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Spatial proximity moderates genotype uncertainty in genetic tagging studies

Augustine, Ben1; Royle, J. Andrew2; Linden, Daniel W.3; Fuller, Angela K.2

Published Jan 02, 2020; Updated Jan 07, 2020 on Dryad. https://doi.org/10.5061/dryad.4qrfj6q6b

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Jan 02, 2020 version files 3.87 GB

Abstract

Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower quality samples cannot be used, and they do not allow for errors in individual identification. We introduce the Genotype Spatial Partial Identity Model (SPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers Pekania pennanti in which 48% of samples were originally discarded because of uncertainty in individual identity. The Genotype SPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected 2 errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63% and 42%, respectively, using 3 PCRs per genetic sample. Further, the simulations showed that the Genotype SPIM model parameters are identifiable with only one PCR per sample, and that accuracy and precision are relatively insensitive to the number of PCRs for high quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high quality samples, but when using the Genotype SPIM, genotyping protocols could be more efficient by devoting more resources to low quality samples.