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bowfin_scripts.rmd
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---
title: "Bowfin figures"
author: "Collin Moratz, Jeremiah Shrovnal, Mia McReynolds"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: pdf_document
toc: TRUE
editor_options:
chunk_output_type: console
---
# Length histogram
```{r length histogram}
library(ggplot2)
setwd("C:/Users/jshro/Documents/Old cpu/Sept 9")
lengthhist <- read.csv("bowfin_lengths.csv")
# Label all ticks
xbreaks2 <- seq(50, 795, 50)
xlabels2 <- as.character(xbreaks2)
# For only labeling every other tick
ybreaks2 <- seq(0, 70, 10)
ylabels2 <- as.character(ybreaks2)
ylabels2[!(ybreaks2 %% 20 == 0)] <- ''
bowfincount <- nrow(lengthhist)
lengthhistogram <- ggplot(lengthhist, aes(x = Length, fill = Sex, color = Sex)) +
geom_histogram(binwidth = 1, alpha = 0.5, position = "stack",
breaks = seq(50, 795, 50)) +
theme_classic() +
scale_fill_manual(values = c("grey10", "grey60", "grey90"),
labels = c("Female", "Male", "Unknown"), name = "Sex") +
scale_color_manual(values = c("Black", "Black", "Black")) +
scale_x_continuous(limits = c(50, 795), breaks = xbreaks2, labels = xlabels2, expand = expand_scale(mult = c(0, 0))) +
scale_y_continuous(limits = c(0,75), breaks = ybreaks2, labels = ylabels2, expand = expand_scale(mult = c(0, 0))) +
labs(x = "Total length (mm)", y = "Number of bowfin captured") +
theme(axis.text.y = element_text(size = 18), legend.title.align = 0.5,
axis.text.x = element_text(angle = 90, hjust = 0, size = 18),
axis.title.x = element_text(size = 20, margin = margin(15, 1, 1, 1)),
axis.title.y = element_text(size = 20, margin = margin(1, 15, 1, 1)),
legend.text = element_text(size = 16), legend.position = c(.1, .97),
legend.title = element_text(size = 18), legend.justification = c("left", "top"),
legend.direction = "horizontal")
# Save histogram
ggsave("lengthhistogram.tiff", plot = lengthhistogram, dpi = 600)
```
# Diet Plot
```{r}
sizediet <- read.csv("bowfindietsize.csv")
#Colorblind friendly palette
cbbPalette <- c("#009E73", "#F0E442", "#000000", "#0072B2",
"#D55E00", "#CC79A7", "#E69F00", "#56B4E9")
ggplot(sizediet, aes(fill=Food, y=Diet, x=Size)) +
geom_bar( stat="identity") +
labs(title="Diet Proportion by Size", y = "Diet Source by count (%)",
x = "Size Class (mm)") +
scale_fill_manual(values=cbbPalette) +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
```
# Model Output
```{r}
library(gridExtra)
library(lemon)
plotdata <- read.csv("collin_SASplot2.csv")
plotdata$Year <- as.factor(plotdata$Year)
(FirstPlot <- ggplot(plotdata, aes(x = RelativeJulianDay, y = TotalCatch)) +
geom_point()+
theme_classic() +
xlim(0,45) + #ylim(0,30) +
geom_line(data = plotdata, aes(x = RelativeJulianDay, y = Mu)) +
geom_ribbon(data = plotdata, aes(ymin = Lower, ymax = Upper), alpha=0.3) +
labs(y = "Total in and outflow count",
x = "Relative Julian Day") +
theme(panel.border=element_blank(), axis.line=element_line()) +
facet_rep_wrap(~ Year, ncol = 1, scales = "free_y",repeat.tick.labels = 'left'))
(SecondPlot <- ggplot(plotdata, aes(x = RelativeJulianDay)) +
geom_line(data = plotdata, aes(y = AvgTemp, color = "Temp")) +
scale_colour_manual(values = "blue") +
theme_classic() +
xlim(0,45) + #ylim(-12,22) +
labs(y = "Average Water Temp (°C)",
x = "Relative Julian Day",
colour = "") +
theme(legend.position = "none") +
facet_wrap(~ Year, ncol = 1,
scales = "free_y"))
grid.arrange(FirstPlot, SecondPlot, nrow = 1, widths = c(1,1))
```
# Migration
```{r migration}
Malchow <- read.csv("BONMalchow.csv")
Malchow$Year <- as.factor(Malchow$Year)
ggplot(Malchow, aes(x = RelativeJulianDay)) +
geom_bar(data = Malchow, stat = "identity", position = "dodge",
aes(y = CatchCount, fill = Catch)) +
geom_line(data = Malchow, aes(y = AvgTemp), color = "black") +
