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nested_mixes.R
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nested_mixes.R
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# Authors: James Sturges, Ryan Rezek, Ryan James
# Last Updated 20 August 2023
# Seasonal differences in brown/green energy pathways and source specific contributions across multiple FCE food webs
# DATASETUP ----
# read in libraries
library(MixSIAR)
library(forcats)
library(car)
library(scales)
library(rgl)
library(flextable)
library(magick)
library(R2jags)
library(rjags)
library(tidyverse)
library(dplyr)
#set print limits high for mixing model outputs
options(max.print = 99999)
# Create mixTable function created by Ryan James -
mixTable = function(file,type,ind = F,nest = F, csv = F){
require(tidyverse)
cn = c('ID', 'Mean', 'SD', '2.5%', '5%', '25%', '50%', '105%', '95%', '910.5%')
if(csv){
x = read_csv(file)
}else{
x = read_fwf(file, skip = 8)
}
names(x) = cn
x$source = NA
x$name = NA
x$code = NA
if (nest == F){
for (i in 1:nrow(x)){
temp = strsplit(x$ID, split = '.', fixed = T)
x$source[i] = temp[[i]][3]
x$name[i] = temp[[i]][2]
x$type = type
x$ymax = x$`105%` + 1.5*(x$`105%` - x$`25%`)
x$ymin = x$`25%` - 1.5*(x$`105%` - x$`25%`)
df = tibble(x$name, x$type, x$source, x$Mean, x$SD, x$`2.5%`, x$`910.5%`,
x$`50%`, x$`25%`, x$`105%`, x$ymax, x$ymin)
colnames(df) = c('name', 'type', 'source', 'mean', 'sd', 'lowend', 'highend',
'mid', 'low', 'up', 'ymax', 'ymin')
}
}else{
for (i in 1:nrow(x)){
temp = strsplit(x$ID, split = '.', fixed = T)
x$source[i] = temp[[i]][4]
x$code[i] = temp[[i]][3]
x$name[i] = temp[[i]][2]
x$type = type
x$ymax = x$`105%` + 1.5*(x$`105%` - x$`25%`)
x$ymin = x$`25%` - 1.5*(x$`105%` - x$`25%`)
df = tibble(x$name, x$type, x$source, x$code, x$Mean, x$SD, x$`2.5%`, x$`910.5%`,
x$`50%`, x$`25%`, x$`105%`, x$ymax, x$ymin)
colnames(df) = c('name', 'type', 'source', 'code', 'mean', 'sd', 'lowend', 'highend',
'mid', 'low', 'up', 'ymax', 'ymin')
}
}
for (i in 1:nrow(df)){
if (df$ymax[i] > df$highend[i]){
df$ymax[i] = df$highend[i]
}
if (df$ymin[i] < df$lowend[i]){
df$ymin[i] = df$lowend[i]
}
}
df = df %>% drop_na %>%
filter(name != 'global')
if (ind == T){
if (nest == T){
df = df %>% select(name, type, code, source, mean) %>%
pivot_wider(names_from = 'source', values_from = 'mean')
}else{
df = df %>% select(name, type, source, mean)%>%
pivot_wider(names_from = 'source', values_from = 'mean')
}
}
return(df)
}
# updated data as of August 2023 includes sulfur reruns and dry 2020 supplemental values
SI <- read.csv("data/FCE_SI_data_13_August_2023.csv",na.strings=c("","NA"))%>%filter(is.na(outlier))
#raw stable isotope values for individual level analysis
SI = SI %>%
select(site,hydroseason,year, group, common_name, functional_grp,d13C,d15N,d34S)
#average stable isotope values for species across sites and seasons
SIa<-SI %>% group_by(site, hydroseason, group, common_name, functional_grp) %>%
summarise(n=n(),md13C = mean(as.numeric(d13C), na.rm = T),d13Csd=sd(as.numeric(d13C),na.rm = T),
md15N=mean(as.numeric(d15N),na.rm = T),d15Nsd=sd(as.numeric(d15N),na.rm = T),
md34S=mean(as.numeric(d34S),na.rm = T),d34Ssd=sd(d34S,na.rm = T)) %>%
filter(!is.na(md13C),!is.na(md15N),!is.na(md34S))
# not including seasonality as a factor
SIb<-SI %>% group_by(site,group, common_name, functional_grp) %>%
summarise(n=n(),md13C = mean(d13C,na.rm = T),d13Csd=sd(d13C,na.rm = T), md15N=mean(d15N,na.rm = T),d15Nsd=sd(d15N,na.rm = T),md34S=mean(d34S,na.rm = T),d34Ssd=sd(d34S,na.rm = T)) %>%
filter(!is.na(md13C),!is.na(md15N),!is.na(md34S))
# Biplots ----
# create list of sites as i vector
sites = c("SRS3", "SRS4","SRS6", "RB10", "TS3", "TS7", "TS9", "TS10", "TS11")
# produces two biplots per site (one CN and one CS)
for(i in 1:length(sites)) {
fish = read_csv(paste0('data/Consumers/', sites[i], 'mix.csv'))
sources = read.csv(paste0('data/Sources/sources', sites[i], '.csv')) %>%
mutate(Meand13C = Meand13C + 1.95,
Meand15N = Meand15N + 5.1,
Meand34S = Meand34S + .75)
wcn = ggplot(data = sources, aes(Meand13C, Meand15N))+
geom_point(data = sources, size = 3, pch=c(20))+
geom_errorbar(data = sources, aes(ymin = Meand15N - SDd15N, ymax = Meand15N + SDd15N), width = 0) +
geom_errorbarh(data = sources, aes(xmin = Meand13C - SDd13C, xmax = Meand13C + SDd13C), height = 0) +
