Lonsinger, R. C., D. Daniel, J. R. Adams, and L. P. Waits. 2019. Consideration of sample source for establishing reliable genetic microsatellite data from mammalian carnivore specimens held in natural history collections. Journal of Mammalogy 100(5): 1678-1689. doi.org/10.1093/jmammal/gyz112
Specimens from natural history collections (NHCs) are increasingly being used for genetic studies and can provide information on extinct populations, facilitate comparisons of historical and contemporary populations, produce baseline data before environmental changes, and elucidate patterns of change. Destructive sampling for DNA may be in disagreement with NHC goals of long-term care and maintenance. Differentiating quality among sample sources can direct destructive sampling to the source predicted to yield the highest quality DNA and most reliable data, potentially reducing damage to specimens, laboratory costs, and genotyping errors. We used the kit fox (Vulpes macrotis) as a model species and evaluated the quality and reliability of genetic data obtained from carnivoran specimens via three different sample sources: cranial bones, nasal bones, and toepads. We quantified variation in microsatellite amplification success and genotyping error rates and assessed the reliability of source- specific genic data. Toepads had the highest amplification success rates and lowest genotyping error rates. Shorter loci had higher amplification success and lower allelic dropout rates than longer loci. There were substantial differences in the reliability of resulting multilocus genotypes. Toepads produced the most reliable data, required the fewest replicates, and therefore, had the lowest costs to achieve reliable data. Our results demonstrate that the quality of DNA obtained from specimens varies by sample source and can inform NHCs when evaluating requests for destructive sampling. Our results suggest that prior to large-scale specimen sampling, researchers should conduct pilot studies to differentiate among source-specific data reliability, identify high performing loci, reduce costs of analyses, and minimize destructive sampling.