When migration leaves a clean trace: Decoupling migration from coalescence in the structured serial coalescent

Area of Science:

• Population genetics
• Computational biology
• Evolutionary dynamics

Background:

• Increasingly large population genomic datasets necessitate advanced demographic inference tools.
• Current methods often lack explicit time-series modeling or fine spatial resolution.
• Distinguishing migration from coalescence rates remains a challenge.

Purpose of the Study:

• To develop a demographic inference framework for estimating time-varying migration rates at fine spatial scales.
• To address the need for methods that can disentangle migration and coalescence processes.
• To leverage the structured serial coalescent model for improved population structure analysis.

Main Methods:

• Investigated pairwise genealogical processes under the structured serial coalescent model.
• Derived evolution equations for pairwise branch length distributions.
• Classified relationships based on parameter dependencies and computational tractability.

Main Results:

• Identified a computationally tractable class of genealogical relationships determined solely by migration rates.
• Developed and simulated a novel inference framework for fine-resolution, time-varying migration rates.
• Demonstrated the feasibility of the proposed inference framework.

Conclusions:

• The proposed framework offers a computationally efficient method for inferring dynamic migration patterns.
• This approach enhances our ability to understand population structure and history from genomic data.
• The framework provides a foundation for extending demographic inference to jointly estimate migration and coalescence rates.