ylab(expression(paste(delta^{15}, "N (\u2030)")))+
xlab(expression(paste(delta^{13}, "C (\u2030)"))) +
labs(title = paste("CN ", sites[i])) + # Add title
theme_classic() + geom_text(data = sources, aes(label = source),hjust=-.1, vjust=-1) +
geom_point(data = fish, aes(x = d13C, y = d15N,color = common_name), size=3, pch=c(20))+
# scale_color_manual(values = cols, drop = F)+
# scale_x_continuous(limits = c(-27, -6))+
# scale_y_continuous(limits = c(0,14))+
theme( legend.title = element_blank(),
legend.text=element_text(size=12))#,legend.position=c(.85,.15))
ggsave(paste0('figures/biplots/nested/',sites[i], 'CN.pdf'), units="in", width=10, height=6)
# C and S
wcs = ggplot(data = sources, aes(Meand13C, Meand34S))+
geom_point(data = fish, aes(x = d13C, y = d34S,color = common_name), size=3, pch=c(20))+
# scale_color_manual(values = cols, drop = F)+
geom_point(data = sources, size = 3, pch=c(20))+
geom_errorbar(data = sources, aes(ymin = Meand34S - SDd34S, ymax = Meand34S + SDd34S), width = 0) +
geom_errorbarh(data = sources, aes(xmin = Meand13C - SDd13C, xmax = Meand13C + SDd13C), height = 0) +
ylab(expression(paste(delta^{34}, "S (\u2030)")))+
xlab(expression(paste(delta^{13}, "C (\u2030)"))) +
labs(title = paste("CS ", sites[i])) + # Add title
theme_classic() + geom_text(data = sources, aes(label = source),hjust=-.1, vjust=-1) +
# scale_x_continuous(limits = c(-27, -6))+
# scale_y_continuous(limits = c(-16.5, 24))+
theme(legend.title = element_blank())#, legend.position=c(.85,.85))
ggsave(paste0('figures/biplots/nested/',sites[i], 'CS.pdf'), units="in", width=10, height=6)
}
# creates the same isotope biplots but colors points by species to look at wet dry trends
for(i in 1:length(sites)) {
fish = read_csv(paste0('data/Consumers/', sites[i], 'mix.csv'))
sources = read.csv(paste0('data/Sources/sources', sites[i], '.csv')) %>%
mutate(Meand13C = Meand13C + 1.95,
Meand15N = Meand15N + 5.1,
Meand34S = Meand34S + .75)
wcn_season = ggplot(data = sources, aes(Meand13C, Meand15N))+
geom_point(data = sources, size = 3, pch=c(20))+
geom_errorbar(data = sources, aes(ymin = Meand15N - SDd15N, ymax = Meand15N + SDd15N), width = 0) +
geom_errorbarh(data = sources, aes(xmin = Meand13C - SDd13C, xmax = Meand13C + SDd13C), height = 0) +
ylab(expression(paste(delta^{15}, "N (\u2030)")))+
xlab(expression(paste(delta^{13}, "C (\u2030)"))) +
labs(title = paste("CN ", sites[i])) + # Add title
theme_classic() + geom_text(data = sources, aes(label = source),hjust=-.1, vjust=-1) +
geom_point(data = fish, aes(x = d13C, y = d15N,color = hydroseason), size=3, pch=c(20))+
# scale_color_manual(values = cols, drop = F)+
# scale_x_continuous(limits = c(-27, -6))+
# scale_y_continuous(limits = c(0,14))+
theme( legend.title = element_blank(),
legend.text=element_text(size=12))#,legend.position=c(.85,.15))
ggsave(paste0('figures/biplots/nested/',sites[i], 'CN_WD.pdf'), units="in", width=10, height=6)
# C and S
wcs_season = ggplot(data = sources, aes(Meand13C, Meand34S))+
geom_point(data = fish, aes(x = d13C, y = d34S,color = hydroseason), size=3, pch=c(20))+
# scale_color_manual(values = cols, drop = F)+
geom_point(data = sources, size = 3, pch=c(20))+
geom_errorbar(data = sources, aes(ymin = Meand34S - SDd34S, ymax = Meand34S + SDd34S), width = 0) +
geom_errorbarh(data = sources, aes(xmin = Meand13C - SDd13C, xmax = Meand13C + SDd13C), height = 0) +
ylab(expression(paste(delta^{34}, "S (\u2030)")))+
xlab(expression(paste(delta^{13}, "C (\u2030)"))) +
labs(title = paste("CS ", sites[i])) + # Add title
theme_classic() + geom_text(data = sources, aes(label = source),hjust=-.1, vjust=-1) +
# scale_x_continuous(limits = c(-27, -6))+
# scale_y_continuous(limits = c(-16.5, 24))+
theme(legend.title = element_blank())#, legend.position=c(.85,.85))
ggsave(paste0('figures/biplots/nested/',sites[i], 'CS_WD.pdf'), units="in", width=10, height=6)
}
# RB10 Mixing Models ----
# RB10 with Floc
RB10mix<-SIa %>% filter(site == 'RB10', common_name!="Egyptian paspalidium",group=='Consumer')%>% rename('d13C'='md13C',"d15N"= "md15N","d34S"="md34S")
write.csv(RB10mix,"data/Consumers/nested/RB10mix.csv",row.names = F)
mix <- load_mix_data(filename="data/Consumers/RB10mix.csv",
iso_names=c("d13C","d15N","d34S"),
