com.index: Computing the moments of the trait distribution and other metrics to test and quantify the non-random assembly of communities

Description

Computing the moments of the trait distribution and other metrics to test and quantify the non-random assembly of communities.

Usage

com.index(traits = NULL, index = NULL, namesindex = NULL, 
	nullmodels = NULL, ind.plot = NULL, sp = NULL, com=NULL, 
	reg.pool=NULL, nperm = 99, printprogress = TRUE, ind.value=TRUE, 
	type="count")

Arguments

traits

Individual Matrix of traits with traits in column.

index

A vector of function to apply to traits vectors in the form "mean(x, na.rm = TRUE)" or "range(x)", see examples for more complexe functions.

namesindex

A vector of names for index.

nullmodels

A vector of values corresponding to null models tu use for each index. A value of 1 corresponds to a randomization of individual values within a given community. A value of 2 corresponds to randomization of individual values within region, ie within all t

ind.plot

Factor defining the name of the plot (site or community) in which the individual is.

Factor defining the species which the individual belong to.

com

Community data matrix with species (or populations) in rows and sites in column. Use only if ind.value=FALSE. "traits" matrix and "com" matrix must have the same number of rows.

reg.pool

Regional pool data for traits. If not informed, traits is considere as the regional pool. This matrix need to be larger than traits. Use only for null model 2.

nperm

Number of permutations. If NULL, only observed values are returned;

printprogress

Logical value; print progress during the calcul or not.

ind.value

Do the data are from individual value. If not, an internal function transform the abundance data matrix of com into a individual like matrix to compute the function.

type

Only if ind.value=FALSE. Either "count" or "abundance". Use abundance when one value or more in the com matrix are inferior to one.

Value

An object of class "com.index" corresponding to a list of lists:
$obsList of observed values for each trait in each community. Each component of the list corresponds to a matrix containing the result for each custom function.
$NullList of null values for each trait in each community. Each component of the list corresponds to an array containing the result of the permutations for each custom function.
$list.indexList of index values and related null models. Internal use in other function. Traits in columns.
$list.index.tList of index values and related null models. Internal use in other function. Traits in rows.
$sites_richnessNumber of species per site.
$namestraitsNames of traits.

Details

Compute statistics (e.g. mean, range, CVNND and kurtosis) to test community assembly using null models. For each statistic this function returns observed values and the related null distribution. This function implement three null models which keep unchanged the number of individual per community. Model 1 corresponds to randomization of individual values within community. Model 2 corresponds to randomization of individual values within region. Model 3 corresponds to randomization of population values within region. In most cases, models 1 and 2 correspond to index at the individual level and the model 3 to index at the species level (or any other aggregate variable like genus, family or functionnal group).

Examples

Run this code

data(finch.ind)
	oldpar<-par()
	
	####
	#The function com.index allow to choose your own function 
	#(like mean, range, variance...) to calculate customize index.
	
	require(e1071)
	
	funct<-c("mean(x, na.rm=TRUE)", "kurtosis(x, na.rm=TRUE)", 
	"max(x, na.rm=TRUE) - min(x, na.rm=TRUE)", "CVNND(x)"  )
	
	res.finch.sp_mn2<-com.index(traits=traits.finch, index=funct, 
		sp=sp.finch, nullmodels=c(2,2,2,2), ind.plot=ind.plot.finch, 
		nperm=9, print=FALSE)
		
		res.finch.sp_mn3<-com.index(traits=traits.finch, index=funct, 
		sp=sp.finch, nullmodels=c(3,3,3,3), ind.plot=ind.plot.finch, 
		nperm=9, print=FALSE)
	
		####
		#We can represent Standardized Effect Size (ses)
		#using the function plot(as.listofindex(list1, list2, list3))
		
		list.ind2<-list(res.finch.sp_mn2, res.finch.sp_mn3)
		index.list2<-as.listofindex(list.ind2)
		
		plot(index.list2, type="bytraits")
		
		plot(index.list2)
	
		####
		#This allows to calcul index per site 
		#for example using "tapply(x, sites, mean)".
		
		funct<-c("tapply(x, ind.plot.finch, function(x) mean(x, na.rm=TRUE))", 
		"tapply(x, ind.plot.finch, function(x) kurtosis(x, na.rm=TRUE))", 
		"tapply(x, ind.plot.finch, function(x) max(x, na.rm=TRUE) - 
		min(x, na.rm=TRUE) )", "tapply(x, ind.plot.finch, function(x) 
		CVNND(x))"  )
		
		
		##Null model 1 is trivial for this function 
		#because randomisation is within community only
		
		res.finch.ind_mn1<-com.index(traits=traits.finch, index=funct, 
		sp=sp.finch, nullmodels=c(1,1,1,1), ind.plot=ind.plot.finch, 
		nperm=9, print=FALSE)
		
		res.finch.ind_mn2<-com.index(traits=traits.finch, index=funct, 
		sp=sp.finch, nullmodels=c(2,2,2,2), ind.plot=ind.plot.finch, 
		nperm=9, print=FALSE)
	
