--- 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) ```