Random encounter model is a reliable method for estimating population density of multiple species using camera traps. Issue 5 (24th June 2022)

• Record Type: Journal Article
• Authors: Palencia, Pablo; Barroso, Patricia; Vicente, Joaquín; Hofmeester, Tim R.; Ferreres, Javier; Acevedo, Pelayo
• Editors: Rowcliffe, Marcus; Caravaggi, Anthony
• Is Part Of: Remote sensing in ecology and conservation. Volume 8:Issue 5(2022)
• Page Start: 670
• Page End: 682
• Publication Date: 2022-06-24
• Subjects: Camera trapping -- non‐invasive -- population abundance -- population density -- random encounter model -- unmarked; Remote sensing -- Periodicals; Ecology -- Research -- Periodicals; Ecology -- Methodology -- Periodicals; Ecology -- Remote sensing -- Periodicals; Nature conservation -- Methodology -- Periodicals; 577.0723
• DOI: 10.1002/rse2.269
• Languages: English
• ISSNs: 2056-3485

Abstract

Abstract: Population density estimates are important for wildlife conservation and management. Several camera trapping‐based methods for estimating densities have been developed, one of which, the random encounter model (REM), has been widely applied due to its practical advantages such as no need for species‐specific study design. Nevertheless, most of the studies in which REM has been assessed against referenced methods have sampled one population, precluding evaluation of the circumstances under which REM does or does not perform well. At this point, a review of all REM assessments could be useful to provide an overview of method reliability and highlight the main factors determining REM performance. Here we used a combination of literature review and empirical study to compare the performance of REM with independent methods. We reviewed 34 studies where REM was applied to 45 species, reporting 77 REM‐reference density comparisons; and we also sampled 13 populations (ungulates and lagomorphs) in which we assessed REM performance against independent densities. The results suggested that appropriate procedures to estimate REM parameters (namely day range, detection zone and encounter rate) are mandatory to obtain unbiased densities. Deficient estimates of day range and encounter rate lead to an overestimation of density, while deficient estimates of detection zone conducted to underestimations. Finally, the precision achieved by REM was lower than reference methods, mainly because of the high levels of spatial aggregation observed in natural populations. In this situation, simulation‐based results suggest that c. 60 camera placements should be sampled to achieve acceptable precision (i.e. coefficient of variation below 0.20). The wide range of situations and scenarios included in this study allow us to conclude that REM is a reliable method for estimating wildlife population density when using appropriate estimates of REM parameters and sampling designs. Overall, these results pave the way to wider application of REM for monitoring terrestrial mammals. Abstract : Several camera trapping‐based methods for estimating wildlife population densities have been developed, one of which, the random encounter model (REM), has been widely applied because of its practical advantages such as no need for species‐specific study design. Here we used a combination of literature review and empirical study to compare the performance of REM with "gold standard" reference methods. We reviewed 77 REM‐reference density comparisons, and we also sampled 13 mammal populations. The results suggested that appropriate procedures to estimate REM parameters (namely day range, detection zone and encounter rate) are mandatory to obtain unbiased densities. Deficient estimates of day range and encounter rate led to overestimation of density, while deficient estimates of detection zone resulted in underestimations. In conclusion, the REM is a reliable method for estimating wildlife population densities when using appropriate estimates of REM parameters and sampling designs. Overall, these results pave the way for wider application of REM for monitoring terrestrial mammals.