Improving Projections of Wildlife and Landscapes for Natural Resource Managers
Coupling Agent-Based and State-And-Transition Simulation Models for Improved Ecological Projections
Dates
Start Date
2017-08-18
End Date
2021-10-17
Release Date
2017
Summary
Managing natural resources is fraught with uncertainties around how complex social-ecological systems will respond to management actions and other forces, such as climate. Modeling tools have emerged to help grapple with different aspects of this challenge, but they are often used independently. The purpose of this project is to link two types of commonly-used simulation models (agent-based models and state-and-transition simulation models) and streamline the handling of model inputs and outputs. This innovation will provide researchers with the capability to simulate the interactions of wildlife, vegetation, management actions, and other drivers, and thus answer questions and inform decisions about how best to manage natural resources.
Summary
Managing natural resources is fraught with uncertainties around how complex social-ecological systems will respond to management actions and other forces, such as climate. Modeling tools have emerged to help grapple with different aspects of this challenge, but they are often used independently. The purpose of this project is to link two types of commonly-used simulation models (agent-based models and state-and-transition simulation models) and streamline the handling of model inputs and outputs. This innovation will provide researchers with the capability to simulate the interactions of wildlife, vegetation, management actions, and other drivers, and thus answer questions and inform decisions about how best to manage natural resources.
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.