Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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    PREDICTING THE SALINITY HISTORY OF OYSTERS IN DELAWARE BAY USING OBSERVING SYSTEMS DATA AND NONLINEAR REGRESSION
    (2022) HOWLADER, ARCHI; NORTH, ELIZABETH; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Salinity is a major environmental factor that influences the population dynamics of fish and shellfish along coasts and estuaries, yet methods for predicting the salinity history at specific sampling stations are not widely available. The specific aim of this research was to predict the history of salinity experienced by juvenile and adult oysters (Crassostrea virginica) collected at sampling stations in Delaware Bay as part of the Selection along Estuarine Gradients in Oysters (SEGO) project. To do so, empirical relationships were created to predict salinity at five oyster bed stations using observing systems data and then applied to construct indices of salinity exposure over an oyster’s lifetime. The desired accuracy was +/- 2 psu. Three independent sources of salinity data were used in conjunction with observing systems data to construct and validate the predictive relationships. Observing systems data from the USGS station at Reedy Island Jetty and continuous near-bottom measurements taken by the U.S. Army Corps of Engineers (ACOE) from 2012-2015 and 2018 were employed to fit nonlinear empirical models at each station. Haskin Shellfish Research Laboratory (Haskin) data were used to evaluate model fit, then ACOE data from 2018 (withheld from model fitting in the validation analysis) and SEGO data from 2021 were used to validate models. The best-fitting models for predicting salinity at the oyster bed stations given the salinity at Reedy Island Jetty were logarithmic in form. The root mean square error (RMSE) of the models ranged from 1.3 to 1.6 psu when model predictions were compared with Haskin data, 0.5 to 1.5 when compared with ACOE data, and 0.6 to 0.8 when compared with SEGO data. All of these models were within the desired accuracy range. Results demonstrate that observing systems data can be used for predicting salinity within +/- 2 psu at oyster bed stations within 39 km in upper Delaware Bay. When these models were applied to estimate low salinity exposure of 2-year-old oysters via the metric of consecutive days below 5 psu, the indices suggested that there could be as much as a 42-day difference in low salinity exposure for oysters at stations 31 km apart. This study helps further our understanding of the salt distribution in Delaware Bay as well as the effect of low-salinity stress on the life cycle and genetic differentiation of oysters. In addition, the approach of using observing systems data to predict salinity could be applied to advance understanding of salt distribution and the effect of low salinity exposure on living resources in other estuaries.
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    Historical Shoreline Changes in Response to Environmental Conditions in West Delaware Bay
    (1990) French, Gregory T.; Leatherman, Stephen
    This study quantified historical changes in the coastline of the west shore of Delaware Bay. Shoreline changes were measured through the compilation of historical maps and photographs utilizing the Metric Mapping technique. These changes were correlated with various environmental conditions and with human influences. The results portray a 135 year pattern of overall erosion, with long-term rates averaging -4.5 ftjyr, which is considerably greater than the u.s. Atlantic coast average. Coastal engineering (e.g., groins, jetties and beach nourishment) were locally effective in reducing erosion rates and in some cases promoting limited accretion. Perhaps more importantly, there were few associated negative effects alongshore suggesting that various forms of coastal engineering can be effective in a low-energy environment, even when done in a somewhat unorganized fashion. A correlation was found between erosion rates and underlying Pleistocene morphology. Where pre-Holocene sediments were exposed in the nearshore, erosion rates were lower. However, erosion rates were substantially higher along marshy shorelines. This erosion is not continuous either spatially or temporally, but instead is largely storm-driven. Periods of relative quiescence corresponded with lowered rates of average annual shoreline recession. With the exception of the northernmost marshy areas, severe erosion occurs along all shorelines, regardless of morphology, in response to major coastal storms.