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Generalized linear mixed models can detect unimodal species-environment relationships

Niche theory predicts that species occurrence and abundance show non-linear, unimodal relationships with respect to environmental gradients. Unimodal models, such as the Gaussian (logistic) model, are however more difficult to fit to data than linear ones, particularly in a multi-species context in...

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Detalles Bibliográficos
Autores principales: Jamil, Tahira, ter Braak, Cajo J.F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3709111/
https://www.ncbi.nlm.nih.gov/pubmed/23862108
http://dx.doi.org/10.7717/peerj.95
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author Jamil, Tahira
ter Braak, Cajo J.F.
author_facet Jamil, Tahira
ter Braak, Cajo J.F.
author_sort Jamil, Tahira
collection PubMed
description Niche theory predicts that species occurrence and abundance show non-linear, unimodal relationships with respect to environmental gradients. Unimodal models, such as the Gaussian (logistic) model, are however more difficult to fit to data than linear ones, particularly in a multi-species context in ordination, with trait modulated response and when species phylogeny and species traits must be taken into account. Adding squared terms to a linear model is a possibility but gives uninterpretable parameters. This paper explains why and when generalized linear mixed models, even without squared terms, can effectively analyse unimodal data and also presents a graphical tool and statistical test to test for unimodal response while fitting just the generalized linear mixed model. The R-code for this is supplied in Supplemental Information 1.
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spelling pubmed-37091112013-07-16 Generalized linear mixed models can detect unimodal species-environment relationships Jamil, Tahira ter Braak, Cajo J.F. PeerJ Ecology Niche theory predicts that species occurrence and abundance show non-linear, unimodal relationships with respect to environmental gradients. Unimodal models, such as the Gaussian (logistic) model, are however more difficult to fit to data than linear ones, particularly in a multi-species context in ordination, with trait modulated response and when species phylogeny and species traits must be taken into account. Adding squared terms to a linear model is a possibility but gives uninterpretable parameters. This paper explains why and when generalized linear mixed models, even without squared terms, can effectively analyse unimodal data and also presents a graphical tool and statistical test to test for unimodal response while fitting just the generalized linear mixed model. The R-code for this is supplied in Supplemental Information 1. PeerJ Inc. 2013-07-09 /pmc/articles/PMC3709111/ /pubmed/23862108 http://dx.doi.org/10.7717/peerj.95 Text en © 2013 Jamil and ter Braak http://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Ecology
Jamil, Tahira
ter Braak, Cajo J.F.
Generalized linear mixed models can detect unimodal species-environment relationships
title Generalized linear mixed models can detect unimodal species-environment relationships
title_full Generalized linear mixed models can detect unimodal species-environment relationships
title_fullStr Generalized linear mixed models can detect unimodal species-environment relationships
title_full_unstemmed Generalized linear mixed models can detect unimodal species-environment relationships
title_short Generalized linear mixed models can detect unimodal species-environment relationships
title_sort generalized linear mixed models can detect unimodal species-environment relationships
topic Ecology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3709111/
https://www.ncbi.nlm.nih.gov/pubmed/23862108
http://dx.doi.org/10.7717/peerj.95
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