Inferring processes of community assembly from macroscopic patterns: the case for inclusive and mechanistic approaches

Abstract

Statistical techniques exist for inferring community assembly processes from community patterns. Habitat filtering, competition, and biogeographical effects have, for example, been inferred from signals in phenotypic and phylogenetic data. The usefulness of current inference techniques is, however, debated as the causal link between process and pattern is often lacking and processes known to be important are ignored. Here, we revisit current knowledge on community assembly across scales and, in line with several reviews that have outlined the features and challenges associated with current inference techniques, we identify a discrepancy between features of real communities and current inference techniques. We argue, that mechanistic eco-evolutionary models in combination with novel model fitting and model evaluation techniques can provide avenues for more accurate, reliable and inclusive inference. To exemplify, we implement a trait-based and spatially explicit dynamic eco-evolutionary model and discuss steps of model modification, fitting, and evaluation as an iterative approach enabling inference from diverse data sources. This suggested approach can be computationally intensive, and model fitting and parameter estimation can be challenging. We discuss optimization of model implementation, data requirements and availability, and Approximate Bayesian Computation (ABC) as potential solutions to challenges that may arise in our quest for better inference techniques.