Inferring landscape resistance to gene flow when genetic drift is spatially heterogeneous

Area of Science:

• Ecology
• Population Genetics
• Conservation Biology

Background:

• Connectivity models use land cover costs to estimate species movement resistance.
• Landscape genetics infers these costs from genetic differentiation and cost distances.
• Spatial heterogeneity in population size (drift), migration, and distribution is often overlooked in cost inference.

Purpose of the Study:

• To assess the reliability of cost value inference under varying migration rates, population spatial patterns, and population size heterogeneity.
• To evaluate if incorporating intra-population variables (e.g., gravity models) improves inference accuracy, especially with heterogeneous genetic drift.
• To identify conditions enabling the accurate identification of true landscape resistance costs.

Main Methods:

• Simulated gene flow between populations with diverse sizes and spatial distributions.
• Fitted gravity models using true/alternative cost distances and intra-population variables (population sizes, patch areas).
• Assessed inference reliability using Mantel correlations between cost distances and identified conditions for accurate cost identification.

Main Results:

• Cost scenario ranking was generally reliable, but the true cost scenario rarely yielded the best model fit.
• Inference inaccuracies increased with restricted migration (<4 dispersal events/generation), high population size heterogeneity, and population aggregation.
• Considering intra-population variables significantly improved the reliable identification of true cost scenarios in challenging situations.

Conclusions:

• Standard landscape genetic inference of resistance costs can be unreliable under realistic demographic complexities.
• Incorporating intra-population variables like population size and area is crucial for accurate cost inference when genetic drift is spatially heterogeneous.
• Improved cost inference enhances the effectiveness of connectivity models for conservation planning and species management.