Microsatellite characterization and panel selection for brown bear (Ursus arctos) population assessment

• Published in: Genes
• Main Authors: Buono, Vincenzo, Burgio, Salvatore, Macrì, Nicole, Catania, Giovanni, Hauffe, Heidi C., Mucci, Nadia, Davoli, Francesca
• Other Authors: Buono, V., Burgio, S., Macrì, N., Catania, G., Hauffe, H.C., Mucci, N., Davoli, F.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: Ursus arctos arctos; Ursus arctos marsicanus; Fragment analysis; Genetic diversity; Individual identification; Invasive and non-invasive genetic profiles; Marker selection; Settore BIO/05 - ZOOLOGIA; Ursus arctos
• Online Access: https://hdl.handle.net/10449/78516; https://doi.org/10.3390/genes13112164

An assessment of the genetic diversity and structure of a population is essential for designing recovery plans for threatened species. Italy hosts two brown bear populations, Ursus arctos marsicanus (Uam), endemic to the Apennines of central Italy, and Ursus arctos arctos (Uaa), in the Italian Alps. Both populations are endangered and occasionally involved in human-wildlife conflict; thus, detailed management plans have been in place for several decades, including genetic monitoring. Here, we propose a simple cost-effective microsatellite-based protocol for the management of populations with low genetic variation. We sampled 22 Uam and 22 Uaa individuals and analyzed a total of 32 microsatellite loci in order to evaluate their applicability in individual identification. Based on genetic variability estimates, we compared data from four different STR marker sets, to evaluate the optimal settings in long-term monitoring projects. Allelic richness and gene diversity were the highest for the Uaa population, whereas depleted genetic variability was noted for the Uam population, which should be regarded as a conservation priority. Our results identified the most effective STR sets for the estimation of genetic diversity and individual discrimination in Uam (9 loci, PIC 0.45; PID 2.0 × 10-5), and Uaa (12 loci, PIC 0.64; PID 6.9 × 10-11) populations, which can easily be utilized by smaller laboratories to support local governments in regular population monitoring. The method we proposed to select the most variable markers could be adopted for the genetic characterization of other small and isolated populations