-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathUBRDriftFiguresRCode.r
2629 lines (1916 loc) · 116 KB
/
UBRDriftFiguresRCode.r
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#Figures Jan 2021
############
##Import Step
###########
library(vegan)
library(MASS)
library(ggplot2)
library(plyr)
library(dplyr)
library(magrittr)
library(scales)
library(grid)
library(reshape2)
library(phyloseq)
library(randomForest)
library(knitr)
library(ggpubr)
library(multcompView)
library(lmerTest)
library(lme4)
library(tiff)
library(glmmTMB)
library(bbmle)
library(GGally)
library(nlme)
library(DHARMa)
library(patchwork)
set.seed(45682)
#DRIFT FILES all taxa
otufull=read.table("DataClean\\SturgeonDriftInvertAbundanceMatrix8.15.19.txt",header=TRUE)
#head(otufull)
metadata=read.csv("DataClean\\SturgeonDriftMetadata10.28.2020.csv",header=TRUE)
metadata<-subset(metadata,Ninverts!=0) #Remove sample dates where inverts were not sampled
metadata$PercentRiverDischargeSampled<-metadata$DischargeSampledByNight/metadata$Q*100
metadata$InvertsByDischargeSampled<-metadata$Ninverts100/metadata$DischargeSampledByNight
metadata$InvertsByRiverDischarge<-metadata$Ninverts100/metadata$Q
metadata$BiomassByRiverDischarge<- metadata$InvertBiomass100/metadata$Q
metadata$BiomassByDischargeSampled<-metadata$InvertBiomass100/metadata$DischargeSampledByNight
metadata$DriftInvertConc<-((metadata$Ninverts100*100)/(60*4*60*metadata$AreaSampled.m2.*metadata$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)
metadata$DriftBiomassConc<-((metadata$InvertBiomass100*100)/(60*4*60*metadata$AreaSampled.m2.*metadata$AverageNetFlowByNight)) #Calculate biomass/ 100 m3 water
metadata$SturgeonConc<-((metadata$Nsturgeon*100)/(60*4*60*metadata$AreaSampled.m2.*metadata$AverageNetFlowByNight)) #Calculate sturgeon larvae/ 100 m3 water
metadata$SuckerConc<-((metadata$Nsuckers100*100)/(60*4*60*metadata$AreaSampled.m2.*metadata$AverageNetFlowByNight)) #Calculate sturgeon larvae/ 100 m3 water
#
# HistDischarge<-ggplot(metadata,aes(x=DischargeSampledByNight))+geom_histogram(fill="grey",color="black")+xlab(expression(Discharge~(m^3/sec)~Sampled))+ylab("Frequency")#+facet_wrap(~Year)
# HistDischarge
# theme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))
# mean(metadata$DischargeSampledByNight,na.rm=T)
# sd(metadata$DischargeSampledByNight,na.rm=T)
#
# dev.off()
# tiff("Figures/Discharge_Sampled.tiff", width = 3.3, height = 3.3, units = 'in', res = 800)
# HistDischarge
# dev.off()
#
#
# HistQ<-ggplot(metadata,aes(x=Q))+geom_histogram(fill="grey",color="black")+xlab(expression(River~Discharge~(m^3/sec)))+ylab("Frequency")#+facet_wrap(~Year)
# HistQ
# theme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))
#
# dev.off()
# tiff("Figures/Discharge_Q.tiff", width = 3.3, height = 3.3, units = 'in', res = 800)
# HistQ
# dev.off()
#discharge sampled sd = 0.407179 mean = 0.9361
#metadataOutliers
taxmatrixfull=as.matrix(read.table("DataClean\\SturgeonDriftInvertTaxNames8.15.19.txt"))
#head(taxmatrixfull)
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#000000","#CC79A7")
theme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))
#sets the plotting theme
OTU=otu_table(otufull, taxa_are_rows=TRUE)
#OTU
TAX=tax_table(taxmatrixfull)
colnames(TAX)=("Family")
sampdat=sample_data(metadata)
sample_names(sampdat)=sampdat$SampleID
#head(sampdat)
taxa_names(TAX)=row.names(OTU)
physeq=phyloseq(OTU,TAX,sampdat)#joins together OTU,TAX, and metadata
physeq
levels(sample_data(physeq)$MoonPhase)
sample_data(physeq)$MoonPhase = factor(sample_data(physeq)$MoonPhase, levels = c("New Moon","Waxing Crescent","First Quarter","Waxing Gibbous","Full Moon","Waning Gibbous","Last Quarter","Waning Crescent"))
#levels(sample_data(physeq)$CODE)=c("NM","WXC","FQ","WXG","FM","WAG","LQ","WNC")
Richness=plot_richness(physeq, x="DPFS", measures=c("Shannon"))#+geom_boxplot(aes(x=DPFS, y=value, color=DPFS), alpha=0.05)
#Richness$data
write.csv(Richness$data, "SturgeonMetadataWDiversity.csv")
ShannonRichness<-read.csv("SturgeonMetadataWDiversity.csv",header=T)
AllData<-metadata
AllData$Date2<-as.Date(AllData$Date,format= "%d-%B")
AllData
####################
#Figure 1
########################
theme_set(theme_bw(base_size = 10)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))
############
#Fig 1A Invert By day and year
############
Trtdata <- ddply(AllData, c("Date2","Year"), summarise,
N = length(Ninverts100),
meanInvert = mean(Ninverts100)
)
#Trtdata
InvertByDayOfYear<-ggplot(Trtdata, aes(x=Date2,y=meanInvert))+geom_bar(colour="black", stat="identity")+xlab("Date")+ylab("Macroinvertebrate Abundance")+
theme(axis.text.x = element_text(angle = 0, hjust = 0.5))+facet_grid(Year~.)#+scale_fill_manual(values=cbPalette)
InvertByDayOfYear
##########
#Figure 1B Sucker abu by day and year
##########
Trtdata <- ddply(AllData, c("Date2","Year"), summarise,
N = length(Nsuckers100),
meanSuckers = mean(Nsuckers100)
)
SuckersByDayOfYear<-ggplot(Trtdata, aes(x=Date2,y=meanSuckers))+geom_bar(colour="black", stat="identity")+xlab("Date")+ylab("Larval Sucker Abundance")+
theme(axis.text.x = element_text(angle = 0, hjust = 0.5))+facet_grid(Year~.)#+scale_fill_manual(values=cbPalette)
