <- "P2" } } diff --git a/R/dht.se.R b/R/dht.se.R index a1c7c87..d467e2a 100644 --- a/R/dht.se.R +++ b/R/dht.se.R @@ -45,9 +45,11 @@ #' estimator forms given in Fewster et al (2009). For line transects: #' \code{"R2"}, \code{"R3"}, \code{"R4"}, \code{"S1"}, \code{"S2"}, #' \code{"O1"}, \code{"O2"} or \code{"O3"} can be used by specifying the -#' \code{ervar=} option (default \code{"R2"}). For point transects only the -#' \code{"P3"} estimator may be used. See \code{\link{varn}} and Fewster et al -#' (2009) for further details on these estimators. +#' \code{ervar=} option (default \code{"R2"}). For points, either the +#' \code{"P2"} or \code{"P3"} estimator can be selected (>=mrds 2.3.0 +#' default \code{"P2"}, <= mrds 2.2.9 default \code{"P3"}). See +#' \code{\link{varn}} and Fewster et al (2009) for further details +#' on these estimators. #' #' Exceptions to the above occur if there is only one sample in a stratum. In #' that case it uses Poisson assumption (\eqn{Var(x)=x}) and it assumes a known diff --git a/R/varn.R b/R/varn.R index 28b0e4a..ce87eb7 100644 --- a/R/varn.R +++ b/R/varn.R @@ -33,7 +33,7 @@ #' Default value is \code{"R2"}, shown in Fewster et al. (2009) to have good #' performance for completely random designs for lines. For systematic parallel #' line transect designs, Fewster et al. recommend \code{"O2"}. For point -#' transects the default is \code{"P3"} (but \code{"P2"} is also available). +#' transects the default is \code{"P2"} (but \code{"P3"} is also available). #' #' For the systematic estimators, pairs are assigned in the order they are #' given in the \code{lengths} and \code{groups} vectors. diff --git a/man/dht.Rd b/man/dht.Rd index c62cd2c..8e6809d 100644 --- a/man/dht.Rd +++ b/man/dht.Rd @@ -190,9 +190,10 @@ For options \code{1} and \code{2}, it is then possible to choose one of the estimator forms given in Fewster et al (2009) for line transects: \code{"R2"}, \code{"R3"}, \code{"R4"}, \code{"S1"}, \code{"S2"}, \code{"O1"}, \code{"O2"} or \code{"O3"} by specifying the \code{ervar=} -option (default \code{"R2"}). For points estimator \code{"P3"} is the only -option. See \code{\link{varn}} and Fewster et al (2009) for further details -on these estimators. +option (default \code{"R2"}). For points, either the \code{"P2"} or +\code{"P3"} estimator can be selected (>=mrds 2.3.0 default \code{"P2"}, +<= mrds 2.2.9 default \code{"P3"}). See \code{\link{varn}} and Fewster +et al (2009) for further details on these estimators. } \section{\code{dht} options}{ @@ -209,7 +210,7 @@ on these estimators. length (Default: \code{1})} \item{\code{ervar}}{encounter rate variance type (see "Uncertainty" and \code{type} argument of \code{\link{varn}}). (Default: \code{"R2"} for - lines and \code{"P3"} for points)} + lines and \code{"P2"} for points)} } } diff --git a/man/dht.se.Rd b/man/dht.se.Rd index 5ab7824..8050e56 100644 --- a/man/dht.se.Rd +++ b/man/dht.se.Rd @@ -83,9 +83,11 @@ For options \code{1} and \code{2}, it is then possible to choose one of the estimator forms given in Fewster et al (2009). For line transects: \code{"R2"}, \code{"R3"}, \code{"R4"}, \code{"S1"}, \code{"S2"}, \code{"O1"}, \code{"O2"} or \code{"O3"} can be used by specifying the -\code{ervar=} option (default \code{"R2"}). For point transects only the -\code{"P3"} estimator may be used. See \code{\link{varn}} and Fewster et al -(2009) for further details on these estimators. +\code{ervar=} option (default \code{"R2"}). For points, either the +\code{"P2"} or \code{"P3"} estimator can be selected (>=mrds 2.3.0 +default \code{"P2"}, <= mrds 2.2.9 default \code{"P3"}). See +\code{\link{varn}} and Fewster et al (2009) for further details +on these estimators. Exceptions to the above occur if there is only one sample in a stratum. In that case it uses Poisson assumption (\eqn{Var(x)=x}) and it assumes a known diff --git a/man/varn.Rd b/man/varn.Rd index 23f196b..c253b31 100644 --- a/man/varn.Rd +++ b/man/varn.Rd @@ -58,7 +58,7 @@ visits per point, model-based estimator} Default value is \code{"R2"}, shown in Fewster et al. (2009) to have good performance for completely random designs for lines. For systematic parallel line transect designs, Fewster et al. recommend \code{"O2"}. For point -transects the default is \code{"P3"} (but \code{"P2"} is also available). +transects the default is \code{"P2"} (but \code{"P3"} is also available). For the systematic estimators, pairs are assigned in the order they are given in the \code{lengths} and \code{groups} vectors. diff --git a/tests/testthat/test_dht.R b/tests/testthat/test_dht.R index 3fd928c..8d8eef6 100644 --- a/tests/testthat/test_dht.R +++ b/tests/testthat/test_dht.R @@ -18,12 +18,18 @@ obs <- book.tee.data$book.tee.obs test_that("golf tees", { # check that errors are thrown when the wrong ER variance is asked for + expect_error(dht(ds.model, region.table=region, + sample.table=samples, obs.table=obs, + options=list(ervar="P2")), + "Encounter rate variance estimator P2 / P3 may only be used with point transects, set with options=list(ervar=...)", fixed=TRUE) + expect_error(dht(ds.model, region.table=region, sample.table=samples, obs.table=obs, options=list(ervar="P3")), - "Encounter rate variance estimator P3 may only be used with point transects, set with options=list(ervar=...)", fixed=TRUE) + "Encounter rate variance estimator P2 / P3 may only be used with point transects, set with options=list(ervar=...)", fixed=TRUE) }) + test_that("ptdata", { # fake up some pt data pt.sample <- data.frame(Sample.Label=1, Region.Label=1, Effort=1) @@ -37,7 +43,7 @@ test_that("ptdata", { expect_warning(dht(model, region.table=pt.region, sample.table=pt.sample, obs.table=pt.obs, options=list(ervar="O1")), - "Point transect encounter rate variance can only use estimators P2 or P3, switching to P3.", fixed=TRUE) + "Point transect encounter rate variance can only use estimators P2 or P3, switching to P2.", fixed=TRUE) }) From b632bc1523d89ea6633a30011df38c8cea51147d Mon Sep 17 00:00:00 2001 From: Laura Marshall Date: Wed, 29 Nov 2023 10:34:47 +0000 Subject: [PATCH 04/11] MCDS optimizer no longer used for double observer models Reference #89 --- DESCRIPTION | 2 +- NEWS | 2 +- R/ddf.R | 21 +++++++++++++++++---- man/ddf.Rd | 11 +++++++---- tests/testthat/test_input.R | 17 +++++++++++++++++ 5 files changed, 43 insertions(+), 10 deletions(-) create mode 100644 tests/testthat/test_input.R diff --git a/DESCRIPTION b/DESCRIPTION index dd720bc..4875483 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,7 @@ Description: Animal abundance estimation via conventional, multiple covariate fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator. -Version: 2.2.9.9002 +Version: 2.2.9.9003 URL: https://github.com/DistanceDevelopment/mrds/ BugReports: https://github.com/DistanceDevelopment/mrds/issues Depends: diff --git a/NEWS b/NEWS index 15f5b4c..cfae3ef 100644 --- a/NEWS +++ b/NEWS @@ -8,7 +8,7 @@ New Features Bug Fixes * Re-formatted the format section of the documentation for the book.tee.data -* +* Ensure that the MCDS optimizer is not used for double observer models as this was generating errors. mrds 2.2.9 ---------- diff --git a/R/ddf.R b/R/ddf.R index 5ed2f7b..4b6c624 100644 --- a/R/ddf.R +++ b/R/ddf.R @@ -181,10 +181,13 @@ #' \code{solnp} when fitting a monotonic model. Default 200.} #' \item{\code{silent}}{silences warnings within ds fitting method (helpful #' for running many times without generating many warning/error messages).} -#' \item{\code{optimizer}}{By default this is set to 'both'. In this case -#' the R optimizer will be used and if present the MCDS optimizer will also -#' be used. The result with the best likelihood value will be selected. To -#' run only a specified optimizer set this value to either 'R' or 'MCDS'. +#' \item{\code{optimizer}}{By default this is set to 'both' for single +#' observer analyses and 'R' for double observer analyses. For single +#' observer analyses where optimizer = 'both', the R optimizer will be used +#' and if present the MCDS optimizer will also be used. The result with the +#' best likelihood value will be selected. To run only a specified optimizer +#' set this value to either 'R' or 'MCDS'. The MCDS optimizer cannot currently +#' be used for detection function fitting with double observer analyses. #' See \code{\link{mcds_dot_exe}} for more information.