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.

Wildlife population dynamics are modulated by abiotic and biotic factors, typically climate, resource availability, density-dependent effects, and predator-prey interactions. Understanding if human-caused disturbances shape these processes is needed for the conservation and management of ecological communities within increasingly human-dominated landscapes. Garnering this understanding is difficult due to the lack of long-term longitudinal data on wildlife populations. Concurrently, the interplay between long-term human-mediated disturbances, climate, and predator density on ungulate population dynamics has been under-studied. Using a 50-year time series (1962-2012) on mule deer (Odocoileus hemionus) demographics, seasonal weather, predator density, and oil and gas development patterns from the North Dakota Badlands to investigate the long-term effects of landscape-level disturbance, we aimed to evaluate if harsh weather conditions in-combination with energy development and predators affected fall mule deer recruitment. We found that density-dependent effects and harsh seasonal weather primarily drove recruitment in the North Dakota Badlands. Recruitment was further shaped by interacting effects of harsh seasonal weather and predator presence in the form of high coyote density. Additionally, we found that fall recruitment was subtly modulated by interactions between seasonal weather and energy development (i.e., lower recruitment when harsher weather was combined with higher density of active oil and gas wells), and that the combined effect of predator density and energy development was not interactive but rather additive. Our analysis demonstrates the effect of energy development by modulating mule deer recruitment fluctuations concurrent with main recruitment drivers being biotic (density-dependency, habitat, predation) and abiotic (harsh seasonal weather, woody vegetation encroachment). A pattern emerges of density dependence, presumably due to limited quality habitat, being the primary factor influencing fall fawn recruitment in mule deer. Secondarily, stochastic weather events periodically cause dramatic declines in recruitment. Finally, the interactions between human disturbance and predation can be additive to the aforementioned drivers of recruitment and subsequently cause further declines.

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.