A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities

Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Within ecological communities drift, dispersal, speciation, and selection operate simultaneously to shape patterns of biodiversity. Reconciling the relative importance of these is hindered by current models and inference methods, which tend to focus on a subset of processes and their resulting predictions. Here we introduce Massive Eco-evolutionary Synthesis Simulations (MESS), a unified mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: i) species richness and abundances; ii) population genetic diversities; and iii) trait variation in a phylogenetic context. Using simulations we demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. MESS is unique in generating predictions of community-scale genetic diversity, and in characterizing joint patterns of genetic diversity, abundance, and trait values. MESS unlocks the full potential for investigation of biodiversity processes using multi-dimensional community data including a genetic component, such as might be produced by contemporary eDNA or metabarcoding studies. We combine with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of data availability scenarios, and spatial and taxonomic scales.Funding was provided by grants from FAPESP (BIOTA, 2013/50297-0 to MJH and AC Carnaval), the Synthesis Centre of iDiv (DFG FZT 118), NASA through the Dimensions of Biodiversity Program (DOB 1343578) and the National Science Foundation (DEB-1253710 to MJH; DEB 1745562 to AC Carnaval; DBI 1927319 to AJR). IO was supported by the Mina Rees Dissertation Fellowship in the Sciences provided by the Graduate Center of the City University of New York. MR was supported by the Bioinformatics and Computational Biology Fellowship through the Institute for Bioinformatics and Evolutionary Studies at the University of Idaho. AJR was supported by the Santa Fe Institute Omidyar Fellowship. JR was supported by fellowships from the Natural Environment Research Council (NERC) (NE/I021179, NE/L011611/1). RSE was supported by an NWO-VICI grant. This work is a contribution to Imperial College’s Grand Challenges in Ecosystems and the Environment initiative, through JR.Peer reviewe