-
Notifications
You must be signed in to change notification settings - Fork 0
/
Peak-Wildflower-Season.Rmd
1145 lines (927 loc) · 46.4 KB
/
Peak-Wildflower-Season.Rmd
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
---
title: "Analyze MeadoWatch Data"
author: "Janneke Hille Ris Lambers, Aji John, Meera Sethi, Elli Theobald"
date: "Updated 12/02/2020"
output: html_document
---
#Setup for R script and R markdown
1. Load all libraries
2. Specify string behavior
3. Load packages (should we add all packages here?)
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
options(stringsAsFactors = FALSE)
library(tidyverse)
library(leaflet)
library(lubridate)
library(boot)
library(readr)
```
#Data munging / merging
1. Read in raw data
2. Remove cases with NA observations in any phenophase
3. Merge plot-specific snowmelt data and individual observations
4. Remove unneeded columns
5. Calculate and add Julian day (DOY) to data
6. Reorganize data columns and rename to PhenoSite
```{r}
# Read in phenology data, station data
PhenoDatall <- read.csv("data/MW_PhenoDat_2013_2019.csv", header=TRUE)
StationDat <- read.csv("data/MW_SiteDat_2013_2019.csv", header=TRUE)
# Remove rows where any phenophases were coded as NA
PhenoDat <- PhenoDatall[complete.cases(PhenoDatall[,c(12, 14, 16, 18)]),]
# Merge by the rows in both the files (Year, Site_Code)
MergePhenoStation <- merge(StationDat,PhenoDat, by=c("Year","Site_Code"))
# Remove unneeded columns
#TODO - might be a better way to do this in case column names change
PhenoSite_0 <- MergePhenoStation[,c(1:6, 9, 12, 15:16, 18:20, 22, 24, 26, 28)]
# Calculate Julian Days of observations; DSS=days since snow; add to PhenoSite
PhenoSite_0 <- PhenoSite_0[order(PhenoSite_0$Year),] #order by year
yrs <- unique(PhenoSite_0$Year);
DOY <- c()
# Convert the 'Date' - observed date to Julian (DOY)
for(i in 1:length(yrs)){
tmpdates <- PhenoSite_0$Date[PhenoSite_0$Year==yrs[i]]
Jan1Jul <- as.Date(paste(yrs[i],"-01-01", sep=""))
ObsJulDayYr <- julian(as.Date(as.character(tmpdates),"%m/%d/%Y"),
origin=Jan1Jul)
DOY <- c(DOY,ObsJulDayYr)
}
# Now add day of year (DOY) to Phenosite Data
PhenoSite_0 <- cbind(PhenoSite_0, DOY)
# Finally, reorganize data and rename as PhenoSite
PhenoSite <- PhenoSite_0[,c(1,3,2,4,8,18,7,10,11,14)] #reorganize data
#Examine the data
head(PhenoSite)
```
#Define wildflower community you are interested in
This chunk of code defines the wildflower community and subsets the data to only include these species. Specifically:
1. Specify which flowering species to include. For reference:
A. Reflection Lakes species include: c("ANOC", "CAPA", "ERMO", "ERPE", "LIGR", "LUAR", "MIAL", "PEBR", "POBI", "VASI")
B. Glacier Basin species include: c("ANAR", "ARLA", "ASLE", "CAMI", "ERGR", "LUAR", "MEPA", "PEBR", "POBI", "VASI")
2. Create PhenoSite_Focal - data set with just species of interest
3. From this, *nspp* (number of species) and *nyears* (number of years) are defined, which are used in for loops
```{r}
# Define the wildflower community: which species to include
species <- c("ANOC", "ASLE", "CAMI", "CAPA", "ERGR", "ERMO",
"ERPE", "LUAR", "PEBR", "VASI")
# Create PhenoSite_Focal - data set with just species of interest
specieskeep <- PhenoSite$Species %in% species
PhenoSite_Focala <- PhenoSite[specieskeep,]
# Determine how many years of data, how many species, etc
yrs <- unique(PhenoSite_Focala$Year)
nyrs <- length(yrs)
nspp <- length(species)
```
#Calculate observer effort and filter / remove observations with too few
Add information to PhenoSite_Focal to allow for outlier detection
1. Calculate total observations per plot / year / species
2. Filter out plots with too little data (see below for parameters that can be modified)
+ *totobs* (min # observations for fitting, currently set at 30)
+ *totyesobs* (min # observations of flowering, currently set at 5)
```{r}
#Calculate total number of observations
Nobs <- PhenoSite_Focala %>%
group_by(Transect.x,Year,Site_Code,Species)%>%
tally() %>% as.data.frame()
#Calculate total number of yes observations
PhenoSite_Focalb <- PhenoSite_Focala[PhenoSite_Focala$Flower==1,]
Nobsyes <- PhenoSite_Focalb %>%
group_by(Transect.x,Year,Site_Code,Species)%>%
tally() %>% as.data.frame()
#merge PhenoSite_Focal.. with Nobs, Nobsyes
PhenoSite_Focalc <- merge(PhenoSite_Focala, Nobs,
by=c("Year","Transect.x",
"Site_Code","Species"))
PhenoSite_Focald <- merge(PhenoSite_Focalc, Nobsyes,
by=c("Year","Transect.x",
"Site_Code","Species"))
dimnames(PhenoSite_Focald)[[2]][11:12] <- c("Nobs","NobsY")
#Filter out plots with fewer than totobs, totyesobs observations
totobs <- 30
totyesobs <- 5
PhenoSite_Focale <- PhenoSite_Focald[PhenoSite_Focald$Nobs>=totobs &
PhenoSite_Focald$NobsY>totyesobs,]
```
#Outliers
Calculate for each observation the closest 'yes' and the closest 'no' observation. This could allow us to detect outliers (i.e. 'yes' observations that are more than a certain number of days separated from other yes observations)
```{r}
#For each observation, calculate nearest yes, no observation
#TODO this takes a LONG time - any suggestions?
