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

Checking IPM simulation output

library(plyr)
library(reshape2)
library(ggplot2)
library(synchrony)
output_list <- readRDS("../../results/ipm_simulation_lists.RDS")
mlist <- melt(output_list)

Look at simulated time series

ggplot(mlist, aes(x=Var1, y=(value*100), color=Var2))+
  geom_line()+
  facet_grid(L1~L2, scale="free")

Run through synchrony library

library(communitySynchrony)
colnames(mlist)[1:3] <- c("year", "species", "cover")
ts_data <- subset(mlist, L2=="ENVDEMO" & L1=="Arizona")[1:3]
test1 <- get_ipm_synchrony(ts_data = ts_data)
str(test1)

Plot results of simulation scenarios

colnames(mlist)[1:3] <- c("year", "species", "cover")
mlist <- subset(mlist, year>500)
sites <- unique(mlist$L1)
sims <- unique(mlist$L2)
pgr_list <- list()
for(dosite in sites){
  tmp_data <- subset(mlist, L1==dosite)
  if(dosite=="Idaho"){
    tmp_data <- subset(tmp_data, species!="ARTR")
  }
  within_site_list <- list()
  for(dosim in sims){
    tmpsim <- subset(tmp_data, L2==dosim)[c("year", "species", "cover")]
    tmpsynch <- get_ipm_synchrony(tmpsim)
    tmp_pgr_synch <- as.numeric(tmpsynch$pgr_synchrony["obs"])
    within_site_list[[dosim]] <- tmp_pgr_synch
  }
  pgr_list[[dosite]] <- within_site_list
}
pgr_synch <- melt(pgr_list) 

ggplot(pgr_synch, aes(x=L2, y=value, fill=L2))+
  geom_bar(stat="identity", position=position_dodge(0.9), color="grey")+
  facet_wrap("L1")+
  scale_fill_manual(values = c("grey45", "steelblue", "slateblue4"))+
  guides(fill=FALSE)+
  scale_y_continuous(limits=c(0,1))+
  xlab("Source of stochasticity")+
  ylab("Species synchrony")

So all sites except Montana show an increase in species synchrony when environmental variation is included in the model. This is probably because they are all perennial grass species in semi-arid grasslands that respond similarly to environmental conditions.

Comparison with niche differecnes

niche_diff <- data.frame(site=sites,
                         ND = c(0.58, 0.59, 0.66, 0.69, 0.68))
pgr_subs <- subset(pgr_synch, L2!="ENVDEMO")
pgr_cast <- dcast(pgr_subs, L1~L2, value.var = "value")
synch_diff <- ddply(pgr_cast, .(L1), summarise,
                    value = ENV-DEMO)
niche_synch <- merge(niche_diff, synch_diff, by.x="site", by.y="L1")
ggplot(niche_synch, aes(x=ND, y=value))+
  geom_point(size=5)

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