Random encounter model is a reliable method for estimating population density of multiple species using camera traps
[EN] Population density estimates are important for wildlife conservation and man-agement. Several camera trapping-based methods for estimating densities havebeen developed, one of which, the random encounter model (REM), has beenwidely applied due to its practical advantages such as no need for spe...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de León |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/26579 |
| Acceso en línea: | https://zslpublications.onlinelibrary.wiley.com/doi/epdf/10.1002/rse2.269 https://hdl.handle.net/10612/26579 |
| Access Level: | acceso abierto |
| Palabra clave: | Veterinaria Camera trapping Non-invasive Population abundance Population density Random encounter model Unmarked 3109 Ciencias Veterinarias |
| Sumario: | [EN] Population density estimates are important for wildlife conservation and man-agement. Several camera trapping-based methods for estimating densities havebeen developed, one of which, the random encounter model (REM), has beenwidely applied due to its practical advantages such as no need for species-specific study design. Nevertheless, most of the studies in which REM has beenassessed against referenced methods have sampled one population, precludingevaluation of the circumstances under which REM does or does not performwell. At this point, a review of all REM assessments could be useful to providean overview of method reliability and highlight the main factors determiningREM performance. Here we used a combination of literature review and empir-ical study to compare the performance of REM with independent methods. Wereviewed 34 studies where REM was applied to 45 species, reporting 77 REM-reference density comparisons; and we also sampled 13 populations (ungulatesand lagomorphs) in which we assessed REM performance against independentdensities. The results suggested that appropriate procedures to estimate REMparameters (namely day range, detection zone and encounter rate) are manda-tory to obtain unbiased densities. Deficient estimates of day range and encoun-ter rate lead to an overestimation of density, while deficient estimates ofdetection zone conducted to underestimations. Finally, the precision achievedby REM was lower than reference methods, mainly because of the high levels ofspatial aggregation observed in natural populations. In this situation,simulation-based results suggest that c. 60 camera placements should be sam-pled 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 toconclude that REM is a reliable method for estimating wildlife population den-sity when using appropriate estimates of REM parameters and samplingdesigns. Overall, these results pave the way to wider application of REM formonitoring terrestrial mammals. |
|---|