-
Notifications
You must be signed in to change notification settings - Fork 4
/
Copy path5_post_GF_TRW_to_biomass.R
448 lines (360 loc) · 18.2 KB
/
5_post_GF_TRW_to_biomass.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
# Transforming DBH reconstructions from Tree-ring Widths into biomass (kg/m2)
library(dplR)
library(ggplot2)
se <- function(x){
sd(x, na.rm=TRUE) / sqrt((length(!is.na(x))))}
# Load in diameter reconstrcutions generated from step 3.
g.filled.diam <- read.csv("processed_data/GapFilling_DBHrecon_ALL.csv", header=T, row.names=1)
# summary(g.filled.diam)
# read in tree data
tree.data <- read.csv("processed_data/TreeData.csv", header=T)
summary(tree.data)
#select sites for which to run the biomass reconstruction
# NOTE: the North Carolina coast doesn't have a density estimate, so we're going to ignore it for now
trees.use <- tree.data[tree.data$TreeID %in% names(g.filled.diam),] # If you want to do this later, it'll be a special case
# trees.use <- tree.data
summary(trees.use)
plot.data <- read.csv("raw input files/DOE_plus_Valles.csv")
plot.data$Year.Sample <- as.numeric(substr(plot.data$date.sample,7,10))
summary(plot.data)
##########################################################################
# Allometric Equations
##########################################################################
#Convert to biomass with the allometric equation
#using the PECAN generated bayesian equations
library(car)
load("processed_data/allometries_list.Rdata")
# Find out what species we don't have allometries for that need to be renames
unique(trees.use$Species)[!(unique(trees.use$Species) %in% names(allometries))]
trees.use$spp.allom <- recode(trees.use$Species, " 'PIEN'='picea.sp'; 'FRAX'='FRAM'; 'ASTR'='e.hard'; 'PRSE'='e.hard'; 'ULRU'='e.hard'")
summary(trees.use)
plots <- unique(trees.use$PlotID) # You had the right idea, but it was throwing errors because you were trying to evaluate plots you haven't gotten to yet
# making the generalized allometry function to run
# log(AGB) = mu0 + mu1*log(DBH) --equaton form of PECAN allometrics
allom.eq <- function(mu0, mu1, DBH) { exp(mu0 + mu1 * log(DBH) )}
# A temporary allometry file that will get filled
allom.temp <- g.filled.diam
allom.temp[,] <- NA
# dbh=0 causes problems, so we're going to make those NA
g.filled.diam[g.filled.diam==0] <- 1e-6
min(g.filled.diam, na.rm=T)
# summary(g.filled.diam)
dim(g.filled.diam)
# First we're going to save the allometric iterations by tree because this gives us more flexibility in the analyses
bm.array <- array(NA, dim=c(nrow(g.filled.diam), ncol(g.filled.diam), nrow(allometries[[1]])))
dimnames(bm.array)[[1]] <- row.names(g.filled.diam) #CRR Added
dimnames(bm.array)[[2]] <- names(g.filled.diam)
# summary(bm.array[,,1])
dim(bm.array)
tree.names <- names(g.filled.diam)
#--------------------------------------------------
# This is one big loop that goes through each layer of the 500 iterations
# and uses it to calculate the biomass of each tree
# Part 2 (t-loop): converts biomass/tree to biomass/m2
#--------------------------------------------------
