From individual traits to the big picture: trait-based approaches to predict zooplankton fate in a rapidly changing Arctic Ocean

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Nowhere on Earth are the impacts of current climate disruption more pronounced than in the Arctic. The Arctic Ocean oceanographic conditions and ecosystems are rapidly drifting towards uncharted territories. To apprehend the future of Arctic marine ecosystems we need a mechanistic understanding of the key processes operating at various levels of organization, within which mesozooplankton (animals between 200 µm to 20 mm in size) plays a crucial role. The functional traits shared by zooplanktonic organisms supporting the functioning of Arctic marine ecosystems are an adaptation to the extreme environmental conditions typical of the Arctic. Hence it appears crucial to improve our ability to identify and quantify these traits rapidly and at a large scale, while the actual "normal" environmental conditions are shifting at an unprecedented rate. The concurrent rapid development of new in situ observing techniques of imagery and of powerful numerical analysis methods based on machine learning offer the unique opportunity to efficiently measure essential functional traits of planktonic organisms and to elucidate their role in marine zooplankton communities' responses to environmental variability. My research plan will 1) characterize, at the pan-Arctic scale, the spatio-temporal distribution of individual mesozooplankton traits in response to environmental variability by using in situ imagery of individuals, 2) explain the ecological mechanisms translating environmental variability into gradual and stepwise changes into the realized distribution of mesozooplankton traits and 3) project mesozooplankton communities' organization and functional properties (traits distributions) in rapidly changing Arctic marine environments by using advanced numerical methods. My research plan will improve the predictive power of marine ecosystem models by implementing interindividual variance of functional traits in models of zooplankton communities. Such models will represent a range of phenotypes and individual responses to environmental variability comparable to newly acquired in situ observations. Non-linear responses ranging from gradual changes to stepwise shifts in community compositions with regards to specific functional traits should allow more nuanced and realistic predictions of marine ecosystems responses to the future oceanographic climate. By informing on current and future dynamics of mesozooplankton traits distributions, this research will provide quantitative information (possibly derived as indices) for pressing issues such as responsive management (changes in endangered species habitat, invasive species), implementation of marine protected areas according to Canada's commitments and the eventual impacts of new kinds of anthropic hazards on Arctic marine ecosystems.