inst/graminoidRemovals/Plots.R
In richardjtelford/seedclim-analysis: Code for Cleaning SeedClim data
source("~/OneDrive - University of Bergen/Research/FunCaB/seedclimComm/inst/graminoidRemovals/plotting_dim.R")
##### FUNCTIONS FOR PLOTTING #####
# per response variable
time.plots.temp <- function(dat, response, save, ylab) {
p <- ggplot(dat, aes_string(x = "Year", y = response, colour = "interaction(Precipitation_level, TTtreat)", alpha = "interaction(Precipitation_level, TTtreat)", shape = "interaction(Precipitation_level, TTtreat)", linetype = "interaction(Precipitation_level, TTtreat)", group = "interaction(Precipitation_level, TTtreat)")) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line") +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_grid(as.formula(paste(".~ ", "Temperature_level"))) +
scale_alpha_manual(legend.title.prec, values = c(0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1)) +
scale_color_manual(legend.title.prec, values = cbPalette[c(1, 1, 1, 1, 7, 2, 4, 3)]) +
scale_linetype_manual(legend.title.prec, values = c("dashed", "dashed", "dashed", "dashed", "solid", "solid", "solid", "solid")) +
scale_shape_manual(legend.title.prec, values = c(0, 1, 2, 8, 15, 16, 17, 8)) +
theme_classic() +
theme(strip.background = element_blank()) +
labs(y = if_else(ylab == TRUE,
paste("Δ", substr(response, 6, nchar(response))),
""), colour = "") +
#theme(legend.text = c("Control", "Treatment")) +
axis.dim
if(save == TRUE){
ggsave(p, filename = paste0(response, "temp.jpg", sep = ""), height = 4, width = 8, dpi = 300, path = "/Users/fja062/Documents/seedclimComm/figures")
}# defaults to save == FALSE, so you don't have to write that in every time
return(p)
}
time.plots.precip <- function(dat, response, save, ylab) {
p <- ggplot(dat, aes_string(x = "Year", y = response, colour = "interaction(Temperature_level, TTtreat)", alpha = "interaction(Temperature_level, TTtreat)", shape = "interaction(Temperature_level, TTtreat)", linetype = "interaction(Temperature_level, TTtreat)", group = "interaction(Temperature_level, TTtreat)")) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line") +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_grid(as.formula(paste(".~ ", "Precipitation_level"))) +
scale_alpha_manual(legend.title.temp, values = c(0.5, 0.5, 0.5, 1, 1, 1)) +
scale_color_manual(legend.title.temp, values = cbPalette[c(1, 1, 1, 9, 8, 7)]) +
scale_linetype_manual(legend.title.temp, values = c("dashed", "dashed", "dashed", "solid", "solid", "solid")) +
scale_shape_manual(legend.title.temp, values = c(0, 1, 2, 15, 16, 17)) +
theme_classic() +
theme(strip.background = element_blank()) +
labs(y = if_else(ylab == TRUE,
paste("Δ", substr(response, 6, nchar(response))),
""), colour = "") +
#theme(legend.text = c("Control", "Treatment")) +
axis.dim
if(save == TRUE){
ggsave(p, filename = paste0(response, "precip.jpg", sep = ""), height = 4, width = 8, dpi = 300, path = "/Users/fja062/Documents/seedclimComm/figures")
}# defaults to save == FALSE, so you don't have to write that in every time
return(p)
}
timedelta %>% filter(TTtreat == "RTC") %>% ggplot(aes(x = Year, y = deltasumcover, colour = as.factor(Precipitation_level), group = as.factor(Temperature_level))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), aes(group = Precipitation_level)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", aes(group = Precipitation_level)) +
facet_grid(. ~ Temperature_level) +
scale_colour_manual(values = cbPalette[c(10, 4, 2, 7)]) +
geom_hline(yintercept = 0, linetype = "dashed")
####### plot for temperature and precipitation interaction
timedelta %>% filter(deltasumcover > -75) %>%
ggplot(aes(x = Year, y = deltasumcover, colour = as.factor(Precipitation_level), alpha = TTtreat, linetype = TTtreat, group = interaction(Precipitation_level, TTtreat))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line") +
facet_grid(. ~ Temperature_level) +
scale_alpha_manual(values = c(0.5, 1)) +
scale_colour_manual(values = cbPalette[c(7, 2, 4, 10)]) +
scale_linetype_manual(values = c("dashed", "solid")) +
geom_hline(yintercept = 0, linetype = "dashed")
time.plots.facet <- function(dat, response) {
p <- ggplot(dat, aes_string(x = "as.numeric(Year)", y = response, colour = "TTtreat")) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line") +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_grid(as.formula(paste("Temperature_level ~ Precipitation_level"))) +
scale_color_manual(values = cbPalette) +
theme(legend.text = c("Control", "Treatment")) +
axis.dim
return(p)
}
density.plots <- function(dat, response, explan) {
p <- ggplot(dat, aes_string(x = response, fill = "funYear")) +
theme_bw() +
scale_color_manual(values = cbPalette) +
geom_density(alpha = 0.5) +
#geom_smooth(method = lm) +
#geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
facet_grid(as.formula(. ~ explan)) +
axis.dim
return(p)
