Camera traps and GPS telemetry can provide similar estimates of daily travel distance in terrestrial mammals
12 affiliations
- 1Department of Biology, Utrecht University, Padualaan 8, Utrecht, 3584 CH the Netherlands
- 2Department of Environmental Sciences, Wageningen University & Research, Wageningen, The Netherlands
- 3Animal Ecology Unit, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all ‘Adige, Italy
- 4NBFC National Biodiversity Future Centre, Palermo, Italy
- 5Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
- 6Department of National Park Monitoring and Animal Management, Bavarian Forest National Park, Grafenau, Germany
- 7Chair of Wildlife Ecology and Wildlife Management, University of Freiburg, Freiburg, Germany
- 8Institute for Forest and Wildlife Management, Inland Norway University of Applied Sciences, Koppang, NO-34 Norway
- 9Department of Game Management and Wildlife Biology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Kamýcká 129 Prague 6, Suchdol, 165 00 Czech Republic
- 10ZSL Institute of Zoology, Regent’s Park, London, NW1 4RY UK
- 11Deptartment of Genetics, Evolution and Environment, University College London, Gower Street, London, WC1E 6BT UK
- 12Smithsonian Tropical Research Institute, Luis Clement Avenue 401 Tupper building, Ancon, Panama
Abstract
Background: Two primary methods exist for estimating the daily travel distance by free-ranging animals: inference from animal location data recorded with biologging or wearable GNSS tags (e.g. GPS), and inference from snapshots of animal movement recorded by stationary sensors (e.g. camera traps). Formal comparisons between the two approaches, which differ in both their assumptions and in choices made during analyses, remain scarce.
Methods: Here, we compare GPS- and camera-based estimates of daily travel distance at matched temporal scales under both simulated and real-world scenarios. First, using simulated trajectories, we varied the GPS sampling interval and estimation method — continuous-time movement models (CTMM) vs. straight-line displacement (SLD) — as well as camera-analysis choices (activity correction, data sparsity) to test how well each approach recovers the true distance travelled. Second, we applied the same workflow to empirical datasets with overlapping GPS telemetry and camera-trap observations from the same populations.
Results: We found that, under the simulated movement and observation scenarios considered here, daily distance travelled could be estimated with limited bias from both camera-trap and GPS data using CTMM, but not with GPS data using SLD. Accurate estimation required correction for GPS-error, sufficiently high GPS sampling frequency, activity estimation for camera-trap data and adequate camera-trap data volume to estimate instantaneous speed and activity. Using empirical data from three case studies, we found that camera trapping and GPS-based CTMM estimates were of similar magnitude with overlapping confidence intervals, while GPS-based SLD estimates were lower than the others.
Conclusions: These findings suggest that, when appropriately implemented, both methods can yield similar and fairly unbiased estimates of daily travel distance. These results provide practical guidance for using and potentially combining GPS-based CTMM and camera trapping-based travel distances in density models such as REM (random encounter model), strengthening ecological inference and the effectiveness of wildlife monitoring and management.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s40462-026-00690-6.
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BibTeX
@article{osinga2026camera,
title = {{Camera traps and GPS telemetry can provide similar estimates of daily travel distance in terrestrial mammals}},
author = {Thomas Osinga and Henrik J. de Knegt and Francesca Cagnacci and Massimo Scandura and Maik Henrich and Marco Heurich and Astrid Olejarz and J. Marcus Rowcliffe and Patrick A. Jansen},
journal = {Movement Ecology},
year = {2026},
month = jul,
doi = {10.1186/s40462-026-00690-6},
url = {https://doi.org/10.1186/s40462-026-00690-6},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Camera traps and GPS telemetry can provide similar estimates of daily travel distance in terrestrial mammals
AU - Thomas Osinga
AU - Henrik J. de Knegt
AU - Francesca Cagnacci
AU - Massimo Scandura
AU - Maik Henrich
AU - Marco Heurich
AU - Astrid Olejarz
AU - J. Marcus Rowcliffe
AU - Patrick A. Jansen
PY - 2026
DA - 2026/07/31
JO - Movement Ecology
DO - 10.1186/s40462-026-00690-6
UR - https://doi.org/10.1186/s40462-026-00690-6
AB - Background: Two primary methods exist for estimating the daily travel distance by free-ranging animals: inference from animal location data recorded with biologging or wearable GNSS tags (e.g. GPS), and inference from snapshots of animal movement recorded by stationary sensors (e.g. camera traps). Formal comparisons between the two approaches, which differ in both their assumptions and in choices made during analyses, remain scarce. Methods: Here, we compare GPS- and camera-based estimates of daily travel distance at matched temporal scales under both simulated and real-world scenarios. First, using simulated trajectories, we varied the GPS sampling interval and estimation method — continuous-time movement models (CTMM) vs. straight-line displacement (SLD) — as well as camera-analysis choices (activity correction, data sparsity) to test how well each approach recovers the true distance travelled. Second, we applied the same workflow to empirical datasets with overlapping GPS telemetry and camera-trap observations from the same populations. Results: We found that, under the simulated movement and observation scenarios considered here, daily distance travelled could be estimated with limited bias from both camera-trap and GPS data using CTMM, but not with GPS data using SLD. Accurate estimation required correction for GPS-error, sufficiently high GPS sampling frequency, activity estimation for camera-trap data and adequate camera-trap data volume to estimate instantaneous speed and activity. Using empirical data from three case studies, we found that camera trapping and GPS-based CTMM estimates were of similar magnitude with overlapping confidence intervals, while GPS-based SLD estimates were lower than the others. Conclusions: These findings suggest that, when appropriately implemented, both methods can yield similar and fairly unbiased estimates of daily travel distance. These results provide practical guidance for using and potentially combining GPS-based CTMM and camera trapping-based travel distances in density models such as REM (random encounter model), strengthening ecological inference and the effectiveness of wildlife monitoring and management. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s40462-026-00690-6.
SN - 2051-3933
ER -