} #' \item{\code{winebin}}{Location of the \code{wine} binary used to run #' \code{MCDS.exe}. See \link{mcds_dot_exe} for more information.} @@ -319,6 +322,16 @@ ddf <- function(dsmodel=call(), mrmodel=call(),data, method="ds", stop("For method=",method,", mrmodel must be specified") } } + if(method %in% c("io","trial","rem")){ + # Do not use the MCDS.exe optimiser + if(is.null(control$optimizer)){ + # If the user has not specified quietly change to use only R optimizer + control$optimizer <- "R" + }else if(control$optimizer == "MCDS"){ + warning("The MCDS optimizer cannot currently be used with double observer analyses, the R opitimizer will be used instead.", call. = FALSE, immediate. = TRUE) + control$optimizer <- "R" + } + } # call method specific fitting function result <- switch(method, diff --git a/man/ddf.Rd b/man/ddf.Rd index d48272d..b51ecbd 100644 --- a/man/ddf.Rd +++ b/man/ddf.Rd @@ -215,10 +215,13 @@ infrequently. The list values include: \code{solnp} when fitting a monotonic model. Default 200.} \item{\code{silent}}{silences warnings within ds fitting method (helpful for running many times without generating many warning/error messages).} - \item{\code{optimizer}}{By default this is set to 'both'. In this case - the R optimizer will be used and if present the MCDS optimizer will also - be used. The result with the best likelihood value will be selected. To - run only a specified optimizer set this value to either 'R' or 'MCDS'. + \item{\code{optimizer}}{By default this is set to 'both' for single + observer analyses and 'R' for double observer analyses. For single + observer analyses where optimizer = 'both', the R optimizer will be used + and if present the MCDS optimizer will also be used. The result with the + best likelihood value will be selected. To run only a specified optimizer + set this value to either 'R' or 'MCDS'. The MCDS optimizer cannot currently + be used for detection function fitting with double observer analyses. See \code{\link{mcds_dot_exe}} for more information.} \item{\code{winebin}}{Location of the \code{wine} binary used to run \code{MCDS.exe}. See \link{mcds_dot_exe} for more information.} diff --git a/tests/testthat/test_input.R b/tests/testthat/test_input.R new file mode 100644 index 0000000..0550af6 --- /dev/null +++ b/tests/testthat/test_input.R @@ -0,0 +1,17 @@ +# Input checks +context("user input") + +test_that("MCDS optimizer is not used with double observer analyses",{ + + data(book.tee.data) + region <- book.tee.data$book.tee.region + egdata <- book.tee.data$book.tee.dataframe + samples <- book.tee.data$book.tee.samples + obs <- book.tee.data$book.tee.obs + + expect_warning(ddf(dsmodel=~cds(key = "hn"), mrmodel=~glm(~distance), + data=egdata, method="io", meta.data=list(width=4), + control = list(optimizer = "MCDS")), + "The MCDS optimizer cannot currently be used with double observer analyses, the R opitimizer will be used instead.") + +}) From d78f2a9b1bd2835cab8a8b8817fb4a4fa4f41abc Mon Sep 17 00:00:00 2001 From: Laura Marshall Date: Thu, 30 Nov 2023 14:00:50 +0000 Subject: [PATCH 05/11] Update documentation in initial values and parameter bounds Reference #90 --- DESCRIPTION | 2 +- NEWS | 7 ++++--- R/ddf.R | 14 ++++++++++---- man/ddf.Rd | 14 ++++++++++---- 4 files changed, 25 insertions(+), 12 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 4875483..f4b4db2 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,7 @@ Description: Animal abundance estimation via conventional, multiple covariate fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator. -Version: 