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ForageFigures_CODE.R
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ForageFigures_CODE.R
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#### Load Libraries ####
library(ggplot2)
library(ggrepel)
library(colorspace)
library(colortools)
library(RColorBrewer)
library(egg)
library(patchwork)
library(tidyverse)
#### Load Plot Data ####
files <- list.files(pattern = "*.RData")
lapply(files, load, .GlobalEnv)
#### Figure 2: Shadeplot ####
Fig2 <- forage_shade
Fig2
ggsave("Fig2.pdf", Fig2, width = 4.5, height = 9)
#### Figure S1: Abundance and Frequency of Occurrence ####
FigS1a <- rankabun_plots$AB +
labs(x = NULL,
title = "Apalachicola Bay")
FigS1b <- rankfreq_plots$AB +
labs(x = NULL)
FigS1c <- rankabun_plots$CK +
labs(x = NULL,
title = "Cedar Key")
FigS1d <- rankfreq_plots$CK +
labs(x = NULL)
FigS1e <- rankabun_plots$TB +
labs(x = NULL,
title = "Tampa Bay")
FigS1f <- rankfreq_plots$TB +
labs(x = NULL)
FigS1g <- rankabun_plots$CH +
labs(title = "Charlotte Harbor")
FigS1h <- rankfreq_plots$CH
FigS1 <- ((FigS1a + FigS1b) +
plot_layout(ncol = 2))/
((FigS1c + FigS1d) +
plot_layout(ncol = 2))/
(FigS1e + FigS1f +
plot_layout(ncol = 2))/
(FigS1g + FigS1h +
plot_layout(ncol = 2))/
(guide_area()) +
plot_layout(nrow = 5) +
plot_layout(guides = 'collect',
heights = c(1,1,1,1,.01)) +
plot_annotation(tag_levels = "a")
FigS1 <- FigS1 & theme(plot.title.position = "plot",
plot.title = element_text(vjust = 4, face = "bold", size = 16),
plot.tag.position = c(0.1, .95),
legend.position = "bottom",
legend.key.height = unit(0,"inches"),
legend.title = element_blank(),
axis.title.y = element_text(hjust = 0.5, vjust = 0),
plot.margin = unit(c(.1,.1,.1,.1), "inches"))
FigS1
ggsave("FigS1.pdf", FigS1, height = 8, width = 15)
#### Figure S2: Richness and Evenness ####
FigS2a <- RichBay_plot +
theme(axis.text.x = element_blank()) +
labs(title = "a")
FigS2b <- RichSeason_plot +
theme(axis.text = element_blank(),
axis.title = element_blank()) +
labs(title = "b")
FigS2c <- EvenBay_plot +
labs(title = "c")
FigS2d <- EvenSeason_plot + theme(axis.text.y = element_blank(), axis.title = element_blank()) + labs(title = "d")
FigS2 <- (FigS2a | FigS2b)/
(FigS2c | FigS2d)
FigS2
ggsave("FigS2.pdf", FigS2, height = 6, width = 6)
#### Figure 3: CAP Plots by Bay and Season ####
# Initialize for loop for Season CAP plots by bay
CAP_plots <- list(Bay = list(), Season = list())
spp_vecs <- list(Bay = list(), Season = list())
cbPalette1 <- brewer.pal(8, "Paired")[c(2, 3, 6, 7)]
cbPalette2 <- brewer.pal(4, "Dark2")
