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biomass_climate_report.R
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## Figures
# 1. Total biomass over time / site
# 2. Total increment over time / site
# 3. Species specific biomass over time / site
# 4. Species specific incrmenet over time / site
# 5. Comparison of species / site on one plot where species overlap
# 6. Average temperature vs increment
# 7. Average precipitation vs increment
# 8. Simple model of temperature, precipitation, increment across sites
# 9. Simple model with biomass instead of increment
rm(list = ls())
library(dplyr)
library(ggplot2)
# library(forecast)
#library(lme4)
# library(performance)
library(tidyr)
library(ggcorrplot)
library(ggthemes)
library(reshape2)
library(broom)
library(gam)
library(correlation)
#library(RColorBrewer)
library(stringr)
library(corrplot)
library(purrr)
library(Hmisc)
#above ground biomass
goose_total_agb <- readRDS('sites/GOOSE/runs/v3.1_012021/output/AGB_TAXA_STAN_GOOSE_v3.1_012021.RDS')
goose_total_agb_subset = subset(goose_total_agb, year>1950)
# harvard_total_agb <- readRDS('sites/HARVARD/runs/v2.0_102020/output/AGB_TAXA_STAN_HARVARD_v2.0_102020.RDS')
northround_total_agb <- readRDS('sites/NORTHROUND/runs/v3.1_082020/output/AGB_TAXA_STAN_NORTHROUND_v3.1_082020.RDS')
northround_total_agb_subset = subset(northround_total_agb, year>1950)
rooster_total_agb <- readRDS('sites/ROOSTER/runs/v3.1_082020/output/AGB_TAXA_STAN_ROOSTER_v3.1_082020.RDS')
rooster_total_agb_subset = subset(rooster_total_agb, year>1950)
sylvania_total_agb <- readRDS('sites/SYLVANIA/runs/v3.1_082020/output/AGB_TAXA_STAN_SYLVANIA_v3.1_082020.RDS')
sylvania_total_agb_subset = subset(sylvania_total_agb, year>1950)
harvard_total_agb <- readRDS('sites/HARVARD/runs/v3.1_102020/output/AGB_TAXA_STAN_HARVARD_v3.1_102020.RDS')
harvard_total_agb_subset = subset(harvard_total_agb, year>1950)
hmc_total_agb <- readRDS('sites/HMC/runs/v3.1_082020/output/AGB_TAXA_STAN_HMC_v3.1_082020.RDS')
hmc_total_agb$year = hmc_total_agb$year + 1900 - 1
hmc_total_agb_subset = subset(hmc_total_agb, year>1950)
#above ground biomass increment
goose_total_agbi <- readRDS('sites/GOOSE/runs/v3.1_012021/output/AGBI_TAXA_STAN_GOOSE_v3.1_012021.RDS')
goose_total_agbi_subset = subset(goose_total_agbi, year>1950)
# harvard_total_agbi <- readRDS('sites/HARVARD/runs/v2.0_102020/output/AGBI_TAXA_STAN_HARVARD_v2.0_102020.RDS')
northround_total_agbi <- readRDS('sites/NORTHROUND/runs/v3.1_082020/output/AGBI_TAXA_STAN_NORTHROUND_v3.1_082020.RDS')
northround_total_agbi_subset = subset(northround_total_agbi, year>1950)
rooster_total_agbi <- readRDS('sites/ROOSTER/runs/v3.1_082020/output/AGBI_TAXA_STAN_ROOSTER_v3.1_082020.RDS')
rooster_total_agbi_subset = subset(rooster_total_agbi, year>1950)
sylvania_total_agbi <- readRDS('sites/SYLVANIA/runs/v3.1_082020/output/AGBI_TAXA_STAN_SYLVANIA_v3.1_082020.RDS')
sylvania_total_agbi_subset = subset(sylvania_total_agbi, year>1950)
harvard_total_agbi <- readRDS('sites/HARVARD/runs/v3.1_102020/output/AGBI_TAXA_STAN_HARVARD_v3.1_102020.RDS')
harvard_total_agbi_subset = subset(harvard_total_agbi, year>1950)
hmc_total_agbi <- readRDS('sites/HMC/runs/v3.1_082020/output/AGBI_TAXA_STAN_HMC_v3.1_082020.RDS')
hmc_total_agbi$year = hmc_total_agbi$year + 1900 - 1
hmc_total_agbi_subset = subset(hmc_total_agbi, year>1950)
# Combining abgi and abg into one dataframe
goose_total <- goose_total_agb_subset |>
left_join(goose_total_agbi_subset, by = c('year', 'iter', 'taxon',
'model', 'plot')) |>
dplyr::rename(AGB = ab,
AGBI = abi) |>
# select(-c(type.x, type.y)) |>
# Add site name for combining sites into one df
mutate(site = 'GOOSE')
nrp_total <- northround_total_agb_subset |>
left_join(northround_total_agbi_subset, by = c('year', 'iter', 'taxon',
'model', 'plot')) |>
dplyr::rename(AGB = ab,
AGBI = abi) |>
# select(-c(type.x, type.y)) |>
mutate(site = 'NRP')
rooster_total <- rooster_total_agb_subset |>
left_join(rooster_total_agbi_subset, by = c('year', 'iter', 'taxon',
'model', 'plot')) |>
dplyr::rename(AGB = ab,
AGBI = abi) |>
# select(-c(type.x, type.y)) |>
mutate(site = 'ROOSTER')
sylvania_total <- sylvania_total_agb_subset |>
left_join(sylvania_total_agbi_subset, by = c('year', 'iter', 'taxon',
'model', 'plot')) |>
dplyr::rename(AGB = ab,
AGBI = abi) |>
# select(-c(type.x, type.y)) |>
mutate(site = 'SYLVANIA')
harvard_total <- harvard_total_agb_subset |>
left_join(harvard_total_agbi_subset, by = c('year', 'iter', 'taxon',
'model', 'plot')) |>
