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environmental_analysis.R
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environmental_analysis.R
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################################################################################
### Environmental Data Analysis ###
### code by HGR 7/2020 ###
################################################################################
### Load Libraries #############################################################
library(data.table)
library(lubridate)
library(readxl)
library(ggfortify)
library(ggpubr)
library(viridis)
library(lme4)
library(lmerTest)
library(emmeans)
library(MuMIn)
library(mgcv)
library(raster)
library(sf)
library(gstat)
library(stars)
library(automap)
library(ggnewscale)
library(magick)
library(tidyverse)
################################################################################
### Load Data ##################################################################
flux.daily <- fread("/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/input_data/flux_daily.csv")
flux.annual <- read.csv("/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/input_data/flux_annual.csv") %>%
mutate(flux.year = as.factor(flux.year))
filenames <- list.files('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/input_data/weather/',
pattern = 'csv$',
full.names = TRUE)
# weather <- fread('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/input_data/hobo_half_hourly_gap_filled.csv')
filenames <- list.files('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/input_data/weather/hourly',
pattern = 'csv$',
full.names = TRUE)
weather <- map_dfr(filenames,
~ read.csv(.x,
header = TRUE) %>%
select(year = matches('[Y|y]ear'), DOY,
hourmin = matches('hour'), Tair, par = PAR,
precip = matches('[P|p]recip'), rh = RH))
weather <- data.table(weather)
# Format time variables
weather[, date := parse_date_time(as.Date(DOY-1, origin = paste(year, '-01-01', sep = '')), orders = c('Y!-m!*-d!'))] # doy-1 because as.Date is 0 indexed, while lubridate::yday() (used to create doy variable) is 1 indexed
weather[, year := year(date)]
weather[, month := month(date)]
weather[, season := fifelse(month <= 4 | month >= 10,
'ngs',
'gs')]
weather[, flux.year := fifelse(month >= 10,
year + 1,
year)]
# Neaten
weather.daily <- weather[flux.year >= 2009,
.(tair.mean = mean(Tair, na.rm = TRUE),
tair.min = min(Tair, na.rm = TRUE),
tair.max = max(Tair, na.rm = TRUE),
precip = sum(precip, na.rm = TRUE),
par = mean(par, na.rm = TRUE)),
by = .(flux.year, month, date)]
weather.seasonal <- weather[flux.year >= 2009,
.(tair.mean = mean(Tair, na.rm = TRUE),
tair.min = min(Tair, na.rm = TRUE),
tair.max = max(Tair, na.rm = TRUE),
precip = sum(precip, na.rm = TRUE),
par = mean(par, na.rm = TRUE)),
by = .(flux.year, season)]
weather.annual <- weather[flux.year >= 2009,
.(tair.mean = mean(Tair, na.rm = TRUE),
tair.min = min(Tair, na.rm = TRUE),
tair.max = max(Tair, na.rm = TRUE),
precip = sum(precip, na.rm = TRUE),
par = mean(par, na.rm = TRUE)),
by = .(flux.year)]
# Snow depth
snow.depth <- read.csv('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/input_data/snow_depth/plot_snow_depth_2009_2021.csv')
# Snow free date
snow.free.2009.2016 <- read.csv('/home/heidi/ecoss_server/Schuur Lab/2020 New_Shared_Files/DATA/CiPEHR & DryPEHR/CO2 fluxes/Snow free/LTER_Data/2016/CiPEHR_dates_snowfree_2010_2016.csv',
na.strings = c('N/A', 'ND')) %>%
filter(Plot.Type == 'F') %>%
select(flux.year = Year, fence = Fence, plot = Plot, treatment = Treatment,
doy.snow.free = DOY.Snow.Free) %>%
mutate(treatment = case_when(plot == 2 | plot == 4 ~ 'Control',
plot == 1 | plot == 3 ~ 'Air Warming',
plot == 6 | plot == 8 ~ 'Soil Warming',
plot == 5 | plot == 7 ~ 'Air + Soil Warming'))
snow.free.2017 <- read_excel('/home/heidi/ecoss_server/Schuur Lab/2020 New_Shared_Files/Computer_Backups/Healy cabin computer backup/2017/CiPEHR_DryPEHR/SnowFree/Date Plots Snow Free_2017.xlsx',
sheet = 1) %>%
slice(-1) %>%
select(plot = `Plot Number`, flux = Flux) %>%
mutate(flux = as_date(as.numeric(flux), origin = '1899-12-30')) %>% # excel uses 1900-01-01, but I think there is a difference in indexing that is causing the 2 day offset?
separate(plot, into = c('fence', 'plot'), sep = '_', convert = TRUE) %>%
mutate(flux.year = 2017,
treatment = case_when(plot == 2 | plot == 4 ~ 'Control',
plot == 1 | plot == 3 ~ 'Air Warming',
plot == 6 | plot == 8 ~ 'Soil Warming',
plot == 5 | plot == 7 ~ 'Air + Soil Warming'),
doy.snow.free = yday(flux)) %>%
select(colnames(snow.free.2009.2016))
snow.free.2018 <- read_excel('/home/heidi/ecoss_server/Schuur Lab/2020 New_Shared_Files/Computer_Backups/Healy cabin computer backup/2018/CiPEHR_DryPEHR/Winter_2017_2018/Date Plots Snow Free_2018.xlsx',
sheet = 1) %>%
slice(-1) %>%
select(plot = `Plot Number`, flux = Flux) %>%
mutate(flux = as_date(as.numeric(flux), origin = '1899-12-30')) %>% # excel uses 1900-01-01, but I think there is a difference in indexing that is causing the 2 day offset?
