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Microsatellite Characterization and Panel Selection for Brown Bear (Ursus arctos) Population Assessment

Authors :
Vincenzo Buono
Salvatore Burgio
Nicole Macrì
Giovanni Catania
Heidi C. Hauffe
Nadia Mucci
Francesca Davoli
Source :
Genes, Vol 13, Iss 11, p 2164 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

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.

Details

Language :
English
ISSN :
20734425
Volume :
13
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Genes
Publication Type :
Academic Journal
Accession number :
edsdoj.fdb2f6780614db7b53d35d57778c810
Document Type :
article
Full Text :
https://doi.org/10.3390/genes13112164