Authors: Pranab Roy Chowdhury*, University of Washington, Daniel G. Brown, University of Washington
Topics: Coupled Human and Natural Systems, Global Change, Spatial Analysis & Modeling
Keywords: Agent-Based Model, Forest Management, Land Change, Sustainability
Session Type: Virtual Paper
Presentation File: No File Uploaded
Forest resources in the Pacific Northwest region of the US significantly contribute to regional and global socio-economic and environmental well-being. These lands are owned and managed by multiple landowners, such as the state and federal agencies, industrial and small public owners, who engage with their lands according to varying management practices and change the spatial structure of the landscape in the process. The management decisions of these landowners are driven by their economic goals, intrinsic values, and social and policy contexts; as the landowners adapt to dynamic socio-economic, policy, and environmental contexts, these practices also vary significantly. The spatial structure of forests has a direct bearing on the flow of ecosystem goods and services from the landscape. Thus, understanding the evolution of forest landscapes under different contexts is crucial for quantifying its impact at landscape levels and the contributions of different landowner segments to shape the landscape in multiple directions, with serious sustainability implications. We developed a spatially-explicit and empirically rich agent-based modeling framework to examine the behavior of different landowner groups, capture the dynamics of their adaptive actions under different contexts and relevant policy measures aimed at promoting sustainable forest management practices, and to analyze its end effects on sustaining carbon storage while ensuring the uninterrupted flow of revenue and timber from the Pacific Northwest forests.
In this presentation, we will discuss the modeling approach, followed by the insights generated, and the scope and challenges to generalizing this framework at regional and global levels.