scale_y_continuous(limits = c(0,20), sec.axis = sec_axis(~ . , name = "Average Temperature (C)")) +
scale_x_continuous(limits=c(0,40)) +
xlim(0,40) + ylim(0,30) +
scale_fill_manual(values = c("blue", "red")) +
theme_classic() +
labs(y = "Catch Count",
x = "Relative Julian Day",
colour = "") +
theme(legend.position = "right") +
facet_grid(Year ~ ., scales = "fixed")
```
# Weight Length
```{r}
bonwl <- read.csv("BON_wl.csv")
wlreg <- lm(log10(Weight) ~ log10(Length), data = bonwl)
library(car)
Anova(wlreg)
summary(wlreg)
confint(wlreg)
plot(log10(bonwl$Weight) ~ log10(bonwl$Length))
abline(lm(log10(bonwl$Weight) ~ log10(bonwl$Length)))
plot(wlreg)
# Random around 0
plot(wlreg$residuals~log10(bonwl$Length))
# Central tendency
hist(residuals(wlreg), col = "darkgray")
plot(resid(wlreg))
# Observed vs expected
qqnorm(wlreg$residuals)
qqline(wlreg$residuals)
# Constant variability
plot(wlreg$residuals~wlreg$fitted)
plot(abs(wlreg$residuals)~wlreg$fitted)
```
# Growth
```{r growth rate}
library(FSA)
library(MASS)
library(nlstools)
library(minpack.lm)
library(cvTools)
library(AICcmodavg)
library(nlme)
library(lattice)
library(msm)
setwd("C:/Users/jshro/Documents/Old cpu/Sept 9")
BON <- read.csv("C:/Users/jshro/Documents/Old cpu/Sept 9/bowfinbc.csv")
# Von Bert
vonbertlog <- function(T, logLinf, K = NULL, t0 = NULL) {
# If passed as list
if(length(logLinf) == 3) {
t0 <- logLinf[[3]]
K <- logLinf[[2]]
logLinf <- logLinf[[1]]
}
exp(logLinf) * (1 - exp(-K * (T - t0)))
}
# Gompertz
gomplog <- function(T, logLinf, tstar = NULL, gstar = NULL) {
# If passed as list
if(length(logLinf) == 3) {
gstar <- logLinf[[3]]
tstar <- logLinf[[2]]
logLinf <- logLinf[[1]]
}
exp(logLinf) * exp(-exp(-gstar * (T - tstar)))
}
# Logistic
logisticlog <- function(T, logLinf, tstar = NULL, gninf = NULL) {
# If passed as a list
if(length(logLinf) == 3) {
gninf <- logLinf[[3]]
tstar <- logLinf[[2]]
logLinf <- logLinf[[1]]
}
exp(logLinf) / (1 + exp(-gninf * (T - tstar)))
}
# Richards
richardslog <- function(T, logLinf, k = NULL, tstar = NULL, b = NULL) {
# If passed as list
if(length(Linf) == 4) {
b <- Linf[[4]]
tstar <- Linf[[3]]
k <- Linf[[2]]
Linf <- Linf[[1]]
}
exp(Linf) * (1 - (1 / b) * exp(-k * (T - tstar)))^b
}
fitVB.01 <- nlme(BC.Len ~ vonbertlog(BC.Age, logLinf, K, t0), data = BON,
fixed = list(logLinf ~ 1, K ~ 1, t0 ~ 1),
random = logLinf + K + t0 ~ 1 | ID,
start = list(fixed = c(logLinf = log(600), k = .5, t0 = 0.5)))
fitG.01 <- nlme(BC.Len ~ gomplog(BC.Age, logLinf, tstar, gstar), data = BON,
fixed = list(logLinf ~ 1, tstar ~ 1, gstar ~ 1),
random = logLinf + tstar + gstar ~ 1 | ID,
start = list(fixed = c(logLinf = log(600), tstar = 2, gstar = 0.5)))
fitL.01 <- nlme(BC.Len ~ logisticlog(BC.Age, logLinf, tstar, gninf), data = BON,
fixed = list(logLinf ~ 1, tstar ~ 1, gninf ~ 1),
random = logLinf + tstar + gninf ~ 1 | ID,
start = list(fixed = c(logLinf = log(600), tstar = 2, gninf = 0.8)))
# Power functions
fitVB.01 <- nlme(BC.Len ~ vonbertlog(BC.Age, logLinf, K, t0), data = BON,
fixed = list(logLinf ~ 1, K ~ 1, t0 ~ 1),
random = logLinf + K + t0 ~ 1 | ID,
start = list(fixed = c(logLinf = log(600), k = .5, t0 = 0.5)),
weights = varPower(form = ~ BC.Age))
fitG.01p <- nlme(BC.Len ~ gomplog(BC.Age, logLinf, tstar, gstar), data = BON,
fixed = list(logLinf ~ 1, tstar ~ 1, gstar ~ 1),
random = logLinf + tstar + gstar ~ 1 | ID,
start = list(fixed = c(logLinf = log(600), tstar = 2, gstar = 0.5)),
weights = varPower(form = ~ BC.Age))
# fitL.01p <- nlme(BC.Len ~ logisticlog(BC.Age, logLinf, tstar, gninf), data = BON,
# fixed = list(logLinf ~ 1, tstar ~ 1, gninf ~ 1),
# random = logLinf + tstar + gninf~1 | ID,
# start = list(fixed = c(logLinf = log(600), tstar = 2, gninf = 0.8)),
# weights = varPower(form = ~ BC.Age))
aictab(list(fitG.01,fitL.01,
fitG.01p),
c("Gompertz","logistic",
"Gompertz hetero."))
