Authors
Boiani, Maria V.Advisors
Geary, MattPublication Date
2024-07
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The recolonization of wolves (Canis lupus) in Europe has become a notable ecological and conservation success in recent decades, though it presents various legislative and management challenges. These challenges are heightened by the wolves' wide-ranging distribution, which often crosses administrative boundaries. Effective management of wolves depends on robust data from well-designed monitoring programs, which are difficult to implement in fragmented regions. Italy has taken significant steps to address these issues, conducting the first national scale monitoring of wolf abundance and distribution in 2020-2021 using non-invasive genetic sampling (NGS) combined with Spatial Capture-Recapture (SCR) modelling. This thesis explores a variety of ways to implement and optimise the current monitoring strategies by focusing on the Italian Alpine region. This area, in particular, can rely on historical information on wolves since the first surveys in the 90’s and on a highly trained network of personnel dedicated to their monitoring that has been implemented over the years. The year of national monitoring has been a great success in coordination and results, but it opens the question of the feasibility of maintaining such high quality in the long term and with possible reductions in economic funds. In fact, with both European and national management of the species expected in the next future, constantly updated abundance estimates will be essential to ensure the conservation of the species. In this thesis I investigated how the combination of NGS and SCR to obtain population size estimates can be optimised by identifying reduction limits for the number of search transects and also for their repetitions within the sampling season. This will allow to reduce both effort and costs for the next years of population size monitoring while maintaining accuracy and precision. Additionally, I explored the feasibility of extending large-scale monitoring of wolf abundance in the Italian Alps using more cost-effective tools, such as camera traps. I demonstrated the unsuitability of a group of statistical models, Spatial Counts or unmarked SCR, which are often used when individuals in the population are not distinguishable from each other. After identifying the limitations of the current modelling framework, I proposed a solution to address some of these issues by incorporating the group-living nature of wolves into the existing model through an extension of the Spatial Counts approach. Finally, I tested whether the drivers of the latest phase of wolf expansion throughout the Italian Alpine region have changed over time. I used a Dynamic Occupancy model to analyse wolf presence-absence data from 2014 to 2021. This analysis revealed the increasing importance of prey richness in colonization and persistence dynamics together with the smoothed effect over time of human densities. Additionally, I identified key areas where new potential conflicts could arise in human-dominated landscapes. Addressing these challenges is crucial for the continued recovery and sustainable coexistence of wolves with human communities in Italy and all Europe. Finally, I discussed all the findings in light of future population management of wolves.Citation
Boiani, M. V. (2024). Spatio-temporal dynamics of wolves in the Italian Alps [Unpublished doctoral thesis]. University of Chester.Publisher
University of ChesterType
Thesis or dissertationLanguage
enCollections
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