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betapart R Documentation
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 components, i.e. total dissimilarity (a monotonic transformation
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.
The package also allows fitting negative exponential, power law or Gompertz distance-decay models for assessing the relationship between assemblage (dis)similarity and spatial (or other) distance. decay.model fits the nonlinear distance-decay function via the minpack.lm package, plot.decay plots the distance-decay pattern and the fitted model, boot.coefs.decay bootstraps the paramaters of the distance-decay model, and zdep assesses the differences between parameters of two distance-decay models.
Author(s)
Andres Baselga, David Orme, Sebastien Villéger, Julien De Bortoli, Fabien Leprieur, Maxime Logez, Sara Martínez-Santalla, Ramiro Martín-Devasa, Carola Gómez-Rodríguez, and Rosa M. Crujeiras
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. 2017. Partitioning abundance-based multiple-site dissimilarity into components: balanced variation in abundance and
abundance gradients. Methods in Ecology and Evolution 8: 799-808
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
Gómez-Rodríguez, C. & Baselga, A. 2018. Variation among European beetle taxa in patterns of distance decay
of similarity suggests a major role of dispersal processes. Ecography, in press
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
Martín-Devasa R, Martínez-Santalla S, Gómez-Rodríguez C, Crujeiras RM, Baselga A. 2022. Species range size shapes distance decay in community similarity. Diversity and Distributions 28: 1348-1357
Martín-Devasa R, Martínez-Santalla S, Gómez-Rodríguez C, Crujeiras RM, Baselga A. 2022. Comparing distance-decay parameters: a novel test under pairwise dependence. Ecological Informatics 72: 101894
Martínez-Santalla S, Martín-Devasa R, Gómez-Rodríguez C, Crujeiras RM, Baselga A. 2022. Assessing the
non-linear decay of community similarity: permutation and site-block resampling significance tests.
Journal of Biogeography 49: 968-978
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
betapart documentation built on March 31, 2023, 10:29 p.m.
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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 components, i.e. total dissimilarity (a monotonic transformation
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.
The package also allows fitting negative exponential, power law or Gompertz distance-decay models for assessing the relationship between assemblage (dis)similarity and spatial (or other) distance. decay.model fits the nonlinear distance-decay function via the minpack.lm package, plot.decay plots the distance-decay pattern and the fitted model, boot.coefs.decay bootstraps the paramaters of the distance-decay model, and zdep assesses the differences between parameters of two distance-decay models.
Author(s)
Andres Baselga, David Orme, Sebastien Villéger, Julien De Bortoli, Fabien Leprieur, Maxime Logez, Sara Martínez-Santalla, Ramiro Martín-Devasa, Carola Gómez-Rodríguez, and Rosa M. Crujeiras
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. 2017. Partitioning abundance-based multiple-site dissimilarity into components: balanced variation in abundance and abundance gradients. Methods in Ecology and Evolution 8: 799-808
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
Gómez-Rodríguez, C. & Baselga, A. 2018. Variation among European beetle taxa in patterns of distance decay of similarity suggests a major role of dispersal processes. Ecography, in press
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
Martín-Devasa R, Martínez-Santalla S, Gómez-Rodríguez C, Crujeiras RM, Baselga A. 2022. Species range size shapes distance decay in community similarity. Diversity and Distributions 28: 1348-1357
Martín-Devasa R, Martínez-Santalla S, Gómez-Rodríguez C, Crujeiras RM, Baselga A. 2022. Comparing distance-decay parameters: a novel test under pairwise dependence. Ecological Informatics 72: 101894
Martínez-Santalla S, Martín-Devasa R, Gómez-Rodríguez C, Crujeiras RM, Baselga A. 2022. Assessing the non-linear decay of community similarity: permutation and site-block resampling significance tests. Journal of Biogeography 49: 968-978
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
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