library(plyr)
library(dplyr)
BONmatchvar <- c("ID", "Site", "Sex")
BONmatch <- BON[BONmatchvar]
coefs <- data.frame(gstar = coef(fitG.01p)[,3], tstar = coef(fitG.01p)[,2],
logLinf = coef(fitG.01p)[,1], fish = rownames(coef(fitG.01p)))
coefsnew <- inner_join(x = coefs,y = BONmatch, by = c("fish" = unique("ID")))
coefsnew <- unique(coefsnew)
coefsmalefit <- filterD(coefsnew, Sex == "M")
coefsfemalefit <- filterD(coefsnew, Sex == "F")
coefsmale <- data.frame(gstar = coefsmalefit["gstar"], tstar = coefsmalefit["tstar"],
logLinf = coefsmalefit["logLinf"])
coefsfemale <- data.frame(gstar = coefsfemalefit["gstar"], tstar = coefsfemalefit["tstar"],
logLinf = coefsfemalefit["logLinf"])
BONplot <- filterD(BON, Sex == "M" | Sex == "F")
Male_count <- nrow(unique(coefsmalefit))
Female_count <- nrow(unique(coefsfemalefit))
Male_label <- paste("Male (N = ", Male_count,")",sep = "")
Female_label <- paste("Female (N = ", Female_count,")",sep = "")
ggplot(BONplot, aes(x = BC.Age, y = BC.Len)) +
scale_x_continuous(limits=c(0,20), breaks = seq(0, 20, 2), "Back-Calculated Age") +
scale_y_continuous(limits=c(0,800), breaks = seq(0, 800, 100),
"Back-Calculated Length at Age (mm)") +
stat_function(fun=function(x){exp(mean(coefsmale[,"logLinf"]))*
exp(-exp(-mean(coefsmale[,"gstar"])*
(x-mean(coefsmale[,"tstar"]))))},
color="blue", linetype = "longdash", size = 1.5) +
stat_function(fun=function(x){exp(mean(coefsfemale[,"logLinf"]))*
exp(-exp(-mean(coefsfemale[,"gstar"])*
(x-mean(coefsfemale[,"tstar"]))))},
color="red", linetype = "longdash", size = 1.5) +
geom_point(aes(color = Sex, shape = Sex), alpha = .75, size = 4) +
scale_color_manual(values = c("Red", "Blue"), labels = c(Female_label, Male_label),
name = "Sex") +
scale_shape_manual(values = c(1,4), labels = c(Female_label, Male_label),
name = "Sex") +
theme_classic() +
theme(axis.text.y = element_text(size = 18), legend.title.align = 0.5,
axis.text.x = element_text(hjust = 0, size = 18),
axis.title.x = element_text(size = 20, margin = margin(15, 1, 1, 1)),
axis.title.y = element_text(size = 20, margin = margin(1, 15, 1, 1)),
legend.text = element_text(size=16), legend.position = c(.95, .1),
legend.title = element_text(size=18), legend.justification = c("right", "bottom"),
legend.direction = "vertical")
```
```{r new_growth_metric}
# Demonstrate other populations
# femaleUMR
806.8 * (1-exp(-0.227*(3 + 0.012)))
#maleUMR
696.7 * (1-exp(-0.328 * (2 - 0.185)))
# Lake Lindsay Grace
603.8 * (1-exp(-0.625 * (2 - 0.779)))
603.8 * (1-exp(-0.625 * (3 - 0.779)))
# Upper Barataria Estuary - needs checking
1131.6 * (1-exp(-0.078 * (2 - 0.523)))
1131.6 * (1-exp(-0.078 * (3 - 0.523)))
# Pool 11 UMR
809.2 * (1-exp(-0.229 * (2 - 0.086)))
809.2 * (1-exp(-0.229 * (3 - 0.086)))
# Pool 13 UMR
783.3 * (1-exp(-0.235 * (2 + 0.004)))
783.3 * (1-exp(-0.235 * (3 + 0.004)))
BCgrowth <- readr::read_csv(
paste0(
here::here(),
"/bowfinbc.csv"
)
)
BC_length_at_age_summary <- BCgrowth |>
dplyr::rename(
`BC Age` = BC.Age
) |>
dplyr::group_by(
Sex,
`BC Age`
) |>
dplyr::summarize(
`Mean BC Length` = mean(BC.Len, na.rm = TRUE),
`SD BC Length` = sd(BC.Len, na.rm = TRUE),
Observations = dplyr::n(),
`Total Sites` = length(unique(Site)),
Sites = tibble::lst(unique(Site)),
dplyr::across(`Mean BC Length`:`SD BC Length`, round)
)
# Length at age table
BC_length_at_age_summary |>
dplyr::filter(
(`BC Age` == 3 & Sex == "F") |
(`BC Age` == 2 & Sex == "M")
) |>
dplyr::select(
1:2, 5, 3:4, 6, 7
) |>
gt::gt()
BC_length_at_age_summary |>
dplyr::filter(
Sex != "U"
) |>
dplyr::mutate(
`BC Age` = factor(`BC Age`)
) |>
tidyr::complete(
`BC Age`
) |>
dplyr::mutate(
`BC Age` = as.numeric(`BC Age`),
Sex = dplyr::if_else(
