This thesis presents improvements to a method for measuring the production of biogenic silica (bSiO2) by diatoms, a group of microscopic algae with siliceous cell walls (frustules) that dominate the marine cycling of silicon (Si) and account for a significant proportion of global marine primary productivity. Using the fluorescent dye PDMPO, diatom bSiO2 can be labeled as it is produced and then quantified using fluorometry to determine community-wide bSiO2 production. A distinct advantage of PDMPO over more traditional tracers of bSiO2 production is that the combination of measurements of PDMPO by fluorometry and by fluorescence microscopy allows for the quantification of cell (and thus taxa) specific bSiO2 production within a mixed community. However, the robustness of PDMPO as a quantitative tracer of diatom bSiO2 production has not been sufficiently investigated. To address this, experiments were conducted both in the lab, and at two field locations where diatoms are known to be abundant, namely the continental shelf off the west coast of Vancouver Island, and Saanich Inlet, a highly productive fjord located on southern Vancouver Island. Laboratory culture experiments demonstrated that concentrations of PDMPO >500 nmol L-1 reduced growth rate in the diatom Thalassiosira pseudonana, and affected the Si:PDMPO ratio of incorporation. The relationship between SiO2 and PDMPO incorporation was significantly affected by diatom species, though this effect was small (8%) when cells were lysed. From these experiments, a Si:PDMPO incorporation ratio of 4200 ± 380:1 was determined, which predicted 30% more bSiO2 production for PDMPO incorporation than previous studies, and better agreed with bSiO2 production rates determined using established methods in Saanich Inlet. However, bSiO2 production rates were over-estimated by the PDMPO method when rates were less than 1 µmol L-1 d-1. In a few cases, this occurred when dinoflagellates were numerically dominant, but for the majority of samples, dinoflagellates were low in abundance, and over-estimation by PDMPO may be related to low dissolved Si(OH)4 concentration. Protocols for quantifying PDMPO fluorescence by microscopy were optimized by using a low numerical aperture microscope objective. Additionally, measurements of fluorescence intensity were calibrated using a fluorescent microscope slide as a standard, which served to correct for unevenness of illumination across the field of view. With these protocol modifications, quantification of PDMPO by microscopy agreed with PDMPO measured by fluorometry. When PDMPO was measured by microscopy in the field, the contribution of diatom taxa to PDMPO fluorescence differed from their contribution to cell numbers. In many cases this was due to large diatom taxa producing more bSiO2 per cell than smaller taxa. However, much of the difference between cell numbers and PDMPO fluorescence was not explained by differences in cell size. This suggests that the diatom taxa had different specific bSiO2 production rates, which could be estimated using PDMPO. This thesis highlights the strength of the PDMPO tracer for understanding diatom community dynamics. The use of PDMPO should allow the relationship between diatom community composition, growth and productivity to be better illuminated in the oceans. / Graduate / 0416 / jelong@uvic.ca

Silification and growth rates of diatoms were measured at three spatially distinct sampling sites in the Římov Reservoir in 2011. Silica deposition measured via PDMPO fluorescence technique was significantly related to growth rates of three dominant diatom species (Fragilaria crotonensis, Asterionella formosa, Aulacoseira italica). Over the study season, light has a decisive effect on diatom growth, which was the highest at the transition zone of the reservoir.

Les Processus Décisionnels de Markov Partiellement Observables (PDMPOs) permettent de modéliser facilement lesproblèmes probabilistes de décision séquentielle dans l'incertain. Lorsqu'il s'agit d'une mission robotique, lescaractéristiques du robot et de son environnement nécessaires à la définition de la mission constituent le système. Son étatn'est pas directement visible par l'agent (le robot). Résoudre un PDMPO revient donc à calculer une stratégie qui remplit lamission au mieux en moyenne, i.e. une fonction prescrivant les actions à exécuter selon l'information reçue par l'agent. Cetravail débute par la mise en évidence, dans le contexte robotique, de limites pratiques du modèle PDMPO: ellesconcernent l'ignorance de l'agent, l'imprécision du modèle d'observation ainsi que la complexité de résolution. Unhomologue du modèle PDMPO appelé pi-PDMPO, simplifie la représentation de l'incertitude: il vient de la Théorie desPossibilités Qualitatives qui définit la plausibilité des événements de manière qualitative, permettant la modélisation del'imprécision et de l'ignorance. Une fois les modèles PDMPO et pi-PDMPO présentés, une mise à jour du modèle possibilisteest proposée. Ensuite, l'étude des pi-PDMPOs factorisés permet de mettre en place un algorithme appelé PPUDD utilisantdes Arbres de Décision Algébriques afin de résoudre plus facilement les problèmes structurés. Les stratégies calculées parPPUDD, testées par ailleurs lors de la compétition IPPC 2014, peuvent être plus efficaces que celles des algorithmesprobabilistes dans un contexte d'imprécision ou de grande dimension. Cette thèse propose d'utiliser les possibilitésqualitatives dans le but d'obtenir des améliorations en termes de temps de calcul et de modélisation. / Partially Observable Markov Decision Processes (POMDPs) define a useful formalism to express probabilistic sequentialdecision problems under uncertainty. When this model is used for a robotic mission, the system is defined as the featuresof the robot and its environment, needed to express the mission. The system state is not directly seen by the agent (therobot). Solving a POMDP consists thus in computing a strategy which, on average, achieves the mission best i.e. a functionmapping the information known by the agent to an action. Some practical issues of the POMDP model are first highlightedin the robotic context: it concerns the modeling of the agent ignorance, the imprecision of the observation model and thecomplexity of solving real world problems. A counterpart of the POMDP model, called pi-POMDP, simplifies uncertaintyrepresentation with a qualitative evaluation of event plausibilities. It comes from Qualitative Possibility Theory whichprovides the means to model imprecision and ignorance. After a formal presentation of the POMDP and pi-POMDP models,an update of the possibilistic model is proposed. Next, the study of factored pi-POMDPs allows to set up an algorithmnamed PPUDD which uses Algebraic Decision Diagrams to solve large structured planning problems. Strategies computedby PPUDD, which have been tested in the context of the competition IPPC 2014, can be more efficient than those producedby probabilistic solvers when the model is imprecise or for high dimensional problems. This thesis proposes some ways ofusing Qualitative Possibility Theory to improve computation time and uncertainty modeling in practice.