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01_ExperimentalSetup.Rmd
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---
title: "Experimental Setup of Reciprocal Transplant Study"
author: "Serena Hackerott"
date: "12/28/2023"
output:
html_document:
toc: yes
df_print: paged
html_notebook:
toc: yes
toc_float: yes
---
# Setup
```{r Setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
```
### Load Packages
```{r}
##Install Packages if Needed
if (!require("viridis")) install.packages("viridis")
if (!require("scales")) install.packages("scales")
if (!require("geodata")) install.packages("geodata")
if (!require("sf")) install.packages("sf")
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("cowplot")) install.packages("cowplot")
if (!require("lubridate")) install.packages("lubridate")
if (!require("Rmisc")) install.packages("Rmisc")
if (!require("png")) install.packages("png")
if (!require("grid")) install.packages("grid")
if (!require("plotrix")) install.packages("plotrix")
if (!require("Hmisc")) install.packages("Hmisc")
if (!require("corrplot")) install.packages("corrplot")
if (!require("factoextra")) install.packages("factoextra")
if (!require("vegan")) install.packages("vegan")
if (!require("ggfortify")) install.packages("ggfortify")
if (!require("dplyr")) install.packages("dplyr")
##Load Packages
library(viridis) #Color-blind and grey-scale-friendly color palettes
library(scales) #Required to view color scale options
library(geodata) #Required for map of Bonaire
library(sf) #Required for working with spatial data
library(ggplot2) #Required for ggplots
library(cowplot) #Required for plotting panel figures
library(lubridate) #Required for date and time formatting
library(Rmisc) #Required for SummarySE function
library(png) #Required for reading in png figures
library(grid) #Required for plotting grids
library(plotrix) #Required for Standard error function
library(Hmisc) #Required for correlation matrix rcorr function
library(corrplot) #Required for correlation plot
library(factoextra) #Required for PCA analysis extras
library(vegan) #Required for multivariate analysis PERM and PCA
library(ggfortify) #Required for autoplot of PCA
library(dplyr) #Required for dataframe organization
```
### Graphing Parameters
```{r Graphing Parameters}
####Colors by Groups
##Sites
show_col(viridis_pal(option = "mako")(25))
Site.colors.o <- c("#3B5698FF", "#5BCDADFF")
#Sites: Klein Bonaire, Something Special
##Origin Colors
Orig.colors.o<- c("#40498EFF", "#357BA2FF", "#5BCDADFF","#B1E4C2FF")
#Origins: KL Native, KL Transplant, SS Native, SS Transplant
##Genotype Colors
show_col(viridis_pal(option = "turbo")(30))
Geno.colors.o<-c("#458AFCFF", "#1FE9AFFF", "#FE942AFF")
#Genotypes: AC8, AC10, AC12
##Genotype Shapes
Geno.shapes.o<-c(15, 17, 19)
#Genotypes: AC8, AC10, AC12
##Heat Assay Treatments
show_col(viridis_pal(option = "turbo")(25))
HC.colors.o<-c("#3BA0FDFF", "#BA1E02FF")
#Heat Assay Treatments: Control, Heated
##Set Shapes for Seasons
Seas.shapes.o<-c(19, 17, 15, 18)
#Seasons: Fall, Winter, Spring, Summer
####Plot Feature Sizes
axis.title.sz=18
axis.txt.sz=14
leg.title.sz=15
leg.txt.sz=12
levels.sz=7
sig.sz=5
panel.lab.sz=25
point.sz=4
bar.sz=1
cap.sz=0.3
```
# Sample Data and Metadata
### Load and Organize Data
```{r}
##Load Data
Sites<-read.csv("Data/BonaireSites.csv", header=TRUE)
Dates<-read.csv("Data/BonaireDates.csv", header=TRUE)
TempCond<-read.csv("Data/TempCond.csv", header=TRUE)
PAR<-read.csv("Data/PAR10.csv", header=TRUE)
Nutrients<-read.csv("Data/Nutrients.csv", header=TRUE)
HCTemp<-read.csv("Data/HCTemp.csv", header=TRUE)
AssayPAR<-read.csv("Data/AssayPAR.csv", header=TRUE)
##Set factor variables
Sites$Site<-factor(Sites$Site, levels=c("SS", "KL", "KR", "OL", "RP", "BN"), ordered=TRUE)
Sites$Type<-factor(Sites$Type, levels=c("Study", "Capital", "Harvest"), ordered=TRUE)
Dates$TimeP<-factor(Dates$TimeP, ordered=TRUE, levels=c("T0", "IN", "TP1", "TP2", "TP3", "TP4"))
TempCond$Site<-factor(TempCond$Site, ordered=TRUE, levels=c("KL", "SS"))
PAR$Site<-factor(PAR$Site, ordered=TRUE, levels=c("KL", "SS"))
Nutrients$Site<-factor(Nutrients$Site, ordered=TRUE, levels=c("KL", "SS"))
Nutrients$Month<-factor(Nutrients$Month, ordered=TRUE, levels=c("Aug", "Sep", "Oct", "Nov", "Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul"))
Nutrients$Month.n<-factor(Nutrients$Month.n, ordered=TRUE, levels=c("M0", "M1", "M2", "M3", "M4", "M5", "M6", "M7", "M8", "M9", "M10", "M11", "M12"))
HCTemp$TimeP<-factor(HCTemp$TimeP, ordered=TRUE, levels=c("IN", "TP1", "TP2", "TP3", "TP4"))
HCTemp$TempTreatment<-factor(HCTemp$TempTreatment, ordered=TRUE, levels=c("Control", "Heated"))
AssayPAR$TimeP<-factor(AssayPAR$TimeP, ordered=TRUE, levels=c("IN", "TP1", "TP2", "TP3", "TP4"))
##Set date and time variables
TempCond$Date<-as.Date(TempCond$Date, format='%d/%m/%Y')
TempCond$Date.Time<-ymd_hm(c(paste(as.character(TempCond$Date), as.character(TempCond$Time))), tz="US/Eastern")
