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usgs_metab_long-term_analysis_v2.Rmd
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usgs_metab_long-term_analysis_v2.Rmd
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---
output:
pdf_document: default
html_document: default
---
------------------------------------------------------------------------
title: 'Data for: Are annual river productivity regimes changing over time?' author: "Nick Marzolf" date: "2023-1010" output: html_document
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
RMarkdown file to accompany: *Are annual river productivity regimes changing over time?,* a manuscript submitted to *Limnology and Oceanography: Letters* Special Issue on "Changing Phenology in Aquatic Ecosystems".
Authors: Nicholas S. Marzolf^1,\*^, Michael J. Vlah^1^, Heili E. Lowman^2^, Weston M. Slaughter^1^, Emily S. Bernhardt^1^
Affiliations; ^1^ Department of Biology, Duke University, Durham, NC, USA
^2^ Department of Natural Resources and Environmental Science, University of Nevada, Reno, Reno, Nevada, USA
\*corresponding author: [nicholas.marzolf\@duke.edu](mailto:nicholas.marzolf@duke.edu){.email} (ORCiD: 0000-0001-9146-1643)
To recreate this HTML, load the data files into a folder named 'data' at the same path as the .rmd file, open the .rmd file, and click 'Knit'.
# Load packages
```{r load-packages, echo = TRUE, message = FALSE, warning = FALSE}
# data manipulation
library(tidyverse)
library(dplyr)
library(lubridate)
library(purrr)
library(tibble)
# plotting
library(ggplot2)
library(ggpubr)
library(ggExtra)
library(GGally)
source("C:/Users/Nick Marzolf/Desktop/Research/R code/theme_nick.R")
ggplot2::theme_set(theme_nick())
# spatial
library(sf)
library(spData)
# statistics
library(lme4)
library(nlme)
library(performance)
library(EnvStats)
library(EflowStats)
library(car)
library(merTools)
# reproducability
library(pander)
```
```{r define-constants, echo=TRUE, message=FALSE, warning=FALSE}
# define constants and axis labels
metab_units_area <- (expression(paste('GPP (g C ', m^-2, ' ',y^-1,')', sep = ' ')))
low_p <- '#103801'
high_p <- '#43fa00'
# quotient to convert GPP from O2 to C
# g C = g O2 * (1 mol O2/32 g O2) * (2 mol O/1 mol O2) * (1 mol C/1 mol O) * (12 g C/1 mol C)
rq <- (1/32)*(2/1)*(1/1)*(12/1)
# where to save figures
save_figs <- TRUE
version <- 'v2'
fig_dir <- glue::glue('manuscript/long-term_GPP/figures/{version}/')
if(!dir.exists(fig_dir)){
dir.create(fig_dir)
}
```
# Explore sites
```{r sites, warning = FALSE, echo = TRUE}
# histogram of watershed areas
table_s1 <- readr::read_csv('data/data_citation/1_site_info.csv')
table_s1
readr::write_csv(table_s1,
'data/data_citation/ms_usgs_annual_metab/table_s1_site_info.csv')
table_s1_plot <- table_s1 %>%
sf::st_as_sf(.,
coords = c('Longitude', 'Latitude'),
crs = 4326)
fig_s1_map <- ggplot()+
geom_sf(data = world %>%
dplyr::filter(name_long == 'United States') %>%
sf::st_set_crs(4326),
fill = 'white')+
geom_sf(data = table_s1_plot)+
coord_sf(xlim = c(-130, -65), ylim = c(23, 55), expand = FALSE)
fig_s1_hist <- ggplot(table_s1,
aes(x = `Drainage Area (km2)`*2.58999))+ # convert to km2
geom_histogram()+
labs(x = expression(paste('Watershed area (', km^2,')')),
y = 'Count')+
scale_x_log10()
fig_s1_hist
```
```{r fig-s1, fig.width = 9, fig.height = 4}
fig_s1 <- cowplot::plot_grid(fig_s1_map,
fig_s1_hist,
ncol = 2,
rel_widths = c(2,1),
axis = 'b',
align = 'v')
fig_s1
