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ibt-d15n.Rmd
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---
title: "How island characteristics affect enrichment of island flora and fauna: d15N"
author: "Debora Obrist"
date: "23/10/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
This analysis shows how island characteristics affect levels of enrichment in island flora and fauna.
# Set up:
```{r, echo = FALSE, warning = FALSE, include = FALSE}
rm(list = ls())
library(tidyverse)
library(lme4)
library(MuMIn)
library(patchwork)
library(glmmTMB)
library(MASS)
library(DHARMa)
# Isotope data:
df <- read.csv("food-web-ibt-2021.csv")
```
# Models by species:
```{r}
# SOIL:
soil_dat <- df %>%
dplyr::filter(taxon == "soil") %>%
dplyr::filter(!is.na(shoredist)) %>% # remove 4 NAs for distance to shoredist
dplyr::mutate(l.area.std = scale(l.area)) %>%
dplyr::mutate(slope.std = scale(slope_mean)) %>%
dplyr::mutate(ln.shoredist.std = scale(ln.shoredist)) %>%
dplyr::mutate(sqrt.wrack.std = scale(sqrt.wrack))
soil_mod <- glmmTMB(d15n ~ l.area.std * ln.shoredist.std + slope.std + sqrt.wrack.std + (1|island),
data = soil_dat,
na.action = "na.fail",
REML = FALSE)
r.squaredGLMM(soil_mod) # Marginal (fixed): 0.57, conditional (inc random): 0.69
soil_dredge <- dredge(soil_mod)
soil_mods <- get.models(soil_dredge, subset = TRUE)
soil_avg <- model.avg(soil_mods, fit = TRUE)
saveRDS(soil_avg, "soil_avg_mod.rds")
# SAL:
SAL_dat <- df %>%
dplyr::filter(taxon == "SAL") %>%
dplyr::filter(!is.na(shoredist)) %>%
dplyr::mutate(l.area.std = scale(l.area)) %>%
dplyr::mutate(slope.std = scale(slope_mean)) %>%
dplyr::mutate(ln.shoredist.std = scale(ln.shoredist)) %>%
dplyr::mutate(sqrt.wrack.std = scale(sqrt.wrack))
SAL_mod <- glmmTMB(d15n ~ l.area.std * ln.shoredist.std + slope.std + sqrt.wrack.std + (1|island),
data = SAL_dat,
na.action = "na.fail",
REML = FALSE)
r.squaredGLMM(SAL_mod) # Marginal (fixed): 0.65, conditional (inc random): 0.79
SAL_dredge <- dredge(SAL_mod)
SAL_avg <- model.avg(SAL_dredge, fit = TRUE)
saveRDS(SAL_avg, "SAL_avg_mod.rds")
FLV_dat <- df %>%
dplyr::filter(taxon == "FLV") %>%
dplyr::filter(!is.na(shoredist)) %>%
dplyr::mutate(l.area.std = scale(l.area)) %>%
dplyr::mutate(slope.std = scale(slope_mean)) %>%
dplyr::mutate(ln.shoredist.std = scale(ln.shoredist)) %>%
dplyr::mutate(sqrt.wrack.std = scale(sqrt.wrack))
FLV_mod <- glmmTMB(d15n ~ l.area.std * ln.shoredist.std + slope.std + sqrt.wrack.std + (1|island),
data = FLV_dat,
na.action = "na.fail",
REML = FALSE)
r.squaredGLMM(FLV_mod) # Marginal (fixed): 0.63, conditional (inc random): 0.82
FLV_dredge <- dredge(FLV_mod)
FLV_avg <- model.avg(FLV_dredge, fit = TRUE)
saveRDS(FLV_avg, "FLV_avg_mod.rds")
CUR_dat <- df %>%
dplyr::filter(taxon == "CUR") %>%
dplyr::filter(!is.na(shoredist)) %>%
dplyr::mutate(l.area.std = scale(l.area)) %>%
dplyr::mutate(slope.std = scale(slope_mean)) %>%
dplyr::mutate(ln.shoredist.std = scale(ln.shoredist)) %>%
dplyr::mutate(sqrt.wrack.std = scale(sqrt.wrack))
CUR_mod <- glmmTMB(d15n ~ l.area.std * ln.shoredist.std + slope.std + sqrt.wrack.std + (1|island),
data = CUR_dat,
na.action = "na.fail",
REML = FALSE)
r.squaredGLMM(CUR_mod) # Marginal (fixed): 0.52, conditional (inc random): 0.67
CUR_dredge <- dredge(CUR_mod)
CUR_avg <- model.avg(CUR_dredge, fit = TRUE)
saveRDS(CUR_avg, "CUR_avg_mod.rds")
ISO_dat <- df %>%
dplyr::filter(taxon == "ISO") %>%
dplyr::filter(!is.na(shoredist)) %>%
dplyr::mutate(l.area.std = scale(l.area)) %>%
dplyr::mutate(slope.std = scale(slope_mean)) %>%
dplyr::mutate(ln.shoredist.std = scale(ln.shoredist)) %>%
dplyr::mutate(sqrt.wrack.std = scale(sqrt.wrack))
ISO_mod <- glmmTMB(d15n ~ l.area.std * ln.shoredist.std + slope.std + sqrt.wrack.std + (1|island),
data = ISO_dat,
na.action = "na.fail",
REML = FALSE)
r.squaredGLMM(ISO_mod) # Marginal (fixed): 0.51, conditional (inc random): 0.71
ISO_dredge <- dredge(ISO_mod)
ISO_avg <- model.avg(ISO_dredge, fit = TRUE)
saveRDS(ISO_avg, "ISO_avg_mod.rds")
COL_dat <- df %>%
dplyr::filter(taxon == "COL") %>%
dplyr::filter(!is.na(shoredist)) %>%
dplyr::mutate(l.area.std = scale(l.area)) %>%
dplyr::mutate(slope.std = scale(slope_mean)) %>%
