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Analysing the distance decay of community similarity in river networks using Bayesian methods

The distance decay of community similarity (DDCS) is a pattern that is widely observed in terrestrial and aquatic environments. Niche-based theories argue that species are sorted in space according to their ability to adapt to new environmental conditions. The ecological neutral theory argues that c...

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Autores principales: Dias, Filipe S., Betancourt, Michael, Rodríguez-González, Patricia María, Borda-de-Água, Luís
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8569194/
https://www.ncbi.nlm.nih.gov/pubmed/34737354
http://dx.doi.org/10.1038/s41598-021-01149-x
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author Dias, Filipe S.
Betancourt, Michael
Rodríguez-González, Patricia María
Borda-de-Água, Luís
author_facet Dias, Filipe S.
Betancourt, Michael
Rodríguez-González, Patricia María
Borda-de-Água, Luís
author_sort Dias, Filipe S.
collection PubMed
description The distance decay of community similarity (DDCS) is a pattern that is widely observed in terrestrial and aquatic environments. Niche-based theories argue that species are sorted in space according to their ability to adapt to new environmental conditions. The ecological neutral theory argues that community similarity decays due to ecological drift. The continuum hypothesis provides an intermediate perspective between niche-based theories and the neutral theory, arguing that niche and neutral factors are at the opposite ends of a continuum that ranges from competitive to stochastic exclusion. We assessed the association between niche-based and neutral factors and changes in community similarity measured by Sorensen’s index in riparian plant communities. We assessed the importance of neutral processes using network distances and flow connection and of niche-based processes using Strahler order differences and precipitation differences. We used a hierarchical Bayesian approach to determine which perspective is best supported by the results. We used dataset composed of 338 vegetation censuses from eleven river basins in continental Portugal. We observed that changes in Sorensen indices were associated with network distance, flow connection, Strahler order difference and precipitation difference but to different degrees. The results suggest that community similarity changes are associated with environmental and neutral factors, supporting the continuum hypothesis.
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spelling pubmed-85691942021-11-05 Analysing the distance decay of community similarity in river networks using Bayesian methods Dias, Filipe S. Betancourt, Michael Rodríguez-González, Patricia María Borda-de-Água, Luís Sci Rep Article The distance decay of community similarity (DDCS) is a pattern that is widely observed in terrestrial and aquatic environments. Niche-based theories argue that species are sorted in space according to their ability to adapt to new environmental conditions. The ecological neutral theory argues that community similarity decays due to ecological drift. The continuum hypothesis provides an intermediate perspective between niche-based theories and the neutral theory, arguing that niche and neutral factors are at the opposite ends of a continuum that ranges from competitive to stochastic exclusion. We assessed the association between niche-based and neutral factors and changes in community similarity measured by Sorensen’s index in riparian plant communities. We assessed the importance of neutral processes using network distances and flow connection and of niche-based processes using Strahler order differences and precipitation differences. We used a hierarchical Bayesian approach to determine which perspective is best supported by the results. We used dataset composed of 338 vegetation censuses from eleven river basins in continental Portugal. We observed that changes in Sorensen indices were associated with network distance, flow connection, Strahler order difference and precipitation difference but to different degrees. The results suggest that community similarity changes are associated with environmental and neutral factors, supporting the continuum hypothesis. Nature Publishing Group UK 2021-11-04 /pmc/articles/PMC8569194/ /pubmed/34737354 http://dx.doi.org/10.1038/s41598-021-01149-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Dias, Filipe S.
Betancourt, Michael
Rodríguez-González, Patricia María
Borda-de-Água, Luís
Analysing the distance decay of community similarity in river networks using Bayesian methods
title Analysing the distance decay of community similarity in river networks using Bayesian methods
title_full Analysing the distance decay of community similarity in river networks using Bayesian methods
title_fullStr Analysing the distance decay of community similarity in river networks using Bayesian methods
title_full_unstemmed Analysing the distance decay of community similarity in river networks using Bayesian methods
title_short Analysing the distance decay of community similarity in river networks using Bayesian methods
title_sort analysing the distance decay of community similarity in river networks using bayesian methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8569194/
https://www.ncbi.nlm.nih.gov/pubmed/34737354
http://dx.doi.org/10.1038/s41598-021-01149-x
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