for(i in 1:nrow(allometries[[1]])){
allom.temp <- g.filled.diam
allom.temp[,] <- NA
# Species loop for calculating tree biomass
for(j in unique(trees.use$spp.allom)){
cols <- which(names(g.filled.diam) %in% trees.use[trees.use$spp.allom==j, "TreeID"])
# Pulling coefficients from the randomly pulled estimates from Pecan;
# Need to use Bg0 because the heierarchical means were being weird
mu0=allometries[[j]][i,"Bg0"]
mu1=allometries[[j]][i,"Bg1"]
allom.temp[,cols] <- allom.eq(mu0=mu0, mu1 = mu1, DBH = g.filled.diam[,cols])
}
# summing to the plot level
allom.temp[is.na(allom.temp)] <- 0
# biomass loop for summing trees to plots in kg/m2
for(t in 1:ncol(allom.temp)){
if(substr(tree.names[t],1,1)=="V" | substr(tree.names[t],1,1)=="N"){
bm.array[,t,i] <- allom.temp[,t]*plot.data[plot.data$PlotID==paste(tree.data[tree.data$TreeID==tree.names[t], "PlotID"]),"Density.Total..stems.ha."]/10000
} else {
# Neil's sampling scheme has different densities based on the size of each tree, so we index it by tree rather than total plot area
bm.array[,t,i] <- allom.temp[,t]*tree.data[tree.data$TreeID==tree.names[t],"Density..stems.ha."]/10000
}
}
}
#--------------------------------------------------
plots <- paste(unique(tree.data[tree.data$TreeID %in% names(g.filled.diam),"PlotID"]))
years <- as.numeric(row.names(g.filled.diam))
for(p in 1:length(plots)){ # will go by plot
cols <- which(dimnames(bm.array)[[2]] %in% tree.data[tree.data$PlotID==plots[p], "TreeID"])
yr.sample <- plot.data[plot.data$PlotID==plots[p], "Year.Sample"]
if(max(years) > yr.sample){ # if tree wasn't cored in the last year of sampling, make it's outer years NA
rows.na <- which(years > yr.sample-1)
bm.array[rows.na,cols,] <- NA
}
}
summary(bm.array[,,1])
#bm.array[,,1]
summary(bm.array[,1:10,1])
save(bm.array, file="processed_data/Biomass_Array_Tree_kgm-2.RData")
# ---------------------------------
# Create a data frame with biomass by tree
# ---------------------------------
bm.mean <- data.frame(apply(bm.array, c(1,2), mean, na.rm=T))
bm.lo <- data.frame(apply(bm.array, c(1,2), quantile, 0.025, na.rm=T))
bm.hi <- data.frame(apply(bm.array, c(1,2), quantile, 0.975, na.rm=T))
names(bm.mean) <- names(bm.lo) <- names(bm.hi) <- tree.names
# This is stacking the tree-level biomass to be easier to work with
bm.tree <- stack(bm.mean)[,c(2,1)]
names(bm.tree) <- c("TreeID", "BM.Mean")
bm.tree$Year <- as.numeric(row.names(g.filled.diam))
bm.tree$BM.CI.lo <- stack(bm.lo)[,1]
bm.tree$BM.CI.hi <- stack(bm.hi)[,1]
summary(bm.tree)
# Merge in a lot of useful data about each tree
bm.tree <- merge(bm.tree, tree.data[,c("TreeID","Site","PlotID", "plot", "Species", "Canopy.Class", "DBH..cm.", "Live.Dead", "Distance", "Azimuth","Dated")], all.x=T, all.y=F)
summary(bm.tree)
write.csv(bm.tree, "processed_data/Biomass_Tree_kgm-2.csv", row.names=F)
# ---------------------------------
# ---------------------------------
# Aggregating to the total plot-level biomass
# ---------------------------------
# doing bm.plot wide first and then we'll stack it
plots <- unique(bm.tree$PlotID)
