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CNH: Co-Evolution of Upstream Human Behavior and Downstream Ecosystem Services in a Changing Climate

Non-point source nutrient and sediment runoff from upstream agricultural production is impairing coastal ecosystem services across the globe, including the Great Lakes, Chesapeake Bay, the northern Gulf of Mexico, and other regions of economic, recreational, and cultural importance. This degradation is projected to worsen with continued climate change, as more intense rains transport more nutrients and sediments downstream with impacts that include reduced water clarity, increases in harmful algal blooms, and a loss of high-valued fish stocks. To address these problems, many agricultural management practices have been identified that can reduce sediment and nutrient runoff. Effective design of policies to encourage adoption of effective agricultural management practices is limited by an important knowledge gap concerning human behavioral responses to ecosystem conditions, however. This interdisciplinary research project will use the Maumee River watershed and western Lake Erie as a model ecosystem to quantify the co-evolution between upstream human behavior and downstream ecosystem services. The investigators will model how public attitudes co-evolve with downstream ecosystem conditions and shape support for policies that impact agricultural management practices, and in turn, how farmers respond to these policies and public attitudes. They also will integrate biophysical models of the study region with behavioral models of public policy and farmer decision making to predict the co-evolution among public policies, farmer behavior, and downstream ecosystems under possible future scenarios. This modeling of the two-way coupling between upstream human behavior and downstream ecosystem services will address the question of whether changes in upstream public attitudes, policies, and farmer behavior will offset anticipated negative impacts of climate change on downstream ecosystem services. This project will develop a coupled human-natural system model that focuses on the dynamic feedback between upstream human behavioral responses and downstream ecosystem change. It will provide an assessment of how farmer behavioral responses mediate the interactions between specific policies and changing ecological conditions, and it will enhance understanding regarding the capacity of state and local policy makers to influence farmer behavior and downstream ecosystem conditions in ways that may counteract the expected negative impacts of climate change. While substantial research has focused on the one-way impacts of upstream nutrient inputs and fluxes on watershed functioning and downstream ecosystem services, key reverse linkages that shape public, policy, and farmer responses to ecological changes have received much less attention. The integrated model will provide the theoretical foundation for assessing the dynamic linkages between human attitudes and behaviors and downstream ecological conditions and will improve predictions of the dynamic effect of changing behavioral activities and climate on the availability and quality of downstream ecosystem services. This predictive knowledge is relevant to societal concerns because it will improve the ability of decision makers in coastal watersheds to manage adaptively to address negative impacts of climate change. The project also will provide education, training, and mentoring for undergraduate and graduate students and post-doctoral researchers. The investigators will work directly with Grade 6-12 teachers within rural school districts in the Maumee watershed to develop watershed science curriculum, and they will disseminate results more broadly by working with the Ohio Sea Grant College Program to develop and distribute materials targeted at decision makers and the general public. This project is supported by the NSF Dynamics of Coupled Natural and Human Systems (CNH) Program.

Status
In progress
Type
Project
Project URL
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1039122
Project Database
Start Date
End Date

The Great Lakes - St. Lawrence Research Inventory is an
interactive, Internet-based, searchable database created as a tool to collect and disseminate
up-to-date information about research projects in the
Great Lakes - St. Lawrence Region.