Collaborative Research: Direct determination and model analysis of elemental stoichiometry of phytoplankton from the Oregon Coast

合作研究：俄勒冈海岸浮游植物元素化学计量的直接测定和模型分析

• 批准号: 2049656
• 负责人: Allen Milligan
• 金额: $ 64.41万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2049656&HistoricalAwards=false
• 关键词: Collaborative; Research; Direct; determination; model

The ratio of carbon:nitrogen:phosphorus (C:N:P) in marine organic matter is used to study biochemical cycling of nutrients in the ocean. The cycling of nutrients is a process thought to be controlled by phytoplankton. Despite variability in individual measurements of the C:N:P in various parts of the ocean, the global plankton C:N:P averages out to a relatively constant value through ecosystem processes. What the important processes are and how the average ratio of these elements is maintained in the ocean has only been examined in modeling exercises. However, the assumption of a constant ratio of C:N:P in phytoplankton is most likely violated in real life, leading to uncertainties in model outputs. Laboratory experiments have shown that there is a large range of possible C:N:P ratios in phytoplankton, but no direct measurements of naturally growing phytoplankton in the ocean have been made to support the laboratory findings. In addition, no current models of phytoplankton cell biology have been tested with field data to determine if is possible to predict changes in the C:N:P ratio given environmental conditions. Through a direct measure of phytoplankton this study will examine the spatial variability in C:N:P across the Oregon coastal upwelling system to the nutrient-poor waters offshore. Using laboratory techniques, researchers will selectively remove phytoplankton from the suspended particles and apply newly-developed, high-sensitivity analyses to determine phytoplankton specific C:N:P. Through a direct measure of phytoplankton we will examine how environmental conditions affect C:N:P in the sampling region. This C:N:P data will be incorporated into a model that predicts C:N:P in phytoplankton under a range of environmental conditions. Success in this endeavor will provide a predictive model for the phytoplankton C:N:P and eliminate the need to make assumptions about a fixed C:N:P in phytoplankton. Results from the proposed research will be used in undergraduate and graduate teaching. Also, relevant science will be disseminated to underrepresented and underserved audiences, through collaboration with The Science & Math Investigative Learning Experiences Program (The SMILE Program) of Oregon State University (OSU). This proposal will support the development of two teacher training workshops that give teachers hands-on experiments that can be used in their classrooms. The proposed research will provide training for a graduate student and several undergraduate students. We have been successful in recruiting under-represented students and will continue the practice. Given the reliance on biogeochemical models to both predict and hindcast ocean productivity and in turn, model reliance on C:N:P assumptions, it is critically important to determine the drivers of phytoplankton C:N:P variability and the extent to which the stoichiometry is flexible in natural oceanic systems. By combining field efforts and numerical modeling we propose to 1) measure and describe the variability in phytoplankton specific C:N:P across a large gradient in nutrient availability (Oregon Coast to offshore), 2) combine observations with a mechanistic phytoplankton model to attribute the role of environmental factors and community composition in generating the observed plankton stoichiometric variability, 3) evaluate the contribution of phytoplankton C:N:P to that of marine particles, and 4) include a mechanistic representation of phytoplankton stoichiometry in a high-resolution regional ocean model (ROMS) to interpret observations and explore their regional implications. Success in this endeavor will provide the oceanographic community with phytoplankton specific C:N:P data that will allow us to test and improve phytoplankton physiology based ecosystem models, improving the predictive capability of biogeochemical cycles, and ecosystem responses to future climate change.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

海洋有机质中碳、氮、磷（C：N：P）的比值是研究海洋营养盐生物化学循环的重要参数。营养物质的循环是一个被认为是由浮游植物控制的过程。尽管海洋不同地区的C：N：P的个别测量值存在差异，但全球浮游生物C：N：P的平均值通过生态系统过程达到相对恒定的值。重要的过程是什么，以及这些元素的平均比例是如何在海洋中保持的，只有在模拟练习中才能得到检验。然而，浮游植物中C：N：P恒定比例的假设在真实的生活中很可能被违反，导致模型输出的不确定性。实验室实验表明，浮游植物中可能存在很大范围的C：N：P比例，但没有对海洋中自然生长的浮游植物进行直接测量以支持实验室发现。此外，目前还没有浮游植物细胞生物学模型与现场数据进行测试，以确定是否有可能预测的C：N：P比给定的环境条件下的变化。 通过浮游植物的直接测量，这项研究将探讨在C：N：P在整个俄勒冈州沿海上升流系统的营养贫乏的沃茨近海的空间变异。利用实验室技术，研究人员将有选择地从悬浮颗粒中去除浮游植物，并应用新开发的高灵敏度分析来确定浮游植物特定的C：N：P。这一C：N：P数据将被纳入一个模型，该模型预测在一系列环境条件下浮游植物中的C：N：P。这一奋进的成功将为浮游植物C：N：P提供一个预测模型，并消除对浮游植物中固定C：N：P的假设。从拟议的研究结果将用于本科和研究生教学。此外，将通过与俄勒冈州州立大学（OSU）的科学数学调查性学习经验计划（SMILE计划）合作，向代表性不足和服务不足的受众传播相关科学。这项建议将支持举办两个教师培训讲习班，使教师能够在课堂上进行实践实验。拟议的研究将为一名研究生和几名本科生提供培训。我们成功地招收了代表性不足的学生，并将继续这种做法。 由于依赖生物地球化学模型来预测和后报海洋生产力，反过来，模型依赖于C：N：P假设，因此确定浮游植物C：N：P变化的驱动因素以及化学计量在自然海洋系统中的灵活程度至关重要。通过结合野外工作和数值模拟，我们提出：1）测量和描述浮游植物特定C：N：P在营养盐可用性的大梯度上的变化（俄勒冈州海岸到近海），2）将联合收割机观测与机械浮游植物模型相结合，以确定环境因素和群落组成在产生观测到的浮游生物化学计量变化中的作用，3）评估浮游植物C：N的贡献：与海洋颗粒的P值相比，4）在高分辨率区域海洋模式（ROMS）中加入浮游植物化学计量学的机械表示解释观察结果并探讨其区域影响。这项奋进的成功将为海洋学界提供浮游植物特有的C：N：P数据，使我们能够测试和改进基于浮游植物生理学的生态系统模型，提高生物地球化学循环的预测能力，以及生态系统对未来气候变化的响应。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。