-
Notifications
You must be signed in to change notification settings - Fork 0
/
NutNet_rare_spp.R
878 lines (719 loc) · 33.7 KB
/
NutNet_rare_spp.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
### NutNet and rare species #####
# June 13 2017
#Close graphics and clear local memory
#graphics.off()
rm(list=ls())
setwd("~/Google Drive/LTER_Biodiversity_Productivity")
#kim's wd
# setwd('C:\\Users\\lapie\\Dropbox (Smithsonian)\\nutrient network\\NutNet data')
library(grid)
library(lme4)
library(sjPlot)
library(merTools)
library(DHARMa)
library(car)
library(AICcmodavg)
library(tidyverse)
library(ggplot2)
theme_set(theme_bw())
theme_update(axis.title.x=element_text(size=20, vjust=-0.35, margin=margin(t=15)), axis.text.x=element_text(size=16),
axis.title.y=element_text(size=20, angle=90, vjust=0.5, margin=margin(r=15)), axis.text.y=element_text(size=16),
plot.title = element_text(size=24, vjust=2),
panel.grid.major=element_blank(), panel.grid.minor=element_blank(),
legend.title=element_blank(), legend.text=element_text(size=20))
###bar graph summary statistics function
#barGraphStats(data=, variable="", byFactorNames=c(""))
barGraphStats <- function(data, variable, byFactorNames) {
count <- length(byFactorNames)
N <- aggregate(data[[variable]], data[byFactorNames], FUN=length)
names(N)[1:count] <- byFactorNames
names(N) <- sub("^x$", "N", names(N))
mean <- aggregate(data[[variable]], data[byFactorNames], FUN=mean)
names(mean)[1:count] <- byFactorNames
names(mean) <- sub("^x$", "mean", names(mean))
sd <- aggregate(data[[variable]], data[byFactorNames], FUN=sd)
names(sd)[1:count] <- byFactorNames
names(sd) <- sub("^x$", "sd", names(sd))
preSummaryStats <- merge(N, mean, by=byFactorNames)
finalSummaryStats <- merge(preSummaryStats, sd, by=byFactorNames)
finalSummaryStats$se <- finalSummaryStats$sd / sqrt(finalSummaryStats$N)
return(finalSummaryStats)
}
#########
nutnetdf <- read.csv("~Dropbox/IV in ecology/NutNet/NutNetCoverData_ProcessedAug2019.csv")
# kim's code: LD changed on sept 30 2019
# nutnetdf <-read.csv("full-cover-21-February-2019.csv")
#
# nutnetpretreatdt <- data.table(nutnetpretreatdf)
# nutnetpretreatdt[, meanPTAbundance:=mean(rel_cover, na.rm=T), .(year, site_code, Taxon)]
# nutnetpretreatdt[, maxPTAbundance:=max(rel_cover, na.rm=T), .(year, site_code, Taxon)]
# nutnetpretreatdt[, PTfreq:=sum(live==1, na.rm=T), .(year, site_code, Taxon)]
#relative cover
nutnetSum <- nutnetdf%>%
group_by(site_code, plot, year_trt)%>%
summarise(total_cover=sum(max_cover))
nutnetRel <- nutnetdf%>%
left_join(nutnetSum)%>%
mutate(rel_cover=(max_cover/total_cover)*100)%>%
filter(live==1)
## compute mean abundance, max abundance and frequency of each species in the pretreatment data
#filter to pretreatment data
nutnetpretreatdf <- nutnetRel[nutnetRel$year_trt == 0,]
meanAb_byspecies <- aggregate(nutnetpretreatdf$rel_cover, by = list(nutnetpretreatdf$year, nutnetpretreatdf$site_code, nutnetpretreatdf$Taxon), FUN = mean, na.rm = TRUE)
names(meanAb_byspecies) = c("year", "site_code", "Taxon", "meanPTAbundance")
meanAb_byspecies <- meanAb_byspecies%>%
select(-year)
max_abund <- aggregate(nutnetpretreatdf$rel_cover, by = list(nutnetpretreatdf$year, nutnetpretreatdf$site_code, nutnetpretreatdf$Taxon), FUN = max, na.rm = TRUE)
names(max_abund) = c("year", "site_code", "Taxon", "maxPTAbundance")
max_abund <- max_abund%>%
select(-year)
#filter to species live in a plot and pretreatment year, then sum to get the # of plots the species appeared in
