functional.beta.pair {betapart}

Pair-wise functional dissimilarities

Description

Computes 3 distance matrices accounting for the spatial turnover and nestedness components of functional beta diversity, and the sum of both values. Functional dissimilarities are based on volume of convex hulls intersections in a multidimensional functional space.

Usage

functional.beta.pair(x, traits, index.family="sorensen")

Arguments

x

data frame, where rows are sites and columns are species. Alternatively x can be a functional.betapart object derived from the functional.betapart.core function or from the functional.betapart.core.pairwise function.

traits

if x is not a functional.betapart object, a data frame, where rows are species and columns are functional space dimensions (i.e. quantitative traits or synthetic axes after PCoA). Number of species in each site must be strictly higher than number of dimensions.

index.family

family of dissimilarity indices, partial match of "sorensen" or "jaccard".

Details

If x is a data.frame then functional.betapart.core.pairwise is called to compute the distance matrices necessary to compute the different components of the beta diversity. Only the default argument values will be used, while functional.betapart.core.pairwise integrates options that could be much more efficient, such as internal parallelisation, or different options for the convexhull volume estimation. Note that the the betapart package now supports external parallel computing for null models. As for internal parallelisation, these functionalities are only availabe in functional.betapart.core or in functional.betapart.core.pairwise. In this case, use the functional.betapart object as x in this function. See functional.betapart.core and functional.betepart.core.pairwise for more details.

Value

The function returns a list with three functional dissimilarity matrices.

For index.family="sorensen" the three matrices are:

funct.beta.sim

dist object, dissimilarity matrix accounting for functional turnover, measured as Simpson derived pair-wise functional dissimilarity

funct.beta.sne

dist object, dissimilarity matrix accounting for nestedness-resultant functional dissimilarity, measured as the nestedness-fraction of Sorensen derived pair-wise functional dissimilarity

funct.beta.sor

dist object, dissimilarity matrix accounting for functional beta diversity, measured as Sorensen derived pair-wise functional dissimilarity

For index.family="jaccard" the three matrices are:

funct.beta.jtu

dist object, dissimilarity matrix accounting for functional turnover, measured as the turnover-fraction of Jaccard derived pair-wise functional dissimilarity

funct.beta.jne

dist object, dissimilarity matrix accounting for nestedness-resultant functional dissimilarity, measured as the nestedness-fraction of Jaccard derived pair-wise functional dissimilarity

funct.beta.jac

dist object, dissimilarity matrix accounting for functional beta diversity, measured as Jaccard derived pair-wise functional dissimilarity

Author(s)

Sébastien Villéger, Andrés Baselga and David Orme

References

Villéger S., Novack-Gottshal P. & Mouillot D. 2011. The multidimensionality of the niche reveals functional diversity changes in benthic marine biotas across geological time. Ecology Letters 14: 561-568

Baselga, A. 2012. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Global Ecology and Biogeography 21: 1223-1232

Villéger, S. Grenouillet, G., Brosse, S. 2013. Decomposing functional beta-diversity reveals that low functional beta-diversity is driven by low functional turnover in European fish assemblages. Global Ecology and Biogeography 22: 671–681

See Also

functional.beta.multi, functional.betapart.core, functional.betapart.core.pairwise, beta.pair

Examples

##### 4 communities in a 2D functional space (convex hulls are rectangles)
traits.test<-cbind( c(1,1,1,2,2,3,3,4,4,5,5) , c(1,2,4,1,2,3,5,1,4,3,5) )
	dimnames(traits.test)<-list(paste("sp",1:11,sep="") , c("Trait 1","Trait 2") ) 

comm.test<-matrix(0,4,11,dimnames=list( c("A","B","C","D") , paste("sp",1:11,sep="") ) )
comm.test["A",c(1,2,4,5)]<-1
comm.test["B",c(1,3,8,9)]<-1
comm.test["C",c(6,7,10,11)]<-1
comm.test["D",c(2,4,7,9)]<-1

plot(5,5,xlim=c(0,6), ylim=c(0,6), type="n", xlab="Trait 1",ylab="Trait 2")
points(traits.test[,1],traits.test[,2], pch=21,cex=1.5,bg="black")
rect(1,1,4,4, col="#458B0050", border="#458B00") ; text(2.5,2.5,"B",col="#458B00",cex=1.5)	
polygon(c(2,1,3,4), c(1,2,5,4), col="#DA70D650", border="#DA70D6") ; 
text(2.5,3,"D",col="#DA70D6",cex=1.5)	
rect(1,1,2,2, col="#FF000050" , border="#FF0000") ; text(1.5,1.5,"A",col="#FF0000",cex=1.5)	
rect(3,3,5,5, col="#1E90FF50", border="#1E90FF") ; text(4,4.2,"C",col="#1E90FF",cex=1.5)	

test.pair<-functional.beta.pair(x=comm.test, traits=traits.test, index.family = "jaccard")
lapply(test.pair,round,2)

#### with functional.betapart.core.pairwise
test1 <- functional.betapart.core.pairwise(comm.test, traits.test)
test.pair <- functional.beta.pair(test1)
## Not run: 
#### if internal parallelisation would be interesting (large community matrix)
test1 <- functional.betapart.core.pairwise(comm.test, traits.test, parallel = TRUE,
                                           opt.parallel = list(nc = 2))
test.pair <- functional.beta.pair(test1)

## End(Not run)