NSF Postdoctoral Fellowship in Biology: Statistical Mechanics Reveals the Long-Term and Multi-Scale Consequences of Perturbations to Ecological and Evolutionary Dynamics

美国国家科学基金会生物学博士后奖学金：统计力学揭示扰动对生态和进化动力学的长期和多尺度后果

• 批准号: 2208901
• 负责人: Renata Diaz
• 金额: $ 13.8万
• 依托单位: Diaz, Renata Montserrat
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208901&HistoricalAwards=false
• 关键词: NSF; Postdoctoral; Fellowship; Biology; Statistical

Fellow's name: Renata DiazProposal number: 2208901Research title: Statistical mechanics reveals the long-term and multi-scale consequences of perturbations to ecological and evolutionary dynamicsSponsoring scientist(s) and host institution(s): Dr. Andrew Rominger, University of MaineThis action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2022, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. A key challenge for ecology in the 21st century is predicting how perturbations, such as species loss, declines in abundance, and changing environmental conditions, propagate across levels of organization and over time in biological systems. Ecological systems can be extremely difficult to predict in specific detail, but often conform to predictable patterns at larger, aggregated scales. In many instances, these aggregate patterns converge with patterns that emerge at random - not because ecological and evolutionary processes do not contribute to these patterns, but because many interacting processes at multiple levels of organization smooth or cancel each other out. However, strong perturbations can temporarily drive aggregate patterns away from the forms they assume at random. The fellow will construct a unified theoretical, empirical, and computational framework for using disruptions to aggregate patterns to detect and diagnose strong disturbances to ecological and evolutionary dynamics. As part of this work, they will develop computational and educational materials to facilitate wider uptake of complex systems approaches in ecology, and lead workshops in active allyship in the university setting and computational skills for early-stage scientists. The fellow will use agent-based simulations of random, process-driven, and experimentally perturbed eco-evolutionary dynamics to characterize the expected impacts of different types and magnitudes of disturbances on aggregated patterns in ecological systems. They will integrate these findings with the predictions from established theories derived from community ecology, population genetics, and statistical mechanics. They will test the causal link between perturbations and disruptions to aggregate patterns in experimentally manipulated empirical systems using case studies and a meta-analysis of disturbance experiments. They will develop a lab module on emergent phenomena for a theoretical ecology course, and produce software packages, workshop curricula, and interactive online tutorials to support applications of this conceptual framework in new contexts. They will receive training in simulation modeling, population genetics, software and curriculum development, and mentoring. The products of this work will be publicly archived and made openly available for re-use.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.

研究员姓名：Renata DiazProposal编号：2208901研究标题：统计力学揭示了生态和进化动力学扰动的长期和多尺度后果赞助科学家（S）和主办机构（S）：安德鲁·罗明格博士，缅因大学这一行动资助了NSF博士后生物学研究奖学金2022财年，综合研究调查基因组，环境和表型之间的生命管理相互作用的规则。该研究金支持研究员的研究和培训，以创新的方式为生活规则领域做出贡献。21世纪生态学面临的一个关键挑战是预测物种丧失、丰度下降和环境条件变化等干扰如何在生物系统中随着时间的推移在组织层面上传播。生态系统的具体细节可能极难预测，但在更大的聚合尺度上往往符合可预测的模式。在许多情况下，这些聚合模式与随机出现的模式趋同--这并不是因为生态和进化过程对这些模式没有贡献，而是因为在多个组织层次上的许多相互作用的过程相互平滑或相互抵消。然而，强扰动可以暂时地驱使聚合模式远离它们随机呈现的形式。该研究员将构建一个统一的理论，经验和计算框架，用于使用中断聚合模式来检测和诊断对生态和进化动力学的强烈干扰。作为这项工作的一部分，他们将开发计算和教育材料，以促进更广泛地吸收生态学中的复杂系统方法，并在大学环境和早期科学家的计算技能中积极开展研讨会。该研究员将使用基于代理的随机，过程驱动和实验扰动的生态进化动力学模拟，以表征不同类型和规模的干扰对生态系统中聚集模式的预期影响。他们将把这些发现与来自社区生态学，种群遗传学和统计力学的既定理论的预测相结合。他们将使用案例研究和干扰实验的元分析，在实验操作的经验系统中测试扰动和破坏之间的因果关系。他们将为理论生态学课程开发一个关于涌现现象的实验室模块，并制作软件包，研讨会课程和互动式在线教程，以支持在新环境中应用这一概念框架。他们将接受模拟建模、群体遗传学、软件和课程开发以及指导方面的培训。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。