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.

Barriers to fieldwork exist for many reasons such as physical ability, financial cost, and time availability. Unfortunately, these barriers disproportionately affect minority communities and create a disparity in access to fieldwork experience in the natural science community. Travel restrictions and the global lockdown has extended this barrier to fieldwork across the community and led to increased anxiety about gaps in productivity, especially for graduate students and early-career researchers. In this paper, we discuss Agent-Based Modeling as an open-source, accessible, and inclusive resource to substitute for lost fieldwork during COVID-19 and future scenarios of travel restrictions such as climate change. We detail the process of model development with a plethora of examples from the literature on how Agent-Based Models can be applied broadly across life-science research. We aim to amplify awareness and adoption of this technique to broaden the diversity and size of the Agent-Based Modeling community in ecology and evolutionary research. We also describe the benefits of Agent-Based models as a teaching and training resource for students across education levels. Finally, we discuss the current challenges facing Agent-Based Modeling and discuss how the field of quantitative ecology can work in tandem with traditional field ecology to improve both methods.