Are camera traps a reliable method for estimating activity patterns? A case study comparing technologies for estimating brown hyaena activity curves

Abstract

Camera traps and radio-tags are both frequently and widely used sampling methods for deriving wildlife activity patterns. While radio-tags continuously monitor a limited number of tagged individuals, camera traps have the potential to monitor all population members, albeit from spatially restricted, fixed points. Such differences might result in differing activity pattern estimates between the two sampling methods. However, few studies have compared the activity patterns derived from simultaneously employed sampling methods, or explored if using combinations of techniques might reveal greater insights into activity patterns. To address this, we compared the activity patterns derived from camera traps placed at latrines and den sites, both in combination and separately, to those from movement rates and the activity sensor collected by GPS collars on brown hyaena Parahyaena brunnea. The activity curve produced by combined camera traps showed a relatively high level of overlap with those produced by the movement rates and activity sensor; 0.88 (95% CI's 0.87-0.89) and 0.85 (95% CI's 0.83-0.86) respectively. However, camera traps reflected higher levels of activity from 00:00 to 06:00, than the radio-tag methods, with data from den sites alone producing this higher level of activity. The results suggest that although hyaenas were active during the 00:00 to 06:00 period, they were so mainly at small, localized den sites, which reflected as lower levels of activity as derived by movement rates and activity sensors. Although our results reflect data from a single species and season, they illustrate the value of using a combination of techniques to disentangle complex behavioural activity patterns.

Towards a best‐practices guide for camera trapping: assessing differences among camera trap models and settings under field conditions

“…Broadly, this is highly relevant for camera trapping studies based on encounter rates (Mills et al, 2020) and those focused on activity patterns (i.e. proportion of the day that animals spend moving), key parameters with which to estimate population density (Palencia et al, 2021a), species interactions (Niedballa et al, 2019) or even when they are the target parameter themselves (Distiller et al, 2020;Edwards et al, 2020). We conclude that the effect of the period of the day should be considered when analysing camera trapping data to avoid biased results (Fig.…”

Camera‐trap data do not indicate scaling of diel activity and cathemerality with body mass in an East African mammal assemblage

“…In the last decades, systematic camera‐trap sampling evolved as a key survey method to assess wildlife populations (Beaudrot et al., 2016 ; O’Connell et al., 2011 ; Rovero & Zimmermann, 2016 ). Conveniently, the resulting time‐stamped pictures can be used to estimate circadian rhythms and activity levels of the photographed species (Caravaggi et al., 2017 ; Edwards et al., 2021 ; Gaynor et al., 2018 ; Rowcliffe et al., 2014 ), which often represent near complete large mammal communities (Steinbeiser et al., 2019 ). The conflicting findings on the relationships between diel activity characteristics and body mass are also reflected in camera‐trap studies.…”

Effect of disturbances and habitat fragmentation on an arboreal habitat specialist mammal using GPS telemetry: a case of the red panda

“…We recorded 55,506 independent images including red pandas in only eight independent events in six trail cameras. Such a small sampling size was insufficient to draw any inference, but telemetry-based data can also be used in temporal interaction studies (Meredith and Ridout 2014 ; Lashley et al 2018 ; Edwards et al 2021 ). So we considered telemetry data falling within a 45 m radius of each camera, including 25 m telemetry error and 20 m detection range, as an independent event of a red panda visiting camera locations which resulted in an additional 77 independent events.…”