distyes <- rep(NA, times=dim(PhenoSite_Focale)[1])
distno <- rep(NA, times=dim(PhenoSite_Focale)[1])
PhenoSite_Focal <- cbind(PhenoSite_Focale, distyes, distno)
#Now for loop to calculate
for(i in 1:dim(PhenoSite_Focal)[1]){
yr <- PhenoSite_Focal$Year[i]
sc <- PhenoSite_Focal$Site_Code[i]
sp <- PhenoSite_Focal$Species[i]
dy <- PhenoSite_Focal$DOY[i]
#exclude current observation
PhenoSite_Focal_comp <- PhenoSite_Focal[-i,]
#extract plot / year / species data
compdat <- PhenoSite_Focal_comp[PhenoSite_Focal_comp$Year==yr&
PhenoSite_Focal_comp$Site_Code==sc&
PhenoSite_Focal_comp$Species==sp,]
#separate into yes and no
compdatyes <- compdat[compdat$Flower==1,]
compdatno <- compdat[compdat$Flower==0,]
#closest yes, no observation
minyes <- min(abs(compdatyes$DOY - dy))
minno <- min(abs(compdatno$DOY - dy))
#Fill in PhenoSite_Focal
PhenoSite_Focal$distyes[i] <- minyes
PhenoSite_Focal$distno[i] <- minno
}
#Examine file
head(PhenoSite_Focal)
#Number of total observations, total yes, total yes very far from others
out_thresh <- 21
outliercheck <- PhenoSite_Focal[PhenoSite_Focal$Flower==1
& PhenoSite_Focal$distyes>out_thresh,]
write.csv(outliercheck, file="data/outliers.csv", quote = FALSE, row.names=FALSE)
Nobs <- dim(PhenoSite_Focal)[1]
Nobsyes <- dim(PhenoSite_Focal[PhenoSite_Focal$Flower==1,])[1]
Noutliers <- dim(outliercheck)[1]
print("Total, Total Yes, Total outliers")
print(c(Nobs, Nobsyes, Noutliers))
#plot per species and year/plot combo
for(i in 1:length(species)){
PhenoSite_Species <- PhenoSite_Focal[PhenoSite_Focal$Species==species[i],]
#make unique identifier - year, site code
yrsite <- paste(PhenoSite_Species$Year, PhenoSite_Species$Site_Code)
yrsites <- unique(yrsite); nyrsite <- length(yrsites)
#assess number of obs, outlier obs
nobsyes_sp <- dim(PhenoSite_Species[PhenoSite_Species$Flower==1,])[1]
noutliers_sp <- dim(PhenoSite_Species[PhenoSite_Species$Flower==1
& PhenoSite_Species$distyes>out_thresh,])[1]
#Make a dummy plot to add points to
namegraph <- paste("figs/",species[i],"_outliers.png", sep="", collapse=NULL)
png(filename = namegraph,
width = 480, height = 720, units = "px")
mxy <- max(PhenoSite_Species$distyes[PhenoSite_Species$Flower==1])
par(mfrow=c(1,1), omi=c(0,0,0,0), mai=c(0.5,0.6,0.5,0.5),
tck=-0.02, mgp=c(1.1,0.5,0))
plot(1,1, type="n", xlim=c(0,mxy), ylim=c(0,(nyrsite+1)), yaxt="n",
xlab="time between observations", ylab="")
title(paste(species[i],"-", noutliers_sp, "outlier / ", nobsyes_sp, "total yes"))
#now add points and labels
for(j in nyrsite:1){
PhenoSite_plt <- PhenoSite_Species[yrsite==yrsites[j],]
PhenoSite_plt_ys <- PhenoSite_plt[PhenoSite_plt$Flower==1,]
PhenoSite_plt_no <- PhenoSite_plt[PhenoSite_plt$Flower==0,]
noy <- jitter(rep(j,times=dim(PhenoSite_plt_no)[1]), amount = 1/nyrsite)
yesy<- jitter(rep(j,times=dim(PhenoSite_plt_ys)[1]), amount = 1/nyrsite)
points(PhenoSite_plt_no$distyes, noy, pch=21, bg="grey")
points(PhenoSite_plt_ys$distyes, yesy, pch=21, bg="black", cex=1.5)
#labels
xposlab <- mxy*1/15
text(-xposlab,j,labels=yrsites[j],cex=0.5, adj=1, xpd=NA)
}
dev.off()
}
```
#Define functions - maximum likelihood models & phenology curves (for plotting)
There are four functions we need, 3 for model fitting and one for drawing curves. They are described here.
1. nullfit - binomial likelihood model that estimates a constant probability of flowering (one parameter) and the negative log likelihood, given yes / no observations. We know this model isn't accurate, but it is useful in providing
a null model for curvefit_perplot (allowing one to compare AIC's or conduct a likelihood test as to whether flowering varies with DOY in a unimodal way).
2. curvefit_perplot - binomial likelihood model that estimates the probability of flowering as a function of DOY; as described by three parameters - range, maximum, and peak. The unimodal curve is fit with a logit transformation (similar to Theobald et al 2017, Sethi et al 2020; albeit with parameters that do NOT vary with climate). Also requires yes / no observations. This function is fit to year-plot-species specific observations
3. curvefit_allplot - binomial likelihood model that estimates the probability of flowering for a species across all plots, years and transects as a function of several parameters. Similar to the second model above, there is a unimodal curve describing flowering, fit with a logit transformation. Peak flowering at a plot is predicted by snowmelt date (intercept, slope parameter estimated). Range and maximum parameters vary by year and plot. This function is fit to all data from a species.