nutnetpretreatdf_live = nutnetpretreatdf[nutnetpretreatdf$live == 1,]
freq <- aggregate(nutnetpretreatdf_live$live, by = list(nutnetpretreatdf_live$year, nutnetpretreatdf_live$site_code, nutnetpretreatdf_live$Taxon), FUN = sum, na.rm = TRUE)
names(freq) = c("year", "site_code", "Taxon", "PTfreq")
tot_plots <- nutnetpretreatdf_live%>%
select(site_code, plot)%>%
unique()%>%
group_by(site_code)%>%
summarise(num_plots=max(plot))
freq <- freq%>%
select(-year)%>%
merge(tot_plots, by='site_code')%>%
mutate(freq=PTfreq/num_plots)
#### Process data to create a max cover or 0 for each species, plot & year ####
nutnetdf_allspp <- nutnetRel%>%
select(-Family, -live:-total_cover)%>%
group_by(site_name, site_code)%>%
nest()%>%
mutate(spread_df = purrr::map(data, ~spread(., key=Taxon, value=rel_cover, fill=0)%>%
gather(key=Taxon, value=rel_cover,
-year:-trt)))%>%
unnest(spread_df)
nutnetdf_allspp <- as.data.frame(nutnetdf_allspp)
###make a column for presence absence of each species in a plot-year
nutnetdf_allspp$PA = ifelse(nutnetdf_allspp$rel_cover > 0, 1, 0)
nutnetdf_length <- as.data.frame(nutnetdf_allspp)%>%
#make a column for max trt year
group_by(site_code)%>%
summarise(length=max(year_trt))
nutnetdf_allspp2 <- nutnetdf_allspp%>%
merge(nutnetdf_length, by='site_code')%>%
select(-year, -rel_cover)%>%
filter(year_trt>0)%>%
mutate(year_trt2=paste("yr", year_trt, sep=''))%>%
select(-year_trt, -trt)%>%
group_by(site_code, Taxon, site_name, block, plot, subplot, year_trt2, length)%>%
summarise(PA2=mean(PA))%>%
ungroup()%>%
group_by(site_code, Taxon, plot, length)%>%
summarise(PA3=sum(PA2))%>%
ungroup()
nutnetpretrt <- nutnetpretreatdf_live%>%
mutate(pretrt_cover=rel_cover)%>%
select(site_code, plot, Taxon, pretrt_cover)
nutnetdf_allspp3 <- nutnetdf_allspp2%>%
mutate(yrs_absent=(length-PA3)/length)%>%
left_join(nutnetpretrt)%>%
filter(!is.na(pretrt_cover))%>%
merge(meanAb_byspecies, by=c('site_code', 'Taxon'))%>%
merge(max_abund, by=c('site_code', 'Taxon'))%>%
merge(freq, by=c('site_code', 'Taxon'))%>%
mutate(abund_metric=((meanPTAbundance/100)+freq)/2)%>%
select(-PTfreq)%>%
filter(length>0)
#get back trt info
trt <- nutnetRel%>%
select(year_trt, site_code, plot, trt)%>%
filter(year_trt>0)%>%
select(-year_trt)%>%
unique()
nutnetdf_allspp3Trt <- nutnetdf_allspp3%>%
merge(trt, by=c('site_code', 'plot'))
###count concecutive 0's: how long is a sp lost?
cumul_zeros <- function(x) {
x <- !x
rl <- rle(x)
len <- rl$lengths
v <- rl$values
cumLen <- cumsum(len)
z <- x
# replace the 0 at the end of each zero-block in z by the
# negative of the length of the preceding 1-block....
iDrops <- c(0, diff(v)) < 0
z[ cumLen[ iDrops ] ] <- -len[ c(iDrops[-1],FALSE) ]
# ... to ensure that the cumsum below does the right thing.
# We zap the cumsum with x so only the cumsums for the 1-blocks survive:
x*cumsum(z)
}
nutnetOrder <- nutnetdf_allspp[with(nutnetdf_allspp, order(site_code, plot, Taxon, year)), ]
nutnetConsAbs <- nutnetOrder%>%
filter(year_trt>0)%>%
select(site_code, year_trt, plot, Taxon, trt, rel_cover, PA)%>%
group_by(site_code, year_trt, plot, Taxon, trt, rel_cover, PA)%>%
unique()%>%
ungroup()%>%
group_by(site_code, plot, Taxon)%>%
nest()%>%
mutate(mutate_df = purrr::map(data, ~mutate(., cons_abs=cumul_zeros(PA))))%>%
unnest(mutate_df)
nutnetConsAbs2 <- nutnetConsAbs%>%
group_by(site_code, plot, Taxon)%>%
summarise(cons_abs_max=max(cons_abs))%>%
ungroup()%>%
merge(trt, by=c('site_code', 'plot'))%>%
merge(meanAb_byspecies, by=c('site_code', 'Taxon'))%>%
merge(max_abund, by=c('site_code', 'Taxon'))%>%
