Accurate recapture identification for genetic mark-recapture studies with error-tolerant likelihood-based match...

Area of Science:

• Population genetics
• Ecological monitoring
• Bioinformatics

Background:

• Genetic mark-recapture studies are crucial for population assessments.
• Genotyping errors can compromise the accuracy of identifying recaptured individuals.
• Accurate capture histories are essential for robust population modeling.

Purpose of the Study:

• To evaluate an error-tolerant likelihood-based match-calling model for genetic mark-recapture.
• To assess the performance of a two-stage sample clustering algorithm for identifying recaptures.
• To determine the utility of single nucleotide polymorphism (SNP) and microsatellite (MSAT) markers in this approach.

Main Methods:

• Utilized simulation analyses and case study data (Pacific walrus, fishers).
• Employed a novel two-stage clustering: identifying within- and across-occasion recaptures.
• Assessed both biallelic (SNP) and multiallelic (MSAT) genetic markers.

Main Results:

• The likelihood-based matching protocol performed well in simulations.
• Moderately sized SNP and MSAT panels yielded accurate match/non-match calls despite genotyping errors.
• Matching accuracy remained stable or improved with an increased number of genetic markers.

Conclusions:

• The error-tolerant likelihood-based approach is effective for genetic mark-recapture studies.
• This method accurately reconstructs capture histories, even with potential genotyping errors.
• SNP and MSAT markers are suitable for robust individual identification in population studies.