Functional Diversity and Community Distances from Species Trees
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). Tree distance is the increase in total branch length when combining two sites.
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Community data frame or matrix.
A dendrogram which for
Force matching of column names in data
Print diagnostic messages and warnings.
Use distances relative to the height of combined tree.
Other arguments passed to functions (ignored).
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), and when applied to phylogenetic trees this is
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
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 species, and
NA for empty
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 2AB - A - B, where
A and B are heights of component trees and
AB is the height of the combined tree. With
relative = TRUE
the dissimilarity is (2AB - A - B)/(AB).
Although the latter formula is similar to
Jaccard dissimilarity (see
designdist), it is not in the range 0
… 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 or phylogenies as an
input. If species traits contain
ordered factor variables, it is recommended to use Gower
distances for mixed data (function
package cluster), and usually the recommended clustering method
is UPGMA (
method = "average" in function
(Podani and Schmera 2006). Phylogenetic trees can be changed into
It is possible to analyse the non-randomness of tree diversity
oecosimu. This needs specifying an adequate Null
model, and the results will change with this choice.
A vector of diversity values or a single tree height, or a
dissimilarity structure that inherits from
can be used similarly.
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.
treedive is similar to the phylogenetic
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.
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## There is no data set on species properties yet, and we demonstrate ## the methods using phylogenetic trees data(dune) data(dune.phylodis) cl <- hclust(dune.phylodis) treedive(dune, cl) ## Significance test using Null model communities. ## The current choice fixes numbers of species and picks species ## proportionally to their overall frequency oecosimu(dune, treedive, "r1", tree = cl, verbose = FALSE) ## Phylogenetically ordered community table dtree <- treedist(dune, cl) tabasco(dune, hclust(dtree), cl) ## Use tree distances in capscale capscale(dtree ~ 1, comm=dune)
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