}
#### COVER ####
# if we dig deeper, and look at the cover of the functional groups, things start to look interesting.
supfig1 <- wholecom %>%
filter(Year == 2011) %>%
ggplot(aes(x = factor(tempLevel), y = sumcover, fill = functionalGroup)) +
geom_boxplot() +
scale_fill_manual("",values = c("grey60", "grey90")) +
facet_wrap(~precipLevel) +
axis.dimLarge +
labs(x = "Mean summer temperature (°C)", y = "Total vegetation cover")
ggsave(supfig1, file = "~/OneDrive - University of Bergen/Research/FunCaB/figures/supfig1.jpg")
###################
deltaheight <- ggplot(timedelta, aes(x = Year, y = deltawmeanheight_local, colour = interaction(Precipitation_level, TTtreat), alpha = interaction(Precipitation_level, TTtreat), shape = interaction(Precipitation_level, TTtreat), linetype = interaction(Precipitation_level, TTtreat), group = interaction(Precipitation_level, TTtreat))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line") +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_alpha_manual(legend.title.prec, values = c(0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1)) +
scale_color_manual(legend.title.prec, values = cbPalette[c(1, 1, 1, 1, 7, 2, 4, 3)]) +
scale_linetype_manual(legend.title.prec, values = c("dashed", "dashed", "dashed", "dashed", "solid", "solid", "solid", "solid")) +
scale_shape_manual(legend.title.prec, values = c(0, 1, 2, 8, 15, 16, 17, 8)) +
theme_classic() +
axis.dim +
facet_wrap(~ Temperature_level) +
theme(legend.position = "none",
strip.background = element_blank())
timedelta %>%
mutate(temp = if_else(grepl("6.5", temp), "alpine", if_else(grepl("8.5", temp), "sub-alpine", "boreal"))) %>%
mutate(temp = factor(temp, levels = c("alpine", "sub-alpine", "boreal"))) %>%
ggplot(aes(x = Year, y = deltawmeanCN_local, shape = TTtreat, alpha = TTtreat, group = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), size = 0.75, colour = "#02401B") +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 0.75, colour = "#02401B") +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_wrap(~ temp) +
scale_alpha_manual(legend.title.climate, values = c(0.5,1), labels = c("Control", "Removal")) +
scale_shape_manual(legend.title.climate, values = c(1, 16), labels = c("Control", "Removal")) +
theme_classic() +
labs(y = paste("Δ forb community evenness")) +
theme(legend.direction = "horizontal",
legend.justification=c(-0.1, 1.5),
legend.position=c(0, 1),
strip.background = element_blank(),
axis.text.x = element_text(angle = 90)) +
axis.dimLarge
ggsave(filename = paste0("fig2A_coverSLA_v2.jpg"), width = 11, height = 4.5, dpi = 300, path = "/Users/fja062/Documents/seedclimComm/figures")
cover_temp.orig <- timedelta %>%
mutate(temp = if_else(grepl("6.5", tempLevel), "alpine", if_else(grepl("8.5", tempLevel), "sub-alpine", "boreal"))) %>%
mutate(temp = factor(temp, levels = c("alpine", "sub-alpine", "boreal"))) %>%
ggplot(aes(x = Year, y = deltasumcover, colour = TTtreat, shape = TTtreat, group = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), size = 0.75) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 0.75) +
scale_colour_manual(legend.title.climate, values = c("Black", "grey80"), labels = c("Removal", "Untreated")) +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_wrap(~ temp) +
scale_shape_manual(legend.title.climate, values = c(16, 1), labels = c( "Removal", "Untreated")) +
theme_classic() +
labs(y = paste("Δ forb cover (%)")) +
theme(legend.direction = "horizontal",
legend.justification=c(-0.1, 1.5),
legend.position=c(0, 1),
strip.background = element_blank(),
axis.text.x = element_text(angle = 90)) +
