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.

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A fragmented landscape, which contains a patchwork of vegetated hospitable areas and a barren intervening matrix, may reduce gene flow in a population and over time result in an increase in population structure. We tested this prediction in crab spiders (Mecaphesa celer (Hentz, 1847)) inhabiting isolated habitat patches in the lava matrix of Craters of the Moon National Monument and Preserve, Idaho, USA. Using reduced‐representation genomic sequencing, we did not find evidence of population structure due to a reduction in gene flow among habitat patches. Instead, our results show strong evidence of panmixia likely due to abundant juvenile dispersal and possible connectivity to outer regions surrounding the lava flows despite the species' habitat specificity.