BayesAssNM

BayesAssNM estimates recent migration rates and joint demographic parameters in metapopulations using a modified Bayesian framework to quantify local gene flow, genetic drift, effective demic population size (Ne), and immigration rate (m), including nonreproductive individuals.

Key Features:

• Modified Bayesian methodology: Employs a Bayesian framework tailored to distinguish gene flow at the deme level from individual migration events and to identify descendants of immigrants during nonmigratory life stages.
• Asymmetric gene flow detection: Detects directional (asymmetric) migration between populations to characterize nonreciprocal movement patterns.
• Joint estimation of Ne and m: Estimates effective demic population size (Ne) and immigration rate (m) jointly, explicitly including nonreproductive individuals.
• Temporal sampling integration: Incorporates temporal sampling to estimate changes in Ne and m over time.
• Genotypic data analysis: Analyzes multilocus genotypic data to infer fine-scale demographic processes and relationships between immigrants and residents.
• Uncertainty and prior incorporation: Uses Bayesian inference to incorporate prior information and quantify uncertainty in parameter estimates.

Scientific Applications:

• Metapopulation connectivity studies: Quantifies recent gene flow and demographic parameters in systems of demes to resolve fine-scale connectivity and genetic exchange.
• Pond-breeding newt case studies: Applied to pond-breeding European newts (Triturus cristatus and T. marmoratus) to assess gene flow between demes, including studies in western France.
• Conservation genetics and microevolution: Provides estimates of Ne and m that inform microevolutionary inference and conservation strategies for maintaining population connectivity and genetic diversity.

Methodology:

Analyzes multilocus genotypic data using a modified Bayesian inference framework that distinguishes deme-level gene flow from individual migration events, incorporates temporal sampling, and allows inclusion of prior information and uncertainty in parameter estimation.