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

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:
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
项目状态:
已结题
起止时间:
2022-09-01 至 2024-08-31

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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.
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