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Title

When Can Species Abundance Data Reveal Non-neutrality?

AuthorsAl Hammal, Omar; Alonso, David ; S. Etienne, Rampal; Cornell, Stephen J.
Issue Date2015
PublisherPublic Library of Science
CitationPLOS Computational Biology0(0) : e1004134 (2015)
AbstractSpecies 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 concensus 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.
Description22 páginas, 4 tablas, 4 figuras
Publisher version (URL)http://dx.doi.org/10.1371/journal.pcbi.1004134
URIhttp://hdl.handle.net/10261/112803
DOI10.1371/journal.pcbi.1004134
ISSN1553-734X
E-ISSN1553-7358
Appears in Collections:(CEAB) Artículos
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