Spatially explicit estimation of recent migration rates in plants using genotypic data

Area of Science:

• Population Genetics
• Conservation Genetics
• Bioinformatics

Background:

• Estimating recent migration rates is crucial for understanding population dynamics and designing conservation strategies.
• Existing methods often struggle to disentangle seed and pollen dispersal or account for spatial effects accurately.

Purpose of the Study:

• To develop a novel hierarchical Bayesian method for estimating recent seed and pollen migration rates in a spatially explicit framework.
• To simultaneously infer population genetic parameters, including allele frequencies, divergence, inbreeding coefficients, ancestries, and allelic dropout rates.

Main Methods:

• A hierarchical Bayesian model utilizing multilocus genotypes.
• Incorporation of distance effects separately for seed and pollen dispersal.
• Numerical simulation analysis to assess method performance and required sample sizes.

Main Results:

• The method provides reliable estimates of seed and pollen migration rates and spatial effects on migration.
• Accurate inference is achievable with 25-50 individuals/population for FST≥0.05, or 100 individuals/population for FST=0.025.
• SNP assays with ~1000 loci approach theoretical maximum accuracy for migration inference.

Conclusions:

• The developed method offers a robust tool for estimating contemporary migration patterns in plant populations.
• Application to Taxus baccata revealed low but significant gene flow, with distance negatively impacting pollen migration.
• The findings highlight the importance of spatial scale in understanding plant population connectivity.