Agent-Based Modeling as a Tool for Ecological Comanagement of Grazing Lands

Abstract

Rangeland management necessitates addressing complex and dynamic social-ecological challenges and opportunities at scales appropriate to target landscapes. We combine geospatial and climate data in an agent-based system dynamics model to simulate temporally and spatially scalable rangeland human-environment-animal-forage relationships. Modeling highly variable grazing system elements requires both adaptability and mathematical realism. Agent-based modeling makes it possible to test alternative approaches to real-world scenarios without taking risks associated with actual experimentation on working operations. Our agent-based model, Ecological Comanagement of Rangelands, or ECo-Range, allows managers to simulate cattle grazing scenarios by setting environmental conditions and management decisions that affect simulation outcomes. In this sense, ECo-Range is not just a product of scientific inquiry, but a tool to be used for collaborative discovery, as it illuminates relationships among environmental conditions, management decisions, and ecological and livestock outcomes for modeled landscapes. ECo-Range explores the complexities of grazing management, embracing rather than excluding variability and heterogeneity inherent in rangeland social-ecological systems. We first present ECo-Range, explaining the model's relevance to the fields of simulation modeling and rangeland management. We then present a case study on the Colorado Front Range as proof of concept to test the utility, validity, and applicability of ECo-Range as a learning tool to explore scenarios related to government-owned landscapes that necessitate comanagement approaches to cattle grazing.