Affiliation:
1. Istituto Superiore per la Protezione e la Ricerca Ambientale Roma Italy
2. Federparchi—Italian Federation of Parks and Natural Reserves Roma Italy
3. Istituto di Ecologia Applicata Roma Italy
4. Department for the Monitoring and Protection of the Environment and for Biodiversity Conservation, Unit for Conservation Genetics (BIO‐CGE) Istituto Superiore per la Protezione e la Ricerca Ambientale Ozzano dell'Emilia Italy
5. Department of Life Sciences and Systems Biology University of Torino Torino Italy
Abstract
AbstractEstimating 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.