---
title: "R Notebook"
output: html_notebook
---
```{r}
rm(list = ls())
library(vegan)
library(ggplot2)
set.seed(250)
```
#Read in vertebrate community data and read in and organize sample metadata
```{r}
df <- read.csv("~/Dropbox/Active/Arizona/1) CA Smith Fellowship/Ecosphere Submission/Datasets/DataS4.csv", header = T, row.names = 1, skip=1)
df <- t(df[,7:88])
df <- df[names(which(rowSums(df) != 0)),]
md <- read.csv("~/Dropbox/Active/Arizona/1) CA Smith Fellowship/Ecosphere Submission/Datasets/DataS2.csv", header = T, skip=1)
md <- md[md$Sampling.x.site.code!="",]
row.names(md) <- md$Sampling.x.site.code
md <- md[,-1]
md <- md[row.names(df),]
###Which env vectors/factors do you wnat to fit
md <- md[,c("Basin","Days.flowing", "Drying.frequency", "Flow.permanence", "Stream.distance.to.perennial.refuge..m.")]
```
#For both basins together
```{r}
nmds = metaMDS(df, distance = "bray")
en = envfit(nmds, md, permutations = 999, na.rm = TRUE)
en
```
#For Chalone Creek
```{r}
md.pin <- md[which(md$Basin=="Pinnacles"),]
df.pin <- df[c(row.names(md.pin)),]
row.names(md.pin) == row.names(df.pin)
nmds = metaMDS(df.pin, distance = "bray")
en = envfit(nmds, md.pin, permutations = 999, na.rm = TRUE)
en
```
#For Point Reyes
```{r}
md.pr<- md[which(md$Basin=="Point Reyes"),]
df.pr <- df[c(row.names(md.pr)),]
row.names(md.pr) == row.names(df.pr)
nmds = metaMDS(df.pr, distance = "bray")
en = envfit(nmds, md.pr, permutations = 999, na.rm = TRUE)
en
```