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Characterizing the Function of Bilin Lyases CpeY, and CpeU in Marine Synechococcus RS9916, an Organism that Undergoes Type IV Chromatic Acclimation
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A Modeling System for Integral Simulation of Propagation of Ocean Surge and Wave and Their Impinging on Coastal Structure (Extended Abstract)
Get PDFNow it has become necessary to develop our capability to directly simulate many emerging coastal ocean flow and wave problems. These flow problems present a common challenge to our modeling capability; they involve multiphysics phenomena spanning a vast range of spatial and temporal scales, however, so far essentially we have no methods and computer software to directly and integrally simulate these phenomena. Now it has become necessary for us to develop new capability to model them in efficient ways and high-fidelity. Towards this goal, we have developed a brand new, unprecedented modeling system that is able to directly evaluate many emerging multiscale, multiphysics flow problems. In this presentation, the newly developed modeling system will be introduced, together with numerical experiments and applications
Session 4 Presentation - Improved Coastal and Nearshore Wave Forecasting
Get PDFAccurate nearshore and coastal wave forecasts are essential for the protection of life and property as well as enhancing the economy through safe and efficient commercial activities. Modeling the nearshore environment has remained both computationally intensive and challenging due to the strong interaction of waves with the ocean bottom in shallow water environments. Here we develop a new wave system approach for nearshore wave modeling that addresses these issues. Wave systems result from specific wind forcing events on the ocean surface. This approach extends earlier work on wave system partitioning and tracking to assimilate coastal buoy observations into model output at the wave system level and model wave system transition through the shallow water environment. The resulting model is affectionately called Nessie
Conflict, constraint, and the evolution of the multivariate performance phenotype
Get PDFPerformance is key to survival. From day-to-day foraging events, to reproductive activities, to life-or-death crises, how well an organism performs these tasks can determine success or failure. Selection, therefore, both natural and sexual, act upon performance, and performance demands on individuals shape a population’s morphological and physiological trait distributions. While studies of morphological adaptations to ecological pressures implicitly center on the idea that responses to selection improve performance via changes in morphology, the relationships between morphology, performance, and fitness are not always well understood. In this dissertation, I investigate these relationships explicitly, as well as determine the effects that different ecological and genetic contexts have on selection and how populations respond to performance pressures.
Using a model of lizard locomotor performance, I address three issues that may impact selection on performance that are often overlooked in performance studies. First, performance is not a static trait. Rather, individuals possess a range of performance abilities or intensities that can be expressed as needed. Using a novel, individual-based, quantitative genetic simulation model, I demonstrate the effects of variable performance expression and genetic constraints on how a population experiences and responds to selection on sprint and endurance performance. Second, sex differences in performance are expected in sexually dimorphic species, but empirical evidence for this is lacking. To this end, I measured and analyzed multivariate morphology and performance in Anolis carolinensis to identify sex-specific patterns in functional morphology and functional trade-offs within a broad suite of performance traits. Third, intralocus sexual conflict should constrain the evolution of the multivariate performance phenotype in both sexes. By extending the simulation model to include correlated trait inheritance between sexes and sex-specific selection on certain performance traits, I demonstrate the extent to which this sexual conflict constrains performance evolution. In combining studies of natural populations with simulation studies of selection, this dissertation embraces the complexity of performance to address the multiple contributing factors and constraints on performance evolution, and demonstrates the importance of accounting for such complexity when studying animal performance
Automated Species Classification Methods for Passive Acoustic Monitoring of Beaked Whales
Get PDFThe Littoral Acoustic Demonstration Center has collected passive acoustic monitoring data in the northern Gulf of Mexico since 2001. Recordings were made in 2007 near the Deepwater Horizon oil spill that provide a baseline for an extensive study of regional marine mammal populations in response to the disaster. Animal density estimates can be derived from detections of echolocation signals in the acoustic data. Beaked whales are of particular interest as they remain one of the least understood groups of marine mammals, and relatively few abundance estimates exist. Efficient methods for classifying detected echolocation transients are essential for mining long-term passive acoustic data. In this study, three data clustering routines using k-means, self-organizing maps, and spectral clustering were tested with various features of detected echolocation transients. Several methods effectively isolated the echolocation signals of regional beaked whales at the species level. Feedforward neural network classifiers were also evaluated, and performed with high accuracy under various noise conditions. The waveform fractal dimension was tested as a feature for marine biosonar classification and improved the accuracy of the classifiers. [This research was made possible by a grant from The Gulf of Mexico Research Initiative. Data are publicly available through the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC) at https://data.gulfresearchinitiative.org.] [DOIs: 10.7266/N7W094CG, 10.7266/N7QF8R9K