factors=c('hydroseason','common_name'),
fac_random=c(F,T),
fac_nested=c(F,T),
cont_effects=NULL)
source <- load_source_data(filename="data/Sources/sourcesRB10.csv",
source_factors=NULL,
conc_dep=T,
data_type="means",
mix)
discr <- load_discr_data(file("data/TEF/FCE_TEF_RB10.csv"), mix)
plot_data(filename="figures/isospace/nested/RB10_isospace_plot", plot_save_pdf=T, plot_save_png=T, mix,source,discr)
model_filename <- "data/Consumers/nested/RB10_mix.txt"
write_JAGS_model(model_filename, resid_err=T, process_err=T, mix, source)
#run a test model to make sure it works
jags.RB10 <- run_model(run="test", mix, source, discr, model_filename,
alpha.prior = 1, resid_err=F, process_err=F)
jags.RB10 <- run_model(run="normal", mix, source, discr, model_filename,
alpha.prior = 1, resid_err=F, process_err=F)
output_jags.RB10 <- list(summary_save = T,
summary_name = "data/JAGS_Output/RB10/nested/FCERB10_sumstats",
sup_post = F,
plot_post_save_pdf = T,
plot_post_name = "data/JAGS_Output/RB10/nested/FCERB10_plot",
sup_pairs = F,
plot_pairs_save_pdf = T,
plot_pairs_name = "data/JAGS_Output/RB10/nested/FCERB10_pairs",
sup_xy = T,
plot_xy_save_pdf = T,
plot_xy_name = "data/JAGS_Output/RB10/nested/FCERB10_plot",
gelman = T,
heidel = F,
geweke = T,
diag_save = T,
diag_name = "data/JAGS_Output/RB10/nested/FCERB10_Diagnostic",
indiv_effect = F,
plot_post_save_png = F,
plot_pairs_save_png = F,
plot_xy_save_png = F)
saveRDS(jags.RB10, 'data/JAGS_Output/RB10/nested/RB10.RDS')
output_JAGS(jags.RB10, mix, source, output_jags.RB10)
mixtable_RB10 = mixTable("data/JAGS_Output/RB10/nested/FCERB10_sumstats.txt",type = "RB10", nest = T)
write.csv(mixtable_RB10, "data/Mix_Quants/nested/MT_RB10.csv", row.names = FALSE)
# combinedRB10 <- combine_sources(jags.RB10, mix, source, alpha.prior=1,
# groups=list(green=c('Epiphytes','Phytoplankton'), brown=c('Mangrove', 'Floc')))
#
# # get posterior medians for new source groupings
# apply(combinedRB10$post, 2, median)
#
# summary_stat(combinedRB10, meanSD=T, quantiles=c(0.025, 0.25, 0.5, 0.75, 0.975), savetxt=T,
# filename = "RB10_combined_sumstats" )
#
# SRS Mixing Models ----
# SRS 3
SRS3mix <- SIa %>% filter(site == 'SRS3', common_name != "Egyptian paspalidium", group == 'Consumer') %>% rename('d13C' = 'md13C', 'd15N' = 'md15N', 'd34S' = 'md34S')
write.csv(SRS3mix, "data/Consumers/SRS3mix.csv", row.names = FALSE)
mix <- load_mix_data(filename = "data/Consumers/SRS3mix.csv",
iso_names = c("d13C", "d15N", "d34S"),
factors = c('common_name', 'hydroseason'),
fac_random = c(FALSE, FALSE),
fac_nested = c(FALSE, FALSE),
cont_effects = NULL)
source <- load_source_data(filename = "data/Sources/sourcesSRS3.csv",
source_factors = NULL,
conc_dep = TRUE,
data_type = "means",
mix)
discr <- load_discr_data(file("data/TEF/FCE_TEF_SRS3.csv"), mix)
plot_data(filename = "figures/isospace/SRS3_isospace_plot",
plot_save_pdf = TRUE,
plot_save_png = TRUE,
mix, source, discr)
model_filename <- "data/Consumers/SRS3_mix.txt"
write_JAGS_model(model_filename, resid_err = FALSE, process_err = TRUE, mix, source)
jags.SRS3 <- run_model(run = "test", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
jags.SRS3 <- run_model(run = "normal", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
output_jags.SRS3 <- list(summary_save = TRUE,
summary_name = "data/JAGS_Output/SRS3/FCESRS3_sumstats",
sup_post = FALSE,
plot_post_save_pdf = T,
plot_post_name = "data/JAGS_Output/SRS3/FCESRS3_plot",
sup_pairs = FALSE,
plot_pairs_save_pdf = T,
plot_pairs_name = "data/JAGS_Output/SRS3/FCESRS3_pairs",
sup_xy = TRUE,
plot_xy_save_pdf = FALSE,
plot_xy_name = "data/JAGS_Output/SRS3/FCESRS3_plot",
gelman = TRUE,
heidel = FALSE,
geweke = TRUE,
diag_save = TRUE,
diag_name = "data/JAGS_Output/SRS3/FCESRS3_Diagnostic",
indiv_effect = FALSE,
plot_post_save_png = FALSE,
plot_pairs_save_png = FALSE,
plot_xy_save_png = FALSE)
output_JAGS(jags.SRS3, mix, source, output_jags.SRS3)
mixtable_SRS3 = mixTable("data/JAGS_Output/SRS3/FCESRS3_sumstats.txt", type = "SRS3", nest = TRUE)
write.csv(mixtable_SRS3, "data/Mix_Quants/MT_SRS3.csv", row.names = FALSE)
