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• W3144748715 abstract "Despite their interdependence, community ecology and evolutionary biology have proven difficult to synthesize empirically in studies of community assembly. This is primarily due to differing temporal and spatial scales of ecological and evolutionary dynamics, ranging from broad‐scale processes like speciation and environmental filtering to local‐scale past and present‐day niche dynamics. Phylogenetic generalized linear mixed modeling (PGLMM) offers a solution to this problem, it can be used to integrate through time by modeling the evolution of trait‐based community assembly, and across space ranging from broad‐scale environmental sensitivities to local‐scale co‐occurrences. As such, PGLMM provides the ability to compare the relative strength of deep versus shallow‐time drivers of biodiversity by including them in a single model. Despite its unique value, the application of PGLMM has been limited because statistical advances have not been adequately matched by conceptual progress. Recent expansion in the availability of cross‐clade assemblage data and phylogenetic tools have increased the urgency of conceptual unification. Here we describe the potential of PGLMM for parsing the evolutionary and ecological drivers of community assembly, focusing on how three major drivers – environmental sensitivities, within‐clade interactions (e.g. competition), and cross‐clade associations (e.g. herbivory) – shape historical and present‐day assemblages. We outline three fundamental questions that PGLMM can address, linked to each of the aforementioned drivers: 1) are species' regional‐scale environmental responses evolutionarily constrained? 2) Do evolved responses to past competition minimize or enhance present‐day competition? 3) Are cross‐clade associations evolutionarily constrained? For each question, we review conceptual advances and opportunities, and demonstrate the application of PGLMM in a supplementary tutorial. We focus on the ecological and evolutionary outcomes of PGLMM and describe the value of these outcomes for conservation and natural resource management, in order to move PGLMM beyond statistical complexities and toward a future with clear conceptual and practical goals." @default.
• W3144748715 created "2021-04-13" @default.
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• W3144748715 date "2021-03-25" @default.
• W3144748715 modified "2023-10-03" @default.
• W3144748715 title "Phylogenetic generalized linear mixed modeling presents novel opportunities for eco‐evolutionary synthesis" @default.
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• W3144748715 doi "https://doi.org/10.1111/oik.08048" @default.
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