Inferring the ecological and evolutionary determinants of community genetic diversity

Understanding the relative contributions of ecological and evolutionary processes to the structuring of ecological communities is needed to improve our ability to predict how communities may respond to future changes in an increasingly human-modified world. Metabarcoding methods make it possible to gather population genetic data for all species within a community, unlocking a new axis of data to potentially unveil the origins and maintenance of biodiversity at local scales. Here, we present a new eco-evolutionary simulation model for investigating community assembly dynamics using metabarcoding data. The model makes joint predictions of species abundance, genetic variation, trait distributions and phylogenetic relationships under a wide range of parameter settings (e.g. high speciation/low dispersal or vice versa) and across a range of community states, from pristine and unmodified to heavily disturbed. We first demonstrate that parameters governing metacommunity and local community processes leave detectable signatures in simulated biodiversity data axes. Next, using a simulation-based machine learning approach we show that neutral and non-neutral models are distinguishable and that reasonable estimates of several model parameters within the local community can be obtained using only community-scale genetic data, while phylogenetic information is required to estimate those describing metacommunity dynamics. Finally, we apply the model to soil microarthropod metabarcoding data from the Troodos mountains of Cyprus, where we find that communities in widespread forest habitats are structured by neutral processes, while high-elevation and isolated habitats act as an abiotic filter generating non-neutral community structure. We implement our model within the ibiogen R package, a package dedicated to the investigation of island, and more generally community-scale, biodiversity using community-scale genetic data.

Agencia Estatal de Investigación, Grant/Award Number: FJC2018-035611-I; Horizon 2020 Framework Programme, Grant/Award Number: 810729.

Peer reviewed