Author Information

Shannon M. Knapp
Bruce A. Craig

Abstract

DNA from non-invasive sources is increasingly being used as molecular tags for markrecapture population estimation. These sources, however, provide small quantities of often contaminated DNA, which can lead to genotyping errors that will bias the population estimate. We describe a novel approach, called Genotyping Uncertainty Added Variance Adjustment (GUAVA), to address this problem. GUAVA incorporates an explicit model of genotyping error to generate a distribution of complete-information capture histories that is used to estimate the population size. This approach both reduces the genotyping-error bias and incorporates the additional uncertainty due to genotyping error into the variance of the estimate. We demonstrate this approach via simulated mark-recapture data with a range of genetic information, population sizes, sample sizes, and genotyping error-rates. The bias, variance, and coverage of the GUAVA estimates are shown to be superior to those of other available methods used to analyze this type of data. Because GUAVA assumes each sample is genotyped only once per locus, it also has the potential to save a great deal of time and money collecting consensus molecular information.

Keywords

DNA markers, genotyping error, mark-release-recapture, microsatellite, non-invasive sampling, population size estimation

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Apr 27th, 11:00 AM

ADJUSTING POPULATION ESTIMATES FOR GENOTYPING ERROR IN NON-INVASIVE DNA-BASED MARK-RECAPTURE EXPERIMENTS

DNA from non-invasive sources is increasingly being used as molecular tags for markrecapture population estimation. These sources, however, provide small quantities of often contaminated DNA, which can lead to genotyping errors that will bias the population estimate. We describe a novel approach, called Genotyping Uncertainty Added Variance Adjustment (GUAVA), to address this problem. GUAVA incorporates an explicit model of genotyping error to generate a distribution of complete-information capture histories that is used to estimate the population size. This approach both reduces the genotyping-error bias and incorporates the additional uncertainty due to genotyping error into the variance of the estimate. We demonstrate this approach via simulated mark-recapture data with a range of genetic information, population sizes, sample sizes, and genotyping error-rates. The bias, variance, and coverage of the GUAVA estimates are shown to be superior to those of other available methods used to analyze this type of data. Because GUAVA assumes each sample is genotyped only once per locus, it also has the potential to save a great deal of time and money collecting consensus molecular information.