treedive(comm, tree, match.force = FALSE)
treeheight(tree)
treedist(x, tree, relative = TRUE, match.force = FALSE, ...)treedive must be for species
(columns).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 ordist and
can be used similarly.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 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.
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.
treedive is similar to the phylogenetic
diversity function pd in treedist is similar to the phylogenetic similarity
phylosor in taxondive is something very similar from another world.
## 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