Inferring the ecological and evolutionary determinants of community genetic diversity

Area of Science:

• Ecology
• Evolutionary Biology
• Genetics

Background:

• Predicting community responses to global change requires understanding ecological and evolutionary drivers.
• Metabarcoding offers population genetic data for all species, enabling new insights into biodiversity origins and maintenance.

Purpose of the Study:

• To develop a novel eco-evolutionary simulation model for analyzing community assembly dynamics with metabarcoding data.
• To investigate how ecological and evolutionary processes influence community structure and biodiversity patterns.

Main Methods:

• Developed an eco-evolutionary simulation model predicting species abundance, genetic variation, traits, and phylogenetic relationships.
• Employed a simulation-based machine learning approach to distinguish neutral and non-neutral community assembly models.
• Applied the model to soil microarthropod metabarcoding data from Cyprus.

Main Results:

• Metacommunity and local processes leave detectable signatures in biodiversity data.
• Community-scale genetic data estimates local parameters, while phylogenetic data estimates metacommunity parameters.
• Neutral processes structure widespread forest communities; abiotic filtering creates non-neutral structure in isolated, high-elevation habitats.

Conclusions:

• The developed model and R package (ibiogen) facilitate the study of community biodiversity using genetic data.
• Distinguishing between neutral and non-neutral community assembly is possible with genetic and phylogenetic data.
• Habitat type significantly influences whether neutral or non-neutral processes dominate community structure.