Dynamics of Phytoplankton in Water Columns: Persistence, Competition, and Evolution

该项目将开发模型来预测对光线和营养物质的竞争如何影响湖泊中的浮游植物。藻类是漂浮在湖泊和海洋水柱中的微生物。它们构成了水生食物网的基础，通过光合作用产生了世界上大约50%到85%的氧气。它们的种群持久性对生态系统健康至关重要。然而，有害的藻类水华在包括伊利湖在内的一些水体中是一个严重的问题。水华可能是由产生毒素的藻类和无毒的硅藻之间的竞争逆转引起的。在混合良好的环境中，蓝藻被硅藻竞争性地排除在外，但在混合程度较低或分层的水中，有毒的蓝藻向上漂浮形成表面浮渣，并排除硅藻。水文措施，如改变湖泊中的湍流混合，在适当的条件下可以解决这一问题。该项目将使用数学模型来了解浮游植物之间的竞争，并帮助设计更有效的水文控制策略。将进行创新的数学分析，以量化各种环境过程对浮游植物种群动态形成的影响。该项目将为博士生和本科生的暑期项目提供研究课题。本科生导师将与俄亥俄州立大学数学生物科学研究所的夏季本科生凯普斯通会议相结合。该项目旨在了解环境驱动因素，包括空间异质性、非均匀混合以及光和营养物质的可用性，在塑造不同浮游植物种群的群落结构方面的影响。我们将建立单物种、多物种和连续物种的浮游植物种群模型，以解决四个关键的生物学问题：(1)在富营养化条件下，营养丰富，但光照有限。水柱分层如何影响单一物种的持久性？(2)人类活动加速了许多淡水湖和沿海生态系统的富营养化，导致有害藻类水华的出现。人工混合能否创造一种竞争逆转，允许无毒硅藻竞争性地排除有毒藻类？(3)浮游植物群落是多样化的，可以按照各种功能权衡来构建。在光获取方面的权衡如何影响浮游植物物种的共存？(4)营养动态如何影响空间异质环境中的浮游植物竞争？对于层状水柱中的两种群竞争，长期动态将根据单调动力系统的结果进行分类。这个项目的一个重要的方法论进步是使用突变-选择模型的博弈论方法。这种方法通过根据浮游植物的功能特征对各种浮游植物物种进行分组，捕捉到了浮游植物令人惊讶的多样性。最后，将在薄层极限下讨论营养动态的影响，重点是深层叶绿素极大值的进化稳定性。这个项目将通过数学建模和分析进一步了解浮游植物的种群持久性、垂直分布和群落多样性。这个奖项反映了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，被认为是值得支持的。

This project will develop models to predict how competition for light and nutrients affect phytoplankton in lakes. Algae are microscopic organisms that drift in the water columns of lakes and oceans. They form the foundation of the aquatic food web and produce approximately 50% to 85% of the world's oxygen via photosynthesis. Their population persistence is critical to ecosystem health. However, harmful algal blooms are a serious problem in some bodies of water, including Lake Erie. Blooms can be caused by the reversal of the competition between toxin-producing algae and the non-toxic diatoms. In well-mixed environments, cyanobacteria are competitively excluded by diatoms, but in poorly mixed or stratified water the toxic cyanobacteria float upwards to form surface scum and exclude diatoms. Hydrologic measures, such as altering the turbulent mixing in lakes, can solve the problem under appropriate conditions. This project will use mathematical models to understand the competition among phytoplankton and help design more effective hydrologic control strategies. Innovative mathematical analysis will be performed to quantify the effect of various environmental processes in shaping the population dynamics of phytoplankton. This project will provide research topics for PhD students and undergraduate summer projects. The undergraduate mentorship will be leveraged with the Summer Undergraduate Capstone Conference of the Mathematical Biosciences Institute at Ohio State University. This project aims to understand the effect of environmental drivers, including spatial heterogeneity, non-uniform mixing, and light and nutrient availability, in shaping the community structure of a diverse phytoplankton population. We will develop phytoplankton population models of a single species, multiple species, and a continuous spectrum of species to address four key biological questions: (1) In eutrophic conditions nutrients are abundant, but light is limited. How does water column stratification affect the persistence of a single species? (2) Human activities have accelerated the eutrophication of many freshwater lakes and coastal ecosystems, causing harmful algal blooms to emerge. Can artificial mixing create a competitive reversal, allowing non-toxic diatoms to competitively exclude the toxic algae? (3) Phytoplankton communities are diverse and can be structured along various functional trade-offs. How do trade-offs in light acquisition affect coexistence among phytoplankton species? (4) How do nutrient dynamics influence phytoplankton competition in spatially heterogeneous environments? For two-species competing in stratified water columns, long-term dynamics will be classified by monotone dynamical systems results. A significant methodological advance of this project is the game-theoretical approach of using mutation-selection models. This approach captures the surprising diversity of the phytoplankton by grouping various phytoplankton species according to their functional traits. Finally, the effect of nutrient dynamics will be addressed in a thin-layer limit, with emphasis on the evolutionary stability of deep chlorophyll maxima. This project will further understanding of population persistence, vertical distribution, and community diversity of phytoplankton through mathematical modeling and analysis.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.