separate(plot, into = c('fence', 'plot'), sep = '_', convert = TRUE) %>%
mutate(flux.year = year(flux),
treatment = case_when(plot == 2 | plot == 4 ~ 'Control',
plot == 1 | plot == 3 ~ 'Air Warming',
plot == 6 | plot == 8 ~ 'Soil Warming',
plot == 5 | plot == 7 ~ 'Air + Soil Warming'),
doy.snow.free = yday(flux)) %>%
select(colnames(snow.free.2009.2016))
snow.free.2019 <- read_excel('/home/heidi/ecoss_server/Schuur Lab/2020 New_Shared_Files/Computer_Backups/Healy cabin computer backup/2019/CiPEHR_DryPEHR/Phenology/SnowFree/Date Plots Snow Free_2019.xlsx',
sheet = 1) %>%
slice(-1) %>%
select(plot = `Plot Number`, flux = Flux) %>%
mutate(flux = as_date(as.numeric(flux), origin = '1899-12-30')) %>% # excel uses 1900-01-01, but I think there is a difference in indexing that is causing the 2 day offset?
separate(plot, into = c('fence', 'plot'), sep = '_', convert = TRUE) %>%
mutate(flux.year = year(flux),
treatment = case_when(plot == 2 | plot == 4 ~ 'Control',
plot == 1 | plot == 3 ~ 'Air Warming',
plot == 6 | plot == 8 ~ 'Soil Warming',
plot == 5 | plot == 7 ~ 'Air + Soil Warming'),
doy.snow.free = yday(flux)) %>%
select(colnames(snow.free.2009.2016))
snow.free.2020 <- read_excel('/home/heidi/ecoss_server/Schuur Lab/2020 New_Shared_Files/Computer_Backups/Healy cabin computer backup/2020/CiPEHR_DryPEHR/Phenology/SnowFree/Date Plots Snow Free_2020.xlsx',
sheet = 1) %>%
slice(-1) %>%
select(plot = `Plot Number`, flux = Flux) %>%
mutate(flux = as_date(as.numeric(flux), origin = '1899-12-30')) %>% # excel uses 1900-01-01, but I think there is a difference in indexing that is causing the 2 day offset?
separate(plot, into = c('fence', 'plot'), sep = '_', convert = TRUE) %>%
mutate(flux.year = year(flux),
treatment = case_when(plot == 2 | plot == 4 ~ 'Control',
plot == 1 | plot == 3 ~ 'Air Warming',
plot == 6 | plot == 8 ~ 'Soil Warming',
plot == 5 | plot == 7 ~ 'Air + Soil Warming'),
doy.snow.free = yday(flux)) %>%
select(colnames(snow.free.2009.2016))
snow.free.2021 <- read_excel('/home/heidi/ecoss_server/Schuur Lab/2020 New_Shared_Files/Computer_Backups/Healy cabin computer backup/2021/CiPEHR_DryPEHR/SnowFree_2021/Date Plots Snow Free_2021.xlsx',
sheet = 1) %>%
slice(-1) %>%
select(plot = `Plot Number`, flux = Flux) %>%
mutate(flux = as_date(as.numeric(flux), origin = '1899-12-30')) %>% # excel uses 1900-01-01, but I think there is a difference in indexing that is causing the 2 day offset?
separate(plot, into = c('fence', 'plot'), sep = '_', convert = TRUE) %>%
mutate(flux.year = 2021,
treatment = case_when(plot == 2 | plot == 4 ~ 'Control',
plot == 1 | plot == 3 ~ 'Air Warming',
plot == 6 | plot == 8 ~ 'Soil Warming',
plot == 5 | plot == 7 ~ 'Air + Soil Warming'),
doy.snow.free = yday(flux)) %>%
select(colnames(snow.free.2009.2016))
snow.free <- snow.free.2009.2016 %>%
rbind.data.frame(snow.free.2017) %>%
rbind.data.frame(snow.free.2018) %>%
rbind.data.frame(snow.free.2019) %>%
rbind.data.frame(snow.free.2020) %>%
rbind.data.frame(snow.free.2021)
rm(snow.free.2009.2016, snow.free.2017, snow.free.2018, snow.free.2019,
snow.free.2020, snow.free.2021)
################################################################################
######################## DEFINE FUNCTIONS TO EXTRACT AND GRAPH CI #########################
#Extract the coefficients for the fixed effects from your model, make a dataframe with them called model
extract_ci <- function(x) {coefs<-fixef(x)
modeldf<-as.data.frame(coefs)
#calculate confidence intervals; merge fixed effects and ci into one dataframe
ci <- confint(x,method="boot",boot.type="norm",level=0.95,nsim=1000)
modelci<-merge(ci,modeldf,by="row.names",all.x=F)
#rename colnames so that they make sense and remove symbols
colnames(modelci)<-c("term","min","max","coefs")
return (modelci)}
# graph CI
graph_ci <- function(ci,figtitle,model) {ggplot(ci,aes(x=names,y=coefs))+
geom_errorbar(aes(ymin=min,ymax=max),width=0,size=1)+
geom_point(aes(size=2))+
labs (title = paste(figtitle, ", AIC:", round(AIC(model),2), sep =" ") , x = "Fixed effect", y = "Effect size and 95% CI") +
guides(size=F,shape=F)+
theme_bw()+
theme(axis.text.x=element_text(size=18),
axis.title.x=element_text(size=26),
axis.title.y=element_text(size=26,vjust=1),
axis.text.y=element_text(size=22),
panel.grid.minor=element_blank(),
panel.grid.major.x=element_blank())+
geom_hline(yintercept=0)+
coord_flip() }
#################################################################################
### PCA ########################################################################
# need to finalize which variables to include
env.annual.plot <- flux.annual %>%
select(-c(matches('rh'),
matches('ndvi'),
gdd, fdd, winter.fdd, precip)) %>%
mutate(flux.year = factor(as.numeric(as.character(flux.year))),