4. predflower - input a DOY (or vector of DOY's) and 3 parameters describing peak, range and duration of flowering; and returns the probability of flowering. This is primarily used for plotting.
```{r}
#Null model - assumes the probability of flowering is constant
nullfit <- function (param){
meanp <- param[1]
pred <- rep(meanp, times=length(phenophase))
llik <- dbinom(phenophase,1,pred, log=TRUE) #this is the likelihood
return(-sum(llik)) #this is the negative log likelihood, which is minimized
}
#Assumes flowering varies with DOY as a function of 3 parameters
#Should be fit per species, plot and year
curvefit_perplot <- function (param){ #curve fitting function for mle
peakp <- param[1]
rangep <- param[2]
maxp <- param[3]
pred <- inv.logit(rangep * (days - peakp)^2 + maxp)
llik <- dbinom(phenophase,1,pred, log=TRUE)
return(-sum(llik))
}
#Fits model per species
#peak flowering varies with snowmelt, and plot/trail specific range and max
#Should be fit per species (but all data for each speices)
curvefit_allplot <- function (param){ #curve fitting function for mle
intpeak <- param[1]
slopepeak <- param[2]
rangep_all <- param[3:(2+ntrlyr)] #different range per trail, year
maxp_all <- param[(3+ntrlyr):(2+2*ntrlyr)] #different max per trail, year
peakp <- intpeak + slopepeak*SDD
rangep <- rangep_all[trlyr]
maxp <- maxp_all[trlyr]
pred <- inv.logit(rangep * (days - peakp)^2 + maxp)
llik <- dbinom(phenophase,1,pred, log=TRUE)
return(-sum(llik))
}
#Predicts flowering phenology as a function of DOY, param (peak, range, max)
predflower <- function (xx, param){
days <- xx
peakp <- param[1]
rangep <- param[2]
maxp <- param[3]
pred <- inv.logit(rangep * (days - peakp)^2 + maxp)
return(pred)
}
```
#Phenological models fit to year-plot-species specific data
This chunk of code uses several for loops to fit phenological curves to year-plot-species specific data (using function *curvefit_perplot*). The advantage of these curves is that they are optimized to individual plots. The disadvantage is that these curves only exist if the species was present in the plot (even if it was nearby). More broadly, these fits can be used to check output from likelihood models fit to all data from a species.
##Code does the following:
1. Pulls out data for each species / plot / year combo. Data in innermost loop is PhenoSite_YearPlotSpecies.
2. The functions nullfit and curvefit_perplot are fit to year-plot-species specific data.
3. AIC's are calculated for both the null model and curve fitting model, which can be used to determine whether time (DOY) helps explain the probability of flowering. This is saved to aics_yrpltsp.
4. Plots individual curves and raw data in separate graphics windows (if *plottrue* is set to "T")
5. Saves the peak, range and maximum parameters to object pars_yrpltsp
6. Turn pars_yrpltsp into a data frame (this happens after the for loop)
7. Write parameter fits to data folder (PerPlotCurves.csv)
```{r}
#define object in which to save parameters
pars_yrpltsp <- c()
#define object in which to save AIC values
aics_yrpltsp <- c()
#set plottrue to 'T' if plots desired; 'F' if not
plottrue <- "F"
#For loops to fit curves per year/plot/species, save parameters, plot
for(i in 1:nyrs){ # First for loop: runs through years
#extract data for that year
PhenoSite_Year <- PhenoSite_Focal[PhenoSite_Focal$Year==yrs[i],]
#pull out unique plots for year in question
plots <- unique(PhenoSite_Year$Site_Code)
#Nested for loop for each plot
for(j in 1:length(plots)){ # Second for loop: each plot
PhenoSite_YearPlot <- PhenoSite_Year[PhenoSite_Year$Site_Code==plots[j],]
#Identify species in plot
spinplt <- unique(PhenoSite_YearPlot$Species)
if(length(spinplt)==0){next} #break if focal spp not in plot
#Nested for loop for each species
for(k in 1:length(spinplt)){ #Third for loop: each species in the pot
#Set up graphics windows for multiple plots, if plottrue set to "T"
if(plottrue=="T"){
if(i==1&j==1&k==1){ #set up graphics window, if plotting
X11(width=8,height=8)
par(mfrow=c(4,4), tck=-0.02, omi=c(0,0,0,0),
mai=c(0.4,0.4,0.4,0.2), mgp=c(1.25,0.5,0))
newplot <- 0
}
if(newplot==16){
X11(width=8,height=8)
par(mfrow=c(4,4), tck=-0.02, omi=c(0,0,0,0),
mai=c(0.4,0.4,0.4,0.2), mgp=c(1.25,0.5,0))
newplot <- 0
}
}
#Extract data for the species in question
PhenoSite_YearPlotSpecies <-
PhenoSite_YearPlot[PhenoSite_YearPlot$Species==spinplt[k],]
#define parameters for curvefitting
days <- PhenoSite_YearPlotSpecies$DOY #explanatory variable: DOY
phenophase <- PhenoSite_YearPlotSpecies$Flower #yes / no flowering
#remove days when no observations were made; NA in phenophase
#TODO is this redundant with cases line? check with earlier code
#days <- days[is.na(phenophase)==FALSE]
#phenophase <- phenophase[is.na(phenophase)==FALSE]
#add three weeks of zeroes before earliest SDD in those plots
SDDplt <- min(PhenoSite_YearPlotSpecies$SDD)