dplyr::mutate(ln.shoredist.std = scale(ln.shoredist)) %>%
dplyr::mutate(sqrt.wrack.std = scale(sqrt.wrack))
COL_mod <- glmmTMB(d15n ~ l.area.std * ln.shoredist.std + slope.std + sqrt.wrack.std + (1|island),
data = COL_dat,
na.action = "na.fail",
REML = FALSE)
r.squaredGLMM(COL_mod) # Marginal (fixed): 0.50, conditional (inc random): 0.64
COL_dredge <- dredge(COL_mod)
COL_avg <- model.avg(COL_dredge, fit = TRUE)
saveRDS(COL_avg, "COL_avg_mod.rds")
feces_dat <- df %>%
dplyr::filter(taxon == "feces") %>%
dplyr::filter(!is.na(shoredist)) %>%
dplyr::mutate(l.area.std = scale(l.area)) %>%
dplyr::mutate(slope.std = scale(slope_mean)) %>%
dplyr::mutate(ln.shoredist.std = scale(ln.shoredist)) %>%
dplyr::mutate(sqrt.wrack.std = scale(sqrt.wrack))
feces_mod <- glmmTMB(d15n ~ l.area.std * ln.shoredist.std + slope.std + sqrt.wrack.std + (1|island),
data = feces_dat,
na.action = "na.fail",
REML = FALSE)
r.squaredGLMM(feces_mod) # Marginal (fixed): 0.30, conditional (inc random): 0.47
feces_dredge <- dredge(feces_mod)
feces_avg <- model.avg(feces_dredge, fit = TRUE)
saveRDS(feces_avg, "feces_avg_mod.rds")
feather_dat <- df %>%
dplyr::filter(taxon == "feather") %>%
dplyr::filter(!is.na(shoredist)) %>%
dplyr::mutate(l.area.std = scale(l.area)) %>%
dplyr::mutate(slope.std = scale(slope_mean)) %>%
dplyr::mutate(ln.shoredist.std = scale(ln.shoredist)) %>%
dplyr::mutate(sqrt.wrack.std = scale(sqrt.wrack))
feather_mod <- glmmTMB(d15n ~ l.area.std * ln.shoredist.std + slope.std + sqrt.wrack.std + (1|island),
data = feather_dat,
na.action = "na.fail",
REML = FALSE)
r.squaredGLMM(feather_mod) # Marginal (fixed): 0.16, conditional (inc random): 0.31
feather_dredge <- dredge(feather_mod)
feather_avg <- model.avg(feather_dredge, fit = TRUE)
saveRDS(feather_avg, "feather_avg_mod.rds")
```
# Check mods:
```{r}
performance::check_collinearity(soil_mod)
# png("figures/diagnostics/soil-d15n-qq.# png", units = "px", width = 300, height = 300)
qqnorm(resid(soil_mod), col = "grey50")
qqline(resid(soil_mod))
# dev.off()
# png("figures/diagnostics/soil-d15n-fitresid.# png", units = "px", width = 600, height = 450)
simulateResiduals(fittedModel = soil_mod, plot = T)
# dev.off()
# png("figures/diagnostics/soil-d15n-qq.# png", units = "px", width = 300, height = 300)
hist(resid(soil_mod), col = "grey50")
# dev.off()
# Check individual predictors. This will work if run in sequence but will rewrite simulated_resids for each model as they are run.
simulated_resids <- simulateResiduals(fittedModel = soil_mod)
# Check individual predictors
plotResiduals(simulated_resids, form = soil_dat$l.area.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = soil_dat$ln.shoredist.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = soil_dat$slope.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = soil_dat$sqrt.wrack.std, quantreg = FALSE)
# Goodness of fit test
testUniformity(simulationOutput = simulated_resids) # Good
# Check for Outliers
testOutliers(simulationOutput = simulated_resids) # These are fine
# Check for overdispersion
testDispersion(simulated_resids) # Looks fine
# Salal:
performance::check_collinearity(SAL_mod)
# png("figures/diagnostics/SAL-d15n-qq.# png", units = "px", width = 300, height = 300)
qqnorm(resid(SAL_mod), col = "#1B7837")
qqline(resid(SAL_mod))
# dev.off()
# png("figures/diagnostics/SAL-d15n-fitresid.# png", units = "px", width = 600, height = 450)
simulateResiduals(fittedModel = SAL_mod, plot = T)
# dev.off()
# png("figures/diagnostics/SAL-d15n-hist.# png", units = "px", width = 300, height = 300)
hist(resid(SAL_mod), col = "#1B7837")
# dev.off()
# Check individual predictors.
simulated_resids <- simulateResiduals(fittedModel = SAL_mod)
# Check individual predictors
plotResiduals(simulated_resids, form = SAL_dat$l.area.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = SAL_dat$ln.shoredist.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = SAL_dat$slope.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = SAL_dat$sqrt.wrack.std, quantreg = FALSE)
# Goodness of fit test
testUniformity(simulationOutput = simulated_resids) # Failed KS test but fine for dispersion test and outlier test.