treatment = factor(treatment,
levels = c('Control',
'Air Warming',
'Soil Warming',
'Air + Soil Warming')),
subsidence = -1*subsidence,
wtd.mean = -1*wtd.mean) %>%
na.omit()
env.annual <- env.annual.plot %>%
select(-c(flux.year, block, fence, plot, plot.id, treatment, matches('sum')))
env.annual.subset <- env.annual %>%
select(t5.mean, t5.sd,
t10.mean, t10.sd,
t20.mean, t20.sd,
t40.mean, t40.sd,
vwc.mean, vwc.sd,
gwc.mean, gwc.sd,
wtd.mean, wtd.sd,
subsidence, # adding or removing subsidence doesn't change much in pca
alt, tp,
biomass,
w.snow.depth = spring.snow.depth,
w.t5.min = winter.t5.min,
w.t10.min = winter.t10.min,
w.t20.min = winter.t20.min,
w.t40.min = winter.t40.min)
env.annual.subset.norm <- env.annual.subset %>%
mutate(across(all_of(colnames(.)), ~(.x - mean(.x))/sd(.x)))
# pca.annual.norm <- prcomp(as.matrix(env.annual.subset.norm))
# saveRDS(pca.annual.norm,
# '/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/model_output/env_pca_normalized.rds')
pca.annual.norm <- readRDS('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/model_output/env_pca_normalized.rds')
# get PCA scores for each plot in each year
env.annual.plot <- env.annual.plot %>%
cbind.data.frame(pca.annual.norm[['x']])
# write.csv(env.annual.plot,
# '/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/model_output/env_pca_output.csv',
# row.names = FALSE)
# # get PCA scores for each variable
# write.csv(pca.annual.norm[['rotation']],
# '/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/model_output/env_pca_variable_rotation.csv',
# row.names = FALSE)
# Environmental PCA colored by subsidence
pca.plot.norm <- autoplot(pca.annual.norm, data = env.annual.plot,
colour = 'flux.year', shape = 'treatment', alpha = 0.8,
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.colour = 'black') +
scale_color_viridis(name = '',
direction = -1,
discrete = TRUE,
breaks = seq(2009, 2021)) +
scale_shape_manual(name = '',
values = c(1, 0, 16, 15)) +
coord_fixed() +
theme_bw() +
coord_cartesian()
pca.plot.norm
# zoom in on the center mass of red
pca.plot.norm.zoom <- autoplot(pca.annual.norm, data = env.annual.plot,
colour = 'flux.year', shape = 'treatment', alpha = 0.8,
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.colour = 'black') +
scale_color_viridis(name = '',
direction = -1,
discrete = TRUE,
breaks = seq(2009, 2021)) +
scale_shape_manual(name = '',
values = c(1, 0, 16, 15)) +
scale_y_continuous(limits = c(-0.05, 0.05)) +
scale_x_continuous(limits = c(-0.05, 0.05)) +
coord_fixed() +
theme_bw()
pca.plot.norm.zoom
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/environmental_pca.jpg',
# pca.plot.norm,
# height = 6,
# width = 6)
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/environmental_pca.pdf',
# pca.plot.norm,
# height = 6,
# width = 6)
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/environmental_pca_zoom.jpg',
# pca.plot.norm.zoom,
# height = 6,
# width = 6)
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/environmental_pca_zoom.pdf',
# pca.plot.norm.zoom,
# height = 6,
# width = 6)
autoplot(pca.annual.norm, data = env.annual.plot, colour = 'plot.id',
loadings = TRUE, loadings.label = TRUE) +
scale_color_viridis(discrete = TRUE)
autoplot(pca.annual.norm, data = env.annual.plot, colour = 'treatment',
loadings = TRUE, loadings.label = TRUE) +
scale_color_manual(breaks = c('Control', 'Air Warming', 'Soil Warming', 'Air + Soil Warming'),
values = c("#0099cc", '#009900', "#990000", '#330000'))
autoplot(pca.annual.norm, data = env.annual.plot, colour = 'flux.year',
loadings = TRUE, loadings.label = TRUE) +
scale_color_viridis(discrete = TRUE)
autoplot(pca.annual.norm, data = env.annual.plot, colour = 'nee.sum',
loadings = TRUE, loadings.label = TRUE) +
scale_color_viridis()
autoplot(pca.annual.norm, data = env.annual.plot, colour = 'gpp.sum',
loadings = TRUE, loadings.label = TRUE) +
scale_color_viridis()
autoplot(pca.annual.norm, data = env.annual.plot, colour = 'reco.sum',
loadings = TRUE, loadings.label = TRUE) +
scale_color_viridis()
################################################################################
### WTD TD Plot ################################################################
wtd.alt.data <- flux.annual %>%
select(flux.year, plot.id, treatment, alt, wtd.mean, wtd.sd) %>%
mutate(year = as.numeric(as.character(flux.year)),
treatment_full = factor(treatment, levels = c('Control', 'Air Warming', 'Soil Warming', 'Air + Soil Warming')),
treatment = factor(case_when(treatment %in% c('Control', 'Air Warming') ~ 'Control',
treatment %in% c('Soil Warming', 'Air + Soil Warming') ~ 'Soil Warming')))
wtd.alt.data.2021 <- wtd.alt.data %>%
filter(year == 2009 | year == 2021)
wtd.alt.start <- wtd.alt.data.2021 %>%
filter(year == 2009) %>%
