---
title: "R Notebook"
output: html_notebook
---
```{r}
rm(list = ls())
require(vegan)
require(bipartite)
set.seed(500)
```
###Chalone Creek analysis
```{r}
rm(list = ls())
require(vegan)
require(bipartite)
```
###Read in community matrix
```{r}
df <- read.csv("~/Dropbox/Active/Arizona/1) CA Smith Fellowship/Ecosphere Submission/Datasets/DataS4.csv", skip=1, header = T)
row.names(df) <- df$TaxonCode
df <- df[,8:89]
```
###Read in metadata
```{r}
md <- read.csv("~/Dropbox/Active/Arizona/1) CA Smith Fellowship/Ecosphere Submission/Datasets/DataS2.csv", skip=1, header = T)
md <- md[md$Sampling.x.site.code!="",]
colnames(md)[1] <- "Site"
md <- md[,c("Site","Basin","Flow.class", "Flow.permanence", "Days.flowing", "Drying.frequency", "Stream.distance.to.perennial.refuge..m.")]
```
###Subset to Pinnacles
```{r}
md <- md[which(md$Basin=="Pinnacles"),]
df <- df[as.character(md$Site)]
df <- df[names(which(rowSums(df) > 0)),]
```
###Order matrices by different environmental predictors (permanent to less permanent)
```{r}
md.fp <- md[order(-md$Flow.permanence),]
md.dfl <- md[order(-md$Days.flowing),]
md.dfr <- md[order(md$Drying.frequency),]
md.dr <- md[order(md$Stream.distance.to.perennial.refuge..m.),]
```
###Test matrix ordered by days flowing against null model
```{r}
df.dfl <- df[,as.character(md.dfl$Site)]
plotmatrix(as.matrix(df.dfl))
oecosimu(df.dfl, nestednodf, "quasiswap",nsimul = 1000, order = FALSE)
```
###Test matrix ordered by drying frequency against null model
```{r}
df.dfr <- df[,as.character(md.dfr$Site)]
plotmatrix(as.matrix(df.dfr))
oecosimu(df.dfr, nestednodf, "quasiswap",nsimul = 1000, order = FALSE)
```
###Test matrix ordered by flow permanence against null model
```{r}
df.fp <- df[,as.character(md.fp$Site)]
plotmatrix(as.matrix(df.fp))
oecosimu(df.fp, nestednodf, "quasiswap",nsimul = 1000, order = FALSE)
```
###Test matrix ordered by distance to refuge against null model
```{r}
df.dr <- df[,as.character(md.dr$Site)]
plotmatrix(as.matrix(df.dr))
oecosimu(df.dr, nestednodf, "quasiswap",nsimul = 1000, order = FALSE)
```
###Pine Gulch analysis
```{r}
rm(list = ls())
require(vegan)
require(bipartite)
```
###Read in community matrix
```{r}
df <- read.csv("~/Dropbox/Active/Arizona/1) CA Smith Fellowship/Ecosphere Submission/Datasets/DataS4.csv", skip=1, header = T)
row.names(df) <- df$TaxonCode
df <- df[,8:89]
```
###Read in metadata
```{r}
md <- read.csv("~/Dropbox/Active/Arizona/1) CA Smith Fellowship/Ecosphere Submission/Datasets/DataS2.csv", skip=1, header = T)
md <- md[md$Sampling.x.site.code!="",]
colnames(md)[1] <- "Site"
md <- md[,c("Site","Basin","Flow.class", "Flow.permanence", "Days.flowing", "Drying.frequency", "Stream.distance.to.perennial.refuge..m.")]
```
###Subset to Pine Gulch
```{r}
md <- md[which(md$Basin=="Point Reyes"),]
df <- df[as.character(md$Site)]
df <- df[names(which(rowSums(df) > 0)),]
```
###Order matrices by different environmental predictors (permanent to less permanent)
```{r}
md.fp <- md[order(-md$Flow.permanence),]
md.dfl <- md[order(-md$Days.flowing),]
md.dfr <- md[order(md$Drying.frequency),]
md.dr <- md[order(md$Stream.distance.to.perennial.refuge..m.),]
```
###Test matrix ordered by days flowing
```{r}
df.dfl <- df[,as.character(md.dfl$Site)]
plotmatrix(as.matrix(df.dfl))
oecosimu(df.dfl, nestednodf, "quasiswap",nsimul = 1000, order = FALSE)
```
###Test matrix ordered by drying frequency against null model
```{r}
df.dfr <- df[,as.character(md.dfr$Site)]
plotmatrix(as.matrix(df.dfr))
oecosimu(df.dfr, nestednodf, "quasiswap",nsimul = 1000, order = FALSE)
```
###Test matrix ordered by flow permanence against null model
```{r}
df.fp <- df[,as.character(md.fp$Site)]
plotmatrix(as.matrix(df.fp))
oecosimu(df.fp, nestednodf, "quasiswap",nsimul = 1000, order = FALSE)
```
###Test matrix ordered by distance to refuge against null model
```{r}
df.dr <- df[,as.character(md.dr$Site)]
plotmatrix(as.matrix(df.dr))
oecosimu(df.dr, nestednodf, "quasiswap",nsimul = 1000, order = FALSE)
```