# Check for Outliers
testOutliers(simulationOutput = simulated_resids) # These are fine
# Check for overdispersion
testDispersion(simulated_resids) # Looks fine
# FLV:
performance::check_collinearity(FLV_mod)
# png("figures/diagnostics/FLV-d15n-qq.# png", units = "px", width = 300, height = 300)
qqnorm(resid(FLV_mod), col = "#7FBF7B")
qqline(resid(FLV_mod))
# dev.off()
# png("figures/diagnostics/FLV-d15n-fitresid.# png", units = "px", width = 600, height = 450)
simulateResiduals(fittedModel = FLV_mod, plot = T)
# dev.off()
# png("figures/diagnostics/FLV-d15n-hist.# png", units = "px", width = 300, height = 300)
hist(resid(FLV_mod), col = "#7FBF7B")
# dev.off()
# Check individual predictors:
simulated_resids <- simulateResiduals(fittedModel = FLV_mod)
# Check individual predictors
plotResiduals(simulated_resids, form = FLV_dat$l.area.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = FLV_dat$ln.shoredist.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = FLV_dat$slope.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = FLV_dat$sqrt.wrack.std, quantreg = FALSE)
# Goodness of fit test
testUniformity(simulationOutput = simulated_resids) # Failed KS test (p = 0.02) but dispersion and deviation fine
# Check for Outliers
testOutliers(simulationOutput = simulated_resids) # These are fine
# Check for overdispersion
testDispersion(simulated_resids) # Looks fine
# CUR:
performance::check_collinearity(CUR_mod)
# png("figures/diagnostics/CUR-d15n-qq.# png", units = "px", width = 300, height = 300)
qqnorm(resid(CUR_mod), col = "#D9F0D3")
qqline(resid(CUR_mod))
# dev.off()
# png("figures/diagnostics/CUR-d15n-fitresid.# png", units = "px", width = 600, height = 450)
DHARMa::simulateResiduals(fittedModel = CUR_mod, plot = T)
# dev.off()
# png("figures/diagnostics/CUR-d15n-hist.# png", units = "px", width = 300, height = 300)
hist(resid(CUR_mod), col = "#D9F0D3")
# dev.off()
# Check individual predictors:
simulated_resids <- simulateResiduals(fittedModel = CUR_mod)
# Check individual predictors
plotResiduals(simulated_resids, form = CUR_dat$l.area.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = CUR_dat$ln.shoredist.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = CUR_dat$slope.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = CUR_dat$sqrt.wrack.std, quantreg = FALSE)
# Goodness of fit test
testUniformity(simulationOutput = simulated_resids) # Significant outlier
# Check for Outliers
testOutliers(simulationOutput = simulated_resids) # These are fine
# Check for overdispersion
testDispersion(simulated_resids) # Looks fine
# ISO:
performance::check_collinearity(ISO_mod)
# png("figures/diagnostics/ISO-d15n-qq.# png", units = "px", width = 300, height = 300)
qqnorm(resid(ISO_mod), col = "#E7D4E8")
qqline(resid(ISO_mod))
# dev.off()
# png("figures/diagnostics/ISO-d15n-fitresid.# png", units = "px", width = 600, height = 450)
DHARMa::simulateResiduals(fittedModel = ISO_mod, plot = T)
# dev.off()
# png("figures/diagnostics/ISO-d15n-hist.# png", units = "px", width = 300, height = 300)
hist(resid(ISO_mod), col = "#E7D4E8")
# dev.off()
# Check individual predictors:
simulated_resids <- simulateResiduals(fittedModel = ISO_mod)
# Check individual predictors
plotResiduals(simulated_resids, form = ISO_dat$l.area.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = ISO_dat$ln.shoredist.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = ISO_dat$slope.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = ISO_dat$sqrt.wrack.std, quantreg = FALSE)
# Goodness of fit test
testUniformity(simulationOutput = simulated_resids) # Good
# Check for Outliers
testOutliers(simulationOutput = simulated_resids) # These are fine
# Check for overdispersion
testDispersion(simulated_resids) # Looks fine
# COL:
performance::check_collinearity(COL_mod)
# png("figures/diagnostics/COL-d15n-qq.# png", units = "px", width = 300, height = 300)
qqnorm(resid(COL_mod), col = "#AF8DC3")
qqline(resid(COL_mod))
# dev.off()
# png("figures/diagnostics/COL-d15n-fitresid.# png", units = "px", width = 600, height = 450)
DHARMa::simulateResiduals(fittedModel = COL_mod, plot = T)
# dev.off()
# png("figures/diagnostics/COL-d15n-hist.# png", units = "px", width = 300, height = 300)
hist(resid(COL_mod), col = "#AF8DC3")
# dev.off()
# Check individual predictors:
simulated_resids <- simulateResiduals(fittedModel = COL_mod)