2.2.9.9003 +Version: 2.2.9.9004 URL: https://github.com/DistanceDevelopment/mrds/ BugReports: https://github.com/DistanceDevelopment/mrds/issues Depends: diff --git a/NEWS b/NEWS index cfae3ef..0226bdf 100644 --- a/NEWS +++ b/NEWS @@ -3,12 +3,13 @@ mrds 2.3.0 New Features -* The 'P2' estimator is now the default for estimating the encouter rate variance for point transect surveys. +* The 'P2' estimator is now the default for estimating the encouter rate variance for point transect surveys. (Issue #65) Bug Fixes -* Re-formatted the format section of the documentation for the book.tee.data -* Ensure that the MCDS optimizer is not used for double observer models as this was generating errors. +* Re-formatted the format section of the documentation for the book.tee.data (Issue #91) +* Ensure that the MCDS optimizer is not used for double observer models as this was generating errors. (Issue #89) +* Improved the documentation on initial values, lower and upper bounds. (Issue #90) mrds 2.2.9 ---------- diff --git a/R/ddf.R b/R/ddf.R index 4b6c624..d878d7f 100644 --- a/R/ddf.R +++ b/R/ddf.R @@ -156,10 +156,16 @@ #' \item{\code{refit}}{if TRUE the algorithm will attempt multiple #' optimizations at different starting values if it doesn't converge} #' \item{\code{nrefits}}{number of refitting attempts} -#' \item{\code{initial}}{a named list of starting values for the parameters -#' (e.g. \code{$scale}, \code{$shape}, \code{$adjustment})} -#' \item{\code{lowerbounds}}{a vector of lowerbounds for the parameters} -#' \item{\code{upperbounds}}{a vector of upperbounds for the parameters} +#' \item{\code{initial}}{a named list of starting values for the dsmodel +#' parameters (e.g. \code{$scale}, \code{$shape}, \code{$adjustment})} +#' \item{\code{lowerbounds}}{a vector of lowerbounds for the dsmodel +#' parameters in the order the ds parameters will appear in the par +#' element of the ddf object, i.e. \code{fit.ddf$par} where \code{fit.ddf} +#' is a fitted ddf model.} +#' \item{\code{upperbounds}}{a vector of upperbounds for the dsmodel +#' parameters in the order the ds parameters will appear in the par +#' element of the ddf object, i.e. \code{fit.ddf$par} where \code{fit.ddf} +#' is a fitted ddf model.} #' \item{\code{limit}}{if TRUE restrict analysis to observations with #' \code{detected}=1} #' \item{\code{debug}}{ if TRUE, if fitting fails, return an object with diff --git a/man/ddf.Rd b/man/ddf.Rd index b51ecbd..83ea685 100644 --- a/man/ddf.Rd +++ b/man/ddf.Rd @@ -190,10 +190,16 @@ infrequently. The list values include: \item{\code{refit}}{if TRUE the algorithm will attempt multiple optimizations at different starting values if it doesn't converge} \item{\code{nrefits}}{number of refitting attempts} - \item{\code{initial}}{a named list of starting values for the parameters - (e.g. \code{$scale}, \code{$shape}, \code{$adjustment})} - \item{\code{lowerbounds}}{a vector of lowerbounds for the parameters} - \item{\code{upperbounds}}{a vector of upperbounds for the parameters} + \item{\code{initial}}{a named list of starting values for the dsmodel + parameters (e.g. \code{$scale}, \code{$shape}, \code{$adjustment})} + \item{\code{lowerbounds}}{a vector of lowerbounds for the dsmodel + parameters in the order the ds parameters will appear in the par + element of the ddf object, i.e. \code{fit.ddf$par} where \code{fit.ddf} + is a fitted ddf model.} + \item{\code{upperbounds}}{a vector of upperbounds for the dsmodel + parameters in the order the ds parameters will appear in the par + element of the ddf object, i.e. \code{fit.ddf$par} where \code{fit.ddf} + is a fitted ddf model.