Maintenance of polymorphism in spatially heterogeneous environments

Area of Science:

• Evolutionary biology
• Theoretical ecology
• Population genetics

Background:

• Local adaptation is crucial for species survival in heterogeneous environments.
• The balance between migration, selection, and genetic drift determines the stability of local adaptation.
• Existing models often oversimplify population structure and environmental variation, limiting their real-world applicability.

Purpose of the Study:

• To develop a novel theoretical framework for analyzing complex multi-population models with spatial heterogeneity.
• To investigate the influence of environmental spatial autocorrelation on the maintenance of local adaptation.

Main Methods:

• Developed a new theoretical method to approximate complex spatial models with a simpler, panmictic one-population model.
• Retained the core stochastic structure to enable conventional diffusion methods.
• Validated the method against simulations across various spatial models with high migration rates.

Main Results:

• The new method accurately describes stochastic evolutionary dynamics in complex spatial models under high migration.
• High spatial autocorrelation in 2D landscapes significantly promotes the maintenance of locally adaptive alleles.
• Environmental heterogeneity patterns demonstrably impact the qualitative outcomes of evolution.

Conclusions:

• The developed theoretical method offers a powerful tool for studying local adaptation in realistic, complex environments.
• Spatial autocorrelation is a key factor influencing the stability and persistence of local adaptation.
• Understanding spatial heterogeneity is essential for predicting evolutionary trajectories in natural populations.