merge(freq, by=c('site_code', 'Taxon'))%>%
mutate(abund_metric=((meanPTAbundance/100)+PTfreq)/2)%>%
merge(nutnetdf_length, by=c('site_code'))
# ggplot(nutnetConsAbs2, aes(x=abund_metric, y=cons_abs_max)) +
# geom_point() +
# xlab('Pre-Treatment Modified Importance Index') +
# ylab('Consecutive Years Absent (max)') +
# geom_smooth(method='loess') +
# facet_wrap(~trt)
###pres/absence
nutnetPresAbs <- nutnetdf_allspp%>%
merge(meanAb_byspecies, by=c('site_code', 'Taxon'))%>%
merge(max_abund, by=c('site_code', 'Taxon'))%>%
merge(freq, by=c('site_code', 'Taxon'))%>%
mutate(abund_metric=((meanPTAbundance/100)+PTfreq)/2)%>%
filter(year_trt>0)
###proportion of years absent
nutnetPropAbs <- nutnetPresAbs%>%
group_by(site_code, Taxon, plot, trt, abund_metric)%>%
summarise(years_present=sum(PA))%>%
ungroup()%>%
left_join(nutnetdf_length)%>%
mutate(prop_years_present=years_present/length)%>%
mutate(prop_years_absent=1-prop_years_present)%>%
filter(prop_years_present>0)
nutnetPropAbsCtl <- nutnetPropAbs%>%
filter(trt=='Control')%>%
rename(prop_years_absent_ctl=prop_years_absent)%>%
select(site_code, Taxon, prop_years_absent_ctl)
nutnetPropAbsDiff <- nutnetPropAbs%>%
filter(trt!='Control')%>%
left_join(nutnetPropAbsCtl)%>%
mutate(prop_years_absent_diff=prop_years_absent-prop_years_absent_ctl)
ggplot(nutnetPropAbsDiff, aes(x=abund_metric, y=prop_years_absent_diff)) +
geom_point() +
xlab('Pre-Treatment Modified Importance Index') +
ylab('Proportion Years Absent') +
geom_smooth(method='loess') +
facet_wrap(~trt)
n <- 100
newdata <- crossing(abund_metric=seq(0,30,length.out=n),
site_code = unique(nutnetPropAbs$site_code))
newdata_fixed <- data.frame(abund_metric=seq(0,30,length.out=n),
site_code = unique(nutnetPropAbs$site_code)[1])
#NPK plot model
modPropAbsentNPK <- glmer(prop_years_absent_diff ~ abund_metric + (1 + abund_metric|site_code),
data = subset(nutnetPropAbsDiff, trt=='NPK'))
pred_final_loss_fixed <- cbind(newdata_fixed, predictInterval(modPropAbsentNPK,
newdata=newdata_fixed,
which="fixed", type="probability",
include.resid.var = FALSE))
pred_final_loss_ranef <- cbind(newdata, predictInterval(modPropAbsentNPK,
newdata=newdata,
which="full", type="probability",
include.resid.var = F))
ggplot(subset(nutnetPropAbs, trt=='NPK'), aes(x=abund_metric, y=prop_years_absent, group=as.character(site_code))) +
geom_point(position=position_jitter(width=0.05, height=0.05),
alpha=0.2, color="grey") +
geom_line(data = pred_final_loss_ranef,
mapping=aes(y=fit, group=as.character(site_code)),
lwd=0.4, alpha=0.4, color="lightgrey") +
geom_line(data = pred_final_loss_fixed,
mapping=aes(y=fit),
color="black", lwd=1.3) +
ylab("Proportion Years Absent") +
xlab("Dominance Indicator Index")
#add in final year abundances to see if species do persist, how does their abundance change
nutnet_finalabund <- nutnetdf_allspp%>%
filter(year_trt==9)%>%
mutate(final_cover=rel_cover)%>%
select(site_code, plot, trt, final_cover, Taxon)
nutnet_finalabund2 <- nutnetdf_allspp3Trt%>%
group_by(site_code, plot, Taxon, length, PA3, yrs_absent, pretrt_cover, meanPTAbundance, maxPTAbundance, num_plots, freq, abund_metric, trt)%>%
unique()%>%
ungroup()%>%
left_join(nutnet_finalabund)%>%
filter(!is.na(final_cover))
ggplot(nutnet_finalabund2, aes(x=abund_metric, y=final_cover)) +
geom_point(size=3) +
xlab('Pre-Treatment Modified Importance Index') +
ylab('Final Year (9) Relative Abundance') +
facet_wrap(~trt)
#export at 1200x1200
###BEF figure (traditional)
#notes, do we want to just include controls? and only 30 pretrt plots?