days <- c(SDDplt-21, SDDplt-14, SDDplt-7,days)
phenophase <- c(0,0,0,phenophase)
#now fit null model
model0 <- optimize(nullfit, c(0.000001,0.999999)) #fit null model
#now fit alternative model - curve
param <- c(mean(days[phenophase[]==1]), -0.001, 0) # initial parameters
model1 <- optim(param, curvefit_perplot, control = list(maxit = 50000))
if(model1$convergence==1){
print(paste(spinplt[k],"no convergence", sep="-"))}
#plot curve, data only if plottrue is set to "T"
if(plottrue == "T"){
plot(days,phenophase, ylab="flower",pch=21, bg="grey")
xx <- seq(min(days),max(days))
yy <- predflower(xx,model1$par)
lines(xx,yy)
title(paste(nyears[i],plots[j],spinplt[k],sep=" "))
newplot <- newplot + 1
}
#write the model tests / AIC to data frame
AICnull <- round(2*(model0$objective+1),1)
AICalt <- round(2*(model1$value + 3),1)
pcurve <- signif(pchisq(model1$value-model0$objective,2),3)
#save AIC values
tmp_aic_vals <- c(yrs[i], as.character(plots[j]),
as.character(spinplt[k]),AICnull,AICalt)
#save parameters to spyrpltpars
tmp_pars <- c(yrs[i], as.character(plots[j]), SDDplt,
as.character(spinplt[k]), model1$par[1:3])
pars_yrpltsp <- rbind(pars_yrpltsp, tmp_pars)
aics_yrpltsp <- rbind(aics_yrpltsp, tmp_aic_vals)
}
}
}
# turn spyrpltpars into a data frame
dimnames(pars_yrpltsp) <- list(c(), c("year", "plot", "SDD", "species",
"peak", "duration", "max"))
pars_yrpltsp <- data.frame(pars_yrpltsp)
#change storage type to numeric - all except plot (since a few plots have a, b)
pars_yrpltsp$year <- as.numeric(pars_yrpltsp$year)
pars_yrpltsp$species <- as.factor(pars_yrpltsp$species)
pars_yrpltsp$SDD <- as.numeric(pars_yrpltsp$SDD)
pars_yrpltsp$peak <- as.numeric(pars_yrpltsp$peak)
pars_yrpltsp$duration <- as.numeric(pars_yrpltsp$duration)
pars_yrpltsp$max <- as.numeric(pars_yrpltsp$max)
# turn aic vector to dataframe
dimnames(aics_yrpltsp) <- list(c(), c("year", "plot", "species", "AICnull", "AICalt"))
aics_yrpltsp <- data.frame(aics_yrpltsp)
aics_yrpltsp$year <- as.numeric(aics_yrpltsp$year)
aics_yrpltsp$species <- as.factor(aics_yrpltsp$species)
aics_yrpltsp$AICnull <- as.numeric(aics_yrpltsp$AICnull)
aics_yrpltsp$AICalt <- as.numeric(aics_yrpltsp$AICalt)
#examine pars, aics
head(pars_yrpltsp)
head(aics_yrpltsp)
#Write output to data folder
write.csv(pars_yrpltsp, "data/PerPlotCurves.csv", quote=FALSE,
row.names=FALSE)
#Write AIC output to data folder
write.csv(aics_yrpltsp, "data/AIC_plotmodel.csv", quote=FALSE,
row.names=FALSE)
```
#Graph to visualize peak flowering as observed by MW volunteers each year
This code takes estimates of peak flowering from year-plot-species specific model fits (in pars_yrpltsp) and plots those estimates by year (year on the x-axis, DOY on the y axis) and by trail.
```{r}
##Plot peak flowering estimates of all species, trails, plots on one graph
par(mfrow=c(1,1),omi=c(0,0,0,0), mai=c(0.5,0.4,0.4,0.2),
tck=-0.01, mgp=c(1.25,0.25,0), xpd=TRUE)
#set plotting colors - 16 total to accommodate all species if needed
plotcol <- c("yellowgreen","magenta","orange","purple","yellow","springgreen",
"pink","purple","navyblue","azure4","yellow4","orchid",
"turquoise","salmon","maroon","black")
#create plot to add points to
#earlypk <- min(spyrpltpars$peak); latepk <- max(spyrpltpars$peak)
earlypk <- 135; latepk <- 255
plot(2016,175, xlim=c(2012, 2020), ylim=c(earlypk,latepk),type="n",
xaxp=c(2012,2020,8), yaxt="n", xlab="Year",ylab="Flowering")
text(2011.5,srt=90, c(135, 165, 195, 225, 255),-0.1,
labels=c("May", "Jun", "Jul", "Aug", "Sept"))
for(trail in 1:2){
if(trail==1){
pars_yrpltsp2 <- pars_yrpltsp[substr(pars_yrpltsp$plot,1,2)=="RL",]}
if(trail==2){
pars_yrpltsp2 <- pars_yrpltsp[substr(pars_yrpltsp$plot,1,2)=="GB",]}
#how many years? differs per trail
yrs2 <- unique(pars_yrpltsp2$year)
#extract data per year, plot
for(i in 1:length(yrs2)){
paryear <- pars_yrpltsp2[pars_yrpltsp2$year==yrs2[i],]
#now pull out all data for a species
for(j in 1:length(species)){
paryearsp <- paryear[paryear$species==species[j],]
if(dim(paryearsp)[1]==0){next}
pks <- paryearsp$peak
tiny <- 0; if(trail==2){tiny <- 0.333}
if(trail==1){pltshp <-21}; if(trail==2){pltshp <- 24}
points((rep(yrs2[i],length(pks))+jitter(rep(tiny,length(pks)),2.5)),
pks,pch=pltshp, bg=plotcol[j], cex=1.25)
}
}
}
legend(x="topleft", legend=c("RL","GB"), pch=c(21,24),
pt.bg="gray", cex=0.75, pt.cex=1.25)
legend(x="bottomleft",legend=species, pch=21,
pt.bg=plotcol[1:length(species)], cex=0.75, pt.cex=1.25)
```
#Graph to visualize AIC for Null and Alt for Year, Plot and Species
## Visual plot, not a great plot as its not by plot/species, but one can see AIC of Alt model is lower than null
#Second graph plots AIC null vs. AIC alt of all combos, and the 1:1 line. This should show most if not all AIC alt are less than AIC null. Could color code / change shapes depending on years / species
```{r}
#Make a plot showing...
