SWIMMING PHYTOPLANKTON IN A TURBULENT ENVIRONMENT

流体环境中游动的微生物无处不在，种类繁多。例如，精子甩动它们长长的鞭状尾巴，长途跋涉去寻找卵子，致病菌可以进入血液，引起诸如伤寒之类的疾病。该项目将建立数学模型来描述流体环境中微生物的分布，特别关注湍流中游动的浮游植物。了解浮游植物的时空动态是一个最及时的研究问题。例如，浮游植物是气候变化的关键参与者：浮游植物在光合作用中固定碳，当它们死亡时可以沉入海底，作为大气二氧化碳的单向通道。此外，某些种类的浮游植物可以形成有害的水华，或“赤潮”。这些水华可能对人类健康有害，甚至致命，并可能造成重大的经济损失，例如对水产养殖者和旅游业。微生物经常以斑驳的结构分布，例如，在地表水中出现大量藻类（“赤潮”），细菌聚集在美味的下沉聚集体上，称为海洋雪。当地人口与其环境之间的相互作用显然取决于人口在环境中的分布方式。例如，集中在水面附近的藻类细胞可能比在整个水柱中充分混合的细胞更有效地进行光合作用，从而生长得更快。因此，开发预测模型来确定微型浮游生物的分布对于了解水生生态系统至关重要。本项目将对环境相关流场中游动浮游植物的空间分布进行预测。在小型浮游生物所经历的小尺度上，流体环境高度受粘性力支配。尽管这些细胞生活在波涛汹涌、狂风呼啸的湍流环境中，但它们只能看到相当简单的剪切流体运动。这些动作如何影响他们的游泳？这会妨碍它们朝自己选择的方向游泳吗？它会强大到使它们停止游泳吗？显然，这取决于气流的能量有多大，而这又取决于物理作用力（如风）的能量有多大。除了单个细胞看到的剪切运动外，生物体还通过更大规模的流体运动进行运输。这些水流会不会把细胞混在一起，以至于所有的游泳努力都白费了？或者流场是否会与游泳相互作用，例如在流体汇聚区形成细胞斑块？为了预测在湍流环境中游动的浮游微生物是如何分布的，我们将：1)建立一个数学模型，描述在简单流场中游动细胞群体的空间分布。这种模式的计算速度比数值模拟大量游动的浮游植物要快，因此在计算强度大的海洋学模拟中很有用。该模型将包括该项目的一个实验部分的结果，该部分将量化示例藻类物种在流体流动中的游泳行为。2)建立合适的浮游植物湍流流场数值模型。这将描述与环境相关的大尺度流场，但也描述了在单个细胞的小尺度上所经历的流场。3)结合I和II的结果，模拟浮游植物在湍流流场中的游动。

Swimming micro-organisms in fluid environments are ubiquitous and diverse. For example, sperm beat their long whiplike tails, travelling long distances to find an egg, and pathogenic bacteria can move into the bloodstream, causing diseases such as typhoid fever. This project will develop mathematical models to describe the distribution of micro-organisms in fluid environments, with a specific focus on swimming phytoplankton in turbulent flow. Understanding the spatial-temporal dynamics of phytoplankton is a most timely research question. For example, phytoplankton are critical players in climate change: phytoplankton fix carbon during photosynthesis, and when they die can sink to the ocean floor, acting as a one-way path for atmospheric carbon dioxide. Also, certain species of phytoplankton can form harmful blooms, or `red tides'. These blooms can be detrimental, or even fatal, to human health, and can cause significant financial losses, for example to aquaculturists and the tourism business. Micro-organisms are frequently distributed in patchy structures, for example blooms of algae appear in surface waters ('red tides'), and bacteria aggregate on tasty sinking aggregates called marine snow. The interaction between a local population and its environment will clearly depend on how the population is distributed in the environment. For example, algal cells that are concentrated near the surface may undergo photosynthesis more efficiently, and thus grow more rapidly, than cells which are well mixed throughout the water column. Developing predictive models to determine the distribution of microplankton is therefore critical for understanding aquatic ecosystems. This project will predict the spatial distribution of swimming phytoplankton in environmentally relevant flow fields. At the small scale experienced by microplankton, the fluid environment is highly dominated by viscous forces. Despite living in a turbulent fluid environment with waves crashing and winds roaring, these cells only see rather simple shearing fluid motions. How do these motions affect their swimming? Will it hamper their ability to swim in their chosen direction? Will it be so strong as to cause them to stop swimming? Clearly it will depend on how energetic the flow is, which in turn will depend on how energetic the physical forcing (e.g. wind) is. In addition to the shearing motion that individual cells see, organisms are also transported by larger scale fluid motions. Will these flows mix cells so vigorously that all swimming efforts are effectively wasted? Or will the flow fields interact with the swimming, for example creating patches of cells at fluid convergence zones? To predict how swimming microplankton are distributed in a turbulent environment, we will: 1)Develop a mathematical model that describes how a population of swimming cells is spatially distributed in simple flow fields. This model will be computationally faster for than numerically simulating large numbers of swimming phytoplankton, and thus will be useful in computationally intense oceanographic simulations. Included in this model will be the results of an experimental component of the project which will quantify the swimming behaviour of example algal species in fluid flow.2)Develop an appropriate numerical model for turbulent flow fields experienced by phytoplankton. This will describe the large-scale environmentally relevant flow fields, and yet also describe the flow field experienced at the small scale of individual cells.3)Combine the results of I and II to model swimming phytoplankton in turbulent flow fields.