bm.plot <- array(dim=c(nrow(g.filled.diam), length(plots), 3)) # years x plots x (mean, ci.low, ci.hi)
dimnames(bm.plot)[[1]] <- row.names(g.filled.diam);
dimnames(bm.plot)[[2]] <- plots;
dimnames(bm.plot)[[3]] <- c("mean", "ci.low", "ci.hi")
for(p in 1:length(plots)){ # will go by plot
cols <- which(dimnames(bm.mean)[[2]] %in% tree.data[tree.data$PlotID==plots[p], "TreeID"])
if(substr(plots[p],1,1)=="V" | substr(plots[p],1,1)=="N"){
bm.plot[,p,1] <- apply(bm.mean[,cols], 1, mean)
bm.plot[,p,2] <- apply(bm.lo[,cols], 1, mean)
bm.plot[,p,3] <- apply(bm.hi[,cols], 1, mean)
} else {
bm.plot[,p,1] <- apply(bm.mean[,cols], 1, sum)
bm.plot[,p,2] <- apply(bm.lo[,cols], 1, sum)
bm.plot[,p,3] <- apply(bm.hi[,cols], 1, sum)
}
}
summary(bm.plot[,,1])
# Stacking it to be a more user-friendly data frame
bm.plot2 <- stack(data.frame(bm.plot[,,1]))[,c(2,1)]
names(bm.plot2) <- c("PlotID", "BM.Mean")
bm.plot2$Plot <- as.factor(substr(bm.plot2$PlotID, 3,3))
bm.plot2$Year <- as.numeric(dimnames(bm.plot)[[1]])
bm.plot2$BM.CI.lo <- stack(data.frame(bm.plot[,,2]))[,1]
bm.plot2$BM.CI.hi <- stack(data.frame(bm.plot[,,3]))[,1]
summary(bm.plot2)
bm.plot2 <- merge(bm.plot2, plot.data[,c("PlotID", "Site..Tower.", "latitude", "longitude", "elevation", "Year.Sample")])
summary(bm.plot2)
# Visualizing everything to make our lives easier
pdf("figures/Biomass_Plot_Total_kgm-2.pdf")
ggplot(bm.plot2[,]) + facet_wrap(~Site..Tower., scales="fixed") +
geom_ribbon(aes(x=Year, ymin= BM.CI.lo, ymax=BM.CI.hi, fill=Plot), alpha=0.5) +
geom_line(aes(x=Year, y=BM.Mean, color=Plot)) +
labs(x="Year", y="Biomass (kg m-2)") +
theme_bw()
dev.off()
write.csv(bm.plot2, "processed_data/Biomass_Plot_Total_kgm-2.csv", row.names=F)
# ---------------------------------
# ---------------------------------
# Aggregating to the species plot-level biomass
# ---------------------------------
# doing bm.plot wide first and then we'll stack it
plots <- unique(bm.tree$PlotID)
spp <- unique(bm.tree$Species)
bm.spp <- list()
bm.spp[["mean"]] <- array(dim=c(nrow(g.filled.diam), length(spp), length(plots))) # years x species x plot
dimnames(bm.spp[["mean"]])[[1]] <- row.names(g.filled.diam);
dimnames(bm.spp[["mean"]])[[2]] <- spp
dimnames(bm.spp[["mean"]])[[3]] <- plots
bm.spp[["ci.lo"]] <- array(dim=c(nrow(g.filled.diam), length(spp), length(plots))) # years x species x plot
bm.spp[["ci.hi"]] <- array(dim=c(nrow(g.filled.diam), length(spp), length(plots))) # years x species x plot
for(s in 1:length(spp)){
for(p in 1:length(plots)){ # will go by plot
cols <- which(dimnames(bm.mean)[[2]] %in% tree.data[tree.data$PlotID==plots[p] & tree.data$Species==spp[s], "TreeID"])
if(length(cols) > 0){
if(substr(plots[p],1,1)=="V" | substr(plots[p],1,1)=="N"){
if(length(cols) > 1) {
bm.spp[["mean"]][,s,p] <- apply(bm.mean[,cols], 1, mean)
bm.spp[["ci.lo"]][,s,p] <- apply(bm.lo[,cols], 1, mean)
bm.spp[["ci.hi"]][,s,p] <- apply(bm.hi[,cols], 1, mean)
} else {
bm.spp[["mean"]][,s,p] <- bm.mean[,cols]
bm.spp[["ci.lo"]][,s,p] <- bm.lo[,cols]
