vegan (version 1.17-4)

treedive: Functional Diversity estimated from a Species Dendrogram

Description

Functional diversity is defined as the total branch length in a trait dendrogram connecting all species, but excluding the unnecessary root segments of the tree (Petchey and Gaston 2006).

Usage

treedive(comm, tree, match.force = FALSE)
treeheight(tree)
treedist(x, tree, ...)

Arguments

comm, x

Community data frame or matrix.

tree

A dendrogram which for treedive must be for species (columns).

match.force

Force matching of column names in comm and labels in tree. If FALSE, matching only happens when dimensions differ, and in that case the species must be in identical order in both.

...

Other arguments passed to functions (ignored).

Value

• A vector of diversity values or a single tree height, or a dissimilarity structure that inherits from dist and can be used similarly.

Details

Function treeheight finds the sum of lengths of connecting segments in a dendrogram produced by hclust, or other dendrogram that can be coerced to a correct type using as.hclust. When applied to a clustering of species traits, this is a measure of functional diversity (Petchey and Gaston 2002, 2006).

Function treedive finds the treeheight for each site (row) of a community matrix. The function uses a subset of dendrogram for those species that occur in each site, and excludes the tree root if that is not needed to connect the species (Petchey and Gaston 2006). The subset of the dendrogram is found by first calculating cophenetic distances from the input dendrogram, then reconstructing the dendrogram for the subset of the cophenetic distance matrix for species occurring in each site.

Function treedist finds the dissimilarities among trees. Pairwise dissimilarity of two trees is found by combining species in a common tree and seeing how much of the tree height is shared and how much is unique. The current function uses a Jaccard like approach and defines dissimilarity as the total unshared height proportional of the total height of the combined tree. The idea is similar as in the UniFrac distance (Lozupone and Knight 2005), but the implementation is completely different and only works in the presence absence framework.

The functions need a dendrogram of species traits as an input. If species traits contain factor or ordered factor variables, it is recommended to use Gower distances for mixed data (function daisy in package cluster), and usually the recommended clustering method is UPGMA (method = "average" in function hclust) (Podani and Schmera 2006).

It is possible to analyse the non-randomness of functional diversity using oecosimu. This provided specifying an adequate Null model, and the results will change with this choice.

References

Lozupone, C. and Knight, R. 2005. UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology 71, 8228--8235.

Petchey, O.L. and Gaston, K.J. 2002. Functional diversity (FD), species richness and community composition. Ecology Letters 5, 402--411. Petchey, O.L. and Gaston, K.J. 2006. Functional diversity: back to basics and looking forward. Ecology Letters 9, 741--758.

Podani J. and Schmera, D. 2006. On dendrogram-based methods of functional diversity. Oikos 115, 179--185.

See Also

taxondive is something very similar from another world.

Examples

## There is no data set on species properties yet, and therefore
## the example uses taxonomy 
data(dune)
data(dune.taxon)
d <- taxa2dist(dune.taxon, varstep=TRUE)
cl <- hclust(d, "aver")
treedive(dune, cl)
## Significance test using Null model communities.
## The current choice fixes only site totals.
oecosimu(dune, treedive, "r0", tree = cl)
## Clustering of tree distances
dtree <- treedist(dune, cl)
plot(hclust(dtree, "aver"))