In atredennick/community_synchrony: Calculates syunchrony and stability metrics from time series data

Appendix X for TITLE: Correlations of Species' Random Year Effects

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Andrew T. Tredennick\footnote{Correspondance: [e-mail]}\textsuperscript{1}, Claire de Mazancourt\textsuperscript{2}, Michel Loreau\textsuperscript{2}, and Peter B. Adler\textsuperscript{1}

\textit{\small{\textsuperscript{1}Department of Wildland Resources and the Ecology Center, 5230 Old Main Hill, Utah State University, Logan, Utah 84322 USA}}

\textit{\small{\textsuperscript{2}Centre for Biodiversity Theory and Modelling, Experimental Ecology Station, Centre National de la Recherche Scientifique, Moulis, 09200, France}}

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####
####  Clean Workspace an Load Libraries ----------------------------------------
####
rm(list = ls()) # wipe the workspace clean
library(reshape2)
library(plyr)
library(ggplot2)
library(xtable)



####
####  Get Vital Rate Satistical Results Files ----------------------------------
####
vital_rates <- c("growth", "surv", "recruit")
result_files <- list.files("../results/")
vr_files <-result_files[grep(paste(vital_rates,collapse="|"), 
                              list.files("../results/"))]



####
####  Loop Over Files and Extract Crowding Effects -----------------------------
####
# Growth and survival first
vital_mat_list <- list()
for(do_vital in vital_rates[c(1,2)]){
  vr_do <- vr_files[grep(do_vital,vr_files)]
  tmp_vital <- readRDS(paste0("../results/",vr_do))
  sites <- names(tmp_vital)
  mat_list <- list()
  for(do_site in sites){
    tmp <- tmp_vital[[do_site]]
    spp <- names(tmp)
    tmpmat <- matrix(0,nrow = length(spp), ncol = length(spp))
    for(i in 1:length(spp)){
      do_spp <- spp[i]
      tmpspp <- tmp[[do_spp]]
      tmpw <- tmpspp[1,grep("crowd*", colnames(tmpspp))]
      tmpmat[i,] <- as.numeric(tmpw)
    }
    rownames(tmpmat) <- spp
    colnames(tmpmat) <- spp
    mat_list[[do_site]] <- tmpmat
  }
  vital_mat_list[[do_vital]] <- mat_list 
}

# Recruitment
vr_do <- vr_files[grep("recruit",vr_files)]
tmp_vital <- readRDS(paste0("../results/",vr_do))
names_vital <- readRDS(paste0("../results/",vr_files[1]))
sites <- names(tmp_vital)
mat_list <- list()
for(do_site in sites){
  tmp <- tmp_vital[[do_site]]
  spp <- names(names_vital[[do_site]])
  tmpdd <- tmp[grep("dd", rownames(tmp)),"Mean"]
  tmpmat <- matrix(tmpdd,length(spp),length(spp), byrow = FALSE)
  rownames(tmpmat) <- spp
  colnames(tmpmat) <- spp
  mat_list[[do_site]] <- tmpmat
}
vital_mat_list[["recruit"]] <- mat_list 

names(vital_mat_list) <- c("growth", "survival", "recruitment")
for(do_vital in names(vital_mat_list)){
  tmp_list <- vital_mat_list[[do_vital]]
  for(do_site in names(tmp_list)){
    site_list <- tmp_list[[do_site]]
    synch_cap <- paste0("Interaction coefficients for ", do_vital, " regressions in ", do_site, ".")
    print(xtable(site_list, caption = synch_cap),
      caption.placement="top",
      include.rownames = TRUE, 
      sanitize.colnames.function = identity,
      comment=FALSE)
  }
}

atredennick/community_synchrony documentation built on May 10, 2019, 2:10 p.m.