bm.spp[["ci.hi"]][,s,p] <- bm.hi[,cols]
}
} else {
if(length(cols) > 1) {
bm.spp[["mean"]][,s,p] <- apply(bm.mean[,cols], 1, sum)
bm.spp[["ci.lo"]][,s,p] <- apply(bm.lo[,cols], 1, sum)
bm.spp[["ci.hi"]][,s,p] <- apply(bm.hi[,cols], 1, sum)
} else {
bm.spp[["mean"]][,s,p] <- bm.mean[,cols]
bm.spp[["ci.lo"]][,s,p] <- bm.lo[,cols]
bm.spp[["ci.hi"]][,s,p] <- bm.hi[,cols]
}
}
}
}
}
summary(bm.spp[["mean"]][,,1])
# Getting the bm.spp into a friendly format
for(p in 1:length(plots)){
temp <- stack(data.frame(bm.spp[["mean"]][,,p]))[,c(2,1)]
names(temp) <- c("Species", "BM.Mean")
temp$PlotID <- as.factor(dimnames(bm.spp[["mean"]])[[3]][p])
temp$Plot <- as.factor(substr(temp$PlotID, 3,3))
temp$Year <- as.numeric(dimnames(bm.spp[["mean"]])[[1]])
temp$BM.CI.lo <- stack(data.frame(bm.spp[["ci.lo"]][,,p]))[,1]
temp$BM.CI.hi <- stack(data.frame(bm.spp[["ci.hi"]][,,p]))[,1]
if(p == 1) bm.spp2 <- temp else bm.spp2 <- rbind(bm.spp2, temp)
}
summary(bm.spp2)
bm.spp2 <- merge(bm.spp2, plot.data[,c("PlotID", "Site..Tower.", "latitude", "longitude", "elevation", "Year.Sample")])
summary(bm.spp2)
pdf("figures/Biomass_Plot_Species_kgm-2.pdf") # NOTE: This will probably stop worrking well very quickly
ggplot(bm.spp2[,]) + facet_grid(Site..Tower. ~ Plot, scales="free") +
geom_ribbon(aes(x=Year, ymin= BM.CI.lo, ymax=BM.CI.hi, fill=Species), alpha=0.5) +
geom_line(aes(x=Year, y=BM.Mean, color=Species)) +
labs(x="Year", y="Biomass (kg m-2)") +
theme_bw()
dev.off()
write.csv(bm.spp2, "processed_data/Biomass_Plot_Species_kgm-2.csv", row.names=F)
# ---------------------------------
# ---------------------------------
# Aggregating to the species SITE-level biomass
# ---------------------------------
# doing bm.plot wide first and then we'll stack it
sites <- unique(substr(bm.spp2$PlotID, 1, 2))
spp <- unique(bm.spp2$Species)
bm.site.spp <- list()
bm.site.spp[["mean"]] <- array(dim=c(nrow(g.filled.diam), length(spp), length(sites))) # years x species x plot
dimnames(bm.site.spp[["mean"]])[[1]] <- row.names(g.filled.diam);
dimnames(bm.site.spp[["mean"]])[[2]] <- spp
dimnames(bm.site.spp[["mean"]])[[3]] <- sites
bm.site.spp[["sd"]] <- array(dim=c(nrow(g.filled.diam), length(spp), length(sites))) # years x species x plot
bm.site.spp[["ci.lo.mean"]] <- array(dim=c(nrow(g.filled.diam), length(spp), length(sites))) # years x species x plot
bm.site.spp[["ci.hi.mean"]] <- array(dim=c(nrow(g.filled.diam), length(spp), length(sites))) # years x species x plot
# Need to make NA 0 because that species doesn't exist in the record
bm.spp$mean[is.na(bm.spp$mean)] <- 0
bm.spp$ci.lo[is.na(bm.spp$ci.lo)] <- 0
bm.spp$ci.hi[is.na(bm.spp$ci.hi)] <- 0
# Altering our 0s to be NA for years more recent than the year the plot was sampled
plots <- paste(unique(tree.data[tree.data$TreeID %in% names(g.filled.diam),"PlotID"]))
years <- as.numeric(row.names(g.filled.diam))
for(p in 1:length(plots)){ # will go by plot
yr.sample <- plot.data[plot.data$PlotID==plots[p], "Year.Sample"]
if(max(years) > yr.sample){ # if tree wasn't cored in the last year of sampling, make it's outer years NA
