inst/graminoidRemovals/predictions2.R
In richardjtelford/seedclim-analysis: Code for Cleaning SeedClim data
#### predictions ####
#----------------------------------------
# SLA model
forbcomSLA <- filter(forbcom, !is.na(wmeanSLA))
modsla <- lmer(wmeanSLA ~ TTtreat*Stemp0916*Sprecip0916*SYear - TTtreat:Stemp0916:Sprecip0916:SYear + (1|siteID/blockID), REML = FALSE, data = forbcomSLA)
modsla %>%
tidy() %>%
mutate(lower = (estimate - std.error*1.96),
upper = (estimate + std.error*1.96))
forbcomSLA$modPreds <- predict(modsla)
forbcomSLA %>% #gather(wmeanSLA, modPreds, key = Model, value = value) %>%
ggplot(aes(x = Year, y = modPreds, colour = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.1)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.1), geom = "line") +
#geom_point(aes(y = wmeanSLA)) +
scale_alpha_manual(values = c(0.5, 1)) +
scale_color_manual(values = pal1) +
scale_linetype_manual(values = c("dashed", "solid")) +
facet_wrap(~ tempLevel)
rtcmeta %>%
filter(Year == 2016) %>%
ggplot(aes(x = precip7010, y = deltawmeanLA, colour = factor(tempLevel))) +
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") +
#scale_alpha_manual(values = c(0.5, 1)) +
scale_color_manual(values = pal1) +
#scale_linetype_manual(values = c("dashed", "solid")) +
geom_hline(yintercept = 0, colour = "grey60")
#----------------- Models -----------------------
# trait means
forbcomCN <- filter(forbcom, !is.na(wmeanseedMass), !is.na(wmeanCN), !is.na(wmeanLDMC), !is.na(wmeanSLA), !is.na(wmeanLA), !is.na(wmeanLTH), !is.na(wmeanheight)) %>%
gather(wmeanLDMC:wmeanseedMass, key = trait, value = value)
modMN <- forbcomCN %>%
group_by(trait) %>%
do({
mod = lmer(value ~ TTtreat*Stemp0916*Sprecip0916*SYear - TTtreat:Stemp0916:Sprecip0916:SYear + (1|siteID/blockID), REML = FALSE, data = .)
tidy(mod, data = .)
}) %>%
arrange(desc(trait)) %>%
mutate(lower = (estimate - std.error*1.96),
upper = (estimate + std.error*1.96)) %>%
ungroup()
modMN <- modMN %>%
mutate(test = case_when(
grepl("wmean", trait) ~ "Mean",
grepl("cwv", trait) ~ "Variance",
grepl("^s|^r|^e", trait) ~ "Mean"),
term = case_when(
term == "(Intercept)" ~ "Control",
term == "Stemp0916" ~ "t",
term == "Sprecip0916" ~ "P",
term == "SYear" ~ "year",
term =="TTtreatRTC:Stemp0916" ~ "t x removal",
term =="TTtreatRTC:Sprecip0916" ~ "P x removal",
term =="TTtreatRTC:Stemp0916:SYear" ~ "t x year x removal",
term =="TTtreatRTC:Sprecip0916:SYear" ~ "P x year x removal",
term =="TTtreatRTC:Stemp0916:Sprecip0916" ~ "P x t x removal",
term =="Stemp0916:SYear" ~ "t x year",
term =="Sprecip0916:SYear" ~ "P x year",
term =="Stemp0916:Sprecip0916" ~ "P x t",
term =="Stemp0916:Sprecip0916:SYear" ~ "P x t x year",
term =="TTtreatRTC:SYear" ~ "Year x removal",
term == "TTtreatRTC" ~ "removal")) %>%
mutate(trait = if_else(grepl("wmean", trait), substr(trait, 6, n()),
if_else(grepl("cwv", trait), substr(trait, 4, n()), trait))) %>%
mutate(sign = recode(trait, sumcover = 1, evenness = 1, richness = 1, seedMass = 1, height = 0, LA = 0, LTH = 0, LDMC = 0, CN = 1, SLA = 1))
modMNA <- forbcomAnalysis %>%
group_by(trait) %>%
do({
mod = lmer(measurement ~ TTtreat*Stemp0916*Sprecip0916*SYear - TTtreat:Stemp0916:Sprecip0916:SYear + (1|siteID/blockID), REML = FALSE, data = .)
tidy(mod, data = .)
