Globally, water bodies are increasingly affected by undesirable harmful algal blooms. This dissertation contributes to research methodology pertaining to quantification of the algal blooms in multiple water bodies of Mississippi using in situ, analytical, and remote sensing techniques. The main objectives of this study were to evaluate the potential of several techniques for phytoplankton enumeration and to develop remote sensing algorithms for several sensors and evaluate the performance of the sensors for quantifying phytoplankton in several water bodies. Analytical techniques such as “FlowCam”, an imaging flow cytometer; “HPLC”, high performance liquid chromatography with the chemical taxonomy program “ChemTax”; spectrofluorometric analyses; and “ELISA” assay were used to quantify a suite of parameters on algal blooms. Additionally, in-situ algal pigment biomass was measured using fluorescence probes. It was found that that each technique has unique potential. While some of the rapid and simpler techniques can be used instead of more involved techniques, sometimes use of several techniques together is beneficial for managing aquatic ecosystems and protecting human health. Algorithms were developed to quantify chlorophyll a using five remote sensing sensors including three currently operational satellite sensors and two popular sensors onboard the Unmanned Aerial Systems (UASs). Empirical band ratio algorithms were developed for each sensor and the best algorithms were chosen. Cluster analysis helped in differentiating the water types and linear regression was used to develop algorithms for each of the water types. The UAS sensor- Micasense was found to be most useful among the UAS sensors and the best overall with highest R2 value 0.75 with p<0.05 and minimum %RMSE of 28.22% and satellite sensor OLCI was found to be most efficient among the three satellite sensors used in the study for chlorophyll a estimation with R2 of 0.75 with p<0.05 and %RMSE 13.19%. The algorithms developed for these sensors in this study represent the best algorithms for chlorophyll a estimation in these water bodies based on R2 and %RMSE. The applicability of the algorithms can be extended to other water bodies directly or the approach developed in this study can be adopted for estimating Chl a in other water bodies.

No description available.

published_or_final_version / Ecology and Biodiversity / Doctoral / Doctor of Philosophy

Macroalgal blooms are responses to nutrient enrichment in shallow seagrass ecosystems like the Indian River Lagoon (IRL), Florida. Little is known about nitrogen (N) and phosphorus (P) limitation or the importance of morphological/physiological characteristics of bloom-forming macroalgae (Ulva lactuca, Hypnea musciformis, and Gracilaria tikvahiae) in the IRL. We hypothesized: 1) all species would proliferate in nutrient-rich Titusville, 2) opportunistic U. lactuca would dominate, 3) Rapid Light Curves (RLCs) would assess nutrient status, and 4) nutrient concentrations would regulate growth more than N:P ratios. Field studies showed rapid biomass doubling times of 2 days (U. lactuca; November 2012) in urbanized Titusville. RLCs in a guano-enriched island off Big Pine Key (BPK) and Titusville (Ulva spp.) were similar due to P-saturation. Laboratory studies showed three-fold higher RLCs and two-fold faster growth at high nutrient concentrations of N and P. Reductions of both N and P will be required to moderate future blooms. / by Lisa N.A. Vlaming. / Thesis (M.S.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

Historical water quality databases from two South Australian drinking water reservoirs were used, in conjunction with various computational modelling methods for the ordination, clustering and forecasting of complex ecological data. Techniques used throughout the study were: Kohonen artificial neural networks (KANN) for data categorisation and the discovery of patterns and relationships, recurrent supervised artificial neural networks (RANN) for knowledge discovery and forecasting of algal dynamics and hybrid evolutionary algorithms (HEA) for rule-set discovery and optimisation for forecasting algal dynamics. These methods were combined to provide an integrated approach to the analysis of algal populations including interactions within the algal community and with other water quality factors, which results in improved understanding and forecasting of algal dynamics. The project initially focussed on KANN for the patternising and classification of the historical data to reveal links between the physical, chemical and biological components of the reservoirs. This offered some understanding of the system and relationships being considered for the construction of the forecasting models. Specific investigations were performed to examine past conditions and the impacts of different management regimes, as well as to discover sets of conditions that correspond with specific algal functional groups. RANN was then used to build models for forecasting both Chl-a and the main nuisance species, Anabaena, up to 7 days in advance. This method also provided sensitivity analyses to demonstrate the relationship between input and output variables by plotting the reaction of the output to variations in the inputs. Initially one year from the data set was selected for the testing of a model, as per the split-sample technique. To further test the models, it was later decided to select several years for testing to ensure the models were useful under changed conditions, and that test results were not misleading regarding the models true capabilities. RANN were firstly used to create reservoir specific or ad-hoc models. Later, the models were trained with the merged data sets of both reservoirs to create one model that could be applied to either reservoir. Another method of forecasting was trialled and compared to RANN. HEA was found to be equal or superior to RANN in predictive power, also allowed sensitivity analysis and provided an explicit, portable rule set. The HEA rule sets were initially tested on selected years of data, however to fully demonstrate the models potential, a process for k-fold cross-validation was developed to test the rule-set on all years of data. To further extend the applicability of the HEA rule-set; the idea of rule-based agents for specific lake ecosystem categories was examined. The generality of a rule-based agent means that, after successful validation on several lakes from one category, the agent could then be applied to other water bodies from within that category that had not been involved in the training process. The ultimate test of the rule-based agent for the warm monomictic and eutrophic lake ecosystem category was to be applied to a real-time monitoring and forecasting situation. The agent was fed with online, real-time data from a reservoir that belonged to the same ecosystem category but was not used in the training process. These preliminary experiments showed promising results. It can be concluded that the concept of rulebased agents will facilitate real-time forecasting of algal blooms in drinking water reservoirs provided on-line monitoring of relevant variables has been implemented. Contributions of this research include: (1) to offer insight into the capabilities of 3 kinds of computational modelling techniques applied to complex water quality data, (2) novel applications of KANN including the division of data into separate management periods for comparison of management efficiency, (3) to both qualitatively and quantitatively elucidate relationships between water quality parameters, (4) research toward the development of a forecasting tool for algal abundance 7 days in advance that could be generic for a particular lake ecosystem category and implemented in real-time, and (5) to suggest a thorough testing method for such models (k-fold cross validation). / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1331584 / Thesis (Ph.D.) -- University of Adelaide, School of Earth and Environmental Sciences, 2008