dplyr::rename(AGB = ab,
AGBI = abi) |>
# select(-c(type.x, type.y)) |>
mutate(site = 'HARVARD')
harvard_total <- subset(harvard_total, model == 'Model RW')
hmc_total <- hmc_total_agb_subset |>
left_join(hmc_total_agbi_subset, by = c('year', 'iter', 'taxon',
'model', 'plot')) |>
dplyr::rename(AGB = ab,
AGBI = abi) |>
# select(-c(type.x, type.y)) |>
mutate(site = 'HMC')
hmc_total$plot = as.numeric(hmc_total$plot)
harvard_total <- subset(harvard_total, model == 'Model RW')
hmc_total <- subset(hmc_total, model == 'Model RW')
#combining data from all sites into one dataframe
all_data <- rbind(goose_total, nrp_total, rooster_total, sylvania_total, harvard_total, hmc_total)
# all_data <- rbind(goose_total, nrp_total, rooster_total, sylvania_total)
ggplot(data=all_data) + geom_histogram(aes(x=AGB)) + facet_wrap(~site)
# sum AGB and AGBI across species for a given site, plot, year, and iter
all_site_plot_by_iter <- all_data |>
group_by(year, iter, plot, model, site) |>
dplyr::summarize(AGB.sum = sum(AGB),
AGBI.sum = sum(AGBI),
.groups = 'keep')
ggplot(data=all_site_plot_by_iter) + geom_histogram(aes(x=AGB.sum)) + facet_wrap(~site)
# #plot summary data
# all_site_plot_summary = all_site_plot_by_iter %>%
# group_by(year, plot, model, site) %>%
# dplyr::summarize(AGB.mean = mean(AGB.sum, na.rm = T),
# AGB.sd = sd(AGB.sum),
# AGB.lo = quantile(AGB.sum, c(0.025), na.rm=TRUE),
# AGB.hi = quantile(AGB.sum, c(0.975), na.rm=TRUE),
# AGBI.mean = mean(AGBI.sum, na.rm = T),
# AGBI.sd = sd(AGBI.sum),
# AGBI.lo = quantile(AGBI.sum, c(0.025), na.rm=TRUE),
# AGBI.hi = quantile(AGBI.sum, c(0.975), na.rm=TRUE),
# .groups='keep')
# head(all_site_plot_summary)
#
# ggplot(data=all_site_plot_summary) + geom_histogram(aes(x=AGB.mean)) + facet_wrap(~site)
# take mean AGB and AGBI across plots for a given site, year, and iter
all_site_by_iter <- all_site_plot_by_iter |>
group_by(year, iter, model, site) |>
dplyr::summarize(AGB.iter = mean(AGB.sum),
AGBI.iter = mean(AGBI.sum),
.groups = 'keep')
ggplot(data=all_site_by_iter) + geom_histogram(aes(x=AGB.iter)) + facet_wrap(~site)
# summarize mean AGB and AGBI across plots for a given site and year
all_site_summary = all_site_by_iter %>%
group_by(year, model, site) %>%
dplyr::summarize(AGB.mean = mean(AGB.iter, na.rm = T),
AGB.sd = sd(AGB.iter),
AGB.lo = quantile(AGB.iter, c(0.025), na.rm=TRUE),
AGB.hi = quantile(AGB.iter, c(0.975), na.rm=TRUE),
AGBI.mean = mean(AGBI.iter, na.rm = T),
AGBI.sd = sd(AGBI.iter),
AGBI.lo = quantile(AGBI.iter, c(0.025), na.rm=TRUE),
AGBI.hi = quantile(AGBI.iter, c(0.975), na.rm=TRUE),
.groups='keep')
head(all_site_summary)
ggplot(data=all_site_summary) + geom_histogram(aes(x=AGB.mean)) + facet_wrap(~site)
# all_site_by_iter <- all_data |>
# group_by(year, iter, model, site) |>
# dplyr::summarize(AGB.sum = sum(AGB),
# AGBI.sum = sum(AGBI),
# .groups = 'keep')
# ggplot(data=all_site_by_iter) + geom_histogram(aes(x=AGB.sum)) + facet_wrap(~site)
# #plot summary data
# all_site_summary = all_site_by_iter %>%
# group_by(year, model, site) %>%
# dplyr::summarize(AGB.mean = mean(AGB.sum, na.rm = T),
# AGB.sd = sd(AGB.sum),
# AGB.lo = quantile(AGB.sum, c(0.025), na.rm=TRUE),
# AGB.hi = quantile(AGB.sum, c(0.975), na.rm=TRUE),
# AGBI.mean = mean(AGBI.sum, na.rm = T),
# AGBI.sd = sd(AGBI.sum),
# AGBI.lo = quantile(AGBI.sum, c(0.025), na.rm=TRUE),
# AGBI.hi = quantile(AGBI.sum, c(0.975), na.rm=TRUE),
# .groups='keep')
# head(all_site_plot_summary)
#
#
# #site summary data
# all_site_summary = all_site_by_iter %>%
# group_by(year, model, site) %>%
# dplyr::summarize(AGB.mean = mean(AGB.sum, na.rm = T),
# AGB.sd = sd(AGB.sum),
# AGB.lo = quantile(AGB.sum, c(0.025), na.rm=TRUE),
# AGB.hi = quantile(AGB.sum, c(0.975), na.rm=TRUE),
# AGBI.mean = mean(AGBI.sum, na.rm = T),
# AGBI.sd = sd(AGBI.sum),
# AGBI.lo = quantile(AGBI.sum, c(0.025), na.rm=TRUE),
# AGBI.hi = quantile(AGBI.sum, c(0.975), na.rm=TRUE),
# .groups='keep')
# head(all_site_summary)
all_site_summary$period = NA
all_site_summary$period[which(all_site_summary$year<1960)] = "past"
all_site_summary$period[which(all_site_summary$year>2000)] = "present"
#AGB ovetime
ggplot(data=all_site_summary) +
geom_ribbon(aes(x=year, ymin=AGB.lo, ymax=AGB.hi, colour=site, fill=site)) +
geom_line(aes(x=year, y=AGB.mean, colour=site)) +
theme_light(14)
#labs( title = "Aboveground biomass over time", x = "Year", y = "AGB (Mg/ha)")
ggsave("report/figures/AGB_over_time.png")
#taxon_group takes the sum of all the trees in one taxon.