}) %>%
arrange(desc(trait)) %>%
mutate(lower = (estimate - std.error*1.96),
upper = (estimate + std.error*1.96)) %>%
ungroup()
modMNA <- modMNA %>%
mutate(test = case_when(
grepl("wmean", trait) ~ "Mean",
grepl("cwv", trait) ~ "Variance",
grepl("^s|^r|^e", trait) ~ "Mean"),
term = case_when(
term == "(Intercept)" ~ "Control",
term == "Stemp0916" ~ "t",
term == "Sprecip0916" ~ "P",
term == "SYear" ~ "year",
term =="TTtreatRTC:Stemp0916" ~ "t x removal",
term =="TTtreatRTC:Sprecip0916" ~ "P x removal",
term =="TTtreatRTC:Stemp0916:SYear" ~ "t x year x removal",
term =="TTtreatRTC:Sprecip0916:SYear" ~ "P x year x removal",
term =="TTtreatRTC:Stemp0916:Sprecip0916" ~ "P x t x removal",
term =="Stemp0916:SYear" ~ "t x year",
term =="Sprecip0916:SYear" ~ "P x year",
term =="Stemp0916:Sprecip0916" ~ "P x t",
term =="Stemp0916:Sprecip0916:SYear" ~ "P x t x year",
term =="TTtreatRTC:SYear" ~ "Year x removal",
term == "TTtreatRTC" ~ "removal")) %>%
mutate(trait = if_else(grepl("wmean", trait), substr(trait, 6, n()),
if_else(grepl("cwv", trait), substr(trait, 4, n()), trait))) %>%
mutate(sign = recode(trait, sumcover = 1, evenness = 1, richness = 1, seedMass = 1, height = 0, LA = 0, LTH = 0, LDMC = 0, CN = 1, SLA = 1))
# run model for predictions
modMN <- forbcomCN %>%
group_by(trait) %>%
do({
mod = lmer(value ~ TTtreat*Stemp0916*Sprecip0916*SYear - TTtreat:Stemp0916:Sprecip0916:SYear + (1|siteID/blockID), REML = FALSE, data = .)
augment(mod, data = .)
}) %>%
mutate(TTtreat = factor(TTtreat, levels = c("RTC", "TTC"))) %>%
rename("preds" = `.fitted`) %>%
gather(preds, value, key = mod, value = data)
# trait variances
forbcomVar <- filter(forbcom, !is.na(cwvseedMass), !is.na(cwvCN), !is.na(cwvLDMC), !is.na(cwvSLA), !is.na(cwvLA), !is.na(cwvLTH), !is.na(cwvheight)) %>%
gather(cwvLDMC:cwvseedMass, key = trait, value = value)
# run model for predictions
modVar <- forbcomVar %>%
group_by(trait) %>%
do({
mod = lmer(value ~ TTtreat*Stemp0916*Sprecip0916*SYear - TTtreat:Stemp0916:Sprecip0916:SYear + (1|siteID/blockID), REML = FALSE, data = .)
augment(mod, data = .)
}) %>%
mutate(TTtreat = factor(TTtreat, levels = c("RTC", "TTC"))) %>%
rename("preds" = `.fitted`) %>%
gather(preds, value, key = mod, value = data)
#------------- TEMPERATURE ---------------------------
plotmodMNt
modMN %>%
filter(!trait %in% c("wmeanseedMass", "wmeanLA")) %>%
filter(mod == "value") %>%
ggplot(aes(x = Year, y = data, colour = factor(tempLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal1[c(3,5,4)]) +
scale_alpha_manual(values = c(1, 0.6)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
plotmodVart <- modVar %>%
filter(!trait %in% c("cwvseedMass", "cwvLDMC")) %>%
filter(mod == "value") %>%
ggplot(aes(x = Year, y = data, colour = factor(tempLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal1[c(3,5,4)]) +
scale_alpha_manual(values = c(1, 0.6)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
tmodPredsTraits <- plot_grid(plotmodMNt, plotmodVart, nrow = 2)
ggsave(tmodPredsTraits, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempmodelPredictionsTraits.jpg", dpi = 300, height = 6, width = 13)
#------------- PRECIPITATION ---------------------------
plotmodMNP
modMN %>%
filter(!trait %in% c("wmeanseedMass", "wmeanLA")) %>%
filter(mod == "value") %>%
ggplot(aes(x = Year, y = data, colour = factor(precipLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3)]) +
scale_alpha_manual(values = c(1, 0.6)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
plotmodVarP <- modVar %>%
ggplot(aes(x = Year, y = data, colour = factor(precipLevel), alpha = mod)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(linetype = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3)]) +
scale_alpha_manual(values = c(0.6, 1)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
PmodPredsTraits <- plot_grid(plotmodMNP, plotmodVarP, nrow = 2)
ggsave(PmodPredsTraits, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/PrecipmodelPredictionsTraits.jpg", dpi = 300, height = 6, width = 14)
#------------- TIME DELTAS ---------------------------
#------------- TEMPERATURE ---------------------------
treatwmTD <- timedelta %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltawmeanSLA", "deltawmeanLTH", "deltawmeanCN", "deltawmeanheight")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel), linetype = TTtreat, alpha = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual(values = pal1[c(3,5,4)]) +
