Description Usage Arguments Value Examples
uniqueness
estimates the functional uniqueness of species, communities by comparing the TPD of lower levels (i.e. species), with that of higher levels (i.e. communities). TPD's are compared by means of overlap. High overlap means low uniqueness (i.e. the species traits are frequent in the community), whereas low overlap means high uniqueness. Uniqueness is then estimated as 1-overlap. The function is hence basically the same as 'dissim', with some slight modifications. Despite functional uniqueness can be estimated at any scale, current implementation is limited to species within communities (although communities can be easily created to represent regions, or regional pools of species).
1 | uniqueness(TPDs = NULL, TPDc = NULL)
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TPDs |
An object of class "TPDsp", generated with the |
TPDc |
An object of class "TPDcomm", generated with the |
uniqueness
returns a matrix, with the communities in rows and the species in columns. The values in the matrix represent the functional uniqueness of each species in each community. Very unique species will have values close to 1, whereas non-unique species will have values close to 0.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | # 1. Compute the TPDs of three different species
traits_iris <- iris[, c("Sepal.Length", "Sepal.Width")]
sp_iris <- iris$Species
example_TPDs <- TPDs(species = sp_iris, traits = traits_iris)
#2. Three different communities with different abundances of each species
example_abundances <- matrix(c(c(0.5, 0.3, 0.2,
0.1, 0.8, 0.1,
0.5, 0, 0.5)), #I. virg. dominates; setosa absent
ncol = 3, byrow = TRUE, dimnames = list(paste0("Comm.",1:3),
unique(iris$Species)))
example_TPDc <- TPDc (TPDs = example_TPDs, sampUnit = example_abundances)
#3. Calculate the uniqueness of each species in each community
example_uniqueness <- uniqueness (TPDs = example_TPDs, TPDc = example_TPDc)
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