# combinedSRS3 <- combine_sources(jags.SRS3, mix, source, alpha.prior=1,
# groups=list(green=c('Phytoplankton','Floc'), brown=c('Sawgrass, Periphyton')))
#
# # get posterior medians for new source groupings
# apply(combinedSRS3$post, 2, median)
# # summary_stat(combinedSRS3, meanSD=T, quantiles=c(0.025, 0.25, 0.5, 0.75, 0.975), savetxt=T,
# # filename = "SRS3_combined_sumstats" )
# SRS4
SRS4mix <- SIa %>% filter(site == 'SRS4', common_name != "Egyptian paspalidium", group == 'Consumer') %>% rename('d13C' = 'md13C', 'd15N' = 'md15N', 'd34S' = 'md34S')
write.csv(SRS4mix, "data/Consumers/SRS4mix.csv", row.names = FALSE)
mix <- load_mix_data(filename = "data/Consumers/SRS4mix.csv",
iso_names = c("d13C", "d15N", "d34S"),
factors = c('common_name', 'hydroseason'),
fac_random = c(FALSE, FALSE),
fac_nested = c(FALSE, FALSE),
cont_effects = NULL)
source <- load_source_data(filename = "data/Sources/sourcesSRS4.csv",
source_factors = NULL,
conc_dep = TRUE,
data_type = "means",
mix)
discr <- load_discr_data(file("data/TEF/FCE_TEF_SRS4.csv"), mix)
plot_data(filename = "figures/isospace/SRS4_isospace_plot",
plot_save_pdf = TRUE,
plot_save_png = TRUE,
mix, source, discr)
model_filename <- "data/Consumers/SRS4_mix.txt"
write_JAGS_model(model_filename, resid_err = FALSE, process_err = TRUE, mix, source)
jags.SRS4 <- run_model(run = "test", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
jags.SRS4 <- run_model(run = "normal", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
output_jags.SRS4 <- list(summary_save = TRUE,
summary_name = "data/JAGS_Output/SRS4/FCESRS4_sumstats",
sup_post = FALSE,
plot_post_save_pdf = T,
plot_post_name = "data/JAGS_Output/SRS4/FCESRS4_plot",
sup_pairs = FALSE,
plot_pairs_save_pdf = T,
plot_pairs_name = "data/JAGS_Output/SRS4/FCESRS4_pairs",
sup_xy = TRUE,
plot_xy_save_pdf = T,
plot_xy_name = "data/JAGS_Output/SRS4/FCESRS4_plot",
gelman = TRUE,
heidel = FALSE,
geweke = TRUE,
diag_save = TRUE,
diag_name = "data/JAGS_Output/SRS4/FCESRS4_Diagnostic",
indiv_effect = FALSE,
plot_post_save_png = FALSE,
plot_pairs_save_png = FALSE,
plot_xy_save_png = FALSE)
output_JAGS(jags.SRS4, mix, source, output_jags.SRS4)
mixtable_SRS4 = mixTable("data/JAGS_Output/SRS4/FCESRS4_sumstats.txt", type = "SRS4", nest = TRUE)
write.csv(mixtable_SRS4, "data/Mix_Quants/MT_SRS4.csv", row.names = FALSE)
# combinedSRS4 <- combine_sources(jags.SRS4, mix, source, alpha.prior=1,
# groups=list(green=c('Phytoplankton',"Epiphytes"), brown=c('Mangrove')))
#
# # get posterior medians for new source groupings
# apply(combinedSRS4$post, 2, median)
# # summary_stat(combinedSRS4, meanSD=T, quantiles=c(0.025, 0.25, 0.5, 0.75, 0.975), savetxt=T,
# # filename = "SRS4_combined_sumstats" )
# SRS 6
SRS6mix <- SIa %>% filter(site == 'SRS6', common_name != "Egyptian paspalidium", group == 'Consumer') %>% rename('d13C' = 'md13C', 'd15N' = 'md15N', 'd34S' = 'md34S')
write.csv(SRS6mix, "data/Consumers/SRS6mix.csv", row.names = FALSE)
mix <- load_mix_data(filename = "data/Consumers/SRS6mix.csv",
iso_names = c("d13C", "d15N", "d34S"),
factors = c('common_name', 'hydroseason'),
fac_random = c(FALSE, FALSE),
fac_nested = c(FALSE, FALSE),
cont_effects = NULL)
source <- load_source_data(filename = "data/Sources/sourcesSRS6.csv",
source_factors = NULL,
conc_dep = TRUE,
data_type = "means",
mix)
discr <- load_discr_data(file("data/TEF/FCE_TEF_SRS6.csv"), mix)
plot_data(filename = "figures/isospace/SRS6_isospace_plot",
plot_save_pdf = TRUE,
plot_save_png = TRUE,
mix, source, discr)
model_filename <- "data/Consumers/SRS6_mix.txt"
write_JAGS_model(model_filename, resid_err = FALSE, process_err = TRUE, mix, source)
jags.SRS6 <- run_model(run = "test", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
jags.SRS6 <- run_model(run = "normal", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
output_jags.SRS6 <- list(summary_save = TRUE,
summary_name = "data/JAGS_Output/SRS6/FCESRS6_sumstats",
sup_post = FALSE,
plot_post_save_pdf = T,
plot_post_name = "data/JAGS_Output/SRS6/FCESRS6_plot",
sup_pairs = FALSE,
plot_pairs_save_pdf = T,
plot_pairs_name = "data/JAGS_Output/SRS6/FCESRS6_pairs",
sup_xy = TRUE,
plot_xy_save_pdf = T,
plot_xy_name = "data/JAGS_Output/SRS6/FCESRS6_plot",
gelman = TRUE,
heidel = FALSE,
geweke = TRUE,
diag_save = TRUE,
diag_name = "data/JAGS_Output/SRS6/FCESRS6_Diagnostic",
indiv_effect = FALSE,
plot_post_save_png = FALSE,
plot_pairs_save_png = FALSE,
plot_xy_save_png = FALSE)
output_JAGS(jags.SRS6, mix, source, output_jags.SRS6)