axis.dimLarge
sla_temp.orig <- timedelta %>%
mutate(temp = if_else(grepl("6.5", tempLevel), "alpine", if_else(grepl("8.5", tempLevel), "sub-alpine", "boreal"))) %>%
mutate(temp = factor(temp, levels = c("alpine", "sub-alpine", "boreal"))) %>%
ggplot(aes(x = Year, y = deltadiversity, shape = TTtreat, colour = TTtreat, group = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), size = 0.75) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 0.75) +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_grid(. ~ temp) +
scale_colour_manual(legend.title.climate, values = c("Black", "grey80"), labels = c("Untreated", "Removal")) +
scale_shape_manual(legend.title.climate, values = c(16, 1), labels = c("Untreated", "Removal")) +
theme_classic() +
labs(y = paste("Δ diversity (Shannon)")) +
theme(legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 90)) +
axis.dimLarge
delta <- plot_grid(cover_temp.orig, sla_temp.orig, labels = c('A', 'B'), ncol = 2, align = 'h')
ggsave(delta, width = 11, height = 4.5, dpi = 300, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/fig2ab.jpg")
abund <- my.GR.data %>%
filter(functionalGroup == "forb", temp > 10, TTtreat == "RTC") %>%
group_by(siteID, Year, species) %>%
mutate(meanCov = mean(cover)) %>%
ungroup() %>%
group_by(precip, species) %>%
mutate(dominance = case_when(
precip == "2700" & mean(meanCov) > 16 ~ "dominant",
precip == "2000" & mean(meanCov) > 16 ~ "dominant",
precip == "1200" & mean(meanCov) > 16 ~ "dominant",
precip == "600" & mean(meanCov) > 16 ~ "dominant",
precip == "2700" & mean(meanCov) < 16 ~ "subordinate",
precip == "2000" & mean(meanCov) < 16 ~ "subordinate",
precip == "1200" & mean(meanCov) < 16 ~ "subordinate",
precip == "600" & mean(meanCov) < 16 ~ "subordinate"
)) %>%
#mutate(dominance = if_else(mean(meanCov, na.rm = TRUE) > 15, "dominant", "subordinate")) %>%
mutate(label = if_else(Year == 2016 & dominance == "dominant", as.character(species), NA_character_))
lm1 <- lm(meanCov ~ species + precip + 0, data = abund) %>%
tidy() %>%
mutate(term = gsub("species", "", term)) %>%
filter(!term == "precip")
summary(lm1)
abund %>%
left_join(lm1, by = c("species" = "term")) %>%
ggplot(aes(x = Year, y = meanCov, colour = dominance, group = species)) +
#stat_summary(fun.data = "mean_cl_boot", geom = "line") +
geom_line() +
geom_label(aes(label = label),
nudge_x = -0.6,
nudge_y = -2.45,
na.rm = TRUE) +
scale_colour_manual(values = c("Black", "grey80")) +
facet_grid(. ~ precip) +
geom_point(size = 3) +
labs(y = "Mean cover (%)") +
axis.dimLarge +
theme(legend.position = "none",
axis.title.x = element_blank())
ggsave(filename = paste0("fig4.jpg"), width = 11, height = 4.5, dpi = 300, path = "~/OneDrive - University of Bergen/Research/FunCaB/figures")
my.GR.data %>%
mutate(mossHeight = if_else(Year == 2016, mossHeight/10, mossHeight)) %>%
ggplot(aes(x = Year, y = mossHeight, colour = TTtreat, shape = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot") +
stat_summary(fun.data = "mean_cl_boot", geom = "line") +
scale_colour_manual(legend.title.weat, values = cbPalette, labels = c("Control", "Removal")) +
scale_shape_manual(legend.title.weat, values = c(1, 16), labels = c("Control", "Removal")) +
theme_classic() +
facet_grid(.~precip) +
axis.dimLarge +
labs(x = "", y = "Moss depth (cm)") +
theme(axis.text.x = element_text(angle = 90)) +
ggsave(filename = "moss_depth_precip.jpg", path = "/Users/fja062/Documents/seedclimComm/figures", height = 4, width = 7.5)
my.GR.data %>%
ggplot(aes(x = Year, y = totalBryophytes, colour = TTtreat, shape = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "line") +
