Loading...
Challenges in estimating wolf population size using spatial count models and camera trap data
Boiani, Maria V. Dupont, Pierre Bischof, Richard Friard, Olivier P. Bisi, Francesco Bombieri, Giulia Calderola, Sonia Carolfi, Sabrina Chioso, Christian Fattori, Umberto
Boiani, Maria V.
Dupont, Pierre
Bischof, Richard
Friard, Olivier P.
Bisi, Francesco
Bombieri, Giulia
Calderola, Sonia
Carolfi, Sabrina
Chioso, Christian
Fattori, Umberto
Advisors
Editors
Other Contributors
EPub Date
Publication Date
2026-06-27
Submitted Date
Collections
Files
Loading...
Published version
Adobe PDF, 2.71 MB
Other Titles
Abstract
Accurate abundance estimates are essential for wildlife conservation but remain difficult to obtain for wide-ranging and elusive species. Camera-traps (CT) are one of the most popular tools to study wildlife. However, the usefulness of CT data to estimate population sizes for species without individual marks remains debated. Several statistical methods showed promise in simulation studies, but researchers urged caution, and large-scale applicability remains unclear. This work aims to evaluate the use of Bayesian Spatial Count (SC) models to estimate the wolf (Canis lupus) population size in the Italian Alps using CT data collected through an opportunistic sampling design. A total of 755 CT were deployed across the entire range of occurrence during 2020/2021. Thanks to the availability of independent abundance estimates during the same period, we were able to investigate the effect of including prior information on the quality of large-scale population size estimates. SC models failed to provide accurate population size estimates, largely overestimating abundance, especially when using non-informative priors. While including informative priors on the effect of density covariates slightly improved estimates, providing informative priors for the spatial scale parameter led to no improvement, contrary to previous suggestions. Our findings highlighted the importance of using an adequate sampling design and accounting for individual heterogeneity in detectability within the population. Overall, SC models based on CT data seem not yet suitable for large-scale monitoring of unmarked, socially structured species like wolves, but improved sampling designs and methodological advances may increase their applicability for cost-effective population assessment.
Citation
Boiani, M. V., Dupont, P., Bischof, R., Friard, O., Bisi, F., Bombieri, G., Calderola, S., Carolfi, S., Chioso, C., Fattori, U., Ferrari, P., Filacorda, S., Molinari, P., Corlatti, L., Pernechele, E., Righetti, D., Truc, F., Tomasella, M., Geary, M.,…Marucco, F. (2026). Challenges in estimating wolf population size using spatial count models and camera trap data. Biological conservation, 321, 111988. https://doi.org/https://doi.org/10.1016/j.biocon.2026.111988
Publisher
Elsevier
Journal
Biological Conservation
Research Unit
PubMed ID
PubMed Central ID
Type
Article
Language
Description
© 2026 The Authors. Published by Elsevier Ltd.
Series/Report no.
ISSN
0006-3207
EISSN
1873-2917
ISBN
ISMN
Gov't Doc
Test Link
Sponsors
The authors thank LIFE Wolfalps EU (LIFE18 NAT/IT/000972, 2020/2024) and LIFE Wild Wolf (LIFE21 NAT/IT/101074417, 2023/2027) for financial support and invaluable contribution to data collection and management. The authors also acknowledge projects WildMap (NFR 286886) and PopFlow (NFR 345279) and the Sustainable Futures PhD scholarship for financial support. We also thank all park rangers, police and forest services, technicians, researchers and volunteers who implemented wolf monitoring. All fieldwork and data collection activities were carried out in accordance with institutional and national guidelines for the ethical treatment of wildlife. The study was also approved by the Ethics Committee of the University of Chester (FSE-REC reference: 193/21/MB/BS) as part of the first author's PhD research project.