Using genetic data to estimate diffusion rates in heterogeneous landscapes

Area of Science:

• Ecology
• Population Genetics
• Mathematical Biology

Background:

• Precise knowledge of population dispersal in heterogeneous environments is crucial for agroecology and conservation.
• Dispersal data informs management strategies for pests and endangered species.

Purpose of the Study:

• To develop a mechanistic-statistical method for estimating space-dependent diffusion parameters in spatially-explicit models.
• To utilize genetic data for inferring population dispersal patterns.

Main Methods:

• Proposed a method based on stochastic differential equations and genetic data.
• Divided populations into habitat patches with known allele frequencies.
• Solved reaction-diffusion equations to compute individual proportions across space.
• Developed a statistically tractable likelihood function for diffusion parameters.

Main Results:

• Successfully estimated diffusion parameters in a simulated heterogeneous environment with distinct regions.
• Demonstrated that higher genetic differentiation among subpopulations improves estimation accuracy.
• Identified the finite size of the genotyped population as a limiting factor for estimation accuracy.

Conclusions:

• The proposed method effectively estimates space-dependent diffusion parameters using genetic data.
• Genetic differentiation is vital for accurate dispersal estimation, but population size also plays a critical role.
• This approach offers valuable tools for ecological management and conservation biology.