--- 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 ```