SuckersByDayOfYear
##########
# Figure 1C sturgeon larvae abu by day and year
##########
head(AllData)
Trtdata <- ddply(AllData, c("Date2","Year"), summarise,
N = length(Nsturgeon),
meanSturgeon = mean(Nsturgeon)
)
#Trtdata
SturgeonByDayOfYear<-ggplot(Trtdata, aes(x=Date2,y=meanSturgeon))+geom_bar(colour="black", stat="identity")+xlab("Date")+ylab("Larval Sturgeon Abundance")+
theme(axis.text.x = element_text(angle = 0, hjust = 0.5))+facet_grid(Year~.)#+scale_fill_manual(values=cbPalette)
SturgeonByDayOfYear
dev.off()
tiff("Figures/SturgeonByDayAndYear.tiff", width = 90, height = 110, units = 'mm', res = 1200)
SturgeonByDayOfYear
dev.off()
#########
#Old Figure 1C Sturgeon larvae CTU from first spawn
#########
# SpawningDischarge<-read.csv("DataClean\\SpawningSiteDischarge2012-2018JR.csv",header=T)
# head(SpawningDischarge)
#
# SturgeonCTU<-ggplot(AllData,aes(x=CTUSturgeon))+geom_point(size=0.35,aes(y=Nsturgeon),shape=19)+geom_line(aes(y=DischargeSpawning/0.002,x=SturgeonCTU),data=SpawningDischarge,color="blue")+scale_y_continuous(sec.axis = sec_axis(~.*0.002,name=expression(River~Discharge~In~Spawning~Area~(m^3/sec))))+xlab("Cumulative Temperature Units")+facet_wrap(~Year)+ylab("Larval Sturgeon Abundance")+
# theme(axis.text.x = element_text(angle = 45, hjust = 1))
# SturgeonCTU
Trtdata <- ddply(AllData, c("CTUSturgeon","Year"), summarise,
N = length(Nsturgeon),
meanSturgeon = mean(Nsturgeon)
)
ggplot(Trtdata, aes(x=CTUSturgeon,y=meanSturgeon))+geom_bar(colour="black", stat="identity",width = 10)+xlab("Date")+ylab("Larval Sturgeon Abundance")+
theme(axis.text.x = element_text(angle = 0, hjust = 0.5))+facet_wrap(Year~.)#+scale_fill_manual(values=cbPalette)
SturgeonCTU<-ggplot(AllData,aes(x=CTUSturgeon,y=SturgeonConc))+geom_point(size=0.5)+xlab("Cumulative Temperature Units")+facet_wrap(~Year)+ylab(expression(Abundance~Per~100~m^3~Drift))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
SturgeonCTU
head(AllData)
dev.off()
tiff("Figures/SturgeonCTUBase5.8.tiff", width = 84, height = 84, units = 'mm', res = 1200)
SturgeonCTU
dev.off()
###########
#Fig 1D Densities by CTU
##########
head(AllData)
Sturgeon<-ggplot(AllData,aes(CTUSturgeon,SturgeonConc))+geom_point(color="#000000",shape=15,size=1)+theme(axis.title.y=element_blank())+xlab("Cumulative Temperature Units (CTU)")+ scale_y_continuous(breaks=c(0,25,50,75,100),limits=c(0,100))
Invert<-ggplot(AllData,aes(CTUSturgeon,DriftInvertConc))+geom_point(color="#E69F00",shape=17,size=1)+theme(axis.title.x=element_blank(),axis.title.y=element_blank())
Cato<-ggplot(AllData,aes(CTUSturgeon,SuckerConc))+geom_point(color="#0072B2",shape=16,size=1)+theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.y = element_text(size = 7))
dev.off()
tiff("Figures/Figure1D DensityByCTU.tiff", width = 87, height = 95, units = 'mm', res = 1200)
ggarrange(Invert,Cato,Sturgeon,
labels = c("", "","",""),
ncol = 1, nrow = 3)
dev.off()
############
#Join together Figure 1
###########
theme_set(theme_bw(base_size = 10)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))
dev.off()
tiff("Figures/Fig1Oct2020.tiff", width = 174, height = 190, units = 'mm', res = 1200)
ggarrange(InvertByDayOfYear,SuckersByDayOfYear,SturgeonByDayOfYear,
labels = c("a", "b","c","d"),
ncol = 2, nrow = 2)
dev.off()
#Fig1D Added in paint
##############
#Figure Sucker GAM
##############
#citation(gratia)
library(itsadug)
library(gratia)
ShannonRichness<-read.csv("SturgeonMetadataWDiversity.csv",header=T)
ShannonSubset<-ShannonRichness
ShannonSubset<-subset(ShannonSubset, Temp!= "NA")
ShannonSubset<-subset(ShannonSubset, DischargeSampledByNight!= "NA")
ShannonSubset<-subset(ShannonSubset, AverageNetFlowByNight!= "NA")
ShannonSubset<-subset(ShannonSubset,ShannonSubset$DischargeSampledByNight < 2) #Remove discharge sampled outliers
ShannonSubset$temp_centered = ShannonSubset$Temp - mean(ShannonSubset$Temp)
ShannonSubset$Qcentered = ShannonSubset$Q - mean(ShannonSubset$Q)
ShannonSubset$AverageFlowCentered = ShannonSubset$AverageNetFlowByNight - mean(ShannonSubset$AverageNetFlowByNight)
ShannonSubset$DischargeCentered = ShannonSubset$DischargeSampledByNight - mean(ShannonSubset$DischargeSampledByNight)
ShannonSubset$DPFSCentered = ShannonSubset$DPFS - mean(ShannonSubset$DPFS)
ShannonSubset$DoYCentered = ShannonSubset$DayOfYear - mean(ShannonSubset$DayOfYear)
head(ShannonSubset)
SuckerAbuLog0 <- gam(log(SuckerConc+10e-5)~ 1+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SuckerAbuLog1 <- gam(log(SuckerConc+10e-5)~ s(CTUSturgeon)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SuckerAbuLog2 <- gam(log(SuckerConc+10e-5)~ s(DayOfYear)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SuckerAbuLog3 <- gam(log(SuckerConc+10e-5)~ s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SuckerAbuLog4 <- gam(log(SuckerConc+10e-5)~ s(temp_centered)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SuckerAbuLog5 <- gam(log(SuckerConc+10e-5)~ s(percillum)+s(CTUSturgeon)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SuckerAbuLog6 <- gam(log(SuckerConc+10e-5)~ s(DayOfYear)+s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SuckerAbuLog7 <- gam(log(SuckerConc+10e-5)~ s(DayOfYear)+s(temp_centered)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SuckerAbuLog8 <- gam(log(SuckerConc+10e-5)~ s(DayOfYear)+s(temp_centered)+s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SuckerAbuLogDischarge <- gam(log(Nsuckers100+10e-5)~ s(DischargeCentered)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
