# Partitioning beta diversity into turnover and nestedness components

### Description

betapart allows computing pair-wise dissimilarities (distance matrices) and multiple-site dissimilarities, separating the turnover and nestedness-resultant components of taxonomic (incidence and abundance based), functional and phylogenetic beta diversity.

### Details

The partitioning of incidence-based dissimilarity can be performed for two different families of indices:
Sorensen and Jaccard. The pairwise function `beta.pair`

yields 3 distance matrices accounting
for the spatial turnover and the nestedness components of beta-diversity. The third distance
matrix accounts for the sum of both componentes, i.e. totaal dissimilarity (a monotonic transoformation
of beta diversity.
The multiple site function `beta.multi`

yields the spatial turnover and the nestedness components of
overall dissimilarity, and the sum of both components, total dissimilarity. The basic calculations for all these
multiple-site measures and pairwise dissimilarity matrices can be computed using the function `betapart.core`

,
which returns an object of class `betapart`

. This is useful for large datasets as the consuming calculations
are done only once, and its result can then be used for computing many indices.
The multiple-site values can be randomly sampled a specified number of times for a specified number of sites using
the function `beta.sample`

.
The aforementioned indices used for assessing spatial patterns can also be used for measuring temporal changes in community composition with the
function `beta.temp`

.
Likewise, an analogous framework has been implemented for separating the two components of abundance-based
dissimilarity (balanced changes in abundance vs. abundance gradients) using commands `beta.pair.abund`

, `beta.multi.abund`

, `betapart.core.abund`

, and `beta.sample.abund`

.
The framework has been extended for functional beta diversity with commands `functional.betapart.core`

,
`functional.beta.pair`

and `functional.beta.multi`

, and for phylogenetic beta diversity with commands `phylo.betapart.core`

,
`phylo.beta.pair`

and `phylo.beta.multi`

.

### Author(s)

Andrés Baselga, David Orme, Sébastien Villeger, Julien De Bortoli and Fabien Leprieur

### References

Baselga, A. 2010. Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography 19:134-143

Baselga, A. 2012. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Global Ecology and Biogeography 21, 1223-1232

Baselga, A. 2013. Separating the two components of abundance-based dissimilarity: balanced changes in abundance vs. abundance gradients. Methods in Ecology and Evolution, 4: 552-557

Baselga, A. 2016. Partitioning abundance-based multiple-site dissimilarity into components: balanced variation in abundance and abundance gradients. Methods in Ecology and Evolution, in press

Baselga A, Leprieur, F. 2015. Comparing methods to separate components of beta diversity. Methods in Ecology and Evolution 6: 1069-1079

Baselga A, Orme CDL. 2012. betapart: an R package for the study of beta diversity. Methods Ecol. Evol. 3: 808-812

Legendre P. 2014. Interpreting the replacement and richness difference components of beta diversity. Global Ecology and Biogeography, 23: 1324–1334

Leprieur F, Albouy C, De Bortoli J, Cowman PF, Belwood DR, Mouillot D. 2012. Quantifying phylogenetic beta diversity: distinguishing between "true" turnover of lineages and phylogenetic diversity gradients. PLoS One 7(8): e42760

Villéger, S. Grenouillet, G., Brosse, S. 2013. Decomposing functional beta-diversity reveals that low functional beta-diversity is driven by low functional turnover in European fish assemblages. Global Ecology and Biogeography, 22: 671-681