CMG: Integrating Mathematical Models, Laboratory Experiments, and Ocean Observatory Data to Understand Phytoplankton Dynamics

0530830浮游植物是重要的初级生产者和介导的海洋地球化学通量在沿海海洋。它们形成复杂的群落，在不同的时间和空间尺度上对生态系统的自然变化作出反应。然而，尽管进行了数十年的研究，目前对它们在自然系统中的调节的理解和预测其种群动态的能力仍然不足。改善这种情况的主要障碍是（1）无法在适当的空间和时间尺度上进行充分的抽样，（2）难以从可以收集的数据中得出人口动态参数（如人口增长率）。在试图克服第一个障碍，新的传感器技术的自主，在现场测量有关自然浮游植物组合的关键信息最近已经开发出来。为了克服第二个障碍，数学科学家和生物海洋学家之间需要进行重要的跨学科合作。该项目的PI将建立这样的合作。智力优势：研究人员--一名应用数学家、一名统计学家和两名生物海洋学家--将密切合作，开发一系列浮游植物细胞生长和分裂的数学模型，反映环境条件变化的影响。这些模型将在实验室中通过控制生长实验进行验证，使用单种浮游植物培养物，指定种群结构中的昼夜模式如何响应生长条件（光照，温度和营养物质）的变化。将开发严格的统计程序，以a）指导模型优化并根据实验室培养结果评价模型性能，以及B）估计模型参数，以预测种群增长率。更广泛的影响：除了固有的社会相关性之外，拟议的活动将在科学界产生更广泛的影响。新一代的沿海观测站，具有产生类似于我们建议分析的长时间序列的能力，将很快投入使用。通过向海洋学界提供统计模型（以及为运行该模型而开发的任何软件），调查人员将为海洋观测站及其生成的数据的广泛和创新的生态应用做出贡献。此外，研究成果将与教学和其他形式的教育在几个层次，从一般公众到研究生整合。这项拟议研究所涉及的沿海科学和创新技术相结合，将通过公开展览为非正规教育工作提供一个令人信服的背景。

0530830Phytoplankton are critically important primary producers and mediators of biogeochemical fluxes in the coastal ocean. They form complex communities that respond to natural changes in the ecosystem at a variety of time and space scales. Despite decades of study, however, the current understanding of their regulation in natural systems and ability to predict their population dynamics remain inadequate. The principal impediments to improving this situation have been (1) the inability to sample adequately at the appropriate space and time scales and (2) the difficulty of deriving population-dynamic parameters (such as population growth rate) from the data that can be collected. In an attempt to overcome the first impediment, new sensor technologies for autonomous, in situ measurement of critical information about natural phytoplankton assemblages have been recently developed. To overcome the second impediment, a significant interdisciplinary collaboration, between mathematical scientists and biological oceanographers, is required. The PIs on this project will establish just such a collaboration.Intellectual merit: The investigators-an applied mathematician, a statistician, and two biological oceanographers-will collaborate closely to develop a series of mathematical models for phytoplankton cell growth and division that reflect effects of changes in environmental conditions. The models will be validated by controlled growth experiments in the laboratory, with mono-specific phytoplankton cultures, that specify how diel patterns in population structure respond to changes in growth conditions (light, temperature, and nutrients). Rigorous statistical procedures will be developed to a) guide model refinement and evaluate model performance against results from laboratory cultures and b) estimate model parameters in order to predict population growth rates. The project will provide fundamental insights into the regulation of coastal ocean ecosystems.Broader impacts: Beyond the inherent societal relevance, the proposed activities will have broader impacts within the scientific community. A new generation of coastal observatories, with the capability to generate long time-series similar to those that we propose to analyze, will soon be operational. By making the statistical model (and any software developed to run it) available to the oceanographic community, the investigators will contribute to broad and innovative ecological applications of ocean observatories and the data they generate. In addition, the research results will be integrated with teaching and other forms of education at several levels from the general public to graduate students. The combination of coastal sciences and innovative technologies that this proposed research involves will provide a compelling context for informal education efforts through public exhibits.