aics_yrpltsp %>% pivot_longer(cols=c("AICnull","AICalt")) %>% mutate(value =as.numeric(value)) %>% ggplot(aes(x=year, y=value, fill=name,color=species,group=species)) +
geom_bar(stat='identity', position='dodge') + theme_minimal(base_size = 14)+
labs(fill='AIC value',y='AIC value',x="Year")
#Make a plot showing AIC null vs. AICalt
plot(aics_yrpltsp$AICnull, aics_yrpltsp$AICalt, pch=21, bg="grey",
xlab="AIC Null", ylab="AIC Curve")
title("Compare Null to Curve")
abline(0,1)
```
#Explore Outliers
The following graphs are intended to detect outliers and model fitting issues
##Graph of plot / Species / Year-specific model fits
In this graph, the predicted peak flowering day of the year (DOY) of each species is graphed by year/plot id. X-axis is the years, Y-axis is DOY, and colors indicate all the species. This allows us to identify species / plot year fits that are anomolous (indicating potential model fitting issues)
```{r}
plot_pars_years <- read_csv("data/PerPlotCurves.csv")
# str_sub subsets first two characters pf the site ID, so here it would be either RL or GB.
# aes - aesthetics -we give x and y variables , and color
# facets add third dimension to the figure here.
# %>% is the pipe operator - which is equivalent to saying make all the field names available to the operation on right, it helps one to write simplified code where we don't need to use $ to access the fields in a dataframe
plot_pars_years %>% mutate(site= str_sub(plot_pars_years$plot,1,2)) %>%
ggplot() +
geom_point(aes(as.factor(year),peak,color=species)) +
facet_grid(.~plot) +
theme_minimal() + theme(axis.text.x = element_text(angle = 90,hjust = 5)) +
geom_hline(aes(yintercept = 152)) +
geom_text(mapping = aes(label = 'June 1st',y=152, x = 0.1), angle = 90,alpha=.4, hjust = 0) +
geom_hline(aes(yintercept = 183)) +
geom_text(mapping = aes(label = 'July 1st',y=183, x = 0.1), angle = 90, alpha=.4,hjust = 0) +
geom_hline(aes(yintercept = 213)) +
geom_text(mapping = aes(label = 'Aug 1st',y=213, x = 0.1), angle = 90, alpha=.4,hjust = 0) +
geom_hline(aes(yintercept = 244)) +
geom_text(mapping = aes(label = 'Sep 1st',y=244, x = 0.1), angle = 90,alpha=.4, hjust = 0) +
# facet_grid(.~year) +
labs(x=" Year", y= "Predicted Peak flowering (DOY)", color="Species")
# Its a comprehensive plot so might be better to view the generated image separately.
ggsave("figs/sanity_check.png",width = 70, height = 20, units = "cm")
```
#Alternative graph of model fits
Alternative plot to visualize outliers, Site level (RL or GB) is on alternative y-axis, X-axis DOY, and Y-axis the focal species. We see the outliers are closer to start of the y-axis scale or towards the end. Columns here are years. SDD gradient can be ignored ( no variation)
```{r}
# note here aes (aesthetics included 'fill' as well)
plot_pars_years %>%
mutate(site= str_sub(plot_pars_years$plot,1,2)) %>%
ggplot( aes(x = peak, y = species,fill=SDD)) +
geom_jitter() +
facet_grid(site~year) +
scale_fill_viridis_c(name = "Snow Melt Date") +
labs(title = 'Peaks across all the years',
subtitle = 'Modelled peak flowering across 10 focal species',
x = "Day of the year") +
theme_minimal(base_size = 14) + theme(axis.title.y = element_blank())
ggsave("figs/sanity_check_revised.png", width = 35, height = 20, units = "cm")
```
#Observation Effort
This code calculates the total number of observations for each plot / year / species combination.
```{r}
#This coce shows the total number of observations by plot / year / species
PhenoSite_Focal %>%
group_by(Transect.x,Year,Site_Code,Species)%>%
tally()
#This code does the same thing, but transforms the tibble into a dataframe
tallyObservations_typs <- PhenoSite_Focal %>%
group_by(Transect.x,Year,Site_Code,Species)%>%
tally() %>% as.data.frame()
```
#Graph showing species/ year / transect observer effort
Notice a perfect cluster of # of observationsin 2015 Reflections lake. Also, what we see is the low number of observations for some of the plot / species combinations
```{r}
# In dply lingo everything is a tibble , we are transforming it to a dataframe for compatibility
tallyObservations_typs %>%
ggplot() +
geom_jitter(aes(as.factor(Year), n,color=Species))+
facet_grid(.~Transect.x) +
labs(y="Number of observations",x="Year") +
theme_minimal(base_size = 14)
```
#Calculate outlier 'yes' observations
This code calculates all pairwise differences in dates between all yes observations in each trail / year / plot / species combo, and identify any observation where the minimum value is > 7
For example, imagine a specific query : Species =='ANOC' & Year=='2013' & Site_Code =='RL9'