scale_alpha_manual("", values = c(1, 0.6), labels = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y",
nrow = 1) +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank(),
axis.title = element_blank(),
axis.text = element_text(size = 10),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "bottom")
treatwvTD <- timedelta %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltacwvSLA", "deltacwvLTH", "deltacwvCN", "deltacwvheight")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel), linetype = TTtreat, alpha = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual(values = pal1[c(3,5,4)]) +
scale_alpha_manual("", values = c(1, 0.6), labels = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y",
nrow = 1) +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank(),
legend.position = "none",
strip.text.x = element_blank(),
axis.title = element_blank(),
axis.text = element_text(size = 10))
legend <- get_legend(treatwmTD)
#------------- PRECIPITATION ---------------------------
timedelta <- mutate(timedelta, deltaExpH = exp(deltawmeanheight))
treatwmPD <- timedelta %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltawmeanSLA", "deltawmeanLTH", "deltawmeanCN", "deltaExpH")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(precipLevel), linetype = TTtreat, alpha = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual(values = pal2[c(7,4,1,3)]) +
scale_alpha_manual("", values = c(1, 0.6), labels = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y",
nrow = 1) +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank(),
axis.title = element_blank(),
axis.text = element_text(size = 10),
legend.position = "bottom")
plotModprecip <- modMNA %>% filter(term %in% c("(Intercept)", "P", "P x removal", "P x year", "P x year x removal")) %>%
filter(trait %in% c("SLA", "LTH", "CN", "height", "LDMC")) %>%
filter(test == "Mean") %>%
ggplot(aes(x = trait, y = estimate, ymin = lower, ymax = upper)) +
geom_errorbar(width = 0, position = position_dodge(width = 0.5), size = 0.8, aes(colour = factor(term, levels = c("(Intercept)", "P", "P x removal", "P x year", "P x year x removal")))) +
geom_hline(yintercept = 0, linetype = "dashed") +
geom_point(aes(colour = factor(term, levels = c("(Intercept)", "P", "P x removal", "P x year", "P x year x removal"))), position = position_dodge(width = 0.5), size = 5) +
#scale_colour_manual("", values = pal2[c(7,1,4,3)]) +
scale_colour_manual("", values = pal2[c(7,4,1,3)]) +
theme(axis.ticks.x = element_blank(),
axis.text.y = element_text(size = 10),
axis.title = element_blank(),
legend.position = "bottom") +
scale_x_discrete(expand=c(0.07, 0.07))
legendPrecip <- get_legend(treatwmPD)
tmodPredsTraits <- plot_grid(treatwmTD + theme(legend.position = "none"), treatwvTD, treatwmPD + theme(legend.position = "none"), nrow = 3, labels = c("A", "B"), align = "hv", axis = "tblr", rel_heights = c(0.5, 0.5))
legends <- plot_grid(legend, legendPrecip, nrow = 1)
combiPlot <- plot_grid(tmodPredsTraits, legends, nrow = 2, ncol = 1, rel_heights = c(0.9,0.1))
ggsave(combiPlot, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/combiTest.jpg", dpi = 300, height = 8, width = 12.5)
ggsave(tmodPredsTraits, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempTdeltaTraits.jpg", dpi = 300, height = 6.5, width = 15)
plotmodMNP
timedelta %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltawmeanSLA", "deltawmeanLTH", "deltawmeanCN", "deltawmeanheight", "deltawmeanLDMC")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(precipLevel), linetype = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3)]) +
scale_alpha_manual("", values = c(1, 0.6), limits = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "") +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank())
plotmodVarP <- timedelta %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltacwvSLA", "deltacwvLTH", "deltacwvCN", "deltacwvheight", "deltacwvLDMC")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(precipLevel), linetype = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual("Mean annual\n precipitation", values = pal2[c(7,1,4,3)]) +
scale_alpha_manual("", values = c(1, 0.6), limits = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "") +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank())