# Check individual predictors
plotResiduals(simulated_resids, form = COL_dat$l.area.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = COL_dat$ln.shoredist.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = COL_dat$slope.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = COL_dat$sqrt.wrack.std, quantreg = FALSE)
# Goodness of fit test
testUniformity(simulationOutput = simulated_resids) # Good
# Check for Outliers
testOutliers(simulationOutput = simulated_resids) # These are fine
# Check for overdispersion
testDispersion(simulated_resids) # Looks fine
# Feces
performance::check_collinearity(feces_mod)
# png("figures/diagnostics/feces-d15n-qq.# png", units = "px", width = 300, height = 300)
qqnorm(resid(feces_mod), col = "#762A83")
qqline(resid(feces_mod))
# dev.off()
# png("figures/diagnostics/feces-d15n-fitresid.# png", units = "px", width = 600, height = 450)
DHARMa::simulateResiduals(fittedModel = feces_mod, plot = T)
# dev.off()
# png("figures/diagnostics/feces-d15n-hist.# png", units = "px", width = 300, height = 300)
hist(resid(feces_mod), col = "#762A83")
# dev.off()
# Check individual predictors:
simulated_resids <- simulateResiduals(fittedModel = feces_mod)
# Check individual predictors
plotResiduals(simulated_resids, form = feces_dat$l.area.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = feces_dat$ln.shoredist.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = feces_dat$slope.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = feces_dat$sqrt.wrack.std, quantreg = FALSE)
# Goodness of fit test
testUniformity(simulationOutput = simulated_resids) # Good
# Check for Outliers
testOutliers(simulationOutput = simulated_resids) # These are fine
# Check for overdispersion
testDispersion(simulated_resids) # Looks fine
# Feathers:
performance::check_collinearity(feather_mod)
# png("figures/diagnostics/feather-d15n-qq.# png", units = "px", width = 300, height = 300)
qqnorm(resid(feather_mod), col = "#762A83")
qqline(resid(feather_mod))
# dev.off()
# png("figures/diagnostics/feather-d15n-fitresid.# png", units = "px", width = 300, height = 300)
DHARMa::simulateResiduals(fittedModel = feather_mod, plot = T)
# dev.off()
# png("figures/diagnostics/feather-d15n-hist.# png", units = "px", width = 300, height = 300)
hist(resid(feather_mod), col = "#762A83")
# dev.off()
# Check individual predictors:
simulated_resids <- simulateResiduals(fittedModel = feather_mod)
# Check individual predictors
plotResiduals(simulated_resids, form = feather_dat$l.area.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = feather_dat$ln.shoredist.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = feather_dat$slope.std, quantreg = FALSE)
plotResiduals(simulated_resids, form = feather_dat$sqrt.wrack.std, quantreg = FALSE)
# Goodness of fit test
testUniformity(simulationOutput = simulated_resids) # Good
# Check for Outliers
testOutliers(simulationOutput = simulated_resids) # These are fine
# Check for overdispersion
testDispersion(simulated_resids) # Looks fine
```
# Results:
## 1.) Make table of RVIs
```{r}
MuMIn::importance(soil_avg)
MuMIn::importance(SAL_avg)
MuMIn::importance(FLV_avg)
MuMIn::importance(CUR_avg)
MuMIn::importance(ISO_avg)
MuMIn::importance(COL_avg)
MuMIn::importance(feces_avg)
MuMIn::importance(feather_avg)
```
## 2.) Coefplot:
Feces:
```{r}
soil_avg <- readRDS("other-models/soil_avg_mod.rds")
SAL_avg <- readRDS("other-models/SAL_avg_mod.rds")
FLV_avg <- readRDS("other-models/FLV_avg_mod.rds")
CUR_avg <- readRDS("other-models/CUR_avg_mod.rds")
ISO_avg <- readRDS("other-models/ISO_avg_mod.rds")
COL_avg <- readRDS("other-models/COL_avg_mod.rds")
feces_avg <- readRDS("other-models/feces_avg_mod.rds")
params <- factor(c("cond(slope.std)", "cond(sqrt.wrack.std)", "cond(l.area.std:ln.shoredist.std)", "cond(ln.shoredist.std)", "cond(l.area.std)"))
# SOIL:
soil.coefs <- coefTable(soil_avg, full = FALSE)
soil.coefs <- as.data.frame(soil.coefs)
soil.coefs$parameter <- rownames(soil.coefs)
soil.coefs$df <- NULL
names(soil.coefs) <- c("fit", "fit.se", "parameter")
soil.coefs$parameter <- factor(soil.coefs$parameter, levels = params)
soil.coefs <- soil.coefs %>%
slice(match(params, parameter)) # This removes the intercept.
# Salal:
SAL.coefs <- coefTable(SAL_avg, full = FALSE)
SAL.coefs <- as.data.frame(SAL.coefs)
SAL.coefs$parameters <- rownames(SAL.coefs)
SAL.coefs$df <- NULL
names(SAL.coefs) <- c("fit", "fit.se", "parameter")
SAL.coefs$parameter <- factor(SAL.coefs$parameter, levels = params)
SAL.coefs <- SAL.coefs %>%
slice(match(params, parameter)) # This removes the intercept.
