Capture–recapture population growth rate as a robust tool against detection heterogeneity for population management

• Main Authors: Marescot, Lucile, Pradel, Roger, Duchamp, Christophe, Cubaynes, Sarah, Marboutin, Eric, Choquet, Rémi, Miquel, Christian, Gimenez, Olivier
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Figshare 2016
• Subjects: Environmental Science; Ecology; FOS Biological sciences; Canis lupus
• Online Access: https://dx.doi.org/10.6084/m9.figshare.c.3295175; https://figshare.com/collections/Capture_recapture_population_growth_rate_as_a_robust_tool_against_detection_heterogeneity_for_population_management/3295175

Managing large carnivores is one of the most controversial issues in wildlife conservation, as the sociopolitical challenges it raises are as important as the biological ones. Such controversial issues in wildlife conservation require objective biological components to be implemented within the management decision process, in particular, a reliable way of estimating trends in abundance. However, these species usually exhibit territoriality, low densities, and social constraints that can generate individual detection heterogeneity (IDH) of methodological (sampling) or biological (social status, marking behavior) origin. If not accounted for, IDH can lead, in turn, to strong bias in the estimation of population abundance. As a complement to population size, we propose to use the population growth rate (λ) estimated with capture–recapture (CR) data, a robust method to detect and account for IDH, to monitor and manage elusive species. As a case study, we consider the natural recovery of the wolf ( Canis lupus ) population in France, for which a previous study has shown strong IDH leading to a 27% underestimation of abundance. Analyzing a nine-year data set based on a noninvasive sampling protocol relying on wolf scat genotyping, we adopt a new CR approach to estimate λ while explicitly accounting for IDH. The annual population growth rate was estimated at 1.271 ± 0.087 (mean ± SE) vs. 1.270 ± 0.095 when not accounting for IDH, indicating that λ is much less sensitive to IDH than is abundance. We evaluated the robustness of our approach in a simulation study using increasing levels of IDH. The bias in λ increased with detection heterogeneity but was low whether we used a model with IDH or not. Finally, we discuss the management implications of our findings in terms of sampling protocols and management strategies of elusive species.