} \item{\code{limit}}{if TRUE restrict analysis to observations with \code{detected}=1} \item{\code{debug}}{ if TRUE, if fitting fails, return an object with From c08f682b962d3f9e8a4b602d3eb98c4d462ed928 Mon Sep 17 00:00:00 2001 From: Laura Marshall Date: Thu, 30 Nov 2023 19:03:48 +0000 Subject: [PATCH 06/11] Closes #90 --- DESCRIPTION | 2 +- NEWS | 2 +- R/ddf.R | 4 ++-- R/mrds-package.R | 19 +++++++++---------- man/ddf.Rd | 4 ++-- man/{mrds-opt.Rd => mrds_opt.Rd} | 20 ++++++++++---------- 6 files changed, 25 insertions(+), 26 deletions(-) rename man/{mrds-opt.Rd => mrds_opt.Rd} (83%) diff --git a/DESCRIPTION b/DESCRIPTION index f4b4db2..37148f6 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,7 @@ Description: Animal abundance estimation via conventional, multiple covariate fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator. -Version: 2.2.9.9004 +Version: 2.2.9.9005 URL: https://github.com/DistanceDevelopment/mrds/ BugReports: https://github.com/DistanceDevelopment/mrds/issues Depends: diff --git a/NEWS b/NEWS index 0226bdf..6ab9a96 100644 --- a/NEWS +++ b/NEWS @@ -9,7 +9,7 @@ Bug Fixes * Re-formatted the format section of the documentation for the book.tee.data (Issue #91) * Ensure that the MCDS optimizer is not used for double observer models as this was generating errors. (Issue #89) -* Improved the documentation on initial values, lower and upper bounds. (Issue #90) +* Improved the documentation on initial values, lower and upper bounds in both the ddf and mrds_opt documentation (mrds _opt was renamed from mrds-opt which was not accessible via ?mrds-opt). (Issue #90) mrds 2.2.9 ---------- diff --git a/R/ddf.R b/R/ddf.R index d878d7f..7481f3d 100644 --- a/R/ddf.R +++ b/R/ddf.R @@ -203,7 +203,7 @@ #' \url{http://examples.distancesampling.org/}. #' #' Hints and tips on fitting (particularly optimisation issues) are on the -#' \code{\link{mrds-opt}} manual page. +#' \code{\link{mrds_opt}} manual page. #' #' @param dsmodel distance sampling model specification #' @param mrmodel mark-recapture model specification @@ -218,7 +218,7 @@ #' @seealso \code{\link{ddf.ds}}, \code{\link{ddf.io}}, #' \code{\link{ddf.io.fi}}, \code{\link{ddf.trial}}, #' \code{\link{ddf.trial.fi}}, \code{\link{ddf.rem}}, \code{\link{ddf.rem.fi}}, -#' \code{\link{mrds-opt}} +#' \code{\link{mrds_opt}} #' @references Laake, J.L. and D.L. Borchers. 2004. Methods for incomplete #' detection at distance zero. In: Advanced Distance Sampling, eds. S.T. #' Buckland, D.R.Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. diff --git a/R/mrds-package.R b/R/mrds-package.R index d67f009..95bbc94 100644 --- a/R/mrds-package.R +++ b/R/mrds-package.R @@ -1161,10 +1161,10 @@ NULL #' #' #' @section Initial values: -#' Initial (or starting) values can be set via the \code{initial} element of -#' the \code{control} list. \code{initial} is a list itself with elements -#' \code{scale}, \code{shape} and \code{adjustment}, corresponding to the -#' associated parameters. If a model has covariates then the \code{scale} or +#' Initial (or starting) values for the dsmodel can be set via the \code{initial} +#' element of the \code{control} list. \code{initial} is a list itself with +#' elements \code{scale}, \code{shape} and \code{adjustment}, corresponding to +#' the associated parameters. If a model has covariates then the \code{scale} or #' \code{shape} elements will be vectors with parameter initial values in the #' same order as they are specific in the model formula (using \code{showit} is #' a good check they are in the correct order). Adjustment starting values are @@ -1180,21 +1180,20 @@ NULL #' parameter (or intercept in a covariate model) on the exponential scale, so #' one must \code{log} this before supplying it to \code{ddf}. #' -#' #' @section Bounds: -#' One can change the upper and lower bounds for the parameters. These specify -#' the largest and smallest values individual parameters can be. By placing -#' these constraints on the parameters, it is possible to "temper" the +#' One can change the upper and lower bounds for the dsmodel parameters. These +#' specify the largest and smallest values individual parameters can be. By +#' placing these constraints on the parameters, it is possible to "temper" the #' optimisation problem, making fitting