Learn R Programming

vegan (version 2.0-10)

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, relative = TRUE, match.force = FALSE, ...)

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 data (comm, x) and labels in tree. If FALSE, matching only happens when dimensions differ (with a warning or message). The order of data must match to the or
relative
Use distances relative to the height of combined tree.
...
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. Diversity is 0 for one spcies, and NA for empty communities.

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. With relative = FALSE the dissimilarity is defined as $2 (A \cup B) - A - B$, where $A$ and $B$ are heights of component trees and $A \cup B$ is the height of the combined tree. With relative = TRUE the dissimilarity is $(2(A \cup B)-A-B)/(A \cup B)$. Although the latter formula is similar to Jaccard dissimilarity (see vegdist, designdist), it is not in the range $0 \ldots 1$, since combined tree can add a new root. When two zero-height trees are combined into a tree of above zero height, the relative index attains its maximum value $2$. The dissimilarity is zero from a combined zero-height tree.

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 needs 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

Function treedive is similar to the phylogenetic diversity function pd in picante, but excludes tree root if that is not needed to connect species. Function treedist is similar to the phylogenetic similarity phylosor in picante, but excludes unneeded tree root and returns distances instead of similarities.

taxondive is something very similar from another world.

Examples

Run this code
## 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"))

Run the code above in your browser using DataLab