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- W2989711901 startingPage "13218" @default.
- W2989711901 abstract "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." @default.
- W2989711901 created "2019-12-05" @default.
- W2989711901 creator A5015120030 @default.
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- W2989711901 creator A5091361743 @default.
- W2989711901 date "2019-11-21" @default.
- W2989711901 modified "2023-10-16" @default.
- W2989711901 title "Identifying models of trait‐mediated community assembly using random forests and approximate Bayesian computation" @default.
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- W2989711901 doi "https://doi.org/10.1002/ece3.5773" @default.
- W2989711901 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6912896" @default.
- W2989711901 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31871640" @default.
- W2989711901 hasPublicationYear "2019" @default.
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