Integrating Modeling, Monitoring, and Management to Reduce Critical Uncertainties in Water Resource Decision Making

Monday, September 9, 2013: 2:00 PM
Harris Brake (The Marriott Little Rock)
James T. Peterson , Department of Fisheries and Wildlife, Oregon State University, USGS Oregon Cooperative Fish and Wildlife Research Unit, Corvallis, OR
Mary C. Freeman , USGS Patuxent Wildlife Research Center, Athens, GA
Quantifying the effects of anthropogenic hydrologic alteration, land development, and climate change on aquatic communities is crucial for effective water resources decision-making. To aid in decision making, managers need models to evaluate the potential effect of conservation actions and choose the best alternative. The principle of requisite decision making dictates that useful decision models should balance the need to faithfully represent complex system dynamics with the ability to estimate the effects of decision alternatives in a timely manner. These decisions also are fraught with complexity and uncertainty associated with ecological dynamics. Adaptive resource management can be used to reduce uncertainties and improve management through the integration of models and monitoring. Thus, an additional consideration for decision model building is the need to estimate ecological responses that can be measured and feasibly monitored. We developed a landscape-scale approach to predict the effects of flow alteration, stream fragmentation, and land cover change on the distribution and persistence of fish communities in the Apalachicola, Chattahoochee, and Flint (ACF) river basins. The approach models the dynamics of fish communities in individual stream segments using empirical estimates of meta-demographic rates (i.e., colonization, extinction, reproduction). We used sensitivity analysis to identify the model components that most strongly influenced the estimated changes to the fish communities and developed an occupancy-based monitoring design to iteratively improve the models and our understanding of the system dynamics. We illustrate the approach using monitoring data collected in 2012 to update initial models and model weights and apply the new estimates to predict changes to fish communities in response to climate change in an urbanizing river basin.