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

• Published in: Molecular ecology resources Vol. 21; no. 8; pp. 2782 - 2800
• Main Authors: Overcast, Isaac, Ruffley, Megan, Rosindell, James, Harmon, Luke, Borges, Paulo, Emerson, Brent C., Etienne, Rampal S., Gillespie, Rosemary G., Krehenwinkel, Henrik, Mahler, D. Luke, Massol, Francois, Parent, Christine E., Patiño, Jairo, Peter, Ben, Week, Bob, Wagner, Catherine, Hickerson, Michael J., Rominger, Andrew
• Format: Journal Article
• Language: English
• Published: England Wiley 01-11-2021; Wiley Subscription Services, Inc; Wiley/Blackwell; John Wiley and Sons Inc
• Subjects: Abundance; Arthropods; Assembly; Axes (reference lines); Bioaccumulation; Biodiversity; Biogeography; Biota; Community Ecology; Community Genetic Diversity; Community Phylogenetics; Comparative Phylogeography; Dispersal; Ecology; Ecology, environment; Evolution; Genetic diversity; Genetic Variation; Island biogeography; Learning algorithms; Life Sciences; Machine learning; Models, Biological; Phylogeny; Population Genetics; Populations and Evolution; Predictions; RESOURCE ARTICLES; Special Issue; Speciation; Species diversity; Species richness; community genetic diversity; population genetics; community phylogenetics; community ecology; comparative phylogeography
• ISSN: 1755-098X; 1755-0998; 1755-0998
• DOI: 10.1111/1755-0998.13514

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 ecoevolutionary 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 multidimensional community data including a genetic component, such as might be produced by contemporary eDNA or metabarcoding studies. We combine MESS 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.
This manuscript is a product of the working group sEcoEvo-Biodiversity Dynamics: The Nexus Between Space & Time, which was kindly supported by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig and the Santa Fe Institute supported additional working group meetings. We thank John Chase, Catherine Graham, Jacopo Grilli, Joaquin Hortal, Petr Keil, Tiffany Knight, Angela McGaughran, Brian McGill, and Pedro Neves for useful conversations. We thank the Morlon group and William Sherwin for useful comments on an early draft of the manuscript, and three anonymous reviewers for useful comments at a later stage. We thank Arianna Kuhn for assistance with Figure 1. 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 Centre 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.