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 (autonomous units, such as wildlife or people); agent characteristics, decision-making, adaptive behavior, and mobility; and agent-environment interactions. STSMs are flexible and intuitive stochastic landscape models that can track scenarios and integrate diverse data. Both can be run spatially and track metrics of management success.
Due to the complementarity of these approaches, we sought to couple them through a dynamic linkage and demonstrate the relevance of this advancement for modeling landscape processes and patterns.
We developed analytical techniques and software tools to couple these modeling approaches using NetLogo, R, and the ST-Sim package for SyncroSim. We demonstrated the capabilities and value of this coupled approach through a proof-of-concept case study of bison-vegetation interactions in Badlands National Park.
The coupled ABM-STSM: (1) streamlined handling of model inputs and outputs; (2) allowed representation of processes at multiple temporal scales; (3) minimized assumptions; and (4) generated spatial and temporal patterns that better reflected agent-environment interactions.
These developments constitute a new approach for representing agent-environment feedbacks; modelers can now use output from an ABM to dictate landscape changes within an STSM that in turn influence agents. This facilitates experimentation across domains (agent and environment) and creation of more realistic and management-relevant projections, and opens new opportunities for communicating models and linking to other methods.
- Learn More: https://doi.org/10.1007/s10980-021-01282-y
- USGS Source: Publications Warehouse (indexId: 70225558)