#iterations for each taxon not individual trees
all_taxon_plot_by_iter <- all_data |>
group_by(year, iter, taxon, plot, model, site) |>
dplyr::summarize(AGB.sum = sum(AGB),
AGBI.sum = sum(AGBI),
.groups = 'keep')
#taxon summary data across plots
all_taxon_site_by_iter = all_taxon_plot_by_iter %>%
group_by(year, iter, taxon, model, site) %>%
dplyr::summarize(AGB.iter.mean = mean(AGB.sum, na.rm = TRUE),
AGBI.iter.mean = mean(AGBI.sum, na.rm = TRUE),
.groups='keep')
head(all_taxon_site_by_iter)
#taxon summary data without plot
all_taxon_site_summary = all_taxon_site_by_iter %>%
group_by(year, taxon, model, site) %>%
dplyr::summarize(AGB.mean = mean(AGB.iter.mean, na.rm = TRUE),
AGB.sd = sd(AGB.iter.mean, na.rm=TRUE),
AGB.lo = quantile(AGB.iter.mean, c(0.025), na.rm=TRUE),
AGB.hi = quantile(AGB.iter.mean, c(0.975), na.rm=TRUE),
AGBI.mean = mean(AGBI.iter.mean, na.rm = TRUE),
AGBI.sd = sd(AGBI.iter.mean, na.rm = TRUE),
AGBI.lo = quantile(AGBI.iter.mean, c(0.025), na.rm=TRUE),
AGBI.hi = quantile(AGBI.iter.mean, c(0.975), na.rm=TRUE),
.groups='keep')
head(all_taxon_site_summary)
# #taxon summary data without plot
# all_taxon_site_summary = all_taxon_site_by_iter %>%
# group_by(year, taxon, model, site) %>%
# dplyr::summarize(AGB.mean = mean(AGB.mean, na.rm = TRUE),
# AGB.sd = sd(AGB.mean, na.rm=TRUE),
# AGB.lo = quantile(AGB.mean, c(0.025), na.rm=TRUE),
# AGB.hi = quantile(AGB.mean, c(0.975), na.rm=TRUE),
# AGBI.mean = mean(AGBI.mean, na.rm = TRUE),
# AGBI.sd = sd(AGBI.mean, na.rm = TRUE),
# AGBI.lo = quantile(AGBI.mean, c(0.025), na.rm=TRUE),
# AGBI.hi = quantile(AGBI.mean, c(0.975), na.rm=TRUE),
# .groups='keep')
# head(all_taxon_site_summary)
#
#
#
# #taxon plot summary with plot data
# all_taxon_plot_summary = all_taxon_plot_by_iter %>%
# group_by(year, taxon, plot, model, site) %>%
# dplyr::summarize(AGB.mean = mean(AGB.sum, na.rm = T),
# AGB.sd = sd(AGB.sum),
# AGB.lo = quantile(AGB.sum, c(0.025), na.rm=TRUE),
# AGB.hi = quantile(AGB.sum, c(0.975), na.rm=TRUE),
# AGBI.mean = mean(AGBI.sum, na.rm = T),
# AGBI.sd = sd(AGBI.sum),
# AGBI.lo = quantile(AGBI.sum, c(0.025), na.rm=TRUE),
# AGBI.hi = quantile(AGBI.sum, c(0.975), na.rm=TRUE),
# .groups='keep')
# head(all_taxon_plot_summary)
#
# #no plot
# all_taxon_by_iter <- all_data |>
# group_by(year, iter, taxon, model, site) |>
# dplyr::summarize(AGB.sum = sum(AGB),
# AGBI.sum = sum(AGBI),
# .groups = 'keep')
#
# #taxon summary data without plot
# all_taxon_summary = all_taxon_by_iter %>%
# group_by(year, taxon, model, site) %>%
# dplyr::summarize(AGB.mean = mean(AGB.sum, na.rm = T),
# AGB.sd = sd(AGB.sum),
# AGB.lo = quantile(AGB.sum, c(0.025), na.rm=TRUE),
# AGB.hi = quantile(AGB.sum, c(0.975), na.rm=TRUE),
# AGBI.mean = mean(AGBI.sum, na.rm = TRUE),
# AGBI.sd = sd(AGBI.sum, na.rm = TRUE),
# AGBI.lo = quantile(AGBI.sum, c(0.025), na.rm=TRUE),
# AGBI.hi = quantile(AGBI.sum, c(0.975), na.rm=TRUE),
# .groups='keep')
# head(all_taxon_summary)
all_taxon_by_iter <- all_taxon_plot_by_iter |>
group_by(year, iter, taxon, model, site) |>
dplyr::summarize(AGB.iter = mean(AGB.sum, na.rm=TRUE),
AGBI.iter = mean(AGBI.sum, na.rm=TRUE),
.groups = 'keep')
#taxon plot summary with plot data
all_taxon_summary = all_taxon_by_iter %>%
group_by(year, taxon, model, site) %>%
dplyr::summarize(AGB.mean = mean(AGB.iter, na.rm = TRUE),
AGB.sd = sd(AGB.iter, na.rm=TRUE),
AGB.lo = quantile(AGB.iter, c(0.025), na.rm=TRUE),
AGB.hi = quantile(AGB.iter, c(0.975), na.rm=TRUE),
AGBI.mean = mean(AGBI.iter, na.rm = T),
AGBI.sd = sd(AGBI.iter, na.rm=TRUE),
AGBI.lo = quantile(AGBI.iter, c(0.025), na.rm=TRUE),
AGBI.hi = quantile(AGBI.iter, c(0.975), na.rm=TRUE),
.groups='keep')
head(all_taxon_summary)
# #no plot
# all_taxon_by_iter <- all_data |>
# group_by(year, iter, taxon, model, site) |>
# dplyr::summarize(AGB.sum = sum(AGB),
# AGBI.sum = sum(AGBI),
# .groups = 'keep')
#
# #taxon summary data without plot
# all_taxon_summary = all_taxon_by_iter %>%
# group_by(year, taxon, model, site) %>%
# dplyr::summarize(AGB.mean = mean(AGB.sum, na.rm = T),
# AGB.sd = sd(AGB.sum),
# AGB.lo = quantile(AGB.sum, c(0.025), na.rm=TRUE),
# AGB.hi = quantile(AGB.sum, c(0.975), na.rm=TRUE),
# AGBI.mean = mean(AGBI.sum, na.rm = TRUE),
# AGBI.sd = sd(AGBI.sum, na.rm = TRUE),
# AGBI.lo = quantile(AGBI.sum, c(0.025), na.rm=TRUE),
# AGBI.hi = quantile(AGBI.sum, c(0.975), na.rm=TRUE),
# .groups='keep')
# head(all_taxon_summary)
#changing to wide format with taxons as column names and values = ABGI.mean
#values_fill=0 does not work
#wide format of taxon data with AGBI as value data
# all_taxon_summary_wide = pivot_wider(data = all_taxon_summary[,(colnames(all_taxon_summary) %in%
# c('year', 'taxon', 'AGBI.mean', 'site'))],
# id_cols = c(year, site),
# names_from = taxon,
# values_from = AGBI.mean,
# values_fill = NA )
all_taxon_summary = all_taxon_site_summary
