Data repository

The conservation and management of large terrestrial carnivores is one of the most challenging tasks for researchers and practitioners seeking to foster human-wildlife coexistence. Agent-based models (ABMs) allow researchers to design a realistic simulation of their study system, including environmental, anthropogenic, and ecological agents and their characteristics to examine interactions at landscape scales and investigate how interventions may alter potential outcomes. Including high-resolution Geographic Information Systems (GIS) data and real-world ecological data streams in ABMs represents an innovative approach for site-specific investigations into how best to manage the return and re-establishment of large terrestrial carnivores. Here, we used GIS-integrated agent-based models to study the outcome of wolf reintroduction to Ireland’s national parks with respect to wolf ecology and wolf-livestock interactions. We introduced pragmatic management strategies and policy interventions to assess how wolf-livestock interactions could be influenced by wildlife managers and whether outcomes were site-specific. Our study found that wolves could persist past the initial introduction in each protected area regardless of which reintroduction strategy is utilised, however, human-wildlife conflict warning signs always emerge. For example, wolves extensively disperse outside of protected areas, den-sites are located very close (c. 1.5km) to park boundaries and livestock-depredations do occur. Management and policy interventions significantly reduced the likelihood of human-wildlife conflict by reducing the number of livestock depredations and creating ecological buffers that reduce wolf-human interactions, however, the individual characteristics of the protected area determined the success of each management and policy intervention. This analysis reveals nuanced differences in the response of each unique study area to the same management and policy interventions, demonstrating that the outcome of management and policy interventions is highly dependent on the specific ecological conditions captured in the simulation using GIS data. This underscores the importance of integrating high-resolution GIS data into ecological ABMs and highlights the power that such integration can bring to these models for delivering tailored management and policy recommendations to decision-makers enabling human-wildlife coexistence with large carnivores in complex landscapes.