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Abstract
Phytoplankton perform a crucial role in ecosystems, as they are responsible for about half of global oxygen production and serve as a major component of biogeochemical nutrient cycling. Long-term trends, bloom patterns, and environmental drivers of the marine diatom Thalassiosira nordenskioeldii were studied. T. nordenskioeldii is abundant in Narragansett Bay seasonally, representing up to 44% of winter diatoms some years, being most prevalent in water from -1 to 1 °C. Water temperature in Narragansett Bay during the T. nordenskioeldii bloom window has increased over 1°C since 1959, which may push winter water temperature past T. nordenskioeldii's optimal in-situ habitat conditions. As climate change continues, increasing water temperatures may alter T. nordenskioeldii bloom patterns. The data analyzed came from a time-series of weekly observations in lower Narragansett Bay, spanning from 1959-2011. Long-term trends show elevated abundance in 1960s and 1970s, followed by declining abundance through 1980s and 1990s. Populations increased in 2000s, but not to the same magnitude seen early in the time-series. Embedded in the long-term pattern were 53-month cycles, with an apparent disappearance in recent years. Cardinal characters were assigned to bloom characteristics (initiation, peak, duration, etc.) and used for analysis. Perhaps most noteworthy was the high variation exhibited, with blooms initiating anywhere from early December to early April and maximum bloom magnitude ranging from 96 to 8137 cells/ml. Multivariate statistical analyses identified three bloom types: an early, moderate bloom; a later intense bloom; and a late bloom with low abundance. Intense blooms came in winters with reduced river flow (37.3 m3/sec) and cold surface water temperatures (3.8°C), compared to smaller blooms occurring in winters with increased river flow (42.4-49.6 m3/sec) and warmer water (4.2-4.6°C). Understanding trends and bloom parameters of T. nordenskioeldii will allow for appropriate analysis of climate effects and prediction of future impacts.