# FLV:
FLV.coefs <- coefTable(FLV_avg, full = FALSE)
FLV.coefs <- as.data.frame(FLV.coefs)
FLV.coefs$parameters <- rownames(FLV.coefs)
FLV.coefs$df <- NULL
names(FLV.coefs) <- c("fit", "fit.se", "parameter")
FLV.coefs$parameter <- factor(FLV.coefs$parameter, levels = params)
FLV.coefs <- FLV.coefs %>%
slice(match(params, parameter)) # This removes the intercept.
# CUR:
CUR.coefs <- coefTable(CUR_avg, full = FALSE)
CUR.coefs <- as.data.frame(CUR.coefs)
CUR.coefs$parameters <- rownames(CUR.coefs)
CUR.coefs$df <- NULL
names(CUR.coefs) <- c("fit", "fit.se", "parameter")
CUR.coefs$parameter <- factor(CUR.coefs$parameter, levels = params)
CUR.coefs <- CUR.coefs %>%
slice(match(params, parameter)) # This removes the intercept.
# ISO:
ISO.coefs <- coefTable(ISO_avg, full = FALSE)
ISO.coefs <- as.data.frame(ISO.coefs)
ISO.coefs$parameters <- rownames(ISO.coefs)
ISO.coefs$df <- NULL
names(ISO.coefs) <- c("fit", "fit.se", "parameter")
ISO.coefs$parameter <- factor(ISO.coefs$parameter, levels = params)
ISO.coefs <- ISO.coefs %>%
slice(match(params, parameter)) # This removes the intercept.
# COL:
COL.coefs <- coefTable(COL_avg, full = FALSE)
COL.coefs <- as.data.frame(COL.coefs)
COL.coefs$parameters <- rownames(COL.coefs)
COL.coefs$df <- NULL
names(COL.coefs) <- c("fit", "fit.se", "parameter")
COL.coefs$parameter <- factor(COL.coefs$parameter, levels = params)
COL.coefs <- COL.coefs %>%
slice(match(params, parameter)) # This removes the intercept.
# feces:
feces.coefs <- coefTable(feces_avg, full = FALSE)
feces.coefs <- as.data.frame(feces.coefs)
feces.coefs$parameters <- rownames(feces.coefs)
feces.coefs$df <- NULL
names(feces.coefs) <- c("fit", "fit.se", "parameter")
feces.coefs$parameter <- factor(feces.coefs$parameter, levels = params)
feces.coefs <- feces.coefs %>%
slice(match(params, parameter)) # This removes the intercept.
param.labs <- c("mean slope", "sqrt(wrack biomass)", "log10(distance to shore)", "log10(island area)")
soil.coefs$taxon <- "soil"
SAL.coefs$taxon <- "SAL"
FLV.coefs$taxon <- "FLV"
CUR.coefs$taxon <- "CUR"
ISO.coefs$taxon <- "ISO"
COL.coefs$taxon <- "COL"
feces.coefs$taxon <- "feces"
coefs <- rbind(soil.coefs, SAL.coefs, FLV.coefs, CUR.coefs, ISO.coefs, COL.coefs, feces.coefs)
coefs$taxon <- factor(coefs$taxon,
levels = c("feces", "COL", "ISO", "CUR", "FLV", "SAL", "soil"),
labels = c("songbirds", "carnivores (beetles)", "detritivores (isopods)", "herbivores (weevils)", "false lily-of-the-valley", "salal", "soil"))
coefs$parameter <- factor(coefs$parameter,
levels = c("cond(l.area.std)", "cond(ln.shoredist.std)", "cond(l.area.std:ln.shoredist.std)", "cond(sqrt.wrack.std)", "cond(slope.std)"),
labels = c("Island area", "Distance to shore", "Island area * distance", "Wrack biomass", "Mean island slope"))
color_pal <- c(RColorBrewer::brewer.pal(n = 6, name = "PRGn"), "grey50")
# function to modify vertical spacing between legend keys
# @clauswilke
draw_key_polygon3 <- function(data, params, size) {
lwd <- min(data$size, min(size) / 4)
grid::rectGrob(
width = grid::unit(0.6, "npc"),
height = grid::unit(0.6, "npc"),
gp = grid::gpar(
col = data$colour,
fill = alpha(data$fill, data$alpha),
lty = data$linetype,
lwd = lwd * .pt,
linejoin = "mitre"
))
}
# register new key drawing function,
# the effect is global & persistent throughout the R session
GeomBar$draw_key = draw_key_polygon3
coefplot_d15n_feces <- ggplot(coefs, aes(x = taxon, y = fit, colour = taxon)) +
theme_bw() +
geom_hline(yintercept = 0, col = "grey50", linetype = "dashed") +
geom_point(size = 2) +
geom_errorbar(aes(ymin = (fit - 1.96 * fit.se),