possible. #' #' Again, one uses the \code{control} list, the elements \code{upperbounds} and #' \code{lowerbounds}. In this case, each of \code{upperbounds} and #' \code{lowerbounds} are vectors, which one can think of as each of the -#' vectors \code{scale}, \code{shape} and \code{adjustment} from the "Initial +#' vectors \code{shape}, \code{scale} and \code{adjustment} from the "Initial #' values" section above, concatenated in that order. If one does not occur #' (e.g. no shape parameter) then it is simple omitted from the vector. #' -#' @name mrds-opt +#' @name mrds_opt #' @docType methods #' @author David L. Miller NULL diff --git a/man/ddf.Rd b/man/ddf.Rd index 83ea685..0ee87eb 100644 --- a/man/ddf.Rd +++ b/man/ddf.Rd @@ -237,7 +237,7 @@ Examples of distance sampling analyses are available at \url{http://examples.distancesampling.org/}. Hints and tips on fitting (particularly optimisation issues) are on the -\code{\link{mrds-opt}} manual page. +\code{\link{mrds_opt}} manual page. } \examples{ # load data @@ -325,7 +325,7 @@ Marques, F.F.C. and S.T. Buckland. 2004. Covariate models for the detection \code{\link{ddf.ds}}, \code{\link{ddf.io}}, \code{\link{ddf.io.fi}}, \code{\link{ddf.trial}}, \code{\link{ddf.trial.fi}}, \code{\link{ddf.rem}}, \code{\link{ddf.rem.fi}}, -\code{\link{mrds-opt}} +\code{\link{mrds_opt}} } \author{ Jeff Laake diff --git a/man/mrds-opt.Rd b/man/mrds_opt.Rd similarity index 83% rename from man/mrds-opt.Rd rename to man/mrds_opt.Rd index 4cb4f40..b4c9dff 100644 --- a/man/mrds-opt.Rd +++ b/man/mrds_opt.Rd @@ -1,8 +1,8 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/mrds-package.R \docType{methods} -\name{mrds-opt} -\alias{mrds-opt} +\name{mrds_opt} +\alias{mrds_opt} \title{Tips on optimisation issues in \code{mrds} models} \description{ Occasionally when fitting an `mrds` model one can run into optimisation @@ -46,10 +46,10 @@ with more observations. \section{Initial values}{ -Initial (or starting) values can be set via the \code{initial} element of -the \code{control} list. \code{initial} is a list itself with elements -\code{scale}, \code{shape} and \code{adjustment}, corresponding to the -associated parameters. If a model has covariates then the \code{scale} or +Initial (or starting) values for the dsmodel can be set via the \code{initial} +element of the \code{control} list. \code{initial} is a list itself with +elements \code{scale}, \code{shape} and \code{adjustment}, corresponding to +the associated parameters. If a model has covariates then the \code{scale} or \code{shape} elements will be vectors with parameter initial values in the same order as they are specific in the model formula (using \code{showit} is a good check they are in the correct order). Adjustment starting values are @@ -68,15 +68,15 @@ one must \code{log} this before supplying it to \code{ddf}. \section{Bounds}{ -One can change the upper and lower bounds for the parameters. These specify -the largest and smallest values individual parameters can be. By placing -these constraints on the parameters, it is possible to "temper" the +One can change the upper and lower bounds for the dsmodel parameters. These +specify the largest and smallest values individual parameters can be. By +placing these constraints on the parameters, it is possible to "temper" the optimisation problem, making fitting possible. Again, one uses the \code{control} list, the elements \code{upperbounds} and \code{lowerbounds}. In this case, each of \code{upperbounds} and \code{lowerbounds} are vectors, which one can think of as each of the -vectors \code{scale}, \code{shape} and \code{adjustment} from the "Initial +vectors \code{shape}, \code{scale} and \code{adjustment} from the "Initial values" section above, concatenated in that order. If one does not occur (e.g. no shape parameter) then it is simple omitted from the vector. } From 6791381e68ec54903819e87865f092cb3a277fec Mon Sep 17 00:00:00 2001 From: Laura Marshall Date: Tue, 5 Dec 2023 13:54:00 +0000 Subject: [PATCH 07/11] Add startup message about default P2 estimator Reference #65 --- R/zzz.