Density‐dependent space use affects interpretation of camera trap detection rates

Abstract

Camera traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes that detection rate has a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement behavior is density‐dependent across taxa is unclear. The potential confounding of population‐level relative abundance indices by movement would depend on how regularly, and by what magnitude, movement rate and home‐range size vary with density. We conducted a systematic review and meta‐analysis to quantify relationships between movement rate, home‐range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rate indices. Overall, movement rate and home‐range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in relative abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should explicitly consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in camera detection rates should be aware that observed differences may be biased low relative to true changes in abundance. Further information on animal movement, or methods that do not depend on assumptions of density‐independent movement, may be required to make robust inferences on population trends.

Evaluating the Mechanisms of Landscape Change on White‐Tailed Deer Populations

“…We provide additional details on how camera trapping images are classified into time and area (Appendix ). We used density for our study instead of a count of independent detections per camera because movement rates and density are inversely related, which can confound interpretations of relative abundance from detection rates (Sollmann et al 2013, Broadley et al 2019). It is important to consider that density estimates presented do not represent the absolute density of the white‐tailed deer population but an index of relative density among sites, though we hereafter use the term density.…”

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

“…Movement is a fundamental component of detection for mammals (Broadley, Burton, Avgar, & Boutin, 2019;Neilson, Avgar, Burton, Broadley, & Boutin, 2018;Stewart, Volpe, & Fisher, 2019) and CT data have been found to be biased against detecting small, fast-moving species (Glen et al, 2014). Within-species differences in site fidelity, for example, around specific resources, decreases use of space and, hence, increases the probability of detection at specific camera locations (e.g., Sanz & Morgan, 2007).…”

“…Species' behaviors can also vary seasonally (Caravaggi et al, 2018;Larrucea et al, 2007;Popescu, de Valpine, & Sweitzer, 2014) and annually, resulting in time-varying patterns in diurnal activity (Frey, Fisher, Burton, & Volpe, 2017), movement (Broadley et al, 2019), habitat use (Kalle, Ramesh, Qureshi, & Sankar, 2014), and social behaviors (Hongo, Nakashima, Akomo-Okoue, & Mindonga-Nguelet, 2016). For example, gregarious species are more likely to be detected by CTs (Treves, Mwima, Plumptre, & Isoke, 2010), but group sizes can vary throughout the year.…”

Spatial structure of reproductive success infers mechanisms of ungulate invasion in Nearctic boreal landscapes

“…In our second approach, we acknowledge that variation in detection–nondetection among secondary surveys (months of detections) can be due to animal movement and temporally variable habitat use; as such, it is an important part of the ecological signal, and not error as assumed in occupancy models (Broadley et al., 2019; Neilson et al., 2018; Stewart et al., 2018b). We therefore also treated zeros as signal, not error, and used an alternative modeling approach—generalized linear models (GLMs)—to determine whether fawn occurrence varied with landscape features.…”