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Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (i.e., autonomous units, such as wildlife, people, or viruses); agent characteristics, decision-making, adaptive behavior, and mobility; and interactions between agents and their environment. STSMs are flexible and intuitive stochastic models of landscape dynamics that can track scenarios and landscape attributes, and integrate diverse data types. Both can be run spatially and track metrics of management success. Due to the complementarity...


    map background search result map search result map Coupled Agent-Based and State-and-Transition Simulation Model Coupled Agent-Based and State-and-Transition Simulation Model