summary(SuckerAbuLogDischarge)
ShannonSubset2<-subset(ShannonSubset, Q!= "NA")
SuckerAbuLogDischarge2 <- gam(log(Nsuckers100+10e-5)~ s(Q)+s(Year,bs="re"), data = ShannonSubset2, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
summary(SuckerAbuLogDischarge2)
#Nsuckers100
#SuckerConc
SuckerAbuGamma <- gam((SuckerConc+10e-5)~ s(CTUSturgeon)+s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
SuckerAbuGamma$aic
summary(SuckerAbuGamma)
appraise(SuckerAbuGamma)
summary(SuckerAbuLog5)
appraise(SuckerAbuLog5)
SuckerAbuLog5$aic
AICctab(SuckerAbuLog0,SuckerAbuLog1,SuckerAbuLog2,SuckerAbuLog3,SuckerAbuLog4,SuckerAbuLog5,SuckerAbuLog6,SuckerAbuLog7,SuckerAbuLog8,
weights=T)
AICctab(SuckerAbuGamma,SuckerAbuLog5,weights=T)
SuckerAbuLog5$aic
SuckerAbuLog5$aic
summary(SuckerAbuLog5)
summary(SuckerAbuLog1)
# SuckerAbuLog6 <- gam(log(Nsuckers100+10e-5)~ s(percillum)+s(CTUSturgeon)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
# SuckerAbuLogConc <- gam(log(SuckerConc+10e-5)~ s(percillum)+s(CTUSturgeon)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
# AICctab(SuckerAbuLog6,SuckerAbuLogConc,weights=T)
#Models using drift conc performing better
##########33
#Top model
#########3
SuckerAbuLog <- gam(log(SuckerConc+10e-5)~ s(percillum)+s(CTUSturgeon)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
dev.off()
tiff("Figures/SuckerGAMAppraisal.tiff", width = 174, height = 174, units = 'mm', res = 1200)
appraise(SuckerAbuLog5)
dev.off()
summary(SuckerAbuLog)
plot<-plot_smooth(SuckerAbuLog, view="CTUSturgeon",rm.ranef=F,sim.ci = T)
plot_smooth(SuckerAbuLog, view="CTUSturgeon",rm.ranef=F,sim.ci = T)
#plot_sm
#plot$fv
FittedValues<-exp(plot$fv$fit)
head(FittedValues)
exp(7.5)
PredictedDataFrame<-data.frame(exp(plot$fv$fit),plot$fv$CTUSturgeon,exp(plot$fv$ul),exp(plot$fv$ll))
colnames(PredictedDataFrame)<-c("SuckerConc","CTUSturgeon","UpperCI","LowerCI")
head(PredictedDataFrame)
ggplot(PredictedDataFrame, aes(x=CTUSturgeon,y=SuckerConc))+geom_point()+geom_line(aes(CTUSturgeon, UpperCI),color="red")+geom_line(aes(CTUSturgeon,LowerCI),color="red")
head(ShannonSubset)
SuckerGAMPlot<-ggplot(ShannonSubset,aes(CTUSturgeon,SuckerConc))+geom_point(color="darkgrey")+geom_line(data=PredictedDataFrame,size=1.5)+
geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype="dashed")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype="dashed")+
xlab("Cumulative Temperature Units")+ylab(expression(Larval~Sucker~Per~100~m^3~Drift))+theme(axis.title.x=element_text(size = 8))#+ylim(NA, 1000)
SuckerGAMPlot
PredictedDataFramelog<-data.frame((plot$fv$fit),plot$fv$CTUSturgeon,(plot$fv$ul),(plot$fv$ll))
colnames(PredictedDataFramelog)<-c("SuckerConc","CTUSturgeon","UpperCI","LowerCI")
ggplot(ShannonSubset,aes(CTUSturgeon,log(SuckerConc)))+geom_point(color="darkgrey")+geom_line(data=PredictedDataFrame,size=1.5,aes(y=log(SuckerConc)))+
geom_line(data = PredictedDataFrame, aes(y = log(LowerCI)), size = .75,linetype="dashed")+geom_line(data = PredictedDataFrame,aes(y=log(UpperCI)),size=0.75,linetype="dashed")+
xlab("Cumulative Temperature Units")+ylab(expression(Log(Larval~Sucker~Per~100~m^3~Drift)))
dev.off()
tiff("Figures/SuckerGAM.tiff", width = 84, height = 84, units = 'mm', res = 1200)
SuckerGAMPlot
dev.off()
#Percent Illumination
plot<-plot_smooth(SuckerAbuLog, view="percillum",rm.ranef=F,sim.ci = T)
plot_smooth(SuckerAbuLog, view="CTUSturgeon",rm.ranef=F,sim.ci = T)
#plot_sm
#plot$fv
FittedValues<-exp(plot$fv$fit)
head(FittedValues)
exp(7.5)
PredictedDataFrame<-data.frame(exp(plot$fv$fit),plot$fv$percillum,exp(plot$fv$ul),exp(plot$fv$ll))
colnames(PredictedDataFrame)<-c("SuckerConc","percillum","UpperCI","LowerCI")
head(PredictedDataFrame)
ggplot(PredictedDataFrame, aes(x=percillum,y=SuckerConc))+geom_point()+geom_line(aes(percillum, UpperCI),color="red")+geom_line(aes(percillum,LowerCI),color="red")
SuckerGAMPlot2<-ggplot(ShannonSubset,aes(percillum,SuckerConc))+geom_point(color="darkgrey")+geom_line(data=PredictedDataFrame,size=1.5)+
geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype="dashed")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype="dashed")+
xlab("Percent Illumination (%)")+ylab(expression(Larval~Sucker~Per~100~m^3~Drift))+theme(axis.title.x=element_text(size = 8))#+ylim(NA, 1000)
SuckerGAMPlot2
PredictedDataFramelog<-data.frame((plot$fv$fit),plot$fv$CTUSturgeon,(plot$fv$ul),(plot$fv$ll))
colnames(PredictedDataFramelog)<-c("SuckerConc","CTUSturgeon","UpperCI","LowerCI")
ggplot(ShannonSubset,aes(CTUSturgeon,log(SuckerConc)))+geom_point(color="darkgrey")+geom_line(data=PredictedDataFrame,size=1.5,aes(y=log(SuckerConc)))+
geom_line(data = PredictedDataFrame, aes(y = log(LowerCI)), size = .75,linetype="dashed")+geom_line(data = PredictedDataFrame,aes(y=log(UpperCI)),size=0.75,linetype="dashed")+