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/zzz.R b/R/zzz.R index 76398d9..3b57cdf 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -7,11 +7,13 @@ hello <- paste0("This is mrds ",version,"\nBuilt: ",built) + varest.info <- "**Change to default variance estimator for point transects. The default encounter rate variance estimator for point transects is now 'P2' (changed from 'P3'). See 'Uncertainty' section of ?dht for more information.**" + mcds.info <- ifelse(system.file("MCDS.exe", package="mrds") == "", "MCDS.exe not detected, single observer analyses will only be run using optimiser in mrds R library. See ?MCDS for details.", "MCDS.exe detected, by default single observer analyses will utilise both the mrds R optimiser and the MCDS.exe fortran optimiser to achieve the best fit. See ?ddf and ?MCDS for details.") - hello <- paste0(hello, "\n", mcds.info) + hello <- paste0(hello, "\n\n", varest.info, "\n\n", mcds.info) packageStartupMessage(hello) } From 914384ff3c063d4e7fc00552979131ba05c5523d Mon Sep 17 00:00:00 2001 From: Laura Marshall Date: Fri, 15 Dec 2023 12:16:18 +0000 Subject: [PATCH 08/11] Bump package version --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 37148f6..20a64fe 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,7 @@ Description: Animal abundance estimation via conventional, multiple covariate fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator. -Version: 2.2.9.9005 +Version: 2.3.0 URL: https://github.com/DistanceDevelopment/mrds/ BugReports: https://github.com/DistanceDevelopment/mrds/issues Depends: From dd5b8830bafc090dc36add50770d1692ad2118dd Mon Sep 17 00:00:00 2001 From: Laura Marshall Date: Fri, 15 Dec 2023 13:13:28 +0000 Subject: [PATCH 09/11] Formatting change to docs Was causing an error when compiling the manual Reference #91 --- R/mrds-package.R | 17 ++++++++++++----- man/book.tee.data.Rd | 17 ++++++++++++----- 2 files changed, 24 insertions(+), 10 deletions(-) diff --git a/R/mrds-package.R b/R/mrds-package.R index 95bbc94..7c5dcaf 100644 --- a/R/mrds-package.R +++ b/R/mrds-package.R @@ -49,32 +49,39 @@ NULL #' A list of 4 dataframes, with the list elements named: book.tee.dataframe, #' book.tee.region, book.tee.samples and book.tee.obs. #' -#' \describe{\strong{book.tee.dataframe} is the distance sampling data +#' \strong{book.tee.dataframe} is the distance sampling data #' dataframe. Used in the call to fit the detection function in \code{ddf}. #' Contains the following columns: +#' +#' \describe{ #' \item{object}{numeric object id}\item{observer}{factor representing observer #' 1 or 2}\item{detected}{numeric 1 if the animal was detected 0 otherwise} #' \item{distance}{numeric value for the distance the animal was detected} #' \item{size}{numeric value for the group size}\item{sex}{numeric value for #' sex of animal}\item{exposure}{numeric value for exposure level 0 or 1}} #' -#' \describe{\strong{book.tee.region}: is the region table dataframe. Used to +#' \strong{book.tee.region}: is the region table dataframe. Used to #' supply the strata areas to the \code{dht} function. Contains the following #' columns: +#' +#' \describe{ #' \item{Region.Label}{factor giving the strata labels} #' \item{Area}{numeric value giving the strata areas}} #' -#' \describe{\strong{book.tee.samples} is the samples table dataframe to match +#' \strong{book.tee.samples} is the samples table dataframe to match #' the transect ids to the region ids and supply the effort. Used in the #' \code{dht} function. Contains the following columns: +#' +#' \describe{ #' \item{Sample.Label}{numeric giving the sample / transect labels} #' \item{Region.Label}{factor giving the strata labels} #' \item{Effort}{numeric value giving the sample / transect lengths}} #' -#' -#' \describe{\strong{book.tee.obs} is the observations table dataframe to match +#' \strong{book.tee.obs} is