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。浮游植物决定湖泊生态系统的健康，从有害的水华降低水质到鱼类生产力。它们适应它们所生活的条件，但对环境的变化很敏感，包括营养，光线，温度和食草动物。如果浮游植物种类繁多，对环境有不同的反应，那么它们就更有可能成功地适应新的环境。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。该项目将描述浮游植物对决定浮游植物生长的关键因素的反应的多样性，包括物种内和物种间的反应。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，并使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，如湖泊采样，实验室实验，基因组分析和数学建模。项目成果将被纳入K-12教学，包括密歇根州农村学校的数据练习和在德克萨斯纪念博物馆提供专业发展学分的教师研讨会。该项目将推进我们对生态上重要的微生物，浮游植物种内性状变异的认识，并提供有关性状多样性如何在物种内和物种间产生和维持的见解。它将使用一个综合的方法相结合的理论框架的发展预测种内性状变异在不同的尺度和测试这一理论，通过实验性状的测定和测量的遗传相关性之间的许多浮游植物菌株分离出不同的湖泊。生态进化的框架将嵌入定量遗传学的方法在空间元生态环境。为了测试该框架对种内性状分布如何取决于选择制度和其他因素的具体预测，研究人员将确定来自密歇根州湖泊主要分类群的几种浮游植物关键功能性状的种内变异程度（平均值和方差），这些湖泊的资源水平和放牧压力不同，创造了选择制度的梯度。这些性状包括资源利用性状（磷、氮和光）、温度反应性状和细胞大小，这些性状通常被认为是抗食草动物的代表。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，这是以前没有为浮游植物做过的，并将它们与种间权衡进行比较。此外，生态进化模型开发，以预测在不同的空间尺度上的多维性状空间的种内变异应该是其他organis.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.