xlab("Cumulative Temperature Units")+ylab(expression(Log(Larval~Catostomidae~Per~100~m^3~Drift)))
dev.off()
tiff("Figures/SuckerGAM2.tiff", width = 84, height = 84, units = 'mm', res = 1200)
SuckerGAMPlot2
dev.off()
##############
#Figure Sturgeon GAM
##############
library(itsadug)
library(gratia)
ShannonRichness<-read.csv("SturgeonMetadataWDiversity.csv",header=T)
ShannonSubset<-ShannonRichness
ShannonSubset<-subset(ShannonSubset, Temp!= "NA")
ShannonSubset<-subset(ShannonSubset, DischargeSampledByNight!= "NA")
ShannonSubset<-subset(ShannonSubset, AverageNetFlowByNight!= "NA")
ShannonSubset<-subset(ShannonSubset,ShannonSubset$DischargeSampledByNight < 2) #Remove discharge sampled outliers
ShannonSubset$temp_centered = ShannonSubset$Temp - mean(ShannonSubset$Temp)
ShannonSubset$Qcentered = ShannonSubset$Q - mean(ShannonSubset$Q)
ShannonSubset$AverageFlowCentered = ShannonSubset$AverageNetFlowByNight - mean(ShannonSubset$AverageNetFlowByNight)
ShannonSubset$DischargeCentered = ShannonSubset$DischargeSampledByNight - mean(ShannonSubset$DischargeSampledByNight)
ShannonSubset$DPFSCentered = ShannonSubset$DPFS - mean(ShannonSubset$DPFS)
ShannonSubset$DoYCentered = ShannonSubset$DayOfYear - mean(ShannonSubset$DayOfYear)
head(ShannonSubset)
SturgeonAbuLog0 <- gam(log(SturgeonConc+10e-5)~ 1+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SturgeonAbuLog1 <- gam(log(SturgeonConc+10e-5)~ s(CTUSturgeon)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SturgeonAbuLog2 <- gam(log(SturgeonConc+10e-5)~ s(DayOfYear)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SturgeonAbuLog3 <- gam(log(SturgeonConc+10e-5)~ s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SturgeonAbuLog4 <- gam(log(SturgeonConc+10e-5)~ s(temp_centered)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SturgeonAbuLog5 <- gam(log(SturgeonConc+10e-5)~ s(percillum)+s(CTUSturgeon)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SturgeonAbuLog6 <- gam(log(SturgeonConc+10e-5)~ s(DayOfYear)+s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SturgeonAbuLog7 <- gam(log(SturgeonConc+10e-5)~ s(DayOfYear)+s(temp_centered)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SturgeonAbuLog7_ <- gam(log(SturgeonConc+10e-5)~ s(DayOfYear)+s(temp_centered)+s(DriftInvertConc)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SturgeonAbuLog8 <- gam(log(SturgeonConc+10e-5)~ s(DayOfYear)+s(temp_centered)+s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SturgeonAbuLog9 <- gam(log(SturgeonConc+10e-5)~ s(DayOfYear)+s(CTUSturgeon)+s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
SturgeonAbuLogDischarge <- gam(log(Nsturgeon+10e-5)~ s(DischargeCentered)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year))
summary(SturgeonAbuLogDischarge)
appraise(SturgeonAbuLogDischarge)
AICctab(SturgeonAbuLog0,SturgeonAbuLog1,SturgeonAbuLog2,SturgeonAbuLog3,SturgeonAbuLog4,SturgeonAbuLog5,SturgeonAbuLog6,SturgeonAbuLog7,SturgeonAbuLog8,SturgeonAbuLog9,
weights=T)
summary(SturgeonAbuLog1)
appraise(SturgeonAbuLog1)
SturgeonAbuLog7$aic
#Gamma Models
SturgeonAbuGamma0 <- gam((SturgeonConc+10e-5)~ 1+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
SturgeonAbuGamma1 <- gam((SturgeonConc+10e-5)~ s(CTUSturgeon)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
SturgeonAbuGamma2 <- gam((SturgeonConc+10e-5)~ s(DayOfYear)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
SturgeonAbuGamma3 <- gam((SturgeonConc+10e-5)~ s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
SturgeonAbuGamma4 <- gam((SturgeonConc+10e-5)~ s(temp_centered)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
SturgeonAbuGamma5 <- gam((SturgeonConc+10e-5)~ s(percillum)+s(CTUSturgeon)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
SturgeonAbuGamma6 <- gam((SturgeonConc+10e-5)~ s(DayOfYear)+s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
SturgeonAbuGamma7 <- gam((SturgeonConc+10e-5)~ s(DayOfYear)+s(temp_centered)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
SturgeonAbuGamma8 <- gam((SturgeonConc+10e-5)~ s(DayOfYear)+s(temp_centered)+s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
#SturgeonAbuGamma9 <- gam((SturgeonConc+10e-5)~ s(CTUSturgeon)+s(percillum)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
SturgeonAbuGamma10 <- gam((SturgeonConc+10e-5)~ s(CTUSturgeon)+s(temp_centered)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
SturgeonAbuGammaDischarge <- gam((Nsturgeon+10e-5)~ s(Q)+s(Year,bs="re"), data = ShannonSubset, method = "REML",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = "log"))
summary(SturgeonAbuGammaDischarge)
appraise(SturgeonAbuGammaDischarge)
cor(ShannonSubset$DayOfYear,ShannonSubset$CTUSturgeon)
cor(ShannonSubset$DayOfYear,ShannonSubset$Temp)
AICctab(SturgeonAbuGamma0,SturgeonAbuGamma1,SturgeonAbuGamma2,SturgeonAbuGamma3,SturgeonAbuGamma4,SturgeonAbuGamma5,SturgeonAbuGamma6,SturgeonAbuGamma7,SturgeonAbuGamma8,
SturgeonAbuGamma10,weights=T)
AICctab(SturgeonAbuGamma10,SturgeonAbuLog1,weights=T)
summary(SturgeonAbuGamma10)
appraise(SturgeonAbuLog1)
appraise(SturgeonAbuGamma10)
SturgeonAbuGamma10$aic
