Harmful phytoplankton occur naturally in British waters. However, little is known about the environmental drivers that lead to the formation and advection of harmful algae blooms (HABs). To minimise adverse effects of HABs it is necessary to improve our understanding of links between advection, environmental changes and HAB development. The aims of this PhD project were therefore to: 1) Provide a better understanding of the relationship between harmful phytoplankton, environmental drivers and key hydrodynamic features such as the shelf edge; 2) Determine the role of advection in bloom transport and development; 3) Utilise computational modelling to study environmental drivers and advection of HABs. To achieve these aims, field data was collected from two cruises and a glider mission. During the cruises, data on phytoplankton community was collected alongside physical data with a focus on key features such as the shelf edge and seasonal coastal fronts. This data provided an updated, detailed assessment of phytoplankton across the Hebridean and Malin Shelves. Field data showed that the European Slope Current (ESC) and Islay front can separate phytoplankton communities on the shelf. Lack of community differences along the ESC suggested stronger transport and exchange of phytoplankton within the ESC than adjacent shelf waters. Nutrients, nutrient ratios and light conditions were also found to play major structuring roles in determining phytoplankton assemblage. Data collected during field work can also be useful for regulatory assessment of shelf seas by providing baseline information about phytoplankton communities in the area. The glider mission provided an additional high resolution dataset of biological and physical water column properties across the Malin shelf. Vertical resolution showed that phytoplankton distribution was strongly linked to thermal stratification and temperature changes. Horizontal resolution was highly patchy, suggesting that scientific cruises could easily miss high density blooms with small spacial extent. The glider successfully monitored a high density HAB, suggesting that gliders could potentially be used for phytoplankton surveillance and detection of high biomass blooms. In addition to field data, a bio-physical individual based model (IBM) was used to simulate HAB progression. The IBM was coupled with a hydrodynamic ocean model to show the role of advection and importance of offshore seed populations in coastal HAB development. Model output was compared to coastal count data and satellite images whenever possible. Running the model under different conditions for phytoplankton growth and behaviour, suggest that it was crucial to include Abstract 5 biological processes to simulate HABs. The IBM could be initialised with data from satellite images, field data or discrete seed populations. Model simulations with different initial cell concentrations and locations could help to explain observed bloom pathways and suggest possible offshore origins for observed exceptional HABs. The results from field work and model simulations showed the role of the ESC in structuring phytoplankton community and transporting seed populations of HABs along the Scottish west coast. This suggests that future cruises and offshore monitoring should focus on the ESC and shelf break region. The bio-physical model could hindcast HAB pathways along the ESC, suggesting that modelling of HAB pathways of known seed populations could be integrated into an early warning system for aquaculture sites along the Scottish west coast in the future. Such an early warning system would allow the protection, relocation or early harvesting of affected aquaculture sites.

