The Coupled Human and Natural Systems Program
The Coupled Human and Natural Systems (CHANS) program is an ongoing effort led by Faculty Associate Prof. Les Kaufman. The project is investigating how governance, social, and economic systems are intricately connected to natural systems, how we can better explore those connections, and how to better understand the trade-offs that confront those making resource management decisions. Specifically, this work explores relationships between biodiversity and human well-being, food-energy-water systems dynamics, recovery of coral reef systems, and the range of dynamic complexity that underpin coupled human-natural systems over space and time.
One of the team’s primary tools is the MIMES-MIDAS approach to modeling ecosystem service flows and trade-offs, and how these relate to the nature and dynamics of regional ecosystems and the human economies they support. Development of MIMES models are led by Visiting Research Fellow Roel Boumans, in conjunction with MIDAS, led by Faculty Research Fellow Prof. Suchi Gopal, and encompass four geographic areas, namely Cambodia (Tonle Sap and the Mekong Delta), East Africa (Lake Victoria), South Florida and Belize (the tropical west Atlantic and Caribbean Basin), and the Gulf of Maine.
A second track for the CHANS team is the exploration of complex dynamics in CHANS in the real world, including chaos and nonlinear behavior. Kaufman is working with Ethan Deyle, a Research Assistant Professor in the Department of Biology, to study the range of these dynamics using empirical dynamic modeling to better understand how such dynamics emerge and interact. This work has implications for how natural systems are studied and managed, especially in a rapidly changing world, and for the underpinnings of ecological theory more broadly.
These two themes come together as the CHANS team is also working to expand MIMES to capture the complexity of dynamics seen in the real world alongside the implementation of people. The team will use this work as a lens to improve complex dynamic models like MIMES-MIDAS more broadly, to both understand CHANS and provide effective decision support.