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Generalized spatial mark–resight models with incomplete identification: An application to red fox density estimates

AuthorsJiménez, José ; Chandler, Richard; Tobajas, Jorge; Descalzo, Esther; Mateo, Rafael ; Ferreras, Pablo
Issue Date2019
PublisherJohn Wiley & Sons
CitationEcology and Evolution 9(8): 4739-4748 (2019)
AbstractThe estimation of abundance of wildlife populations is an essential part of ecological research and monitoring. Spatially explicit capture–recapture (SCR) models are widely used for abundance and density estimation, frequently through individual identification of target species using camera‐trap sampling. Generalized spatial mark–resight (Gen‐SMR) is a recently developed SCR extension that allows for abundance estimation when only a subset of the population is recognizable by artificial or natural marks. However, in many cases, it is not possible to read the marks in camera‐trap pictures, even though individuals can be recognized as marked. We present a new extension of Gen‐SMR that allows for this type of incomplete identification. We used simulation to assess how the number of marked individuals and the individual identification rate influenced bias and precision. We demonstrate the model's performance in estimating red fox (Vulpes vulpes ) density with two empirical datasets characterized by contrasting densities and rates of identification of marked individuals. According to the simulations, accuracy increases with the number of marked individuals (m ), but is less sensitive to changes in individual identification rate (δ). In our case studies of red fox density estimation, we obtained a posterior mean of 1.60 (standard deviation SD: 0.32) and 0.28 (SD : 0.06) individuals/km2, in high and low density, with an identification rate of 0.21 and 0.91, respectively. This extension of Gen‐SMR is broadly applicable as it addresses the common problem of incomplete identification of marked individuals during resighting surveys.
Publisher version (URL)https://doi.org/10.1002/ece3.5077
Appears in Collections:(IREC) Artículos
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