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

地球上没有任何地方比北极地区受到当前气候破坏的影响更明显。北冰洋的海洋条件和生态系统正迅速向未知领域漂移。为了了解北极海洋生态系统的未来，我们需要对在不同组织层面上运作的关键过程有一个机械性的了解，其中中游动物(大小在200微米到20毫米之间的动物)在其中起着关键作用。支持北极海洋生态系统运作的浮游动物共有的功能特征是对北极典型的极端环境条件的适应。因此，在实际的“正常”环境条件正在以前所未有的速度变化的同时，提高我们快速和大规模地识别和量化这些特征的能力似乎至关重要。新的图像原位观测技术和基于机器学习的强大数值分析方法的同时快速发展，为有效测量浮游生物的基本功能特征和阐明它们在海洋浮游动物群落对环境变化的反应中的作用提供了独特的机会。我的研究计划将1)通过使用个体的原位图像，在泛北极尺度上表征个体中浮游动物特征对环境变化的时空分布，2)解释将环境变化转化为渐进和逐步变化的生态机制，以及3)利用先进的数值方法研究快速变化的北极海洋环境中中浮游动物群落的组织和功能特性(特征分布)。我的研究计划将通过在浮游动物群落模型中实施功能特征的个体间差异来提高海洋生态系统模型的预测能力。这种模型将代表一系列表型和个体对环境变异性的反应，可与新获得的现场观测相媲美。在特定功能特征方面，从群落组成的逐步变化到逐步变化的非线性反应，应该能够对海洋生态系统对未来海洋气候的反应作出更微妙和现实的预测。通过介绍当前和未来中浮游动物特征分布的动态，这项研究将为应对管理(濒危物种栖息地、入侵物种的变化)、根据加拿大的承诺实施海洋保护区以及新型人为灾害对北极海洋生态系统的最终影响等紧迫问题提供量化信息(可能作为指数得出)。

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.