Spatial proximity moderates genotype uncertainty in genetic tagging studies.

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 assume individuals are identified without error. We introduce the genotype spatial partial identity model (gSPIM), 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 37% of ha

SUBMITTER: Augustine BC 

PROVIDER: S-EPMC7395497 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature