Tiger abundance and density estimates from non-invasive sources: Camera-trap and genetic capture-recapture analyses
9 affiliations
- 1Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, Virginia, United States of America
- 2WWF Nepal, Baluwatar, Kathmandu, Nepal
- 3Laboratory for Ecological, Evolutionary and Conservation Genetics, Department of Fish and Wildlife Sciences, University of Idaho, Moscow, Idaho, United States of America
- 4Center for Molecular Dynamics Nepal, Thapathali, Kathmandu, Nepal
- 5Ministry of Agriculture, Forests and Environment, Babarmahal, Kathmandu, Nepal
- 6National Trust for Nature Conservation, Kathmandu, Nepal
- 7Department of Wildlife and Natural Resources, Tarleton State University, Stephenville, Texas, United States of America
- 8Independent Researcher, Kathmandu, Nepal
- 9School of the Environment, University of Queensland, St. Lucia, Queensland, Australia
Abstract
We present tiger density and abundance estimates derived independently from two non-invasive sampling techniques: camera-trapping and fecal genetic sampling in same geographic areas. Camera-trap data collected in 2009 covered 359.2 km2 of lowland core area of Chitwan National Park using 187 camera-trap locations and 2,821 trap-nights obtaining 264 identifiable photographs of 41 tigers with 103 recaptures. Fecal genetic sampling in 2011 surveyed 325 km2 along three spatial routes covering 1,173 km and collecting 257 tiger fecal samples. An eight multi-locus genotyping assay identified 31 tigers with 42 recaptures. We analyzed each dataset separately using spatially explicit capture-recapture models under Maximum likelihood and Bayesian framework. Maximum Likelihood-based density estimates from DENSITY were 2.55 (SE 0.6) tigers per 100 km² for camera traps and 2.57 (SE 0.9) for genetic data, while Bayesian estimates from SPACECAP were slightly lower at 2.4 (SE 0.3) and 2.2 (SE 0.5), respectively. Maximum Likelihood abundance estimates were 66 (SE 7.9) for camera data and 88 (SE 19.3) for genetic data, whereas Bayesian estimates were 64 (SE 7.9) and 93 (SE 19), respectively. Two independent data sources broadly comparable density estimates, though uncertainty was greater for the genetic dataset, which included fewer identified individuals and spatial recaptures. Because the data were surveyed in different years and differed in sampling effort, differences in precision cannot be attributed solely to sampling method. Non-invasive genetic sampling nonetheless produced density and abundance estimates broadly consistent with an established camera trap protocol, suggesting that integrating the data sources can enhance robust tiger monitoring and inform effective conservation planning.
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BibTeX
@article{thapa2026tiger,
title = {{Tiger abundance and density estimates from non-invasive sources: Camera-trap and genetic capture-recapture analyses}},
author = {Kanchan Thapa and Marcella J. Kelly and Lisette Waits and Manisha Bista and Jivan Shakya and Maheshwar Dhakal and Naresh Subedi and Samundra Ambuhang Subba and Netra Sharma Sapkota and Dibesh Karmacharya},
journal = {PLOS One},
year = {2026},
month = nov,
doi = {10.1371/journal.pone.0361369},
url = {https://doi.org/10.1371/journal.pone.0361369},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Tiger abundance and density estimates from non-invasive sources: Camera-trap and genetic capture-recapture analyses
AU - Kanchan Thapa
AU - Marcella J. Kelly
AU - Lisette Waits
AU - Manisha Bista
AU - Jivan Shakya
AU - Maheshwar Dhakal
AU - Naresh Subedi
AU - Samundra Ambuhang Subba
AU - Netra Sharma Sapkota
AU - Dibesh Karmacharya
PY - 2026
DA - 2026/11/04
JO - PLOS One
DO - 10.1371/journal.pone.0361369
UR - https://doi.org/10.1371/journal.pone.0361369
AB - We present tiger density and abundance estimates derived independently from two non-invasive sampling techniques: camera-trapping and fecal genetic sampling in same geographic areas. Camera-trap data collected in 2009 covered 359.2 km2 of lowland core area of Chitwan National Park using 187 camera-trap locations and 2,821 trap-nights obtaining 264 identifiable photographs of 41 tigers with 103 recaptures. Fecal genetic sampling in 2011 surveyed 325 km2 along three spatial routes covering 1,173 km and collecting 257 tiger fecal samples. An eight multi-locus genotyping assay identified 31 tigers with 42 recaptures. We analyzed each dataset separately using spatially explicit capture-recapture models under Maximum likelihood and Bayesian framework. Maximum Likelihood-based density estimates from DENSITY were 2.55 (SE 0.6) tigers per 100 km² for camera traps and 2.57 (SE 0.9) for genetic data, while Bayesian estimates from SPACECAP were slightly lower at 2.4 (SE 0.3) and 2.2 (SE 0.5), respectively. Maximum Likelihood abundance estimates were 66 (SE 7.9) for camera data and 88 (SE 19.3) for genetic data, whereas Bayesian estimates were 64 (SE 7.9) and 93 (SE 19), respectively. Two independent data sources broadly comparable density estimates, though uncertainty was greater for the genetic dataset, which included fewer identified individuals and spatial recaptures. Because the data were surveyed in different years and differed in sampling effort, differences in precision cannot be attributed solely to sampling method. Non-invasive genetic sampling nonetheless produced density and abundance estimates broadly consistent with an established camera trap protocol, suggesting that integrating the data sources can enhance robust tiger monitoring and inform effective conservation planning.
SN - 1932-6203
ER -