Dimensions: Collaborative Research: Biological controls on the ocean C:N:P ratios

维度：合作研究：海洋 C:N:P 比率的生物控制

• 批准号: 1046001
• 负责人: Simon Levin
• 金额: $ 49.74万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-01-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1046001&HistoricalAwards=false
• 关键词: Dimensions; Collaborative; Research; Biological; controls

Intellectual merit. One of the fundamental patterns of ocean biogeochemistry is the Redfield ratio, linking the stoichiometry of surface plankton with the chemistry of the deep ocean. There is no obvious mechanism for the globally consistent C:N:P ratio of 106:16:1 (Redfield ratio), especially as there is substantial elemental variation among plankton communities in different ocean regions. Thus, knowing how biodiversity regulates the elemental composition of the ocean is important for understanding the ocean and climate as a whole -- now and in the future. The conceptual hypotheses for this study are as follows:1. The C:N:P ratio of a cell is constrained by its broad taxonomic group, which determines, for example, whether it has an outer shell, its size, functional metabolism, membrane lipid composition.2. Within a taxon, there is high genetic diversity. Some of this genetic diversity is potentially laterally transferred, or can be lost within taxa, and confers various functional abilities (organic phosphate assimilation, nitrate assimilation, photoheterotrophy, etc.). Functional diversity provides the cell with further flexibility, such as the ability to respond to varying nutrient supply rates/ratios, and affects a cell's C:N:P ratio within the range specified by the taxon.3. Given these taxonomic and genetic constraints, a cell is physiologically plastic and modifies how it allocates cellular resources in response to nutrient supply rates/ratios in the environment.4. The microbial diversity (taxonomic, genetic, and functional) of the surface ocean varies over time and space, driven by many factors in addition to nutrients. The sum of this mixture composes the ecosystem C:N:P, the ratio that Redfield described. Based on this framework, the CoPIs will make field observations of taxon-specific stoichiometry and growth rates, genomic analyses, and conduct laboratory chemostat experiments to improve understanding of how ocean taxonomic, genetic, and functional biodiversity control the stoichiometry of the surface ocean plankton. Their analyses of these data would lead to a mechanistic understanding of variations in the Redfield ratio, both spatially and temporally.Broader impacts. This study will greatly expand knowledge of the genomic diversity among ocean microbes and how this diversity affects biogeochemistry. The stoichiometry of the ocean's microbes is a parameter that nearly every chemical or biological oceanographer uses, from converting measurements made in one element to another, to estimating regional and global nitrogen budgets. The research also has important implications for the global carbon budget and any changes that might result from climate change. Beyond training three postdoctoral scholars and two graduate students, a Gateway Mentoring Program will be established to recruit undergraduates (total of 12) transferring from community colleges in the Southern California area, training and preparing them for careers in research-oriented science. The program will consist of extensive mentoring, research experiences at UCI, internships at BIOS, Princeton, or UCSD, and presentations at national conferences. This intensive mentoring and research experience prepares students well for a career in science, and enhances acceptance to post-graduate schools. The Program will have a very high proportion of underrepresented groups as reflected in the targeted colleges.Integration. To understand mechanistically temporal and spatial variability of the plankton C:N:P ratio, biodiversity must be studied not only at the traditional taxonomic level, but at the genetic and functional levels which dictate organism response to their environment. Data will be integrated into a combined ocean ecological, evolutionary, and biogeochemical model, with flexible stoichiometry, including cellular biochemical allocations. Seeding a coupled physical-biological model of the oceans with multiple competing genotypes enables the exploration of ecological and evolutionary patterns of resource acquisition and C:N:P ratios. Developing a more mechanistic examination of the course of ecology and evolution, in which laboratory and field data define tradeoffs between different growth and nutrient acquisition strategies, would estabblish the framework of adaptive dynamics for determining "evolutionarily convergence". Finally, model outcomes will be evaluated against field data.

智力上的优点。海洋生物地球化学的基本模式之一是雷德菲尔德比率，它将表层浮游生物的化学计量与深海的化学计量联系起来。全球一致的C：N：P比为106：16：1（Redfield比），没有明显的机制，特别是在不同海洋区域的浮游生物群落之间存在很大的元素差异。 因此，了解生物多样性如何调节海洋的元素组成，对于了解现在和未来的海洋和气候整体都很重要。本研究的概念假设如下：1. 细胞的C：N：P比例受其广泛的分类群的限制，这决定了例如它是否有外壳，大小，功能代谢，膜脂组成。 在一个分类单元内，有很高的遗传多样性。 这种遗传多样性中的一些可能是横向转移的，或者可以在分类群中丢失，并赋予各种功能能力（有机磷酸盐同化，硝酸盐同化，光异养等）。 功能多样性为细胞提供了进一步的灵活性，例如对不同营养供应速率/比率做出反应的能力，并在分类单元指定的范围内影响细胞的C：N：P比率。 考虑到这些分类学和遗传学的限制，细胞在生理上是可塑的，并且改变它如何分配细胞资源以响应环境中的营养供应速率/比率。 海洋表层的微生物多样性（分类、遗传和功能）随时间和空间的变化而变化，除了营养物质外，还受到许多因素的影响。 这些混合物的总和构成了生态系统的C：N：P，即Redfield描述的比例。 基于这一框架，CoPI将对特定分类群的化学计量和生长速率进行实地观察，进行基因组分析，并进行实验室恒化器实验，以提高对海洋分类学，遗传学和功能生物多样性如何控制海洋浮游生物表面化学计量的理解。 他们对这些数据的分析将导致对雷德菲尔德比率在空间和时间上的变化的机械理解。 这项研究将大大扩展海洋微生物基因组多样性的知识，以及这种多样性如何影响海洋地球化学。 海洋微生物的化学计量是几乎每个化学或生物海洋学家都使用的参数，从将一种元素的测量值转换为另一种元素，到估计区域和全球氮预算。 这项研究对全球碳预算和气候变化可能导致的任何变化也具有重要意义。 除了培养三名博士后学者和两名研究生外，还将建立一个门户导师计划，以招募从南加州地区社区学院转来的本科生（共12名），为他们在研究型科学领域的职业生涯做好培训和准备。该计划将包括广泛的指导，在UCI的研究经验，在BIOS，普林斯顿大学或UCSD实习，并在全国会议上介绍。 这种密集的指导和研究经验为学生在科学领域的职业生涯做好了准备，并提高了对研究生院的接受程度。 该方案将有很高比例的代表性不足的群体，反映在目标学院。 为了从机制上理解浮游生物C：N：P比值的时空变异性，不仅必须在传统的分类学水平上研究生物多样性，而且必须在遗传和功能水平上研究生物对环境的反应。 数据将被整合到一个海洋生态、进化和生物地球化学相结合的模型中，该模型具有灵活的化学计量，包括细胞生物化学分配。 播种耦合的物理-生物模型的海洋与多种竞争的基因型，使资源获取和C：N：P比例的生态和进化模式的探索。 发展一个更机械的生态学和进化过程的检查，其中实验室和现场数据定义不同的生长和养分获取策略之间的权衡，将建立适应动力学的框架，以确定“进化收敛”。 最后，将根据实地数据对模型结果进行评估。