Many ecosystems exhibit spatially spreading processes that we would like to manage to either promote or prevent. For example, we seek to prevent the spread of invasive species while promoting the spread of endangered species. For wildland fire, we seek to promote the spread of low-intensity ground fires that prevent the buildup of dangerous fuels while preventing the spread of high-intensity crown fires that destroy endangered species habitat and valuable timber. These management problems can be formulated mathematically as Markov Decision Problems (MDPs) defined over spatial regions. However, because of the spatial nature of these problems, the MDPs are immense and cannot be solved by any existing algorithms. This project develops new MDP solution algorithms for spatial MDPs. These algorithms will work with ecosystem simulators (rather than requiring explicit models) and they will also address the risk of catastrophic outcomes such as species extinction or catastrophic wildfires.
To bridge the gap between the computational solution of an MDP and the actual adoption of such solutions by policymakers, this research develops visualization and interaction methods that will allow stakeholders (e.g., policymakers, land owners, timber industry representatives, conservation biologists) to understand and critique both the problem formulations and the resulting solutions.
Broader Impacts
The new methods will be tested on five management problems: (a) Tamarisk spread in river networks, (b) Cheatgrass spread in Western US range lands, (c) Sudden Oak Death spread in California and Oregon, (d) deciding when to let a wildfire burn versus suppressing it, and (e) deciding where to place fuel reduction treatments in the landscape to reduce fire risk. The Tamarisk, Cheatgrass, and Sudden Oak Death problems will be studied in stylized settings where the relevant environmental properties and costs can be varied. The goal of these studies is to understand how the different spatial spreading processes (exhibited by these different invasive species) determine the structure of the optimal management policy. The results will be published in the literature on natural resource economics and discussed with policymakers in these areas. The wildfire problems ("let burn," and "fuel treatment") will be studied in a real landscape-a publicly-owned site in the Deschutes National Forest containing a mix of Ponderosa and Lodgepole pine. A collaborating fire manager with the US Forest Service will recruit a panel of stakeholders to analyze and critique the proposed management policies using the visualization and interaction tools that we will develop.
Problems and techniques developed in this project will form the core of the first Summer School in Computational Sustainability, which will be organized by the research team. The results will also be integrated into the OSU Summer Institute in Eco-Informatics (an NSF REU Site) and the OSU Spring Break Course in Monte Carlo AI for junior undergraduates. Four Ph.D. students and one Postdoc will be trained during this project.