Sex == "M",
"Male",
"Female"
)
) |>
dplyr::mutate_at(
3:6, ~tidyr::replace_na(., 0)
) |>
ggplot2::ggplot(
ggplot2::aes(
x = `BC Age`,
y = `Mean BC Length`,
fill = Sex,
group = Sex
)
) +
ggplot2::geom_col(
position = ggplot2::position_dodge2()
) +
ggplot2::geom_errorbar(
data = BC_length_at_age_summary |>
dplyr::filter(
Sex != "U",
`BC Age` < 13,
!(Sex == "M" && `BC Age` > 7)
) |>
dplyr::mutate(
`BC Age` = factor(`BC Age`)
) |>
tidyr::complete(
`BC Age`
) |>
dplyr::mutate(
`BC Age` = as.numeric(`BC Age`),
Sex = dplyr::if_else(
Sex == "M",
"Male",
"Female"
)
) |>
dplyr::mutate_at(
3:6, ~tidyr::replace_na(., 0)
),
ggplot2::aes(
ymin = `Mean BC Length` - `SD BC Length`,
ymax = `Mean BC Length` + `SD BC Length`
),
width = 0.25,
position = ggplot2::position_dodge(0.9)
) +
# ggplot2::geom_text(
# data = BC_length_at_age_summary |>
# dplyr::filter(
# Sex != "U"
# ) |>
# dplyr::mutate(
# `BC Age` = factor(`BC Age`)
# ) |>
# tidyr::complete(
# `BC Age`
# ) |>
# dplyr::mutate(
# `BC Age` = as.numeric(`BC Age`),
# Sex = dplyr::if_else(
# Sex == "M",
# "Male",
# "Female"
# )
# ),
# ggplot2::aes(
# x = `BC Age`,
# y = 685,
# label = Observations,
# color = Sex
# ),
# position = ggplot2::position_dodge(0.9),
# size = 3
# ) +
ggplot2::scale_y_continuous(
"Back-calculated length",
breaks = seq(0, 700, 100),
limits = c(0, 700),
expand = ggplot2::expansion(
mult = c(0, 0)
)
) +
ggplot2::scale_x_continuous(
"Back-calculated age",
breaks = seq(0, max(BC_length_at_age_summary$`BC Age`)[1], by = 1),
expand = ggplot2::expansion(
mult = c(0.005, 0.005)
)
) +
ggplot2::scale_fill_manual(
"",
values = c(
"Male" = "gray60",
"Female" = "gray30"
)
) +
ggplot2::scale_color_manual(
"",
values = c(
"Male" = "gray60",
"Female" = "gray30"
)
) +
ggplot2::theme_classic()
```
# Diet Metrics
```{r diet metrics}
library(knitr)
library(matrixStats)
diet_full <- read.csv("Bowfin_Diet_Data01.csv")
#Create a total for each fish
diet_full$Total_Diet_N <- rowSums(diet_full[,5:10])
#Remove empties
diet <- diet_full[which(diet_full$Total_Diet_N > 0),]
diet$Fish_percentN <- diet$Fish / diet$Total_Diet_N
diet$Insecta_percentN <- diet$Insecta / diet$Total_Diet_N
diet$Amphipoda_percentN <- diet$Amphipod / diet$Total_Diet_N
diet$Decapoda_percentN <- diet$Decapoda / diet$Total_Diet_N
diet$Isopoda_percentN <- diet$Isopoda / diet$Total_Diet_N
diet$Other_percentN <- diet$Other / diet$Total_Diet_N
diet
#Divide by adult/YOY
Adult_diets <- diet[which(diet$Length >= 200),]
YOY_diets <- diet[which(diet$Length < 200),]
PENS_Adult_diets <- Adult_diets[which(Adult_diets$Site == "PENS"),]
Other_Adult_diets <- Adult_diets[which(Adult_diets$Site != "PENS"),]
Adult_count <- nrow(Adult_diets)
Adult_Fishcount <- nrow(subset(Adult_diets, Fish > 0))
Adult_Insectacount <- nrow(subset(Adult_diets, Insecta > 0))
Adult_Amphipodacount <- nrow(subset(Adult_diets, Amphipoda > 0))
Adult_Decapodacount <- nrow(subset(Adult_diets, Decapoda > 0))
Adult_Isopodacount <- nrow(subset(Adult_diets, Isopoda > 0))
Adult_Othercount <- nrow(subset(Adult_diets, Other > 0))
PENS_Adult_count <- nrow(PENS_Adult_diets)
PENS_Adult_Fishcount <- nrow(subset(PENS_Adult_diets, Fish > 0))
PENS_Adult_Insectacount <- nrow(subset(PENS_Adult_diets, Insecta > 0))
PENS_Adult_Amphipodacount <- nrow(subset(PENS_Adult_diets, Amphipoda > 0))
PENS_Adult_Decapodacount <- nrow(subset(PENS_Adult_diets, Decapoda > 0))
PENS_Adult_Isopodacount <- nrow(subset(PENS_Adult_diets, Isopoda > 0))
PENS_Adult_Othercount <- nrow(subset(PENS_Adult_diets, Other > 0))
Other_Adult_count <- nrow(Other_Adult_diets)
Other_Adult_Fishcount <- nrow(subset(Other_Adult_diets, Fish > 0))
Other_Adult_Insectacount <- nrow(subset(Other_Adult_diets, Insecta > 0))
