Calibrating an occupancy metric to monitor elusive territorial species at large scale: Application to the grey wolf population in France

Abstract

Monitoring species in time and space is vital to identify changes in their status and to help define management measures to improve their conservation. Abundance is often used to monitor species at the population level. However, this metric is costly and requires intensive fieldwork, reaching limits in the context of elusive territorial species distributed over large areas. As a cost-effective alternative, we developed a spatial metric to monitor such species using the grey wolf in France as a case study. We built a dynamic occupancy model for the wolf population in France and calibrated some parameters to turn site occupancy probabilities into occupied versus unoccupied sites. Then, we evaluated the performance of the model under different monitoring-focus scenarios to catch, both at the national and local scales, trends in wolf demography in order to monitor the population and inform its status for the current and previous years. We calibrated the spatial metric to achieve the best trade-off between avoiding false positives and false negatives. To do so, we aimed to match known occurrences of wolf permanent presence areas (packs and non-packs) identified from naïve detection, as well as population sizes estimated from capture–recapture analysis. The best scenario performed efficiently to predict wolf presence and absence, as well as new permanent presence area occurrences. However, it detected less the disappearance of such areas, which are usually quickly recolonized when located in the core area of the species distribution. The spatial metric results were presented through three elements: annual maps of the predicted wolf presence, annual estimates of the total area occupied by the wolves at the national scale and evaluation of the changes in occupancy at a local scale from year to year. Practical implication. This spatial metric could be efficient to monitor species trends at a large scale while being more cost-effective in data collection compared to capture–recapture analysis at such a large scale. While accounting for imperfect detection with presence–absence data, the different elements of the spatial metric also provide dynamic maps over the years, allowing for management processes at both national and local scales.