PAR$Date<-as.Date(PAR$Date, format='%d/%m/%Y')
PAR$Date.Time<-ymd_hms(c(paste(as.character(PAR$Date), as.character(PAR$Time))), tz="US/Eastern")
Nutrients$Date<-as.Date(Nutrients$Date, format='%m/%d/%Y')
HCTemp$Date.Time<-mdy_hm(as.character(HCTemp$Date.Time), tz="US/Eastern")
AssayPAR$Date.Time<-mdy_hm(as.character(AssayPAR$Date.Time), tz="US/Eastern")
AssayPAR$Time<-hms(AssayPAR$Time)
```
# Site Map
### Load Map Data
```{r}
##Initial Download of Bonaire Map Data
#Only done once then commented out
#gadm("BES", level=1, path="Data")
##Read in rds file
BES.sv<-readRDS("Data/gadm/gadm41_BES_1_pk.rds")
##Convert to sf object
BES.sf<-st_as_sf(BES.sv)
##Subset Bonaire
Bonaire.sf<-BES.sf[1,]
```
### Plot map of Bonaire with Study Sites
```{r}
Study_Sites.plot<-ggplot(Bonaire.sf)+
geom_sf(fill="white")+
labs(x="Longitude", y="Latitude")+
geom_point(data=Sites,
aes(x=Long, y=Lat, colour=Site, shape=Type), size=c(rep(point.sz+3.5, 3), rep(point.sz+1,3)))+
scale_colour_manual(values=c("#5BCDADFF", "#3B5698FF", "darkred", "grey25", "grey25", "grey25"))+
scale_shape_manual(values=c(19, 17, 9))+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
annotate("text", x=Sites$Long[which(Sites$Site=="SS")],
y=Sites$Lat[which(Sites$Site=="SS")]+0.01,
label = Sites$SiteName[which(Sites$Site=="SS")],
size=sig.sz, fontface="bold", hjust=0)+
annotate("text", x=Sites$Long[which(Sites$Site=="KL")],
y=Sites$Lat[which(Sites$Site=="KL")]+0.01,
label = Sites$SiteName[which(Sites$Site=="KL")],
size=sig.sz, fontface="bold", hjust=1)+
annotate("text", x=Sites$Long[which(Sites$Site=="KR")]+0.01,
y=Sites$Lat[which(Sites$Site=="KR")],
label = Sites$SiteName[which(Sites$Site=="KR")],
size=sig.sz, fontface="bold", hjust=0)+
annotate("text", x=Sites$Long[which(Sites$Genotype=="AC8")],
y=Sites$Lat[which(Sites$Genotype=="AC8")]+0.01,
label = Sites$Genotype[which(Sites$Genotype=="AC8")],
size=sig.sz, fontface="bold")+
annotate("text", x=Sites$Long[which(Sites$Genotype=="AC10")],
y=Sites$Lat[which(Sites$Genotype=="AC10")]-0.01,
label = Sites$Genotype[which(Sites$Genotype=="AC10")],
size=sig.sz, fontface="bold")+
annotate("text", x=Sites$Long[which(Sites$Genotype=="AC12")],
y=Sites$Lat[which(Sites$Genotype=="AC12")]+0.01,
label = Sites$Genotype[which(Sites$Genotype=="AC12")],
size=sig.sz, fontface="bold"); Study_Sites.plot
```
# Environmental Conditions
### Temperature and Conductivity
#### Plot Temperature Profiles
```{r Plot Temperature}
##Subset Sampling Dates
SampDates<-na.omit(subset(Dates, Task=="Sample"))
SampDates$Date.Time<-mdy_hm(c(paste(SampDates$Date, as.character("00:12"))), tz="US/Eastern")
##Plot Temperature
Temp.plot<-ggplot()+
geom_line(data=na.omit(TempCond), aes(color=Site, x=Date.Time, y=Temp_C), linewidth=bar.sz-.25, alpha=0.7) +
theme_bw()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.direction = "horizontal", legend.position=c(0.5,0.9))+
scale_color_manual(values = c(Site.colors.o))+
labs(x="Date", y="Temperature (\u00B0C)")+
geom_point(data=SampDates, aes(x=Date.Time, y=c(29, 28, 26.75, 27.5)), size=point.sz)+
annotate("text", x = SampDates$Date.Time[1], y = 29.25, label = "TP1", size=sig.sz)+
annotate("text", x = SampDates$Date.Time[2], y = 28.25, label = "TP2", size=sig.sz)+
annotate("text", x = SampDates$Date.Time[3], y = 27, label = "TP3", size=sig.sz)+
annotate("text", x = SampDates$Date.Time[4], y = 27.75, label = "TP4", size=sig.sz, hjust=0.25);Temp.plot
```
#### Plot Conductivity Profiles
```{r Plot Conductivity}
Cond.plot<-ggplot()+
geom_line(data=na.omit(TempCond), aes(color=Site, x=Date.Time, y=Cond_uS.cm), linewidth=bar.sz-.25, alpha=0.7) +
theme_bw()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.direction = "horizontal", legend.position=c(0.5,0.1))+
scale_color_manual(values = c(Site.colors.o))+
labs(x="Date", y="Conductivity (\u03BCS/cm)");Cond.plot
```
#### Daily Stats
Calculate daily average, minimum, maximum, standard deviation, and range by site and date.
```{r}
##Mean
TC.mean<-aggregate(TempCond[4:5], list(TempCond$Site, TempCond$Date), mean)
names(TC.mean)<-c("Site", "Date", "Temp.a", "Cond.a")
##Min
TC.min<-aggregate(TempCond[4:5], list(TempCond$Site, TempCond$Date), min)
names(TC.min)<-c("Site", "Date", "Temp.min", "Cond.min")
##Max
TC.max<-aggregate(TempCond[4:5], list(TempCond$Site, TempCond$Date), max)
names(TC.max)<-c("Site", "Date", "Temp.max", "Cond.max")
##SD
TC.sd<-aggregate(TempCond[4:5], list(TempCond$Site, TempCond$Date), sd)
names(TC.sd)<-c("Site", "Date", "Temp.sd", "Cond.sd")
##Merge together
TC.daily<-merge(TC.mean, TC.min)
TC.daily<-merge(TC.daily, TC.max)
TC.daily<-merge(TC.daily, TC.sd)
##Range
TC.daily$Temp.r<-TC.daily$Temp.max-TC.daily$Temp.min
TC.daily$Cond.r<-TC.daily$Cond.max-TC.daily$Cond.min
```
#### Bi-monthly Stats
Set Bi-monthly variable based on first or second half of each month and average daily stats by bi-monthly bins.