rows.na <- which(years > yr.sample-1)
bm.spp$mean[rows.na,,p] <- NA
bm.spp$ci.lo[rows.na,,p] <- NA
bm.spp$ci.hi[rows.na,,p] <- NA
}
}
summary(bm.spp$mean[,,6])
for(s in 1:length(spp)){
for(p in 1:length(sites)){ # will go by plot
cols <- which(substr(dimnames(bm.spp$mean)[[3]],1,2) == sites[p])
bm.site.spp[["mean"]][,s,p] <- apply(bm.spp$mean[,s,cols], 1, mean)
bm.site.spp[["sd"]][,s,p] <- apply(bm.spp$mean[,s,cols], 1, sd)
bm.site.spp[["ci.lo.mean"]][,s,p] <- apply(bm.spp$ci.lo[,s,cols], 1, mean)
bm.site.spp[["ci.hi.mean"]][,s,p] <- apply(bm.spp$ci.hi[,s,cols], 1, mean)
}
}
summary(bm.site.spp[["mean"]][,,1])
# Getting the bm.site.spp into a friendly format
for(p in 1:length(sites)){
temp <- stack(data.frame(bm.site.spp[["mean"]][,,p]))[,c(2,1)]
names(temp) <- c("Species", "BM.Mean")
temp$SiteID <- as.factor(dimnames(bm.site.spp[["mean"]])[[3]][p])
# temp$Plot <- as.factor(substr(temp$PlotID, 3,3))
temp$Year <- as.numeric(dimnames(bm.site.spp[["mean"]])[[1]])
temp$BM.SD <- stack(data.frame(bm.site.spp[["sd"]][,,p]))[,1]
temp$BM.CI.lo <- stack(data.frame(bm.site.spp[["ci.lo.mean"]][,,p]))[,1]
temp$BM.CI.hi <- stack(data.frame(bm.site.spp[["ci.hi.mean"]][,,p]))[,1]
temp$Site <- unique(plot.data[substr(plot.data$PlotID,1,2)==sites[p],"Site..Tower."])
temp$latitude <- mean(plot.data[substr(plot.data$PlotID,1,2)==sites[p],"latitude"], na.rm=T)
temp$longitude <- mean(plot.data[substr(plot.data$PlotID,1,2)==sites[p],"longitude"], na.rm=T)
temp$elevation <- mean(plot.data[substr(plot.data$PlotID,1,2)==sites[p],"elevation"], na.rm=T)
temp$n.plots <- length(plot.data[substr(plot.data$PlotID,1,2)==sites[p],"PlotID"])
if(p == 1) bm.site.spp2 <- temp else bm.site.spp2 <- rbind(bm.site.spp2, temp)
}
summary(bm.site.spp2)
pdf("figures/Biomass_Site_Species_kgm-2_SE.pdf") # NOTE: This will probably stop worrking well very quickly
ggplot(bm.site.spp2) + facet_wrap(~Site, scales="free") +
# geom_ribbon(aes(x=Year, ymin=BM.CI.lo, ymax=BM.CI.hi, fill=Species), alpha=0.5) +
geom_ribbon(aes(x=Year, ymin=BM.Mean - BM.SD/sqrt(n.plots), ymax=BM.Mean + BM.SD/sqrt(n.plots), fill=Species), alpha=0.5) +
geom_line(aes(x=Year, y=BM.Mean, color=Species)) +
labs(x="Year", y="Biomass (kg m-2)") +
theme_bw()
dev.off()
pdf("figures/Biomass_Site_Species_kgm-2_Allom_CI.pdf") # NOTE: This will probably stop worrking well very quickly
ggplot(bm.site.spp2) + facet_wrap(~Site, scales="free") +
geom_ribbon(aes(x=Year, ymin=BM.CI.lo, ymax=BM.CI.hi, fill=Species), alpha=0.5) +
# geom_ribbon(aes(x=Year, ymin=BM.Mean - BM.SD/sqrt(n.plots), ymax=BM.Mean + BM.SD/sqrt(n.plots), fill=Species), alpha=0.5) +
geom_line(aes(x=Year, y=BM.Mean, color=Species)) +
labs(x="Year", y="Biomass (kg m-2)") +
theme_bw()
dev.off()
write.csv(bm.site.spp2, "processed_data/Biomass_Site_Species_kgm-2.csv", row.names=F)
# ---------------------------------
# Aggregating to the TOTAL SITE-level biomass
# ---------------------------------
bm.site.spp2$n.species <- ifelse(bm.site.spp2$BM.Mean>0,1,0)
summary(bm.site.spp2)
