Quantifying levels of animal activity using camera trap data

Abstract

Summary1. 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. 2. 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. 3. 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. 4. 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.

Wildlife speed cameras: measuring animal travel speed and day range using camera traps

“…We apply these methods to a community of terrestrial animals in Panama, and validate the resulting estimates by comparing body mass scaling patterns and, where possible, specific day range estimates with independent estimates for the same species in the same habitat from the literature. These methods build on our previous work, which used movement-speed and turning-angle estimates from camera traps to parameterize simulations of movement (Rowcliffe et al 2012), and developed methods to estimate activity level from camera-trap data (Rowcliffe et al 2014). The novelty of this paper lies in: (1) a more detailed consideration of field methods; (2) a new, statistically robust approach to the estimation of average travel speed; (3) the combination of travel speed with activity level to estimate day range and (4) empirical validation of the methods.…”

<b>Observations of occurrence and daily activity patterns of ungulates in the Endau Rompin Landscape, peninsular Malaysia</b>

“…2). Through simulations, Rowcliffe et al (2014) found that with a sample size of 20, the kernel model described by Ridout & Linkie (2009) consistently produced <20% median bias. Due to the small sample size of Sambar Deer (n = 15), we made no conclusion about its activity pattern.…”

Estimation of Species Area Abundance from Point Abundance Data, Using Effective Detection Areas from Camera Traps

“…If activity is defined as an animal being in movement [25], then the total amount of time in which detections could occur is equal to the total time the camera traps are operated, multiplied by the proportion of time spent active by the animal (v x ). A flexible circular distribution to time-of-detection from camera trap data can be used to estimate the proportion of time spent active [25].…”

“…If activity is defined as an animal being in movement [25], then the total amount of time in which detections could occur is equal to the total time the camera traps are operated, multiplied by the proportion of time spent active by the animal (v x ). A flexible circular distribution to time-of-detection from camera trap data can be used to estimate the proportion of time spent active [25]. This method has two assumptions: the level of activity is the only determinant of the rate at which the camera detects animals i.e., the camera operating times and animal activity times are independent of one another, and all individuals in the sampled population are active at the peak of the daily activity cycle.…”