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CAP_CODE.R
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CAP_CODE.R
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# Load Required Packages
library(BiodiversityR)
library(readr)
library(labdsv)
library(ggplot2)
library(colortools)
library(ggrepel)
library(RColorBrewer)
library(egg)
# Parse Data
dat <- read_csv("Forage_Permanova.csv")
tax <- dat[, 9:58]
tax <- tax[, taxID]
dis <- vegdist(tax^0.25)
samp_info <- dat[, 7:8]
fact <- as.data.frame(dat[, 1:6])
# Clear original variable
rm(dat)
# Detrend for sample size
detrend_mod <- dbrda(dis ~ nseines + ntrawls, dat = samp_info, sqrt.dist = T)
detrend_scores <- detrend_mod$CA$u %*% diag(sqrt(detrend_mod$CA$eig))
# Convert factor columns to factors
fact$Bay <- as.factor(fact$Bay)
fact$Zone <- as.factor(fact$Zone)
fact$Year <- as.factor(fact$Year)
fact$Season <- as.factor(fact$Season)
# Calculate indvals and isolate top taxa
clust_all <- rep(1, nrow(tax))
indval_all <- indval(tax, clust_all)
avg_abund <- apply(tax, 2, mean)
rel_abund <- avg_abund/sum(avg_abund)
indvals_all <- data.frame(indval = indval_all$relfrq * rel_abund,
relfrq = indval_all$relfrq,
relabn = rel_abund)
indval_top <- indvals_all[order(indvals_all$indval, decreasing = TRUE), ]
indval_top15 <- indval_top[1:15, ]
load("TaxaID.RData")
colnames(tax) <- as.character(1:dim(tax)[2])
cbPalette1 <- brewer.pal(8, "Paired")[c(2, 3, 6, 7)]
cbPalette2 <- brewer.pal(4, "Dark2")
CAP <- list(Season = list(mods = list(), plots = list()), Bay = list(mods = list(), plots = list()))
# Initialize for loop for Season CAPs by bay
for (i in levels(fact$Bay)) {
print(i)
# Subset Data
dis_temp <- dist((detrend_scores[fact$Bay == i, ]))
fact_temp <- fact[fact$Bay == i, ]
tax_temp <- tax[fact$Bay == i, ]
tax_temp <- tax_temp[, colSums(tax_temp) > 0]
clust_temp <- rep(1, nrow(tax_temp))
indval_temp <- indval(tax_temp, clust_temp)
avg_abund_temp <- apply(tax_temp, 2, mean)
rel_abund_temp <- avg_abund_temp/sum(avg_abund_temp)
indvals_temp <- data.frame(indval = indval_temp$relfrq * rel_abund_temp,
relfrq = indval_temp$relfrq,
relabn = rel_abund_temp)
indval_temp_top <- indvals_temp[order(indvals_temp$indval, decreasing = TRUE), ]
indval_temp_top10 <- indval_temp_top[1:10, ]
tax_subset <- tax_temp[, rownames(indval_temp_top10)]
# Conduct analysis
CAP$Season$mods[[i]] <- CAPdiscrim(dis_temp ~ Season, data = fact_temp, permutations = 0, mmax = 500)
CAP$Season$mods[[i]] <- add.spec.scores(CAP$Season$mods[[i]], tax_subset)
}
# Initialize for loop for Bay CAPs by season
for (i in levels(fact$Season)) {
print(i)
# Subset Data
dis_temp <- dist((detrend_scores[fact$Season == i, ]))
fact_temp <- fact[fact$Season == i, ]
tax_temp <- tax[fact$Season == i, ]
tax_temp <- tax_temp[, colSums(tax_temp) > 0]
clust_temp <- rep(1, nrow(tax_temp))
indval_temp <- indval(tax_temp, clust_temp)
avg_abund_temp <- apply(tax_temp, 2, mean)
rel_abund_temp <- avg_abund_temp/sum(avg_abund_temp)
indvals_temp <- data.frame(indval = indval_temp$relfrq * rel_abund_temp,
relfrq = indval_temp$relfrq,
relabn = rel_abund_temp)
indval_temp_top <- indvals_temp[order(indvals_temp$indval, decreasing = TRUE), ]
indval_temp_top10 <- indval_temp_top[1:10, ]
tax_subset <- tax_temp[, rownames(indval_temp_top10)]
# Conduct analysis
CAP$Bay$mods[[i]] <- CAPdiscrim(dis_temp ~ Bay, data = fact_temp, permutations = 0, mmax = 500)
CAP$Bay$mods[[i]] <- add.spec.scores(CAP$Bay$mods[[i]], tax_subset)
}
# Initialize for loop for Season CAP plots by bay
for (i in levels(fact$Bay)) {
# Parse variation
var_expl <- round(100 * sum(CAP$Season$mods[[i]][["manova"]][["SS"]][["y[, group]"]])/CAP$Season$mods[[i]][["tot"]], 2)
var_CA <- round(100 * CAP$Season$mods[[i]][["lda.other"]][["svd"]]^2/sum(CAP$Season$mods[[i]][["lda.other"]][["svd"]]^2), 2)
# Parse data
site_scores <- data.frame(CAP$Season$mods[[i]]$x)
site_scores$Season <- factor(CAP$Season$mods[[i]]$group, levels = c("1", "2", "3", "4"))
spp_vecs <- data.frame(CAP$Season$mods[[i]]$cproj)