#------------- TEMPERATURE RAW ---------------------------
rawWm <- forbcom %>%
mutate(Year = as.numeric(as.character(Year))) %>%
gather(key = trait, value = measurement, c(richness, evenness, sumcover, wmeanLDMC:cwvCN)) %>%
filter(trait %in% c("wmeanSLA", "wmeanLTH", "wmeanCN", "wmeanheight", "wmeanLDMC")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel), linetype = TTtreat, alpha = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual("Mean summer\n temperature", values = pal1[c(3,5,4)]) +
scale_alpha_manual("", values = c(1, 0.6), labels = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "") +
#geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank())
rawWv <- forbcom %>%
mutate(Year = as.numeric(as.character(Year))) %>%
gather(key = trait, value = measurement, c(richness, evenness, sumcover, wmeanLDMC:cwvCN)) %>%
filter(trait %in% c("cwvSLA", "cwvLTH", "cwvCN", "cwvheight", "cwvLDMC")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel), linetype = TTtreat, alpha = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual("Mean summer\n temperature", values = pal1[c(3,5,4)]) +
scale_alpha_manual("", values = c(1, 0.6), labels = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "") +
#geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank(),
legend.position = "none")
legend <- get_legend(rawWm)
trawTraits <- plot_grid(rawWm + theme(legend.position = "none"), rawWv, nrow = 2)
trawTraits <- plot_grid(trawTraits, legend, rel_widths = c(0.9,0.1))
ggsave(trawTraits, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempRawTraits.jpg", dpi = 300, height = 6.5, width = 15)
treatwvD <- rtcmeta %>%
mutate(Year = as.numeric(as.character(Year))) %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltacwvSLA", "deltacwvLTH", "deltacwvCN", "deltacwvheight")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual("Mean summer\n temperature", values = pal1[c(3,5,4)]) +
facet_wrap(~ trait, scales = "free_y", nrow = 1) +
labs(x = "", y = "") +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank(),
legend.position = "none")
#------------- TREATMENT DELTA ------------------
rtcmetaPlot <- rtcmeta %>%
mutate(Year = as.numeric(as.character(Year))) %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
mutate(trait = substr(trait, 6, n())) %>%
mutate(test = case_when(
grepl("wmean", trait) ~ "Mean",
grepl("cwv", trait) ~ "Variance",
grepl("^s|^r|^e|^d", trait) ~ "Mean")) %>%
mutate(trait = if_else(grepl("wmean", trait), substr(trait, 6, n()),
if_else(grepl("cwv", trait), substr(trait, 4, n()), trait)))
treatwmD <- rtcmetaPlot %>%
filter(trait %in% c("SLA", "LTH", "CN", "height"), test == "Mean") %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 0.75) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), shape = 1, geom = "point") +
scale_colour_manual("Mean summer\n temperature", values = pal1[c(3,5,4)]) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "", y = "") +
geom_hline(yintercept = 0) +
theme(strip.background = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.margin = margin(unit(c(0, 0, 0, 0), "cm")))
treatwmDprecip <- rtcmetaPlot %>%
filter(trait %in% c("SLA", "LTH", "CN", "height"), test == "Mean") %>%
ggplot(aes(x = Year, y = measurement, colour = factor(precipLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), shape = 1, geom = "point") +
scale_colour_manual("Mean annual\n precipitation", values = c('#BF5C6A','#D8B772','#68AB82','#3E6E3F')) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "", y = "") +
geom_hline(yintercept = 0) +
theme(strip.background = element_blank(),
strip.text = element_blank(),
plot.margin = margin(unit(c(0, 0, 0, 0), "cm")))
legendT <- get_legend(treatwmD)
legendP <- get_legend(treatwmDprecip)
tmodPredsTraits <- plot_grid(treatwmD + theme(legend.position = "none"), treatwmDprecip + theme(legend.position = "none"), nrow = 2, align = "hv")
legends <- plot_grid(legendT, legendP, ncol = 1)
tmodPredsTraits <- plot_grid(tmodPredsTraits, legends, rel_widths = c(0.9,0.1))
ggsave(tmodPredsTraits, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempdeltaTraitsII.jpg", dpi = 300, height = 7, width = 13.846)
# ------------- 2016 ONLY --------------------
## TEMP/PRECIP INTERACTION
modMN %>% filter(Year == 2016) %>%
filter(!trait %in% c("wmeanseedMass", "wmeanLA")) %>%
filter(mod == "value") %>%
ggplot(aes(x = temp7010, y = data, colour = factor(precipLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3)]) +
