UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
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 given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Dynamics of a large submersed plant bed in upper Chesapeake Bay(2016) Gurbisz, Cassie; Kemp, Michael; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A large SAV bed in upper Chesapeake Bay has experienced several abrupt shifts over the past half-century, beginning with near-complete loss after a record-breaking flood in 1972, followed by an unexpected, rapid resurgence in the early 2000’s, then partial decline in 2011 following another major flood event. Together, these trends and events provide a unique opportunity to study a recovering SAV ecosystem from several different perspectives. First, I analyzed and synthesized existing time series datasets to make inferences about what factors prompted the recovery. Next, I analyzed existing datasets, together with field samples and a simple hydrodynamic model to investigate mechanisms of SAV bed loss and resilience to storm events. Finally, I conducted field deployments and experiments to explore how the bed affects internal physical and biogeochemical processes and what implications those effects have for the dynamics of the system. I found that modest reductions in nutrient loading, coupled with several consecutive dry years likely facilitated the SAV resurgence. Furthermore, positive feedback processes may have played a role in the sudden nature of the recovery because they could have reinforced the state of the bed before and after the abrupt shift. I also found that scour and poor water clarity associated with sediment deposition during the 2011 flood event were mechanisms of plant loss. However, interactions between the bed, water flow, and waves served as mechanisms of resilience because these processes created favorable growing conditions (i.e., clear water, low flow velocities) in the inner core of the bed. Finally, I found that that interactions between physical and biogeochemical processes led to low nutrient concentrations inside the bed relative to outside the bed, which created conditions that precluded algal growth and reinforced vascular plant dominance. This work demonstrates that positive feedbacks play a central role in SAV resilience to both chronic eutrophication as well as acute storm events. Furthermore, I show that analysis of long-term ecological monitoring data, together with field measurements and experiments, can be an effective approach for understanding the mechanisms underlying ecosystem dynamics.Item A Study of Resting State FMRI Dynamic Functional Network Analysis of MTBI(2015) Hou, Wenshuai; JaJa, Joseph; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Mild Traumatic Brain Injury (MTBI) is one of the most common neurological disorders. A subset of patients develop persistent cognitive deficits. A number of the brain studies have been conducted to discover the abnormalities and disruptions in the brain functional networks using similar methods to those employed in more severe brain disorders such as Alzheimer's or Schizophrenia. Static functional network analysis using resting state brain fMRI images has shown some promising results in identifying characteristics of MTBI. However, recent development in the dynamics of functional networks have been able to reveal insightful information about anomalies in brain activities that have not been observed when using traditional static analysis. Our work focuses on both static and dynamic functional analysis of the brain. Our overall analysis pipeline is data-driven using a dataset of 47 MTBI subjects and a demographically matching healthy control group size of 30. The data-driven approach proactively removes noise, focuses on the entire brain functional networks and performs advanced independent component analysis, followed by statistical tests to characterize the functional networks of MTBI patients. A key distinction of our research is the finer labeling of MTBI subject according to their long term 6 months recovery status. Our results suggest that those MTBI subject who suffer prolonged recovery exhibit disturbed functional networks, and slowed dynamism in functional connectivity than those of the healthy control or those MTBI participants who recovered quickly. A number of useful network measurements have been found to capture the states and changes of the brain functional networks for healthy and different types of MTBI subjects in their resting state. We believe that our findings can shed more light into the impact of MTBI on the effectiveness of several functional networks and can contribute to helping clinicians make more informed decisions to aid in the recovery of MTBI patients.