		
		####
		#We can calcul metrics with or without intraspecific variance.
		#Calculation of trait averages per population 
		#(name_sp_site is a name of a population) 
		#like in the function com.index
		#and determine the site for each population (sites_bypop)
	
		name_sp_sites=paste(sp.finch, ind.plot.finch, sep="_")
		traits.by.pop<-apply(traits.finch, 2 , function (x) 
		tapply(x, name_sp_sites, mean , na.rm=TRUE))
		
		sites_bypop<-lapply(strsplit(paste(rownames(traits.by.pop), sep="_"), 
		split="_"), function(x) x[3])
		
		funct.withoutIV<-c("tapply(x, unlist(sites_bypop), function(x) 
		mean(x, na.rm=TRUE))", "tapply(x, unlist(sites_bypop), function(x) 
		kurtosis(x, na.rm=TRUE))", "tapply(x, unlist(sites_bypop), function(x) 
		max(x, na.rm=TRUE) - min(x, na.rm=TRUE) )", 
		"tapply(x, unlist(sites_bypop), function(x) CVNND(x))"  )
		
		
		funct.withIV<-c("tapply(x, ind.plot.finch, function(x) 
		mean(x, na.rm=TRUE))", "tapply(x, ind.plot.finch, function(x) 
		kurtosis(x, na.rm=TRUE))", "tapply(x, ind.plot.finch, function(x) 
		max(x, na.rm=TRUE) - min(x, na.rm=TRUE) )", 
		"tapply(x, ind.plot.finch, function(x) CVNND(x))"  )
		
		
		res.finch.withIV<-com.index(traits=traits.finch, index=funct.withIV, 
		sp=sp.finch, nullmodels=c(2,2,2,2), ind.plot=ind.plot.finch, 
		nperm=9, print=FALSE)
		
		res.finch.withoutIV<-com.index(traits=traits.finch, index=funct.withoutIV, 
		sp=sp.finch, nullmodels=c(3,3,3,3), ind.plot=ind.plot.finch, 
		nperm=9, print=FALSE)
		
		
		####
		#We can also represent T-statistics and custom index thanks to
		#the plot.listofindex function.
		
		res.finch<-Tstats(traits.finch, ind_plot=ind.plot.finch, sp=sp.finch, 
		nperm=9, print=FALSE)
	
		list.ind<-list(res.finch.withIV, res.finch.withoutIV ,res.finch)
		
		index.list1<-as.listofindex(list.ind, namesindex=c("mean", "kurtosis", 
		"range", "CVNND", "mean.pop", "kurtosis.pop", "range.pop", "CVNND.pop", 
		"T_IP.IC", "T_IC.IR", "T_PC.PR"))
		
		class(index.list1)
		
		par(mfrow=c(2,3))
		plot(index.list1,type="bytraits", bysite=TRUE)
		
		par(mfrow=c(2,2))
		plot(index.list1,type="bytraits")
		par(mfrow=c(1,1))
		
		plot(index.list1,type="simple")
		plot(index.list1,type="simple_range")
		plot(index.list1,type="normal")
		plot(index.list1,type="barplot")
	
	############################
	####Using ind.value=FALSE and community data matrix if there is no data 
	#available at the individual level.
	#create traits data at the species level
		traits_by_sp<-apply(traits.finch,2,function(x) tapply(x,sp.finch,
		function(x) mean(x, na.rm=T)))  
		
		#create traits data at the populational level
		names_sp_ind_plot<-as.factor(paste(sp.finch, ind.plot.finch, sep="@")) 
		traits_by_pop<-apply(traits.finch,2,function(x) tapply(x,names_sp_ind_plot, 
		function(x) mean(x, na.rm=T) ))  
		
		#create community data matrix at the species or populational level
		w1<-table(sp.finch,ind.plot.finch)
		dim(w1)
		dim(traits_by_sp)
		
		w2<-table(names_sp_ind_plot,ind.plot.finch)
		dim(w2)
		dim(traits_by_pop)
		
		#Choose indices
		require(e1071)
		
		funct<-c("mean(x, na.rm=TRUE)", "kurtosis(x, na.rm=TRUE)", 
		"max(x, na.rm=TRUE) - min(x, na.rm=TRUE)", "CVNND(x)"  )
		
		
		#################
		#with species value
		
		res<-ab_to_ind(traits_by_sp, w1)
		
		com.index(traits_by_sp, nullmodels=2, ind.value=FALSE, index=funct, 
		sp=rownames(traits_by_sp), com=w1, nperm=9)
		
		
		#################
		#with population value
		res<-ab_to_ind(traits_by_pop, w2)
		sp.sp<-unlist(strsplit(rownames(traits_by_pop),"@"))[seq(1,39*2,2)]
		
		com.index(traits_by_pop, nullmodels=2, ind.value=FALSE, index=funct, 
		sp=sp.sp, com=w2)

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