An integrated model of population genetics and community ecology

Abstract

Aim Quantifying abundance distributions is critical for understanding both how communities assemble, and how community structure varies through time and space, yet estimating abundances requires considerable investment in fieldwork. Community‐level population genetic data potentially offer a powerful way to indirectly infer richness, abundance and the history of accumulation of biodiversity within a community. Here we introduce a joint model linking neutral community assembly and comparative phylogeography to generate both community‐level richness, abundance and genetic variation under a neutral model, capturing both equilibrium and non‐equilibrium dynamics. Location Global. Methods Our model combines a forward‐time individual‐based community assembly process with a rescaled backward‐time neutral coalescent model of multi‐taxa population genetics. We explore general dynamics of genetic and abundance‐based summary statistics and use approximate Bayesian computation (ABC) to estimate parameters underlying the model of island community assembly. Finally, we demonstrate two applications of the model using community‐scale mtDNA sequence data and densely sampled abundances of an arachnid community on La Réunion. First, we use genetic data alone to estimate a summary of the abundance distribution, ground‐truthing this against the observed abundances. Then, we jointly use the observed genetic data and abundances to estimate the proximity of the community to equilibrium. Results Simulation experiments of our ABC procedure demonstrate that coupling abundance with genetic data leads to improved accuracy and precision of model parameter estimates compared with using abundance‐only data. We further demonstrate reasonable precision and accuracy in estimating a metric underlying the shape of the abundance distribution, temporal progress towards local equilibrium and several key parameters of the community assembly process. For the insular arachnid assemblage, we find the joint distribution of genetic diversity and abundance approaches equilibrium expectations, and that the Shannon entropy of the observed abundances can be estimated using genetic data alone. Main conclusions The framework that we present unifies neutral community assembly and comparative phylogeography to characterize the community‐level distribution of both abundance and genetic variation through time, providing a resource that should greatly enhance understanding of both the processes structuring ecological communities and the associated aggregate demographic histories.

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

“…The foundations of the community dynamics underlying MESS are based on the joint neutral model of abundance and genetic diversity described in Overcast et al (2019). Briefly, we simulate an individual-based model of community assembly inspired by the ecological neutral theory of Hubbell (2001), with assembly in a local community proceeding by a process of birth, death, and colonization from the metacommunity (following Rosindell & Harmon 2013).…”

“…Following Overcast et al (2019), the forward-time histories of colonization and abundance changes through time per species are used to parameterize backward-time coalescent models with immigration for each species (Kelleher et al 2016) to generate sampled local nucleotide diversities ( π ; Nei & Li, 1979). For reasons of computational efficiency, and to achieve a realistic scale in terms of numbers of individual organisms, we use a scaling parameter ( ) to specify α the number of individuals per deme, thus the total number of organisms in the local community is given by .…”

“…Recent advances in simulation-based inference under increasingly complex models provide a third option of unifying multiple processes and multiple data types across different scales (e.g. Overcast et al 2019;Pontarp et al 2019b). This unified model of community assembly, which accounts for fundamental processes underlying biodiversity across spatial and temporal scales, could be used to make predictions about multiple axes of biodiversity data that include species richness and abundances, distributions of species genetic diversities, and trait variation.…”

Identifying models of trait‐mediated community assembly using random forests and approximate Bayesian computation

“…These aspects may be more or less relevant depending on the taxonomic scale of the community being investigated (Weiher et al, 2011). Furthermore, the inference power could expand by making CAMI an individual-based model of community assembly (Pontarp et al, 2019;Rosindell, Harmon, & Etienne, 2015), where individuals can diverge to speciate and harbor intraspecific diversity among phenotypes (Jung et al, 2014;Jung, Violle, Mondy, Hoffmann, & Muller, 2010), all while abundance distributions and population demographics are being tracked (HilleRisLambers et al, 2012;Overcast, Emerson, & Hickerson, 2019). A spatially explicit model (see Pontarp et al, 2019) could allow for the exploration of how geography, or even local topography, impacts the assembly process.…”

Assessing metacommunity processes through signatures in spatiotemporal turnover of community composition

“…This approach is still to be extended to deal with temporal abundance data (Ovaskainen et al 2017). Other even more 528 demanding approaches rely on detailed individual-based models of metacommunity dynamics that can be compared to field data thanks to computer-intensive statistical techniques such as 530 approximate Bayesian computation (ABC, Beaumont 2010, Jabot et al 2013, Overcast et al 2019).…”