ymax = (fit + 1.96 * fit.se),
width = 0)) +
facet_wrap(~ parameter, nrow = 5, strip.position = "left") +
theme(panel.grid = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.text.y.left = element_text(angle = 0, size = 8),
strip.background = element_blank(),
legend.title = element_blank(),
axis.text = element_text(size = 8),
axis.title = element_text(size = 12),
legend.text = element_text(size = 8),
legend.key.size = unit(0.3, "cm")) +
ylab((expression(paste("\n"," ", delta^{15}, "N coef")))) +
xlab("") +
scale_color_manual(values = color_pal) +
scale_y_continuous(limits = c(-5, 2),
breaks = c(-6.0, -4.0, -2.0, 0.0, 2.0),
labels = c("-6.0", "-4.0", "-2.0", "0.0", "2.0")) +
guides(color = guide_legend(reverse = TRUE)) +
coord_flip();coefplot_d15n_feces
tiff("figures/coefplot-d15n-feces.tiff", units = "px", width = 3200, height = 3000, res = 600)
coefplot_d15n_feces
# dev.off()
# png("figures/coefplot-d15n-feces-int.# png", units = "px", width = 300, height = 300)
coefplot_d15n_feces
# dev.off()
panel_coefplot_d15n <- ggplot(coefs, aes(x = taxon, y = fit, colour = taxon)) +
theme_bw() +
geom_hline(yintercept = 0, col = "grey50", linetype = "dashed") +
geom_point(size = 3) +
geom_errorbar(aes(ymin = (fit - 1.96 * fit.se),
ymax = (fit + 1.96 * fit.se),
width = 0)) +
facet_wrap(~ parameter, nrow = 5, strip.position = "left") +
theme(panel.grid = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.text.y.left = element_text(angle = 0, size = 12),
strip.background = element_blank(),
legend.title = element_blank(),
axis.text = element_text(size = 12),
axis.title = element_text(size = 16),
legend.position = 'top',
legend.text = element_text(size = 12)) +
guides(color = guide_legend(reverse = TRUE)) +
ylab((expression(paste("\n"," ", delta^{15}, "N coef")))) +
xlab("") +
scale_color_manual(values = color_pal) +
scale_y_continuous(limits = c(-6, 1.8),
breaks = c(-6.0, -4.0, -2.0, 0.0, 2.0),
labels = c("-6.0", "-4.0", "-2.0", "0.0", "2.0")) +
coord_flip();panel_coefplot_d15n
dat_text <- data.frame(
label = "A",
parameter = coefs$parameter[5],
taxon = "soil"
)
dat_text2 <- data.frame(
label = "B",
parameter = coefs$parameter[5],
taxon = "soil"
)
panel_coefplot_d15n2 <- panel_coefplot_d15n +
geom_text(data = dat_text,
mapping = aes(x = 5.5, y = -6, label = label),
hjust = 0,
vjust = 0,
colour = "black",
size = 6)
panel_coefplot_d13c2 <- panel_coefplot_d13c +
geom_text(data = dat_text2,
mapping = aes(x = 6.25, y = -1.45, label = label),
# hjust = 0,
# vjust = 0,
colour = "black",
size = 6)
# Weird workaround to get patchwork's legend collecting function to work:
# from https://github.com/thomasp85/patchwork/issues/170
p_theme <- panel_coefplot_d15n2 + theme(legend.position = "none")
p_guide <- panel_coefplot_d15n2 + guides(colour = "none")
p_theme + panel_coefplot_d13c2 +
plot_layout(guides = "collect") & theme(legend.position = "top")
plot <- p_guide + panel_coefplot_d13c2 +
plot_layout(guides = "collect") & theme(legend.position = "top");plot
tiff("coefplots-d15n-d13c-2021.tiff", units = "px", res = 600, width = 5200, height = 4000)
plot
# dev.off()
```
Feathers:
```{r}
# feathers:
feather_avg <- readRDS("other-models/feather_avg_mod.rds")
feather.coefs <- coefTable(feather_avg, full = FALSE)
feather.coefs <- as.data.frame(feather.coefs)
feather.coefs$parameters <- rownames(feather.coefs)
feather.coefs$df <- NULL
names(feather.coefs) <- c("fit", "fit.se", "parameter")
feather.coefs$parameter <- factor(feather.coefs$parameter, levels = params)
feather.coefs <- feather.coefs %>%
slice(match(params, parameter)) # This removes the intercept.