facet_grid(. ~ precip) +
scale_colour_manual(legend.title.weat, values = cbPalette, labels = c("Control", "Removal")) +
scale_shape_manual(legend.title.weat, values = c(1, 16), labels = c("Control", "Removal")) +
axis.dimLarge +
theme_classic() +
theme(axis.text.x = element_text(angle = 90)) +
ggsave(filename = "moss_coverPRECIP.jpg", width = 7.5, height = 4, path = "/Users/fja062/Documents/seedclimComm/figures")
wholecom %>%
filter(Year == 2011) %>%
ggplot(aes(x = wmeanSLA_local, colour = as.factor(temp), fill = as.factor(temp), shape = as.factor(temp))) +
geom_density(alpha = 0.3) +
scale_colour_manual("Mean summer\ntemperature (°C)", values = cbPalette) +
scale_fill_manual("Mean summer\ntemperature (°C)", values = cbPalette) +
facet_grid(.~functionalGroup) +
theme_classic() +
axis.dimLarge +
labs(x = expression("Community weighted mean SLA "(cm^2/g)), y = "Frequency density")
ggsave(filename = paste0("figsupp1.jpg"), width = 11, height = 4.5, dpi = 300, path = "~/OneDrive - University of Bergen/Research/FunCaB/figures")
rtcLDMC <- forbcom %>%
mutate(nYear = as.numeric(as.character(Year))) %>%
filter(!is.na(wmeanLDMC_local))
modLDMC <- rtcLDMC %>%
lmer(wmeanLDMC_local ~ TTtreat*scale(summer_temp)*scale(annPrecip)*scale(nYear) - TTtreat*scale(summer_temp)*scale(annPrecip)*scale(nYear) + (1|siteID), REML = FALSE, data = .)
LDMCpred <- predict(modLDMC, newdata = LDMCnewDat)
rtcLDMC <- rtcLDMC %>%
mutate(LDMCpredL = (LDMCpred - sqrt(wmeanLDMC_local)*1.96),
LDMCpredH = (LDMCpred + sqrt(wmeanLDMC_local)*1.96)) %>%
group_by(Year, siteID, TTtreat) %>%
mutate(mLDMCpredL = mean(LDMCpredL),
mLDMCpredH = mean(LDMCpredH))
rtcLDMC %>%
#gather(deltawmeanCN_local,deltawmeanheight_local,deltawmeanLA_local,deltawmeanLDMC_local,deltawmeanLTH_local,deltawmeanSLA_local, key = "trait", value = "value") %>%
ggplot(aes(nYear, LDMCpred, colour = factor(temp), fill = factor(temp), group = factor(temp))) +
geom_point(aes(nYear, wmeanLDMC_local)) +
geom_line() +
#stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "line") +
geom_ribbon(aes(ymin = mLDMCpredL, ymax = mLDMCpredH), alpha=0.3) +
scale_color_viridis_d() +
scale_fill_viridis_d() +
#geom_hline(yintercept = 0, linetype = "dashed", colour = "grey60", size = 1) #+
facet_wrap(. ~ TTtreat, scales = "free_y")
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source("~/OneDrive - University of Bergen/Research/FunCaB/seedclimComm/inst/graminoidRemovals/plotting_dim.R")
##### FUNCTIONS FOR PLOTTING #####
# per response variable
time.plots.temp <- function(dat, response, save, ylab) {
p <- ggplot(dat, aes_string(x = "Year", y = response, colour = "interaction(Precipitation_level, TTtreat)", alpha = "interaction(Precipitation_level, TTtreat)", shape = "interaction(Precipitation_level, TTtreat)", linetype = "interaction(Precipitation_level, TTtreat)", group = "interaction(Precipitation_level, TTtreat)")) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line") +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_grid(as.formula(paste(".~ ", "Temperature_level"))) +
scale_alpha_manual(legend.title.prec, values = c(0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1)) +
scale_color_manual(legend.title.prec, values = cbPalette[c(1, 1, 1, 1, 7, 2, 4, 3)]) +
scale_linetype_manual(legend.title.prec, values = c("dashed", "dashed", "dashed", "dashed", "solid", "solid", "solid", "solid")) +
scale_shape_manual(legend.title.prec, values = c(0, 1, 2, 8, 15, 16, 17, 8)) +
theme_classic() +
theme(strip.background = element_blank()) +
labs(y = if_else(ylab == TRUE,