```{r}
##Bi-monthly Bins
TC.daily$BiMonth<-ifelse(TC.daily$Date < as.Date("2021-08-16"), "M0.1",
ifelse(TC.daily$Date > as.Date("2021-08-15") & TC.daily$Date < as.Date("2021-09-01"), "M0.2",
ifelse(TC.daily$Date > as.Date("2021-08-31") & TC.daily$Date < as.Date("2021-09-16"), "M1.1",
ifelse(TC.daily$Date > as.Date("2021-09-15") & TC.daily$Date < as.Date("2021-10-01"), "M1.2",
ifelse(TC.daily$Date > as.Date("2021-09-30") & TC.daily$Date < as.Date("2021-10-16"), "M2.1",
ifelse(TC.daily$Date > as.Date("2021-10-15") & TC.daily$Date < as.Date("2021-11-01"), "M2.2",
ifelse(TC.daily$Date > as.Date("2021-10-31") & TC.daily$Date < as.Date("2021-11-16"), "M3.1",
ifelse(TC.daily$Date > as.Date("2021-11-15") & TC.daily$Date < as.Date("2021-12-01"), "M3.2",
ifelse(TC.daily$Date > as.Date("2021-11-30") & TC.daily$Date < as.Date("2021-12-16"), "M4.1",
ifelse(TC.daily$Date > as.Date("2021-12-15") & TC.daily$Date < as.Date("2022-01-01"), "M4.2",
ifelse(TC.daily$Date > as.Date("2021-12-31") & TC.daily$Date < as.Date("2022-01-16"), "M5.1",
ifelse(TC.daily$Date > as.Date("2022-01-15") & TC.daily$Date < as.Date("2022-02-01"), "M5.2",
ifelse(TC.daily$Date > as.Date("2022-01-31") & TC.daily$Date < as.Date("2022-02-16"), "M6.1",
ifelse(TC.daily$Date > as.Date("2022-02-15") & TC.daily$Date < as.Date("2022-03-01"), "M6.2",
ifelse(TC.daily$Date > as.Date("2022-02-28") & TC.daily$Date < as.Date("2022-03-16"), "M7.1",
ifelse(TC.daily$Date > as.Date("2022-03-15") & TC.daily$Date < as.Date("2022-04-01"), "M7.2",
ifelse(TC.daily$Date > as.Date("2022-03-31") & TC.daily$Date < as.Date("2022-04-16"), "M8.1",
ifelse(TC.daily$Date > as.Date("2022-04-15") & TC.daily$Date < as.Date("2022-05-01"), "M8.2",
ifelse(TC.daily$Date > as.Date("2022-04-30") & TC.daily$Date < as.Date("2022-05-16"), "M9.1",
ifelse(TC.daily$Date > as.Date("2022-05-15") & TC.daily$Date < as.Date("2022-06-01"), "M9.2",
ifelse(TC.daily$Date > as.Date("2022-05-31") & TC.daily$Date < as.Date("2022-06-16"), "M10.1",
ifelse(TC.daily$Date > as.Date("2022-06-15") & TC.daily$Date < as.Date("2022-07-01"), "M10.2",
ifelse(TC.daily$Date > as.Date("2022-06-30") & TC.daily$Date < as.Date("2022-07-16"), "M11.1",
ifelse(TC.daily$Date > as.Date("2022-07-15") & TC.daily$Date < as.Date("2022-08-01"), "M11.2",
ifelse(TC.daily$Date > as.Date("2022-07-31") & TC.daily$Date < as.Date("2022-08-16"), "M12.1", NA)))))))))))))))))))))))))
##Average
TC.bimonth<-aggregate(TC.daily[3:12], list(TC.daily$Site, TC.daily$BiMonth), mean)
names(TC.bimonth)<-c("Site", "BiMonth", names(TC.daily)[3:12])
```
### Light
```{r}
##Remove measurements at 5 min increments from PAR
PAR<-PAR[-c(which(PAR$Minute==5 | PAR$Minute==15 | PAR$Minute==25 | PAR$Minute==35 | PAR$Minute==45 | PAR$Minute==55)),]
```
### Plot Light Profiles
```{r Plot Light}
Light.plot<-ggplot()+
geom_line(data=na.omit(PAR), aes(color=Site, x=Date.Time, y=PAR_umol.m2.s), linewidth=bar.sz-.25, alpha=0.7) +
theme_bw()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.direction = "horizontal", legend.position=c(0.5,0.9))+
scale_color_manual(values = c(Site.colors.o))+
labs(x="Date", y=expression(paste('PAR (\u03BCmol/cm'^-2*"/s)")));Light.plot
```
#### Daily Stats
Calculate daily average, maximum, and standard deviation, by site and date.
```{r}
##Mean
PAR.mean<-aggregate(PAR[4], list(PAR$Site, PAR$Date), mean)
names(PAR.mean)<-c("Site", "Date", "PAR.a")
##Max
PAR.max<-aggregate(PAR[4], list(PAR$Site, PAR$Date), max)
names(PAR.max)<-c("Site", "Date", "PAR.max")
##SD
PAR.sd<-aggregate(PAR[4], list(PAR$Site, PAR$Date), sd)
names(PAR.sd)<-c("Site", "Date", "PAR.sd")
##Merge together
PAR.daily<-merge(PAR.mean, PAR.max)
PAR.daily<-merge(PAR.daily, PAR.sd)
```
Also calculate hours over saturating irradiance of 270 umol/m2/s based on [Chalker 1981](https://link.springer.com/article/10.1007/BF00406821)
```{r}
##Calculate Hours over Saturating
PAR$Sat<-ifelse(PAR$PAR_umol.m2.s>270, 1, 0)
##Sum number of measurements over Saturating
PAR.sat<-aggregate(PAR[7], list(PAR$Site, PAR$Date), sum)
names(PAR.sat)<-c("Site", "Date", "Sat.sum")
##Calculate Hours
#(Number of 10-min increment measurements - 1 Assumed as a starting point) / 6 increments per Hour)
PAR.sat$Sat.hr<-(PAR.sat$Sat.sum-1)/6
##Add to PAR.daily dataframe
PAR.daily<-merge(PAR.daily, PAR.sat)
```
#### Bi-monthly Stats
Set Bi-monthly variable based on first or second half of each month and average daily stats by bi-monthly bins.
