High winter respiration has been observed in a subarctic estuary with high levels of organic matter inputs, while winter is generally thought to be a non-productive season. We constructed an oxygen and carbon budget of the system to validate the high respiration rate, including the resulting low production-to-respiration ratio, and to identify important carbon and energy sources. Measurement data of production and respiration parameters from running monitoring programs were used. Furthermore, we set up a microcosm experiment in order to study effects of iron increases by riverine organic matter inputs. The carbon balance of this subarctic estuary shows a small deficiency of carbon on an annual basis, but is able to explain how winter respiration is fueled by carbon fixed in the autumnal season and inputs of riverine material. Also, the balance calculation was able to predict oxygen deficiencies on a seasonal basis. The effect of riverine organic matter on biological activity was clearly present, but iron did not appear to affect responses in primary or secondary producers. Additional studies will be needed to fully understand the role of iron additions to marine microbial communities, particularly focusing on fractioning of iron and organic matter species.

Among the these proposed methods (i.e., random subspace, bagging, and pairwise classification), the pairwise classification method produces the highest accuracy at the expense of more computation time for training classifiers. The random subspace method and bagging approach have similar performance. To recognize a testing plankton pattern, the computational costs of the these methods are alike. / Due to the complexity of plankton recognition problem, it is difficult to pursue a single optimal classifier to meet all the requirements. In this work, instead of developing a single sophisticated classifier, we propose an ensemble learning framework based on the random sampling techniques including random subspace and bagging. In the random subspace method, a set of low-dimensional subspaces are generated by randomly sampling on the feature space, and multiple classifiers constructed from these random subspaces are combined to yield a powerful classifier. In the bagging approach, a number of independent bootstrap replicates are generated by randomly sampling with replacement on the training set. A classifier is trained on each replicate, and the final result is produced by integrating all the classifiers using majority voting. Using random sampling, the constructed classifiers are stable and multiple classifiers cover the entire feature space or the whole training set without losing discriminative information. Thus, good performance can be achieved. Experimental results demonstrate the effectiveness of the random sampling techniques for improving the system performance. / On the other hand, in previous approaches, normally the samples of all the plankton classes are used for a single classifier training. It may be difficult to select one feature space to optimally represent and classify all the patterns. Therefore, the overall accuracy rate may be low. In this work, we propose a pairwise classification framework, in which the complex multi-class plankton recognition problem is transformed into a set of two-class problems. Such a problem decomposition leads to a number of simpler classification problems to be solved, and it provides an approach for independent feature selection for each pair of classes. This is the first time for such a framework introduced in plankton recognition. We achieve nearly perfect classification accuracy on every pairwise classifier with less number of selected features, since it is easier to select an optimal feature vector to discriminate the two-class patterns. The ensemble of these pairwise classifiers will increase the overall performance. A high accuracy rate of 94.49% is obtained from a collection of more than 3000 plankton images, making it comparable with what a trained biologist can achieve by using conventional manual techniques. / Plankton including phytoplankton and zooplankton form the base of the food chain in the ocean and are a fundamental component of marine ecosystem dynamics. The rapid mapping of plankton abundance together with taxonomic and size composition can help the oceanographic researchers understand how climate change and human activities affect marine ecosystems. / Recently the University of South Florida developed the Shadowed Image Particle Profiling and Evaluation Recorder (SIPPER), an underwater video system which can continuously capture the magnified plankton images in the ocean. The SIPPER images differ from those used for most previous research in four aspects: (i) the images are much noisier, (ii) the objects are deformable and often partially occluded, (iii) the images are projection variant, i.e., the images are video records of three-dimensional objects in arbitrary positions and orientations, and (iv) the images are binary thus are lack of texture information. To deal with these difficulties, we implement three most valuable general features (i.e., moment invariants, Fourier descriptors, and granulometries) and propose a set of specific features such as circular projections, boundary smoothness, and object density to form a more complete description of the binary plankton patterns. These features are translation, scale, and rotation invariant. Moreover, they are less sensitive to noise. High-quality features will surely benefit the overall performance of the plankton recognition system. / Since all the features are extracted from the same plankton pattern, they may contain much redundant information and noise as well. Different types of features are incompatible in length and scale and the combined feature vector has a higher dimensionality. To make the best of these features for the binary SIPPER plankton image classification, we propose a two-stage PCA based scheme for feature selection, combination, and normalization. The first-stage PCA is used to compact every long feature vector by removing the redundant information and reduce noise as well, and the second-stage PCA is employed to compact the combined feature vector by eliminating the correlative information among different types of features. In addition, we normalize every component in the combined feature vector to the same scale according to its mean value and variance. In doing so, we reduce the computation time for the later recognition stage, and improve the classification accuracy. / Zhao Feng. / "May 2006." / Adviser: Xiaoou Tang. / Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6666. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 121-136). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

No description available.