##########33
#Top model
#########3
summary(SturgeonAbuGamma10)
gam.check(SturgeonAbuGamma10)
plot(SturgeonAbuGamma10)
appraise(SturgeonAbuGamma10) #Model ok at best, not great
cor(ShannonSubset$CTUSturgeon,ShannonSubset$Temp)
plot<-plot_smooth(SturgeonAbuGamma10, view="CTUSturgeon",rm.ranef=F,sim.ci = T)
plot_smooth(SturgeonAbuGamma10, view="temp_centered",rm.ranef=F,sim.ci = T)
#plot_sm
#plot$fv
FittedValues<-exp(plot$fv$fit)
head(FittedValues)
exp(7.5)
PredictedDataFrame<-data.frame(exp(plot$fv$fit),plot$fv$CTUSturgeon,exp(plot$fv$ul),exp(plot$fv$ll))
colnames(PredictedDataFrame)<-c("SturgeonConc","CTUSturgeon","UpperCI","LowerCI")
head(PredictedDataFrame)
ggplot(PredictedDataFrame, aes(x=CTUSturgeon,y=SturgeonConc))+geom_point()+geom_line(aes(CTUSturgeon, UpperCI),color="red")+
geom_line(aes(CTUSturgeon,LowerCI),color="red")
ggplot(ShannonSubset,aes(CTUSturgeon,SturgeonConc))+geom_point()
SturgeonGAMPlot<-ggplot(ShannonSubset,aes(CTUSturgeon,SturgeonConc))+geom_point(color="darkgrey")+geom_line(data=PredictedDataFrame,size=1.5)+
geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype="dashed")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype="dashed")+
xlab("Cumulative Temperature Units")+ylab(expression(Larval~Sturgeon~Per~100~m^3~Drift))+theme(axis.title.x=element_text(size = 8))#+ylim(NA, 1000)
SturgeonGAMPlot
# mean(ShannonSubset$Temp)
# PredictedDataFramelog<-data.frame((plot$fv$fit),plot$fv$temp_centered+19.207,(plot$fv$ul),(plot$fv$ll))
# colnames(PredictedDataFramelog)<-c("SturgeonConc","temp_centered","UpperCI","LowerCI")
#
# #ggplot(ShannonSubset,aes(CTUSturgeon,log(SturgeonConc)))+geom_point(color="darkgrey")+geom_line(data=PredictedDataFrame,size=1.5,aes(y=log(SturgeonConc)))+
# geom_line(data = PredictedDataFrame, aes(y = log(LowerCI)), size = .75,linetype="dashed")+geom_line(data = PredictedDataFrame,aes(y=log(UpperCI)),size=0.75,linetype="dashed")+
# xlab("Temperature Centered")+ylab(expression(Log(Larval~Catostomidae~Per~100~m^3~Drift)))
#
dev.off()
tiff("Figures/SturgeonGAM.tiff", width = 84, height = 84, units = 'mm', res = 1200)
SturgeonGAMPlot
dev.off()
dev.off()
tiff("Figures/SturgeonGAMAppraisal.tiff", width = 174, height = 174, units = 'mm', res = 1200)
appraise(SturgeonAbuGamma10)
dev.off()
#Temperature
plot<-plot_smooth(SturgeonAbuGamma10, view="temp_centered",rm.ranef=F,sim.ci = T)
#plot_smooth(SturgeonAbuLog, view="temp_centered",rm.ranef=F,sim.ci = T)
#plot_sm
#plot$fv
FittedValues<-exp(plot$fv$fit)
head(FittedValues)
exp(7.5)
PredictedDataFrame<-data.frame(exp(plot$fv$fit),plot$fv$temp_centered,exp(plot$fv$ul),exp(plot$fv$ll))
colnames(PredictedDataFrame)<-c("SturgeonConc","temp_centered","UpperCI","LowerCI")
PredictedDataFrame$Temp<-PredictedDataFrame$temp_centered+mean(ShannonSubset$Temp) #Remove centering
head(PredictedDataFrame)
ggplot(PredictedDataFrame, aes(x=Temp,y=SturgeonConc))+geom_point()+geom_line(aes(Temp, UpperCI),color="red")+geom_line(aes(Temp,LowerCI),color="red")
ggplot(ShannonSubset,aes(temp_centered,SturgeonConc))+geom_point()
SturgeonGAMPlot2<-ggplot(ShannonSubset,aes(Temp,SturgeonConc))+geom_point(color="darkgrey")+geom_line(data=PredictedDataFrame,size=1.5)+
geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype="dashed")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype="dashed")+
xlab("Temperature (C)")+ylab(expression(Larval~Sturgeon~Per~100~m^3~Drift))#+ylim(NA, 1000)
SturgeonGAMPlot2
dev.off()
tiff("Figures/SturgeonGAMTemp.tiff", width = 84, height = 84, units = 'mm', res = 1200)
SturgeonGAMPlot2
dev.off()
#Sturgeon River Discharge
plot<-plot_smooth(SturgeonAbuGammaDischarge, view="Q",rm.ranef=F,sim.ci = T)
#plot_smooth(SturgeonAbuLog, view="temp_centered",rm.ranef=F,sim.ci = T)
#plot_sm
#plot$fv
FittedValues<-exp(plot$fv$fit)
head(FittedValues)
exp(7.5)
PredictedDataFrame<-data.frame(exp(plot$fv$fit),plot$fv$Q,exp(plot$fv$ul),exp(plot$fv$ll))
colnames(PredictedDataFrame)<-c("Nsturgeon","Q","UpperCI","LowerCI")
head(PredictedDataFrame)
ggplot(PredictedDataFrame, aes(x=Q,y=Nsturgeon))+geom_point()+geom_line(aes(Q, UpperCI),color="red")+geom_line(aes(Q,LowerCI),color="red")
ggplot(ShannonSubset,aes(Q,Nsturgeon))+geom_point()
SturgeonGAMPlot3<-ggplot(ShannonSubset,aes(Q,Nsturgeon))+geom_point(color="darkgrey")+geom_line(data=PredictedDataFrame,size=1.5)+
geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype="dashed")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype="dashed")+
xlab("River Discharge (m3/sec)")+ylab(expression(Nightly~Larval~Sturgeon))#+ylim(NA, 1000)
SturgeonGAMPlot3
appraise(SturgeonAbuGammaDischarge)
dev.off()
tiff("Figures/SturgeonGAMDischarge.tiff", width = 84, height = 84, units = 'mm', res = 1200)
SturgeonGAMPlot3
dev.off()
##########
#LMM Invertebrate Abundance Model
###########
ShannonRichness<-read.csv("SturgeonMetadataWDiversity.csv",header=T)
ShannonSubset<-ShannonRichness
ShannonSubset<-subset(ShannonSubset, Temp!= "NA")
ShannonSubset<-subset(ShannonSubset, AverageNetFlowByNight!= "NA")
ShannonSubset<-subset(ShannonSubset,ShannonSubset$DischargeSampledByNight < 2) #Remove discharge sampled outliers
