README.Rmd

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#  Improving Estimates of the State of Global Fisheries Depends on Better Data Data
## Fish and Fisheries (Link TBD)
  
  - Daniel Ovando
  - Ray Hilborn
  - Cole Monnahan
  - Merrill Rudd
  - Rishi Sharma
  - James Thorson
  - Yannick Rousseau
  - Yimin Ye
  
## Abstract

Implementation of the United Nations Sustainable Development Goals requires assessments of the global state of fish populations. While we have reliable estimates of stock status for fish populations accounting for approximately half of recent global catch, our knowledge of the state of the majority of the world's 'unassessed' fish stocks remains highly uncertain. Numerous publications have produced estimates of the global status of these unassessed fisheries, but limited quantity and quality of data along with methodological differences have produced counterintuitive and conflicting results. Here, we show that despite numerous efforts, our understanding of the status of global fish stocks remains incomplete, even when new sources of broadly available data are added. Estimates of fish populations based primarily on catch histories on average performed 25% better than a random guess. But, on average these methods assigned fisheries to the wrong FAO status category 57% of the time. Within these broad summaries the performance of models trained on our tested data sources varied widely across regions. Substantial improvements to estimates of the state of the world's exploited fish populations depends more on expanded collection of new information and efficient use of existing data than development of new modeling methods.


<!-- *Mean classification accuracy (assignment to FAO stock status category) by FAO statistical area arising from different data sources. Data source panels are ordered in descending (starting from top left) mean accuracy at the FAO region level. RLSADB Index refers to catch and abundance index drawn from RLSADB. Effective CPUE refers to an index of abundance based on reconstructed effort data. Effective CPUE+ uses CPUE along with Fisheries Management Index (FMI) and/or swept area ratio (SAR) data. For both CPUE series 'nominal' assumes a 0% technology creep, for 'effective' a 2.6% technology creep is assumed. FMI uses FMI scores to develop a prior on recent fishing mortality rates, SAR does the same but based on swept area ratio. CMSY uses the methods from Froese et al. 2017 [@froese2017]. Guess assigns a random recent B/B~MSY~ of 0.4,1, or 1.6.* -->

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# Reproducing Results

All materials needed to reproduce our results and manuscript are contained in this repository. In order to reproduce

1. Fork the repository and clone to your machine

2. Open R and set your working directory of the cloned repository (or just use RStudio projects)

3. This project is set up with [`renv`](https://rstudio.github.io/renv/articles/renv.html) to manage package dependencies. Inside R (and with your working directory set correctly) run `renv::restore()`. This will install the correct versions of all the packages needed to replicate our results. Packages are installed in a stand-alone project library for this paper, and will not affect your installed R packages anywhere else. 


4. Run make-assessing-global-fisheries.R, setting all run_ options to TRUE (this will likely take over 48 hours to run). This will knit the manuscript for this paper automatically, generating ovando-etal-assessing-global-fisheries.docx in the documents folder