Quantifying imperfect camera-trap detection probabilities: implications for density modelling

Abstract

ContextData obtained from camera traps are increasingly used to inform various population-level models. Although acknowledged, imperfect detection probabilities within camera-trap detection zones are rarely taken into account when modelling animal densities. AimsWe aimed to identify parameters influencing camera-trap detection probabilities, and quantify their relative impacts, as well as explore the downstream implications of imperfect detection probabilities on population-density modelling. MethodsWe modelled the relationships between the detection probabilities of a standard camera-trap model (n=35) on a remotely operated animal-shaped soft toy and a series of parameters likely to influence it. These included the distance of animals from camera traps, animal speed, camera-trap deployment height, ambient temperature (as a proxy for background surface temperatures) and animal surface temperature. We then used this detection-probability model to quantify the likely influence of imperfect detection rates on subsequent population-level models, being, in this case, estimates from random encounter density models on a known density simulation. Key resultsDetection probabilities mostly varied predictably in relation to measured parameters, and decreased with an increasing distance from the camera traps and speeds of movement, as well as heights of camera-trap deployments. Increased differences between ambient temperature and animal surface temperature were associated with increased detection probabilities. Importantly, our results showed substantial inter-camera (of the same model) variability in detection probabilities. Resulting model outputs suggested consistent and systematic underestimation of true population densities when not taking imperfect detection probabilities into account. ConclusionsImperfect, and individually variable, detection probabilities inside the detection zones of camera traps can compromise resulting population-density estimates. ImplicationsWe propose a simple calibration approach for individual camera traps before field deployment and encourage researchers to actively estimate individual camera-trap detection performance for inclusion in subsequent modelling approaches.

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

“…For example, when using machine learning model output to design occupancy and abundance models, we can incorporate accuracy estimates that were generated when conducting model testing. The error of a machine learning model in identifying species from camera traps is similar to the problem of imperfect detection of wildlife when conducting field surveys (McIntyre, Majelantle, Slip, & Harcourt, 2020). Wildlife are often not detected when they are present (false negatives) and occasionally detected when they are absent (false positives); ecologists have developed models to effectively estimate occupancy when data have these types of errors (Guillera‐Arroita, Lahoz‐Monfort, van Rooyen, Weeks, & Tingley, 2017; Royle & Link, 2006).…”

Counting giraffes: A comparison of abundance estimators on the Ongava Game Reserve, Namibia

“…On OGR, giraffes may find enough water in forage or access to small, non-monitored water sources, making the need to visit larger but dangerous waterholes less stringent. An alternative would be that camera traps might fail to trigger in the presence of an animal, which is sensitive to camera placement, settings and performance (Rovero et al 2013;McIntyre et al 2020), or because the photograph was of too low quality to allow for individual identification (e.g. blurry or dark images).…”

Wildlife trail or systematic? Camera trap placement has little effect on estimates of mammal diversity in a tropical forest in Gabon

“…CT technology has enabled a tremendous leap forward for monitoring medium‐ to large‐bodied terrestrial mammals in remote areas as complex and diversified as tropical moist forests. Although species characteristics (Harmsen et al, 2010; Rowcliffe et al, 2011), abiotic factors (Noss et al, 2003) and camera‐related parameters (McIntyre et al, 2020; Moore et al, 2020; Rovero et al, 2013) have been shown to influence the detection process, the impact of the placement strategy on the detected diversity has been little studied in tropical forests. Here, we demonstrated that the CT placement had little impact on species richness and composition and provided a similar picture of the particularly rich ground‐dwelling mammal community in a tropical forest in Gabon.…”