Monitoring of snow leopards in the Sarychat-Ertash State Reserve (Kyrgyzstan), between 2011 and 2019, through scat genotyping

Snow leopards (Panthera uncia) are a keystone species of Asia’s high mountain ecosystem. The species is assessed as Vulnerable by the IUCN Red List of Threatened Species and is elusive, limiting accurate population assessments that could inform conservation actions. Non-invasive genetic monitoring conducted by citizen scientists offers avenues to provide key data on this species. From 2011 to 2019, OSI-Panthera citizen science expeditions tracked signs of presence of snow leopards and collected scat samples along transects in the main valleys and crests of the Sarychat-Ertash State Reserve (Kyrgyzstan). Scat samples were genotyped at twenty autosomal microsatellite loci and at a X/Y locus (sex identification), allowing an estimation of a minimum of 17 individuals. The genetic recapture of 12 of them provided indications of individuals’ habitat use throughout the reserve. We found putative family relationships between several individuals; however, further research is needed to validate these findings. Our results demonstrate the potential of a citizen science program to collect meaningful data that can inform the conservation management of snow leopards.

Authors

Institutions

Publication Details

Journal
Snow Leopard Reports
Published
2024-10-20
DOI
https://doi.org/10.56510/slr.v3.17674
Primary Topic
Wildlife Ecology and Conservation
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Monitoring of snow leopards in the Sarychat-Ertash State Reserve (Kyrgyzstan), between 2011 and 2019, through scat genotyping

Guillaume Queney, Anne-Lise Cabanat, Bastien Chaix, Didier Casañe et al.
Snow Leopard Reports
Wildlife Ecology and Conservation
article

Monitoring of snow leopards in the Sarychat-Ertash State Reserve (Kyrgyzstan), between 2011 and 2019, through scat genotyping

Guillaume Queney, Anne-Lise Cabanat, Bastien Chaix, Didier Casañe, Cécile Kaerle, Agnès Pelletier, Alexander Vereshagin, J. Rode
article en

Abstract

Snow leopards (Panthera uncia) are a keystone species of Asia’s high mountain ecosystem. The species is assessed as Vulnerable by the IUCN Red List of Threatened Species and is elusive, limiting accurate population assessments that could inform conservation actions. Non-invasive genetic monitoring conducted by citizen scientists offers avenues to provide key data on this species. From 2011 to 2019, OSI-Panthera citizen science expeditions tracked signs of presence of snow leopards and collected scat samples along transects in the main valleys and crests of the Sarychat-Ertash State Reserve (Kyrgyzstan). Scat samples were genotyped at twenty autosomal microsatellite loci and at a X/Y locus (sex identification), allowing an estimation of a minimum of 17 individuals. The genetic recapture of 12 of them provided indications of individuals’ habitat use throughout the reserve. We found putative family relationships between several individuals; however, further research is needed to validate these findings. Our results demonstrate the potential of a citizen science program to collect meaningful data that can inform the conservation management of snow leopards.

Snow Leopard ReportsVol. 3
Panthera Corporation (US), Université Paris Cité (FR), Université Paris-Saclay (FR), Government of British Columbia (CA), Antagen Pharmaceuticals (United States) (US), Panthera Biopartners (GB), Baikal Nature Reserve (RU)
Panthera, Muséum National d'Histoire Naturelle
Openalex Percentile: Top 82%
Wildlife Ecology and Conservation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.