Science Serving Maryland's Coasts

R/P-59a

Forecasting the Response of Delmarva Lagoons to Changing Land use and Climate: Alternative Stable States and Recovery Trajectories

Principal Investigator: 

Lora Harris

Start/End Year: 

2009 to 2011

Institution: 

Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science

Co-Principal investigator: 

Walter Boynton, Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science; Mark Brush, Virginia Institute of Marine Science

Strategic focus area: 

Resilient ecosystem processes and responses

Description: 

*This project is a continuing project initially funded through award NA05OAR4171042 (year one) and currently funded through award NA10OAR4170072 (year two)* Objective: The objective of this proposal is to create a coupled ecosystem model to explore the response of Chincoteague Bay to watershed nitrogen loading. Existing models that simulate the dynamics of eelgrass, benthic microalgae, phytoplankton, and macroalgae will be linked using formulations that model patterns of autotrophic dominance under various nitrogen loading scenarios. Special attention will be paid to nitrogen cycling, feedbacks on light attenuation, and sedimentation processes mediated by SAV. Nitrogen loading models will be adapted to GIS systems. Field surveys using an AUV will be supplemented by ADCP deployments to align varying spatial scales in an existing hydrodynamic model connected to the ecosystem model and simulation scenarios. Methodology: This work leverages existing and ongoing studies that have adapted nitrogen loading models to watersheds (Chincoteague, Gargathy Bays) in the Delmarva system to expand predictions across the peninsula. Previously developed lagoon ecosystem models that predict phytoplankton and macroalgae (Brush, VIMS) will be combined with models that simulate growth of eelgrass (Harris, CBL). Tracer studies in eelgrass will be performed to create empirical nutrient cycling relationships in the eelgrass model that are necessary to align this approach with the existing lagoon ecosystem model. Rationale: The resulting modeling tool will be capable of providing management targets for nutrient loading required to reduce nuisance blooms, improve water quality, and support eelgrass restoration or conservation. The combined modeling and experimental approach will also improve our capacity to quantify the potential for non-linear recovery trajectories by considering nitrogen retention and cycling among sediments and primary producers.

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