richness <- nutnetRel%>%
filter(live==1, Family!='NULL')%>%
group_by(year_trt, site_code, plot, trt)%>%
summarise(richness=length(rel_cover))%>%
ungroup()
biomassRichness <- biomass%>%
left_join(richness)%>%
filter(year_trt!=0)%>%
filter(trt=='Control'|trt=='NPK'|trt=='Fence'|trt=='NPK+Fence')%>%
mutate(logRich=log10(richness+1), logBio=log10(anpp+1))
bef_mod <- lmer(log10(anpp+1) ~ log10(richness+1)*year_trt + (log10(richness+1) | site_code), data=subset(biomassRichness, trt=='Control'))
#evaluate autocor
biomassRichnessModOut <- biomassRichness%>%
ungroup()%>%
filter(trt=='Control')%>%
mutate(res_bef_mod = residuals(bef_mod))%>%
group_by(site_code)%>%
mutate(lag_res_bef_mod = lag(res_bef_mod))%>%
ungroup()
qplot(lag_res_bef_mod, res_bef_mod, data = biomassRichnessModOut)
pred_bef_fix <- cbind(biomassRichnessModOut,
predictInterval(bef_mod,
newdata=biomassRichnessModOut%>%mutate(year_trt=1),
which="fixed"))
pred_bef_ranef <- cbind(biomassRichnessModOut,
predictInterval(bef_mod, newdata=biomassRichnessModOut,
which="full"))
BEFstrawmanFig <- ggplot(biomassRichness, aes(x=log10(richness+1), y=log10(anpp+1), color=as.character(site_code))) +
guides(color = "none") +
geom_ribbon(pred_bef_fix, mapping=aes(ymin=lwr, ymax=upr), fill="lightgrey", color=NA, alpha=0.6) +
geom_smooth(pred_bef_ranef, method='lm', mapping=aes(y=fit), alpha=0.7, lwd=1.3, se=F) +
geom_smooth(pred_bef_fix, method='lm', mapping=aes(y=fit), color="black", lwd=1.5) +
xlab("Log Richness + 1") + ylab("Log Biomass + 1")
###figures comparable to predicts database
# #biomass response by trt
# biomassResp <- biomass%>%
# left_join(trt)%>%
# filter(year_trt!=0)%>%
# group_by(site_code, year_trt, trt)%>%
# summarise(anpp_mean=mean(anpp))%>%
# ungroup()%>%
# spread(key=trt, value=anpp_mean)%>%
# mutate(NPK_diff=(NPK-Control)/Control, Fence_diff=(Fence-Control)/Control, NPKfence_diff=(NPK+Fence-Control)/Control)%>%
# select(site_code, year_trt, NPK_diff, Fence_diff, NPKfence_diff)%>%
# na.omit()%>%
# gather(key=trt, value=diff, NPK_diff:NPKfence_diff)
#
# ggplot(data=barGraphStats(data=biomassResp, variable="diff", byFactorNames=c("year_trt", "trt")), aes(x=year_trt, y=mean, color=trt)) +
# geom_point(size=5) +
# stat_smooth(method = "lm", formula = y ~ x + I(x^2)) +
# geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.2) +
# xlab('Treatment Year') + ylab('Biomass Difference (%)') +
# geom_hline(yintercept=0)
# #export at 1200x800
###split species into groups within sites by dominance
#make a new dataframe with just the label
site_code=nutnetdf_allspp3Trt%>%
select(site_code)%>%
unique()
#makes an empty dataframe
nutnetSppGroups=data.frame(row.names=1)
#loop through sites to get their species' dominance ranks (3 groups)
for(i in 1:length(site_code$site_code)) {
#creates a dataset for each unique year, trt, exp combo
subset=nutnetdf_allspp3Trt[nutnetdf_allspp3Trt$site_code==as.character(site_code$site_code[i]),]%>%
select(site_code, Taxon, abund_metric, meanPTAbundance)%>%
unique()
#group by Dominance Indicator index (DI)
subset$DI_group <- ntile(subset$abund_metric, 3)
#group by mean abundance in pretrt data
subset$abund_group <- ntile(subset$meanPTAbundance, 3)
#pasting dispersions into the dataframe made for this analysis
nutnetSppGroups=rbind(subset, nutnetSppGroups)
}
####do everything at site level (lost from entire trt, rather than just a plot)
nutnetAbsSite <- nutnetPresAbs%>%
#get site level presence/absence by trt
group_by(site_code, Taxon, year_trt, trt)%>%
summarise(presence=sum(PA))%>%
ungroup()%>%
mutate(PA=ifelse(presence>0, 1, 0))%>%
#proportion of years absent for trts
group_by(site_code, Taxon, trt)%>%
summarise(years_present=sum(PA))%>%
ungroup()%>%
left_join(nutnetdf_length)%>%
#calculate proportion of years absent
mutate(prop_years_absent=1-(years_present/length))%>%
#merge the species categories
left_join(nutnetSppGroups)
#calculate differences -- this shows the propotional differences in the number of years a species is absent in the trt compared to years species is absent in the ctl plots (doesn't communicate loss from ctl to trt)
nutnetAbsSiteCtl <- nutnetAbsSite%>%
filter(trt=='Control')%>%
rename(prop_years_absent_ctl=prop_years_absent)%>%
select(site_code, Taxon, prop_years_absent_ctl)
nutnetAbsSiteDiff <- nutnetAbsSite%>%
filter(trt!='Control')%>%
left_join(nutnetAbsSiteCtl)%>%
