Predicting spatio-temporal recolonization of large carnivore populations and livestock depredation risk: wolves in the Italian Alps

Authored by F Marucco, E J B McIntire

Date Published: 2010

DOI: 10.1111/j.1365-2664.2010.01831.x

Sponsors: National Science and Engineering Research Council of Canada (NSERC)

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts

Model Code URLs: Model code not found

Abstract

P>1. Wolves Canis lupus recently recolonized the Western Alps through dispersal from the Italian Apennines, representing one of several worldwide examples of large carnivores increasing in highly human-dominated landscapes. Understanding and predicting expansion of this population is important for conservation because of its direct impact on livestock and its high level of societal opposition. 2. We built a predictive, spatially explicit, individual-based model to examine wolf population expansion in this fragmented landscape, and livestock depredation risk. We developed the model based on known demographic processes, social structure, behaviour and habitat selection of wolves collected during a 10-year intensive field study of this wolf population. 3. During model validation, our model accurately described the recolonization process within the Italian Alps, correctly predicting wolf pack locations, pack numbers and wolf population size, between 1999 and 2008. 4. We then projected packs and dispersers over the entire Italian Alps for 2013, 2018 and 2023. We predicted 25 packs (95\% CI: 19-32) in 2013, 36 (23-47) in 2018 and 49 (29-68) in 2023. The South-Western Alps were the main source for wolves repopulating the Alps from 1999 to 2008. The source area for further successful dispersers will probably shift to the North-Western Alps after 2008, but the large lakes in the Central Alps will probably act as a spatial barrier slowing the wolf expansion. 5. Using the pack presence forecasts, we estimated spatially explicit wolf depredation risk on livestock, allowing tailored local and regional management actions. 6. Synthesis and applications. Our predictive model is novel because we follow the spatio-temporal dynamics of packs, not just population size, which have substantially different requirements and impacts on wolf-human conflicts than wandering dispersers. Our approach enables prioritization of management efforts, including minimizing livestock depredations, identifying important corridors and barriers, and locating future source populations for successful wolf recolonization of the Alps.

Tags

connectivity Dynamics Conservation Dispersal Mortality Model habitat Landscapes Restoration Wolf canis-lupus