cap_pctvars <- function(cap_out){
# A function that takes the output of the CAPdiscrim function and
# produces the percent of among-group variability explained by each
# canonical axis.
#
# Inputs:
# cap_out = 'CAPDiscrim' function list output
#
# Output:
# pct_var = A numerical array with dimensions 1 x p, where p is the
# number of canonical axes, corresponding to the percent
# of among-group variability explained by each axis.
#
# Extract eigenvalues from manova sublist
eig <- cap_out[["manova"]][["Eigenvalues"]]
# Convert eigenvalues to analog of canonical variability
vars <- eig*cap_out$tot*(cap_out$varm/100)/(eig+1)
# Calculate percent of variability explained by each axis
pct_var <- t(100*vars/sum(vars))
# Trim rows not corresponding to canonical axes
pct_var <- pct_var[1:ncol(cap_out$x),]
return(pct_var)
}
for (i in c("AP", "CK", "TB", "CH")) {
# Parse variation
var_expl <- round(100 * sum(CAP$Season$mods[[i]][["manova"]][["SS"]][["y[, group]"]])/CAP$Season$mods[[i]][["tot"]], 2)
var_CA <- round(cap_pctvars(CAP$Season$mods[[i]]), 2)
# Parse data
site_scores <- data.frame(CAP$Season$mods[[i]]$x)
site_scores$Season <- factor(CAP$Season$mods[[i]]$group, levels = c("1", "2", "3", "4"))
spp_vecs$Season[[i]] <- data.frame(CAP$Season$mods[[i]]$cproj, labs = rownames(CAP$Season$mods[[i]]$cproj))
mult <- 12
# Plot data
CAP_plots$Season[[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$Season[[i]]$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 = 1.5,
stroke = 0.001,
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$Season[[i]],
aes(x = 0,
xend = mult*LD1,
y = 0,
yend = mult*LD2),
arrow = arrow(length = unit(0.05,"inches")),
color = "grey1",
size = 0.5,
alpha = 0.6) +
geom_label_repel(data = spp_vecs$Season[[i]],
aes(x = mult*LD1 + 0.2*cos(atan(LD2/LD1))*sign(LD1),
y = mult*LD2 + 0.2*sin(atan(LD2/LD1))*sign(LD2),
label = labs),
segment.alpha = 0,
size = 2.5,
color = "blue",
fontface = "bold",
fill = "white",
alpha = 0.7,
box.padding = .25,
lineheight = 0.4,
label.size = 0.25,
nudge_x = ifelse(spp_vecs$Season[[i]]$LD1>0,0.05,-0.05),
nudge_y = ifelse(spp_vecs$Season[[i]]$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))
}
# Initialize for loop for Bay CAP plots by season
for (i in c("1", "2", "3", "4")) {
# Parse variation
var_expl <- round(100 * sum(CAP$Bay$mods[[i]][["manova"]][["SS"]][["y[, group]"]])/CAP$Bay$mods[[i]][["tot"]], 2)
var_CA <- round(cap_pctvars(CAP$Bay$mods[[i]]), 2)
# Parse data
site_scores <- data.frame(CAP$Bay$mods[[i]]$x)
site_scores$Bay <- factor(CAP$Bay$mods[[i]]$group, levels = c("AP", "CK", "TB", "CH"))
spp_vecs$Bay[[i]] <- data.frame(CAP$Bay$mods[[i]]$cproj, labs = rownames(CAP$Bay$mods[[i]]$cproj))
mult <- 12
# Plot data
CAP_plots$Bay[[i]] <- ggplot(site_scores) +
geom_vline(xintercept = 0,
colour = "grey70",
size = .25) +
geom_hline(yintercept = 0,
colour = "grey70",
size = .25) +
scale_x_continuous(limits = symmetric_range((1+mult)*spp_vecs$Bay[[i]]$LD1),
breaks = c(-6,-3,0,3,6),
minor_breaks = NULL) +
scale_y_continuous(limits = symmetric_range,
breaks = c(-6,-3,0,3,6),
minor_breaks = NULL) +
geom_point(aes(x = LD1,
y = LD2,
fill = Bay),
size = 1.5,
stroke = 0.001,
pch = 21,
colour = "black") +
labs(title = NULL,
x = paste("CA1 (",var_CA[1],"%)",sep=""),
y = paste("CA2 (",var_CA[2],"%)",sep=""),
fill = NULL) +
scale_fill_manual(values = cbPalette2,
labels = c("AB","CK","TB","CH")) +
