Statistical Mechanics Modeling of Critical Phenomena in Phytoplankton Living in a Cold Environment

寒冷环境中浮游植物关键现象的统计力学模型

• 批准号: 2102906
• 负责人: Ivan Sudakow
• 金额: $ 20.17万
• 依托单位: University of Dayton
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102906&HistoricalAwards=false
• 关键词: Statistical; Mechanics; Modeling; Critical; Phenomena

Phytoplankton are a critical component of Earth's carbon cycle and thus play an important role in the climate system. Recent observations have shown that climate change can be a leading factor influencing ecosystem behavior. One specific example where environmental conditions may be related to the dynamics of the plankton ecosystem is an unusual massive phytoplankton bloom that was observed underneath the ice pack in the Arctic Ocean. During the Arctic melt season, the sea ice surface undergoes a remarkable transformation to a complex mosaic of melt ponds, snow and ice. The transition in pond fractal geometry revolves around a critical length scale of about 100 square meters in area, as isolated ponds grow and coalesce into larger connected structures with complex, self-similar boundaries. Moreover, the transition from isolated sunlight penetration associated with individual ponds to a continuous matrix of light associated with large connected pond configurations could help trigger the under-ice blooms and influence biological productivity and bio-geochemical processes. This work will shed light on key concerns such as ecosystem transformations under changing conditions of solar radiation; the melting process and the dynamics of living matter; energy balance in the Arctic climate system; and, the contribution of phytoplankton in the Arctic to climate feedbacks. The PI will explore how conceptual physical models arising in statistical mechanics can be used to efficiently characterize and quantify Arctic phytoplankton under nonlinear dynamics. In outreach efforts, the PI will convey to the general public the importance of rigorous and fundamental approaches to climate and ecosystem research. The PI will prepare illustrative material (scientific infographics) about this research that will explain a strong interdisciplinary approach to this problem. The scientific infographics will be made freely downloadable by the general public and educators from a project webpage. The fundamental concepts of ecosystem modeling will be used in a teaching context in computational and statistical physics classes. A board game based on these principles will be created and tested in local K-12 schools. This research will also provide funding for an undergraduate and PhD student, providing valuable experience in theoretical applications within the broad framework of interdisciplinary research. The PI will involve undergraduate students who are participating in the summer research program of the University of Dayton for minority-serving institutions to participate in this project, which will enable them to gain skills in modeling and teamwork.This project will offer the universality of statistical mechanics models for making robust, quantitative predictions about critical transitions in phytoplankton populations where the full complexity of local dynamics could never be modeled explicitly. The project will reveal some universal links between recent environmental changes and complex behavior of phytoplankton communities and increase our knowledge of phytoplankton ecosystems, including bloom development. The methods and physical interpretation of living matter-climate interaction discovered in this research will also be applicable to future studies of possible organisms in polar regions of the Earth and "extraterrestrial oceans" beneath icy surfaces. This project synthesizes available remote sensing and fieldwork data in an innovative quantitative framework to improve description of phytoplankton dynamics.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.

浮游植物是地球碳循环的重要组成部分，因此在气候系统中发挥着重要作用。最近的观察表明，气候变化可能是影响生态系统行为的主要因素。环境条件可能与浮游生物生态系统的动态相关的一个具体例子是在北冰洋冰层下面观察到的一种不寻常的大规模浮游植物大量繁殖。在北极融化季节，海冰表面发生显着转变，变成由融化的池塘、雪和冰组成的复杂镶嵌体。池塘分形几何的转变围绕着面积约为 100 平方米的临界长度尺度，随着孤立的池塘生长并合并成具有复杂、自相似边界的更大的连接结构。此外，从与单个池塘相关的孤立阳光穿透到与大型相连池塘配置相关的连续光矩阵的转变可能有助于引发冰下水华并影响生物生产力和生物地球化学过程。这项工作将阐明关键问题，例如太阳辐射变化条件下的生态系统转变；生命物质的熔化过程和动力学；北极气候系统的能量平衡；以及北极浮游植物对气候反馈的贡献。 PI将探索如何使用统计力学中产生的概念物理模型来有效地表征和量化非线性动力学下的北极浮游植物。在推广工作中，首席研究员将向公众传达气候和生态系统研究严格和基本方法的重要性。 PI 将准备有关这项研究的说明性材料（科学信息图表），以解释解决该问题的强有力的跨学科方法。公众和教育工作者可以从项目网页免费下载科学信息图表。生态系统建模的基本概念将在计算和统计物理课程的教学中使用。基于这些原则的棋盘游戏将在当地 K-12 学校创建和测试。这项研究还将为本科生和博士生提供资助，在跨学科研究的广泛框架内提供理论应用的宝贵经验。该项目负责人将让参加代顿大学少数族裔服务机构夏季研究项目的本科生参与该项目，这将使他们获得建模和团队合作的技能。该项目将提供统计力学模型的普适性，以便对浮游植物种群的关键转变做出稳健的定量预测，而局部动态的全部复杂性永远无法明确建模。该项目将揭示近期环境变化与浮游植物群落复杂行为之间的一些普遍联系，并增加我们对浮游植物生态系统（包括水华发育）的了解。这项研究中发现的生命物质与气候相互作用的方法和物理解释也将适用于未来对地球极地地区和冰面下方的“外星海洋”中可能存在的生物体的研究。该项目在创新的定量框架中综合了可用的遥感和实地工作数据，以改进对浮游植物动态的描述。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

Excitable media store and transfer complicated information via topological defect motion

可兴奋介质通过拓扑缺陷运动存储和传输复杂信息

• DOI: 10.1016/j.cnsns.2022.106844
• 发表时间: 2023
• 期刊: Communications in Nonlinear Science and Numerical Simulation
• 影响因子: 3.9
• 作者: Sudakow, Ivan;Vakulenko, Sergey A.;Grigoriev, Dima
• 通讯作者: Grigoriev, Dima

Statistical mechanics in climate emulation: Challenges and perspectives

• DOI: 10.1017/eds.2022.15
• 发表时间: 2022-11
• 期刊: Environmental Data Science
• 作者: I. Sudakow;Michael Pokojovy;D. Lyakhov

MeltPondNet: A Swin Transformer U-Net for Detection of Melt Ponds on Arctic Sea Ice

• DOI: 10.1109/jstars.2022.3213192
• 发表时间: 2022
• 期刊: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• 影响因子: 5.5
• 作者: I. Sudakow;V. Asari;Ruixu Liu;D. Demchev

Knowledge gaps and missing links in understanding mass extinctions: Can mathematical modeling help?

• DOI: 10.1016/j.plrev.2022.04.001
• 发表时间: 2022-04
• 期刊: Physics of life reviews
• 影响因子: 11.7
• 作者: I. Sudakow;C. Myers;S. Petrovskii;C. Sumrall;J. Witts