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.
Highlights
Though methods to infer community assembly vary, many ap‐ proaches share a central idea based on phylogenetics; the pattern of shared evolutionary history between species that coexist pro‐ vides insight into the historical processes that assembled the com‐ munity (Brooks & McLennan, 1991; Grandcolas, 1998; Losos, 1996; Thompson et al, 2001; Webb, 2000; Webb, Ackerly, McPeek, & Donoghue, 2002)
Measures of phylogenetic diversity and dis‐ persion, which carry more information than higher taxonomic cat‐ egories and hopefully, encompass trait information, have become widely used in community ecology to infer community assembly processes (Cavender‐Bares, Keen, & Miles, 2006; Kembel et al, 2010; Miller, Farine, & Trisos, 2017; Webb, 2000; Webb, Ackerly, & Kembel, 2008; Webb et al, 2002). These metrics focus on iden‐ tifying alternative models of community assembly, environmental filtering and competitive exclusion
We focus on three community assembly models: neutral, environ‐ mental filtering, and competitive exclusion
Summary
Though methods to infer community assembly vary, many ap‐ proaches share a central idea based on phylogenetics; the pattern of shared evolutionary history between species that coexist pro‐ vides insight into the historical processes that assembled the com‐ munity (Brooks & McLennan, 1991; Grandcolas, 1998; Losos, 1996; Thompson et al, 2001; Webb, 2000; Webb, Ackerly, McPeek, & Donoghue, 2002). Measures of phylogenetic diversity and dis‐ persion, which carry more information than higher taxonomic cat‐ egories and hopefully, encompass trait information, have become widely used in community ecology to infer community assembly processes (Cavender‐Bares, Keen, & Miles, 2006; Kembel et al, 2010; Miller, Farine, & Trisos, 2017; Webb, 2000; Webb, Ackerly, & Kembel, 2008; Webb et al, 2002) These metrics focus on iden‐ tifying alternative models of community assembly, environmental filtering and competitive exclusion. Inference is conditional on the assumption that the rele‐ vant phenotypes for the environment or competition are phyloge‐ netically conserved among the species in the community, or harbor strong phylogenetic signal within the community of focus If this assumption is true, and environmental filtering has predominately impacted the assembly process, the phylogenetic data are expected to be significantly clustered, or under‐dispersed, in the local com‐ munity.
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