1.*group_by* orders the rows by given set of fields (in order)
2. *lag* gets the previous row
Returns
# Groups: Transect, Year, Site_Code, Species [1]
Transect Year Site_Code Species Date Observer QA.QC
<chr> <int> <chr> <chr> <chr> <chr> <int>
1 Reflection Lakes 2013 RL9 ANOC 7/21/2013 Dave Purdon 0
2 Reflection Lakes 2013 RL9 ANOC 7/23/2013 Anna Wilson; Cherry Chen 1
3 Reflection Lakes 2013 RL9 ANOC 7/23/2013 Weedy McCauley 0
4 Reflection Lakes 2013 RL9 ANOC 7/24/2013 Brooke Upton 0
5 Reflection Lakes 2013 RL9 ANOC 7/24/2013 Rita Moore; Dan Paquette 0
6 Reflection Lakes 2013 RL9 ANOC 7/25/2013 Carol Miltimore 0
7 Reflection Lakes 2013 RL9 ANOC 7/26/2013 Carol Clingan 0
8 Reflection Lakes 2013 RL9 ANOC 8/4/2013 Johndavid Hascup; Kristalyn Hascup 0
9 Reflection Lakes 2013 RL9 ANOC 8/10/2013 Bonnie Scott 0
Row # 7 and 8 is what we use to find the difference which is greater than 7
# Figure out the flowering day by year
```{r}
qual_col_pals = RColorBrewer::brewer.pal.info[RColorBrewer::brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(RColorBrewer::brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
cols_need=sample(col_vector, 17)
PhenoSite%>%
filter (Flower == 1) %>%
mutate (doy = lubridate::yday(as.Date(Date, "%m/%d/%Y")) ) %>%
group_by(Transect,Year,Site_Code,Species) %>%
mutate(pdiff = doy -lag(doy, default = doy[1])) %>%
filter (pdiff > 7) %>%
ggplot( aes(x = Site_Code, y = DOY,fill=Species,color=Species)) +
geom_jitter() +
facet_grid(Transect~Year) +
scale_color_manual(values = cols_need)+
# scale_fill_viridis_c(colours = cols_need,name = "Species") +
#scale_fill_distiller(type="seq",palette = "Spectral")
labs(title = '',
subtitle = 'Flowering',
x = "Site Code") +
theme_minimal(base_size = 28) + theme(axis.title.y = element_blank(),axis.text.x = element_text(angle = 35, vjust = 0.5, hjust=1))
PhenoDat%>%
filter (Flower == 1) %>%
mutate (doy = lubridate::yday(as.Date(Date, "%m/%d/%Y")) ) %>%
group_by(Transect,Year,Site_Code,Species) %>%
# mutate(pdiff = doy -lag(doy, default = doy[1])) %>%
# filter (pdiff > 7) %>%
ggplot( aes(x = Year, y = doy,fill=Species,color=Species)) +
geom_jitter() +
facet_grid(.~Transect) +
scale_color_manual(values = cols_need)+
# scale_fill_viridis_c(colours = cols_need,name = "Species") +
#scale_fill_distiller(type="seq",palette = "Spectral")
labs(title = '',
subtitle = 'Flowering',
x = "", y= "Day of year") +
theme_minimal(base_size = 28) + theme(axis.text.x = element_text(angle = 35, vjust = 0.5, hjust=1))
ggsave("figs/casestudy-flowering.png",dpi=300, dev='png', height=10, width=12,units="in")
case_study_d <- PhenoSite%>%
filter (Flower == 1) %>%
mutate (doy = lubridate::yday(as.Date(Date, "%m/%d/%Y")) ) %>%
group_by(Transect,Year,Site_Code,Species) %>%
mutate(pdiff = doy -lag(doy, default = doy[1])) %>%
filter (pdiff > 7) %>%
ggplot( aes(x = Site_Code, y = DOY,fill=Species,color=Species)) +
geom_jitter() +
facet_grid(Transect~Year) +
scale_color_manual(values = cols_need)+
# scale_fill_viridis_c(colours = cols_need,name = "Species") +
#scale_fill_distiller(type="seq",palette = "Spectral")
labs(title = '',
subtitle = 'Flowering',
x = "Site Code") +
theme_minimal(base_size = 14) + theme(axis.title.y = element_blank(),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
case_study_d_r1 <- PhenoSite%>%
filter (Flower == 1) %>%
mutate (doy = lubridate::yday(as.Date(Date, "%m/%d/%Y")) ) %>%
group_by(Transect,Year,Site_Code,Species) %>%
mutate(pdiff = doy -lag(doy, default = doy[1])) %>%
#filter (pdiff > 7) %>%
ggplot( aes(x = Year, y = DOY,fill=Species,color=Species)) +
geom_jitter() +
facet_grid(.~Transect) +
scale_color_manual(values = cols_need)+
# scale_fill_viridis_c(colours = cols_need,name = "Species") +
#scale_fill_distiller(type="seq",palette = "Spectral")
labs(title = '',
subtitle = 'Flowering',
x = "Year",y= "Day of year") +
theme_minimal(base_size = 14) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
```
# Figure out the flowering day by year
```{r}
PhenoDat%>%
filter (Bud == 1) %>%
mutate (doy = lubridate::yday(as.Date(Date, "%m/%d/%Y")) ) %>%
group_by(Transect,Year,Site_Code,Species) %>%
# mutate(pdiff = doy -lag(doy, default = doy[1])) %>%
# filter (pdiff > 7) %>%
ggplot( aes(x = Year, y = doy,fill=Species,color=Species)) +
geom_jitter() +
facet_grid(.~Transect) +
scale_color_manual(values = cols_need)+
# scale_fill_viridis_c(colours = cols_need,name = "Species") +
#scale_fill_distiller(type="seq",palette = "Spectral")
labs(title = '',
subtitle = 'Bud breaks',
x = "", y= "Day of year") +
theme_minimal(base_size = 28) + theme(axis.text.x = element_text(angle = 35, vjust = 0.5, hjust=1))