ShannonSubset$temp_centered = ShannonSubset$Temp - mean(ShannonSubset$Temp)
ShannonSubset$Qcentered = ShannonSubset$Q - mean(ShannonSubset$Q)
ShannonSubset$AverageFlowCentered = ShannonSubset$AverageNetFlowByNight - mean(ShannonSubset$AverageNetFlowByNight)
ShannonSubset$DischargeCentered = ShannonSubset$DischargeSampledByNight - mean(ShannonSubset$DischargeSampledByNight)
ShannonSubset$DPFSCentered = ShannonSubset$DPFS - mean(ShannonSubset$DPFS)
ShannonSubset$DoYCentered = ShannonSubset$DayOfYear - mean(ShannonSubset$DayOfYear)
min(ShannonSubset$DischargeSampledByNight,na.rm=T)
max(ShannonSubset$DischargeSampledByNight,na.rm=T)
mean(ShannonSubset$DischargeSampledByNight,na.rm=T)
min(ShannonSubset$Q,na.rm=T)
max(ShannonSubset$Q,na.rm=T)
mean(ShannonSubset$Q,na.rm=T)
InvertsByDischargeSampledDL0 = lme(log(DriftInvertConc)~1,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))
InvertsByDischargeSampledDL1 = lme(log(DriftInvertConc)~percillum,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))
InvertsByDischargeSampledDL2 = lme(log(DriftInvertConc)~temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))
InvertsByDischargeSampledDL3 = lme(log(DriftInvertConc)~DoYCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))
InvertsByDischargeSampledDL4 = lme(log(DriftInvertConc)~percillum+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))
InvertsByDischargeSampledDL5 = lme(log(DriftInvertConc)~percillum+DoYCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))
InvertsByDischargeSampledDL6 = lme(log(DriftInvertConc)~temp_centered+DoYCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))
InvertsByDischargeSampledDL7 = lme(log(DriftInvertConc)~percillum+DoYCentered+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))
InvertsByDischargeSampledDL8 = lme(log(DriftInvertConc)~CTUSturgeon,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))
InvertsByDischargeSampledDL9 = lme(log(DriftInvertConc)~CTUSturgeon+percillum,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))
AICctab(InvertsByDischargeSampledDL0,InvertsByDischargeSampledDL1,InvertsByDischargeSampledDL2,InvertsByDischargeSampledDL3,
InvertsByDischargeSampledDL4,InvertsByDischargeSampledDL5,InvertsByDischargeSampledDL6,InvertsByDischargeSampledDL7,
InvertsByDischargeSampledDL8,InvertsByDischargeSampledDL9, weights=TRUE)
summary(InvertsByDischargeSampledDL3) #AICc weight = .691
summary(InvertsByDischargeSampledDL8) #AICc weight = .113
summary(InvertsByDischargeSampledDL6) #
summary(InvertsByDischargeSampledDL0) #
summary(InvertsByDischargeSampledDL5) #
summary(InvertsByDischargeSampledDL4) #
summary(InvertsByDischargeSampledDL1) #
summary(InvertsByDischargeSampledDL7) #
#Model no AR1 structure
InvertsByDischargeSampledDL3NoCor = lme(log(DriftInvertConc)~DoYCentered,random =~1|Year,data=ShannonSubset)
InvertsByDischargeSampledDL3NoCor
AICctab(InvertsByDischargeSampledDL3NoCor,InvertsByDischargeSampledDL3)
#######
#Best model residual plots
#######
plot(resid(InvertsByDischargeSampledDL3) ~ temp_centered, data=ShannonSubset)
lines(lowess(resid(InvertsByDischargeSampledDL3) ~ ShannonSubset$temp_centered), col=2)
plot(resid(InvertsByDischargeSampledDL3) ~ percillum, data=ShannonSubset)
lines(lowess(resid(InvertsByDischargeSampledDL3) ~ ShannonSubset$percillum), col=2)
plot(resid(InvertsByDischargeSampledDL3) ~ DayOfYear, data=ShannonSubset)
lines(lowess(resid(InvertsByDischargeSampledDL3) ~ ShannonSubset$DayOfYear), col=2)
hist(resid(InvertsByDischargeSampledDL3))
plot(resid(InvertsByDischargeSampledDL3) ~ DischargeSampledByNight, data=ShannonSubset)
lines(lowess(resid(InvertsByDischargeSampledDL3) ~ ShannonSubset$DischargeSampledByNight), col=2)
plot(resid(InvertsByDischargeSampledDL3) ~ predict(InvertsByDischargeSampledDL3))
lines(lowess(resid(InvertsByDischargeSampledDL3) ~ predict(InvertsByDischargeSampledDL3)), col=2)
summary(InvertsByDischargeSampledDL3)
confint(InvertsByDischargeSampledDL3)
fixef(InvertsByDischargeSampledDL3)
ranef(InvertsByDischargeSampledDL3)
coef(InvertsByDischargeSampledDL3)
#######
#Invert abu model parameter estimates
#######
summary(InvertsByDischargeSampledDL3)
intervals(InvertsByDischargeSampledDL3,which=c("fixed"))
#Intercept
exp(2.29942397) #9.97 estimate Intercept (centered)
exp(2.00731098) #7.44 CI
exp(2.591537) #13.35 CI
#DayOfYear
(exp(-0.03322902)-1)*100 #-3.27 estimate Intercept (centered)
(exp(-0.04684404)-1)*100 #-4.58 CI
(exp(-0.019614)-1)*100 #-1.94 CI
mean(ShannonSubset$DayOfYear) #158.4 june 6th or 7th
#Second best model
summary(InvertsByDischargeSampledDL8)
intervals(InvertsByDischargeSampledDL8,which=c("fixed"))
#Intercept
exp(3.268941490) #26.28 estimate Intercept (centered)
exp(2.793188599) #16.33 CI
exp(3.744694381) #42.29 CI
(exp(-0.002595848)-1)*100 #-0.2592
(exp(-0.001576312)-1)*100 #-0.157% CI
(exp(-0.003615384)-1)*100 #-0.3608% CI
################
#Figure 2C Invert Abu Lmm DayOfYear plot
################
#Bootstrapped CIs rom https://stats.stackexchange.com/questions/231074/confidence-intervals-on-predictions-for-a-non-linear-mixed-model-nlme
ShannonRichness<-read.csv("SturgeonMetadataWDiversity.csv",header=T)
ShannonSubset<-ShannonRichness
