Quantifying imperfect camera-trap detection probabilities: implications for density modelling
T. McIntyre
A B E , T. L. Majelantle B , D. J. Slip C D and R. G. Harcourt D
+ Author Affiliations
- Author Affiliations
A Department of Life and Consumer Sciences, College of Agriculture and Environmental Sciences, University of South Africa, Private Bag X6, Florida, 1710, South Africa.
B Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa.
C Taronga Conservation Society Australia, Bradley’s Head Road, Mosman, NSW 2088, Australia.
D Marine Predator Research Group, Department of Biological Sciences, Macquarie University, North Ryde, NSW 2113, Australia.
E Corresponding author. Email: trevmcnt@gmail.com
Wildlife Research 47(2) 177-185 https://doi.org/10.1071/WR19040
Submitted: 1 March 2019 Accepted: 21 October 2019 Published: 17 February 2020
Abstract
Context: Data 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.
Aims: We 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.
Methods: We 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 results: Detection 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.
Conclusions: Imperfect, and individually variable, detection probabilities inside the detection zones of camera traps can compromise resulting population-density estimates.
Implications: We 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.
Additional keywords: detectability, mark–recapture, performance, random encounter model.
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