mutate(prop_years_absent_diff=(prop_years_absent-prop_years_absent_ctl)/(prop_years_absent_ctl))%>%
mutate(prop_years_absent_diff_corr=ifelse(is.nan(prop_years_absent_diff), 0, ifelse(is.infinite(prop_years_absent_diff), 1, prop_years_absent_diff)))%>%
#drop spp that are always absent from controls, because those are gains not losses
filter(prop_years_absent_ctl<1)%>%
mutate(dom_group=ifelse(DI_group==1, 'rare', ifelse(DI_group==2, 'intermediate', 'common')))
ggplot(data=barGraphStats(data=subset(nutnetAbsSiteDiff, trt=='NPK'|trt=='N'|trt=='NP'), variable="prop_years_absent_diff_corr", byFactorNames=c("trt", "dom_group")), aes(x=trt, y=mean, color=as.factor(dom_group))) +
geom_point(size=5) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.2) +
xlab('Treatment') + ylab('Difference in Proportion of Years Absent (%)') +
geom_hline(yintercept=0)
#export at 800x800
#figure out when a spp is absent from trt but present in ctl
nutnetPASite <- nutnetPresAbs%>%
#get site level presence/absence by trt
group_by(site_code, Taxon, year_trt, trt)%>%
summarise(presence=sum(PA))%>%
ungroup()%>%
mutate(PA=ifelse(presence>0, 1, 0))
nutnetPACtl <- nutnetPASite%>%
filter(trt=='Control')%>%
rename(PA_ctl=PA)%>%
select(site_code, Taxon, year_trt, PA_ctl)
nutnetPASiteTrt <- nutnetPASite%>%
filter(trt!='Control')%>%
left_join(nutnetPACtl)%>%
#drop species that are never present in control in a year (because those are gains in trts)
filter(PA_ctl>0)%>%
#merge dominance categories
left_join(nutnetSppGroups)
#proportion of years absent
nutnetAbsentSiteTrt <- nutnetPASiteTrt%>%
group_by(site_code, Taxon, trt, abund_metric, DI_group)%>%
summarise(present=sum(PA))%>%
ungroup()%>%
#merge in length of exp
left_join(nutnetdf_length)%>%
mutate(prop_years_absent=(length-present)/length)
ggplot(subset(nutnetAbsentSiteTrt, trt=='N'), aes(x=abund_metric, y=prop_years_absent, group=as.character(site_code))) +
geom_point(position=position_jitter(width=0.05, height=0.05),
alpha=0.2, color="grey") +
geom_smooth(method='loess', color='black', se=F, group=(1)) +
ylab("Proportion Years Absent") +
xlab("Dominance Indicator Index")
ggplot(subset(nutnetAbsentSiteTrt, trt=='NPK'), aes(x=abund_metric, y=prop_years_absent, group=as.character(site_code))) +
geom_point(position=position_jitter(width=0.05, height=0.05),
alpha=0.2, color="grey") +
geom_smooth(method='loess', color='black', se=F, group=(1)) +
ylab("Proportion Years Absent") +
xlab("Dominance Indicator Index")
#for each site, how many spp in each category lost?
nutnetLossSite <- nutnetPASiteTrt%>%
filter(PA==0)%>%
group_by(site_code, year_trt, trt, DI_group)%>%
summarise(num_loss=length(PA))%>%
ungroup()
nutnetNotLossSite <- nutnetPASiteTrt%>%
filter(PA==1)%>%
group_by(site_code, year_trt, trt, DI_group)%>%
summarise(num_notloss=length(PA))%>%
ungroup()
nutnetLossorNotSite <- nutnetLossSite%>%
full_join(nutnetNotLossSite)%>%
mutate(num_loss=replace_na(num_loss, 0))%>%
mutate(num_notloss=replace_na(num_notloss, 0))%>%
mutate(total_spp=num_loss+num_notloss)%>%
mutate(prop_loss=num_loss/total_spp)%>%
#remove sites with no DI groups (no pretrt data)
filter(!is.na(DI_group))%>%
#remove sites where trt year is not recorded
filter(year_trt<11)
#across all years
ggplot(barGraphStats(data=subset(nutnetLossorNotSite, trt=='N'|trt=='NP'|trt=='NPK'), variable="prop_loss", byFactorNames=c("trt", "DI_group")), aes(x=as.factor(DI_group), y=mean, color=trt)) +
geom_point(stat='identity', position=position_dodge(width=0.7)) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.2, position=position_dodge(width=0.7)) +
scale_x_discrete(breaks=c("1", "2", "3"), labels=c("Rare", "Intermediate", "Dominant")) +
xlab('') + ylab('Proportion of Species Lost')
#####FIX THIS -- put in model results instead of raw data, drop weird years beyond which most sites have data
#time series
badLossFig <- ggplot(barGraphStats(data=subset(nutnetLossorNotSite, trt=='N'|trt=='NP'|trt=='NPK'), variable="prop_loss", byFactorNames=c("trt", "DI_group", "year_trt")), aes(x=year_trt, y=mean, color=as.factor(DI_group))) +
geom_point(stat='identity', position=position_dodge(width=0.7)) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.2, position=position_dodge(width=0.7)) +
xlab('Year') + ylab('Proportion of Species Lost') +
scale_color_discrete(breaks=c("1", "2", "3"), labels=c("Rare", "Intermediate", "Dominant")) +
facet_wrap(~trt)