Zentrales Ziel dieser Arbeit war es, Methoden der Mikroskopie, Bildverarbeitung und Bilderkennung für die Charakterisierungen verschiedener Phyotplankter zu nutzen, um deren Analyse zu verbessern und zu vereinfachen. Der erste Schwerpunkt der Arbeit lag auf der Analyse von Phytoplanktongemeinschaften, die im Rahmen der Überprüfung der Süßwasserqualität als Marker dienen. Die konventionelle Analyse ist dabei sehr aufwendig, da diese noch immer vollständig von Hand durchgeführt wird und hierfür speziell ausgebildetes Personal eingesetzt werden muss. Ziel war es, ein System zur automatischen Erkennung aufzubauen, um die Analyse vereinfachen zu können. Mit Hilfe von automatischer Mikroskopie war es möglich Plankter unterschiedlicher Ausdehnung durch die Integration mehrerer Schärfeebenen besser in einem Bild aufzunehmen. Weiterhin wurden verschiedene Fluoreszenzeigenschaften in die Analyse integriert. Mit einem für ImageJ erstellten Plugin können Organismen vom Hintergrund der Aufnahmen abgetrennt und eine Vielzahl von Merkmalen berechnet werden. Über das Training von neuralen Netzen wird die Unterscheidung von verschieden Gruppen von Planktontaxa möglich. Zudem können weitere Taxa einfach in die Analyse integriert und die Erkennung erweitert werden. Die erste Analyse von Mischproben, bestehend aus 10 verschiedenen Taxa, zeigte dabei eine durchschnittliche Erkennungsrate von 94.7% und eine durchschnittliche Falsch-Positiv Rate von 5.5%. Im Vergleich mit bestehenden Systemen konnte die Erkennungsrate verbessert und die Falsch Positiv Rate deutlich gesenkt werde. Bei einer Erweiterung des Datensatzes auf 22 Taxa wurde darauf geachtet, Arten zu verwenden, die verschiedene Stadien in ihrem Wachstum durchlaufen oder höhere Ähnlichkeiten zu den bereits vorhandenen Arten aufweisen, um evtl. Schwachstellen des Systemes erkennen zu können. Hier ergab sich eine gute Erkennungsrate (86.8%), bei der der Ausschluss von nicht-planktonischen Partikeln (11.9%) weiterhin verbessert war. Der Vergleich mit weiteren Klassifikationsverfahren zeigte, dass neuronale Netze anderen Verfahren bei dieser Problemstellung überlegen sind. Ähnlich gute Klassifikationsraten konnten durch Support Vektor Maschinen erzielt werden. Allerdings waren diese bei der Unterscheidung von unbekannten Partikeln dem neuralen Netz deutlich unterlegen. Der zweite Abschnitt stellt die Entwicklung einer einfachen Methode zur Viabilitätsanalyse von Cyanobakterien, bei der keine weitere Behandlung der Proben notwendig ist, dar. Dabei wird die rote Chlorophyll - Autofluoreszenz als Marker für lebende Zellen und eine grüne unspezifische Fluoreszenz als Marker für tote Zellen genutzt. Der Assay wurde mit dem Modellorganismus Synechocystis sp. PCC 6803 etabliert und validiert. Die Auswahl eines geeigeneten Filtersets ermöglicht es beide Signale gleichzeitig anzuregen und zu beobachten und somit direkt zwischen lebendenden und toten Zellen zu unterscheiden. Die Ergebnisse zur Etablierung des Assays konnten durch Ausplattieren, Chlorophyllbestimmung und Bestimmung des Absorbtionsspektrums bestätigt werden. Durch den Einsatz von automatisierter Mikroskopie und einem neu erstellten ImageJ Plugin wurde eine sehr genaue und schnelle Analyse der Proben möglich. Der Einsatz beim Monitoring einer mutagenisierten Kultur zur Erhöhung der Temperaturtoleranz ermöglichte genaue und zeitnahe Einblicke in den Zustand der Kultur. Weitere Ergebnisse weisen darauf hin, dass die Kombination mit Absorptionsspektren es ermöglichen können bessere Einblicke in die Vitalität der Kultur zu erhalten. / Central goal of this work was to improve and simplify the characterization of different phytoplankter by the use of automated microscopy, image processing and image analysis. The first part of the work dealt with the analysis of pytoplankton communities, which are used as a marker for the determination of fresh water quality. The current routine analysis, is very time consuming and expensive, as it is carried out manually by trained personnel. Thus the goal of this work was to develop a system for automating the analysis. With the use of automated microscopy different focal planes could be integrated into one image, which made it possible to image plankter of different focus levels simultaneously. Additionally it allowed the integration of different fluorescence characteristics into the analysis. An image processing routine, developed in ImageJ, allows the segmentation of organisms from the image background and the calculation of a large range of features. Neural networks are then used for the classification of previously defined groups of plankton taxa. The program allows easy integration of additional taxa and expansion of the recognition targets. The analysis of samples containing 10 different taxa showed an average recognition rate of 94.7% and an average error rate of 5.5%. The obtained recognition rate was better than those of existing systems and the exclusion of non-plankton particles could be greatly improved. After extending the data set to 22 different classes of (more demanding) taxa a still good recognition (86.9 %) and still improved error rate (11.9 %) were obtained. This extended set was specifically selected in order to target potential weaknesses of the system. It contained mainly taxa that showed strong similarities to each other or taxa that go through various different morphological stages during their growth. The obtained recognition rates were comparable or better than those of existing systems and the exclusion of non-plankton particles could be greatly improved. A comparison of different classification methods showed, that neural networks are superior to all other investigated methods when used for this specific task. While similar recognition rates could be achieved with the use of support vector machines they were vastly inferior for the differentiation of unknown particles. The second part focused on the development of a simple live - dead assay for unicellular cyanobacteria without the need of sample preparation. The assay uses red chlorophyll fluorescence, corresponding to viable cells, and an unspecific green autofluorescence, that can only be observed in non viable cells. The assay was established and validated for the model organism Synechocystis sp. PCC 6803. With the selection of a suitable filter-set both signals could be excited and observed simultaneously, allowing a direct classification of viable and non-viable cells. The results were confirmed by plating/colony count, absorption spectra and chlorophyll measurements. The use of an automated fluorescence microscope and an ImageJ based image analysis plugin allows a very precise and fast analysis. The monitoring of a random mutagenized culture undergoing selection for improved temperature tolerance allowed an accurate and prompt insight into the condition of the culture. Further results indicate that a combination of the new assay with absorption spectra or chlorophyll concentration measurements allows the estimation of the vitality of cells.