if(save_figs){
ggsave(plot = fig_s1,
glue::glue(fig_dir, 'fig_s1.png'),
dpi = 1200,
width = 8.5, height = 3)
}
```
# Load GPP data
```{r load-data, warning = FALSE, echo = TRUE}
river_metab <- readr::read_csv('data/data_citation/5_usgs_metabolism.csv')
head(river_metab)
n_obs <- length(river_metab$GPP)
n_sites <- length(unique(river_metab$site))
```
# 1) Calculate cumulative daily GPP and total annual GPP
```{r GPP-cumsum, warning = FALSE, echo = TRUE}
# calculate site-year cumulative GPP
river_metab_cumsum <- river_metab %>%
dplyr::mutate(year = lubridate::year(date)) %>%
dplyr::group_by(site, year) %>%
dplyr::mutate(jday = lubridate::yday(date),
GPP_C_cumsum = cumsum(GPP*rq))
# calculate annual GPP for each site-year
metab_all_annual <- river_metab %>%
dplyr::mutate(year = lubridate::year(date)) %>%
dplyr::group_by(site, year) %>%
dplyr::summarise(GPP_ann_C = sum(GPP*rq, na.rm = TRUE),
GPP_daily_cv = EnvStats::cv(GPP*rq,
na.rm = TRUE,
method = 'l.moments')*100)
summary(metab_all_annual$GPP_ann_C)
# plot of annual productivity time-series
fig_s2 <- ggplot2::ggplot(metab_all_annual,
aes(x = year,
y = GPP_ann_C,
group = site,
color = GPP_ann_C))+
ggplot2::geom_line()+
ggplot2::geom_point()+
ggplot2::scale_color_gradient(name = metab_units_area,
low = low_p,
high = high_p)+
ggplot2::ylab(metab_units_area)+
ggplot2::theme(axis.title.x = element_blank(),
legend.position = 'none')+
scale_y_continuous(limits = c(0 , 4000))
fig_s2
if(save_figs){
ggsave(plot = fig_s2,
glue::glue(fig_dir,'fig_s2.png'),
dpi = 1200, width = 6, height = 4)
}
# calculate median annual and CV for each site time series
site_mean_ann_gpp <- metab_all_annual %>%
dplyr::group_by(site) %>%
dplyr::summarise(median_ann_GPP = median(GPP_ann_C,
na.rm = TRUE),
sd_ann_GPP = sd(GPP_ann_C, na.rm = TRUE),
min_ann_GPP = min(GPP_ann_C, na.rm = TRUE),
max_ann_GPP = max(GPP_ann_C, na.rm = TRUE),
GPP_percent_diff = ((max_ann_GPP - min_ann_GPP)/min_ann_GPP)*100,
GPP_ann_cv = EnvStats::cv(GPP_ann_C ,
na.rm = TRUE,
method = 'l.moments')*100) %>%
dplyr::arrange(desc(max_ann_GPP)) %>%
dplyr::mutate(clean_names = substr(site, 6,30))
```
```{r fig-1, fig.width=15, fig.height=15}
# cumulative site-year GPP
fig_1 <- ggplot(river_metab_cumsum %>%
dplyr::mutate(clean_names = substr(site, 6,30)),
aes(x = jday,
y = GPP_C_cumsum,
group = interaction(year, site),
color = GPP_C_cumsum))+
geom_line(show.legend = FALSE)+
facet_wrap(forcats::fct_relevel(clean_names,
site_mean_ann_gpp %>%
dplyr::pull(clean_names)) ~ .,
scales = 'free',
ncol = 6)+
scale_color_gradient(low = low_p,
high = high_p)+
#scale_y_continuous(limits = c(-1, 4000))+
labs(x = 'Day of Year',
y = metab_units_area)
fig_1
if(save_figs){
ggsave(plot = fig_1,
glue::glue(fig_dir, 'fig_1.png'),
dpi = 1200, width = 13, height = 12)
}
```
# 2) Annual productivity range
```{r viz-ann-prod, warning = FALSE, echo = TRUE, fig.width=11, fig.height=5}
# boxplot of annual GPP for each site
fig_2_a <- ggplot(metab_all_annual %>%
dplyr::mutate(clean_names = substr(site, 6,30)),
aes(x = forcats::fct_relevel(clean_names,
site_mean_ann_gpp %>%
dplyr::pull(clean_names)),
y = GPP_ann_C))+
geom_boxplot(width = 0.75,
outlier.shape = NA,
show.legend = FALSE,)+
geom_point(aes(color = GPP_ann_C),
show.legend = FALSE,
size = 1)+
# geom_line()+
labs(x = 'Site')+
scale_y_continuous(name = metab_units_area,
limits = c(-1, 4000))+
scale_color_gradient(low = low_p,
high = high_p)+
theme(axis.text.x = element_text(angle = 90, size = 8,