#time series with only 10 year datasets
nutnetLossorNotSite10yr <- nutnetLossorNotSite%>%
left_join(nutnetdf_length)%>%
filter(length==10)
ggplot(barGraphStats(data=subset(nutnetLossorNotSite10yr, trt=='N'|trt=='NP'|trt=='NPK'), variable="prop_loss", byFactorNames=c("trt", "DI_group", "year_trt")), aes(x=year_trt, y=mean, color=as.factor(DI_group))) +
geom_point(stat='identity', position=position_dodge(width=0.7)) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.2, position=position_dodge(width=0.7)) +
xlab('Year') + ylab('Proportion of Species Lost') +
scale_color_discrete(breaks=c("1", "2", "3"), labels=c("Rare", "Intermediate", "Dominant")) +
facet_wrap(~trt)
nutnetAbsSiteCtl <- nutnetAbsSite%>%
filter(trt=='Control')%>%
rename(prop_years_absent_ctl=prop_years_absent)%>%
select(site_code, Taxon, prop_years_absent_ctl)
nutnetAbsSiteDiff <- nutnetAbsSite%>%
filter(trt!='Control')%>%
left_join(nutnetAbsSiteCtl)%>%
mutate(prop_years_absent_diff=(prop_years_absent-prop_years_absent_ctl)/(prop_years_absent_ctl))%>%
mutate(prop_years_absent_diff_corr=ifelse(is.nan(prop_years_absent_diff), 0, ifelse(is.infinite(prop_years_absent_diff), 1, prop_years_absent_diff)))%>%
#drop spp that are always absent from controls, because those are gains not losses
filter(prop_years_absent_ctl<1)%>%
mutate(dom_group=ifelse(DI_group==1, 'rare', ifelse(DI_group==2, 'intermediate', 'common')))
ggplot(data=barGraphStats(data=subset(nutnetAbsSiteDiff, trt=='NPK'|trt=='N'|trt=='NP'), variable="prop_years_absent_diff_corr", byFactorNames=c("trt", "dom_group")), aes(x=trt, y=mean, color=as.factor(dom_group))) +
geom_point(size=5) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.2) +
xlab('Treatment') + ylab('Difference in Proportion of Years Absent (%)') +
geom_hline(yintercept=0)
#export at 800x800
#########
#everything below is really messy and needs shortening
#########
###proportion of years absent
nutnetAbsentDI <- nutnetdf_allspp3Trt%>%
select(site_code, Taxon, plot, trt, yrs_absent, length)%>%
left_join(nutnetSppGroups)%>%
mutate(DI=ifelse(DI_group==1, 'rare', ifelse(DI_group==2, 'int', 'common')))%>%
mutate(prop_absent=yrs_absent/length)%>%
filter(length>2)%>% #sets dataset length to be at least 3, so we don't have species 100% present or absent based on one year
group_by(site_code, trt, DI)%>%
summarise(prop_years_absent=mean(prop_absent))%>%
ungroup()%>%
filter(trt!='NA')%>%
spread(key=trt, value=prop_years_absent)%>%
select(-N, -P, -K, -NP, -NK, -PK)%>%
mutate(NPK_absent=(NPK-Control)/Control, Fence_absent=(Fence-Control)/Control, NPKfence_absent=(NPK+Fence-Control)/Control)%>%
select(site_code, DI, NPK_absent, Fence_absent, NPKfence_absent)%>%
gather(key=trt, value=absent_diff, NPK_absent:NPKfence_absent)%>%
na.omit()%>%
filter(absent_diff<1000)
ggplot(data=barGraphStats(data=nutnetAbsentDI, variable="absent_diff", byFactorNames=c("trt", "DI")), aes(x=trt, y=mean, color=DI)) +
geom_point(size=5) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.2) +
xlab('Treatment') + ylab('Difference in Proportion of Years Absent (%)') +
geom_hline(yintercept=0)
#export at 800x800
nutnetAbsentAbund <- nutnetdf_allspp3Trt%>%
select(site_code, Taxon, plot, trt, yrs_absent, length)%>%
left_join(nutnetSppGroups)%>%
mutate(abund=ifelse(abund_group==1, 'rare', ifelse(abund_group==2, 'int', 'common')))%>%
mutate(prop_absent=yrs_absent/length)%>%
filter(length>2)%>% #sets dataset length to be at least 3, so we don't have species 100% present or absent based on one year
group_by(site_code, trt, abund)%>%
summarise(prop_years_absent=mean(prop_absent))%>%
ungroup()%>%
filter(trt!='NA')%>%
spread(key=trt, value=prop_years_absent)%>%
select(-N, -P, -K, -NP, -NK, -PK)%>%
mutate(NPK_absent=(NPK-Control)/Control, Fence_absent=(Fence-Control)/Control, NPKfence_absent=(NPK+Fence-Control)/Control)%>%
select(site_code, abund, NPK_absent, Fence_absent, NPKfence_absent)%>%
gather(key=trt, value=absent_diff, NPK_absent:NPKfence_absent)%>%
na.omit()%>%
filter(absent_diff<1000)
ggplot(data=barGraphStats(data=nutnetAbsentAbund, variable="absent_diff", byFactorNames=c("trt", "abund")), aes(x=trt, y=mean, color=abund)) +
geom_point(size=5) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.2) +
xlab('Treatment') + ylab('Difference in Proportion of Years Absent (%)') +
geom_hline(yintercept=0)
#export at 800x800
###cumulative number of years absent