scale_alpha_manual(values = c(0.6, 1)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
ggsave(tempPrecip2016, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempPrecip2016.jpg", dpi = 300, height = 4.5, width = 16)
## TEMP ALONE
modMN %>% filter(Year == 2016) %>%
filter(!trait %in% c("wmeanseedMass", "wmeanLA")) %>%
filter(mod == "value") %>%
ggplot(aes(x = tempLevel, y = data, colour = trait)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3,2)]) +
scale_alpha_manual(values = c(0.6, 1)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
## PRECIP ALONE
modMN %>% filter(Year == 2016) %>%
filter(!trait %in% c("wmeanseedMass", "wmeanLA")) %>%
filter(mod == "value") %>%
ggplot(aes(x = precipLevel, y = data, colour = trait)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3,2)]) +
scale_alpha_manual(values = c(0.6, 1)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
#ggsave(tempPrecip2016, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempPrecip2016.jpg", dpi = 300, height = 4.5, width = 16)
richardjtelford/seedclim-analysis documentation built on Dec. 22, 2021, 3:11 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#### predictions ####
#----------------------------------------
# SLA model
forbcomSLA <- filter(forbcom, !is.na(wmeanSLA))
modsla <- lmer(wmeanSLA ~ TTtreat*Stemp0916*Sprecip0916*SYear - TTtreat:Stemp0916:Sprecip0916:SYear + (1|siteID/blockID), REML = FALSE, data = forbcomSLA)
modsla %>%
tidy() %>%
mutate(lower = (estimate - std.error*1.96),
upper = (estimate + std.error*1.96))
forbcomSLA$modPreds <- predict(modsla)
forbcomSLA %>% #gather(wmeanSLA, modPreds, key = Model, value = value) %>%
ggplot(aes(x = Year, y = modPreds, colour = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.1)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.1), geom = "line") +
#geom_point(aes(y = wmeanSLA)) +
scale_alpha_manual(values = c(0.5, 1)) +
scale_color_manual(values = pal1) +
scale_linetype_manual(values = c("dashed", "solid")) +
facet_wrap(~ tempLevel)
rtcmeta %>%
filter(Year == 2016) %>%
ggplot(aes(x = precip7010, y = deltawmeanLA, colour = factor(tempLevel))) +
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") +
#scale_alpha_manual(values = c(0.5, 1)) +
scale_color_manual(values = pal1) +
#scale_linetype_manual(values = c("dashed", "solid")) +
geom_hline(yintercept = 0, colour = "grey60")
#----------------- Models -----------------------
# trait means
forbcomCN <- filter(forbcom, !is.na(wmeanseedMass), !is.na(wmeanCN), !is.na(wmeanLDMC), !is.na(wmeanSLA), !is.na(wmeanLA), !is.na(wmeanLTH), !is.na(wmeanheight)) %>%
gather(wmeanLDMC:wmeanseedMass, key = trait, value = value)
modMN <- forbcomCN %>%
group_by(trait) %>%
do({
mod = lmer(value ~ TTtreat*Stemp0916*Sprecip0916*SYear - TTtreat:Stemp0916:Sprecip0916:SYear + (1|siteID/blockID), REML = FALSE, data = .)
tidy(mod, data = .)
}) %>%
arrange(desc(trait)) %>%
mutate(lower = (estimate - std.error*1.96),
upper = (estimate + std.error*1.96)) %>%
ungroup()
modMN <- modMN %>%
mutate(test = case_when(
grepl("wmean", trait) ~ "Mean",
grepl("cwv", trait) ~ "Variance",
grepl("^s|^r|^e", trait) ~ "Mean"),
term = case_when(
term == "(Intercept)" ~ "Control",
term == "Stemp0916" ~ "t",
term == "Sprecip0916" ~ "P",
term == "SYear" ~ "year",
term =="TTtreatRTC:Stemp0916" ~ "t x removal",
term =="TTtreatRTC:Sprecip0916" ~ "P x removal",
term =="TTtreatRTC:Stemp0916:SYear" ~ "t x year x removal",
term =="TTtreatRTC:Sprecip0916:SYear" ~ "P x year x removal",
term =="TTtreatRTC:Stemp0916:Sprecip0916" ~ "P x t x removal",
term =="Stemp0916:SYear" ~ "t x year",
term =="Sprecip0916:SYear" ~ "P x year",
term =="Stemp0916:Sprecip0916" ~ "P x t",
term =="Stemp0916:Sprecip0916:SYear" ~ "P x t x year",
term =="TTtreatRTC:SYear" ~ "Year x removal",
term == "TTtreatRTC" ~ "removal")) %>%
mutate(trait = if_else(grepl("wmean", trait), substr(trait, 6, n()),
if_else(grepl("cwv", trait), substr(trait, 4, n()), trait))) %>%
mutate(sign = recode(trait, sumcover = 1, evenness = 1, richness = 1, seedMass = 1, height = 0, LA = 0, LTH = 0, LDMC = 0, CN = 1, SLA = 1))
modMNA <- forbcomAnalysis %>%
group_by(trait) %>%
do({
mod = lmer(measurement ~ TTtreat*Stemp0916*Sprecip0916*SYear - TTtreat:Stemp0916:Sprecip0916:SYear + (1|siteID/blockID), REML = FALSE, data = .)
tidy(mod, data = .)
