Improving Projections of Wildlife and Landscapes for Natural Resource Managers

Start Date

2017-08-18

End Date

2021-10-17

Release Date

2017

Simulation models are useful for exploring “what if…?” questions about the implications of different climate futures and resource management alternatives for social-ecological systems. Agent-based models (ABMs) and state-and-transition simulation models (STSMs) are two classes of simulations that have proven useful for evaluating different future scenarios and resource responses. ABMs can simulate many types of agents (i.e., autonomous units, such as wildlife, livestock, people, or viruses), and are advantageous because they can capture agent decision-making, characteristics, mobility, and interactions, and feedbacks between agents and their environment. STSMs are flexible and intuitive models of landscape vegetation dynamics that can track landscape attributes and management scenarios, and integrate diverse data types (e.g., output from correlative and mechanistic models). Both ABMs and STSMs can be run spatially and track important metrics of management success, including costs. Despite the complementary strengths of these methods, they have not been a used in combination. This project is developing analytical techniques and software tools to dynamically link these modeling approaches in order to support natural resource decision-making. We will demonstrate the capabilities and value of this work through a proof-of-concept modeling example focused on bison-vegetation interactions in Badlands National Park.

ScienceBase WMS

• National and Regional Climate Adaptation Science Centers
• North Central CASC