mixtable_SRS6 = mixTable("data/JAGS_Output/SRS6/FCESRS6_sumstats.txt", type = "SRS6", nest = TRUE)
write.csv(mixtable_SRS6, "data/Mix_Quants/MT_SRS6.csv", row.names = FALSE)
# combinedSRS6 <- combine_sources(jags.SRS6, mix, source, alpha.prior=1,
# groups=list(green=c('Phytoplankton','Filamentous Green Algae' ), brown=c('Mangrove', 'Red Macroalgae')))
#
# # get posterior medians for new source groupings
# apply(combinedSRS6$post, 2, median)
# # summary_stat(combinedSRS6, meanSD=T, quantiles=c(0.025, 0.25, 0.5, 0.75, 0.975), savetxt=T,
# # filename = "SRS6_combined_sumstats" )
# TS Mixing Models ----
# TS3
TS3mix <- SIa %>% filter(site == 'TS3', common_name != "Egyptian paspalidium", group == 'Consumer') %>% rename('d13C' = 'md13C', 'd15N' = 'md15N', 'd34S' = 'md34S')
write.csv(TS3mix, "data/Consumers/TS3mix.csv", row.names = FALSE)
mix <- load_mix_data(filename = "data/Consumers/TS3mix.csv",
iso_names = c("d13C", "d15N", "d34S"),
factors = c('common_name', 'hydroseason'),
fac_random = c(FALSE, FALSE),
fac_nested = c(FALSE, FALSE),
cont_effects = NULL)
source <- load_source_data(filename = "data/Sources/sourcesTS3.csv",
source_factors = NULL,
conc_dep = TRUE,
data_type = "means",
mix)
discr <- load_discr_data(file("data/TEF/FCE_TEF_TS3.csv"), mix)
plot_data(filename = "figures/isospace/TS3_isospace_plot",
plot_save_pdf = TRUE,
plot_save_png = TRUE,
mix, source, discr)
model_filename <- "data/Consumers/TS3_mix.txt"
write_JAGS_model(model_filename, resid_err = FALSE, process_err = TRUE, mix, source)
jags.TS3 <- run_model(run = "test", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
jags.TS3 <- run_model(run = "normal", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
output_jags.TS3 <- list(summary_save = TRUE,
summary_name = "data/JAGS_Output/TS3/FCETS3_sumstats",
sup_post = FALSE,
plot_post_save_pdf = T,
plot_post_name = "data/JAGS_Output/TS3/FCETS3_plot",
sup_pairs = FALSE,
plot_pairs_save_pdf = T,
plot_pairs_name = "data/JAGS_Output/TS3/FCETS3_pairs",
sup_xy = TRUE,
plot_xy_save_pdf = T,
plot_xy_name = "data/JAGS_Output/TS3/FCETS3_plot",
gelman = TRUE,
heidel = FALSE,
geweke = TRUE,
diag_save = TRUE,
diag_name = "data/JAGS_Output/TS3/FCETS3_Diagnostic",
indiv_effect = FALSE,
plot_post_save_png = FALSE,
plot_pairs_save_png = FALSE,
plot_xy_save_png = FALSE)
output_JAGS(jags.TS3, mix, source, output_jags.TS3)
mixtable_TS3 = mixTable("data/JAGS_Output/TS3/FCETS3_sumstats.txt", type = "TS3", nest = TRUE)
write.csv(mixtable_TS3, "data/Mix_Quants/MT_TS3.csv", row.names = FALSE)
# ##combine posterior ground into brown/green
# combinedTS3 <- combine_sources(jags.TS3, mix, source, alpha.prior=1,
# groups=list(green=c('Periphyton'), brown=c('Dry Sawgrass','Wet Sawgrass', 'Floc' )))
#
# # get posterior medians for new source groupings
# apply(combinedTS3$post, 2, median)
# summary_stat(combinedTS3, meanSD=FALSE, quantiles=c(0.025, 0.25, 0.5, 0.75, 0.975), savetxt=T,
# filename = "TS3_combined_sumstats", na.rm = TRUE)
# TS7
TS7mix <- SIa %>% filter(site == 'TS7', common_name != "Egyptian paspalidium", group == 'Consumer') %>% rename('d13C' = 'md13C', 'd15N' = 'md15N', 'd34S' = 'md34S')
write.csv(TS7mix, "data/Consumers/TS7mix.csv", row.names = FALSE)
mix <- load_mix_data(filename = "data/Consumers/TS7mix.csv",
iso_names = c("d13C", "d15N", "d34S"),
factors = c('common_name', 'hydroseason'),
fac_random = c(FALSE, FALSE),
fac_nested = c(FALSE, FALSE),
cont_effects = NULL)
source <- load_source_data(filename = "data/Sources/sourcesTS7.csv",
source_factors = NULL,
conc_dep = TRUE,
data_type = "means",
mix)
discr <- load_discr_data(file("data/TEF/FCE_TEF_TS7.csv"), mix)
plot_data(filename = "figures/isospace/TS7_isospace_plot",
plot_save_pdf = TRUE,
plot_save_png = TRUE,
mix, source, discr)
model_filename <- "data/Consumers/TS7_mix.txt"
write_JAGS_model(model_filename, resid_err = FALSE, process_err = TRUE, mix, source)
jags.TS7 <- run_model(run = "test", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
jags.TS7 <- run_model(run = "normal", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
output_jags.TS7 <- list(summary_save = TRUE,
summary_name = "data/JAGS_Output/TS7/FCETS7_sumstats",
sup_post = FALSE,
plot_post_save_pdf = T,
plot_post_name = "data/JAGS_Output/TS7/FCETS7_plot",
