ps_dissim {phylospatial} | R Documentation |
Quantitative phylogenetic dissimilarity
Description
This function calculates pairwise phylogenetic dissimilarity between communities. It works with both binary and
quantitative community data sets. A wide range of phylogentic community dissimilarity metrics are supported,
including phylogenetic Sorensen's and Jaccard's distances, turnover and nestedness components of Sorensen's distance
(Baselga & Orme, 2012), and phylogenetic versions of all community distance indices provided through the vegan
library.
The function also includes options to scale the community matrix in order to focus the analysis on endemism and/or
on proportional differences in community composition. The results from this function can be visualized using
ps_rgb or ps_regions, or used in a variety of statistical analyses.
Usage
ps_dissim(
ps,
method = "sorensen",
fun = c("vegdist", "designdist", "chaodist"),
endemism = FALSE,
normalize = FALSE,
...
)
Arguments
ps |
phylospatial object. |
method |
Character indicating the dissimilarity index to use:
|
fun |
Character indicating which general distance function from the |
endemism |
Logical indicating whether community values should be divided by column totals (taxon range sizes) to derive endemism before computing distances. |
normalize |
Logical indicating whether community values should be divided by row totals (community sums) before
computing distances. If |
... |
Additional arguments passed to |
Value
A pairwise phylogenetic dissimilarity matrix of class dist
.
References
Graham, C. H., & Fine, P. V. (2008). Phylogenetic beta diversity: linking ecological and evolutionary processes across space in time. Ecology Letters, 11(12), 1265-1277.
Baselga, A., & Orme, C. D. L. (2012). betapart: an R package for the study of beta diversity. Methods in Ecology and Evolution, 3(5), 808-812.
Pavoine, S. (2016). A guide through a family of phylogenetic dissimilarity measures among sites. Oikos, 125(12), 1719-1732.
See Also
Examples
# example data set:
ps <- ps_simulate(n_tips = 50)
# The default arguments give Sorensen's quantitative dissimilarity index
# (a.k.a. Bray-Curtis distance):
d <- ps_dissim(ps)
# Specifying a custom formula explicitly via `designdist`;
# (this is the Bray-Curtis formula, so it's equivalent to the prior example)
d <- ps_dissim(ps, method = "(b+c)/(2*a+b+c)",
fun = "designdist", terms = "minimum", abcd = TRUE)
# Alternative arguments can specify a wide range of dissimilarity measures;
# here's endemism-weighted Jaccard's dissimilarity:
d <- ps_dissim(ps, method = "jaccard", endemism = TRUE)