ggsave("figs/casestudy-budbreaks.png",dpi=300, dev='png', height=10, width=12,units="in")
case_study_c <- PhenoDat%>%
filter (Bud == 1) %>%
mutate (doy = lubridate::yday(as.Date(Date, "%m/%d/%Y")) ) %>%
group_by(Transect,Year,Site_Code,Species) %>%
# mutate(pdiff = doy -lag(doy, default = doy[1])) %>%
# filter (pdiff > 7) %>%
ggplot( aes(x = Year, y = doy,fill=Species,color=Species)) +
geom_jitter() +
facet_grid(.~Transect) +
scale_color_manual(values = cols_need)+
# scale_fill_viridis_c(colours = cols_need,name = "Species") +
#scale_fill_distiller(type="seq",palette = "Spectral")
labs(title = '',
subtitle = 'Bud breaks',
x = "Year", y= "Day of year") +
theme_minimal(base_size = 28) + theme(axis.title.x=element_blank(),axis.ticks.x=element_blank(), axis.text.x=element_blank())
```
# Figure out the presence of snow
```{r}
qual_col_pals = RColorBrewer::brewer.pal.info[RColorBrewer::brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(RColorBrewer::brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
cols_need=sample(col_vector, 17)
PhenoDat%>%
filter (Snow == 1) %>%
mutate (doy = lubridate::yday(as.Date(Date, "%m/%d/%Y")) ) %>%
group_by(Transect,Year,Site_Code,Species) %>%
# mutate(pdiff = doy -lag(doy, default = doy[1])) %>%
# filter (pdiff > 7) %>%
ggplot( aes(x = Year, y = doy)) +
geom_jitter() +
facet_grid(.~Transect) +
# scale_fill_viridis_c(colours = cols_need,name = "Species") +
#scale_fill_distiller(type="seq",palette = "Spectral")
labs(title = '',
subtitle = 'Presence of snow',
x = "Year", y= "Day of year") +
theme_minimal(base_size = 28) + theme(axis.text.x = element_text(angle = 35, vjust = 0.5, hjust=1))
ggsave("figs/casestudy-snow.png",dpi=300, dev='png', height=10, width=12,units="in")
case_study_b <- PhenoDat%>%
filter (Snow == 1) %>%
mutate (doy = lubridate::yday(as.Date(Date, "%m/%d/%Y")) ) %>%
group_by(Transect,Year,Site_Code,Species) %>%
# mutate(pdiff = doy -lag(doy, default = doy[1])) %>%
# filter (pdiff > 7) %>%
ggplot( aes(x = Year, y = doy)) +
geom_jitter() +
facet_grid(.~Transect) +
# scale_fill_viridis_c(colours = cols_need,name = "Species") +
#scale_fill_distiller(type="seq",palette = "Spectral")
labs(title = '',
subtitle = 'Presence of snow',
x = "Year", y= "Day of year") +
theme_minimal(base_size = 14) + theme(axis.title.x=element_blank(),axis.ticks.x=element_blank(), axis.text.x=element_blank())
```
```{r }
library(readr)
MW_PhenoSite_2013_2018 <- read_csv("data/MW_SiteDat_2013_2018.csv")
head(MW_PhenoSite_2013_2018)
```
```{r }
library(raster)
library(leaflet)
library(ggrepel)
Sites_2013 <- MW_PhenoSite_2013_2018 %>%
filter(Year == 2013 & Transect== "Reflection Lakes") %>%
as.data.frame()
AllSites_2018 <- MW_PhenoSite_2013_2018 %>%
filter(Year == 2018 & Transect %in% c( "Reflection Lakes","Glacier Basin")) %>%
as.data.frame()
xy <- cbind(x=Sites_2013$longitude+0.008,
y=Sites_2013$latitude-0.005)
S <- SpatialPoints(xy)
bbox(S)
library(ggmap)
pp_stamen <- get_stamenmap(bbox = bbox(S),
zoom = 13)
pp_stamen
ggmap(pp_stamen) +
geom_point(data = Sites_2013,
mapping = aes(x = Sites_2013$longitude,
y = Sites_2013$latitude,color=as.factor(Site_Num)))
```
```{r }
xy <- cbind(x=AllSites_2018$longitude,
y=AllSites_2018$latitude)
S <- SpatialPoints(xy)
bbox(S)
library(ggmap)
pp_stamen <- get_stamenmap(bbox = bbox(S),
zoom = 12)
pp_stamen
ggmap(pp_stamen) +
geom_point(data = AllSites_2018,
mapping = aes(x = AllSites_2018$longitude,
y = AllSites_2018$latitude,color=as.factor(Site_Num))) +
labs(color="", x="Longitude",y="Latitude") +
theme_minimal(base_size = 18) +
theme(legend.position = "none" , axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
case_study_a <- ggmap(pp_stamen) +
geom_point(data = AllSites_2018,
mapping = aes(x = AllSites_2018$longitude,
y = AllSites_2018$latitude,color=as.factor(Site_Num))) +
labs(color="", x="Longitude",y="Latitude") +
theme_minimal(base_size = 18) +
theme(legend.position = "none" , axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
```
```{r , echo=FALSE}
library(patchwork)
layout <- "
A
B
C"
case_study_b+
case_study_c +
case_study_d_r1 +
plot_layout(design = layout,guides = "collect") +
plot_annotation(tag_levels = 'A', title = '',
subtitle = '',
caption = '') +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("figs/casestudy.png",dpi=300, dev='png', height=10, width=12,units="in")
```
#Question - does this only look at the difference between each observation and a previous flowering observation, or also the difference between previous and next? In the latter case, what happens to the first observation of flowering (no previous observation?)