mult <- 12
# Plot data
CAP$Season$plots[[i]] <- ggplot(site_scores) +
geom_vline(xintercept = 0,
colour = "grey70",
size = .25) +
geom_hline(yintercept = 0,
colour = "grey70",
size = .25) +
scale_x_continuous(limits = symmetric_range((3+mult)*spp_vecs$LD1),
breaks = c(-6,-3,0,3,6),
minor_breaks = NULL) +
scale_y_continuous(limits = symmetric_range,
breaks = c(-6,-3,0,3,6),
minor_breaks = NULL) +
geom_point(aes(x = LD1,
y = LD2,
fill = Season),
size = 1,
stroke = 0.1,
pch = 21,
colour = "black") +
labs(title = NULL,
x = paste("CA1 (",var_CA[1],"%)",sep=""),
y = paste("CA2 (",var_CA[2],"%)",sep=""),
fill = NULL) +
scale_fill_manual(values = cbPalette1,
labels = c("Wi","Sp","Su","Fa")) +
theme_bw() +
theme(legend.text = element_text(size=rel(0.8)),
legend.position = c(0.051,0.89),
legend.background = element_blank(),
legend.key = element_blank(),
panel.grid = element_blank()) +
geom_segment(data = spp_vecs,
aes(x = 0,
xend = mult*LD1,
y = 0,
yend = mult*LD2),
arrow = arrow(length = unit(0.1,"inches")),
color = "grey1",
size = .75,
alpha = 0.6) +
geom_label_repel(data = spp_vecs,
aes(x = mult*LD1 + 0.2*cos(atan(LD2/LD1))*sign(LD1),
y = mult*LD2 + 0.2*sin(atan(LD2/LD1))*sign(LD2),
label = rownames(spp_vecs)),
segment.alpha = 0,
size = 3.25,
color = "blue",
fontface = "bold",
fill = "white",
alpha = 0.7,
box.padding = .25,
lineheight = 0.4,
label.size = 0.25,
nudge_x = ifelse(spp_vecs$LD1>0,0.05,-0.05),
nudge_y = ifelse(spp_vecs$LD2>-0.02,0.05,-0.05),
force = 0.3) +
guides(fill = guide_legend(override.aes = list(size = 2.5),
keyheight = 0.15,
keywidth = 0.2))
ggsave(paste("CAP_Season_", i, ".tiff", sep = ""), CAP$Season$plots[[i]], width = 4, height = 2.5, dpi = 600)
}
# Initialize for loop for Bay CAP plots by season
for (i in levels(fact$Season)) {
# Parse variation
var_expl <- round(100 * sum(CAP$Bay$mods[[i]][["manova"]][["SS"]][["y[, group]"]])/CAP$Bay$mods[[i]][["tot"]], 2)
var_CA <- round(100 * CAP$Bay$mods[[i]][["lda.other"]][["svd"]]^2/sum(CAP$Bay$mods[[i]][["lda.other"]][["svd"]]^2), 2)
# Parse data
site_scores <- data.frame(CAP$Bay$mods[[i]]$x)
site_scores$Bay <- factor(CAP$Bay$mods[[i]]$group, levels = c("AP", "CK", "TB", "CH"))
spp_vecs <- data.frame(CAP$Bay$mods[[i]]$cproj)
mult <- 12
# Plot data
CAP$Bay$plots[[i]] <- ggplot(site_scores) +
geom_vline(xintercept = 0,
colour = "grey70",
size = .25) +
geom_hline(yintercept = 0,
colour = "grey70",
size = .25) +
scale_x_continuous(limits = symmetric_range((1+mult)*spp_vecs$LD1),
breaks = c(-6,-3,0,3,6),
minor_breaks = NULL) +
scale_y_continuous(limits = symmetric_range,
breaks = c(-6,-3,0,3,6),
minor_breaks = NULL) +
geom_point(aes(x = LD1,
y = LD2,
fill = Bay),
size = 1,
stroke = 0.1,
pch = 21,
colour = "black") +
labs(title = NULL,
x = paste("CA1 (",var_CA[1],"%)",sep=""),
y = paste("CA2 (",var_CA[2],"%)",sep=""),
fill = NULL) +
scale_fill_manual(values = cbPalette2,
labels = c("AB","CK","TB","CH")) +
theme_bw() +
theme(legend.text = element_text(size=rel(0.8)),
legend.position = c(0.055,0.89),
legend.background = element_blank(),
legend.key = element_blank(),
panel.grid = element_blank()) +
geom_segment(data = spp_vecs,
aes(x = 0,
xend = mult*LD1,
y = 0,
yend = mult*LD2),
arrow = arrow(length = unit(0.1,"inches")),
color = "grey1",
size = .75,
alpha = 0.6)+
geom_label_repel(data = spp_vecs,
aes(x = mult*LD1 + 0.2*cos(atan(LD2/LD1))*sign(LD1),
y = mult*LD2 + 0.2*sin(atan(LD2/LD1))*sign(LD2),
label = rownames(spp_vecs)),
segment.alpha = 0,
size = 3.25,
color = "blue",
fontface = "bold",
fill = "white",
alpha = 0.7,
box.padding = .25,
lineheight = 0.4,
label.size = 0.25,
nudge_x = ifelse(spp_vecs$LD1>0,0.05,-0.05),
nudge_y = ifelse(spp_vecs$LD2>-0.02,0.05,-0.05),
force = 0.3) +
guides(fill = guide_legend(override.aes = list(size = 2.5),
keyheight = 0.15,
keywidth = 0.2))
ggsave(paste("CAP_Bay_", c("Wi", "Sp", "Su", "Fa")[as.numeric(i)], ".tiff", sep = ""), CAP$Bay$plots[[i]], width = 4, height = 2.5, dpi = 600)
}
# Save plot data
save(CAP, file = "CAP_Plots.RData")