nutnetConsAbsentDI <- nutnetConsAbs2%>%
select(site_code, Taxon, plot, trt, cons_abs_max, length)%>%
left_join(nutnetSppGroups)%>%
mutate(DI=ifelse(DI_group==1, 'rare', ifelse(DI_group==2, 'int', 'common')))%>%
mutate(cons_abs=cons_abs_max)%>%
filter(length>2)%>% #sets dataset length to be at least 3, so we don't have species 100% present or absent based on one year
group_by(site_code, trt, DI)%>%
summarise(cons_abs_max=mean(cons_abs))%>%
ungroup()%>%
filter(trt!='NA')%>%
spread(key=trt, value=cons_abs_max)%>%
select(-N, -P, -K, -NP, -NK, -PK)%>%
mutate(NPK_absent=(NPK-Control)/Control, Fence_absent=(Fence-Control)/Control, NPKfence_absent=(NPK+Fence-Control)/Control)%>%
select(site_code, DI, NPK_absent, Fence_absent, NPKfence_absent)%>%
gather(key=trt, value=absent_diff, NPK_absent:NPKfence_absent)%>%
na.omit()%>%
filter(absent_diff<1000)
ggplot(data=barGraphStats(data=nutnetConsAbsentDI, variable="absent_diff", byFactorNames=c("trt", "DI")), aes(x=trt, y=mean, color=DI)) +
geom_point(size=5) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.2) +
xlab('Treatment') + ylab('Difference in Proportion of Years Absent (%)') +
geom_hline(yintercept=0)
#export at 800x800
nutnetConsAbsentAbund <- nutnetConsAbs2%>%
select(site_code, Taxon, plot, trt, cons_abs_max, length)%>%
left_join(nutnetSppGroups)%>%
mutate(abund=ifelse(abund_group==1, 'rare', ifelse(abund_group==2, 'int', 'common')))%>%
mutate(cons_abs=cons_abs_max)%>%
filter(length>2)%>% #sets dataset length to be at least 3, so we don't have species 100% present or absent based on one year
group_by(site_code, trt, abund)%>%
summarise(cons_abs_max=mean(cons_abs))%>%
ungroup()%>%
filter(trt!='NA')%>%
spread(key=trt, value=cons_abs_max)%>%
select(-N, -P, -K, -NP, -NK, -PK)%>%
mutate(NPK_absent=(NPK-Control)/Control, Fence_absent=(Fence-Control)/Control, NPKfence_absent=(NPK+Fence-Control)/Control)%>%
select(site_code, abund, NPK_absent, Fence_absent, NPKfence_absent)%>%
gather(key=trt, value=absent_diff, NPK_absent:NPKfence_absent)%>%
na.omit()%>%
filter(absent_diff<1000)
ggplot(data=barGraphStats(data=nutnetConsAbsentAbund, variable="absent_diff", byFactorNames=c("trt", "abund")), aes(x=trt, y=mean, color=abund)) +
geom_point(size=5) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.2) +
xlab('Treatment') + ylab('Difference in Proportion of Years Absent (%)') +
geom_hline(yintercept=0)
#export at 800x800
#spread(key=year_trt2, value=PA2)%>% ###I don't think we want to do this, because it is then unclear which are missing data and which are true 0s
#mutate(yr_present=(length-yr1-yr2-yr3-yr4-yr5-yr6-yr7-yr8-yr9)/length) ###problem: can't sum over cells with NA, but most experiments have short datasets or missing data
# library(data.table)
# nutnetdt <- data.table(nutnetRel)
#
# # create a data.table of unique species x year combos by site
# site_spp_yr_dt <- nutnetdt[,expand.grid(unique(plot), unique(Taxon), unique(year)), by=site_code]
# names(site_spp_yr_dt) <- c("site_code", "plot", "Taxon", "year")
# filled_nutnetdt <- merge(nutnetdt, site_spp_yr_dt, by=c("site_code", "plot", "Taxon", "year"), all=T)
# filled_nutnetdt[,PA:=!is.na(rel_cover)]
# filled_nutnetdt[,tot_site_years:=length(unique(year)), by=site_code]
# filled_nutnetdt[,tot_spp_plot_years_present:=sum(PA), by=.(plot, site_code, Taxon)]
# filled_nutnetdt[,frac_absent:=(tot_site_years-tot_spp_plot_years_present)/tot_site_years]
# # make a variable that is just tot_site_years-tot_spp_plot_years_present
# head(filled_nutnetdt)
# summary(filled_nutnetdt$frac_absent)
## merge back into the full nutnet data (not only pretreatment year)
# nutnetdf_allspp <- merge(nutnetdf_allspp ,meanAb_byspecies, by = c("site_code", "Taxon"), all.x = T)
# nutnetdf_allspp <- merge(max_abund, nutnetdf_allspp , by = c("site_code", "Taxon"), all = T)
# nutnetdf_allspp <- merge(freq, nutnetdf_allspp , by = c("site_code", "Taxon"), all = T)
#nutnetdf_allspp2 <- reshape(nutnetdf_allspp, idvar=c('site_code', 'Taxon', 'PTfreq', 'maxPTAbundance', 'meanPTAbundance', 'site_name', 'trt'), timevar='year_trt', direction='wide')