param.labs <- c("mean slope", "sqrt(wrack biomass)", "log10(distance to shore)", "log10(island area)")
soil.coefs$taxon <- "soil"
SAL.coefs$taxon <- "SAL"
FLV.coefs$taxon <- "FLV"
CUR.coefs$taxon <- "CUR"
ISO.coefs$taxon <- "ISO"
COL.coefs$taxon <- "COL"
feather.coefs$taxon <- "feather"
feces.coefs$taxon <- "feces"
coefs <- rbind(soil.coefs, SAL.coefs, FLV.coefs, CUR.coefs, ISO.coefs, COL.coefs, feather.coefs)
coefs$taxon <- factor(coefs$taxon,
levels = c("feather", "COL", "ISO", "CUR", "FLV", "SAL", "soil"),
labels = c("songbirds - feathers", "carnivores (beetles)", "detritivores (isopods)", "herbivores (weevils)", "false lily-of-the-valley", "salal", "soil"))
coefs$parameter <- factor(coefs$parameter,
levels = c("cond(l.area.std)", "cond(ln.shoredist.std)", "cond(l.area.std:ln.shoredist.std)", "cond(sqrt.wrack.std)", "cond(slope.std)"),
labels = c("Island area", "Distance to shore", "Island area * distance", "Wrack biomass", "Mean island slope"))
# write.csv(coefs, "data-generated/table-coefs-d15n.csv", row.names = FALSE)
color_pal <- c(RColorBrewer::brewer.pal(n = 6, name = "PRGn"), "grey50")
panel_d15n_feathers <- ggplot(coefs, aes(x = taxon, y = fit, colour = taxon)) +
theme_bw() +
geom_hline(yintercept = 0, col = "grey50", linetype = "dashed") +
geom_point(size = 3) +
geom_errorbar(aes(ymin = (fit - 1.96 * fit.se),
ymax = (fit + 1.96 * fit.se),
width = 0)) +
facet_wrap(~ parameter, nrow = 5, strip.position = "left") +
theme(panel.grid = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.text.y.left = element_text(angle = 0, size = 12),
strip.background = element_blank(),
legend.title = element_blank(),
axis.text = element_text(size = 12),
axis.title = element_text(size = 16),
legend.position = "top",
legend.text = element_text(size = 12)) +
guides(color = guide_legend(reverse = TRUE)) +
ylab((expression(paste("\n"," ", delta^{15}, "N coef")))) +
xlab("") +
scale_color_manual(values = color_pal) +
scale_y_continuous(limits = c(-6, 1.8),
breaks = c(-6.0, -4.0, -2.0, 0.0, 2.0),
labels = c("-6.0", "-4.0", "-2.0", "0.0", "2.0")) +
coord_flip();panel_d15n_feathers
dat_text <- data.frame(
label = "A",
parameter = coefs$parameter[5],
taxon = "soil"
)
dat_text2 <- data.frame(
label = "B",
parameter = coefs$parameter[5],
taxon = "soil"
)
panel_d15n_feathers2 <- panel_d15n_feathers +
geom_text(data = dat_text,
mapping = aes(x = 5.5, y = -6, label = label),
hjust = 0,
vjust = 0,
colour = "black",
size = 6)
panel_d13c_feathers2 <- panel_d13c_feathers +
geom_text(data = dat_text2,
mapping = aes(x = 6.25, y = -1.45, label = label),
# hjust = 0,
# vjust = 0,
colour = "black",
size = 6)
tiff("figures/coefplots-d15n-d13c-feathers.tiff", units = "px", res = 600, width = 5200, height = 4000)
panel_d15n_feathers2 + panel_d13c_feathers2 + plot_layout(guides = "collect") & theme(legend.position = 'top')
# dev.off()
coefs_table_d15n <- rbind(soil.coefs, SAL.coefs, FLV.coefs, CUR.coefs, ISO.coefs, COL.coefs, feces.coefs, feather.coefs)
# write.csv(coefs_table_d15n, "d15n-coef-table.csv")
```
## 3.) Biplots:
### Area:
Bootstrapped CIs and PIs:
```{r}
# Simulate the uncertainty in parameter estimates and generate 95% credible intervals
coef_a <- c(soil_mod$fit$par[1:6]) # Extract the point estimates for coefficients from your model (eg. intercept and slope)
coef_b <- c(SAL_mod$fit$par[1:6])
coef_c <- c(FLV_mod$fit$par[1:6])
coef_d <- c(CUR_mod$fit$par[1:6])
coef_e <- c(ISO_mod$fit$par[1:6])
coef_f <- c(COL_mod$fit$par[1:6])
coef_g <- c(feces_mod$fit$par[1:6])
coef_h <- c(feather_mod$fit$par[1:6])
vcov_a <- vcov(soil_mod)[[1]] # Variance-covariance matrix of the parameter estimates
vcov_b <- vcov(SAL_mod)[[1]]
vcov_c <- vcov(FLV_mod)[[1]]
vcov_d <- vcov(CUR_mod)[[1]]
vcov_e <- vcov(ISO_mod)[[1]]
vcov_f <- vcov(COL_mod)[[1]]
vcov_g <- vcov(feces_mod)[[1]]
vcov_h <- vcov(feather_mod)[[1]]
pars.resamp.a <- mvrnorm(500, mu = coef_a, Sigma = vcov_a) # Multivariate normal simulation of errors - gives the mean and the VCV matrix
pars.resamp.b <- mvrnorm(500, mu = coef_b, Sigma = vcov_b)
pars.resamp.c <- mvrnorm(500, mu = coef_c, Sigma = vcov_c)
pars.resamp.d <- mvrnorm(500, mu = coef_d, Sigma = vcov_d)