hjust = 0.95,vjust = 0.2))
fig_2_a
fig_2_b <- ggplot2::ggplot(data = site_mean_ann_gpp,
aes(y = median_ann_GPP,
x = GPP_ann_cv,
group = site))+
ggplot2::geom_point(size = 2)+
geom_errorbar(aes(ymin = median_ann_GPP - sd_ann_GPP,
ymax = median_ann_GPP + sd_ann_GPP))+
ggplot2::labs(x = expression(paste(CV[GPP],' (%)')),
y = expression(paste('Median Annual GPP (g C ',m^-2, ' ',y^-1,')')))+
lims(y = c(0, 4000),
x = c(5, 40))
fig_2_b
fig_2 <- ggpubr::ggarrange(fig_2_a,
fig_2_b,
widths = c(2,1),
labels = 'auto',
hjust = c(-8.5,-8),
vjust = c(1.7, 1.7),
ncol = 2,
align = 'h')
fig_2
if(save_figs){
ggsave(plot = fig_2,
glue::glue(fig_dir, 'fig_2.png'),
<<<<<<< HEAD
dpi = 850,
=======
dpi = 1000,
>>>>>>> 8781f6f41f93ea6feea8077c199ec6b153c4d917
width = 11, height = 4.5)
}
```
# 3) When are rivers productive
```{r gpp-quantiles, warning = FALSE, echo = TRUE, fig.width=6, fig.height=10}
list_quants <- list()
quantiles <- c(0.25, 0.5, 0.75, 0.95)
for(i in 1:length(quantiles)){
quantile <- quantiles[i]
list_quants[[i]] <- river_metab_cumsum %>%
dplyr::select(site, date, year, GPP_C_cumsum) %>%
dplyr::group_by(site,
year = lubridate::year(date)) %>%
dplyr::mutate(jday = lubridate::yday(date),
gpp_cdf = ecdf(GPP_C_cumsum)(GPP_C_cumsum)) %>%
dplyr::arrange(site, year, gpp_cdf) %>%
dplyr::slice(which.min(abs(gpp_cdf - quantile)))
}
names(list_quants) <- quantiles
df_quants <- map_df(list_quants,
~as.data.frame(.x),
.id = 'quantile')
# by quantile plots
# rank sites by increasing variability of 50th percentile
quant_50_rank <- list_quants[[1]] %>%
dplyr::group_by(site) %>%
dplyr::summarise(iqr = IQR(jday)) %>%
dplyr::arrange(desc(iqr)) %>%
dplyr::pull(site)
quant_75_rank <- list_quants[[2]] %>%
dplyr::group_by(site) %>%
dplyr::summarise(iqr = IQR(jday)) %>%
dplyr::arrange(desc(iqr)) %>%
dplyr::pull(site)
quant_95_rank <- list_quants[[3]] %>%
dplyr::group_by(site) %>%
dplyr::summarise(iqr = IQR(jday)) %>%
dplyr::arrange(desc(iqr)) %>%
dplyr::pull(site)
```
```{r quant-trends, echo=TRUE, warning = FALSE, fig.height=7, fig.width=7}
# trend analysis for quantiles
quantile_trends <- df_quants %>%
dplyr::group_by(site, quantile) %>%
tidyr::nest() %>%
dplyr::mutate(sens = purrr::map(data, ~trend::sens.slope(x = .$jday)),
mk_test = purrr::map(data, ~trend::mk.test(x = .$jday))) %>%
dplyr::mutate(sens_p = purrr::map(sens, ~.$p.value),
sens_s = purrr::map_dbl(sens, ~.$estimates),
mk_p = purrr::map_dbl(mk_test, ~.$p.value),
mk_s = purrr::map_dbl(mk_test, ~.$estimates['S']),
sens_sig = ifelse(sens_p <= 0.05, 'significant', 'non-significant'),
sens_slope = ifelse(sens_s > 0, 'increasing', 'decreasing'),
mk_sig = ifelse(mk_p <= 0.05, 'significant', 'non-significant'),
mk_slope = ifelse(mk_s > 0, 'increasing', 'decreasing'))
quantile_sum_sigs <- quantile_trends %>%
dplyr::ungroup() %>%
dplyr::mutate(sens_p = unlist(sens_p)) %>%
dplyr::select(quantile, site, sens_s, sens_p, sens_sig, sens_slope,
mk_p, mk_s, mk_sig, mk_slope)
quants_n_sig <- quantile_sum_sigs %>%
dplyr::filter(sens_p <= 0.05) %>%
dplyr::group_by(quantile,sens_slope) %>%
summarise(n = n()) %>%
data.frame()
quantile_trends_sum <- df_quants %>%
dplyr::left_join(quantile_sum_sigs) %>%
dplyr::mutate(mk_meaning = case_when(mk_slope == 'increasing' ~ 'Later',
mk_slope == 'decreasing' ~ 'Earlier'))
table_s2 <- quantile_trends_sum %>%
dplyr::filter(mk_p < 0.05) %>%
dplyr::select(quantile, site, year, date, jday, sens_s, sens_p, mk_meaning) %>%