```{r}
##Bi-monthly Bins
PAR.daily$BiMonth<-ifelse(PAR.daily$Date < as.Date("2021-08-16"), "M0.1",
ifelse(PAR.daily$Date > as.Date("2021-08-15") & PAR.daily$Date < as.Date("2021-09-01"), "M0.2",
ifelse(PAR.daily$Date > as.Date("2021-08-31") & PAR.daily$Date < as.Date("2021-09-16"), "M1.1",
ifelse(PAR.daily$Date > as.Date("2021-09-15") & PAR.daily$Date < as.Date("2021-10-01"), "M1.2",
ifelse(PAR.daily$Date > as.Date("2021-09-30") & PAR.daily$Date < as.Date("2021-10-16"), "M2.1",
ifelse(PAR.daily$Date > as.Date("2021-10-15") & PAR.daily$Date < as.Date("2021-11-01"), "M2.2",
ifelse(PAR.daily$Date > as.Date("2021-10-31") & PAR.daily$Date < as.Date("2021-11-16"), "M3.1",
ifelse(PAR.daily$Date > as.Date("2021-11-15") & PAR.daily$Date < as.Date("2021-12-01"), "M3.2",
ifelse(PAR.daily$Date > as.Date("2021-11-30") & PAR.daily$Date < as.Date("2021-12-16"), "M4.1",
ifelse(PAR.daily$Date > as.Date("2021-12-15") & PAR.daily$Date < as.Date("2022-01-01"), "M4.2",
ifelse(PAR.daily$Date > as.Date("2021-12-31") & PAR.daily$Date < as.Date("2022-01-16"), "M5.1",
ifelse(PAR.daily$Date > as.Date("2022-01-15") & PAR.daily$Date < as.Date("2022-02-01"), "M5.2",
ifelse(PAR.daily$Date > as.Date("2022-01-31") & PAR.daily$Date < as.Date("2022-02-16"), "M6.1",
ifelse(PAR.daily$Date > as.Date("2022-02-15") & PAR.daily$Date < as.Date("2022-03-01"), "M6.2",
ifelse(PAR.daily$Date > as.Date("2022-02-28") & PAR.daily$Date < as.Date("2022-03-16"), "M7.1",
ifelse(PAR.daily$Date > as.Date("2022-03-15") & PAR.daily$Date < as.Date("2022-04-01"), "M7.2",
ifelse(PAR.daily$Date > as.Date("2022-03-31") & PAR.daily$Date < as.Date("2022-04-16"), "M8.1",
ifelse(PAR.daily$Date > as.Date("2022-04-15") & PAR.daily$Date < as.Date("2022-05-01"), "M8.2",
ifelse(PAR.daily$Date > as.Date("2022-04-30") & PAR.daily$Date < as.Date("2022-05-16"), "M9.1",
ifelse(PAR.daily$Date > as.Date("2022-05-15") & PAR.daily$Date < as.Date("2022-06-01"), "M9.2",
ifelse(PAR.daily$Date > as.Date("2022-05-31") & PAR.daily$Date < as.Date("2022-06-16"), "M10.1",
ifelse(PAR.daily$Date > as.Date("2022-06-15") & PAR.daily$Date < as.Date("2022-07-01"), "M10.2",
ifelse(PAR.daily$Date > as.Date("2022-06-30") & PAR.daily$Date < as.Date("2022-07-16"), "M11.1",
ifelse(PAR.daily$Date > as.Date("2022-07-15") & PAR.daily$Date < as.Date("2022-08-01"), "M11.2",
ifelse(PAR.daily$Date > as.Date("2022-07-31") & PAR.daily$Date < as.Date("2022-08-16"), "M12.1", NA)))))))))))))))))))))))))
##Average
PAR.bimonth<-aggregate(PAR.daily[3:7], list(PAR.daily$Site, PAR.daily$BiMonth), mean)
names(PAR.bimonth)<-c("Site", "BiMonth", names(PAR.daily)[3:7])
```
### Nutrients
```{r}
##Calculate Total N/ Total P Ratio
Nutrients$N.P<-Nutrients$TN_uM/Nutrients$TP_uM
##Calculate DIN as NO3+NO2+NH4
Nutrients$DIN_uM<-Nutrients$NO3.NO2_uM+Nutrients$NH4_uM
##Add Bi-monthly variable
Nutrients$BiMonth<-paste(Nutrients$Month.n, Nutrients$Sample, sep=".")
##Check for Outliers
boxplot(Nutrients$DIN_uM~Nutrients$Site)
Nutrients$BiMonth[which(Nutrients$DIN_uM>20)]
boxplot(Nutrients$N.P~Nutrients$Site)
Nutrients$BiMonth[which(Nutrients$N.P>25)]
##Remove Outliers
Nutrients.o<-Nutrients[-c(which(Nutrients$DIN_uM>20 | Nutrients$N.P>25)),]
```
### Plot N to P Profiles
```{r Plot N to P}
N.P.plot<-ggplot()+
geom_line(data=na.omit(Nutrients.o), aes(color=Site, x=Date, y=N.P), linewidth=bar.sz) +
geom_point(data=na.omit(Nutrients.o), aes(color=Site, x=Date, y=N.P), size=point.sz) +
theme_bw()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.direction = "horizontal", legend.position=c(0.5,0.9))+
scale_color_manual(values = c(Site.colors.o))+
labs(x="Date", y="Total Nitrogen : Phosphorus");N.P.plot
```
### Plot DIN Profiles
```{r Plot DIN}
DIN.plot<-ggplot()+
geom_line(data=na.omit(Nutrients.o), aes(color=Site, x=Date, y=DIN_uM), linewidth=bar.sz) +
geom_point(data=na.omit(Nutrients.o), aes(color=Site, x=Date, y=DIN_uM), size=point.sz) +
theme_bw()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.direction = "horizontal", legend.position=c(0.5,0.9))+
scale_color_manual(values = c(Site.colors.o))+
labs(x="Date", y="Dissolved Inorg. Nitrogen (\u03BCM)");DIN.plot
```
### Multivariate Analysis
Merge bi-monthly datasets of environmental conditions together for multivariate analysis.
