Simulation-based spatially explicit close-kin mark-recapture.

Area of Science:

• Ecology
• Conservation Biology
• Population Genetics

Background:

• Estimating wild population size is vital but challenging.
• Genetic methods offer a less invasive and potentially cheaper alternative to traditional techniques.
• Existing close-kin mark-recapture (CKMR) models struggle with spatial heterogeneity.

Purpose of the Study:

• To develop a novel CKMR approach that accounts for spatial heterogeneity in population density and sampling effort.
• To create a robust method for estimating population size using genetic data.

Main Methods:

• A simulation-based approach integrating spatially explicit individual-based simulation.
• Utilized a deep convolutional neural network (CKMRnn) for population size estimation.
• Validated the method through extensive simulations and an empirical study on Ugandan elephants.

Main Results:

• CKMRnn demonstrated high accuracy in population size estimation, even with spatial heterogeneity.
• The method successfully accounted for potential confounders like unknown population histories.
• Empirical application to Ugandan elephants yielded comparable point estimates to traditional methods but with a ~30% reduction in confidence interval width.

Conclusions:

• CKMRnn represents a significant advancement in genetic population estimation, overcoming limitations of previous CKMR models.
• This approach offers a more precise and reliable tool for ecologists and conservationists.
• The method's ability to handle spatial complexity and reduce uncertainty enhances its applicability in real-world conservation scenarios.