Estimating animal density for a community of species using information obtained only from camera‐traps

• Published in: Methods in ecology and evolution Vol. 13; no. 10; pp. 2248 - 2261
• Main Authors: Wearn, Oliver R., Bell, Thomas E. M., Bolitho, Adam, Durrant, James, Haysom, Jessica K., Nijhawan, Sahil, Thorley, Jack, Rowcliffe, J. Marcus
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.10.2022
• Subjects: animal activity; animal day‐range; animal movement rate; Availability; Bayesian analysis; Body mass; Cameras; camera‐trap; density estimation; Forest biomass; Forests; Herbivores; Mathematical models; Omnivores; Parameter estimation; Population density; random encounter model; Resource availability; selective logging; Sequences; Species; Statistics; Traps; wildlife monitoring
• ISSN: 2041-210X; 2041-210X
• DOI: 10.1111/2041-210X.13930

Animal density is a fundamental parameter in ecology and conservation, and yet it has remained difficult to measure. For terrestrial mammals and birds, camera‐traps have dramatically improved our ability to collect systematic data across a large number of species, but density estimation (except for species with natural marks) is still faced with statistical and logistical hurdles, including the requirement for auxiliary data and large sample sizes, and an inability to incorporate covariates. To fill this gap in the camera‐trapper's statistical toolbox, we extended the existing Random Encounter Model (REM) to the multi‐species case in a Bayesian framework. This multi‐species REM can incorporate covariates and provides parameter estimates for even the rarest species. As input to the model, we used information directly available in the camera‐trap data. The model outputs posterior distributions for the REM parameters—movement speed, activity level, the effective angle and radius of the camera‐trap detection zone, and density—for each species. We applied this model to an existing dataset for 35 species in Borneo, collected across old‐growth and logged forest. Here, we added animal position data derived from the image sequences in order to estimate the speed and detection zone parameters. The model revealed a decrease in movement speeds, and therefore day‐range, across the species community in logged compared to old‐growth forest, whilst activity levels showed no consistent trend. Detection zones were shorter, but of similar width, in logged compared to old‐growth forest. Overall, animal density was lower in logged forest, even though most species individually occurred at higher density in logged forest. However, the biomass per unit area was substantially higher in logged compared to old‐growth forest, particularly among herbivores and omnivores, likely because of increased resource availability at ground level. We also included body mass as a variable in the model, revealing that larger‐bodied species were more active, had more variable speeds, and had larger detection zones. Caution is warranted when estimating density for semi‐arboreal and fossorial species using camera‐traps, and more extensive testing of assumptions is recommended. Nonetheless, we anticipate that multi‐species density estimation could have very broad application.