#### Check for Outliers
```{r}
##Merge dataframes
EnvData<-merge(TC.bimonth, PAR.bimonth)
EnvData<-merge(EnvData, Nutrients.o[,c(1,16, 8:15)])
names(EnvData)
boxplot(EnvData$Temp.a~EnvData$Site)
boxplot(EnvData$Temp.sd~EnvData$Site)
boxplot(EnvData$Cond.a~EnvData$Site)
boxplot(EnvData$Cond.sd~EnvData$Site)
boxplot(EnvData$PAR.a~EnvData$Site)
boxplot(EnvData$PAR.sd~EnvData$Site)
boxplot(EnvData$Sat.hr~EnvData$Site)
boxplot(EnvData$DIN_uM~EnvData$Site)
boxplot(EnvData$N.P~EnvData$Site)
```
#### Check Correlation
```{r}
##Check correlation between variables
Env.cor<-rcorr(as.matrix(EnvData[,-c(1:2)]), type="pearson")
Env.cor
diag(Env.cor$P)<-0
corrplot(Env.cor$r, type="upper", order="hclust",
p.mat = Env.cor$P, sig.level = 0.01, insig = "blank")
```
Keep main parameters of interest Temp.a, Temp.sd, Cond.a, Cond.sd, Sat hr, DIN, N.P
```{r}
names(EnvData)
EnvData.cut<-EnvData[,c(1:4, 9:10, 17, 24:25 )]
##Check correlation between variables
Env.cor2<-rcorr(as.matrix(EnvData.cut[,-c(1:2)]), type="pearson")
Env.cor2
diag(Env.cor2$P)<-0
corrplot(Env.cor2$r, type="upper", order="hclust",
p.mat = Env.cor2$P, sig.level = 0.01, insig = "blank")
```
#### PERMANOVA
```{r}
names(EnvData.cut)
##Standardize Variables for similar scales
EnvData_St <- EnvData.cut %>% mutate_at(c("Temp.a", "Cond.a", "Temp.sd", "Cond.sd", "Sat.hr", "N.P", "DIN_uM"), ~(scale(.) %>% as.vector))
adonis2(vegdist(EnvData_St[,3:9], "euclidean")~EnvData_St$Site, data=EnvData_St, method="euclidean")
##Check dispersion by Site
anova(betadisper(vegdist(EnvData_St[,3:9], "euclidean"), EnvData_St$Site))
```
#### PCA
```{r}
##Rename Variables
EnvData.clean<-EnvData_St %>% dplyr::rename("Avg Temp" = Temp.a, "Avg Cond" = Cond.a, "Temp SD" = Temp.sd, "Cond SD" = Cond.sd, "Sat PAR" = Sat.hr, "N:P" = N.P, "DIN" = DIN_uM)
##Add Months and Seasons
EnvData.clean$Month<-ifelse(EnvData.clean$BiMonth=="M0.2" | EnvData.clean$BiMonth=="M12.1", "Aug",
ifelse(EnvData.clean$BiMonth=="M1.1" | EnvData.clean$BiMonth=="M1.2", "Sep",
ifelse(EnvData.clean$BiMonth=="M2.1" | EnvData.clean$BiMonth=="M2.2", "Oct",
ifelse(EnvData.clean$BiMonth=="M3.1" | EnvData.clean$BiMonth=="M3.2", "Nov",
ifelse(EnvData.clean$BiMonth=="M4.1" | EnvData.clean$BiMonth=="M4.2", "Dec",
ifelse(EnvData.clean$BiMonth=="M5.1" | EnvData.clean$BiMonth=="M5.2", "Jan",
ifelse(EnvData.clean$BiMonth=="M6.1" | EnvData.clean$BiMonth=="M6.2", "Feb",
ifelse(EnvData.clean$BiMonth=="M7.1" | EnvData.clean$BiMonth=="M7.2", "Mar",
ifelse(EnvData.clean$BiMonth=="M8.1" | EnvData.clean$BiMonth=="M8.2", "Apr",
ifelse(EnvData.clean$BiMonth=="M9.1" | EnvData.clean$BiMonth=="M9.2", "May",
ifelse(EnvData.clean$BiMonth=="M10.1" | EnvData.clean$BiMonth=="M10.2", "Jun",
ifelse(EnvData.clean$BiMonth=="M11.1" | EnvData.clean$BiMonth=="M11.2", "Jul",
NA))))))))))))
EnvData.clean$Season<-ifelse(EnvData.clean$Month=="Sep" | EnvData.clean$Month=="Oct" |
EnvData.clean$Month=="Nov", "Fall",
ifelse(EnvData.clean$Month=="Dec" | EnvData.clean$Month=="Jan" |
EnvData.clean$Month=="Feb", "Winter",
ifelse(EnvData.clean$Month=="Mar" | EnvData.clean$Month=="Apr" |
EnvData.clean$Month=="May", "Spring",
ifelse(EnvData.clean$Month=="Jun" | EnvData.clean$Month=="Jul" |
EnvData.clean$Month=="Aug", "Summer", NA))))
EnvData.clean$Season<-factor(EnvData.clean$Season, levels=c("Fall", "Winter", "Spring", "Summer"), ordered=TRUE)
##Add rownames
rownames(EnvData.clean)<-paste(EnvData.clean$Site, EnvData.clean$BiMonth, sep="_")
##PCA
Env.pca<-prcomp(EnvData.clean[,c(3:9)], scale=TRUE)
summary(Env.pca)
```
```{r Plot Environment PCA}
Env.pca.plot<-fviz_pca_biplot(Env.pca, repel=TRUE, geom = "none", col.var="black", labelsize=sig.sz)+
geom_point(aes(colour=EnvData.clean$Site, shape=EnvData.clean$Season, size=EnvData.clean$Season), alpha=0.9)+
scale_colour_manual(values =Site.colors.o)+
scale_shape_manual(values=Seas.shapes.o)+
scale_size_manual(values=c(rep(point.sz, 3), point.sz+2))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.title.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="PC 1 (31.85%)", y="PC 2 (24.03%)", colour="Site", shape="Season", size=NULL, title=NULL)+