Harmful algal blooms, commonly referred to as red tides, occur yearly with dramatic impacts on marine ecology, coastal economies, and human health. As a consequence, research into the zooplankton grazers that consume HABs is highly important. However, changes in ocean temperature may increase the range of many HABs, exposing historically naive copepods to new species and their associated chemicals. Little research into the impact of allopatric verses sympatric species, particularly on the immediate behavioral impact, has been performed, leaving the indirect fitness effects of HAB exposure and consumption relatively unknown. We measured alterations in the swimming behavior of the calanoid copepod Temora longicornis following exposure to sympatric Alexandrium fundyense and allopatric Karenia brevis treatments. After a 15-16 hours depuration period postA. fundyense exposure, T. longicornis exhibited increased average swimming speed and an elevated net to gross displacement ratio (NGDR). During exposure toK. brevis, copepods exhibited an immediate decrease in swimming speed and NGDR, as well as an increased frequency of jump behavior. However, these effects faded after an one-hour depuration period, and disappeared after a 15-16 hour depuration period. The alterations in swimming behavior demonstrated by the copepods treated A. fundyense may increase encounter rate with predators, while copepods treated with K. brevis remain in bloom conditions for longer periods of time, negatively affecting survivorship. Temora longicornis individuals also may be made more visible to predators due to the increase in jumps seen during treatment with K. brevis. These behavioral changes suggest how HABs escape from zooplankton grazer control by altering copepod swimming behavior, and the pattern of predator-prey evolution that occurs over time.

Thesis (M.S.)--University of North Carolina at Wilmington, 2005. / Includes bibliographical references (leaves: [70]-74)

Algal blooms plague freshwaters across the globe, as increased nutrient loads lead to eutrophication of inland waters and the presence of potentially harmful cyanobacteria. In this context, remote sensing is a valuable approach to monitor water quality over broad temporal and spatial scales. However, there remain several challenges to the accurate retrieval of water quality parameters, and the research in this thesis investigates these in an optically complex lake (Lake Balaton, Hungary). This study found that bulk and specific inherent optical properties [(S)IOPs] showed significant spatial variability over the trophic gradient in Lake Balaton. The relationships between (S)IOPs and biogeochemical parameters differed from those reported in ocean and coastal waters due to the high proportion of particulate inorganic matter (PIM). Furthermore, wind-driven resuspension of mineral sediments attributed a high proportion of total attenuation to particulate scattering and increased the mean refractive index (n̅p) of the particle assemblage. Phytoplankton pigment concentrations [chlorophyll-a (Chl-a) and phycocyanin (PC)] were also accurately retrieved from a times series of satellite data over Lake Balaton using semi-analytical algorithms. Conincident (S)IOP data allowed for investigation of the errors within these algorithms, indicating overestimation of phytoplankton absorption [aph(665)] and underestimation of the Chl-a specific absorption coefficient [a*ph(665)]. Finally, Chl-a concentrations were accurately retrieved in a multiscale remote sensing study using the Normalized Difference Chlorophyll Index (NDCI), indicating hyperspectral data is not necessary to retrieve accurate pigment concentrations but does capture the subtle heterogeneity of phytoplankton spatial distribution. The results of this thesis provide a positive outlook for the future of inland water remote sensing, particularly in light of contemporary satellite instruments with continued or improved radiometric, spectral, spatial and temporal coverage. Furthermore, the value of coincident (S)IOP data is highlighted and contributes towards the improvement of remote sensing pigment retrieval in optically complex waters.

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published_or_final_version / Civil Engineering / Master / Master of Philosophy