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When Can Species Abundance Data Reveal Non-neutrality?

Species abundance distributions (SAD) are probably ecology’s most well-known empirical pattern, and over the last decades many models have been proposed to explain their shape. There is no consensus over which model is correct, because the degree to which different processes can be discerned from SA...

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Detalles Bibliográficos
Autores principales: Al Hammal, Omar, Alonso, David, Etienne, Rampal S., Cornell, Stephen J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4368519/
https://www.ncbi.nlm.nih.gov/pubmed/25793889
http://dx.doi.org/10.1371/journal.pcbi.1004134
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author Al Hammal, Omar
Alonso, David
Etienne, Rampal S.
Cornell, Stephen J.
author_facet Al Hammal, Omar
Alonso, David
Etienne, Rampal S.
Cornell, Stephen J.
author_sort Al Hammal, Omar
collection PubMed
description Species abundance distributions (SAD) are probably ecology’s most well-known empirical pattern, and over the last decades many models have been proposed to explain their shape. There is no consensus over which model is correct, because the degree to which different processes can be discerned from SAD patterns has not yet been rigorously quantified. We present a power calculation to quantify our ability to detect deviations from neutrality using species abundance data. We study non-neutral stochastic community models, and show that the presence of non-neutral processes is detectable if sample size is large enough and/or the amplitude of the effect is strong enough. Our framework can be used for any candidate community model that can be simulated on a computer, and determines both the sampling effort required to distinguish between alternative processes, and a range for the strength of non-neutral processes in communities whose patterns are statistically consistent with neutral theory. We find that even data sets of the scale of the 50 Ha forest plot on Barro Colorado Island, Panama, are unlikely to be large enough to detect deviations from neutrality caused by competitive interactions alone, though the presence of multiple non-neutral processes with contrasting effects on abundance distributions may be detectable.
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spelling pubmed-43685192015-03-27 When Can Species Abundance Data Reveal Non-neutrality? Al Hammal, Omar Alonso, David Etienne, Rampal S. Cornell, Stephen J. PLoS Comput Biol Research Article Species abundance distributions (SAD) are probably ecology’s most well-known empirical pattern, and over the last decades many models have been proposed to explain their shape. There is no consensus over which model is correct, because the degree to which different processes can be discerned from SAD patterns has not yet been rigorously quantified. We present a power calculation to quantify our ability to detect deviations from neutrality using species abundance data. We study non-neutral stochastic community models, and show that the presence of non-neutral processes is detectable if sample size is large enough and/or the amplitude of the effect is strong enough. Our framework can be used for any candidate community model that can be simulated on a computer, and determines both the sampling effort required to distinguish between alternative processes, and a range for the strength of non-neutral processes in communities whose patterns are statistically consistent with neutral theory. We find that even data sets of the scale of the 50 Ha forest plot on Barro Colorado Island, Panama, are unlikely to be large enough to detect deviations from neutrality caused by competitive interactions alone, though the presence of multiple non-neutral processes with contrasting effects on abundance distributions may be detectable. Public Library of Science 2015-03-20 /pmc/articles/PMC4368519/ /pubmed/25793889 http://dx.doi.org/10.1371/journal.pcbi.1004134 Text en © 2015 Al Hammal et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Al Hammal, Omar
Alonso, David
Etienne, Rampal S.
Cornell, Stephen J.
When Can Species Abundance Data Reveal Non-neutrality?
title When Can Species Abundance Data Reveal Non-neutrality?
title_full When Can Species Abundance Data Reveal Non-neutrality?
title_fullStr When Can Species Abundance Data Reveal Non-neutrality?
title_full_unstemmed When Can Species Abundance Data Reveal Non-neutrality?
title_short When Can Species Abundance Data Reveal Non-neutrality?
title_sort when can species abundance data reveal non-neutrality?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4368519/
https://www.ncbi.nlm.nih.gov/pubmed/25793889
http://dx.doi.org/10.1371/journal.pcbi.1004134
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