guides(size="none", colour = guide_legend(override.aes = list(size=point.sz)),
shape = guide_legend(override.aes = list(size=c(rep(point.sz, 3), point.sz+2))))+
annotate("text", x=0.25, y=-2.75,label = "Site PERMANOVA p=0.008",
size=sig.sz-1, fontface="bold.italic", hjust=0)+
annotate("text", x=0.25, y=-3.25,label = "Site PERMDISP p=0.722",
size=sig.sz-1, fontface="italic", hjust=0);Env.pca.plot
```
Check variable contributions
```{r Plot PCA Variable Contributions}
##Variables by PC's
Env.pca_var<-get_pca_var(Env.pca)
corrplot(Env.pca_var$contrib, is.corr=FALSE)
Env.pca_var$contrib
##PC1
Env.pca.var.PC1.plot<-fviz_contrib(Env.pca, choice = "var", axes = 1, top = 10)+
ggtitle("PC 1")+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.title.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5))+
labs(x="Environmental Parameter");Env.pca.var.PC1.plot
##PC2
Env.pca.var.PC2.plot<-fviz_contrib(Env.pca, choice = "var", axes = 2, top = 10)+
ggtitle("PC 2")+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.title.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5))+
labs(x="Environmental Parameter");Env.pca.var.PC2.plot
##PC1 and 2
Env.pca.var.plot<-fviz_contrib(Env.pca, choice = "var", axes = 1:2, top = 10)+
ggtitle("PC 1 and 2")+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.title.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5))+
labs(x="Environmental Parameter");Env.pca.var.plot
```
### Univariate Analysis
#### Average Temperature
```{r}
shapiro.test(TC.daily$Temp.a)
##Not normal
##Non parametric test
wilcox.test(TC.daily$Temp.a[which(TC.daily$Site=="KL")], TC.daily$Temp.a[which(TC.daily$Site=="SS")], alternative = "two.sided")
##Save Results
Env.res_Temp.a<-data.frame(Parameter="Daily Average Temperature", Test="Two-sided Wilcoxon Rank Sum",
p=wilcox.test(TC.daily$Temp.a[which(TC.daily$Site=="KL")], TC.daily$Temp.a[which(TC.daily$Site=="SS")], alternative = "two.sided")$p.value)
```
#### Temperature Standard Deviation
```{r}
shapiro.test(TC.daily$Temp.sd)
##Not normal
##Non parametric test
wilcox.test(TC.daily$Temp.sd[which(TC.daily$Site=="KL")], TC.daily$Temp.sd[which(TC.daily$Site=="SS")], alternative = "two.sided")
##Save Results
Env.res_Temp.sd<-data.frame(Parameter="Daily Standard Deviation of Temperature", Test="Two-sided Wilcoxon Rank Sum", p=wilcox.test(TC.daily$Temp.sd[which(TC.daily$Site=="KL")], TC.daily$Temp.sd[which(TC.daily$Site=="SS")], alternative = "two.sided")$p.value)
```
#### Average Conductivity
```{r}
shapiro.test(TC.daily$Cond.a)
##Not normal
##Non parametric test
wilcox.test(TC.daily$Cond.a[which(TC.daily$Site=="KL")], TC.daily$Cond.a[which(TC.daily$Site=="SS")], alternative = "two.sided")
wilcox.test(TC.daily$Cond.a[which(TC.daily$Site=="KL")], TC.daily$Cond.a[which(TC.daily$Site=="SS")], alternative = "greater")
##Save Results
Env.res_Cond.a<-data.frame(Parameter="Daily Average Conductivity",
Test=c("Two-sided Wilcoxon Rank Sum", "One-sided (KL>SS) Wilcoxon Rank Sum"), p=c(wilcox.test(TC.daily$Cond.a[which(TC.daily$Site=="KL")], TC.daily$Cond.a[which(TC.daily$Site=="SS")], alternative = "two.sided")$p.value, wilcox.test(TC.daily$Cond.a[which(TC.daily$Site=="KL")], TC.daily$Cond.a[which(TC.daily$Site=="SS")], alternative = "greater")$p.value))
```
#### Conductivity Standard Deviation
```{r}
shapiro.test(TC.daily$Cond.sd)
##Not normal
##Non parametric test
wilcox.test(TC.daily$Cond.sd[which(TC.daily$Site=="KL")], TC.daily$Cond.sd[which(TC.daily$Site=="SS")], alternative = "two.sided")
wilcox.test(TC.daily$Cond.sd[which(TC.daily$Site=="KL")], TC.daily$Cond.sd[which(TC.daily$Site=="SS")], alternative = "greater")
##Save Results
Env.res_Cond.sd<-data.frame(Parameter="Daily Standard Deviation of Conductivity",
Test=c("Two-sided Wilcoxon Rank Sum", "One-sided (KL>SS) Wilcoxon Rank Sum"), p=c(wilcox.test(TC.daily$Cond.sd[which(TC.daily$Site=="KL")], TC.daily$Cond.sd[which(TC.daily$Site=="SS")], alternative = "two.sided")$p.value, wilcox.test(TC.daily$Cond.sd[which(TC.daily$Site=="KL")], TC.daily$Cond.sd[which(TC.daily$Site=="SS")], alternative = "greater")$p.value))