goose_taxa <- all_taxon_summary %>%
filter(site == "GOOSE") %>%
select(year, taxon, AGBI.mean, site) %>%
pivot_wider(
names_from = taxon,
values_from = AGBI.mean,
values_fill = list(AGBI.mean = NA))
harvard_taxa <- all_taxon_summary %>%
filter(site == "HARVARD") %>%
select(year, taxon, AGBI.mean, site) %>%
pivot_wider(
names_from = taxon,
values_from = AGBI.mean,
values_fill = list(AGBI.mean = NA))
NRP_taxa <- all_taxon_summary %>%
filter(site == "NRP") %>%
select(year, taxon, AGBI.mean, site) %>%
pivot_wider(
names_from = taxon,
values_from = AGBI.mean,
values_fill = list(AGBI.mean = NA))
rooster_taxa <- all_taxon_summary %>%
filter(site == "ROOSTER") %>%
select(year, taxon, AGBI.mean, site) %>%
pivot_wider(
names_from = taxon,
values_from = AGBI.mean,
values_fill = list(AGBI.mean = NA))
sylvania_taxa <- all_taxon_summary %>%
filter(site == "SYLVANIA") %>%
select(year, taxon, AGBI.mean, site) %>%
pivot_wider(
names_from = taxon,
values_from = AGBI.mean,
values_fill = list(AGBI.mean = NA))
hmc_taxa <- all_taxon_summary %>%
filter(site == "HMC") %>%
select(year, taxon, AGBI.mean, site) %>%
pivot_wider(
names_from = taxon,
values_from = AGBI.mean,
values_fill = list(AGBI.mean = NA))
#wide format of site data with AGBI as value
all_site_summary_wide = pivot_wider(data = all_site_summary[,(colnames(all_site_summary) %in%
c('year','AGBI.mean', 'site'))],
id_cols = c(year),
names_from = site,
values_from = AGBI.mean,
values_fill = NA )
AGB_mean_wide = pivot_wider(data = all_site_summary[,(colnames(all_site_summary) %in%
c('year','AGB.mean', 'site'))],
id_cols = c(year),
names_from = site,
values_from = AGB.mean,
values_fill = NA)
#plotting histogram of AGBI for different time periods (past and present)
ggplot(data = all_site_summary %>% filter(!is.na(period))) +
geom_histogram(aes(x = AGBI.mean, fill = period)) +
facet_wrap(site ~ .)+
theme(legend.position = "bottom")+
theme_light(14)
ggsave('report/figures/AGBI_hist_past_present.jpg')
#Plotting histogram of AGBI for the time period past, year<1960
ggplot(data = all_site_summary %>% filter(period == "past")) +
geom_histogram(aes(x = AGBI.mean, fill = period)) +
facet_wrap(site ~ .)+
theme_light(14)
ggsave('report/figures/AGBI_hist_past.jpg')
#plotting histogram of AGBI for the time period present, year>2000
ggplot(data = all_site_summary %>% filter(period == "present")) +
geom_histogram(aes(x = AGBI.mean, fill = period)) +
facet_wrap(site ~ .)+
theme_light(14)
ggsave('report/figures/AGBI_hist_present.jpg')
#plotting the overall AGBI over time on a histogram
ggplot()+
geom_histogram(data =all_site_summary, aes(x = AGBI.mean, fill = site))+
facet_grid(site~.)+
theme_light(14)+
labs(x = 'Aboveground biomass increment')
ggsave("report/figures/AGBI_site_over_time_histogram.jpg")
#all taxon_summary
ggplot()+
geom_histogram(data = all_taxon_summary, aes(x=AGBI.mean, fill =taxon))+
facet_wrap(site~., scales = "free_x")+
theme(legend.position = "bottom")
ggplot()+
geom_histogram(data = subset(all_taxon_summary, taxon %in% c('ACRU','QURU', 'PIST')),
aes(x=AGBI.mean))+
facet_grid(site~taxon, scales = "free_x")
#above ground biomass nrp vs harvard
ggplot()+
geom_point(data= AGB_mean_wide, aes(x= NRP, y=HARVARD))+
theme_light(14)
ggsave("report/figures/AGB_NRP_HARVARD.jpg")
######################################
#plotting pairwise combinations of the different sites AGBI.mean
########################3333
# List of site columns
sites <- c("GOOSE", "HARVARD", "NRP", "ROOSTER", "SYLVANIA", "HMC")
# Generate all unique pairs of sites
site_pairs <- combn(sites, 2, simplify = FALSE)
# Function to create a scatter plot for each pair
plot_list <- map(site_pairs, ~{
ggplot(data = all_site_summary_wide) +
geom_point(aes_string(x = .x[1], y = .x[2])) +
# geom_smooth(aes_string(x = .x[1], y = .x[2]))+
# geom_smooth(method=lm,fill="blue", color="blue", ...) +
labs(x = .x[1], y = .x[2], title = paste(.x[1], "vs", .x[2])) +
theme_light(base_size = 14)
})
# Display all plots in a single layout (optional)
library(gridExtra)
do.call(grid.arrange, plot_list)
pairs(all_site_summary_wide[,2:7])
library(GGally)
ggpairs(all_site_summary_wide[,2:7], lower=list(continuous="smooth"))+
theme_light(14)
#####################################
#AGBI over time starting at the year 1900
ggplot(data=all_site_summary) +
geom_ribbon(aes(x=year, ymin=AGBI.lo, ymax=AGBI.hi, colour=site, fill=site)) +
geom_line(aes(x=year, y=AGBI.mean, colour=site)) +
theme_light(14) +
labs( x = "Year", y = "AGBI (Mg/ha)")
ggsave("report/figures/AGBI_over_time.jpg")
#AGB ovetime
ggplot(data=all_site_summary) +
geom_ribbon(aes(x=year, ymin=AGB.lo, ymax=AGB.hi, colour=site, fill=site)) +