theme_bw() +
theme(legend.text = element_text(size=rel(0.8)),
legend.position = c(0.055,0.89),
legend.background = element_blank(),
legend.key = element_blank(),
panel.grid = element_blank()) +
geom_segment(data = spp_vecs$Bay[[i]],
aes(x = 0,
xend = mult*LD1,
y = 0,
yend = mult*LD2),
arrow = arrow(length = unit(0.05,"inches")),
color = "grey1",
size = 0.5,
alpha = 0.6)+
geom_label_repel(data = spp_vecs$Bay[[i]],
aes(x = mult*LD1 + 0.2*cos(atan(LD2/LD1))*sign(LD1),
y = mult*LD2 + 0.2*sin(atan(LD2/LD1))*sign(LD2),
label = labs),
segment.alpha = 0,
size = 2.5,
color = "blue",
fontface = "bold",
fill = "white",
alpha = 0.7,
box.padding = .25,
lineheight = 0.4,
label.size = 0.25,
nudge_x = ifelse(spp_vecs$Bay[[i]]$LD1>0,0.05,-0.05),
nudge_y = ifelse(spp_vecs$Bay[[i]]$LD2>-0.02,0.05,-0.05),
force = 0.3) +
guides(fill = guide_legend(override.aes = list(size = 2.5),
keyheight = 0.15,
keywidth = 0.2))
}
Fig3a <- CAP_plots$Bay$`1` +
labs(title = "a) Winter (15.855)")
Fig3a
Fig3b <- CAP_plots$Bay$`2` +
labs(title = "b) Spring (6.56%)")
Fig3b
Fig3c <- CAP_plots$Bay$`3` +
labs(title = "c) Summer (2.05%)")
Fig3c
Fig3d <- CAP_plots$Bay$`4` +
labs(title = "d) Fall (10.63%)")
Fig3d
Fig3e <- CAP_plots$Season$AP +
labs(title = "e) Apalachicola Bay (6.23%)")
Fig3e
Fig3f <- CAP_plots$Season$CK +
labs(title = "f) Cedar Key (15.28%)")
Fig3f
Fig3g <- CAP_plots$Season$TB +
labs(title = "g) Tampa Bay (0.26%)")
Fig3g
Fig3h <- CAP_plots$Season$CH +
labs(title = "h) Charlotte Harbor (7.55%)")
Fig3h
Fig3 <- (Fig3a + Fig3b +
Fig3c + Fig3d +
Fig3e + Fig3f +
Fig3g + Fig3h) +
plot_layout(ncol = 2)
Fig3
ggsave("Fig3.pdf", Fig3, width = 8.5, height = 11)
#### Figure 4 and S2S1: RDA Plot ####
# Set scaling factors
mult_vars_pred <- 9
mult_scores_pred <- 1
mult_vars_hab <- 7
mult_scores_hab <- 2.5
Fig4 <- rdaAll_hab +
labs(title = "Total variability explained: 11.82%")
FigS2S1 <- rdaAll_pred +
labs(title = "Total variability explained: 3.64%")
ggsave("Fig4.pdf", Fig4, width = 8.5, height = 5.5)
ggsave("FigS2S1.pdf", FigS2S2, width = 8.5, height = 5.5)
#### Figure S3: Time Series ####
FigS3a <- rich_timePlot +
theme(axis.title.y = element_text(hjust = 1, vjust = 1))
FigS3b <- rich_spec_plot
FigS3c <- even_timePlot +
theme(axis.title.y = element_text(hjust = 1, vjust = 1))
FigS3d <- even_spec_plot
FigS3e <- pco_plots[[1]] +
theme(axis.title.y = element_text(hjust = 0, vjust = 1))
FigS3f <- spec_plots[[1]]
FigS3g <- pco_plots[[2]] +
theme(axis.title.y = element_text(hjust = 0, vjust = 1))
FigS3h <- spec_plots[[2]]
FigS3i <- pco_plots[[3]] +
theme(axis.title.y = element_text(hjust = 0, vjust = 1))
FigS3j <- spec_plots[[3]]
FigS3k <- pco_plots[[4]] +
theme(axis.title.y = element_text(hjust = 0, vjust = 1),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = rel(0.7)))
FigS3l <- spec_plots[[4]] +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = rel(0.7)))
FigS3 <-
(guide_area())/
(FigS3a + FigS3b +
FigS3c + FigS3d +
FigS3e + FigS3f +
FigS3g + FigS3h +
FigS3i + FigS3j +
FigS3k + FigS3l +
plot_layout(ncol = 2)) +
plot_layout(guides = 'collect',heights = c(.05,1)) +
plot_annotation(tag_levels = "a")
FigS3 <- FigS3 &
theme(plot.tag.position = c(0, 1.2),
legend.position = "top",
legend.key.height = unit(0,"inches"),
axis.title.y = element_text(hjust = 0.5,vjust = 0),
plot.margin = unit(c(.15,.1,.15,.1),"inches"))
FigS3
ggsave("FigS3.pdf", FigS3, height = 6, width = 8.5)