Florian Hartig Theoretical Ecology & Ecological Data Science

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Deer behavior affects density estimates with camera traps, but is outweighed by spatial variability

Maik Henrich, Florian Hartig, Carsten F. Dormann, Hjalmar S. Kühl, Wibke Peters, Frederik Franke, Tomáš Peterka, Pavel Šustr, Marco Heurich

Frontiers in Ecology and Evolution, 10, 881502 (2022)
Cite this
@article{henrich2022deer,
  author = {Maik Henrich and Florian Hartig and Carsten F. Dormann and Hjalmar S. Kühl and Wibke Peters and Frederik Franke and Tomáš Peterka and Pavel Šustr and Marco Heurich},
  title = {Deer behavior affects density estimates with camera traps, but is outweighed by spatial variability},
  journal = {Frontiers in Ecology and Evolution},
  volume = {10},
  pages = {881502},
  year = {2022},
  doi = {10.3389/fevo.2022.881502},
}

DOI: 10.3389/fevo.2022.881502
Cited by 50 (Google Scholar) · 39 (OpenAlex), as of 07 September 2026

View article (DOI)

Abstract

Density is a key trait of populations and an essential parameter in ecological research, wildlife conservation and management. Several models have been developed to estimate population density based on camera trapping data, including the random encounter model (REM) and camera trap distance sampling (CTDS). Both models need to account for variation in animal behavior that depends, for example, on the species and sex of the animals along with temporally varying environmental factors. We examined whether the density estimates of REM and CTDS can be improved for Europe’s most numerous deer species, by adjusting the behavior-related model parameters per species and accounting for differences in movement speeds between sexes, seasons, and years. Our results showed that bias through inadequate consideration of animal behavior was exceeded by the uncertainty of the density estimates, which was mainly influenced by variation in the number of independent observations between camera trap locations. The neglection of seasonal and annual differences in movement speed estimates for REM overestimated densities of red deer in autumn and spring by ca. 14%. In CTDS, density estimates of red deer improved foremost through the consideration of behavioral reactions to the camera traps (avoiding bias of max. 19%), while species-specific delays between photos had a larger effect for roe deer. In general, the applicability of both REM and CTDS would profit profoundly from improvements in their precision along with the reduction in bias achieved by exploiting the available information on animal behavior in the camera trap data.

What the paper shows and why it matters (AI-generated)

Two widely used camera-trap density estimators can be measurably biased by how deer behave around the cameras themselves — vigilance, avoidance, movement-speed differences by season and sex — and this paper quantifies exactly how much, up to 19% for one method. The bigger surprise is what that bias compares against: uneven camera placement across the landscape introduces even more error, a practical argument for wildlife monitoring programmes to spend at least as much effort on survey design as on correcting for animal behaviour after the fact.