Other_Adult_Amphipodacount <- nrow(subset(Other_Adult_diets, Amphipoda > 0))
Other_Adult_Decapodacount <- nrow(subset(Other_Adult_diets, Decapoda > 0))
Other_Adult_Isopodacount <- nrow(subset(Other_Adult_diets, Isopoda > 0))
Other_Adult_Othercount <- nrow(subset(Other_Adult_diets, Other > 0))
YOY_count <- nrow(YOY_diets)
YOY_Fishcount <- nrow(subset(YOY_diets, Fish > 0))
YOY_Insectacount <- nrow(subset(YOY_diets, Insecta > 0))
YOY_Amphipodacount <- nrow(subset(YOY_diets, Amphipoda > 0))
YOY_Decapodacount <- nrow(subset(YOY_diets, Decapoda > 0))
YOY_Isopodacount <- nrow(subset(YOY_diets, Isopoda > 0))
YOY_Othercount <- nrow(subset(YOY_diets, Other > 0))
Adult_dietcount <- rbind(Adult_Fishcount, Adult_Insectacount,
Adult_Amphipodacount, Adult_Decapodacount,
Adult_Isopodacount, Adult_Othercount)
PENS_Adult_dietcount <- rbind(PENS_Adult_Fishcount, PENS_Adult_Insectacount,
PENS_Adult_Amphipodacount, PENS_Adult_Decapodacount,
PENS_Adult_Isopodacount, PENS_Adult_Othercount)
Other_Adult_dietcount <- rbind(Other_Adult_Fishcount, Other_Adult_Insectacount,
Other_Adult_Amphipodacount, Other_Adult_Decapodacount,
Other_Adult_Isopodacount, Other_Adult_Othercount)
YOY_dietcount <- rbind(YOY_Fishcount, YOY_Insectacount, YOY_Amphipodacount,
YOY_Decapodacount, YOY_Isopodacount, YOY_Othercount)
Adult_Oi <- (Adult_dietcount / Adult_count) * 100
PENS_Adult_Oi <- (PENS_Adult_dietcount / PENS_Adult_count) * 100
Other_Adult_Oi <- (Other_Adult_dietcount / Other_Adult_count) * 100
YOY_Oi <- (YOY_dietcount / YOY_count) * 100
total_adult_count_by_item <- colSums(Adult_diets[,5:10])
total_adult_cumulative <- sum(Adult_diets[,11])
total_pens_adult_count_by_item <- colSums(PENS_Adult_diets[,5:10])
total_pens_adult_cumulative <- sum(PENS_Adult_diets[,11])
total_other_adult_count_by_item <- colSums(Other_Adult_diets[,5:10])
total_other_adult_cumulative <- sum(Other_Adult_diets[,11])
total_YOY_count_by_item <- colSums(YOY_diets[,5:10])
total_YOY_cumulative <- sum(YOY_diets[,11])
Adult_Ni <- (total_adult_count_by_item / total_adult_cumulative) * 100
PENS_Adult_Ni <- (total_adult_count_by_item / total_adult_cumulative) * 100
Other_Adult_Ni <- (total_adult_count_by_item / total_adult_cumulative) * 100
YOY_Ni <- (total_YOY_count_by_item / total_YOY_cumulative) * 100
Adult_MNi <- (colMeans(Adult_diets[,12:17])) * 100
PENS_Adult_MNi <- (colMeans(PENS_Adult_diets[,12:17])) * 100
Other_Adult_MNi <- (colMeans(Other_Adult_diets[,12:17])) * 100
YOY_MNi <- (colMeans(YOY_diets[,12:17])) * 100
Adult_MNi_sd <- (colSds(as.matrix(Adult_diets[,12:17]))) * 100
PENS_Adult_MNi_sd <- (colSds(as.matrix(PENS_Adult_diets[,12:17]))) * 100
Other_Adult_MNi_sd <- (colSds(as.matrix(Other_Adult_diets[,12:17]))) * 100
YOY_MNi_sd <- (colSds(as.matrix(YOY_diets[,12:17]))) * 100
(Adult_Oi)
(PENS_Adult_Oi)
(Other_Adult_Oi)
(YOY_Oi)
(Adult_Ni)
(PENS_Adult_Oi)
(Other_Adult_Oi)
(YOY_Ni)
(Adult_MNi)
(PENS_Adult_MNi)
(Other_Adult_MNi)
(YOY_MNi)
(Adult_MNi_sd)
(PENS_Adult_MNi_sd)
(Other_Adult_MNi_sd)
(YOY_MNi_sd)
SchoenerAdultYOY <- 100 - (0.5 * sum(abs(Adult_MNi - YOY_MNi)))
SchoenerAdultPENSvs <- 100 - (0.5 * sum(abs(PENS_Adult_MNi - Other_Adult_MNi)))
SchoenerAdultYOY
SchoenerAdultPENSvs
```
# PCoA
```{r PCoA}
library(vegan)
# load bowfin prop 03
Bowfin_prop03 <- read.csv("Bowfin_prop03.csv")
pco.gc01 <- wcmdscale(vegdist(Bowfin_prop03[,-c(1:5)]), k = 42, eig = TRUE, add="lingoes")
# which prey items contribute significantly to differences
efit_age<- envfit(pco.gc01, Bowfin_prop03[,-c(1:5)], permutations=9999,
strata=Bowfin_prop03$AgeClass)
efit_age
# length regression
efit_len <- envfit(pco.gc01, Bowfin_prop03[,4],permutations=9999)