# # If not already done above to do the species-level BM, do this here
# # Need to make NA 0 because that species doesn't exist in the record
# bm.spp$mean[is.na(bm.spp$mean)] <- 0
# bm.spp$ci.lo[is.na(bm.spp$ci.lo)] <- 0
# bm.spp$ci.hi[is.na(bm.spp$ci.hi)] <- 0
# # Altering our 0s to be NA for years more recent than the year the plot was sampled
# plots <- paste(unique(tree.data[tree.data$TreeID %in% names(g.filled.diam),"PlotID"]))
# years <- as.numeric(row.names(g.filled.diam))
# for(p in 1:length(plots)){ # will go by plot
# yr.sample <- plot.data[plot.data$PlotID==plots[p], "Year.Sample"]
# if(max(years) > yr.sample){ # if tree wasn't cored in the last year of sampling, make it's outer years NA
# rows.na <- which(years > yr.sample-1)
# bm.spp$mean[rows.na,,p] <- NA
# bm.spp$ci.lo[rows.na,,p] <- NA
# bm.spp$ci.hi[rows.na,,p] <- NA
# }
# }
# summary(bm.spp$mean[,,6])
sites <- unique(substr(bm.plot2$PlotID, 1, 2))
bm.site <- array(dim=c(nrow(g.filled.diam), length(sites), 4)) # years x plots x (mean, sd, ci.low, ci.hi)
dimnames(bm.site)[[1]] <- row.names(g.filled.diam);
dimnames(bm.site)[[2]] <- sites;
dimnames(bm.site)[[3]] <- c("mean", "sd", "ci.lo", "ci.hi")
for(p in 1:length(sites)){ # will go by plot
cols <- which(substr(dimnames(bm.plot)[[2]],1,2) == sites[p])
bm.site[,p,1] <- apply(bm.plot[,cols,1], 1, mean)
bm.site[,p,2] <- apply(bm.plot[,cols,1], 1, sd)
bm.site[,p,3] <- apply(bm.plot[,cols,2], 1, mean)
bm.site[,p,4] <- apply(bm.plot[,cols,3], 1, mean)
}
summary(bm.site[,,2])
# Stacking it to be a more user-friendly data frame
bm.site2 <- stack(data.frame(bm.site[,,1]))[,c(2,1)]
names(bm.site2) <- c("SiteID", "BM.Mean")
bm.site2$Year <- as.numeric(dimnames(bm.site)[[1]])
bm.site2$BM.SD <- stack(data.frame(bm.site[,,2]))[,1]
bm.site2$BM.CI.lo <- stack(data.frame(bm.site[,,3]))[,1]
bm.site2$BM.CI.hi <- stack(data.frame(bm.site[,,4]))[,1]
summary(bm.site2)
# Getting some other site-level characteristics to tag along
bm.site3 <- aggregate(bm.site.spp2[,"n.species"], by=list(bm.site.spp2$SiteID, bm.site.spp2$Site, bm.site.spp2$Year), FUN=sum)
names(bm.site3)[1:4] <- c("SiteID", "Site", "Year", "n.species" )
summary(bm.site3)
bm.site2 <- merge(bm.site2, bm.site3)
summary(bm.site2)
bm.site4 <- aggregate(bm.site.spp2[,c("latitude", "longitude", "elevation", "n.plots")], by=list(bm.site.spp2$SiteID, bm.site.spp2$Site, bm.site.spp2$Year), FUN=mean)
names(bm.site4)[1:3] <- c("SiteID", "Site", "Year")
summary(bm.site4)
bm.site2 <- merge(bm.site2, bm.site4, all.x=T, all.y=T)
summary(bm.site2)
pdf("figures/Biomass_Site_Total_kgm-2_SD.pdf") # NOTE: This will probably stop worrking well very quickly
ggplot(bm.site2) +
# geom_ribbon(aes(x=Year, ymin=BM.CI.lo, ymax=BM.CI.hi, fill=Site), alpha=0.5) +
geom_ribbon(aes(x=Year, ymin=BM.Mean - BM.SD, ymax=BM.Mean + BM.SD, fill=Site), alpha=0.5) +
geom_line(aes(x=Year, y=BM.Mean, color=Site), size=1.5) +
labs(x="Year", y="Biomass (kg m-2)") +
theme_bw()
dev.off()
write.csv(bm.site2, "processed_data/Biomass_Site_Total_kgm-2.csv", row.names=F)
# ---------------------------------