Science Serving Maryland's Coasts

R/EH-6

Quantifying Ecological Feedbacks and Transition-Points for Enhanced Restoration of Submerged Aquatic Vegetation

Principal Investigator: 

W. Michael Kemp

Start/End Year: 

2007 to 2010

Institution: 

Horn Point Laboratory, University of Maryland Center for Environmental Science

Co-Principal investigator: 

Laura Murray, University of Maryland Center for Environmental Science, Horn Point Laboratory

Description: 

Objectives: The overall objective is to quantify and model ecological feedback effects whereby seagrass and SAV beds enhance water and sediment conditions across a range of scales to promote improved growth and survival of plants. We will investigate how different plant beds affect key water quality and sediment variables using models to examine how gradual inter-annual trends in bed size and density are related to variations in water quality and climate and to define what sequence in environmental condition leads to abrupt changes in SAV density. Methodology: This study combines comparative fine-scale field sampling with GIS analysis and simulation modeling for SAV beds of different size, density, species composition and spatial orientation. Field sampling includes high-frequency time series of T, sal, O2, chl-a, and turbidity (15 min, YSI 6600) plus nutrients (3 hr, ISCO), spatial mapping of the same variables (underway Dataflow) and sediment coring. GIS analysis of aerial photos will define historical changes in selected beds relative to water quality and climate. A spatial articulated simulation model with simple statistical and mechanistic equations will simulate SAV feedback dynamics. Rationale: Results from this study will substantially improve understanding of how fine-scale mechanisms produce bed-scale feedbacks by which SAV reinforce their own growth. Model experiments will illustrate how these processes can lead to abrupt changes in plant abundance. Comparison of model dynamics, field observations and historical trends will provide insights on how to recognize incipient transition-points; this knowledge will help to form efficient strategies for re-establishing SAV using nascent beds as nursery sites for active restoration efforts.

Impact/Outcome: 

This section describes how this project has advanced scientific knowledge and/or made a difference in the lives of coastal residents, communities, and environments. Maryland Sea Grant has reported these details to the National Oceanic and Atmospheric Administration (NOAA), one of our funding sponsors.

SUMMARY: Researchers produced a deeper understanding of the conditions under which underwater grasses grow and shared these findings with policy-makers working to restore this important vegetation.

RELEVANCE: Underwater grasses (known as submerged aquatic vegetation or SAV) play an important ecological role by improving water quality and providing food and refuge for marine life. But they have declined in the Chesapeake, and restoration efforts have shown mixed results.

RESPONSE: Research by Michael Kemp of the University of Maryland Center for Environmental Science documented conditions that best promote restoration and conservation of submerged aquatic vegetation. Growth rates increase with the grass beds' size, density, and height; Kemp and a coworker quantified minimum requirements for growth. Bigger grass beds slow down the waves within them, allowing water-borne particles to settle. The particles can act as fertilizer, and the clearer water allows more light to reach the grasses and promote their growth. Of particular interest, the restoration of damaged or destroyed grass beds requires better growing conditions than those required to maintain existing beds, which has implications for managing restoration. This project built on an earlier one that studied the ecological role of grass beds.

RESULTS: Kemp has shared the results and participated in semi-annual meetings with federal and state agencies through the Chesapeake Bay Program's SAV Workgroup. The findings will aid resource managers in constructing models to simulate the effects of different management methods and of climate variation on the Bay's SAV restoration.

Related Publications: