Phytoplankton blooms over Arctic Ocean continental shelves are thought to be restricted to waters free of sea ice. Here, we document a massive phytoplankton bloom beneath fully consolidated pack ice far from the ice edge in the Chukchi Sea, where light transmission has increased in recent decades because of thinning ice cover and proliferation of melt ponds. The bloom was characterized by high diatom biomass and rates of growth and primary production. Evidence suggests that under-ice phytoplankton blooms may be more widespread over nutrient-rich Arctic continental shelves and that satellite-based estimates of annual primary production in these waters may be underestimated by up to 10-fold.
Protozoa play important roles in grazing and nutrient recycling, but quantifying these roles has been hindered by difficulties in collecting, culturing, and observing these often-delicate cells. During long-term deployments at the Martha's Vineyard Coastal Observatory (Massachusetts, USA), Imaging FlowCytobot (IFCB) has been shown to be useful for studying live cells in situ without the need to culture or preserve. IFCB records images of cells with chlorophyll fluorescence above a trigger threshold, so to date taxonomically resolved analysis of protozoa has presumably been limited to mixotrophs and herbivores which have eaten recently. To overcome this limitation, we have coupled a broad-application 'live cell' fluorescent stain with a modified IFCB so that protozoa which do not contain chlorophyll (such as consumers of unpigmented bacteria and other heterotrophs) can also be recorded. Staining IFCB (IFCB-S) revealed higher abundances of grazers than the original IFCB, as well as some cell types not previously detected. Feeding habits of certain morphotypes could be inferred from their fluorescence properties: grazers with stain fluorescence but without chlorophyll cannot be mixotrophs, but could be either starving or feeding on heterotrophs. Comparisons between cell counts for IFCB-S and manual light microscopy of Lugol's stained samples showed consistently similar or higher counts from IFCB-S. We show how automated classification through the extraction of image features and application of a machine-learning algorithm can be used to evaluate the large high-resolution data sets collected by IFCBs; the results reveal varying seasonal patterns in abundance among groups of protists.
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