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

Abstract

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.

Estimating animal density with camera traps: a practitioner’s guide of the REST model

“…3a-i). This may discourage vigilant animals from entering the zone or alter their travel speed within the zone (Caravaggi et al 2020). Moreover, the detection zone may be squashed vertically flat in the video images, making it difficult to measure the staying time in subsequent video analysis.…”

Mating behavior of Eastern Spotted Skunk Spilogale putorius Linnaeus, 1758 (Mammalia: Carnivora: Mephitidae) revealed by camera trap in Texas, USA

“…Our detection of Eastern Spotted Skunks mating highlights the utility of camera traps for documenting rarely observed behaviors while monitoring wildlife (Caravaggi et al 2020). Depending on the study design, remote recording may allow for documentation of rarely exhibited behaviors unbiased by human presence (Pesendorfer et al 2018;Farías-González & Vega-Flores 2019;Caravaggi et al 2020). Adjusting camera settings (i.e., increasing the number of pictures taken per trigger, decreasing lag times between triggers, or taking videos) can further improve the likelihood of capturing behaviors.…”

Innovations in movement and behavioural ecology from camera traps: Day range as model parameter

“…foraging) regarding the faster ones (e.g. fleeing; Caravaggi et al., 2020; Rowcliffe et al., 2016). With regard to the identification of behaviour, the classification is normally based on the visual inspection of the CT records, that is, observed‐based classification when working with CT (Dalloz et al., 2012; Leuchtenberger et al., 2014; Palencia et al., 2019).…”