The objective of this dissertation is to gain a better understanding of the environmental and climatic effects on the temporal and spatial variability of phytoplankton biomass along the West Florida Shelf. Chapter 1 examines temporal and spatial patterns in chlorophyll concentrations using satellite data collected between 1997 and 2003. Chlorophyll data derived from the SeaWiFS sensor are validated with in-situ data and analyzed. Wind, current, sea surface temperature, river, and rain data are used to better understand the factors responsible for the patterns observed in the satellite data. My question is whether the standard OC4 algorithm is adequate for studying short-term variability of chlorophyll concentrations along the WFS. I will examine temporal and spatial trends using the OC4 and compare them to the Carder semianalytical algorithm which uses remote sensing reflectances at 412nm, 443nm, 490nm,and 555nm to estimate chlorophyll concentrations separately from CDOM estimates. In Chapters 2 and 3 the potential problems due to CDOM and bottom reflectance are examined. In Chapter 2 I analyze the influence of riverine induced CDOM. Water leaving radiances are analyzed in an effort to discriminate true chlorophyll patterns from CDOM contaminated signals. Chapter 3 examines the impact of bottom reflectance on the satellite signal by using the percentage of remote sensing reflectance at a wavelength of 555 to differentiate between optically shallow waters and optically deep waters. Optically shallow waters are defined as those with the percentage of Rrs at 555 due to bottom reflectance greater than or equal to 25 percent, while optically deep waters have percent bottom reflectance less than or equal to 25 percent. These analyses will help assess the validity of the temporal and spatial patterns ofchlorophyll concentration observed with the SeaWiFS data described in Chapter 1.

Some climate models predict that, within the next 30-50 years, sea surface temperatures (SSTs) will frequently exceed the current thermal tolerance of corals (Fitt et al. 2001; Hughes et al. 2003; Hoegh-Guldberg et al. 2007). A potential consequence is that mass coral bleaching may take place (i) during warm El Niño-Southern Oscillation (ENSO) events which are predicted to occur in some regions more frequently than the current 3-7 year periodicity (Hoegh-Guldberg 1999; Sheppard 2003) or (ii) perhaps as often as annually or biannually if corals and their symbionts are unable to acclimate to the higher SSTs (Donner et al. 2005, 2007). Global data also indicate an upward trend toward increasing frequencies, intensities, and durations of tropical hurricanes and cyclones (Emanual 2005; Webster et al. 2005). As coral communities have been shown to require at least 10-30 years to recover after a major disturbance (e.g. Connell 1997; Ninio et al. 2000; Bruno & Selig 2007; Burt et al. 2008), it is possible that future coral communities may be in a constant state of recovery, with regeneration times exceeding the periods between disturbances. Life history traits (e.g. reproduction, recruitment, growth and mortality) vary among species of hard corals; thus, gradients in community structures may have a strong influence on susceptibilities to disturbance and rates of recovery (Connell 1997; Ninio & Meekan 2002). Taxa which are more susceptible to bleaching and mechanical disturbance (e.g. tabular and branching acroporids and pocilloporids) may experience continual changes in population structure due to persistent cycles of regeneration or local extirpation, while the more resistant taxa (e.g. massive poritids and faviids) may display relatively stable population structures (Woodley et al. 1981; Hughes & Connell 1999; Baird & Hughes 2000; Marshall & Baird 2000; Loya et al. 2001; McClanahan & Maina 2003). Determining whether resistant coral taxa have predictable responses to disturbances, with consistent patterns over wide spatial scales, may improve predictions for the future affects of climate change and the composition of reefs (Done 1999; Hoegh-Guldberg 1999; McClanahan et al. 2004). The work presented in this dissertation describes the spatial and temporal patterns in community structures for high latitude coral assemblages that have experienced the types of natural disturbances which are predicted to occur in tropical reef systems with increasing frequency as a result of climate change. The primary area of focus is the southeastern Arabian Gulf, where the coral communities are exposed to natural conditions that exceed threshold limits of corals elsewhere in the world, with annual temperature ranges between 14-36°C (Kinzie 1973; Shinn 1976) and salinities above 40 ppt. Two additional regions are included in this study for comparisons of high latitude coral community structures. The northwestern Gulf of Oman is adjacent to the southeastern Arabian Gulf (i.e. the two bodies of water are connected by the Strait of Hormuz); however, the environmental conditions are milder in the Gulf of Oman such that the number of coral taxa therein is threefold that found in the southeastern Arabian Gulf (i.e. 107 coral species in the Gulf of Oman compared to 34 species in this region of the Arabian Gulf (Riegl 1999; Coles 2003; Rezai et al. 2004)). Broward County, Florida is geographically remote from the Gulfs and, therefore, serves as a benchmark for testing whether consistent patterns in community structures exist despite different climatic and anthropogenic influences. The coral communities within the southeastern Arabian Gulf, the northwestern Gulf of Oman, and Broward County, Florida have been exposed to recurrent elevated sea surface temperature (SST) anomalies, sequential cyclone and red tide disturbances, and frequent hurricanes and tropical storms, respectively. These disturbances and other impacts (e.g. bleaching episodes, disease outbreaks, anthropogenic stresses) have affected the more susceptible acroporids and pocilloporids, resulting in significant losses of coral cover by these families and shifts towards massive corals as the dominant taxa. During the post-disturbance scarcity or absence of branching and tabular corals, the resistant massive taxa have become the crux of the essential hard coral habitat for fish, invertebrates and other marine organisms. Because recovery to pre-disturbance community structures may take decades or may not occur at all, it is vital that scientists and resource managers have a better understanding of the spatial and temporal ecology patterns of the corals that survive and fill in the functional gaps that are created by such disturbances. To aid in this understanding, this dissertation presents spatial and temporal patterns for the coral assemblages which have developed after the respective disturbances. Spatial ecology patterns are analyzed using graphical descriptions (e.g. taxa inventories, area cover, densities, size frequency distributions), univariate techniques (e.g. diversity indices), distributional techniques (e.g. k-dominance curves) and multivariate techniques (e.g. hierarchical clustering, multidimensional scaling). Temporal comparisons at monitoring sites within the southeastern Arabian Gulf and northwestern Gulf of Oman describe the coral population dynamics and are used to create size class transition models that project future population structures of massive corals in the recovering habitats.

