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Abstract Detail


Ruffley, Megan [1], Katie, Peterson [2], Week, Bob [1], Tank, David [2], Harmon, Luke [3].

Identifying Models of Trait-Mediated Community Assembly Using Random Forest and Approximate Bayesian Computation.

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. 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. We find that our approach is more accurate than using dispersion metrics and accounts for uncertainty. 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.

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1 - University of Idaho, Bioinformatics and Computational Biology, 875 Perimeter Drive MS-3051, Moscow, Id, 83844-3051, USA
2 - University of Idaho, Biological Sciences, 875 Perimeter Dr. MS 3051, Moscow, Id, 83844-3051
3 - University of Idaho, Biological Sciences, 875 Perimeter Dr. MS 3051, Moscow, Id, 83844-3051, USA

community assembly
environmental filtering
competititve exclusion
random forest

Presentation Type: Oral Paper
Session: ECO3, Ecology 3: Vegetation, Community and Restoration Ecology
Location: Tucson A/Starr Pass
Date: Tuesday, July 30th, 2019
Time: 2:45 PM
Number: ECO3006
Abstract ID:411
Candidate for Awards:Ecological Section Best Graduate Student Paper

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