pez.endemism | R Documentation |
At present, only a small number of metrics, but we intend for this to grow with time. Note that metrics that incorporate abundance are mixed in with those that do not. Some of these metrics make sense when used with probabilities, for example those derived from an SDM; some do not. You will have to use your own judgement (as with everything in science!).
pez.endemism(data, sqrt.phy = FALSE)
data |
|
sqrt.phy |
If TRUE (default is FALSE) your phylogenetic distance matrix will be square-rooted; specifying TRUE will force the square-root transformation on phylogenetic distance matrices (in the spirit of Leitten and Cornwell, 2014). See ‘details’ for details about different metric calculations when a distance matrix is used. |
data.frame
with metric values.
Will Pearse, Dan Rosauer
BED
Cadotte, M. W., & Jonathan Davies,
T. (2010). Rarest of the rare: advances in combining
evolutionary distinctiveness and scarcity to inform
conservation at biogeographical scales. Diversity and
Distributions, 16(3), 376-385.
PE
Rosauer, D. A. N., Laffan, S. W., Crisp,
M. D., Donnellan, S. C., & Cook, L. G. (2009). Phylogenetic
endemism: a new approach for identifying geographical
concentrations of evolutionary history. Molecular Ecology,
18(19), 4061-4072.
pez.shape
pez.evenness
pez.dispersion
pez.dissimilarity
data(laja) data <- comparative.comm(invert.tree, river.sites, invert.traits) (output<-pez.endemism(data))
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