ShannonSubset<-subset(ShannonSubset, Temp!= "NA")
ShannonSubset<-subset(ShannonSubset, AverageNetFlowByNight!= "NA")
ShannonSubset<-subset(ShannonSubset,ShannonSubset$DischargeSampledByNight < 2) #Remove discharge sampled outliers
ShannonSubset$temp_centered = ShannonSubset$Temp - mean(ShannonSubset$Temp)
ShannonSubset$Qcentered = ShannonSubset$Q - mean(ShannonSubset$Q)
ShannonSubset$AverageFlowCentered = ShannonSubset$AverageNetFlowByNight - mean(ShannonSubset$AverageNetFlowByNight)
ShannonSubset$DischargeCentered = ShannonSubset$DischargeSampledByNight - mean(ShannonSubset$DischargeSampledByNight)
ShannonSubset$DPFSCentered = ShannonSubset$DPFS - mean(ShannonSubset$DPFS)
ShannonSubset$DoYCentered = ShannonSubset$DayOfYear - mean(ShannonSubset$DayOfYear)
ShannonSubset$Year<-as.factor(ShannonSubset$Year)
ShannonSubset$DriftLog<-log(ShannonSubset$DriftInvertConc)
InvertsByDischargeSampledDL3 = lme((DriftLog)~DoYCentered,random=~1|Year,data=ShannonSubset)#correlation=corAR1(form = ~DayOfYear|Year)
xvals <- with(ShannonSubset,seq(min(DoYCentered),max(DoYCentered),length.out=100))
nresamp<-1000
## pick new parameter values by sampling from multivariate normal distribution based on fit
pars.picked <- mvrnorm(nresamp, mu = fixef(InvertsByDischargeSampledDL3), Sigma = vcov(InvertsByDischargeSampledDL3))
## predicted values: useful below
pframe <- with(ShannonSubset,data.frame(DoYCentered))
pframe$DriftLog <- predict(InvertsByDischargeSampledDL3,newdata=pframe,level=0)
head(pframe)
## utility function
get_CI <- function(y,pref="") {
r1 <- t(apply(y,1,quantile,c(0.025,0.975)))
setNames(as.data.frame(r1),paste0(pref,c("lwr","upr")))
}
## bootstrapping
sampfun <- function(fitted,data,idvar="Year") {
pp <- predict(fitted,levels=1)
rr <- residuals(fitted)
dd <- data.frame(data,pred=pp,res=rr)
## sample groups with replacement
iv <- levels(data[[idvar]])
bsamp1 <- sample(iv,size=length(iv),replace=TRUE)
bsamp2 <- lapply(bsamp1,
function(x) {
## within groups, sample *residuals* with replacement
ddb <- dd[dd[[idvar]]==x,]
## bootstrapped response = pred + bootstrapped residual
ddb$DriftLog <- ddb$pred +
sample(ddb$res,size=nrow(ddb),replace=TRUE)
return(ddb)
})
res <- do.call(rbind,bsamp2) ## collect results
if (is(data,"groupedData"))
res <- groupedData(res,formula=formula(data))
return(res)
}
pfun <- function(fm) {
predict(fm,newdata=pframe,level=0)
}
set.seed(101)
yvals2 <- replicate(nresamp,
pfun(update(InvertsByDischargeSampledDL3,data=sampfun(InvertsByDischargeSampledDL3,ShannonSubset,"Year"))))
c2 <- get_CI(yvals2,"boot_")
pframe2 <- data.frame(pframe,c2)
head(pframe2)
pframe2$DayOfYear<-pframe2$DoYCentered+mean(ShannonSubset$DayOfYear) #Remove centering on DOY for plotting
pframe2$DriftInvertConc<-exp(pframe2$DriftLog)
pframe2$boot_lwr<-exp(pframe2$boot_lwr)
pframe2$boot_upr<-exp(pframe2$boot_upr)
DOYInvertModelBoot<-ggplot(ShannonSubset,aes(DayOfYear,DriftInvertConc))+geom_point(color="darkgrey")+geom_line(data=pframe2,size=1.5)+
geom_line(data = pframe2, aes(y = boot_lwr), size = .75,linetype="dashed")+geom_line(data = pframe2,aes(y=boot_upr),size=0.75,linetype="dashed")+
xlab("Day of Year")+ylab(expression(Macroinvertebrates~Per~100~m^3~Drift))+theme(axis.title.y=element_text(size = 11))
DOYInvertModelBoot
dev.off()
tiff("Figures/DOYInvertModelBoot.tiff", width = 84, height = 84, units = 'mm', res = 1000)
DOYInvertModelBoot
dev.off()
#####################
#Combine Abundance Model Figure
#####################
DOYInvertModelBoot
SturgeonGAMPlot
SuckerGAMPlot
dev.off()
tiff("Figures/Fig2AbundanceModels.tiff", width = 174, height = 84, units = 'mm', res = 1200)
ggarrange(SturgeonGAMPlot,SuckerGAMPlot,DOYInvertModelBoot,
labels = c("a", "b","c"),
ncol = 3, nrow = 1)
dev.off()
###########
#Total InvertN By Moon Phase Normalized
############
# Model<-aov(log(DriftInvertConc)~MoonPhase,data=ShannonRichness)
# summary(Model)
# hist(resid(Model))
#
# kruskal.test(DriftInvertConc~MoonPhase, data=ShannonRichness)
# Tukey<-TukeyHSD(Model,"MoonPhase")
#
# Tukey
# difference<-Tukey$MoonPhase[,"p adj"]
# Letters<-multcompLetters(difference)
#
# Letters
kruskal.test(DriftInvertConc~MoonPhase, data=ShannonRichness)
compare_means(DriftInvertConc ~ MoonPhase, data = ShannonRichness, p.adjust.method = "fdr",method="wilcox.test")
Means=compare_means(DriftInvertConc ~ MoonPhase, data = ShannonRichness, p.adjust.method = "fdr",method="wilcox.test")
Hyphenated<-as.character(paste0(Means$group1,"-",Means$group2))
difference<-Means$p.adj
names(difference)<-Hyphenated
Letters<-multcompLetters(difference)
Letters
Letters$Letters
#manually renamed due to some weirdness with plotting where new moon donsn't start with a
LettersRearranged<-c("bc","abc","abc","a","ab","ab","ab","c")
DriftPlotNAsRemoved<-subset(ShannonRichness, DriftInvertConc!="NA")
DriftPlotNAsRemoved
Trtdata <- ddply(DriftPlotNAsRemoved, c("MoonPhase"), summarise,
N = length(DriftInvertConc),
meanSturgeon = mean(DriftInvertConc),
sd = sd(DriftInvertConc),
se = sd / sqrt(N),na.rm =T
)
Trtdata
Trtdata$MoonPhase = factor(Trtdata$MoonPhase, levels = c("New Moon","Waxing Crescent","First Quarter","Waxing Gibbous","Full Moon","Waning Gibbous","Last Quarter","Waning Crescent"))