efit_len
## graph by stream
Bowfin_prop03$Site <- as.factor(Bowfin_prop03$Site)
Bowfin_prop03$AgeClass <- as.factor(Bowfin_prop03$AgeClass)
coltreat <- c("deepskyblue4", "darkolivegreen4")
par(mar = c(0, 0, 0, 0), oma = c(4, 4, 0.5, 0.5))
par(tcl = -0.25)
par(mgp = c(2, 0.6, 0))
par(font.lab=2, cex.lab=1.5)
plot(pco.gc01$points, type = "p", bg=coltreat[Bowfin_prop03$AgeClass], pch = 22, cex=0.75, asp = 1/1)
lev <- levels(Bowfin_prop03$AgeClass)
for (i in seq_along(lev)) {
ordiellipse(pco.gc01$points, draw="polygon", kind = "se", #can also plot standard errors
groups = Bowfin_prop03$AgeClass, alpha= 40,
col = coltreat[i], border=coltreat[i],
lwd=2, show.groups = lev[i],
label=FALSE)
}
plot(efit_len, col = "red", cex = 0.9, p.max=0.05)
plot(efit_age, col = "black", cex = 0.9, p.max=0.05)
mtext("Axis 2", side = 2, outer = TRUE, cex = 1.5, font =2, line = 2.2)
mtext("Axis 1", side = 1, outer = TRUE, cex = 1.5, font =2, line = 2.2)
legend("topleft",legend=c(levels(Bowfin_prop03$AgeClass)), title="Age Class", pch = 22,
pt.bg=c("deepskyblue4", "darkolivegreen4", "goldenrod3", "darkmagenta",
"indianred3","darkorange3", "red"),
pt.cex=1, cex=1, xpd=NA, bty="n")
## PERMANOVA by age class
( mod01 <- adonis(vegdist(Bowfin_prop03[,-c(1:5)], method="bray") ~ factor(AgeClass),
data = Bowfin_prop03, permutations = 999) )
# meets assumption of equal variance
dist15<-vegdist(Bowfin_prop03[,-c(1:5)])
mod <- with(Bowfin_prop03[,-c(1:2, 4:5)], betadisper(dist15, AgeClass))
anova(mod)
# 3: PERMANOVA by site
# subset into only sites with more than 3 reps
Bowfin_prop_site <- Bowfin_prop03 %>%
filter(AgeClass=="Adult") %>%
filter(Site %in% c("PENS", "PESH", "PTSA", "DEHO"))
( mod02 <- adonis(vegdist(Bowfin_prop_site[,-c(1:5)], method="bray") ~ factor(Site),
data = Bowfin_prop_site, permutations = 999) )
dist16<-vegdist(Bowfin_prop_site[,-c(1:5)])
mod <- with(Bowfin_prop_site[,-c(2:5)], betadisper(dist16, Site))
anova(mod)
```
# tRophic Position
```{r trophic position}
library(tRophicPosition)
library(coda)
library(bayestestR)
pensBONiso <- read.csv("pens_BON.csv")
# Adult identified in rf prediction
pensBONiso <- pensBONiso[which(pensBONiso$ID != "penswlbon6"),]
TDF_values <- TDF()
BONtpList <- loadIsotopeData(pensBONiso, consumer = "Adult", b1 = "YOY",
baselineColumn = "Development",
consumersColumn = "Development",
deltaC = TDF_values$deltaC,
deltaN = TDF_values$deltaN)
model.string <- jagsBayesianModel(model = "oneBaseline", TP = "dnorm(1, 0.1)", lambda = 0)
BON_tp_model <- TPmodel(BONtpList, model.string = model.string,
n.adapt = 20000, n.chains = 3)
BONposterior.samples <- posteriorTP(model = BON_tp_model, n.iter = 20000,
variable.names = c("TP", "muDeltaN"))
# Check Convergence
gelman.diag(BONposterior.samples)
BONposterior.combined <- coda::mcmc(do.call(rbind, BONposterior.samples))
BONposteriormode <- getPosteriorMode(BONposterior.combined)
BONposteriormode
BON_tp_map <- map_estimate(as.numeric(BONposterior.combined[,"TP"]))
BON_dN_map <- map_estimate(as.numeric(BONposterior.combined[,"muDeltaN"]))
ci_hdi <- ci(BONposterior.combined, ci = 0.95, method = "HDI")
ci_hdi
BON_tp_graph_info <- estimate_density(BONposterior.combined, extend = TRUE)
BON_tp_graph <- ggplot(BON_tp_graph_info[which(BON_tp_graph_info$Parameter == "TP"),],
aes(x = x, y = y)) +
geom_area(fill = "gray") +
theme_classic() +
geom_vline(xintercept = ci_hdi$CI_low[1], color = "black",
linetype = "longdash", size = 0.5) +
geom_vline(xintercept = ci_hdi$CI_high[1], color = "black",
linetype = "longdash", size = 0.5) +
geom_vline(xintercept = BONposteriormode[1], color = "black",
linetype = "dotdash", size = 0.5) +
