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

海洋有机质中碳、氮、磷的比值(C：N：P)被用来研究海洋营养物质的生化循环。营养物质的循环被认为是由浮游植物控制的过程。尽管海洋不同部分对C：N：P的单独测量存在差异，但全球浮游生物C：N：P通过生态系统过程平均为一个相对恒定的值。什么是重要的过程，以及这些元素在海洋中的平均比例是如何保持的，只有在建模练习中才会进行研究。然而，浮游植物中C：N：P比例恒定的假设很可能在现实生活中被违反，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比率，但没有对海洋中自然生长的浮游植物进行直接测量来支持实验室的发现。此外，目前还没有浮游植物细胞生物学的模型用现场数据进行测试，以确定在给定环境条件下是否有可能预测C：N：P比率的变化。通过对浮游植物的直接测量，这项研究将考察从俄勒冈州沿岸上升流系统到近海营养不良水域的C：N：P的空间变异性。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有的C：N：P。通过直接测量浮游植物，我们将研究环境条件如何影响采样区域的C：N：P。这个C：N：P数据将被合并到一个模型中，该模型预测一系列环境条件下浮游植物的C：N：P。这一努力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的C：N：P做出假设的必要性。拟议的研究结果将用于本科生和研究生的教学。此外，相关科学将通过与俄勒冈州立大学(OSU)的科学与数学研究性学习体验计划(SILE计划)合作，向未得到充分代表和服务的受众传播。这项提议将支持发展两个教师培训讲习班，为教师提供可在课堂上使用的动手实验。拟议的研究将为一名研究生和几名本科生提供培训。我们已经成功地招收了人数不足的学生，并将继续这一做法。鉴于依赖生物地球化学模型来预测和事后预测海洋生产力，并反过来依赖于C：N：P假设建立模型，至关重要的是确定浮游植物C：N：P变异性的驱动因素以及化学计量在自然海洋系统中的灵活程度。通过结合现场工作和数值模拟，我们建议1)测量和描述营养物质可获得性的大梯度(从俄勒冈海岸到近海)中浮游植物特定C：N：P的可变性，2)将观测与机械浮游植物模型相结合，以归因于环境因素和群落组成在产生所观察到的浮游生物化学计量可变性中的作用，3)评估浮游植物C：N：P对海洋颗粒的贡献，以及4)在高分辨率区域海洋模式(ROMS)中包括浮游植物化学计量的机械表示，以解释观测结果并探索其区域影响。这项工作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，以及对未来气候变化的生态系统响应。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.