paste("Δ", substr(response, 6, nchar(response))),
""), colour = "") +
#theme(legend.text = c("Control", "Treatment")) +
axis.dim
if(save == TRUE){
ggsave(p, filename = paste0(response, "temp.jpg", sep = ""), height = 4, width = 8, dpi = 300, path = "/Users/fja062/Documents/seedclimComm/figures")
}# defaults to save == FALSE, so you don't have to write that in every time
return(p)
}
time.plots.precip <- function(dat, response, save, ylab) {
p <- ggplot(dat, aes_string(x = "Year", y = response, colour = "interaction(Temperature_level, TTtreat)", alpha = "interaction(Temperature_level, TTtreat)", shape = "interaction(Temperature_level, TTtreat)", linetype = "interaction(Temperature_level, TTtreat)", group = "interaction(Temperature_level, TTtreat)")) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line") +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_grid(as.formula(paste(".~ ", "Precipitation_level"))) +
scale_alpha_manual(legend.title.temp, values = c(0.5, 0.5, 0.5, 1, 1, 1)) +
scale_color_manual(legend.title.temp, values = cbPalette[c(1, 1, 1, 9, 8, 7)]) +
scale_linetype_manual(legend.title.temp, values = c("dashed", "dashed", "dashed", "solid", "solid", "solid")) +
scale_shape_manual(legend.title.temp, values = c(0, 1, 2, 15, 16, 17)) +
theme_classic() +
theme(strip.background = element_blank()) +
labs(y = if_else(ylab == TRUE,
paste("Δ", substr(response, 6, nchar(response))),
""), colour = "") +
#theme(legend.text = c("Control", "Treatment")) +
axis.dim
if(save == TRUE){
ggsave(p, filename = paste0(response, "precip.jpg", sep = ""), height = 4, width = 8, dpi = 300, path = "/Users/fja062/Documents/seedclimComm/figures")
}# defaults to save == FALSE, so you don't have to write that in every time
return(p)
}
timedelta %>% filter(TTtreat == "RTC") %>% ggplot(aes(x = Year, y = deltasumcover, colour = as.factor(Precipitation_level), group = as.factor(Temperature_level))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), aes(group = Precipitation_level)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", aes(group = Precipitation_level)) +
facet_grid(. ~ Temperature_level) +
scale_colour_manual(values = cbPalette[c(10, 4, 2, 7)]) +
geom_hline(yintercept = 0, linetype = "dashed")
####### plot for temperature and precipitation interaction
timedelta %>% filter(deltasumcover > -75) %>%
ggplot(aes(x = Year, y = deltasumcover, colour = as.factor(Precipitation_level), alpha = TTtreat, linetype = TTtreat, group = interaction(Precipitation_level, TTtreat))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line") +
facet_grid(. ~ Temperature_level) +
scale_alpha_manual(values = c(0.5, 1)) +
scale_colour_manual(values = cbPalette[c(7, 2, 4, 10)]) +
scale_linetype_manual(values = c("dashed", "solid")) +
geom_hline(yintercept = 0, linetype = "dashed")
time.plots.facet <- function(dat, response) {
p <- ggplot(dat, aes_string(x = "as.numeric(Year)", y = response, colour = "TTtreat")) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line") +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_grid(as.formula(paste("Temperature_level ~ Precipitation_level"))) +
scale_color_manual(values = cbPalette) +
theme(legend.text = c("Control", "Treatment")) +
axis.dim
return(p)
}
density.plots <- function(dat, response, explan) {
p <- ggplot(dat, aes_string(x = response, fill = "funYear")) +
theme_bw() +
scale_color_manual(values = cbPalette) +
geom_density(alpha = 0.5) +
#geom_smooth(method = lm) +
#geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
facet_grid(as.formula(. ~ explan)) +
axis.dim
return(p)