```{r}
PhenoSite%>%
filter (Flower == 1) %>%
mutate (doy = lubridate::yday(as.Date(Date, "%m/%d/%Y")) ) %>%
group_by(Transect,Year,Site_Code,Species) %>%
mutate(pdiff = doy -lag(doy, default = doy[1])) %>%
filter (pdiff > 7) %>%
ggplot( aes(x = Site_Code, y = Species,fill=pdiff)) +
geom_jitter() +
facet_grid(Transect~Year) +
scale_fill_viridis_c(name = "Difference in DOY") +
labs(title = 'Pairwise differences',
subtitle = 'All species',
x = "Site Code") +
theme_minimal(base_size = 14) + theme(axis.title.y = element_blank(),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
```
# Pairwise differences but filter it only by focal species
#I got rid of this snippet of code, as I changed code above to only look at focal species
# Plot pairwise for RL only, looks like 128 observations for RL (no jitter)
```{r}
PhenoSite %>%
filter (Transect== 'Reflection Lakes' & Flower == 1 & Species %in% species) %>%
mutate (doy = lubridate::yday(as.Date(Date, "%m/%d/%Y")) ) %>%
group_by(Transect,Year,Site_Code,Species) %>% mutate(pdiff = doy - lag(doy, default = doy[1])) %>%
filter (pdiff > 7) %>% ggplot( aes(x = Site_Code, y = Species,fill=pdiff)) +
geom_point() +
facet_grid(Transect~Year) +
scale_fill_viridis_c(name = "Difference in DOY") +
labs(title = 'Pairwise differences',
subtitle = 'All species',
x = "Site Code") +
theme_minimal(base_size = 14) + theme(axis.title.y = element_blank(),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
```
#Plot pairwise for RL only, looks like 128 observations for RL (with jitter)
```{r}
PhenoSite %>%
filter (Transect== 'Reflection Lakes' & Flower == 1 & Species %in% species) %>%
mutate (doy = lubridate::yday(as.Date(Date, "%m/%d/%Y")) ) %>%
group_by(Transect,Year,Site_Code,Species) %>% mutate(pdiff = doy - lag(doy, default = doy[1])) %>%
filter (pdiff > 7) %>%
ggplot( aes(x = Site_Code, y = Species,fill=pdiff)) +
geom_jitter() +
facet_grid(Transect~Year) +
scale_fill_viridis_c(name = "Difference in DOY") +
labs(title = 'Pairwise differences',
subtitle = 'All species (jittered)',
x = "Site Code") +
theme_minimal(base_size = 14) + theme(axis.title.y = element_blank(),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
```
#Fit phenology model for all species data, using second model
This code fits a predictive phenology model to all observations of flowering for a particular species (function *curvefit_allplot*), for the same flowering community as defined above. So, this code chunk expects that the data frame *PhenoSite_Focal*, the vector *species*, and the objects *nspp*, *totobs* and *totyesobs* have been defined. Note that this takes a bit of time to run (5ish minutes). The code does the following:
1. Subset data for a particular species.
2. For each species, use a for loop to assemble year-plot specific data for the species in question. This requires extracting year-plot specific data, assessing whether there is sufficient data (so only plots with *totobs* observations and *totyesobs* flowering observations) are included, and adding three no flowering observations before snowmelt (as was done for model fitting for individual year-plot-species data), and creating variables *days* (vector of DOY for all plots included), *phenophase* (vector of 1's and 0's observed), *SDD* (snow disappearance date at each plot for the observation in question) and *trlyr* which trail / year combo the observation came from (this is for fitting range, max parameters).
3. Fit the maximum likelihood model (defined by *curvefit_allplot* above) to the data. This requires setting initial parameters first - which was done by fitting the relationship between peak flowering and SDD from year-plot-species specific data (in *pars_sppltsp*), and taking the average range and duration values for those same model fits.
4. Saves the parameters to *pars_spp_peak*, *pars_spp_range*, *pars_spp_max*
+ *pars_spp_peak* per-species slope / intercept relates SDD to peak
+ *pars_spp_range* includes the year / trail specific range parameters
+ *pars_spp_max* includes the year / trail specific max parameters
5. Also saves the AIC values to aics_sp
6. After the for loop, turns *pars_spp* objects into data framed, view it
7. Write *pars_spp_peak*, *pars_spp_range*, *pars_spp_max* as .csv files to the data folder.
```{r}
#define where to save output
pars_spp_peak <- c() #save peak parameters for per species fit
pars_spp_range <- c() #save range parameters for per species fit
pars_spp_max <- c() #save max parameters for per species fit
aics_sp <-c() #save AIC of overall model
##Now nested for loops to fit curves for all years, species, plots
for(i in 1:nspp){ #loop for each species, to assemble data
#extract data for that year
PhenoSite_Species <- PhenoSite_Focal[PhenoSite_Focal$Species==species[i],]
#assemble data - need to add zeros before SDD, define trail year combos
yrplt <- paste(PhenoSite_Species$Year, PhenoSite_Species$Site_Code, sep="")
yrplts <- unique(yrplt)
trailyear <- unique(paste(substr(yrplts,5,6),substr(yrplts,1,4),sep=""))
trailyear <- trailyear[order(trailyear)]
ntrlyr <- length(trailyear)
trlyr <- c()
days <- c()
phenophase <- c()
SDD <- c()
#Now go through each unique year - plot combination per species
for(j in 1:length(yrplts)){
PhenoSite_YearPlotSpecies <- PhenoSite_Species[yrplt[]==yrplts[j],]
tmpdays <- PhenoSite_YearPlotSpecies$DOY #explanatory variable: DOY
tmpphenophase <- PhenoSite_YearPlotSpecies$Flower #yes / no flowers
tmpSDD <- PhenoSite_YearPlotSpecies$SDD
#Do not include if fewer totobs obs, totyesobs flowering obs
if(length(tmpdays)<=totobs){next}
if(sum(tmpphenophase)<=totyesobs){next}
#remove days when no observations were made; NA in phenophase
tmpdays <- tmpdays[is.na(tmpphenophase)==FALSE]
tmpSDD <- tmpSDD[is.na(tmpphenophase)==FALSE]
tmpphenophase <- tmpphenophase[is.na(tmpphenophase)==FALSE]
#Add observations of no flowering 3 weeks prior to snowmelt
SDDplt <- min(tmpSDD)