TotalInvertAbuMoonPhase<-ggplot(Trtdata, aes(x=MoonPhase,y=meanSturgeon))+geom_bar(aes(fill=MoonPhase),stat="identity")+xlab("Moon Phase")+ylab(expression(Macroinvertebrates~Per~100~m^3~Drift~(SE)))+
geom_errorbar(aes(ymin=meanSturgeon-se,ymax=meanSturgeon+se))+ theme(axis.text.x = element_text(angle = 0, hjust = 0.5),axis.title.y = element_text(size = 10))+scale_fill_manual(values=cbPalette)+
geom_text(aes(x=MoonPhase, y=meanSturgeon+se+1,label=LettersRearranged))+theme(legend.position = "none")+geom_text(aes(x=5,y=25,label= "KW, Chi-sq = 39.98, P < 0.001"),size=4)+
scale_x_discrete(labels=c("New","WXC","FQ","WXG","Full","WAG","LQ","WNC"))
TotalInvertAbuMoonPhase
theme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))
dev.off()
tiff("Figures/TotalInvertebrateAbuByPhaseNormalized.tiff", width = 84, height = 84, units = 'mm', res = 1200)
TotalInvertAbuMoonPhase
dev.off()
compare_means(DriftInvertConc ~ MoonPhase, data = ShannonRichness, p.adjust.method = "fdr",method="wilcox.test")
###############
#Hourly Results
###############
Hourly<-read.csv("C:\\Users\\Joe Receveur\\Documents\\MSU data\\Sturgeon\\SturgeonDrift\\SturgeonDrift\\DataClean\\SturgeonDriftDataHourly12.21.19.csv",header=T)
head(Hourly)
Hourly$Time = factor(Hourly$Time, levels = c("Ten","Eleven","Twelve","One","Two"))
Hourly$DriftInvertConc<-((Hourly$InvertSample100*100)/(60*60*Hourly$AreaSampled*Hourly$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)
Hourly$DriftSuckerConc<-(Hourly$Suckers100*100/(60*60*Hourly$AreaSampled*Hourly$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)
Hourly$DriftSturgeonConc<-(Hourly$LiveSturgeon*100/(60*60*Hourly$AreaSampled*Hourly$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)
head(Hourly$Suckers100)
StatSubset<-subset(Hourly, InvertSample100 > 0&DriftInvertConc!= "NA") #Remove sample dates where Inverts were not collected (no dates where drift collections occured which had no drifting invertebrates)
length(StatSubset$ï..SampleID)
length(Hourly$ï..SampleID)
hist(log(Hourly$InvertSample100))
plot(DriftInvertConc~Time,data= StatSubset)
#
#
# Model<-aov(log(DriftInvertConc)~Time,data=StatSubset)
#
# summary(Model)
# plot(Model)
# Tukey<-TukeyHSD(Model,"Time")
# plot(Tukey)
# Tukey
# (exp(1.510104612)-1)*100 #352.7%Difference between 10 pm collection and midnight
# (exp(1.1965172)-1)*100 #230.8574% lower CI
# (exp(1.8236920)-1)*100 #519.4687% upper CI
#
# difference<-Tukey$Time[,"p adj"]
# Letters<-multcompLetters(difference)
# Letters
# #rearrange letters so a is at ten pm, c=a,a=b,c=c
# LetterRearranged<-c("a","b","c","c","c")
# hist(resid(Model))
kruskal.test((DriftInvertConc)~Time,data=StatSubset)
compare_means(DriftInvertConc ~ Time, data = StatSubset, p.adjust.method = "fdr",method="wilcox.test")
Means=compare_means(DriftInvertConc ~ Time, data = StatSubset, p.adjust.method = "fdr",method="wilcox.test")
Hyphenated<-as.character(paste0(Means$group1,"-",Means$group2))
difference<-Means$p.adj
names(difference)<-Hyphenated
LettersInvert<-multcompLetters(difference)
LettersInvert<-LettersInvert$Letters
Trtdata <- ddply(StatSubset, c("Time"), summarise,
N = length(DriftInvertConc),
meanInverts = mean(DriftInvertConc),
sd = sd(DriftInvertConc),
se = sd / sqrt(N)
)
Trtdata
HourlyInvertAbu<-ggplot(Trtdata,aes(x=Time,y=meanInverts))+geom_point()+xlab("Collection Time")+
geom_errorbar(aes(ymin=meanInverts-se,ymax=meanInverts+se))+ylab(expression(Macroinvertebrates~Per~100~m^3~Drift~(SE)))+
scale_x_discrete(labels=c("22:00","23:00","0:00","1:00","2:00"))+geom_text(aes(x=Time, y=meanInverts+se+0.5,label=LettersInvert))+
annotate("text", label = "Kruskal-Wallis,\n Chi-sq = 188.4,\n P < 0.001", size = 3.5, x = 1.5, y = 15)+ theme(axis.title.y = element_text(size = 10))
HourlyInvertAbu
dev.off()
tiff("Figures/Hourly_InvertAbundance.tiff", width = 84, height = 84, units = 'mm', res = 1200)
HourlyInvertAbu
dev.off()
#Sucker by collection
TrtdataSucker <- ddply(StatSubset, c("Time"), summarise,
N = length(DriftSuckerConc),
meanSucker = mean(DriftSuckerConc),
sd = sd(DriftSuckerConc),
se = sd / sqrt(N)
)
TrtdataSucker
plot(DriftSuckerConc~Time,data= StatSubset)
kruskal.test((DriftSuckerConc)~Time,data=StatSubset)
compare_means(DriftSuckerConc ~ Time, data = StatSubset, p.adjust.method = "fdr",method="wilcox.test")
Means=compare_means(DriftSuckerConc ~ Time, data = StatSubset, p.adjust.method = "fdr",method="wilcox.test")
Hyphenated<-as.character(paste0(Means$group1,"-",Means$group2))
difference<-Means$p.adj
names(difference)<-Hyphenated
LettersSucker<-multcompLetters(difference)
LettersSucker
HourlySuckerAbu<-ggplot(TrtdataSucker,aes(x=Time,y=meanSucker))+geom_point()+xlab("Collection Time")+
geom_errorbar(aes(ymin=meanSucker-se,ymax=meanSucker+se))+ylab(expression(Sucker~Larvae~Per~100~m^3~Drift~(SE)))+
scale_x_discrete(labels=c("22:00","23:00","0:00","1:00","2:00"))+geom_text(aes(x=Time, y=meanSucker+se+5,label=LettersSucker$Letters))+
annotate("text", label = "Kruskal-Wallis,\n Chi-sq = 126.1,\n P < 0.001", size = 3.5, x = 4.5, y = 90)+ theme(axis.title.y = element_text(size = 10))