geom_vline(xintercept = BON_tp_map[1], color = "black",
linetype = "dotted", size = 0.5) +
labs(title = "Trophic Position Distribution", x = "TP", y = "Density") +
theme(plot.title = element_text(hjust = 0.5))
BON_tp_graph
BON_dn_graph <- ggplot(BON_tp_graph_info[which(BON_tp_graph_info$Parameter == "muDeltaN"),],
aes(x = x, y = y)) +
geom_area(fill = "gray") +
theme_classic() +
geom_vline(xintercept = ci_hdi$CI_low[2], color = "black",
linetype = "longdash", size = 0.5) +
geom_vline(xintercept = ci_hdi$CI_high[2], color = "black",
linetype = "longdash", size = 0.5) +
geom_vline(xintercept = BONposteriormode[2], color = "black",
linetype = "dotdash", size = 0.5) +
geom_vline(xintercept = BON_dN_map[1], color = "black",
linetype = "dotted", size = 0.5) +
labs(title = expression(Delta*" N Distribution"),
x = expression("Mean "*Delta*" N Distribution"),
y = "Density") +
theme(plot.title = element_text(hjust = 0.5))
BON_dn_graph
Length_d13C_relationship <- lm(d13C ~ Length, data = pensBONiso)
summary(Length_d13C_relationship)
summary(Length_d13C_relationship)$r.squared
penBON_dC_Length <- ggplot(pensBONiso, aes(x = Length, y = d13C)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE) +
labs(title = expression("Pensaukee Bowfin "*delta*"13C-Length Regression"),
y = expression(delta*"13C")) +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
penBON_dC_Length
Length_d15N_relationship <- lm(d15N ~ Length, data = pensBONiso)
summary(Length_d15N_relationship)
summary(Length_d15N_relationship)$r.squared
penBON_dN_Length <- ggplot(pensBONiso, aes(x = Length, y = d15N)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE) +
labs(title = expression("Pensaukee Bowfin "*delta*"15N-Length Regression"),
y = expression(delta*"15N")) +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
penBON_dN_Length
```
# nicheROVER
```{r nicherover}
library(nicheROVER)
bowfin <- pensBONiso[,c("Development", "d13C", "d15N", "Spp")]
aggregate(bowfin[2:3], bowfin[1], mean)
clrs <- c("red", "blue")
# generate parameter draws from the 'default' posteriors of each fish
set.seed(152)
nsamples <- 10000
system.time({
bon.par <- tapply(1:nrow(bowfin), bowfin$Development, function(ii) niw.post(nsamples = nsamples,
X = bowfin[ii, 2:3]))
})
# 2-d projections of 10 niche regions
clrs <- c("deepskyblue4", "darkolivegreen4") # colors for each species
nsamples <- 10
set.seed(152)
bon.par <- tapply(1:nrow(bowfin), bowfin$Development, function(ii) niw.post(nsamples = nsamples,
X = bowfin[ii, 2:3]))
# format data for plotting function
bon.data <- tapply(1:nrow(bowfin), bowfin$Development, function(ii) X = bowfin[ii, 2:3])
limitmatrix <- matrix(c(round_any(min(bowfin$d13C), 5, f = floor),
round_any(max(bowfin$d13C), 5, f = ceiling),
round_any(min(bowfin$d15N), 5, f = floor),
round_any(max(bowfin$d15N), 5, f = ceiling)),
nrow = 2, ncol = 2)
niche.plot <- function(niche.par, niche.data, alpha = .95,
species.names, iso.names, lims,
col, ndens = 512, pfrac = 0, xlab) {
niso <- ncol(niche.par[[1]]$mu)
nspec <- length(niche.par)
npts <- 100 # number of points for each ellipse
nell <- sapply(niche.par, function(x) nrow(x$mu)) # number of ellipses per species
if(missing(species.names)) species.names <- names(niche.par)
if(missing(iso.names)) iso.names <- colnames(niche.par[[1]]$mu)
# create all the ellipses to get the plot limits right.
ell <- vector("list", nspec)
names(ell) <- names(species.names)
D <- combn(niso, 2)
for(ii in 1:nspec) {
ell.tmp <- array(NA, c(nell[ii], ncol(D), npts+1, 2))
for(jj in 1:nell[ii]) {
for(kk in 1:ncol(D)) {
ell.coord <- ellipse(niche.par[[ii]]$mu[jj, D[,kk]],
V = niche.par[[ii]]$Sigma[D[,kk], D[,kk], jj],
alpha = alpha, n = npts)
