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Abstract 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.
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This content will become publicly available on May 12, 2026
The distribution of genetic diversity in ecological communities: A unifying measure for monitoring biodiversity change
Monitoring the “health” of an ecological community is a critical component of conservation planning. We propose that aggregating intraspecific genetic variation across all species of an ecological community (Community Genetic Distribution; CGD) provides a new way to measure biodiversity that is unifying across taxa, economically scalable, and geographically transferable. Such community-scale data provides information about past dynamics that can unveil processes structuring contemporary biodiversity, and can identify communities that are resilient to perturbation. Using the CGD, high-throughput biodiversity genetic inventories (e.g. metabarcoding/eDNA) can be leveraged to identify the genetic signatures of pristine and disturbed systems. We show examples of the CGD from empirical systems, how it responds through space and time to human disturbance, and how it successfully recovers restoration and succession gradients from metabarcoding datasets with the goal of obtaining insight on community genetic health and developing indicator metrics which can identify communities that are resilient to perturbation. We outline ways in which the CGD complements and extends information in the suite of currently described essential biodiversity variables, and how it can contribute to the targets of the Kunming-Montreal Global Biodiversity Framework.
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- Award ID(s):
- 2135502
- PAR ID:
- 10589544
- Publisher / Repository:
- EcoEvoRxiv
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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