Graduation date: 1963

Harmful algal blooms' global amplification has driven research on growth characteristics and instigating mechanisms. These blooms prosper under diverse environmental conditions, creating challenges identifying bloom initiation. The haptophyte, Prymnesium parvum, plagues the southwestern United States with massive system disruptions and huge fish kills caused by its toxin. Despite many abiotic factors' association with P. parvum blooms, low nutrient levels stress the alga increasing toxin production, eliminating nutrient competition, and alleviating grazing pressures. This model examines the relationship between nutrient availability and P. parvum toxin production against another phytoplankton and a single grazing zooplankton, using a Monod function relating population growth rate with limiting nutrient concentrations. Sensitivity analyses emphasize plankton biological parameters most influential in accumulating biomass. The impact of toxin production on zooplankton grazing rates underscores P. parvum's need for top-down control suppression. The toxin production equation increases production when P. parvum experiences low specific growth rates from nutrient availability and low biomass. This equation is analyzed against previously published allelopathic relationships, comparing plankton reactions and bloom endurance. The model's toxin production equation proves more ecologically feasible, incorporating competing phytoplankton species' mortality and variables easily verified through laboratory experiments. Though not intended for management strategy development, the model explores and supports the proposed strategy of incorporating hydraulic flushing, pulsed and continuous inflows, to eliminate biomass accumulation. Inflows relieve stressful nutrient-limiting conditions, introducing resources affecting bloom stability and plankton community dynamics. The faster-growing competing phytoplankton gains survival advantages when inflow rates fall lower than its maximum specific growth rate, but greater than P. Parvum's, emphasizing the accurate measuring of competitors' maximum specific growth rates and identifying a dilution rate range where P. parvum loses at nutrient intake. Inflows with various nutrient levels representing different source waters from freshwater lakes were tested for impacts on plankton dynamics. Adding any hydrological effect reduced P. parvum biomass. Disruptions create disturbance, removing P. parvum's system-dominating position, allowing the phytoplankton to exceed P. parvum's density. The model highlights the importance of P. parvum's toxin's presence to maintain dominance and emphasizes flushing agitation as potential and feasible management schemes to deter bloom continuation and increase species diversity.

Po-210 and Pb-210 are particle-reactive natural radionuclides which can be removed from seawater by their incorporation with various particulate materials, resulting in radioactive disequilibria with respect to their parent nuclides. Previous studies showed that Po-210/Pb-210 activity ratio in surface seawater ranges from about 0.2 to 1 due to atmospheric input of Pb-210 and effects of biological productivity and upwelling. In order to evaluate the effects of plankton on Po-210 and Pb-210 distributions, plankton samples were collected by surface-trawling of plankton net in Taiwanese water. These plankton samples were analyzed for Po-210 and Pb-210 in order to obtain their concentrations and activity ratio, which may indicate the enrichment or discrimination of these nuclides in the marine planktonic phase around Taiwan. The results show that the Po-210/Pb-210 activity ratio of planktonic samples ranges from 23.06 to 51.40 in northeastern Taiwan, whereas the activity ratio in southwest Taiwan varies within and outside of the sampleing locations: stations 1, 2, 3 and 4 yield range of 10.72-43.90, 16.40-47.87, 13.86-23.37 and 9.35-13.82, respectively. Two marine algae (Sargassum polycystum C.Agardh and Hypnea charoides Lamouroux) yield values of 2.24-8.57, and 3.72-5.96, respectively. These Po-210/Pb-210 activity ratios are much greater than unity, and their activities comparison to those of inorganic particulates support the contention that the plankton preferentially incorporates Po-210 but discriminates against Pb-210. However, the mechanism or process of Po-210 to be incorporated in the plankton remains unclear. In comparison of the activities between Po-210 and Pb-210 in organic particulates, one finds that Po-210 is effectively enriched while Pb-210 is clearly discriminatied by the organic particulates. These results are consistent with those reported in literatures.

