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。它们适应生活的条件，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许多类型，对环境的反应不同，那么它们更有可能成功地适应新的条件。为了了解浮游植物可能如何应对未来的湖泊环境，例如更温暖、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推动了这种多样性。该项目将描述物种内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的多样性。研究人员还将使用数学模型来开发一种新的理论，说明是什么推动了物种内部和物种之间的多样性，并用经验数据进行了检验。这些结果将使人们能够更好地预测湖泊将如何应对不断变化的环境。研究生和博士后助理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进化专业发展学分的教师讲习班。这个项目将促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，并为如何在物种内和物种间产生和维持性状多样性提供见解。它将使用一种综合的方法，结合建立预测不同尺度上的种内性状变化的理论框架，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测试这一理论。生态进化框架将在空间元群落环境中嵌入数量遗传学方法。为了测试这一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，研究人员将确定密歇根州湖泊中资源水平和放牧压力不同的主要分类群中几种浮游植物关键功能性状的种内变异程度(均值和方差)，从而产生选择制度的梯度。这些特性包括资源利用特性(磷、氮和光)、温度响应特性和细胞大小，这些特性通常被认为是牧草抗性的代用品。同时观察几个特征，该项目将能够探索物种内成对的和更高维度的权衡，这是以前对浮游植物所没有做过的，并将它们与物种间的权衡进行比较。此外，为预测不同空间尺度上多维特征空间的种内变异而开发的生态进化模型应该与其他组织相关。这一奖项反映了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。

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.