Metabolomics is the methodical scientific study of biochemical processes associated with the metabolome—which comprises the entire collection of metabolites in any biological entity. Metabolome changes occur as a result of modifications in the genome and proteome, and are, therefore, directly related to cellular phenotype. Thus, metabolomic analysis is capable of providing a snapshot of cellular physiology. Untargeted metabolomics is an impartial, all-inclusive approach for detecting as many metabolites as possible without a priori knowledge of their identity. Hence, it is a valuable exploratory tool capable of providing extensive chemical information for discovery and hypothesis-generation regarding biochemical processes. A history of metabolomics and advances in the field corresponding to improved analytical technologies are described in Chapter 1 of this dissertation. Additionally, Chapter 1 introduces the analytical workflows involved in untargeted metabolomics research to provide a foundation for Chapters 2 – 5. Part I of this dissertation which encompasses Chapters 2 – 3 describes the utilization of mass spectrometry (MS)-based untargeted metabolomic analysis to acquire new insight into cancer detection. There is a knowledge deficit regarding the biochemical processes of the origin and proliferative molecular mechanisms of many types of cancer which has also led to a shortage of sensitive and specific biomarkers. Chapter 2 describes the development of an in vitro diagnostic multivariate index assay (IVDMIA) for prostate cancer (PCa) prediction based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) metabolic profiling of blood serum samples from 64 PCa patients and 50 healthy individuals. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent prostate-specific antigen blood test, thus, highlighting that a combination of multiple discriminant features yields higher predictive power for PCa detection than the univariate analysis of a single marker. Chapter 3 describes two approaches that were taken to investigate metabolic patterns for early detection of ovarian cancer (OC). First, Dicer-Pten double knockout (DKO) mice that phenocopy many of the features of metastatic high-grade serous carcinoma (HGSC) observed in women were studied. Using UPLC-MS, serum samples from 14 early-stage tumor DKO mice and 11 controls were analyzed. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for early-stage HGSC detection. In the second approach, serum metabolic phenotypes of an early-stage OC pilot patient cohort were characterized. Serum samples were collected from 24 early-stage OC patients and 40 healthy women, and subsequently analyzed using UPLC-MS. Multivariate statistical analysis employing support vector machine learning methods and recursive feature elimination selected a panel of metabolites that differentiated between age-matched samples with 100% cross-validated accuracy, sensitivity, and specificity. This small pilot study demonstrated that metabolic phenotypes may be useful for detecting early-stage OC and, thus, supports conducting larger, more comprehensive studies. Many challenges exist in the field of untargeted metabolomics. Part II of this dissertation which encompasses Chapters 4 – 5 focuses on two specific challenges. While metabolomic data may be used to generate hypothesis concerning biological processes, determining causal relationships within metabolic networks with only metabolomic data is impractical. Proteins play major roles in these networks; therefore, pairing metabolomic information with that acquired from proteomics gives a more comprehensive snapshot of perturbations to metabolic pathways. Chapter 4 describes the integration of MS- and NMR-based metabolomics with proteomics analyses to investigate the role of chemically mediated ecological interactions between Karenia brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana. This integrated systems biology approach showed that K. brevis allelopathy distinctively perturbed the metabolisms of these two competitors. A. glacialis had a more robust metabolic response to K. brevis allelopathy which may be a result of its repeated exposure to K. brevis blooms in the Gulf of Mexico. However, K. brevis allelopathy disrupted energy metabolism and obstructed cellular protection mechanisms including altering cell membrane components, inhibiting osmoregulation, and increasing oxidative stress in T. pseudonana. This work represents the first instance of metabolites and proteins measured simultaneously to understand the effects of allelopathy or in fact any form of competition. Chromatography is traditionally coupled to MS for untargeted metabolomics studies. While coupling chromatography to MS greatly enhances metabolome analysis due to the orthogonality of the techniques, the lengthy analysis times pose challenges for large metabolomics studies. Consequently, there is still a need for developing higher throughput MS approaches. A rapid metabolic fingerprinting method that utilizes a new transmission mode direct analysis in real time (TM-DART) ambient sampling technique is presented in Chapter 5. The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. In terms of reproducibility, TM-DART compared favorably with traditional probe mode DART analysis, with coefficients of variation as low as 16%. TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera and was adapted for exhaled breath condensate (EBC) analysis. To determine the feasibility of utilizing TM-DART for metabolomics investigations, TM-DART was interfaced with traveling wave ion mobility spectrometry (TWIMS) time-of-flight (TOF) MS for the analysis of EBC samples from cystic fibrosis patients and healthy controls. TM-DART-TWIMS-TOF MS was able to successfully detect cystic fibrosis in this small sample cohort, thereby, demonstrating it can be employed for probing metabolome changes. Finally, in Chapter 6, a perspective on the presented work is provided along with goals on which future studies may focus.