summarize(wtd.mean.initial = mean(wtd.mean, na.rm = TRUE),
wtd.sd.initial = sd(wtd.mean, na.rm = TRUE),
alt.mean.initial = mean(alt, na.rm = TRUE),
alt.sd.initial = sd(alt, na.rm = TRUE))
wtd.alt.start.treatment <- wtd.alt.data.2021 %>%
filter(year == 2009) %>%
group_by(treatment) %>%
summarize(wtd.mean.initial = mean(wtd.mean, na.rm = TRUE),
wtd.sd.initial = sd(wtd.mean, na.rm = TRUE),
alt.mean.initial = mean(alt, na.rm = TRUE),
alt.sd.initial = sd(alt, na.rm = TRUE))
wtd.alt.delta <- wtd.alt.data.2021 %>%
filter(year == 2021) %>%
summarize(wtd.mean.end = mean(wtd.mean, na.rm = TRUE),
wtd.sd.end = sd(wtd.mean, na.rm = TRUE),
alt.mean.end = mean(alt, na.rm = TRUE),
alt.sd.end = sd(alt, na.rm = TRUE)) %>%
cbind.data.frame(wtd.alt.start) %>%
mutate(treatment = 'Overall',
wtd.diff = wtd.mean.end - wtd.mean.initial,
wtd.diff.sd = sqrt(wtd.sd.end^2 + wtd.sd.initial^2),
alt.diff = alt.mean.end - alt.mean.initial,
alt.diff.sd = sqrt(alt.sd.end^2 + alt.sd.initial^2))
wtd.alt.delta.treatment <- wtd.alt.data.2021 %>%
filter(year == 2021) %>%
group_by(treatment) %>%
summarize(wtd.mean.end = mean(wtd.mean, na.rm = TRUE),
wtd.sd.end = sd(wtd.mean, na.rm = TRUE),
alt.mean.end = mean(alt, na.rm = TRUE),
alt.sd.end = sd(alt, na.rm = TRUE)) %>%
full_join(wtd.alt.start.treatment, by = 'treatment') %>%
mutate(wtd.diff = wtd.mean.end - wtd.mean.initial,
wtd.diff.sd = sqrt(wtd.sd.end^2 + wtd.sd.initial^2),
alt.diff = alt.mean.end - alt.mean.initial,
alt.diff.sd = sqrt(alt.sd.end^2 + alt.sd.initial^2))
wtd.alt.delta <- rbind.data.frame(wtd.alt.delta.treatment,
wtd.alt.delta) %>%
mutate(wtd.cov.initial = wtd.sd.initial/wtd.mean.initial,
wtd.cov.end = wtd.sd.end/wtd.mean.end,
alt.cov.initial = alt.sd.initial/alt.mean.initial,
alt.cov.end = alt.sd.end/alt.mean.end)
rm(wtd.alt.start, wtd.alt.start.treatment, wtd.alt.delta.treatment)
# write.csv(wtd.alt.delta,
# '/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/tables/wtd_change.csv',
# row.names = FALSE)
# Take a look at plots which got drier
drier.plots <- wtd.alt.data.2021 %>%
select(flux.year, plot.id, wtd.mean) %>%
pivot_wider(names_from = flux.year, values_from = wtd.mean, names_prefix = 'year.') %>%
filter(year.2009 < year.2021)
# Compare treatments in 2021
t.test(alt ~ treatment,
data = wtd.alt.data.2021 %>%
filter(year == 2021),
alternative = 'less')
alt.treatment.anova <- lm(alt ~ treatment_full,
data = wtd.alt.data.2021 %>%
filter(year == 2021))
summary(alt.treatment.anova)
emmeans(alt.treatment.anova, specs = pairwise ~ treatment_full)
t.test(wtd.mean ~ treatment,
data = wtd.alt.data.2021 %>%
filter(year == 2021),
alternative = 'greater')
wtd.treatment.anova <- lm(wtd.mean ~ treatment_full,
data = wtd.alt.data.2021 %>%
filter(year == 2021))
summary(wtd.treatment.anova)
emmeans(wtd.treatment.anova, specs = pairwise ~ treatment_full)
# compare start and end
t.test(alt ~ flux.year, data = wtd.alt.data.2021,
alternative = 'less')
t.test(wtd.mean ~ flux.year, data = wtd.alt.data.2021,
alternative = 'greater')
# plot ALT and WTD at start and end
alt.start <- wtd.alt.delta$alt.mean.initial[3]
wtd.start <- wtd.alt.delta$wtd.mean.initial[3]
arrows <- data.frame(xmin = c(max(wtd.alt.data$alt) - (max(wtd.alt.data$alt) - min(wtd.alt.data$alt))/3,
max(wtd.alt.data$alt)),
xmax = c(max(wtd.alt.data$alt) - (max(wtd.alt.data$alt) - min(wtd.alt.data$alt))/15,
max(wtd.alt.data$alt)),
ymin = c(min(wtd.alt.data$wtd.mean*-1),
min(wtd.alt.data$wtd.mean*-1) - (min(wtd.alt.data$wtd.mean*-1) - max(wtd.alt.data$wtd.mean*-1))/15),
ymax = c(min(wtd.alt.data$wtd.mean*-1),
min(wtd.alt.data$wtd.mean*-1) - (min(wtd.alt.data$wtd.mean*-1) - max(wtd.alt.data$wtd.mean*-1))/3))
labels <- data.frame(x = c(max(wtd.alt.data$alt) - (max(wtd.alt.data$alt) - min(wtd.alt.data$alt))/5,
max(wtd.alt.data$alt)),
y = c(min(wtd.alt.data$wtd.mean*-1),
min(wtd.alt.data$wtd.mean*-1) - (min(wtd.alt.data$wtd.mean*-1) - max(wtd.alt.data$wtd.mean*-1))/5),
labels = c('Warmer',
'Wetter'),
angles = c(0, 90))
wtd.alt.plot <- ggplot(wtd.alt.data.2021,
aes(x = alt, y = wtd.mean*-1,
color = treatment,
shape = flux.year)) +
geom_hline(yintercept = 0, linetype = 'dashed') +
geom_vline(xintercept = alt.start, linetype = 'dashed') +
geom_segment(data = arrows,
aes(x = xmin, y = ymin, xend = xmax, yend = ymax),
inherit.aes = FALSE,
arrow = arrow(length = unit(0.25, 'cm'))) +
geom_text(data = labels,
aes(x = x, y = y, label = labels, angle = angles),
inherit.aes = FALSE,
hjust = 0.5,
vjust = -0.5) +
geom_point(size = 2) +
scale_x_continuous(name = 'Active Layer Thickness (cm)') +
scale_y_continuous(name = 'Mean Water Table Depth (cm)') +
scale_color_manual(name = 'Treatment',
values = c('#33AAFF', '#DD0000'),
labels = c('Control', 'Soil\nWarming')) +
scale_shape_manual(name = 'Year',
values = c(1, 16)) +
theme_bw() +