geom_line(aes(x=year, y=AGB.mean, colour=site)) +
theme_light(14) +
labs( x = "Year", y = "AGB (Mg/ha)")
ggsave("report/figures/AGB_over_time.png")
#AGBI over time by taxon
ggplot(data=all_taxon_summary) +
geom_ribbon(aes(x=year, ymin=AGBI.lo, ymax=AGBI.hi, colour=taxon, fill=taxon)) +
geom_line(aes(x=year, y=AGBI.mean, colour=taxon)) +
theme_bw(14) +
xlab('Year') +
ylab('AGBI (Mg/ha)') +
facet_wrap(~site)
ggsave("report/figures/AGBI_over_time_taxons.jpg")
#AGBI over time by taxon, scales = free_y
ggplot(data=all_taxon_summary) +
geom_ribbon(aes(x=year, ymin=AGBI.lo, ymax=AGBI.hi, colour=taxon, fill=taxon)) +
geom_line(aes(x=year, y=AGBI.mean, colour=taxon)) +
theme_light(14) +
xlab('Year') +
ylab('AGBI (Mg/ha)') +
facet_wrap(~site, scales = 'free_y')+
theme(axis.text.x = element_text(angle = -45))
ggsave("report/figures/AGBI_over_time_taxons_freey.jpg")
#AGBI over time by taxon with sd
ggplot(data=all_taxon_summary) +
geom_ribbon(aes(x=year, ymin=AGBI.mean-2*AGBI.sd,
ymax=AGBI.mean+2*AGBI.sd, color = taxon, fill = taxon), alpha=0.3) +
geom_line(aes(x=year, y=AGBI.mean, color = taxon)) +
facet_wrap(~site)
ggsave("report/figures/AGBI_site_taxon_with_sd.jpg")
#########################################################################################
#CORRELATION AT THE SITE LEVEL
################################################################################################
#correlation between sites of AGBI
cor_site_AGBI = data.frame(cor(all_site_summary_wide[, c('GOOSE', 'ROOSTER', 'SYLVANIA', 'NRP', 'HARVARD', 'HMC')],
use = "complete.obs"))
write.csv(cor_site_AGBI, "correlation_AGBI_site.csv")
#plotting correlaiton data
ggcorrplot(cor_site_AGBI, method = "square", type = "lower",show.diag = TRUE, hc.order = FALSE)+
scale_fill_distiller(
palette = "PuOr", na.value = "white",
direction = 1, limits = c(-1, 1),
name = "Pearson\nCorrelation:")
ggtitle("AGBI correlation")
#calculating correlation and pvalues for AGBI between sites
cor_results <- rcorr(as.matrix(all_site_summary_wide
[, c('GOOSE', 'ROOSTER', 'SYLVANIA', 'NRP', 'HARVARD', 'HMC')]))
# Extract correlation coefficients and p-values
cor_coefficients <- cor_results$r
p_values <- cor_results$P
# Convert to data frame
cor_site_AGBI_p <- data.frame(
Variable1 = rep(colnames(cor_coefficients), each = ncol(cor_coefficients)),
Variable2 = rep(colnames(cor_coefficients), times = ncol(cor_coefficients)),
Correlation = as.vector(cor_coefficients),
P_Value = as.vector(p_values))
# Filter for unique pairs (optional, to avoid duplicates)
cor_site_AGBI_p <- cor_site_AGBI_p[upper.tri(cor_coefficients, diag = FALSE), ]
#correlation between sites AGB
cor_site_AGB = data.frame(cor(AGB_mean_wide[, c('GOOSE', 'ROOSTER', 'SYLVANIA', 'NRP', 'HARVARD', 'HMC')],
use = "complete.obs"))
ggcorrplot(cor_site_AGB, method = "square", type = "lower", show.diag = TRUE, hc.order = FALSE)+
ggtitle("AGB correlation")
#goose correlation
#complete.obs excludes any rows with NA values
goose_correlations = data.frame( cor(goose_taxa [,c(4:length(goose_taxa))], use = "complete.obs"))
harvard_correlations = data.frame( cor(harvard_taxa [,c(4:length(harvard_taxa))], use = "complete.obs"))
NRP_correlations = data.frame( cor(NRP_taxa [,c(4:length(NRP_taxa))], use = "complete.obs"))
rooster_correlations = data.frame( cor(rooster_taxa [,c(4:length(rooster_taxa))], use = "complete.obs"))
sylvania_correlations = data.frame( cor(sylvania_taxa [,c(4:length(sylvania_taxa))], use = "complete.obs"))
hmc_correlations = data.frame( cor(hmc_taxa [,c(4:length(hmc_taxa))], use = "complete.obs"))
#plotting correlation plots for each site between each taxa
pdf('report/figures/site_taxa_cor.pdf')
ggcorrplot(goose_correlations, method = "square", type = "lower", hc.order = FALSE, show.diag = TRUE) +
scale_fill_distiller(
palette = "PuOr", na.value = "white",
direction = 1, limits = c(-1, 1),
name = "Pearson\nCorrelation:") +
ggtitle("Goose Correlation")
ggcorrplot(harvard_correlations, method = "square", type = "lower", hc.order = FALSE, show.diag = TRUE) +
scale_fill_distiller(
palette = "PuOr", na.value = "white",
direction = 1, limits = c(-1, 1),
name = "Pearson\nCorrelation:") +
ggtitle("Harvard Correlation")
ggcorrplot(NRP_correlations, method = "square", type = "lower", hc.order = FALSE, show.diag = TRUE) +
scale_fill_distiller(
palette = "PuOr", na.value = "white",
direction = 1, limits = c(-1, 1),
name = "Pearson\nCorrelation:") +
ggtitle("NRP Correlation")
ggcorrplot(rooster_correlations, method = "square", type = "lower", hc.order = FALSE, show.diag = TRUE) +
scale_fill_distiller(
palette = "PuOr", na.value = "white",
direction = 1, limits = c(-1, 1),
name = "Pearson\nCorrelation:") +
ggtitle("Rooster Correlation")