sup_pairs = FALSE,
plot_pairs_save_pdf = T,
plot_pairs_name = "data/JAGS_Output/TS7/FCETS7_pairs",
sup_xy = TRUE,
plot_xy_save_pdf = T,
plot_xy_name = "data/JAGS_Output/TS7/FCETS7_plot",
gelman = TRUE,
heidel = FALSE,
geweke = TRUE,
diag_save = TRUE,
diag_name = "data/JAGS_Output/TS7/FCETS7_Diagnostic",
indiv_effect = FALSE,
plot_post_save_png = FALSE,
plot_pairs_save_png = FALSE,
plot_xy_save_png = FALSE)
output_JAGS(jags.TS7, mix, source, output_jags.TS7)
mixtable_TS7 = mixTable("data/JAGS_Output/TS7/FCETS7_sumstats.txt", type = "TS7", nest = TRUE)
write.csv(mixtable_TS7, "data/Mix_Quants/MT_TS7.csv", row.names = FALSE)
# ##combine posterior ground into brown/green
# combinedTS7 <- combine_sources(jags.TS7, mix, source, alpha.prior=1,
# groups=list(green=c('SPOM', 'Epiphytes'), brown=c('Seagrass','Mangrove' )))
#
# # get posterior medians for new source groupings
# apply(combinedTS7$post, 2, median)
# summary_stat(combinedTS7, meanSD=FALSE, quantiles=c(0.025, 0.25, 0.5, 0.75, 0.975), savetxt=T,
# filename = "TS7_combined_sumstats", na.rm = TRUE)
# TS9
TS9mix <- SIa %>% filter(site == 'TS9', common_name != "Egyptian paspalidium", group == 'Consumer') %>% rename('d13C' = 'md13C', 'd15N' = 'md15N', 'd34S' = 'md34S')
write.csv(TS9mix, "data/Consumers/TS9mix.csv", row.names = FALSE)
mix <- load_mix_data(filename = "data/Consumers/TS9mix.csv",
iso_names = c("d13C", "d15N", "d34S"),
factors = c('common_name', 'hydroseason'),
fac_random = c(FALSE, FALSE),
fac_nested = c(FALSE, FALSE),
cont_effects = NULL)
source <- load_source_data(filename = "data/Sources/sourcesTS9.csv",
source_factors = NULL,
conc_dep = TRUE,
data_type = "means",
mix)
discr <- load_discr_data(file("data/TEF/FCE_TEF_TS9.csv"), mix)
plot_data(filename = "figures/isospace/TS9_isospace_plot",
plot_save_pdf = TRUE,
plot_save_png = TRUE,
mix, source, discr)
model_filename <- "data/Consumers/TS9_mix.txt"
write_JAGS_model(model_filename, resid_err = FALSE, process_err = TRUE, mix, source)
jags.TS9 <- run_model(run = "test", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
jags.TS9 <- run_model(run = "normal", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
output_jags.TS9 <- list(summary_save = TRUE,
summary_name = "data/JAGS_Output/TS9/FCETS9_sumstats",
sup_post = FALSE,
plot_post_save_pdf = T,
plot_post_name = "data/JAGS_Output/TS9/FCETS9_plot",
sup_pairs = FALSE,
plot_pairs_save_pdf = T,
plot_pairs_name = "data/JAGS_Output/TS9/FCETS9_pairs",
sup_xy = TRUE,
plot_xy_save_pdf = T,
plot_xy_name = "data/JAGS_Output/TS9/FCETS9_plot",
gelman = TRUE,
heidel = FALSE,
geweke = TRUE,
diag_save = TRUE,
diag_name = "data/JAGS_Output/TS9/FCETS9_Diagnostic",
indiv_effect = FALSE,
plot_post_save_png = FALSE,
plot_pairs_save_png = FALSE,
plot_xy_save_png = FALSE)
output_JAGS(jags.TS9, mix, source, output_jags.TS9)
mixtable_TS9 = mixTable("data/JAGS_Output/TS9/FCETS9_sumstats.txt", type = "TS9", nest = TRUE)
write.csv(mixtable_TS9, "data/Mix_Quants/MT_TS9.csv", row.names = FALSE)
# ##combine posterior ground into brown/green
# combinedTS9 <- combine_sources(jags.TS9, mix, source, alpha.prior=1,
# groups=list(green=c('Epiphytes', 'SPOM'), brown=c('Seagrass', 'Mangrove' )))
#
# # get posterior medians for new source groupings
# apply(combinedTS9$post, 2, median)
# # summary_stat(combinedTS9, meanSD=FALSE, quantiles=c(0.025, 0.25, 0.5, 0.75, 0.975), savetxt=T,
# # filename = "TS9_combined_sumstats" )
#
# TS10
TS10mix <- SIa %>% filter(site == 'TS10', common_name != "Egyptian paspalidium", group == 'Consumer') %>% rename('d13C' = 'md13C', 'd15N' = 'md15N', 'd34S' = 'md34S')
write.csv(TS10mix, "data/Consumers/TS10mix.csv", row.names = FALSE)
mix <- load_mix_data(filename = "data/Consumers/TS10mix.csv",
iso_names = c("d13C", "d15N", "d34S"),
factors = c('common_name', 'hydroseason'),
fac_random = c(FALSE, FALSE),
fac_nested = c(FALSE, FALSE),
cont_effects = NULL)
source <- load_source_data(filename = "data/Sources/sourcesTS10.csv",
source_factors = NULL,
conc_dep = TRUE,
data_type = "means",
mix)
discr <- load_discr_data(file("data/TEF/FCE_TEF_TS10.csv"), mix)
plot_data(filename = "figures/isospace/TS10_isospace_plot",
plot_save_pdf = TRUE,
plot_save_png = TRUE,
mix, source, discr)
model_filename <- "data/Consumers/TS10_mix.txt"
