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Sustainable Seagrass Restoration in the Virginia Coastal Bays

Al-Haj, Alia
Thesis/Dissertation; Online
Al-Haj, Alia
McGlathery, Karen
Seagrass depth limits are important to consider when thinking about the future of coastal ecosystems through climate change and nutrient loading scenarios. Seagrasses provide many ecosystem services to coastal areas worldwide, including providing habitat for many economically important species, acting as a significant carbon sink, and improving water quality. Because seagrasses are declining globally, it is important to be able to identify areas for restoration where seagrasses could be successful in order to maximize use of time and money. Current models for the Virginia Coast Reserve (VCR) only consider effects of light and point measurements of temperature on the maximum depth limit for eelgrass. However, it has been shown that multiple factors can affect light requirements in plants such as sediment characteristics and pore-water chemistry. Sediment characteristics, such as grain size and organic matter content increase light requirements in plants by physically blocking light and decreasing oxygen concentrations in the sediments allowing for the intrusion of phytotoxins such as pore-water sulfide and ammonium. With climate change causing a rise in global temperatures, seagrasses will become even more sensitive to changes in their light environment, such as those caused by coastal eutrophication, and will need to increase light requirements further to maintain a positive carbon balance. This may affect depth limits in seagrasses by limiting their range for growth at the minimum depth limit due to increases in temperature and at the maximum depth limit due to declining light conditions with depth. Because the persistence of restoration projects is dependent on the feedbacks between hydrodynamics, light attenuation, and temperature at the meadow scale, it is important to consider the effects of light and temperature measurements over time in terms of other stressors such as pore-water chemistry and sediment characteristics to accurately find the maximum and minimum depths for eelgrass growth. This thesis addresses how maximum and minimum depth limits change over an environmental gradient of sediment grain size and organic matter content in the Virginia coastal bays. The impact of changes in light attenuation in terms of water quality and temperature on maximum and minimum depth limits was investigated through spatial analysis of field and bathymetry data. The predicted depth ranges were compared to ranges of transplanted plants along a depth gradient from 0.4 m to 2.0 m MSL (mean sea level) bracketing the known range for eelgrass growth in Hog Island Bay, 0.8 m to 1.6 m MSL. I found that the maximum depth limit for eelgrass growth can be predicted by light levels in areas with low pore-water sulfide concentrations; however, in areas with high sediment pore-water sulfide concentrations there may be a more complex interaction occurring where light requirements increase due to sulfide intrusion. Predicting the minimum depth limit involves considering a more complex interaction between light and temperature. In a mesocosm study, I attempted to separate the effects of light and sediment characteristics, specifically pore-water sulfide concentrations, by measuring optimal quantum yield using a pulse amplitude modulated fluorometer and plant productivity. There was no significant relationship between sediment treatment and photosynthesis; however, I did find a significant effect of light on photosynthesis. This indicates that in this controlled environment, sediment grain size and organic matter content do not have an effect on the efficiency of photosystem II or productivity of eelgrass. However, results may have been skewed due to poor mesocosm set-up and a drop in sediment sulfide concentrations between the field and mesocosm experiments. With this knowledge, these effects can be separated in future experiments and the information from this thesis can be used to constrain eelgrass growth and distribution models in the VCR.
University of Virginia, Department of Environmental Sciences, MS (Master of Science), 2014
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MS (Master of Science)
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