The concentration of phytoplankton in waters off the Western Australian coastline contrast with the coastal waters west of southern Africa and South America. The lack of favourable upwelling conditions results in the majority of the southeastern Indian Ocean surface waters being nutrient poor. Which is reflected in their low productivity. Several areas either on or in close proximity to the coastline are notably more productive. The associated forcing terms generating phytoplankton favourable conditions differ between areas. as do the seasons in which they occur. Measurements of chlorophyll a concentration. the major photosynthetic pigment contained in phytoplankton, may be directly related to oceanic bioproductivity. Using data collected by the Coastal Zone Color Seamier between 1979-86, this work aims to improve the understanding of the spatial and temporal changes that occurred in chlorophyll a abundance in the southeastern Indian Ocean. The highest seasonal mean concentrations occur in Summer (January-March) and Autumn (April-June); the former occurring in waters of the North West Shelf and the latter in close coastal areas of Western Australia south of North West. Cape. Concentrations observed in the offshore oceanic regions are mostly poor. Exceptions to this occur in proximity to the adjacent Indonesian islands and directly south of Albany (possibly due to northwards flow of subantarctic nutrient-rich waters). A considerable interannual variation was also noted, with the highest mean chlorophyll concentrations occurring in 1981. 1982 and 1983. / The influence of the forcing terms on chlorophyll a appears to vary significantly among the waters of North West Shelf, Western and southern Western Australian coastline. This is most notable in the interseasonal variations. The changes observed interannually and their influence on chlorophyll a are not easily discernible. but there may be some connection with the La Nina/El Nino related changes in both currents and winds.

