Spatial proximity moderates genotype uncertainty in genetic tagging studies

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 hair samples were originally discarded because of uncertainty in individual identity. The gSPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected three 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 three replicated assignments (e.g., PCRs for microsatellites) per genetic sample. Further, the simulations showed that the gSPIM model parameters are identifiable with only one replicated assignment per sample and that accuracy and precision are relatively insensitive to the number of replicated assignments for high-quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high-quality samples, but when using the gSPIM, genotyping protocols could be more efficient by devoting more resources to low-quality samples.

A New Panel of SNP Markers for the Individual Identification of North American Pumas

Pumas Puma concolor are one of the most studied terrestrial carnivores because of their widespread distribution, substantial ecological impacts, and conflicts with humans. Over the past decade, managing pumas has involved extensive efforts including the use of genetic methods. Microsatellites have been the most commonly used genetic markers; however, technical artifacts and little overlap of frequently used loci render large-scale comparison of puma genetic data across studies challenging. Therefore, a panel of genetic markers that can produce consistent genotypes across studies without the need for extensive calibrations is essential for range-wide genetic management of puma populations. Here, we describe the development of PumaPlex, a high-throughput assay to genotype 25 single nucleotide polymorphisms in pumas. We validated PumaPlex in 748 North American pumas Puma concolor couguar, and demonstrated its ability to generate reproducible genotypes and accurately identify individuals. Furthermore, in a test using fecal deoxyribonucleic acid (DNA) samples, we found that PumaPlex produced significantly more genotypes with fewer errors than 12 microsatellite loci, 8 of which are commonly used. Our results demonstrate that PumaPlex is a valuable tool for the genetic monitoring and management of North American puma populations. Given the analytical simplicity, reproducibility, and high-throughput capability of single nucleotide polymorphisms, PumaPlex provides a standard panel of markers that promotes the comparison of genotypes across studies and independent of the genotyping technology used.