write_JAGS_model(model_filename, resid_err = FALSE, process_err = TRUE, mix, source)
jags.TS10 <- run_model(run = "test", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
jags.TS10 <- run_model(run = "normal", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
output_jags.TS10 <- list(summary_save = TRUE,
summary_name = "data/JAGS_Output/TS10/FCETS10_sumstats",
sup_post = FALSE,
plot_post_save_pdf = T,
plot_post_name = "data/JAGS_Output/TS10/FCETS10_plot",
sup_pairs = FALSE,
plot_pairs_save_pdf = T,
plot_pairs_name = "data/JAGS_Output/TS10/FCETS10_pairs",
sup_xy = TRUE,
plot_xy_save_pdf = T,
plot_xy_name = "data/JAGS_Output/TS10/FCETS10_plot",
gelman = TRUE,
heidel = FALSE,
geweke = TRUE,
diag_save = TRUE,
diag_name = "data/JAGS_Output/TS10/FCETS10_Diagnostic",
indiv_effect = FALSE,
plot_post_save_png = FALSE,
plot_pairs_save_png = FALSE,
plot_xy_save_png = FALSE)
output_JAGS(jags.TS10, mix, source, output_jags.TS10)
mixtable_TS10 = mixTable("data/JAGS_Output/TS10/FCETS10_sumstats.txt", type = "TS10", nest = TRUE)
write.csv(mixtable_TS10, "data/Mix_Quants/MT_TS10.csv", row.names = FALSE)
# ##combine posterior ground into brown/green
# combinedTS10 <- combine_sources(jags.TS10, mix, source, alpha.prior=1,
# groups=list(green=c('Epiphytes', 'SPOM'), brown=c('Seagrass', 'Mangrove' )))
#
# # get posterior medians for new source groupings
# apply(combinedTS10$post, 2, median)
# # summary_stat(combinedTS10, meanSD=FALSE, quantiles=c(0.025, 0.25, 0.5, 0.75, 0.975), savetxt=T,
# # filename = "TS10_combined_sumstats" )
# TS11 Mixing Model
TS11mix <- SIa %>% filter(site == 'TS11', common_name != "Egyptian paspalidium", group == 'Consumer') %>% rename('d13C' = 'md13C', 'd15N' = 'md15N', 'd34S' = 'md34S')
write.csv(TS11mix, "data/Consumers/TS11mix.csv", row.names = FALSE)
mix <- load_mix_data(filename = "data/Consumers/TS11mix.csv",
iso_names = c("d13C", "d15N", "d34S"),
factors = c('common_name', 'hydroseason'),
fac_random = c(FALSE, FALSE),
fac_nested = c(FALSE, FALSE),
cont_effects = NULL)
source <- load_source_data(filename = "data/Sources/sourcesTS11.csv",
source_factors = NULL,
conc_dep = TRUE,
data_type = "means",
mix)
discr <- load_discr_data(file("data/TEF/FCE_TEF_TS11.csv"), mix)
plot_data(filename = "figures/isospace/TS11_isospace_plot",
plot_save_pdf = TRUE,
plot_save_png = TRUE,
mix, source, discr)
model_filename <- "data/Consumers/TS11_mix.txt"
write_JAGS_model(model_filename, resid_err = FALSE, process_err = TRUE, mix, source)
jags.TS11 <- run_model(run = "test", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
jags.TS11 <- run_model(run = "normal", mix, source, discr, model_filename,
alpha.prior = 1, resid_err = FALSE, process_err = FALSE)
output_jags.TS11 <- list(summary_save = TRUE,
summary_name = "data/JAGS_Output/TS11/FCETS11_sumstats",
sup_post = FALSE,
plot_post_save_pdf = T,
plot_post_name = "data/JAGS_Output/TS11/FCETS11_plot",
sup_pairs = FALSE,
plot_pairs_save_pdf = T,
plot_pairs_name = "data/JAGS_Output/TS11/FCETS11_pairs",
sup_xy = TRUE,
plot_xy_save_pdf = T,
plot_xy_name = "data/JAGS_Output/TS11/FCETS11_plot",
gelman = TRUE,
heidel = FALSE,
geweke = TRUE,
diag_save = TRUE,
diag_name = "data/JAGS_Output/TS11/FCETS11_Diagnostic",
indiv_effect = FALSE,
plot_post_save_png = FALSE,
plot_pairs_save_png = FALSE,
plot_xy_save_png = FALSE)
output_JAGS(jags.TS11, mix, source, output_jags.TS11)
mixtable_TS11 = mixTable("data/JAGS_Output/TS11/FCETS11_sumstats.txt", type = "TS11", nest = TRUE)
write.csv(mixtable_TS11, "data/Mix_Quants/MT_TS11.csv", row.names = FALSE)
# ##combine posterior ground into brown/green
# combinedTS11 <- combine_sources(jags.TS11, mix, source, alpha.prior=1,
# groups=list(green=c('SPOM','Epiphytes'), brown=c('Seagrass', 'Mangrove')))
#
# # get posterior medians for new source groupings
# apply(combinedTS11$post, 2, median)
# summary_stat(combinedTS11, meanSD=FALSE, quantiles=c(0.025, 0.25, 0.5, 0.75, 0.975), savetxt=T,
# filename = "TS11_combined_sumstats" )
# Aggregating by Energy Channel ----
MixOut_RB10 = read.csv('data/Mix_Quants/MT_RB10.csv')
MixOut_SRS3 = read.csv('data/Mix_Quants/MT_SRS3.csv')
MixOut_SRS4 = read.csv('data/Mix_Quants/MT_SRS4.csv')
MixOut_SRS6 = read.csv('data/Mix_Quants/MT_SRS6.csv')
MixOut_TS3 = read.csv('data/Mix_Quants/MT_TS3.csv')
