Quantifying levels of animal activity using camera trap data

By J. Marcus Rowcliffe, R. Kays, Bart Kranstauber, Chris Carbone, Patrick A. Jansen. 2014

518 citations recorded by the source corpus.

Activity level (the proportion of time that animals spend active) is a behavioural and ecological metric that can provide an indicator of energetics, foraging effort and exposure to risk. However, activity level is poorly known for free‐living animals because it is difficult to quantify activity in the field in a consistent, cost‐effective and non‐invasive way. This article presents a new method to estimate activity level with time‐of‐detection data from camera traps (or more generally any remote sensors), fitting a flexible circular distribution to these data to describe the underlying activity schedule, and calculating overall proportion of time active from this. Using simulations and a case study for a range of small‐ to medium‐sized mammal species, we find that activity level can reliably be estimated using the new method. The method depends on the key assumption that all individuals in the sampled population are active at the peak of the daily activity cycle. We provide theoretical and empirical evidence suggesting that this assumption is likely to be met for many species, but may be less likely met in large predators, or in high‐latitude winters. Further research is needed to establish stronger evidence on the validity of this assumption in specific cases; however, the approach has the potential to provide an effective, non‐invasive alternative to existing methods for quantifying population activity levels.

Subfields: Computer Vision and Pattern Recognition, Ecology, Ecology, Evolution, Behavior and Systematics, Small Animals

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Citations in the corpus

SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision (2022, methodology): “Furthermore, we use the Activity R package (Rowcliffe et al., 2014) with a bandwidth parameter of 1 and a single replicate to estimate animal activity levels and calculate an activity rate of 0.”
Distance sampling with camera traps (2017, methodology): “Methods in Ecology and Evolution © 2017 British Ecological Society, Methods in Ecology and Evolution, 8, 1558–1565 regarding animal activity patterns (Lynam et al. 2013; Cruz et al. 2014; Rowcliffe et al. 2014).”
Use of object detection in camera trap image identification: assessing a method to rapidly and accurately classify human and animal detections for research and application in recreation ecology (2022, background): “…remote cameras, or camera traps, which allow researchers to collect extensive observational data while minimally disturbing the wild species of interest, for a relatively low monetary cost (Burton et al., 2015; Caravaggi et al., 2017; Glover-Kapfer et al., 2019; Rowcliffe et al., 2014).”
Estimating animal density without individual recognition using information derivable exclusively from camera traps (2018, methodology): “The method of Rowcliffe et al. (2014) relies on the key assumption that all individuals in the sampled animal population are active at the peak of the daily activity cycle.”
Investigating animal activity patterns and temporal niche partitioning using camera‐trap data: challenges and opportunities (2017, methodology): “123 Only recently have researchers focused attention on the finer scaled temporal data provided by time-stamped camera-trap images (e.g. Ridout and Linkie 2009; Rowcliffe et al. 2014), which detail the timing of wildlife occurrences across points in space.”