Incorporating genotype uncertainty into mark-recapture-type models for estimating abundance using DNA samples

Area of Science:

• Wildlife biology
• Genetics
• Population ecology

Background:

• Noninvasive DNA sampling aids animal identification for mark-recapture studies.
• Genotyping errors, particularly allelic dropout, can lead to misidentification and inaccurate population estimates.
• Discarding low-quality samples results in data loss, despite containing valuable partial genotype information.

Purpose of the Study:

• To develop a method for modeling allelic dropout in noninvasive DNA samples.
• To enable the use of uncertain genotypes in population modeling.
• To improve the accuracy of individual identification and population size estimation in wildlife research.

Main Methods:

• A data augmentation approach is utilized to model uncertain genotypes.
• The method accounts for allelic dropout rather than attempting to minimize it.
• The technique was applied to DNA data from European badger (Meles meles) populations.

Main Results:

• The developed method successfully models data with uncertain genotypes, incorporating allelic dropout.
• This approach allows for the retention and analysis of previously discarded low-quality samples.
• Accurate individual identification and population size estimation are improved by accounting for genotyping errors.

Conclusions:

• Modeling allelic dropout provides a robust solution for analyzing noninvasive DNA data.
• This method enhances the utility of low-quality samples, preserving valuable genetic information.
• The approach offers a significant advancement for mark-recapture studies and wildlife population assessments.