```
#### Average PAR
```{r}
shapiro.test(PAR.daily$PAR.a)
##Not normal
##Non parametric test
wilcox.test(PAR.daily$PAR.a[which(PAR.daily$Site=="KL")], PAR.daily$PAR.a[which(PAR.daily$Site=="SS")], alternative = "two.sided")
wilcox.test(PAR.daily$PAR.a[which(PAR.daily$Site=="KL")], PAR.daily$PAR.a[which(PAR.daily$Site=="SS")], alternative = "less")
##Save Results
Env.res_PAR.a<-data.frame(Parameter="Daily Average PAR",
Test=c("Two-sided Wilcoxon Rank Sum", "One-sided (KL<SS) Wilcoxon Rank Sum"), p=c(wilcox.test(PAR.daily$PAR.a[which(PAR.daily$Site=="KL")], PAR.daily$PAR.a[which(PAR.daily$Site=="SS")], alternative = "two.sided")$p.value, wilcox.test(PAR.daily$PAR.a[which(PAR.daily$Site=="KL")], PAR.daily$PAR.a[which(PAR.daily$Site=="SS")], alternative = "less")$p.value))
```
#### Hours of Saturating Irradiance
```{r}
shapiro.test(PAR.daily$Sat.hr)
##Not normal
##Non parametric test
wilcox.test(PAR.daily$Sat.hr[which(PAR.daily$Site=="KL")], PAR.daily$Sat.hr[which(PAR.daily$Site=="SS")], alternative = "two.sided")
wilcox.test(PAR.daily$Sat.hr[which(PAR.daily$Site=="KL")], PAR.daily$Sat.hr[which(PAR.daily$Site=="SS")], alternative = "less")
##Save Results
Env.res_Sat.hr<-data.frame(Parameter="Daily Daily Hours above Saturating Irradiance",
Test=c("Two-sided Wilcoxon Rank Sum", "One-sided (KL<SS) Wilcoxon Rank Sum"), p=c(wilcox.test(PAR.daily$Sat.hr[which(PAR.daily$Site=="KL")], PAR.daily$Sat.hr[which(PAR.daily$Site=="SS")], alternative = "two.sided")$p.value, wilcox.test(PAR.daily$Sat.hr[which(PAR.daily$Site=="KL")], PAR.daily$Sat.hr[which(PAR.daily$Site=="SS")], alternative = "less")$p.value))
```
#### Total N to P
```{r}
shapiro.test(Nutrients.o$N.P)
##Normal
var.test(N.P ~ Site, Nutrients.o, alternative = "two.sided")
##Equal Variance
##Parametric test
t.test(Nutrients.o$N.P[which(Nutrients.o$Site=="KL")], Nutrients.o$N.P[which(Nutrients.o$Site=="SS")], var.equal=TRUE, alternative = "two.sided")
##Save Results
Env.res_N.P<-data.frame(Parameter="Bi-monthly Total Nitrogen : Phosphorus", Test="Two-sided Student's t", p=t.test(Nutrients.o$N.P[which(Nutrients.o$Site=="KL")], Nutrients.o$N.P[which(Nutrients.o$Site=="SS")], var.equal=TRUE, alternative = "two.sided")$p.value)
```
#### Total N
```{r}
shapiro.test(Nutrients.o$TN_uM)
##Normal
var.test(TN_uM ~ Site, Nutrients.o, alternative = "two.sided")
##Equal Variance
##Parametric test
t.test(Nutrients.o$TN_uM[which(Nutrients.o$Site=="KL")], Nutrients.o$TN_uM[which(Nutrients.o$Site=="SS")], var.equal=TRUE, alternative = "two.sided")
##Save Results
Env.res_TN<-data.frame(Parameter="Bi-monthly Total Nitrogen", Test="Two-sided Student's t", p=t.test(Nutrients.o$TN_uM[which(Nutrients.o$Site=="KL")], Nutrients.o$TN_uM[which(Nutrients.o$Site=="SS")], var.equal=TRUE, alternative = "two.sided")$p.value)
```
#### Total P
```{r}
shapiro.test(Nutrients.o$TP_uM)
##Not normal
##Non parametric test
wilcox.test(Nutrients.o$TP_uM[which(Nutrients.o$Site=="KL")], Nutrients.o$TP_uM[which(Nutrients.o$Site=="SS")], alternative = "two.sided")
##Save Results
Env.res_TP<-data.frame(Parameter="Bi-monthly Total Phosphorus", Test="Two-sided Wilcoxon Rank Sum", p=wilcox.test(Nutrients.o$TP_uM[which(Nutrients.o$Site=="KL")], Nutrients.o$TP_uM[which(Nutrients.o$Site=="SS")], alternative = "two.sided")$p.value)
```
#### DIN
```{r}
shapiro.test(Nutrients.o$DIN)
##Not normal
##Non parametric test
wilcox.test(Nutrients.o$DIN[which(Nutrients.o$Site=="KL")], Nutrients.o$DIN[which(Nutrients.o$Site=="SS")], alternative = "two.sided")
##Save Results
Env.res_DIN<-data.frame(Parameter="Bi-monthly Dissolved Inorganic Nitrogen", Test="Two-sided Wilcoxon Rank Sum", p=wilcox.test(Nutrients.o$DIN[which(Nutrients.o$Site=="KL")], Nutrients.o$DIN[which(Nutrients.o$Site=="SS")], alternative = "two.sided")$p.value)
```
# Calulcate Averages per Timepoint
To pair environmental parameters with coral physiology, calculating averages within bins around each timepoint.