theme(legend.title = element_blank())
wtd.alt.plot
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/alt_wtd_trajectory.jpg',
# wtd.alt.plot,
# height =3.5,
# width = 4)
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/alt_wtd_trajectory.pdf',
# wtd.alt.plot,
# height = 3.5,
# width = 4)
wtd.alt.plot.bw <- ggplot(wtd.alt.data.2021,
aes(x = alt, y = wtd.mean*-1,
shape = flux.year,
group = plot.id)) +
# geom_hline(yintercept = 0, color = 'gray') +
# geom_hline(yintercept = wtd.start*-1, linetype = 'dashed') +
geom_hline(yintercept = 0, linetype = 'dashed') +
geom_vline(xintercept = alt.start, linetype = 'dashed') +
geom_segment(data = arrows,
aes(x = xmin, y = ymin, xend = xmax, yend = ymax),
inherit.aes = FALSE,
arrow = arrow(length = unit(0.25, 'cm'))) +
geom_text(data = labels,
aes(x = x, y = y, label = labels, angle = angles),
inherit.aes = FALSE,
hjust = 0.5,
vjust = -0.5) +
# geom_line(color = 'gray90', linewidth = 0.25) +
geom_point(size = 2,
aes(color = treatment)) +
scale_x_continuous(name = 'Active Layer Thickness (cm)') +
scale_y_continuous(name = 'Mean Water Table Depth (cm)') +
scale_color_manual(name = 'Treatment',
values = c('gray65', 'black'),
labels = c('Control', 'Soil\nWarming')) +
scale_shape_manual(name = 'Year',
labels = c('1', '13'),
values = c(1, 16)) +
theme_bw() +
guides(color = guide_legend(order = 1),
shape = guide_legend(order = 2))
# theme(legend.title = element_blank())
wtd.alt.plot.bw
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/alt_wtd_trajectory_bw.jpg',
# wtd.alt.plot.bw,
# height =3.5,
# width = 4)
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/alt_wtd_trajectory_bw.pdf',
# wtd.alt.plot.bw,
# height = 3.5,
# width = 4)
# gif of changing soil conditions at CiPEHR
plots <- list()
for (n.year in seq(2009, max(wtd.alt.data$year))) {
data <- wtd.alt.data %>%
filter(flux.year == 2009 | flux.year == n.year)
plots[[n.year - 2008]] <- ggplot(data,
aes(x = alt, y = wtd.mean*-1,
color = treatment,
shape = flux.year)) +
geom_hline(yintercept = 0, linetype = 'dashed') +
geom_vline(xintercept = alt.start, linetype = 'dashed') +
geom_segment(data = arrows,
aes(x = xmin, y = ymin, xend = xmax, yend = ymax),
inherit.aes = FALSE,
arrow = arrow(length = unit(0.25, 'cm'))) +
geom_text(data = labels,
aes(x = x, y = y, label = labels, angle = angles),
inherit.aes = FALSE,
hjust = 0.5,
vjust = -0.5) +
geom_point(size = 3) +
scale_x_continuous(name = 'Active Layer Thickness (cm)',
limits = c(min(wtd.alt.data$alt), max(wtd.alt.data$alt))) +
scale_y_continuous(name = 'Mean Water Table Depth (cm)',
limits = c(min(wtd.alt.data$wtd.mean*-1), max(wtd.alt.data$wtd.mean*-1))) +
scale_color_manual(name = 'Treatment',
values = c('#33AAFF', '#DD0000'),
labels = c('Control', 'Soil\nWarming')) +
scale_shape_manual(name = 'Year',
values = c(1, 16)) +
theme_bw() +
ggtitle(paste(n.year)) +
theme(legend.title = element_blank(),
plot.title = element_text(hjust = 0.5),
text = element_text(size = 16)) +
guides(color = guide_legend(order = 1),
shape = guide_legend(order = 2))
}
# repeat first and last images for longer viewing
plots.long <- list()
for (n in 1:length(plots)) {
if (n == 1 | n == length(plots)) {
for (rep in 1:5) {
plots.long <- append(plots.long, plots[n])
}
} else {
plots.long <- append(plots.long, plots[n])
}
}
img <- image_graph(800, 700, res = 96)
map(plots.long,
~ print(.x))
dev.off()
wtd.alt.gif <- image_animate(img, delay = 50, loop = 1)
# # save gif
# image_write(image = wtd.alt.gif,
# path = '/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/alt_wtd_trajectory.gif')
################################################################################
### Annual Variability #########################################################
# Air temp
ggplot(weather.annual, aes(x = flux.year)) +
geom_point(aes(y = tair.mean, color = 'Mean Air Temp')) +
geom_point(aes(y = tair.max, color = 'Max Air Temp')) +
geom_point(aes(y = tair.min, color = 'Min Air Temp')) +
scale_x_continuous(breaks = seq(2009, 2020))
### Test if there's a detectable trend in air temp
lm.tair <- lm(tair.mean ~ I(flux.year-2009), data = weather.annual)
summary(lm.tair)
ggplot(weather.annual, aes(x = flux.year, y = tair.mean)) +
geom_point() +
geom_smooth(method = 'lm',
color = 'black')
# no detectable trend in air temp over time
# (although there was a non-significant increase in mean and min)
tair.max.lm <- lm(tair.max ~ I(flux.year-2009),
data = weather.annual)
summary(tair.max.lm)
tair.min.lm <- lm(tair.min ~ I(flux.year-2009),
data = weather.annual)
summary(tair.min.lm)
# Unusual years
# Z-scores
weather.annual <- weather.annual %>%
mutate(tair.mean.z = (tair.mean - mean(tair.mean))/sd(tair.mean),
tair.min.z = (tair.min - mean(tair.min))/sd(tair.min),
tair.max.z = (tair.max - mean(tair.max))/sd(tair.max),
