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

Abstract

Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Reconciling the relative importance of these processes is hindered by current theory, which tends to focus on a single spatial, temporal or taxonomic scale. We introduce a 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. We demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. We combine our massive eco-evolutionary synthesis simulations (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 spatial scales.

Detecting the ecological footprint of selection

“…To explore the behavior of the proposed competition models and understand how the different models affect the outcome of community assembly, we ran 10 000 simulations for each of the five community assembly models (neutral, filtering, mean competition, pairwise competition and β-competition), with parameter ranges following Overcast et al (2021) (see Appendix A.3). We explored temporal trends in richness and abundance, genetic diversity, and trait variation using least square polynomial regressions using the polyfit function of Numpy v.19.0 (Oliphant 2006).…”

“…We explored temporal trends in richness and abundance, genetic diversity, and trait variation using least square polynomial regressions using the polyfit function of Numpy v.19.0 (Oliphant 2006). The temporal trends are studied in terms of Λ, a parameter used to quantify the progress of the simulation toward equilibrium (Overcast et al 2019), consistent with the original MESS model Overcast et al (2021). We visually inspected the resulting simulations by collapsing simulated summary statistics into principal components using the built-in PCA function of MESS (Overcast et al 2021).…”

“…The temporal trends are studied in terms of Λ, a parameter used to quantify the progress of the simulation toward equilibrium (Overcast et al 2019), consistent with the original MESS model Overcast et al (2021). We visually inspected the resulting simulations by collapsing simulated summary statistics into principal components using the built-in PCA function of MESS (Overcast et al 2021). This enabled us to distinguish between the different community assembly models.…”

“…Following the machine learning method from Overcast et al (2021), we compared summary statistics linked to the SAD, genetic diversity and traits computed on the empirical data to those computed on 50 000 simulations (10 000 for each community assembly model).…”

“…The Massive Eco-evolutionary Synthesis Simulations (MESS) model of Overcast et al (2021) allows testing mechanistic hypotheses jointly across a combination of three data axes: species abundances, population genetic variation and trait values. These three axes reflect a variety of processes operating over a variety of time scales, from a few generations (abundances) to several tens of thousands of generations (genetic variation).…”