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Title
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Trait-environment relationships and tiered forward model selection in linear mixed models
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Author
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Abstract
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| To understand patterns of variation in species biomass in terms of species traits and environmental variables a one-to-one approach might not be sufficient, and a multitrait multienvironment approach will be necessary. A multitrait multienvironment approach is proposed, based on a mixed model for species biomass. In the model, environmental variables are species-dependent random terms, whereas traits are fixed terms, and trait-environment relationships are fixed interaction terms. In this approach, identifying the important trait-environment relationship becomes a model selection problem. Because of the mix of fixed and random terms, we propose a novel tiered forward selection approach for this. In the first tier, the random factors are selected; in the second, the fixed effects; in the final tier, nonsignificant terms are removed using a modified Akaike information criterion. We complement this tiered selection with an alternative selection method, namely, type II maximum likelihood. A mesocosm experiment on early community assembly in wetlands with three two-level environmental factors is analyzed by the new approach. The results are compared with the fourth corner problem and the linear trait-environment method. Traits related to germination and seedling establishment are selected as being most important in the community assembly in these wetland mesocosms. |
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Language
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English
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Source (journal)
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International journal of ecology
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Publication
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2012
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ISSN
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1687-9708
1687-9716
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DOI
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10.1155/2012/947103
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Volume/pages
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(2012)
, 12 p.
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Article Reference
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947103
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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