precip.z = (precip - mean(precip))/sd(precip),
year.label = paste0(flux.year - 2008, ' (', flux.year, ')'))
annual.temp.precip <- ggplot(weather.annual, aes(x = tair.mean, y = precip)) +
geom_hline(aes(yintercept = mean(weather.annual$precip), linetype = 'Mean'), # use this one to create a legend item with a horizontal line only
linewidth = 0.1) +
geom_vline(xintercept = mean(weather.annual$tair.mean),
linewidth = 0.1) + # don't use the linetype in previous line, because it will add a vertical line
geom_hline(aes(yintercept = mean(weather.annual$precip) + sd(weather.annual$precip),
linetype = '1 SD'),
linewidth = 0.1) +
geom_vline(xintercept = mean(weather.annual$tair.mean) + sd(weather.annual$tair.mean),
linewidth = 0.1,
linetype = 'dashed') +
geom_hline(yintercept = mean(weather.annual$precip) - sd(weather.annual$precip),
linewidth = 0.1,
linetype = 'dashed') +
geom_vline(xintercept = mean(weather.annual$tair.mean) - sd(weather.annual$tair.mean),
linewidth = 0.1,
linetype = 'dashed') +
geom_point() +
geom_text(aes(label = year.label),
color = 'black',
size = 2.5,
position = position_nudge(x = 0, y = 12)) +
scale_linetype_manual(name = NULL,
breaks = c('Mean', '1 SD'),
values = c('solid', 'dashed')) +
scale_x_continuous(name = expression('Mean Temp' ~ (degree*C)),
limits = c(-4.33, 1.33)) +
scale_y_continuous(name = expression('Precip (mm)')) +
theme_bw()
annual.temp.precip
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/annual_temp_precip.jpg',
# annual.temp.precip,
# height = 4,
# width = 5)
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/annual_temp_precip.pdf',
# annual.temp.precip,
# height = 4,
# width = 5)
### Coldest and Warmest 3 years
# mean air temp
tair.mean.q <- quantile(weather.annual$tair.mean, probs = c(0.25, 0.75))
tair.mean.cold <- subset(weather.annual,
tair.mean < 3*sd(tair.mean))$flux.year
tair.mean.warm <- subset(weather.annual,
tair.mean > tair.mean.q[2])$flux.year
# min air temp
tair.min.q <- quantile(weather.annual$tair.min, probs = c(0.25, 0.75))
tair.min.outliers.cold <- subset(weather.annual,
tair.min < tair.min.q[1])$flux.year
tair.min.outliers.warm <- subset(weather.annual,
tair.min > tair.min.q[2])$flux.year
# max air temp
tair.max.q <- quantile(weather.annual$tair.max, probs = c(0.25, 0.75))
tair.max.outliers.cold <- subset(weather.annual,
tair.max < tair.max.q[1])$flux.year
tair.max.outliers.warm <- subset(weather.annual,
tair.max > tair.max.q[2])$flux.year
### Unusual Years with Seasonal Information
snow.depth.control <- snow.depth %>%
filter(exp == 'CiPEHR' & treatment == 'c') %>%
mutate(flux.year = year) %>%
left_join(snow.free %>%
filter(treatment %in% c('Control', 'Air Warming')) %>%
mutate(plot = as.character(plot)) %>%
select(-treatment),
by = c('flux.year', 'fence', 'plot')) %>%
group_by(flux.year) %>%
summarise(snow.depth = mean(snow.depth, na.rm = TRUE),
snow.free.date = mean(doy.snow.free, na.rm = TRUE)) %>%
mutate(snow.free.date = ifelse(is.nan(snow.free.date),
NA,
snow.free.date))
weather.seasonal.wide <- weather.seasonal %>%
select(-c(tair.min, tair.max)) %>%
pivot_wider(names_from = 'season',
values_from = c(tair.mean:par),
names_sep = '.') %>%
full_join(select(weather.annual, flux.year, precip), by = 'flux.year') %>%
mutate(ngs.precip.percent = precip.ngs/precip) %>%
full_join(snow.depth.control, by = 'flux.year') %>%
mutate(across(tair.mean.ngs:snow.free.date,
~(.x - mean(.x, na.rm = TRUE))/sd(.x, na.rm = TRUE),
.names = '{col}.z'))
mean.temp <- mean(weather.annual$tair.mean)
mean.temp.gs <- mean(weather.seasonal.wide$tair.mean.gs)
mean.temp.ngs <- mean(weather.seasonal.wide$tair.mean.ngs)
mean.precip <- mean(weather.annual$precip)
weather.seasonal.control.z <- weather.seasonal.wide %>%
select(flux.year, contains('z')) %>%
select(-c(precip.gs.z, precip.ngs.z, ngs.precip.percent.z)) %>%
rename(precip.gs.z = precip.z) %>% # use total annual rainfall rather than May-Oct rainfall
pivot_longer(tair.mean.ngs.z:snow.free.date.z, names_to = 'measurement', values_to = 'z.score') %>%
mutate(flux.year = factor(flux.year),
measurement = factor(str_sub(measurement, 1, -3),
levels = c('tair.mean.ngs',
'snow.depth',
'par.ngs',
'snow.free.date',
'tair.mean.gs',
'precip.gs',
'par.gs')),
season = factor(case_when(str_detect(measurement, 'ngs') ~ 'NGS',
str_detect(measurement, 'depth') ~ 'NGS',
str_detect(measurement, 'gs') ~ 'GS'),
levels = c('NGS',
'GS')),
measurement.type = factor(case_when(str_detect(measurement, 'tair') ~ 'Air Temp',
str_detect(measurement, 'snow.depth') | str_detect(measurement, 'precip') ~ 'Precip/Snow Depth',
str_detect(measurement, 'par') ~ 'PAR',
str_detect(measurement, 'date') ~ 'Snow Free Date'),
levels = c('Air Temp',
'Precip/Snow Depth',
'PAR',
'Snow Free Date')))