MixOut_TS7 = read.csv('data/Mix_Quants/MT_TS7.csv')
MixOut_TS9 = read.csv('data/Mix_Quants/MT_TS9.csv')
MixOut_TS10 = read.csv('data/Mix_Quants/MT_TS10.csv')
MixOut_TS11 = read.csv('data/Mix_Quants/MT_TS11.csv')
SRSMixout_gb <- rbind(MixOut_RB10, MixOut_SRS3, MixOut_SRS4, MixOut_SRS6)
SRSMixout_gb = SRSMixout_gb %>%
rename(site = type, season = code) %>%
mutate(path = case_when(
source %in% c("Epiphytes", "Phytoplankton", "Filamentous Green Algae", "Periphyton") ~ "green",
source %in% c("Mangrove", "Sawgrass", "Red Macroalgae",'Floc') ~ "brown",
TRUE ~ NA_character_ # For other cases, you can assign NA or something else if needed
))
SRSMixout_gb = SRSMixout_gb %>%
group_by(name, site, season, path) %>%
summarize(value = sum(mean)) %>%
pivot_wider(names_from = path, values_from = value)
TSMixout_gb <- rbind(MixOut_TS3, MixOut_TS7, MixOut_TS9, MixOut_TS10, MixOut_TS11)
TSMixout_gb = TSMixout_gb %>%
rename(site = type, season = code) %>%
mutate(path = case_when(
source %in% c("Epiphytes", "Phytoplankton", "Filamentous Green Algae", "Periphyton", "Epiphytic microalgae", 'SPOM') ~ "green",
source %in% c("Mangrove", "Sawgrass", "Red Macroalgae", "Seagrass", 'Floc') ~ "brown",
TRUE ~ NA_character_ # For other cases, you can assign NA or something else if needed
))
TSMixout_gb = TSMixout_gb %>%
group_by(name, site, season, path) %>%
summarize(value = sum(mean)) %>%
pivot_wider(names_from = path, values_from = value)
y_label_formatter <- function(x) {
ifelse(x %% 1 == 0, formatC(x, format = "f", digits = 0), formatC(x, format = "f", digits = 2))
}
combined_df = bind_rows(SRSMixout_gb,TSMixout_gb) %>%
mutate(transect = case_when(
site %in% c("SRS3", "SRS4","SRS6", "RB10") ~ "Shark River Slough",
site %in% c("TS3", "TS7", "TS9", "TS10", "TS11") ~ "Taylor Slough"))
unique_names <- combined_df %>%
group_by(site, season) %>%
summarize(unique_names = n_distinct(name))
# Combined Brown vs Green boxplot----
combined_df <- combined_df %>%
mutate(site = ifelse(site == "RB10", "Upper River",
ifelse(site == "SRS3", "SRS Marsh",
ifelse(site == "SRS4", "Mid River",
ifelse(site == "SRS6", "Lower River",
ifelse(site == "TS3", "TS Marsh",
ifelse(site == "TS7", "Mangrove Ecotone",
ifelse(site == "TS9", "Inner Bay",
ifelse(site == "TS10", "Mid Bay",
ifelse(site == "TS11", "Outer Bay", site)
)
)
)
)
)
)
)
),
site = factor(site, levels = c( "SRS Marsh","Upper River","Mid River","Lower River", "TS Marsh", "Mangrove Ecotone", "Inner Bay", "Mid Bay", "Outer Bay")))
combined_df = combined_df %>%
group_by(site, season) %>%
mutate(fill = mean(green),
site = factor(site, levels = c( "Outer Bay","Mid Bay","Inner Bay","Mangrove Ecotone" ,"TS Marsh", "Lower River", "Mid River","Upper River" ,"SRS Marsh" )))
# generates a table of average contributions for brown vs green at each site during each season
bvg_table = combined_df %>%
group_by(site,season) %>%
summarise(green = mean(green))
bvg_table_ft <- flextable(bvg_table,
col_keys = c("site", "season","green")) %>%
add_header_row(colwidths = c(1,1,1), values = c("Site", "Season", "Mean Green Pathway Contribution")) %>%
set_header_labels(site = "Site",
season = "Season",
green = "Mean Green Pathway Contribution") %>%
colformat_double(digits = 2) %>%
theme_box() %>%
align(align = "center") %>%
align(part = "header", align = "center") %>%
# compose(j = "Genus_spp",
# value = as_paragraph(as_i(Genus_spp))) %>%
merge_v(part = "header")
bvg_table_ft
save_as_docx(bvg_table_ft, path = "tables/bvg_table_flex.docx")
mixoutput_bxplt_gb_combined <-ggplot(combined_df,aes(x=site, y = green, fill=fill, width=0.8))+
geom_boxplot()+
theme_bw()+
scale_fill_gradient2(low = "#663300",
high = "#92D050",
mid = 'white',
midpoint = 0.5,
limits = c(0,1),
na.value = "grey50") +
facet_grid(transect~season, scales = "free_y") +
theme(axis.title = element_text(size = 20),
axis.text.y = element_text(size = 20, colour = "black"),
axis.text.x = element_text(size = 18, colour = "black"),
plot.title = element_text(size = 18, hjust=0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = 'right',
legend.title = element_text(size = 14),
strip.text.x = element_text(size = 18),
strip.text.y = element_text(size = 18),
legend.text = element_text(size = 16)) +
scale_y_continuous(
breaks = c(0.0, 0.25, 0.5, 0.75, 1.0),
limits = c(0,1),