Biology Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2749
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Item SHIFTING INPUTS AND TRANSFORMATIONS OF NITROGEN IN FORESTED AND MIXED LAND USE BASINS: IMPLICATIONS FOR HYDROLOGIC NITROGEN LOSS(2018) Sabo, Robert Daniel; Eshleman, Keith N.; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Increased N inputs along with changes in population, land use, and climate have globally altered the N cycle. This alteration has been associated with increased food, energy, and fiber availability, but has also contributed to the degradation of human health conditions and diminishment of expected ecosystem services in many regions throughout the world. In this context, my research explored the impact of shifting anthropogenic N inputs and other environmental drivers on terrestrial N surpluses and linked changes in terrestrial surpluses to observed changes in N loss to aquatic systems. Working in both forested and mixed land use catchments in the eastern USA, I hypothesized that processes that reduced terrestrial N surpluses in catchments by 1) reducing N inputs, 2) increasing plant uptake, and/or 3) increasing gaseous efflux would result in decreased hydrologic N export. Identification of potential processes was accomplished by first generating long-term atmospheric, remote sensing, terrestrial, and hydrologic datasets for individual catchments. The first two components of my dissertation highlighted potential interactions between atmospheric N deposition, acidic deposition, climate, and disturbance in influencing terrestrial N availability, as indicated by N isotopes in tree rings, in forested catchments. Leveraging trend analysis and statistical models, I identified continued long-term declines in terrestrial N availability in forests, but this decline was likely being modified by disturbance and long-term reductions in acidic deposition. The final component of my dissertation involved developing a lumped conceptual model to explain water quality trends in three mixed land use catchments within the Chesapeake Bay watershed. This study assessed the relative influence of point source N loading, agricultural practices, and atmospheric N deposition on long-term trends in riverine N loss. Insights from the simple N loading model strongly suggested that declines in atmospheric N deposition and point source loading were key drivers of historical water quality improvement. Whether relying on quasi-mass balances or dendroisotopic records, findings from this research emphasize the usefulness of constructing proxy datasets of terrestrial N surpluses in identifying likely processes driving changes in hydrologic N loss in forested and mixed land use catchments.Item Memory-related cognitive modulation of human auditory cortex: Magnetoencephalography-based validation of a computational model(2008-04-09) Rong, Feng; Contreras-Vidal, José L; Neuroscience and Cognitive Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)It is well known that cognitive functions exert task-specific modulation of the response properties of human auditory cortex. However, the underlying neuronal mechanisms are not well understood yet. In this dissertation I present a novel approach for integrating 'bottom-up' (neural network modeling) and 'top-down' (experiment) methods to study the dynamics of cortical circuits correlated to shortterm memory (STM) processing that underlie the task-specific modulation of human auditory perception during performance of the delayed-match-to-sample (DMS) task. The experimental approach measures high-density magnetoencephalography (MEG) signals from human participants to investigate the modulation of human auditory evoked responses (AER) induced by the overt processing of auditory STM during task performance. To accomplish this goal, a new signal processing method based on independent component analysis (ICA) was developed for removing artifact contamination in the MEG recordings and investigating the functional neural circuits underlying the task-specific modulation of human AER. The computational approach uses a large-scale neural network model based on the electrophysiological knowledge of the involved brain regions to simulate system-level neural dynamics related to auditory object processing and performance of the corresponding tasks. Moreover, synthetic MEG and functional magnetic resonance imaging (fMRI) signals were simulated with forward models and compared to current and previous experimental findings. Consistently, both simulation and experimental results demonstrate a DMSspecific suppressive modulation of the AER and corresponding increased connectivity between the temporal auditory and frontal cognitive regions. Overall, the integrated approach illustrates how biologically-plausible neural network models of the brain can increase our understanding of brain mechanisms and their computations at multiple levels from sensory input to behavioral output with the intermediate steps defined.