Broadening the toolset for stakeholder engagement to explore consensus over wolf management.

Facilitating coexistence between people and large carnivores is critical for large carnivore conservation in human-dominated landscapes, when their presence impacts negatively on human interests. Such situations will often require novel ways of mediating between different values, worldviews and opinions about how carnivores should be managed. We report on such a process in an agricultural area of recent wolf recovery in central Italy where unsolved social tensions over wolf presence have radicalized opinions on either side of the wolf debate, resulting in a stalemate. Where previous mitigation policies based on top-down damage compensation have failed, we tested the potential for applying a participatory approach to engage different stakeholder groups in a dialogue aimed at sharing a deep understanding of the problem and co-creating potential solutions. We based our approach on the theory of meta-consensus, using a decision support tool known as Multi Criteria Decision Analysis (MCDA). Over the course of three months, we carried out five workshops with stakeholder representatives from farming, hunting and environmental associations, and one biologist. Stakeholders shared several objectives and agreed over many management interventions, including the management of free-ranging dogs, the implementation of damage prevention measures, and a damage compensation system suitable for farmers. The process facilitated agreement over actions aimed at improving relations between stakeholders and enhancing the state of knowledge on the issues at stake. Most importantly, we recorded positive social and relationship outcomes from the workshops, and observed a willingness from participants to engage in further discussions over disputed management preferences. Overall, we found MCDA to be a useful tool for laying the groundwork for further participatory and deliberative processes on wolf management. However, challenges ahead included the involvement of a larger number of representatives of different social sectors, and a simplification of the methodology which some participants found too complicated and time consuming.

Estimating distribution and abundance of wide-ranging species with integrated spatial models: Opportunities revealed by the first wolf assessment in south-central Italy.

Estimating demographic parameters for wide-ranging and elusive species living at low density is challenging, especially at the scale of an entire country. To produce wolf distribution and abundance estimates for the whole south-central portion of the Italian wolf population, we developed an integrated spatial model, based on the data collected during a 7-month sampling campaign in 2020-2021. Data collection comprised an extensive survey of wolf presence signs, and an intensive survey in 13 sampling areas, aimed at collecting non-invasive genetic samples (NGS). The model comprised (i) a single-season, multiple data-source, multi-event occupancy model and (ii) a spatially explicit capture-recapture model. The information about species' absence was used to inform local density estimates. We also performed a simulation-based assessment, to estimate the best conditions for optimizing sub-sampling and population modelling in the future. The integrated spatial model estimated that 74.2% of the study area in south-central Italy (95% CIs = 70.5% to 77.9%) was occupied by wolves, for a total extent of the wolf distribution of 108,534 km2 (95% CIs = 103,200 to 114,000). The estimate of total population size for the Apennine wolf population was of 2557 individuals (SD = 171.5; 95% CIs = 2127 to 2844). Simulations suggested that the integrated spatial model was associated with an average tendency to slightly underestimate population size. Also, the main contribution of the integrated approach was to increase precision in the abundance estimates, whereas it did not affect accuracy significantly. In the future, the area subject to NGS should be increased to at least 30%, while at least a similar proportion should be sampled for presence-absence data, to further improve the accuracy of population size estimates and avoid the risk of underestimation. This approach could be applied to other wide-ranging species and in other geographical areas, but specific a priori evaluations of model requirements and expected performance should be made.