# ### Code from Kim using Codyn
#
# library(codyn)
#
# # codyn function modification ---------------------------------------------
# #modifying codyn functions to output integer numbers of species appearing and disappearing, plus total spp number over two year periods, rather than ratios
# turnover_allyears <- function(df,
# time.var,
# species.var,
# abundance.var,
# metric=c("total", "disappearance","appearance")) {
#
# # allows partial argument matching
# metric = match.arg(metric)
#
# # sort and remove 0s
# df <- df[order(df[[time.var]]),]
# df <- df[which(df[[abundance.var]]>0),]
#
# ## split data by year
# templist <- split(df, df[[time.var]])
#
# ## create two time points (first year and each other year)
# t1 <- templist[1]
# t2 <- templist[-1]
#
# ## calculate turnover for across all time points
# out <- Map(turnover_twoyears, t1, t2, species.var, metric)
# output <- as.data.frame(unlist(out))
# names(output)[1] = metric
#
# ## add time variable column
# alltemp <- unique(df[[time.var]])
# output[time.var] = alltemp[2:length(alltemp)]
#
# # results
# return(output)
# }
#
# turnover_twoyears <- function(d1, d2,
# species.var,
# metric=c("total", "disappearance","appearance")){
#
# # allows partial argument matching
# metric = match.arg(metric)
#
# # create character vectors of unique species from each df
# d1spp <- as.character(unique(d1[[species.var]]))
# d2spp <- as.character(unique(d2[[species.var]]))
#
# # ID shared species
# commspp <- intersect(d1spp, d2spp)
#
# # count number not present in d2
# disappear <- length(d1spp)-length(commspp)
#
# # count number that appear in d2
# appear <- length(d2spp)-length(commspp)
#
# # calculate total richness
# totrich <- sum(disappear, appear, length(commspp))
#
# # output based on metric
# if(metric == "total"){
# output <- totrich
# } else {
# if(metric == "appearance"){
# output <- appear
# } else {
# if(metric == "disappearance"){
# output <- disappear
# }
# }
# }
#
# # results
# return(output)
# }
#
#
# # generating appearances and disappearances for each experiment ---------------------------------------------
# #make a new dataframe with just the label;
# site_code=nutnetdf_allspp%>%
# select(site_code)%>%
# unique()
#
# #makes an empty dataframe
# for.analysis=data.frame(row.names=1)
#
# for(i in 1:length(site_code$site_code)) {
#
# #creates a dataset for each unique site-year
# subset=nutnetdf_allspp[nutnetdf_allspp$site_code==as.character(site_code$site_code[i]),]%>%
# select(site_code, year, Taxon, rel_cover, plot)%>%
# group_by(site_code, year, plot, Taxon)%>%
# summarise(rel_cover=max(rel_cover))%>%
# ungroup()%>%
# filter(rel_cover>0)
#
# #need this to keep track of sites
# labels=subset%>%
# select(year, site_code)%>%
# unique()
#
# #calculating appearances and disappearances (from previous year): for each year
# appear<-turnover_allyears(df=subset, time.var='year', species.var='Taxon', abundance.var='rel_cover', metric='appearance')
# disappear<-turnover_allyears(df=subset, time.var='year', species.var='Taxon', abundance.var='rel_cover', metric='disappearance')
# total<-turnover_allyears(df=subset, time.var='year', species.var='Taxon', abundance.var='rel_cover', metric='total')
#
# #merging back with labels to get back experiment labels
# turnover<-merge(appear, disappear, by=c('year'))
# turnoverAll<-merge(turnover, total, by=c('year'))
# turnoverLabel<-merge(turnoverAll, labels, by=c('year'), all=T)
#
# #pasting into the dataframe made for this analysis
# for.analysis=rbind(turnoverLabel, for.analysis)
# }
#biomass response by trt
biomassResp <- biomass%>%
left_join(trt)%>%
filter(year_trt!=0)%>%
group_by(site_code, year_trt, trt)%>%
summarise(anpp_mean=mean(anpp))%>%
ungroup()%>%
spread(key=trt, value=anpp_mean)%>%
mutate(NPK_diff=(NPK-Control)/Control, N_diff=(N-Control)/Control, NP_diff=(NP+Fence-Control)/Control)%>%
select(site_code, year_trt, NPK_diff, N_diff, NP_diff)%>%
na.omit()%>%
gather(key=trt, value=diff, NPK_diff:NP_diff)
badBiomassFig <- ggplot(data=barGraphStats(data=biomassResp, variable="diff", byFactorNames=c("year_trt", "trt")), aes(x=year_trt, y=mean, color=trt)) +
geom_point(size=5) +
stat_smooth(method = "lm", formula = y ~ x + I(x^2)) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.2) +
xlab('Treatment Year') + ylab('Biomass Difference (%)') +
geom_hline(yintercept=0)
#export at 1200x800
#figure placeholder - needs updates
pushViewport(viewport(layout=grid.layout(1,3)))
print(BEFstrawmanFig, vp=viewport(layout.pos.row = 1, layout.pos.col = 1))
print(badLossFig, vp=viewport(layout.pos.row = 1, layout.pos.col = 2))
print(badBiomassFig, vp=viewport(layout.pos.row = 1, layout.pos.col = 3))