TP1: Aug-Oct 2021
TP2: Nov 2021-Jan 2022
TP3: Feb-Apr 2022
TP4: May-Jul/Aug 2022
### Temperature and Conductivity
```{r}
##Add Timepoint Variable
TC.daily$TimeP<-ifelse(TC.daily$BiMonth=="M0.1" | TC.daily$BiMonth=="M0.2" |
TC.daily$BiMonth=="M1.1" | TC.daily$BiMonth=="M1.2" |
TC.daily$BiMonth=="M2.1" | TC.daily$BiMonth=="M2.2", "TP1",
ifelse(TC.daily$BiMonth=="M3.1" | TC.daily$BiMonth=="M3.2" |
TC.daily$BiMonth=="M4.1" | TC.daily$BiMonth=="M4.2" |
TC.daily$BiMonth=="M5.1" | TC.daily$BiMonth=="M5.2", "TP2",
ifelse(TC.daily$BiMonth=="M6.1" | TC.daily$BiMonth=="M6.2" |
TC.daily$BiMonth=="M7.1" | TC.daily$BiMonth=="M7.2" |
TC.daily$BiMonth=="M8.1" | TC.daily$BiMonth=="M8.2", "TP3",
ifelse(TC.daily$BiMonth=="M9.1" | TC.daily$BiMonth=="M9.2" |
TC.daily$BiMonth=="M10.1" | TC.daily$BiMonth=="M10.2" |
TC.daily$BiMonth=="M11.1" | TC.daily$BiMonth=="M11.2" |
TC.daily$BiMonth=="M12.1", "TP4", "NA" ))))
##Averages by Timepoint
names(TC.daily)
TC.TP<-aggregate(TC.daily[,c(3:12)], list(TC.daily$Site, TC.daily$TimeP), mean)
names(TC.TP)[1:2]<-c("Site", "TimeP")
```
### PAR
```{r}
##Add Timepoint Variable
PAR.daily$TimeP<-ifelse(PAR.daily$BiMonth=="M0.1" | PAR.daily$BiMonth=="M0.2" |
PAR.daily$BiMonth=="M1.1" | PAR.daily$BiMonth=="M1.2" |
PAR.daily$BiMonth=="M2.1" | PAR.daily$BiMonth=="M2.2", "TP1",
ifelse(PAR.daily$BiMonth=="M3.1" | PAR.daily$BiMonth=="M3.2" |
PAR.daily$BiMonth=="M4.1" | PAR.daily$BiMonth=="M4.2" |
PAR.daily$BiMonth=="M5.1" | PAR.daily$BiMonth=="M5.2", "TP2",
ifelse(PAR.daily$BiMonth=="M6.1" | PAR.daily$BiMonth=="M6.2" |
PAR.daily$BiMonth=="M7.1" | PAR.daily$BiMonth=="M7.2" |
PAR.daily$BiMonth=="M8.1" | PAR.daily$BiMonth=="M8.2", "TP3",
ifelse(PAR.daily$BiMonth=="M9.1" | PAR.daily$BiMonth=="M9.2" |
PAR.daily$BiMonth=="M10.1" | PAR.daily$BiMonth=="M10.2" |
PAR.daily$BiMonth=="M11.1" | PAR.daily$BiMonth=="M11.2" |
PAR.daily$BiMonth=="M12.1", "TP4", "NA" ))))
##Averages by Timepoint
names(PAR.daily)
PAR.TP<-aggregate(PAR.daily[,c(3:7)], list(PAR.daily$Site, PAR.daily$TimeP), mean)
names(PAR.TP)[1:2]<-c("Site", "TimeP")
```
### Nutrients
```{r}
##Add Timepoint Variable
Nutrients.o$TimeP<-ifelse(Nutrients.o$BiMonth=="M0.1" | Nutrients.o$BiMonth=="M0.2" |
Nutrients.o$BiMonth=="M1.1" | Nutrients.o$BiMonth=="M1.2" |
Nutrients.o$BiMonth=="M2.1" | Nutrients.o$BiMonth=="M2.2", "TP1",
ifelse(Nutrients.o$BiMonth=="M3.1" | Nutrients.o$BiMonth=="M3.2" |
Nutrients.o$BiMonth=="M4.1" | Nutrients.o$BiMonth=="M4.2" |
Nutrients.o$BiMonth=="M5.1" | Nutrients.o$BiMonth=="M5.2", "TP2",
ifelse(Nutrients.o$BiMonth=="M6.1" | Nutrients.o$BiMonth=="M6.2" |
Nutrients.o$BiMonth=="M7.1" | Nutrients.o$BiMonth=="M7.2" |
Nutrients.o$BiMonth=="M8.1" | Nutrients.o$BiMonth=="M8.2", "TP3",
ifelse(Nutrients.o$BiMonth=="M9.1" | Nutrients.o$BiMonth=="M9.2" |
Nutrients.o$BiMonth=="M10.1" | Nutrients.o$BiMonth=="M10.2" |
Nutrients.o$BiMonth=="M11.1" | Nutrients.o$BiMonth=="M11.2" |
Nutrients.o$BiMonth=="M12.1", "TP4", "NA" ))))
##Averages by Timepoint
names(Nutrients.o)
Nutrients.TP<-aggregate(Nutrients.o[,c(8:15)], list(Nutrients.o$Site, Nutrients.o$TimeP), mean)
names(Nutrients.TP)[1:2]<-c("Site", "TimeP")
```
### Environmetnal Conditions by Timepoint
```{r}
##Merge all Environmental Data
#Merges by Site and Timepoint
EnvData.TP<-merge(TC.TP, PAR.TP)
EnvData.TP<-merge(EnvData.TP, Nutrients.TP)
##Retain Variables of Interest
EnvData.TP<-EnvData.TP[,c("Site", "TimeP", "Temp.a", "Cond.a", "Temp.sd", "Cond.sd", "PAR.a", "Sat.hr", "TN_uM", "TP_uM", "N.P", "DIN_uM")]
##Save Environmental Dataset
write.csv(EnvData.TP, "Outputs/AvgEnvData.csv", row.names=FALSE)
```
# Thermal Tolerance Assay
### Daigram of Exposure
```{r Plot Heat Assay Design}
## Design Data
HA_Design<-data.frame(Hours=c(rep(c(0, 3, 6, 7, 18),2)), TempTreatment=c(rep("Control",5), rep("Heated", 5)), Temp_C=c(rep(0, 5), 0.1, 8, 8, 0.1, 0.1))
HA_Design$TempTreatment<-factor(HA_Design$TempTreatment, ordered=TRUE, levels=c("Control", "Heated"))
HA_SampTimes<-data.frame(Hours=c(7, 9, 11, 18), Sample=c(rep("FvFm", 3), "Bleaching"), Temp_C=c(rep(0.25,4)))
##Plot Temperature Profiles
HA_Design.plot<-ggplot(data=HA_Design, aes(color=TempTreatment, x=Hours, y=Temp_C)) +
annotate("rect", xmin=6, xmax=7, ymin=-Inf, ymax=Inf, alpha=0.8, fill="grey80")+
annotate("rect", xmin=8, xmax=9, ymin=-Inf, ymax=Inf, alpha=0.8, fill="grey80")+
annotate("rect", xmin=10, xmax=11, ymin=-Inf, ymax=Inf, alpha=0.8, fill="grey80")+
geom_line(data=HA_Design, aes(color=TempTreatment, x=Hours, y=Temp_C), linewidth=bar.sz+1) +
scale_color_manual(values = c(HC.colors.o))+
theme_bw()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.direction = "vertical", legend.position=c(0.8,0.8))+
labs(x="Time (hr)", y="\u00B0C + Ambient", colour="Treatment")+
ylim(0, 8.5)+
geom_point(data=HA_SampTimes, aes(x=Hours,y=Temp_C), color="grey0", size=point.sz, shape=c(8, 8, 8, 16));HA_Design.plot
```
### Average Temperature by Treatment by Timepoint
```{r}