ggcorrplot(sylvania_correlations, method = "square", type = "lower", hc.order = FALSE, show.diag = TRUE) +
scale_fill_distiller(
palette = "PuOr", na.value = "white",
direction = 1, limits = c(-1, 1),
name = "Pearson\nCorrelation:") +
ggtitle("Sylvania Correlation")
ggcorrplot(hmc_correlations, method = "square", type = "lower", hc.order = FALSE, show.diag = TRUE) +
scale_fill_distiller(
palette = "PuOr", na.value = "white",
direction = 1, limits = c(-1, 1),
name = "Pearson\nCorrelation:") +
ggtitle("HMC Correlation")
dev.off()
#########################################################################################
#CLIMATE
################################################################################################
load('climate/prism_clim.RData')
clim_data = prism_long
clim_data = clim_data %>%
dplyr::rename(site = loc, PPT = PPT2, Tmean = Tmean2, Tmin = Tmin2, Tmax = Tmax2,
Vpdmin = Vpdmin2, Vpdmax = Vpdmax2)
#putting the climat variables in wide format
clim_wide = pivot_wider(data = clim_data,
names_from = month,
values_from = c(PPT, Tmean, Tmin, Tmax, Vpdmin, Vpdmax))
# clim_melt = melt(clim_data,
# id.vars = c('year', 'month', 'site'))
#
# clim_melt$year = as.integer(clim_melt$year)
# summary(clim_melt)
#PPT_mean is the mean of each month summed to get the mean of the year
#yearly_meanT is the yearly temperature mean based on monthly means
# clim_summary = clim_wide |>
# mutate(PPT_total = rowSums(dplyr::select(clim_wide, starts_with('PPT'))),
# PPT_total_prev_tree = rowSums(dplyr::pick('PPT_09', 'PPT_10', 'PPT_11', 'PPT_12')),
# PPT_total_current_tree = rowSums(dplyr::pick('PPT_01', 'PPT_02', 'PPT_03', 'PPT_04',
# 'PPT_05', 'PPT_06', 'PPT_07', 'PPT_08')),
# yearly_meanT = rowMeans(dplyr::select(clim_wide, starts_with('Tmean'))),
# T_min_mean = rowMeans(dplyr::select(clim_wide, starts_with('Tmin'))),
# T_max_mean = rowMeans(dplyr::select(clim_wide, starts_with('Tmax'))),
# )
clim_summary = clim_wide |>
dplyr::mutate(PPT_total = rowSums(dplyr::select(clim_wide, starts_with('PPT'))),
PPT_total_prev_tree = rowSums(dplyr::pick('PPT_09', 'PPT_10', 'PPT_11', 'PPT_12')),
PPT_total_current_tree = rowSums(dplyr::pick('PPT_01', 'PPT_02', 'PPT_03', 'PPT_04',
'PPT_05', 'PPT_06', 'PPT_07', 'PPT_08')))
Vpd_sets = list(c("Vpdmin_01", "Vpdmax_01"), c("Vpdmin_02", "Vpdmax_02"),
c("Vpdmin_03", "Vpdmax_03"), c("Vpdmin_04", "Vpdmax_04"),
c("Vpdmin_05", "Vpdmax_05"), c("Vpdmin_06", "Vpdmax_06"),
c("Vpdmin_07", "Vpdmax_07"), c("Vpdmin_08", "Vpdmax_08"),
c("Vpdmin_09", "Vpdmax_09"), c("Vpdmin_10", "Vpdmax_10"),
c("Vpdmin_11", "Vpdmax_11"), c("Vpdmin_12", "Vpdmax_12")
)
#writing a loop to calculate vpd mean insert it into clim_summary dataframe
for (i in seq_along(Vpd_sets)) {
set <- Vpd_sets[[i]]
# Add a leading 0 for 1-9, otherwise just use i as is
clim_summary <- clim_summary %>%
dplyr::mutate(!!paste0("Vpdmean_", ifelse(i < 10, paste0("0", i), i)) := rowMeans(select(., all_of(set))))
}
N_years = nrow(clim_summary)
clim_summary$PPT_total_tree = NA
clim_summary$PPT_total_tree[2:N_years] = clim_summary$PPT_total_prev_tree[1:(N_years-1)] +
clim_summary$PPT_total_current_tree[2:N_years]
clim_summary = clim_summary[, !(colnames(clim_summary) %in% c('PPT_total_prev_tree', 'PPT_total_current_tree', 'PPT_total'))]
clim_summary$year <- as.numeric(clim_summary$year)
#joiing climate data with biomass data, climate data still in wide format
#clim_summary is year, site, all climate data in wide format
clim_agb <- all_site_summary|>
left_join(clim_summary, by = c('year', 'site'))
#melting climate data
clim_melt = melt(clim_summary,
id.vars = c('year', 'site'))
clim_melt$year = as.integer(clim_melt$year)
summary(clim_melt)
#dataframe with AGB data (no taxon) + climate data in melted format
#AGBI.mean represents each site
clim_total <- all_site_summary|>
left_join(clim_melt, by = c('year', 'site'))
head(clim_total)
summary(clim_melt)
#biomass data with taxon data and climate in melted format
#AGBI.mean is for each taxon at a site
clim_taxon <- all_taxon_summary|>
left_join(clim_melt, by = c('year', 'site')) %>%
select(-site)
head(clim_taxon)
summary(clim_melt)
#annual_vars = select(clim_agb, year, AGBI.mean, site,
# yearly_meanT, PPT_total_tree, T_min_mean, T_max_mean)
annual_vars = select(clim_agb, year, AGBI.mean, site, PPT_total_tree)
annual_vars_melt = melt(annual_vars, id.vars = c('model', 'year', 'AGBI.mean', 'site'))
##################################################################################
#separating climate variables into separate dataframes
############PPT##################################################
#ppt data with AGBI.mean at the site level
ppt = select(clim_agb, year, AGBI.mean, site,
starts_with('PPT'))
colnames(ppt)[which(names(ppt) == "AGBI.mean")] <- "AGBI.mean.site"
#melt at the site level
