Estimating wolf ( Canis lupus) densities using video camera traps and spatial capture–recapture analysis

• Published in: Ecosphere
• Main Authors: Jiménez, José, Cara, Daniel, García‐Dominguez, Francisco, Barasona, Jose Angel
• Other Authors: Ministerio para la Transición Ecológica y el Reto Demográfico
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2023
• Subjects: Canis lupus
• Online Access: http://dx.doi.org/10.1002/ecs2.4604; https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.4604

Abstract Estimating population density is critical for effective species conservation, wildlife management planning, and long‐term monitoring. Obtaining accurate estimates is especially important for the wolf ( Canis lupus ), a widely distributed northern hemisphere apex predator whose management and conservation are highly controversial in most of its range, and whose presence usually generates high‐profile media coverage. The peculiarities of wolf social spatial organization and behavior can violate the assumptions of capture–recapture models (uniformity and independence, respectively) to a greater or lesser extent and make it difficult to obtain precise and reliable density estimates. This paper presents a case study, which estimated the population density of the Iberian wolf in the Dorsal Gallega mountain ridge (Galicia, NW Spain) based on the identification of individual wolves from their traits and behavior using video camera traps and spatially explicit capture–recapture (SCR) analyses. The study followed three phases. Firstly, field data were collected by installing camera traps and changing their location until the entire area was sampled. Second, a complete morphological and behavioral study of the wolves recorded was performed to facilitate individual recognition. Third, overdispersion due to gregariousness and other sources of heterogeneity was modeled in the SCR analyses comparing Poisson and negative binomial observation models with different random effects on the baseline detection probability. We estimated a density of 2.88 (SD: 0.37) wolves/100 km 2 in the study area. We concluded that estimating wolf population size using camera trap videos, individual identification, and SCR provides a feasible method and can be used for estimating the density in similar species.