ell.tmp[jj,kk,,] <- ell.coord
}
}
ell[[ii]] <- ell.tmp
}
# plot limits.
if(missing(lims)) {
# data
lims <- array(sapply(niche.data, function(x) apply(x, 2, range)),
dim = c(2, niso, nspec))
lims <- apply(lims, 2, range)
# ellipse
DD <- t(sapply(1:niso, function(ii) which(D == ii, arr.ind = TRUE)[1,]))
elims <- array(sapply(ell, function(x) {
tmp <- apply(x, c(4,2), range)
rbind(as.matrix(tmp[1,,])[DD], as.matrix(tmp[2,,])[DD])
}), dim = c(2, niso, nspec))
elims <- apply(elims, 2, range)
lims <- apply(rbind(lims, elims), 2, range)
}
# plots
opar <- par(mfcol = c(niso,niso), mar = rep(.5, 4), oma = rep(4,4))
for(ci in 1:niso) {
for(ri in 1:niso) {
# initialize plot
plot.new()
plot.window(lims[,ci], lims[,ri])
if (ci == ri) {
# diagonals: density plots
xdens <- matrix(NA, ndens, nspec)
ydens <- xdens
for(ii in 1:nspec) {
den <- density(niche.data[[ii]][,ci], n = ndens)
xdens[,ii] <- den$x
ydens[,ii] <- den$y
}
for(ii in 1:nspec) {
ly <- par("usr")[1:2]
ly[2] <- ly[1] + pfrac*(ly[2]-ly[1])
ly[3] <- (ly[2]-ly[1])/nspec
segments(x0 = niche.data[[ii]][,ci],
y0 = ly[1]+(ii-1)*ly[3], y1 = ly[1]+ii*ly[3], col = col[ii])
ly <- ly[2] + ydens[,ii]/max(ydens)*(lims[2,ci]-ly[2])
lines(xdens[,ii], ly, col = col[ii])
}
}
if (ri > ci) {
# lower triangle: point plots
for(ii in 1:nspec) {
points(niche.data[[ii]][,c(ci,ri)], col = col[ii], pch = 16)
}
}
if (ri < ci) {
# upper triangle: ellipses
for(ii in 1:nspec) {
for(jj in 1:nell[ii]) {
lines(ell[[ii]][jj,which(D[1,] == ri & D[2,] == ci),,2:1], col = col[ii])
}
}
}
box()
if(ci == niso) axis(side = 4) else axis(side = 4, labels = FALSE)
if(ri == niso) axis(side = 1) else axis(side = 1, labels = FALSE)
if(ri == 1) mtext(text = iso.names[ci], side = 3, line = 1)
if(ci == 1) mtext(text = iso.names[ri], side = 2, line = 1)
}
}
if(!missing(xlab)) {
mtext(text = xlab, side = 1, outer = TRUE, line = 2.2, cex = .9)
}
legend(x = "topright", legend = species.names, fill = col, bty = "n", cex = 1.25)
par(opar) # reset par
}
niche.plot(niche.par = bon.par, niche.data = bon.data, pfrac = 0.05, iso.names = expression(delta^{
15
} * N, delta^{
13
} * C), col = clrs, xlab = expression("Isotope Ratio (per mil)"), lims = limitmatrix)
# niche overlap plots for 95% niche region sizes
nsamples <- 10000
bon.par <- tapply(1:nrow(bowfin), bowfin$Development, function(ii) niw.post(nsamples = nsamples,
X = bowfin[ii, 2:3]))
# Drop package using same internal function name
#detach("package:bayestestR", unload=TRUE)
# Overlap calculation. use nsamples = nprob = 10000 (1e4) for higher
# accuracy. the variable over.stat can be supplied directly to the
# overlap.plot function
over.stat <- overlap(bon.par, nreps = nsamples, nprob = 10000, alpha = 0.95)
# The mean overlap metrics calculated across iterations for both niche
# region sizes (alpha = .95 and alpha = .99) can be calculated and displayed
# in an array.
over.mean <- apply(over.stat, c(1:2), mean) * 100
round(over.mean, 2)
over.median <- apply(over.stat, c(1:2), median) * 100
round(over.median, 2)
over.cred <- apply(over.stat * 100, c(1:2), quantile, prob = c(0.025, 0.975),
na.rm = TRUE)
round(over.cred[ , , ]) # display alpha = .95 niche region
overlap.plot(over.stat, col = clrs, mean.cred.col = "turquoise", equal.axis = TRUE,
xlab = "Overlap Probability (%) -- Niche Region Size: 95%")
nsamples <- 1000
bon.par <- tapply(1:nrow(bowfin), bowfin$Development,
function(ii) niw.post(nsamples = nsamples, X = bowfin[ii,2:3]))
niche.size <- function(Sigma, alpha = .95) {
Sigma <- as.matrix(Sigma)
n <- nrow(Sigma)
sz <- as.numeric(determinant(Sigma, logarithm = TRUE)$modulus)
sz <- .5 * (sz + n * (log(pi) + log(qchisq(alpha, df = n)))) - lgamma(.5*n+1)
exp(sz)
}