the observations table dataframe to match #' the object ids in the distance data to the transect labels. Used in the #' \code{dht} function. Contains the following columns: +#' +#' \describe{ #' \item{object}{numeric value object id} #' \item{Region.Label}{factor giving the strata labels} #' \item{Sample.Label}{numeric giving the sample / transect labels}} diff --git a/man/book.tee.data.Rd b/man/book.tee.data.Rd index cbe2910..76c0e84 100644 --- a/man/book.tee.data.Rd +++ b/man/book.tee.data.Rd @@ -8,32 +8,39 @@ A list of 4 dataframes, with the list elements named: book.tee.dataframe, book.tee.region, book.tee.samples and book.tee.obs. - \describe{\strong{book.tee.dataframe} is the distance sampling data + \strong{book.tee.dataframe} is the distance sampling data dataframe. Used in the call to fit the detection function in \code{ddf}. Contains the following columns: + + \describe{ \item{object}{numeric object id}\item{observer}{factor representing observer 1 or 2}\item{detected}{numeric 1 if the animal was detected 0 otherwise} \item{distance}{numeric value for the distance the animal was detected} \item{size}{numeric value for the group size}\item{sex}{numeric value for sex of animal}\item{exposure}{numeric value for exposure level 0 or 1}} - \describe{\strong{book.tee.region}: is the region table dataframe. Used to + \strong{book.tee.region}: is the region table dataframe. Used to supply the strata areas to the \code{dht} function. Contains the following columns: + + \describe{ \item{Region.Label}{factor giving the strata labels} \item{Area}{numeric value giving the strata areas}} - \describe{\strong{book.tee.samples} is the samples table dataframe to match + \strong{book.tee.samples} is the samples table dataframe to match the transect ids to the region ids and supply the effort. Used in the \code{dht} function. Contains the following columns: + + \describe{ \item{Sample.Label}{numeric giving the sample / transect labels} \item{Region.Label}{factor giving the strata labels} \item{Effort}{numeric value giving the sample / transect lengths}} - - \describe{\strong{book.tee.obs} is the observations table dataframe to match + \strong{book.tee.obs} is the observations table dataframe to match the object ids in the distance data to the transect labels. Used in the \code{dht} function. Contains the following columns: + + \describe{ \item{object}{numeric value object id} \item{Region.Label}{factor giving the strata labels} \item{Sample.Label}{numeric giving the sample / transect labels}} From 533869cab6f333e11d77569afc3515ab43c64855 Mon Sep 17 00:00:00 2001 From: Laura Marshall Date: Fri, 15 Dec 2023 13:59:52 +0000 Subject: [PATCH 10/11] Removed line in DESCRIPTION re VignetteBuilder was generating a NOTE --- DESCRIPTION | 1 - 1 file changed, 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 20a64fe..237415a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -28,6 +28,5 @@ Suggests: knitr, rmarkdown, bookdown -VignetteBuilder: knitr RoxygenNote: 7.2.3 Encoding: UTF-8 From f4004cdee049986dface268b0d176576c96549ad Mon Sep 17 00:00:00 2001 From: Laura Marshall Date: Mon, 18 Dec 2023 00:10:21 +0000 Subject: [PATCH 11/11] Fix typo in NEWS --- NEWS | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NEWS b/NEWS index 6ab9a96..a195664 100644 --- a/NEWS +++ b/NEWS @@ -9,7 +9,7 @@ Bug Fixes * Re-formatted the format section of the documentation for the book.tee.data (Issue #91) * Ensure that the MCDS optimizer is not used for double observer models as this was generating errors. (Issue #89) -* Improved the documentation on initial values, lower and upper bounds in both the ddf and mrds_opt documentation (mrds _opt was renamed from mrds-opt which was not accessible via ?mrds-opt). (Issue #90) +* Improved the documentation on initial values, lower and upper bounds in both the ddf and mrds_opt documentation (mrds_opt was renamed from mrds-opt which was not accessible via ?mrds-opt). (Issue #90) mrds 2.2.9 ----------