dplyr::mutate(across(where(is.numeric), round, 3))
readr::write_csv(table_s2,
'data/data_citation/ms_usgs_annual_metab/table_s2_quantile_trends.csv')
fig_3 <- ggplot(quantile_trends_sum,
aes(x = year,
y = jday,
group = site,
color = mk_meaning))+
# geom_point()+
geom_line(linewidth = 1)+
gghighlight::gghighlight(mk_sig == 'significant',
use_direct_label = FALSE,
keep_scales = TRUE,
unhighlighted_params = list(alpha("grey40", 0.4),
linewidth = 0.1),
calculate_per_facet = TRUE,)+
# ggside::geom_ysidedensity(aes(x = stat(density)))+
scale_y_continuous(limits = c(0, 366))+
scale_color_manual(name = element_blank(),
values = c(high_p, low_p))+
facet_wrap(. ~ quantile,
scales = 'free',
nrow = 2, ncol = 2)+
labs(x = 'Year',
y = 'Day of Year')+
theme(legend.title = element_blank())
fig_3
if(save_figs){
ggsave(plot = fig_3,
glue::glue(fig_dir, 'fig_3.png'),
dpi = 1000, width = 6.5, height = 5)
}
```
# 4) Annual productivity trends
```{r prod-trends, warning = FALSE, echo = TRUE, fig.width=5, fig.height=5}
annual_C_sum_trends <- metab_all_annual %>%
# dplyr::select(site, year, GPP_ann_C) %>%
dplyr::group_by(site) %>%
tidyr::nest() %>%
dplyr::mutate(sens = purrr::map(data, ~trend::sens.slope(x = .$GPP_ann_C)),
mk_test = purrr::map(data, ~trend::mk.test(x = .$GPP_ann_C))) %>%
dplyr::mutate(sens_p = purrr::map(sens, ~.$p.value),
sens_s = purrr::map_dbl(sens, ~.$estimates),
mk_p = purrr::map_dbl(mk_test, ~.$p.value),
mk_s = purrr::map_dbl(mk_test, ~.$estimates['S']),
sens_sig = ifelse(sens_p <= 0.05, 'significant', 'non-significant'),
sens_slope = ifelse(sens_s > 0, 'increasing', 'decreasing'),
mk_sig = ifelse(mk_p <= 0.05, 'significant', 'non-significant'),
mk_slope = ifelse(mk_s > 0, 'increasing', 'decreasing'))
annual_C_sum_sigs <- annual_C_sum_trends %>%
dplyr::ungroup() %>%
dplyr::mutate(sens_p = unlist(sens_p)) %>%
dplyr::select(site,
sens_s, sens_p, sens_sig, sens_slope,
mk_p, mk_s, mk_sig, mk_slope)
n_sig <- annual_C_sum_sigs %>%
dplyr::filter(sens_p <= 0.05) %>%
dplyr::group_by(sens_slope) %>%
summarise(n = n()) %>%
data.frame()
river_trends <- metab_all_annual %>%
dplyr::left_join(annual_C_sum_sigs)
fig_4 <- ggplot2::ggplot(river_trends,
ggplot2::aes(x = year,
y = GPP_ann_C,
color = sens_slope))+
ggplot2::geom_line(aes(group = site),
linewidth = 1.1)+
scale_y_continuous(limits = c(0 , 4000))+
scale_x_continuous(n.breaks = 10)+
gghighlight::gghighlight(sens_sig == 'significant',
use_direct_label = FALSE,
keep_scales = TRUE,
unhighlighted_params = list(alpha("grey", 0.4),
linewidth = 0.5))+
ggplot2::labs(y = metab_units_area)+
scale_color_manual(name = element_blank(),
values = c(low_p,high_p),
labels = c(glue::glue('Decreasing (n = ', n_sig[1,2],')'),
glue::glue('Increasing (n = ', n_sig[2,2],')')))+
theme(axis.title.x = element_blank(),
legend.justification = c(0.05, 0.95),
legend.position = c(0.05, 0.95),
legend.text = element_text(size = 10),
legend.background = element_rect(color = 'black',
linewidth = 0.25),
legend.margin = margin(-0.5, 1, 0.4, 0.1))
fig_4
if(save_figs){
ggsave(plot = fig_4,
glue::glue(fig_dir, 'fig_4.png'),
dpi = 1200,
width = 5, height = 4)
}
table_s3 <- annual_C_sum_sigs %>%
dplyr::select(site, sens_s, sens_p, mk_s,mk_p)
colnames(table_s3) <- c('Site',
"Sen's Slope",
"Sen's p-value",
"Mann-Kendall S",
"Mann-Kendall p-value")
readr::write_csv(table_s3,
'data/data_citation/ms_usgs_annual_metab/table_s3_trend_stats.csv')
```
# Session Info
```{r session-info}
pander::pander(sessionInfo())
```