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OBJECTIVES: The overall objective is to explain recent increases in submersed aquatic vegetation (SAV) in upper Bay regions by analyzing existing long-term time-series data on SAV, water quality, climate and ecological conditions. This study focuses on the historically important Susquehanna Flats SAV bed, which has experienced a dramatic, but yet unexplained resurgence. We will rigorously analyze parallel dataset for trends, periodicities, change-points, covariance, and time-lags. We hypothesize (1) that these patterns will be related to complex interactions and subtle changes in water quality during key seasons and (2) that climate cycles can exert overriding controls on environmental conditions. We will translate these findings into statistical models that will serve to improve SAV model skills and restoration effectiveness.
METHODOLOGY: Time-series data (and proxies) for SAV, water quality, ecological and climate variables from Susquehanna Flats region will be compiled into an existing data management system for graphical and statistical analyses. Data series for various indices of SAV abundance are available (at 1-2 yr intervals) for 3 periods: 1958-1975, 1978-2010, and 1880-1980. Water quality and climate data are available at frequencies ranging from min to hr to wk to mo over the period or record. We will use a suite of methods for statistical and spectral analysis to identify and characterize trends, break-points, and time-lags, and to test for correlation, covariance and coherence within and among time-series. These and other analyses (e.g., ARIMA, CART, PCA, GAM) will be used to infer effects of climatic and ecological factors on SAV responses to water quality conditions and to explain the unexpected SAV resurgence.
RATIONALE: Effective management of SAV beds in Chesapeake Bay requires improved understanding of interacting water quality, ecological and climatic controls. Although early SAV simulation models generally captured regional and seasonal variations in plant abundance, recent analyses with these models have failed to reproduce observed trends of SAV recovery. Data analysis in this project combined with recent findings in previous projects will help to refine SAV simulation models and to improve site-selection protocols for dependable SAV restoration.
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.
RELEVANCE: Effective management of submerged aquatic vegetation (SAV) beds in Chesapeake Bay requires improved understanding of how water quality, climate, and other factors influence the beds' growth and survival. Early SAV simulation models have failed to reproduce observed trends of SAV recovery. One research project by this investigator was undertaken to help refine SAV simulation models and improve site selection for restoration. Another research project by the same investigator focused on the Susquehanna Flats' SAV bed in the upper Chesapeake Bay. The bed experienced a dramatic, but unexplained, resurgence and then was disrupted by back-to-back tropical storms in 2011. Restoration of SAV is important for improving water quality and fish habitat in the Chesapeake Bay.
RESPONSE: The principal investigator on this project is W. Michael Kemp, University of Maryland Center for Environmental Science, Horn Point Laboratory. The researchers have conducted aerial photo surveys and have collected samples for water quality and plant biomass analyses during several field visits. They also measured temperature, salinity, oxygen, chlorophyll, and turbidity.
RESULTS: Strong feedback processes are likely important in hastening recovery, according to time-series data about water quality that were collected inside and outside SAV beds. The growth of SAV improved water clarity within SAV beds, which in turn facilitated the further growth of SAV. This growth was influenced by high river discharge from the Susquehanna River caused by storms; the sediment-laden discharges tended to degrade water quality, but only extreme storms tended to overwhelm the feedback processes. During drought years, river flow declined; water quality improved, providing favorable growth conditions; and the bed expanded.
Researchers have met with colleagues from numerous agencies who have expressed interest in the study's implications for improving water quality models. A better understanding of the processes affecting the bed's growth may influence computation of TMDL (total maximum daily load) allocations, which restrict the loads of nutrients and sediments that may be released into the Bay. The researchers also presented their results to teachers and high-school and college biology students.
Gurbisz, CW; Kemp, M. 2014. Unexpected resurgence of a large submersed plant bed in Chesapeake Bay: Analysis of time series data. Limnology & Oceanography59(2):482 -494. doi:10.4319/lo.2014.59.2.0482. UM-SG-RS-2014-03.