}
#### COVER ####
# if we dig deeper, and look at the cover of the functional groups, things start to look interesting.
supfig1 <- wholecom %>%
filter(Year == 2011) %>%
ggplot(aes(x = factor(tempLevel), y = sumcover, fill = functionalGroup)) +
geom_boxplot() +
scale_fill_manual("",values = c("grey60", "grey90")) +
facet_wrap(~precipLevel) +
axis.dimLarge +
labs(x = "Mean summer temperature (°C)", y = "Total vegetation cover")
ggsave(supfig1, file = "~/OneDrive - University of Bergen/Research/FunCaB/figures/supfig1.jpg")
###################
deltaheight <- ggplot(timedelta, aes(x = Year, y = deltawmeanheight_local, colour = interaction(Precipitation_level, TTtreat), alpha = interaction(Precipitation_level, TTtreat), shape = interaction(Precipitation_level, TTtreat), linetype = interaction(Precipitation_level, TTtreat), group = interaction(Precipitation_level, TTtreat))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line") +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_alpha_manual(legend.title.prec, values = c(0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1)) +
scale_color_manual(legend.title.prec, values = cbPalette[c(1, 1, 1, 1, 7, 2, 4, 3)]) +
scale_linetype_manual(legend.title.prec, values = c("dashed", "dashed", "dashed", "dashed", "solid", "solid", "solid", "solid")) +
scale_shape_manual(legend.title.prec, values = c(0, 1, 2, 8, 15, 16, 17, 8)) +
theme_classic() +
axis.dim +
facet_wrap(~ Temperature_level) +
theme(legend.position = "none",
strip.background = element_blank())
timedelta %>%
mutate(temp = if_else(grepl("6.5", temp), "alpine", if_else(grepl("8.5", temp), "sub-alpine", "boreal"))) %>%
mutate(temp = factor(temp, levels = c("alpine", "sub-alpine", "boreal"))) %>%
ggplot(aes(x = Year, y = deltawmeanCN_local, shape = TTtreat, alpha = TTtreat, group = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), size = 0.75, colour = "#02401B") +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 0.75, colour = "#02401B") +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_wrap(~ temp) +
scale_alpha_manual(legend.title.climate, values = c(0.5,1), labels = c("Control", "Removal")) +
scale_shape_manual(legend.title.climate, values = c(1, 16), labels = c("Control", "Removal")) +
theme_classic() +
labs(y = paste("Δ forb community evenness")) +
theme(legend.direction = "horizontal",
legend.justification=c(-0.1, 1.5),
legend.position=c(0, 1),
strip.background = element_blank(),
axis.text.x = element_text(angle = 90)) +
axis.dimLarge
ggsave(filename = paste0("fig2A_coverSLA_v2.jpg"), width = 11, height = 4.5, dpi = 300, path = "/Users/fja062/Documents/seedclimComm/figures")
cover_temp.orig <- timedelta %>%
mutate(temp = if_else(grepl("6.5", tempLevel), "alpine", if_else(grepl("8.5", tempLevel), "sub-alpine", "boreal"))) %>%
mutate(temp = factor(temp, levels = c("alpine", "sub-alpine", "boreal"))) %>%
ggplot(aes(x = Year, y = deltasumcover, colour = TTtreat, shape = TTtreat, group = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), size = 0.75) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 0.75) +
scale_colour_manual(legend.title.climate, values = c("Black", "grey80"), labels = c("Removal", "Untreated")) +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_wrap(~ temp) +
scale_shape_manual(legend.title.climate, values = c(16, 1), labels = c( "Removal", "Untreated")) +
theme_classic() +
labs(y = paste("Δ forb cover (%)")) +
theme(legend.direction = "horizontal",
legend.justification=c(-0.1, 1.5),
legend.position=c(0, 1),
strip.background = element_blank(),
axis.text.x = element_text(angle = 90)) +
axis.dimLarge
sla_temp.orig <- timedelta %>%
mutate(temp = if_else(grepl("6.5", tempLevel), "alpine", if_else(grepl("8.5", tempLevel), "sub-alpine", "boreal"))) %>%
mutate(temp = factor(temp, levels = c("alpine", "sub-alpine", "boreal"))) %>%
ggplot(aes(x = Year, y = deltadiversity, shape = TTtreat, colour = TTtreat, group = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), size = 0.75) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 0.75) +
geom_hline(yintercept = 0, linetype = "dashed") +
facet_grid(. ~ temp) +
scale_colour_manual(legend.title.climate, values = c("Black", "grey80"), labels = c("Untreated", "Removal")) +
scale_shape_manual(legend.title.climate, values = c(16, 1), labels = c("Untreated", "Removal")) +
theme_classic() +
labs(y = paste("Δ diversity (Shannon)")) +
theme(legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 90)) +
axis.dimLarge
delta <- plot_grid(cover_temp.orig, sla_temp.orig, labels = c('A', 'B'), ncol = 2, align = 'h')
ggsave(delta, width = 11, height = 4.5, dpi = 300, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/fig2ab.jpg")
abund <- my.GR.data %>%
filter(functionalGroup == "forb", temp > 10, TTtreat == "RTC") %>%
group_by(siteID, Year, species) %>%
mutate(meanCov = mean(cover)) %>%
ungroup() %>%
group_by(precip, species) %>%
mutate(dominance = case_when(
precip == "2700" & mean(meanCov) > 16 ~ "dominant",
precip == "2000" & mean(meanCov) > 16 ~ "dominant",
precip == "1200" & mean(meanCov) > 16 ~ "dominant",
precip == "600" & mean(meanCov) > 16 ~ "dominant",
precip == "2700" & mean(meanCov) < 16 ~ "subordinate",
precip == "2000" & mean(meanCov) < 16 ~ "subordinate",
precip == "1200" & mean(meanCov) < 16 ~ "subordinate",
precip == "600" & mean(meanCov) < 16 ~ "subordinate"
)) %>%
#mutate(dominance = if_else(mean(meanCov, na.rm = TRUE) > 15, "dominant", "subordinate")) %>%
mutate(label = if_else(Year == 2016 & dominance == "dominant", as.character(species), NA_character_))
lm1 <- lm(meanCov ~ species + precip + 0, data = abund) %>%
tidy() %>%
mutate(term = gsub("species", "", term)) %>%
filter(!term == "precip")
summary(lm1)
abund %>%
left_join(lm1, by = c("species" = "term")) %>%
ggplot(aes(x = Year, y = meanCov, colour = dominance, group = species)) +
#stat_summary(fun.data = "mean_cl_boot", geom = "line") +
geom_line() +
geom_label(aes(label = label),
nudge_x = -0.6,
nudge_y = -2.45,
na.rm = TRUE) +
scale_colour_manual(values = c("Black", "grey80")) +
facet_grid(. ~ precip) +
geom_point(size = 3) +
labs(y = "Mean cover (%)") +
axis.dimLarge +
theme(legend.position = "none",
axis.title.x = element_blank())
ggsave(filename = paste0("fig4.jpg"), width = 11, height = 4.5, dpi = 300, path = "~/OneDrive - University of Bergen/Research/FunCaB/figures")
my.GR.data %>%
