Collaborative Research: Intraspecific trait variation in phytoplankton at different scales

被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，从有害的水华降低水质到鱼类产量。它们适应它们所处的环境，但对环境的变化很敏感，包括营养、光线、温度和食草动物。如果浮游植物有多种类型，对环境有不同的反应，那么它们更有可能成功地适应新的环境。为了了解浮游植物如何对未来的湖泊环境做出反应，比如更温暖、更富营养的条件，我们需要了解浮游植物的多样性，以及是什么驱动了这种多样性。本项目将描述浮游植物对决定浮游植物生长的关键因素的多样性反应，包括物种内部和物种之间的反应。研究人员还将使用数学模型来开发一种新的理论，解释是什么驱动了物种内部和物种之间的多样性，并用经验数据对其进行测试。研究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湖泊采样、实验室实验、基因组分析和数学建模等多种科学方法的培训。项目成果将纳入K-12教学，包括密歇根州农村学校的数据练习，以及在德克萨斯纪念博物馆提供进化论专业发展学分的教师讲习班。该项目将提高我们对生态重要微生物、浮游植物种内性状变异的认识，并为物种内和物种间性状多样性的产生和维持提供见解。它将采用一种综合的方法，结合理论框架的发展，预测不同尺度的种内性状变异，并通过实验性状测定和测量从不同湖泊分离的众多浮游植物菌株之间的遗传相关性来检验这一理论。生态进化框架将在空间元群落设置中嵌入定量遗传学方法。为了验证基于该框架的具体预测，即种内性状分布是如何依赖于选择机制和其他因素的，研究人员将确定来自密歇根湖泊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（平均值和方差），这些浮游植物的资源水平和放牧压力进行了对比，从而创建了一个选择机制的梯度。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，这些性状通常被认为是食草动物抗性的代表。同时观察几个特征，该项目将能够探索物种内的成对和更高维度的权衡，这是以前没有对浮游植物做过的，并将它们与种间的权衡进行比较。此外，用于预测不同空间尺度下多维性状空间种内变异的生态进化模型也应适用于其他生物。该奖项反映了美国国家科学基金会的法定使命，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。

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.