}) %>%
arrange(desc(trait)) %>%
mutate(lower = (estimate - std.error*1.96),
upper = (estimate + std.error*1.96)) %>%
ungroup()
modMNA <- modMNA %>%
mutate(test = case_when(
grepl("wmean", trait) ~ "Mean",
grepl("cwv", trait) ~ "Variance",
grepl("^s|^r|^e", trait) ~ "Mean"),
term = case_when(
term == "(Intercept)" ~ "Control",
term == "Stemp0916" ~ "t",
term == "Sprecip0916" ~ "P",
term == "SYear" ~ "year",
term =="TTtreatRTC:Stemp0916" ~ "t x removal",
term =="TTtreatRTC:Sprecip0916" ~ "P x removal",
term =="TTtreatRTC:Stemp0916:SYear" ~ "t x year x removal",
term =="TTtreatRTC:Sprecip0916:SYear" ~ "P x year x removal",
term =="TTtreatRTC:Stemp0916:Sprecip0916" ~ "P x t x removal",
term =="Stemp0916:SYear" ~ "t x year",
term =="Sprecip0916:SYear" ~ "P x year",
term =="Stemp0916:Sprecip0916" ~ "P x t",
term =="Stemp0916:Sprecip0916:SYear" ~ "P x t x year",
term =="TTtreatRTC:SYear" ~ "Year x removal",
term == "TTtreatRTC" ~ "removal")) %>%
mutate(trait = if_else(grepl("wmean", trait), substr(trait, 6, n()),
if_else(grepl("cwv", trait), substr(trait, 4, n()), trait))) %>%
mutate(sign = recode(trait, sumcover = 1, evenness = 1, richness = 1, seedMass = 1, height = 0, LA = 0, LTH = 0, LDMC = 0, CN = 1, SLA = 1))
# run model for predictions
modMN <- forbcomCN %>%
group_by(trait) %>%
do({
mod = lmer(value ~ TTtreat*Stemp0916*Sprecip0916*SYear - TTtreat:Stemp0916:Sprecip0916:SYear + (1|siteID/blockID), REML = FALSE, data = .)
augment(mod, data = .)
}) %>%
mutate(TTtreat = factor(TTtreat, levels = c("RTC", "TTC"))) %>%
rename("preds" = `.fitted`) %>%
gather(preds, value, key = mod, value = data)
# trait variances
forbcomVar <- filter(forbcom, !is.na(cwvseedMass), !is.na(cwvCN), !is.na(cwvLDMC), !is.na(cwvSLA), !is.na(cwvLA), !is.na(cwvLTH), !is.na(cwvheight)) %>%
gather(cwvLDMC:cwvseedMass, key = trait, value = value)
# run model for predictions
modVar <- forbcomVar %>%
group_by(trait) %>%
do({
mod = lmer(value ~ TTtreat*Stemp0916*Sprecip0916*SYear - TTtreat:Stemp0916:Sprecip0916:SYear + (1|siteID/blockID), REML = FALSE, data = .)
augment(mod, data = .)
}) %>%
mutate(TTtreat = factor(TTtreat, levels = c("RTC", "TTC"))) %>%
rename("preds" = `.fitted`) %>%
gather(preds, value, key = mod, value = data)
#------------- TEMPERATURE ---------------------------
plotmodMNt
modMN %>%
filter(!trait %in% c("wmeanseedMass", "wmeanLA")) %>%
filter(mod == "value") %>%
ggplot(aes(x = Year, y = data, colour = factor(tempLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal1[c(3,5,4)]) +
scale_alpha_manual(values = c(1, 0.6)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
plotmodVart <- modVar %>%
filter(!trait %in% c("cwvseedMass", "cwvLDMC")) %>%
filter(mod == "value") %>%
ggplot(aes(x = Year, y = data, colour = factor(tempLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal1[c(3,5,4)]) +
scale_alpha_manual(values = c(1, 0.6)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
tmodPredsTraits <- plot_grid(plotmodMNt, plotmodVart, nrow = 2)
ggsave(tmodPredsTraits, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempmodelPredictionsTraits.jpg", dpi = 300, height = 6, width = 13)
#------------- PRECIPITATION ---------------------------
plotmodMNP
modMN %>%
filter(!trait %in% c("wmeanseedMass", "wmeanLA")) %>%
filter(mod == "value") %>%
ggplot(aes(x = Year, y = data, colour = factor(precipLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3)]) +
scale_alpha_manual(values = c(1, 0.6)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
plotmodVarP <- modVar %>%
ggplot(aes(x = Year, y = data, colour = factor(precipLevel), alpha = mod)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(linetype = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3)]) +
scale_alpha_manual(values = c(0.6, 1)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
PmodPredsTraits <- plot_grid(plotmodMNP, plotmodVarP, nrow = 2)
ggsave(PmodPredsTraits, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/PrecipmodelPredictionsTraits.jpg", dpi = 300, height = 6, width = 14)
#------------- TIME DELTAS ---------------------------
#------------- TEMPERATURE ---------------------------
treatwmTD <- timedelta %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltawmeanSLA", "deltawmeanLTH", "deltawmeanCN", "deltawmeanheight")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel), linetype = TTtreat, alpha = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual(values = pal1[c(3,5,4)]) +
scale_alpha_manual("", values = c(1, 0.6), labels = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y",
nrow = 1) +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank(),
axis.title = element_blank(),
axis.text = element_text(size = 10),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "bottom")
treatwvTD <- timedelta %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltacwvSLA", "deltacwvLTH", "deltacwvCN", "deltacwvheight")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel), linetype = TTtreat, alpha = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual(values = pal1[c(3,5,4)]) +