mutate(mossHeight = if_else(Year == 2016, mossHeight/10, mossHeight)) %>%
ggplot(aes(x = Year, y = mossHeight, colour = TTtreat, shape = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot") +
stat_summary(fun.data = "mean_cl_boot", geom = "line") +
scale_colour_manual(legend.title.weat, values = cbPalette, labels = c("Control", "Removal")) +
scale_shape_manual(legend.title.weat, values = c(1, 16), labels = c("Control", "Removal")) +
theme_classic() +
facet_grid(.~precip) +
axis.dimLarge +
labs(x = "", y = "Moss depth (cm)") +
theme(axis.text.x = element_text(angle = 90)) +
ggsave(filename = "moss_depth_precip.jpg", path = "/Users/fja062/Documents/seedclimComm/figures", height = 4, width = 7.5)
my.GR.data %>%
ggplot(aes(x = Year, y = totalBryophytes, colour = TTtreat, shape = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "line") +
facet_grid(. ~ precip) +
scale_colour_manual(legend.title.weat, values = cbPalette, labels = c("Control", "Removal")) +
scale_shape_manual(legend.title.weat, values = c(1, 16), labels = c("Control", "Removal")) +
axis.dimLarge +
theme_classic() +
theme(axis.text.x = element_text(angle = 90)) +
ggsave(filename = "moss_coverPRECIP.jpg", width = 7.5, height = 4, path = "/Users/fja062/Documents/seedclimComm/figures")
wholecom %>%
filter(Year == 2011) %>%
ggplot(aes(x = wmeanSLA_local, colour = as.factor(temp), fill = as.factor(temp), shape = as.factor(temp))) +
geom_density(alpha = 0.3) +
scale_colour_manual("Mean summer\ntemperature (°C)", values = cbPalette) +
scale_fill_manual("Mean summer\ntemperature (°C)", values = cbPalette) +
facet_grid(.~functionalGroup) +
theme_classic() +
axis.dimLarge +
labs(x = expression("Community weighted mean SLA "(cm^2/g)), y = "Frequency density")
ggsave(filename = paste0("figsupp1.jpg"), width = 11, height = 4.5, dpi = 300, path = "~/OneDrive - University of Bergen/Research/FunCaB/figures")
rtcLDMC <- forbcom %>%
mutate(nYear = as.numeric(as.character(Year))) %>%
filter(!is.na(wmeanLDMC_local))
modLDMC <- rtcLDMC %>%
lmer(wmeanLDMC_local ~ TTtreat*scale(summer_temp)*scale(annPrecip)*scale(nYear) - TTtreat*scale(summer_temp)*scale(annPrecip)*scale(nYear) + (1|siteID), REML = FALSE, data = .)
LDMCpred <- predict(modLDMC, newdata = LDMCnewDat)
rtcLDMC <- rtcLDMC %>%
mutate(LDMCpredL = (LDMCpred - sqrt(wmeanLDMC_local)*1.96),
LDMCpredH = (LDMCpred + sqrt(wmeanLDMC_local)*1.96)) %>%
group_by(Year, siteID, TTtreat) %>%
mutate(mLDMCpredL = mean(LDMCpredL),
mLDMCpredH = mean(LDMCpredH))
rtcLDMC %>%
#gather(deltawmeanCN_local,deltawmeanheight_local,deltawmeanLA_local,deltawmeanLDMC_local,deltawmeanLTH_local,deltawmeanSLA_local, key = "trait", value = "value") %>%
ggplot(aes(nYear, LDMCpred, colour = factor(temp), fill = factor(temp), group = factor(temp))) +
geom_point(aes(nYear, wmeanLDMC_local)) +
geom_line() +
#stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "line") +
geom_ribbon(aes(ymin = mLDMCpredL, ymax = mLDMCpredH), alpha=0.3) +
scale_color_viridis_d() +
scale_fill_viridis_d() +
#geom_hline(yintercept = 0, linetype = "dashed", colour = "grey60", size = 1) #+
facet_wrap(. ~ TTtreat, scales = "free_y")
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