weather.seasonal.control <- weather.seasonal.wide %>%
select(-c(contains('z'), precip.gs, precip.ngs, ngs.precip.percent)) %>%
rename(precip.gs = precip) %>%
pivot_longer(tair.mean.ngs:snow.free.date, names_to = 'measurement', values_to = 'value') %>%
mutate(flux.year = factor(flux.year),
measurement = factor(measurement,
levels = c('tair.mean.ngs',
'snow.depth',
'par.ngs',
'snow.free.date',
'tair.mean.gs',
'precip.gs',
'par.gs')),
season = factor(case_when(str_detect(measurement, 'ngs') ~ 'NGS',
str_detect(measurement, 'depth') ~ 'NGS',
str_detect(measurement, 'gs') ~ 'GS'),
levels = c('NGS',
'GS')),
measurement.type = factor(case_when(str_detect(measurement, 'tair') ~ 'Air Temp',
str_detect(measurement, 'snow.depth') | str_detect(measurement, 'precip') ~ 'Precip/Snow Depth',
str_detect(measurement, 'par') ~ 'PAR',
str_detect(measurement, 'date') ~ 'Snow Free Date'),
levels = c('Air Temp',
'Precip/Snow Depth',
'PAR',
'Snow Free Date'))) %>%
full_join(weather.seasonal.control.z, by = c('flux.year', 'measurement', 'measurement.type', 'season'))
rm(weather.seasonal.control.z)
weather.seasonal.plot <- ggplot(weather.seasonal.control,
aes(x = flux.year,
y = z.score,
# color = measurement.type,
fill = measurement.type,
group = measurement,
alpha = season),
color = NULL) +
geom_hline(yintercept = 0, size = 0.1) +
geom_col(position = position_dodge(width = 0.6), width = 0.4) +
# scale_color_manual(values = c('#990000',
# '#006666',
# '#CC9900',
# '#66CC00')) +
scale_fill_manual(name = 'Measurement',
values = c('#990000',
'#006666',
'#CC9900',
'#66CC00')) +
scale_alpha_manual(name = 'Season',
values = c(0.4, 1),
na.translate = FALSE) +
scale_y_continuous(name = 'Normalized Value (Z-Score)') +
facet_grid(measurement.type ~ .) +
theme_bw() +
theme(axis.title.x = element_blank())
weather.seasonal.plot
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/seasonal_weather.jpg',
# weather.seasonal.plot,
# height = 6.5,
# width = 6.5)
# ggsave('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/figures/seasonal_weather.pdf',
# weather.seasonal.plot,
# height = 6.5,
# width = 6.5)
### Create a table of environmental variables by year
env.summary <- weather.seasonal[, .(flux.year, group = season, tair.mean, par, precip)]
env.summary <- rbind(env.summary,
weather.annual[, .(flux.year, group = 'Annual', tair.mean, par, precip)])
env.summary[, ':=' (tair.mean = round(tair.mean, 2),
par = round(par, 2),
precip = round(precip, 2))]
env.summary <- melt(env.summary,
measure.vars = c('tair.mean', 'par', 'precip'),
variable.name = 'measurement')
env.summary <- dcast(env.summary, measurement + group ~ flux.year)
# Add snow depth
snow.depth <- flux.annual %>%
filter(as.numeric(as.character(flux.year)) >= 2010) %>%
mutate(group = treatment,
measurement = 'snow.depth') %>%
group_by(flux.year, measurement, group) %>%
summarise(snow.depth.mean = round(mean(spring.snow.depth, na.rm = TRUE), 2),
snow.depth.sd = round(sd(spring.snow.depth, na.rm = TRUE), 2)) %>%
mutate(snow.depth.mean = paste0(snow.depth.mean, ' +- ', snow.depth.sd)) %>%
select(-snow.depth.sd) %>%
ungroup() %>%
pivot_wider(names_from = 'flux.year',
values_from = 'snow.depth.mean')
env.summary <- rbind(env.summary, snow.depth, fill = TRUE)
# Add snow free date
snow.free.date <- snow.free %>%
mutate(group = treatment,
measurement = 'snow.free.date') %>%
group_by(flux.year, measurement, group) %>%
summarise(mean.doy.snow.free = round(mean(doy.snow.free, na.rm = TRUE)),
snow.free.sd = round(sd(doy.snow.free, na.rm = TRUE))) %>%
mutate(doy.snow.free = paste0(mean.doy.snow.free, ' +- ', snow.free.sd)) %>%
select(-c(mean.doy.snow.free, snow.free.sd)) %>%
ungroup() %>%
pivot_wider(names_from = 'flux.year',
values_from = 'doy.snow.free')
env.summary <- rbind(env.summary, snow.free.date, fill = TRUE)
# write.csv(env.summary, '/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/tables/meteorological_summary.csv',
# row.names = FALSE)
################################################################################
### Calculate Microtopography ##################################################
# For all plots in all years
elev <- list(brick('/home/heidi/Documents/School/NAU/Schuur Lab/GPS/Kriged_Surfaces/Elevation_Variance/ALT_Sub_Ratio_Corrected/Elevation_Stacks/AElevStack_filled_unclipped.tif'),
brick('/home/heidi/Documents/School/NAU/Schuur Lab/GPS/Kriged_Surfaces/Elevation_Variance/ALT_Sub_Ratio_Corrected/Elevation_Stacks/BElevStack_filled_unclipped.tif'),
brick('/home/heidi/Documents/School/NAU/Schuur Lab/GPS/Kriged_Surfaces/Elevation_Variance/ALT_Sub_Ratio_Corrected/Elevation_Stacks/CElevStack_filled_unclipped.tif'))
elev.clipped <- list(brick('/home/heidi/Documents/School/NAU/Schuur Lab/GPS/Kriged_Surfaces/Elevation_Variance/ALT_Sub_Ratio_Corrected/Elevation_Stacks/AElevStack_filled_clipped.tif'),