scale_alpha_manual("", values = c(1, 0.6), labels = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y",
nrow = 1) +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank(),
legend.position = "none",
strip.text.x = element_blank(),
axis.title = element_blank(),
axis.text = element_text(size = 10))
legend <- get_legend(treatwmTD)
#------------- PRECIPITATION ---------------------------
timedelta <- mutate(timedelta, deltaExpH = exp(deltawmeanheight))
treatwmPD <- timedelta %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltawmeanSLA", "deltawmeanLTH", "deltawmeanCN", "deltaExpH")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(precipLevel), linetype = TTtreat, alpha = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual(values = pal2[c(7,4,1,3)]) +
scale_alpha_manual("", values = c(1, 0.6), labels = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y",
nrow = 1) +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank(),
axis.title = element_blank(),
axis.text = element_text(size = 10),
legend.position = "bottom")
plotModprecip <- modMNA %>% filter(term %in% c("(Intercept)", "P", "P x removal", "P x year", "P x year x removal")) %>%
filter(trait %in% c("SLA", "LTH", "CN", "height", "LDMC")) %>%
filter(test == "Mean") %>%
ggplot(aes(x = trait, y = estimate, ymin = lower, ymax = upper)) +
geom_errorbar(width = 0, position = position_dodge(width = 0.5), size = 0.8, aes(colour = factor(term, levels = c("(Intercept)", "P", "P x removal", "P x year", "P x year x removal")))) +
geom_hline(yintercept = 0, linetype = "dashed") +
geom_point(aes(colour = factor(term, levels = c("(Intercept)", "P", "P x removal", "P x year", "P x year x removal"))), position = position_dodge(width = 0.5), size = 5) +
#scale_colour_manual("", values = pal2[c(7,1,4,3)]) +
scale_colour_manual("", values = pal2[c(7,4,1,3)]) +
theme(axis.ticks.x = element_blank(),
axis.text.y = element_text(size = 10),
axis.title = element_blank(),
legend.position = "bottom") +
scale_x_discrete(expand=c(0.07, 0.07))
legendPrecip <- get_legend(treatwmPD)
tmodPredsTraits <- plot_grid(treatwmTD + theme(legend.position = "none"), treatwvTD, treatwmPD + theme(legend.position = "none"), nrow = 3, labels = c("A", "B"), align = "hv", axis = "tblr", rel_heights = c(0.5, 0.5))
legends <- plot_grid(legend, legendPrecip, nrow = 1)
combiPlot <- plot_grid(tmodPredsTraits, legends, nrow = 2, ncol = 1, rel_heights = c(0.9,0.1))
ggsave(combiPlot, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/combiTest.jpg", dpi = 300, height = 8, width = 12.5)
ggsave(tmodPredsTraits, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempTdeltaTraits.jpg", dpi = 300, height = 6.5, width = 15)
plotmodMNP
timedelta %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltawmeanSLA", "deltawmeanLTH", "deltawmeanCN", "deltawmeanheight", "deltawmeanLDMC")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(precipLevel), linetype = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3)]) +
scale_alpha_manual("", values = c(1, 0.6), limits = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "") +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank())
plotmodVarP <- timedelta %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltacwvSLA", "deltacwvLTH", "deltacwvCN", "deltacwvheight", "deltacwvLDMC")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(precipLevel), linetype = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual("Mean annual\n precipitation", values = pal2[c(7,1,4,3)]) +
scale_alpha_manual("", values = c(1, 0.6), limits = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "") +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank())
#------------- TEMPERATURE RAW ---------------------------
rawWm <- forbcom %>%
mutate(Year = as.numeric(as.character(Year))) %>%
gather(key = trait, value = measurement, c(richness, evenness, sumcover, wmeanLDMC:cwvCN)) %>%
filter(trait %in% c("wmeanSLA", "wmeanLTH", "wmeanCN", "wmeanheight", "wmeanLDMC")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel), linetype = TTtreat, alpha = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual("Mean summer\n temperature", values = pal1[c(3,5,4)]) +
scale_alpha_manual("", values = c(1, 0.6), labels = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "") +
#geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank())
rawWv <- forbcom %>%
mutate(Year = as.numeric(as.character(Year))) %>%
gather(key = trait, value = measurement, c(richness, evenness, sumcover, wmeanLDMC:cwvCN)) %>%
filter(trait %in% c("cwvSLA", "cwvLTH", "cwvCN", "cwvheight", "cwvLDMC")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel), linetype = TTtreat, alpha = TTtreat)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual("Mean summer\n temperature", values = pal1[c(3,5,4)]) +