ppt_melt = melt(ppt, id.vars = c('model', 'year', 'AGBI.mean.site', 'site'))
ppt_melt$site = factor(ppt_melt$site, levels = c('GOOSE', 'NRP', 'ROOSTER', 'SYLVANIA', 'HARVARD', 'HMC'))
#ppt at the taxon level
ppt_taxon = all_taxon_summary %>%
left_join(ppt, by = c('year', 'site', 'model'))
#melt at the taxon level
ppt_melt_taxon = melt(ppt_taxon, id.vars = c('model', 'year', 'AGBI.mean','AGBI.mean.site', 'site','taxon', 'AGB.mean',
'AGB.sd','AGB.lo','AGB.hi','AGBI.sd',
'AGBI.lo','AGBI.hi'))
##########VPD#########################################################
vpd = select(clim_agb, year, AGBI.mean, site,
starts_with('Vpd'))
colnames(vpd)[which(names(vpd) == "AGBI.mean")] <- "AGBI.mean.site"
#vpd at the site level
vpd_melt = melt(vpd,
id.vars = c('model', 'year', 'AGBI.mean.site', 'site'))
vpd_melt$site = factor(vpd_melt$site, levels = c('GOOSE', 'NRP', 'ROOSTER', 'SYLVANIA', 'HARVARD', 'HMC'))
vpd_taxon = all_taxon_summary %>%
left_join(vpd, by = c('year', 'site', 'model'))
#vpd at the taxon level
vpd_melt_taxon = melt(vpd_taxon, id.vars = c('model', 'year', 'AGBI.mean', 'AGBI.mean.site', 'site','taxon', 'AGB.mean',
'AGB.sd','AGB.lo','AGB.hi','AGBI.sd',
'AGBI.lo','AGBI.hi'))
# vpd_melt$variable = sapply(as.vector(vpd_melt$variable), function(x) {strsplit(x, '\\_')[[1]][2]})
######TMIN and TMAX#############################################################
tmin = select(clim_agb, year, AGBI.mean, site,
starts_with('Tmin'))
colnames(tmin)[which(names(tmin) == "AGBI.mean")] <- "AGBI.mean.site"
tmin_melt = melt(tmin,
id.vars = c('model', 'year', 'AGBI.mean.site', 'site'))
tmin_melt$site = factor(tmin_melt$site, levels = c('GOOSE', 'NRP', 'ROOSTER', 'SYLVANIA', 'HARVARD', 'HMC'))
tmin_taxon = all_taxon_summary %>%
left_join(tmin, by = c('year', 'site', 'model'))
tmin_melt_taxon = melt(tmin_taxon, id.vars = c('model', 'year', 'AGBI.mean', 'AGBI.mean.site', 'site','taxon', 'AGB.mean',
'AGB.sd','AGB.lo','AGB.hi','AGBI.sd',
'AGBI.lo','AGBI.hi'))
tmax = select(clim_agb, year, AGBI.mean, site,
starts_with('Tmax'))
colnames(tmax)[which(names(tmax) == "AGBI.mean")] <- "AGBI.mean.site"
tmax_melt = melt(tmax,
id.vars = c('model', 'year', 'AGBI.mean.site', 'site'))
tmax_melt$site = factor(tmax_melt$site, levels = c('GOOSE', 'NRP', 'ROOSTER', 'SYLVANIA', 'HARVARD', 'HMC'))
tmax_taxon = all_taxon_summary %>%
left_join(tmax, by = c('year', 'site', 'model'))
tmax_melt_taxon = melt(tmax_taxon, id.vars = c('model', 'year', 'AGBI.mean', 'AGBI.mean.site', 'site','taxon', 'AGB.mean',
'AGB.sd','AGB.lo','AGB.hi','AGBI.sd',
'AGBI.lo','AGBI.hi'))
tmean = select(clim_agb, year, AGBI.mean, site,
starts_with('Tmean'))
colnames(tmean)[which(names(tmean) == "AGBI.mean")] <- "AGBI.mean.site"
tmean_melt = melt(tmean,
id.vars = c('model', 'year', 'AGBI.mean.site', 'site'))
tmean_melt$site = factor(tmean_melt$site, levels = c('GOOSE', 'NRP', 'ROOSTER', 'SYLVANIA', 'HARVARD', 'HMC'))
tmean_taxon = all_taxon_summary %>%
left_join(tmean, by = c('year', 'site', 'model'))
tmean_melt_taxon = melt(tmean_taxon, id.vars = c('model', 'year', 'AGBI.mean', 'AGBI.mean.site', 'site','taxon', 'AGB.mean',
'AGB.sd','AGB.lo','AGB.hi','AGBI.sd',
'AGBI.lo','AGBI.hi'))
######################################################################################################################
#correlation between tree ring data and climate variables
######################################################################################################################
# clim_total = clim_total
clim_total = na.omit(clim_total)
# clim_total = clim_total[which(clim_total$site != 'HARVARD'),]
# clim_taxon = clim_taxon[which(clim_taxon$site != 'HARVARD'),]
clim_total$variable = as.character(clim_total$variable)
clim_total$type = sapply(strsplit(clim_total$variable, split='_'), function(x) x[1])
clim_total$period = sapply(strsplit(clim_total$variable, split='_'), function(x) x[2])
periods = unique(clim_total$period)
period_names = c('jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul',
'aug', 'sep', 'oct', 'nov', 'dec', 'total tree')
clim_total$period_names = period_names[match(clim_total$period, periods)]
clim_total$period_names = factor(clim_total$period_names,
levels = period_names)
clim_taxon$variable = as.character(clim_taxon$variable)
clim_taxon$type = sapply(strsplit(clim_taxon$variable, split='_'), function(x) x[1])
clim_taxon$period = sapply(strsplit(clim_taxon$variable, split='_'), function(x) x[2])
periods = unique(clim_taxon$period)
clim_taxon$period_names = period_names[match(clim_taxon$period, periods)]
clim_taxon$period_names = factor(clim_taxon$period_names,
levels = period_names)
#correlation between AGBI and all climate variables, site level
cor_clim_AGBI <- clim_total %>%
