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Identifying models of trait-mediated community assembly using random forests and approximate Bayesian computation.

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A new simulation approach accurately infers ecological community assembly processes, outperforming traditional dispersion metrics. This method helps estimate the strength of environmental filtering and competitive exclusion in species assembly.

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Area of Science:

  • Ecology
  • Community Ecology
  • Ecological Modeling

Background:

  • Ecologists infer community assembly processes (environmental filtering, competitive exclusion, neutral assembly) using dispersion metrics and hypothesis testing.
  • Traditional metrics have limited power due to often-violated assumptions, hindering accurate inference of ecological assembly models.

Purpose of the Study:

  • To adapt a phenotypic similarity and repulsion model for simulating community assembly.
  • To parameterize the strength of environmental filtering and competitive exclusion during community formation.
  • To develop a more accurate method for distinguishing between ecological assembly models using simulated data.

Main Methods:

  • Adapted a phenotypic similarity and repulsion model to simulate community assembly processes.
  • Parameterized the strength of environmental filtering and competitive exclusion in simulations.
  • Utilized random forests and approximate Bayesian computation for model selection based on simulated data.

Main Results:

  • The novel approach demonstrated higher accuracy in inferring assembly models compared to traditional dispersion metrics.
  • The method effectively accounts for uncertainty in ecological model selection.
  • Accurate estimation of parameters governing the strength of assembly processes was achieved.

Conclusions:

  • The developed simulation-based approach provides a more robust framework for understanding community assembly.
  • The R package CAMI (Community Assembly Model Inference) implements this approach, offering a practical tool for ecologists.
  • Effectiveness demonstrated using plant communities on lava flow islands, highlighting its applicability in real-world ecological studies.