adiv (version 2.2.1)

betaUniqueness: Algorithmic index of plot-to-plot functional (or phylogenetic) dissimilarity and uniqueness

Description

The function betaUniqueness calculates uniqueness and redundancy taking account of functional dissimilarities between species using equation 5 and 6 in Ricotta et al. (2021). Note that functional dissimilarities could be replaced by any other type of dissimilarities between species, including phylogenetic dissimilarities.

Usage

betaUniqueness(comm, dis, Nind = 10000)

Value

The function betaUniqueness returns a list with the following objects:

- betaUniqueness: a matrix with the values of the proposed beta uniqueness (\(U\beta\)=DKG/DR) for each pair of plots (Ricotta et al. (2021), eq. 6);

- betaRedundancy: a matrix with the values of the proposed beta redundancy (\(R\beta\)=1-DKG/DR) for each pair of plots (Ricotta et al. (2021), eq. 5);

- dissimilarityGap: a matrix with the values of the dissimilarity gap index (DR-DKG) for each pair of plots;

- DR: a matrix with the values of the species-based (Rogers) dissimilarity index (DR) for each pair of plots (Ricotta et al. (2021), eq. 4);

- DKG: a matrix with the values of the algorithmic functional dissimilarity index (DKG) for each pair of plots (Ricotta et al. (2021), eq. 3).

Arguments

comm

a matrix containing the relative or absolute abundance of all species in plots. Columns are species and plots are rows. Column labels (species names) should be assigned as in dis.

dis

a matrix or an object of class dist providing the functional dissimilarities between species (dissimilarities are nonnegative, symmetric, and the dissimilarity between a species and itself is zero). Species here must be in the same order as in the columns of comm.

Nind

an integer. The algorithmic index will be applied by assuming that each plot contains Nind individuals. The highest Nind, the most precise the index value will be (see Gregorius et al. 2003, for more details).

Author

Sandrine Pavoine

References

Ricotta, C., Kosman, E., Laroche, F., Pavoine, S. (2021) Beta redundancy for functional ecology. Methods in Ecology and Evolution, 12, 1062--1069. tools:::Rd_expr_doi("10.1111/2041-210X.13587")

Gregorius, H.-R., Gillet, E.M., Ziehe, M. (2003) Measuring differences of trait distributions between populations. Biometrical Journal, 8, 959--973. tools:::Rd_expr_doi("10.1002/bimj.200390063")

See Also

betaTreeUniqueness adapted to the use of phylogenetic trees with species as tips, dislptransport for the algorithmic functional dissimilarity index (DKG in Ricotta et al. 2021), and uniqueness for alpha uniqueness

Examples

if (FALSE) {
data(RutorGlacier)
fundis <- dist(scale(RutorGlacier$Traits2[1:6]))
fundis <- fundis/max(fundis)
frameDKG <- betaUniqueness(RutorGlacier$Abund, fundis)

f1 <- unlist(sapply(1:58, function(i) rep(RutorGlacier$Fac[i], 59-i)))
f2 <- unlist(sapply(1:58, function(i) RutorGlacier$Fac[-(1:i)]))
f <- paste(f1, f2, sep="-")
F <- factor(f, levels=c("early-early", "mid-mid", "late-late", "early-mid", 
    "mid-late", "early-late"))

vbetaU_A <- as.vector(as.dist(frameDKG$betaUniqueness))

boxplot(vbetaU_A~F, ylab="Beta uniqueness", xlab="Compared successional stages")
# See Ricotta et al. 2021 Electronic Appendix 3 for for details
}