brick('/home/heidi/Documents/School/NAU/Schuur Lab/GPS/Kriged_Surfaces/Elevation_Variance/ALT_Sub_Ratio_Corrected/Elevation_Stacks/BElevStack_filled_clipped.tif'),
brick('/home/heidi/Documents/School/NAU/Schuur Lab/GPS/Kriged_Surfaces/Elevation_Variance/ALT_Sub_Ratio_Corrected/Elevation_Stacks/CElevStack_filled_clipped.tif'))
plots <- st_read('/home/heidi/Documents/School/NAU/Schuur Lab/GPS/All_Points/Site_Summary_Shapefiles/plot_coordinates_from_2017.shp') %>%
filter(exp == 'CiPEHR')
# need to clip the 2009-2016 unclipped data
for (i in 1:length(elev.clipped)) {
for (j in 1:8) { # 2009 - 2016
elev[[i]][[j]] <- mask(elev[[i]][[j]], elev.clipped[[i]][[j]])
}
}
weights <- list()
elev.med <- list()
mtopo <- list()
for (i in 1:length(elev)) {
# neighborhood window weights
weights[[i]] <- focalWeight(elev[[i]], 5, type = 'circle')
weights[[i]][which(weights[[1]] > 0)] <- 1
# prepare output data format
elev.med[[i]] <- list()
mtopo[[i]] <- list()
for (j in 1:nlayers(elev[[i]])) {
elev.med[[i]][[j]] <- focal(elev[[i]][[j]],
weights[[i]],
fun = median,
na.rm = TRUE)
mtopo[[i]][[j]] <- elev[[i]][[j]] - elev.med[[i]][[j]]
}
elev.med[[i]] <- brick(elev.med[[i]])
mtopo[[i]] <- brick(mtopo[[i]])
}
# extract microtopography values at all plots in all years
new.names <- c(paste0('layer.', seq(1, 12)))
names(new.names) <- paste0('mtopo.', seq(2009, 2020))
mtopo.df <- data.frame()
for (i in 1:length(mtopo)) {
mtopo.extract <- raster::extract(mtopo[[i]], plots, df = TRUE) %>%
filter(!(rowSums(is.na(.)) == ncol(.) - 1)) %>%
rename(all_of(new.names))
mtopo.df <- rbind.data.frame(mtopo.df, mtopo.extract)
}
plot.mtopo <- plots %>%
cbind.data.frame(mtopo.df) %>%
pivot_longer(names(new.names), names_to = 'flux.year', values_to = 'mtopo') %>%
mutate(flux.year = as.factor(as.numeric(str_extract(flux.year, '[:digit:]+'))),
plot.id = paste(fence, plot, sep = '_')) %>%
select(flux.year, plot.id, mtopo)
flux.annual <- flux.annual %>%
left_join(plot.mtopo, by = c('flux.year', 'plot.id'))
ggplot(flux.annual, aes(x = subsidence, y = mtopo)) +
geom_point(aes(color = treatment))
ggplot(subset(flux.annual, flux.year == 2020), aes(x = subsidence, y = mtopo)) +
geom_point(aes(color = plot.id, shape = factor(treatment,
levels = c('Control',
'Air Warming',
'Soil Warming',
'Air + Soil Warming')))) +
geom_text(aes(label = plot.id),
hjust = 1.5) +
scale_color_viridis(discrete = TRUE,
direction = -1) +
scale_shape_manual(name = 'Treatment',
values = c(1, 0, 16, 15)) +
facet_grid(block~.)
################################################################################
### Impact of Subsidence on Soil Moisture (Interannual) ########################
# Random effects:
# by plot (nested in treatment, nested in fence, nested in block) allows for
# original differences in microtopography and how that might impact the response
# of wtd to subsidence (a relatively high plot could subside a lot but not see
# much difference in wtd for awhile)
#
# will not include a random effect for year, because this causes singular fit
# and differences in measurements or precipitation across years can be included
# in the residuals
# read in thermokarst class data
plots.tk.class <- read.csv('/home/heidi/Documents/School/NAU/Schuur Lab/Autochamber/autochamber_c_flux/model_output/thermokarst_classes.csv') %>%
filter(flux.year == 2021) %>%
select(flux.year, fence, plot, tk.class.factor) %>%
mutate(tk.class.factor = factor(tk.class.factor,
levels = c('Non-TK', 'TK Margin', 'TK Center')))
sub.moisture <- flux.annual %>%
filter(as.numeric(as.character(flux.year)) >= 2009) %>%
mutate(treatment = factor(treatment,
levels = c('Control',
'Air Warming',
'Soil Warming',
'Air + Soil Warming')),
subsidence = subsidence*-1,
wtd.mean = wtd.mean*-1) %>%
select(flux.year, fence, plot, plot.id, treatment, subsidence, # mtopo,
wtd.mean, wtd.sd, wtd.n,
vwc.mean, vwc.sd, gwc.mean, gwc.sd) %>%
full_join(weather.annual %>%
select(flux.year, precip, precip.z) %>%
mutate(flux.year = factor(flux.year)),
by = 'flux.year') %>%
full_join(plots.tk.class %>%
mutate(flux.year = factor(flux.year)),
by = c('fence', 'plot', 'flux.year')) %>%
mutate(precip = precip/10, # convert to cm
precip.group = factor(case_when(precip.z >= 0.75 ~ 'wet',
precip.z > -0.75 ~ 'average',
precip.z <= -0.75 ~ 'dry'),
levels = c('dry', 'average', 'wet')),
sub.group = factor(case_when(subsidence < 15 ~ '<15 cm Subsidence',
subsidence < 30 ~ '15-30 cm Subsidence',
subsidence < 45 ~ '30-45 cm Subsidence',
subsidence >= 45 ~ '>=45 cm Subsidence'),
levels = c('<15 cm Subsidence', '15-30 cm Subsidence',
'30-45 cm Subsidence', '>=45 cm Subsidence')),
time = factor(as.numeric(flux.year)),
year.label = factor(as.numeric(as.character(flux.year)) - 2008),