Identifying models of trait-mediated community assembly using random forests and approximate Bayesian computation.
approximate Bayesian computation
community assembly
competitive exclusion
environmental filtering
random forest
Journal
Ecology and evolution
ISSN: 2045-7758
Titre abrégé: Ecol Evol
Pays: England
ID NLM: 101566408
Informations de publication
Date de publication:
Dec 2019
Dec 2019
Historique:
received:
08
04
2019
revised:
23
07
2019
accepted:
15
09
2019
entrez:
25
12
2019
pubmed:
25
12
2019
medline:
25
12
2019
Statut:
epublish
Résumé
Ecologists often use dispersion metrics and statistical hypothesis testing to infer processes of community formation such as environmental filtering, competitive exclusion, and neutral species assembly. These metrics have limited power in inferring assembly models because they rely on often-violated assumptions. Here, we adapt a model of phenotypic similarity and repulsion to simulate the process of community assembly via environmental filtering and competitive exclusion, all while parameterizing the strength of the respective ecological processes. We then use random forests and approximate Bayesian computation to distinguish between these models given the simulated data. We find that our approach is more accurate than using dispersion metrics and accounts for uncertainty in model selection. We also demonstrate that the parameter determining the strength of the assembly processes can be accurately estimated. This approach is available in the R package CAMI; Community Assembly Model Inference. We demonstrate the effectiveness of CAMI using an example of plant communities living on lava flow islands.
Identifiants
pubmed: 31871640
doi: 10.1002/ece3.5773
pii: ECE35773
pmc: PMC6912896
doi:
Types de publication
Journal Article
Langues
eng
Pagination
13218-13230Informations de copyright
© 2019 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Déclaration de conflit d'intérêts
None declared.
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