Collaborative Proposal: Intraspecific Trait Variation in Phytoplankton at Different Scales

合作提案：不同规模浮游植物的种内性状变异

• 批准号: 1754614
• 负责人: Edward Theriot
• 金额: $ 44.91万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1754614&HistoricalAwards=false
• 关键词: Collaborative; Proposal; Intraspecific; Trait; Variation

Tiny microscopic organisms called phytoplankton form the base of lake food webs. Phytoplankton determine the health of lake ecosystems, from harmful blooms reducing water quality to fish productivity. They adapt to the conditions they live in but are sensitive to changes in the environment, including nutrients, light, temperature, and grazers. If phytoplankton species have many types with different responses to the environment, then they are more likely to successfully adapt to novel conditions. To understand how phytoplankton may respond to future lake environments, such as warmer and more nutrient-rich conditions, we need to understand how diverse phytoplankton are and what drives that diversity. This project will characterize the diversity of phytoplankton responses, both within and across species, to key factors that determine phytoplankton growth. The researchers will also use mathematical models to develop a new theory of what drives the diversity within and among species and test it with the empirical data. The results will enable better predictions of how lakes will respond to changing environments. Graduate students and postdoctoral associates will be trained in multiple scientific approaches such as lake sampling, laboratory experiments, genomic analyses and mathematical modeling. Project results will be incorporated into K-12 teaching, including data exercises for rural schools in Michigan and teacher workshops that provide Professional Development Credit in evolution at the Texas Memorial Museum. This project will advance our knowledge of intraspecific trait variation in ecologically important microbes, phytoplankton, and provide insights into how trait diversity is generated and maintained, both within and among species. It will use an integrated approach combining the development of a theoretical framework for predicting intraspecific trait variation at different scales and tests of this theory though experimental trait determination and measurements of genetic relatedness among numerous phytoplankton strains isolated from different lakes. The eco-evolutionary framework will embed quantitative genetics approaches in a spatial metacommunity setting. To test the specific predictions from this framework for how intraspecific trait distributions depend on selection regime and other factors, the researchers will determine the degree of intraspecific variation (mean and variance) in key functional traits for several species of phytoplankton from major taxonomic groups in Michigan lakes of contrasting resource levels and grazing pressure, creating a gradient of selection regimes. The traits include resource utilization traits (phosphorus, nitrogen and light), temperature response traits, and cell size, often considered a proxy for grazer resistance. Looking at several traits simultaneously, the project will be able to explore pairwise and higher dimensional trade-offs within species, which has not previously been done for phytoplankton, and compare them to interspecific trade-offs. Additionally, the eco-evolutionary models developed to predict intraspecific variation in multidimensional trait space at different spatial scales should be relevant for other organisms.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.

称为浮游植物的微小微生物构成了湖泊食物网的基础。浮游植物决定着湖泊生态系统的健康，从降低水质的有害藻华到鱼类生产力。它们适应生活条件，但对环境的变化很敏感，包括营养物质、光线、温度和食草动物。如果浮游植物物种有多种类型，对环境有不同的反应，那么它们更有可能成功适应新的条件。为了了解浮游植物如何应对未来的湖泊环境，例如更温暖和营养更丰富的条件，我们需要了解浮游植物的多样性以及驱动这种多样性的因素。该项目将描述物种内部和物种间浮游植物对决定浮游植物生长的关键因素的反应多样性。研究人员还将使用数学模型来开发一种新的理论，解释物种内部和物种之间多样性的驱动因素，并用经验数据对其进行测试。这些结果将有助于更好地预测湖泊将如何应对不断变化的环境。研究生和博士后将接受多种科学方法的培训，例如湖泊采样、实验室实验、基因组分析和数学建模。项目成果将纳入 K-12 教学，包括密歇根州乡村学校的数据练习以及在德克萨斯纪念博物馆举办的提供进化专业发展学分的教师讲习班。该项目将增进我们对生态上重要的微生物、浮游植物的种内性状变异的了解，并深入了解物种内部和物种之间性状多样性如何产生和维持。它将使用一种综合方法，结合开发预测不同尺度的种内性状变异的理论框架，并通过实验性状测定和从不同湖泊分离的众多浮游植物菌株之间遗传相关性的测量来测试该理论。生态进化框架将在空间元社区环境中嵌入定量遗传学方法。为了测试该框架的具体预测，即种内性状分布如何取决于选择制度和其他因素，研究人员将确定来自密歇根州主要分类群的几种浮游植物的关键功能性状的种内变异程度（均值和方差）资源水平和放牧压力不同的湖泊，形成了选择制度的梯度。这些性状包括资源利用性状（磷、氮和光）、温度响应性状和细胞大小，通常被认为是食草动物抗性的代表。同时观察多个特征，该项目将能够探索物种内的成对和更高维度的权衡，这是以前从未对浮游植物进行过的，并将它们与种间权衡进行比较。此外，为预测不同空间尺度的多维性状空间的种内变异而开发的生态进化模型应该与其他生物体相关。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。