scale_alpha_manual("", values = c(1, 0.6), labels = c("Removal", "Untreated")) +
scale_linetype_discrete("", labels = c("Removal", "Untreated")) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "") +
#geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank(),
legend.position = "none")
legend <- get_legend(rawWm)
trawTraits <- plot_grid(rawWm + theme(legend.position = "none"), rawWv, nrow = 2)
trawTraits <- plot_grid(trawTraits, legend, rel_widths = c(0.9,0.1))
ggsave(trawTraits, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempRawTraits.jpg", dpi = 300, height = 6.5, width = 15)
treatwvD <- rtcmeta %>%
mutate(Year = as.numeric(as.character(Year))) %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
filter(trait %in% c("deltacwvSLA", "deltacwvLTH", "deltacwvCN", "deltacwvheight")) %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
scale_colour_manual("Mean summer\n temperature", values = pal1[c(3,5,4)]) +
facet_wrap(~ trait, scales = "free_y", nrow = 1) +
labs(x = "", y = "") +
geom_hline(yintercept = 0) +
theme_cowplot() +
theme(strip.background = element_blank(),
legend.position = "none")
#------------- TREATMENT DELTA ------------------
rtcmetaPlot <- rtcmeta %>%
mutate(Year = as.numeric(as.character(Year))) %>%
gather(key = trait, value = measurement, c(deltarichness, deltaevenness, deltasumcover, deltawmeanLDMC:deltacwvCN)) %>%
mutate(trait = substr(trait, 6, n())) %>%
mutate(test = case_when(
grepl("wmean", trait) ~ "Mean",
grepl("cwv", trait) ~ "Variance",
grepl("^s|^r|^e|^d", trait) ~ "Mean")) %>%
mutate(trait = if_else(grepl("wmean", trait), substr(trait, 6, n()),
if_else(grepl("cwv", trait), substr(trait, 4, n()), trait)))
treatwmD <- rtcmetaPlot %>%
filter(trait %in% c("SLA", "LTH", "CN", "height"), test == "Mean") %>%
ggplot(aes(x = Year, y = measurement, colour = factor(tempLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 0.75) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), shape = 1, geom = "point") +
scale_colour_manual("Mean summer\n temperature", values = pal1[c(3,5,4)]) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "", y = "") +
geom_hline(yintercept = 0) +
theme(strip.background = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.margin = margin(unit(c(0, 0, 0, 0), "cm")))
treatwmDprecip <- rtcmetaPlot %>%
filter(trait %in% c("SLA", "LTH", "CN", "height"), test == "Mean") %>%
ggplot(aes(x = Year, y = measurement, colour = factor(precipLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), shape = 1, geom = "point") +
scale_colour_manual("Mean annual\n precipitation", values = c('#BF5C6A','#D8B772','#68AB82','#3E6E3F')) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
labs(x = "", y = "") +
geom_hline(yintercept = 0) +
theme(strip.background = element_blank(),
strip.text = element_blank(),
plot.margin = margin(unit(c(0, 0, 0, 0), "cm")))
legendT <- get_legend(treatwmD)
legendP <- get_legend(treatwmDprecip)
tmodPredsTraits <- plot_grid(treatwmD + theme(legend.position = "none"), treatwmDprecip + theme(legend.position = "none"), nrow = 2, align = "hv")
legends <- plot_grid(legendT, legendP, ncol = 1)
tmodPredsTraits <- plot_grid(tmodPredsTraits, legends, rel_widths = c(0.9,0.1))
ggsave(tmodPredsTraits, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempdeltaTraitsII.jpg", dpi = 300, height = 7, width = 13.846)
# ------------- 2016 ONLY --------------------
## TEMP/PRECIP INTERACTION
modMN %>% filter(Year == 2016) %>%
filter(!trait %in% c("wmeanseedMass", "wmeanLA")) %>%
filter(mod == "value") %>%
ggplot(aes(x = temp7010, y = data, colour = factor(precipLevel))) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3)]) +
scale_alpha_manual(values = c(0.6, 1)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
ggsave(tempPrecip2016, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempPrecip2016.jpg", dpi = 300, height = 4.5, width = 16)
## TEMP ALONE
modMN %>% filter(Year == 2016) %>%
filter(!trait %in% c("wmeanseedMass", "wmeanLA")) %>%
filter(mod == "value") %>%
ggplot(aes(x = tempLevel, y = data, colour = trait)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3,2)]) +
scale_alpha_manual(values = c(0.6, 1)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
## PRECIP ALONE
modMN %>% filter(Year == 2016) %>%
filter(!trait %in% c("wmeanseedMass", "wmeanLA")) %>%
filter(mod == "value") %>%
ggplot(aes(x = precipLevel, y = data, colour = trait)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.6), geom = "line", size = 1, mapping = aes(alpha = TTtreat)) +
scale_colour_manual(values = pal2[c(7,1,4,3,2)]) +
scale_alpha_manual(values = c(0.6, 1)) +
facet_wrap(~trait, scales = "free_y", nrow = 1) +
axis.dimLarge
#ggsave(tempPrecip2016, filename = "~/OneDrive - University of Bergen/Research/FunCaB/paper 1/figures/tempPrecip2016.jpg", dpi = 300, height = 4.5, width = 16)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.