Thesis (Ph. D.)--York University, 2000. / Typescript. Includes bibliographical references. Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ56267.

Thesis (M.S.)--University of North Carolina at Wilmington, 2003.

As the base of the food chain, plankton affect the cycling of nutrients and organic matter within ecosystems and support production at higher trophic levels. The overall goal of this project was to examine how natural water quality fluctuations, such as changes in nutrients, temperature, and salinity, influence estuarine plankton community structure. To achieve this, I examined water quality as well as the diversity and biomass of eukaryotic plankton communities in a subtropical estuary located within the Mission-Aransas National Estuarine Research Reserve. The sampling sites included in this study consisted of three bay (Copano Bay West, Copano Bay East, Aransas Bay) and two river (Mission River Estuary, Aransas River Estuary) estuary sites. Water samples were collected monthly at the five sites from September 2011 to August 2012 and analyzed for a suite of abiotic and biotic variables. Eukaryotic plankton diversity and community structure were evaluated by using the terminal restriction fragment length polymorphism (t-RFLP) method. Although a narrow salinity gradient was present at the sampling sites, seasonal changes in water quality conditions were observed. In the river estuaries, water quality parameters defined three significant temporal periods at the Mission River Estuary site, whereas only one month differed at the Aransas River Estuary site, indicating little seasonal variation. The Copano Bay sites exhibited a seasonal pattern consisting of four periods, marked by a distinct fall (October, November, December) grouping, while Aransas Bay showed a seasonal pattern consisting of three periods, with no fall group. Even though the water quality conditions define different monthly groupings in the bay and river estuary sites, the same parameters – DOC, TDN, and pH – are the strongest drivers of the patterns at all of the sites. Seasonal and spatial distinctions in the Mission-Aransas Estuary eukaryotic plankton community composition were determined using t-RFLP. Frequent shifts in composition were apparent across samples collected at approximately bi-weekly to monthly intervals. There were significant differences (ANOSIM, p < 0.05) in community composition between the Aransas and Mission River Estuary and Aransas Bay sites. Although the overall ANOSIM tests show significance between eukaryotic plankton communities monthly and between the bay water quality periods, none of the pairwise comparisons were significantly different. However, the ANOSIM R-statistic for the monthly pairwise comparisons displays a general increasing trend over time from sampling, further highlighting the dynamic nature of the microbial eukaryotic assemblage within sites. / text

Pelagic ecosystems are inherently complex in coastal inlets where they are controlled by physical processes and influenced by biogeochemical and foodweb interactions. Meteorological events are important drivers of this ecological variability. This thesis investigates their effect on the plankton dynamics of Lunenburg Bay, an inlet on the Atlantic coast of Nova Scotia (Canada). In this region, meteorological events are dominated by upwelling in summer, which are a dominant source of flushing and nutrient variability for the inlets. Despite these events, which induce phytoplankton blooms in other regions, the concentration of phytoplankton as chlorophyll remains relatively low throughout the summer in Lunenburg Bay. To reveal the underlying processes limiting the development of phytoplankton biomass, and therefore to improve our understanding of the factors regulating plankton dynamics in this inlet, the objectives of this thesis are to determine the main drivers of variability in phytoplankton biomass and plankton community structure, and to identify the factors limiting the development of phytoplankton biomass in Lunenburg Bay. For that, I use a dataset collected at a coastal observatory located in Lunenburg Bay that covers the years 2003–2006, complemented by a series of transects carried out in summer 2006. The dataset covers physical, chemical and biological properties of the bay, including plankton taxonomy. Two types of physical-biological coupled models are developed: a low-resolution box model of Lunenburg Bay with steady-state wind forcing, and a high-resolution nested model of Lunenburg Bay using the Regional Ocean Modelling System (ROMS) to hindcast a series of upwelling events in 2006. The results reveal that four factors regulate the phytoplankton response to upwelling events in Lunenburg Bay, namely (1) the duration of an upwelling event, (2) the low nitrate concentration in source waters, (3) the flushing rate of the inlet (hence transport), and (4) the bathymetry along the inshore-offshore axis of the bay. In addition, (5) the occurrence of upwelling and (6) the inshore-offshore gradient of increasing depth influence the structure of respectively phytoplankton and zooplankton communities, indicating a dissimilarity in the processes structuring plankton communities in the lower food web. A conceptual model is then developed to describe the role of transport and nitrate concentration in source waters in controlling plankton dynamics in an inlet.