Community Dynamics Toolbox

######################

##Population stability

#data format: long

#summing cover values by plot then by species

require(plyr)

ps.sum <- ddply(data_raw.long, .(plot, species), summarise,

					sum=sum(cover))

#calculating the mean sum and sd for each treatment

ps.sum.mean <- ddply(ps.sum, .(plot), summarise, 

						mean=mean(sum))

ps.sum.sd <- ddply(ps.sum, .(plot), summarise,

						sd=sd(sum))

ps.m.sd <- join(ps.sum.mean, ps.sum.sd, type="full")

#calculating final comm. stability value per plot						

ps.plot_final <- ddply(ps.m.sd, .(plot), summarise,

					ps=mean/sd)

#calculating final comm. stability value per treatment (must create trt column for ps.plot_final)

ps.trt_final <- ddly(ps.plot_final, .(treatment), summarise,

						mean=mean(ps))

#ANOVA on ps.sum values (must create trt column for ps.sum)

require(car)

lm.ps.sum <- lm(sum ~ treatment, data=ps.sum)

Anova(lm.ps.sum, type=3)

summary(lm.ps.sum)

#KW ANOVA

fit.ps.kw <- kruskal.test(sum ~ treatment, data=pc.sum)