pars.resamp.e <- mvrnorm(500, mu = coef_e, Sigma = vcov_e)
pars.resamp.f <- mvrnorm(500, mu = coef_f, Sigma = vcov_f)
pars.resamp.g <- mvrnorm(500, mu = coef_g, Sigma = vcov_g)
pars.resamp.h <- mvrnorm(500, mu = coef_h, Sigma = vcov_h)
x.a <- seq(min(soil_dat$l.area.std), max(soil_dat$l.area.std), length.out = 1000) # dummy variable for your predictor
x.b <- seq(min(SAL_dat$l.area.std), max(SAL_dat$l.area.std), length.out = 1000)
x.c <- seq(min(FLV_dat$l.area.std), max(FLV_dat$l.area.std), length.out = 1000)
x.d <- seq(min(CUR_dat$l.area.std), max(CUR_dat$l.area.std), length.out = 1000)
x.e <- seq(min(ISO_dat$l.area.std), max(ISO_dat$l.area.std), length.out = 1000)
x.f <- seq(min(COL_dat$l.area.std), max(COL_dat$l.area.std), length.out = 1000)
x.g <- seq(min(feces_dat$l.area.std), max(feces_dat$l.area.std), length.out = 1000)
x.h <- seq(min(feather_dat$l.area.std), max(feather_dat$l.area.std), length.out = 1000)
y.conf <- list() #Create an empty list for the estimate y values for each simulation run
# Create dataframe for each value of X, the limits on y from the simulation
pred.frame.a <- expand.grid(x = x.a,
l.ci = NA,
u.ci = NA,
l.pi = NA,
u.pi = NA,
m = NA)
pred.frame.b <- expand.grid(x = x.b,
l.ci = NA,
u.ci = NA,
l.pi = NA,
u.pi = NA,
m = NA)
pred.frame.c <- expand.grid(x = x.c,
l.ci = NA,
u.ci = NA,
l.pi = NA,
u.pi = NA,
m = NA)
pred.frame.d <- expand.grid(x = x.d,
l.ci = NA,
u.ci = NA,
l.pi = NA,
u.pi = NA,
m = NA)
pred.frame.e <- expand.grid(x = x.e,
l.ci = NA,
u.ci = NA,
l.pi = NA,
u.pi = NA,
m = NA)
pred.frame.f <- expand.grid(x = x.f,
l.ci = NA,
u.ci = NA,
l.pi = NA,
u.pi = NA,
m = NA)
pred.frame.g <- expand.grid(x = x.g,
l.ci = NA,
u.ci = NA,
l.pi = NA,
u.pi = NA,
m = NA)
pred.frame.h <- expand.grid(x = x.h,
l.ci = NA,
u.ci = NA,
l.pi = NA,
u.pi = NA,
m = NA)
# Soil model:
# Calculate additional variance due to random effects & random noise:
summary(soil_mod)
sqrt(2.624 + 6.662) # 3.047294
y.conf <- list()
for(z in 1:length(x.a)){
y.conf[[z]] <- pars.resamp.a[,1] + pars.resamp.a[,2] * x.a[z] # fit the estimate based on the sampled intercept and slopes
pred.temp <- list()
for(t in 1:500){
pred.temp[[t]]<- rnorm(500,pars.resamp.a[t,1] + pars.resamp.a[t,2]*x.a[z], 3.047294) # add in model standard deviation
}
pred.temp.all<- do.call(rbind, lapply(pred.temp, data.frame, stringsAsFactors=FALSE))
pred.frame.a[z,2]<- quantile(y.conf[[z]], 0.025)
pred.frame.a[z,3]<- quantile(y.conf[[z]], 0.975)
pred.frame.a[z,4]<- quantile(pred.temp.all[,1], 0.025)
pred.frame.a[z,5]<- quantile(pred.temp.all[,1], 0.975)
pred.frame.a[z,6] <- coef_a[1] + coef_a[2] * x.a[z]
}
# Salal model:
# Calculate additional variance due to random effects & random noise:
summary(SAL_mod)
sqrt(5.420 + 8.487) # 3.729209
y.conf <- list()
for(z in 1:length(x.b)){
y.conf[[z]] <- pars.resamp.b[,1] + pars.resamp.b[,2] * x.b[z]
pred.temp <- list()
for(t in 1:500){
pred.temp[[t]]<- rnorm(500,pars.resamp.b[t,1] + pars.resamp.b[t,2]*x.b[z], 3.729209) # add in model standard deviation
}
pred.temp.all<- do.call(rbind, lapply(pred.temp, data.frame, stringsAsFactors=FALSE))
pred.frame.b[z,2]<- quantile(y.conf[[z]], 0.025)
pred.frame.b[z,3]<- quantile(y.conf[[z]], 0.975)
pred.frame.b[z,4]<- quantile(pred.temp.all[,1], 0.025)
pred.frame.b[z,5]<- quantile(pred.temp.all[,1], 0.975)
pred.frame.b[z,6] <- coef_b[1] + coef_b[2] * x.b[z]
}
# False lily-of-the-Valley model:
# Calculate additional variance due to random effects & random noise:
summary(FLV_mod)
sqrt(9.52 + 9.11) # 4.316248
y.conf <- list()
for(z in 1:length(x.c)){
y.conf[[z]] <- pars.resamp.c[,1] + pars.resamp.c[,2] * x.c[z]
pred.temp <- list()
for(t in 1:500){
pred.temp[[t]]<- rnorm(500,pars.resamp.c[t,1] + pars.resamp.c[t,2]*x.c[z], 4.316248) # add in model standard deviation
}
pred.temp.all<- do.call(rbind, lapply(pred.temp, data.frame, stringsAsFactors=FALSE))
pred.frame.c[z,2]<- quantile(y.conf[[z]], 0.025)
pred.frame.c[z,3]<- quantile(y.conf[[z]], 0.975)
pred.frame.c[z,4]<- quantile(pred.temp.all[,1], 0.025)
pred.frame.c[z,5]<- quantile(pred.temp.all[,1], 0.975)
pred.frame.c[z,6] <- coef_c[1] + coef_c[2] * x.c[z]
}