A review of factors to consider when using camera traps to study animal behavior to inform wildlife ecology and conservation

By Anthony Caravaggi, A. C. Burton, D. Clark, Jason T. Fisher, Amelia A. Grass, S. Green, Catherine Hobaiter, T. Hofmeester, Ammie K. Kalan, D. Rabaiotti and 1 more. Conservation Science and Practice, 2020

87 citations recorded by the source corpus.

Camera traps (CTs) are an increasingly popular method of studying animal behavior. However, the impact of cameras on detected individuals—such as from mechanical noise, odor, and emitted light—has received relatively little attention. These impacts are particularly important in behavioral studies in conservation that seek to ascribe changes in behavior to relevant environmental factors. In this article, we discuss three sources of bias that are relevant to conservation behavior studies using CTs: (a) disturbance caused by cameras; (b) variation in animal‐detection parameters across camera models; and (c) biased detection across individuals and age, sex, and behavioral classes. We propose several recommendations aimed at mitigating responses to CTs by wildlife. Our recommendations offer a platform for the development of more rigorous and robust behavioral studies using CT technology and, if adopted, would result in greater applied benefits for conservation and management.

Subfields: Developmental Biology, Ecology, Small Animals

View Paper: https://www.semanticscholar.org/paper/db9274eb48474abb3adf1fcfacdd6f5abf39846d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Small Animals

Citations in the corpus

Exploiting Depth Information for Wildlife Monitoring (2021, background): “…ﬁrst contribution to apply RGB-D camera traps and RGB-D instance segmentation to wildlife and animal monitoring (cf. surveys of Kays et al. (2020), Caravaggi et al. (2020), Wearn and Glover-Kapfer (2019), Schneider et al. (2018), Scotson et al. (2017), Li et al. (2014), Rovero et al. (2013),…”