Andrew Irwin, Ph.D.

Professor, Department of Mathematics and Statistics, Dalhousie UniversityAndrew Irwin’s website

CBIOMES Project: Statistical Modeling of Microbial Communities: Niches, Traits and Interactions

Marine microbial communities form the base of the marine food web and perform about half of the photosynthesis on Earth. They are key components of the global carbon cycle and help to sequester carbon in the deep ocean, removing it from the atmosphere, through the burial of organic matter on the seafloor. The productivity and function of marine microbial communities is determined in part by the biogeography — which species live where. We will assemble and analyze observations of microbial communities to develop predictive statistical models that describe community composition. Additional statistical models will be developed to determine the physiological and ecological traits of species and groups of species that determine the growth rates of marine microbes. Finally we will analyze community composition data to determine sub-communities of species that tend to co-occur or tend to not be found together to better understand the complex interactions among species and the suites of traits of species that form stable communities.

SCOPE-Gradients Project: Macromolecular Bases of Carbon-Nitrogen-Phosphorus (CNP) Variation: Modeling and Statistical Analyses

Our central hypothesis is that changes in environmental conditions and microbial taxonomic composition determine variability in microbial macromolecular and storage allocation strategies, influencing growth rate and particulate C:N:P. The quantitative importance of different environmental conditions and variation in taxonomic composition to the variability in particulate C:N:P is not known. Limited laboratory work indicates that mineral nutrient limitation/supply/ratios (nitrate, phosphate, iron) is likely the dominant factor. We propose a series of field observations and complementary laboratory experiments to quantify how phytoplankton alter their growth rate, macromolecular content and elemental composition in response to key environmental variables. The laboratory work will focus on poorly studied pico- and nano-phytoplankton groups found along the Gradients cruises’ transects. This data will be used in the development and testing of new macromolecule-based models of phytoplankton growth that will be used to better understand how environmental conditions drive changes in phytoplankton taxonomic structure and C:N:P.

My training is in limnology and oceanography with a focus on phytoplankton ecology. In recent years I have been teaching oceanography and biometry in China. I have worked on phytoplankton physiological modeling, modeling arctic ice seasonality in Hudson Bay, Canada, and the timing of the spring phytoplankton bloom in the Gulf of Saint Lawrence. This work involves considerable expertise with computer models and large amounts of output. These skills will enable me to be productive working on ecological/biological oceanographic questions associated with this proposal.

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