# Filter to keep only the relevant rows for correlation
filter(str_detect(variable, "^(PPT|Tmean|Tmax|Tmin|Vpdmin|Vpdmax|Vpdmean)")) %>%
# Group by site and variable
group_by(site, variable, type, period, period_names) %>%
# Summarize by calculating correlation between AGBI.mean and value
mutate(correlation = cor(AGBI.mean, value, use = "complete.obs"), .groups = 'drop')
head(cor_clim_AGBI)
write.csv(cor_clim_AGBI, file = "report/AGBI_clim_correlation_site.csv")
#correlation between AGBI and clim with pvalues, only at the site level
cor_clim_AGBI_site_pvalue <- clim_total %>%
# Filter to keep only the relevant rows for correlation
filter(str_detect(variable, "^(PPT|Tmean|Tmax|Tmin|Vpdmin|Vpdmax|Vpdmean)")) %>%
# Group by site and variable
group_by(site, variable, type, period, period_names) %>%
# Summarize by calculating correlation between AGBI.mean and value
dplyr::mutate(correlation = cor.test(AGBI.mean, value, use = "complete.obs")$estimate,
p_value = cor.test(AGBI.mean, value, use = "complete.obs")$p.value, .groups = 'drop')
cor_clim_p_site_subset = subset(cor_clim_AGBI_site_pvalue, p_value < 0.05)
#for each climate variable at which site is it the highest
max_variable_cor = cor_clim_AGBI %>%
group_by(variable) %>%
dplyr::mutate(max_cor = max(correlation, na.rm = TRUE),
site_with_max_cor = site[which.max(correlation)])
#calculating correlation between AGBI.mean and climate variables for each
#climate variable at each site, for each taxon
cor_clim_vars_taxon <- clim_taxon %>%
# Filter to keep only the relevant rows for correlation
filter(str_detect(variable, "^(PPT|Tmean|Tmax|Tmin|Vpdmin|Vpdmax|Vpdmean)")) %>%
# Group by site and variable
group_by(site, taxon, variable, type, period, period_names) %>%
# Summarize by calculating correlation between AGBI.mean and value
mutate(correlation = cor(AGBI.mean, value, use = "complete.obs"), .groups = 'drop')
write.csv(cor_clim_vars_taxon, file = "report/AGBI_clim_taxon_correlation.csv")
#generating the pvalues of the correlation between AGBI.mean and climate variables
#at the taxon level
cor_clim_taxon_pvalue <- clim_taxon %>%
# Filter to keep only the relevant rows for correlation
filter(str_detect(variable, "^(PPT|Tmean|Tmax|Tmin|Vpdmin|Vpdmax|Vpdmean)")) %>%
# Group by site and variable
group_by(site, taxon, variable, type, period, period_names) %>%
# Summarize by calculating correlation between AGBI.mean and value
dplyr::mutate(correlation = cor.test(AGBI.mean, value, use = "complete.obs")$estimate,
p_value = cor.test(AGBI.mean, value, use = "complete.obs")$p.value, .groups = 'drop')
#subsetting data set to only have pvalues <0.05
cor_clim_p_taxon_subset = subset(cor_clim_taxon_pvalue, p_value < 0.05)
head(cor_clim_p_taxon_subset)
clim_vars = c("PPT", "Tmean", "Tmin", "Tmax", "Vpdmin", "Vpdmax", "Vpdmean")
sites = c('GOOSE', 'ROOSTER', 'NRP', 'HARVARD', 'SYLVANIA', 'HMC')
cor_max = max(cor_clim_taxon_pvalue$correlation)
cor_min = min(cor_clim_taxon_pvalue$correlation)
#generatting correlation plots for all taxa for all sites for all clim variables
#with significance shown
# Open a PDF device
pdf("report/figures/AGBI_clim_cor_taxon.pdf", width = 10, height = 8)
for (site in sites) {
# Loop through each climate variable
for (var in clim_vars) {
# Generate the plot for the current climate variable
cor_taxa_p = cor_clim_taxon_pvalue[which((cor_clim_taxon_pvalue$site == site )&(cor_clim_taxon_pvalue$type == var)),]
cor_taxa_p$sig = ifelse(cor_taxa_p$p_value<0.05, TRUE, NA)
p = ggplot()+
geom_tile(data= cor_taxa_p, aes(x=period_names, y= taxon, fill = correlation))+
scale_fill_gradient2(limits = c(-0.6, 0.6),
low = "red", mid = "white", high = "blue",
midpoint = 0)+
geom_point(data = cor_taxa_p, aes(x=period_names, y= taxon, shape = sig), size=3)+
scale_shape_manual(values=c(1, NA)) +
xlab('Period') +
ylab('Species') +
ggtitle(paste0(site, '; ', var)) +
theme(plot.title = element_text(size=18))
# Print the plot to the PDF
print(p)
}}
# Close the PDF device
dev.off()
#plotting y=site x= period (jan, feb, etc..) where each page shows each climate variable
#correlation of each site between the different climate variables
pdf("report/figures/AGBI_clim_cor_sites.pdf", width = 10, height = 8)
# Loop through each climate variable
for (var in clim_vars) {
# Filter data for the current climate variable
cor_var_p <- cor_clim_AGBI_site_pvalue[which(cor_clim_AGBI_site_pvalue$type == var), ]
cor_var_p$sig = ifelse(cor_var_p$p_value<0.05, TRUE, NA)
# Generate the plot for the current climate variable
p <- ggplot() +
geom_tile(data = cor_var_p, aes(x = period_names, y = site, fill = correlation)) +
scale_fill_gradient2(limits = c(-0.6, 0.6),
low = "red", mid = "white", high = "purple",
midpoint = 0) +
geom_point(data = cor_var_p, aes(x= period_names, y= site, shape = sig), size=3)+