GIS-integrated agent-based simulations to model wolf reintroduction
management scenarios in Ireland.
The conservation and management of large carnivores is a challenging
task for researchers seeking to foster human-wildlife coexistence.
Agent-based models (ABMs) allow researchers to design realistic
simulations 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 of large carnivores. We used
GIS-integrated ABMs to study the outcome of wolf reintroduction to
Ireland’s national parks with respect to wolf ecology and wolf-livestock
interactions. We introduced 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 emerged. Wolves
extensively disperse outside protected areas, den-sites are located
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 study
area to the same management and policy interventions, demonstrating that
the outcome of management and policy interventions is highly dependent
on specific ecological conditions captured in GIS data. This underscores
the importance of integrating high-resolution GIS data into ecological
ABMs and the power that such integration can bring to these models for
delivering tailored recommendations to decision-makers enabling
human-wildlife coexistence with large carnivores in complex landscapes.