The Maketu Estuary is a shallow intertidal estuary (2.3 km2) located in the Bay of Plenty, North Island, New Zealand. The Kaituna River contributes the largest freshwater flow into the estuary through control gates. Lake Rotoiti and indirectly Lake Rotorua supply the base flow to the Kaituna River, with tributaries along the 50 km reach also significantly contributing to the flow. Water quality within the river is affected by elevated nutrients, faecal coliforms, high oxygen demand and algae concentrations derived from the lakes as well as contributions from tributaries and industrial and urban discharge. Through the use of a coupled hydrodynamic-biogeochemical numerical model ELCOM-CAEDYM, this study aims to examine the nutrient, phytoplankton and hydrodynamics of the Maketu Estuary and lower Kaituna River. Water quality and hydrodynamic measurements were sourced from Environment Bay of Plenty's data archives as well as a number of instrument deployments to collect water velocity, tidal elevation and salinity and temperature measurements during the course of this study. Included in the field work was a survey of the lower river and estuary bathymetry. Model simulations predicted that the maximum residence time in the Maketu Estuary is 1.5 days, occurring in the inner western region. Residence time in the lower river (mouth to 8.5 km upstream) is in the order of hours although some variations were predicted near the river mouth. Growth rates of four phytoplankton groups where assessed over a 15 day period in January 2004. In the Kaituna River ELCOM-CAEDYM predicted that the community growth rates were small with the exception of a slight increase in biomass of the two freshwater groups in a semi-detached river bend. The increase in the loop was correlated with an increase of residence time. In the estuary, marine diatoms showed the highest growth rates in the western region which is expected to relate to retention time and available nutrients. Dinoflagellates showed the smallest variation in predicted growth rates, most likely due to their broad salinity tolerance. The two freshwater species showed a reduction in abundance when mixed with marine water. A principle limiting factor to phytoplankton growth in both the river and estuary is the low residence time. A number of scenarios were simulated in the river and estuary by altering the forcing conditions in the model. A simulation of the increased nutrient load associated with the Rotoiti diversion wall revealed that phytoplankton growth in the river and estuary will not be significantly affected. Because of the close proximity of the control gates to the river mouth, a proportion of water drawn through the structure can be marine. By opening the old river channel, model simulations predicted that a reduction in salinity would be possible, however the outcome of complete freshwater is probably not achievable. Increasing the discharge volume from the river into the estuary was also simulated. The results indicated that increasing the freshwater inflow at Fords Cut would reduce the salinity in the estuary while increasing the net (residual) flow towards the estuary mouth. Increasing the flow would also result in a greater range of salinity in regions of the estuary. Changing the inflow location to the historic Papahikahawai Channel also affected the salinity in the estuary. The most significant effect of an inflow at this location was a reduction of the residual currents in the western region of the estuary.

Thesis (Ph. D.)--University of Washington, 1981. / Vita. Bibliography: leaves [109]-116.

Since the 'discovery' of the high abundance of viruses in aquatic environments, it has been generally assumed that viruses in lakes are similar to those in oceans. I directly compared these two systems using a large, robust data set. Viral abundance was significantly different among the surveyed environments. The relationship between viral and bacterial abundance indicated a fundamental difference between lakes and oceans, and suggested that viruses infecting phytoplankton may be more important in lakes. Molecular techniques (PCR & DGGE) were used to document spatial and temporal variations in the richness of viruses that infect eukaryotic phytoplankton (Phycodnaviridae) in lakes at the Experimental Lakes Area (ELA). Phycodnavirus richness was highest in the eutrophic lake, and during the spring/early summer in all the lakes. Viral richness was closely associated with phytoplankton abundance and composition. As a result, richness was influenced by trophic status, while patterns of richness were affected by regional climatic conditions. Phylogenetic analysis of environmental Phycodnavirus DNA polymerase (pol) sequences indicated that freshwater Phycodnaviruses are genetically different from cultured isolates and marine environmental sequences. A genetic distance analysis indicated that polsequences > 7 % different infected different host species. Therefore, the 20 different freshwater sequences likely infected nine different hosts. Multivariate statistics identified seven possible phytoplankton hosts, including chlorophytes, chrysophytes, diatoms and dinoflagellates. Finally, the modified dilution experiment was evaluated as an approach for estimating viral-mediated phytoplankton mortality in two lakes at the ELA. Experiments resulted in non-significant apparent growth rate regressions. While a model analysis, indicated that the method was sensitive to poorly constrained parameters such as burst size and length of the lytic cycle, making it unsuitable for estimating mortality rates in these lakes. These studies indicate that Phycodnaviridae are a genetically rich and dynamic component of lakes. Their richness is influenced by both the chemical and physical components of their environment. Although the presence of these viruses indicates that they are a source of phytoplankton mortality, the magnitude of their impact on structuring phytoplankton communities awaits methodological advances. Nonetheless, these findings support the view that viruses infecting phytoplankton are ecologically important componentsof lake ecosystems.

Thesis (Ph. D.)--Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2001. / "February, 2001." "Funding was provided by a grant from the National Science Foundation (OCE-9618729), the National Science Foundation Coastal Traineeship (DGE-9454129), and by an Environmental Protection Agency STAR Graduate Fellowship (U-914966-01-0)." Includes bibliographical references.

Thesis (M.S.)--Oregon State University, 2002. / Typescript (photocopy). Includes bibliographical references (leaves 70-74). Also available online.

published_or_final_version / Botany / Master / Master of Philosophy

No description available.