CyberSEES: Type 2: Collaborative Research: A Computational and Analytic Laboratory for Modeling and Predicting Marine Biodiversity and Indicators of Sustainable Ecosystems

Cyber​​SEES：类型 2：协作研究：用于建模和预测海洋生物多样性和可持续生态系统指标的计算和分析实验室

• 批准号: 1539256
• 负责人: Heidi Sosik
• 金额: $ 62.92万
• 依托单位: Woods Hole Oceanographic Institution
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1539256&HistoricalAwards=false
• 关键词: CyberSEES; Type; Collaborative; Research; Computational

Marine ecosystems are complex and variable networks of interacting organisms that support roughly half of global productivity and play important roles in sustaining global processes. The lower trophic levels that are the focus of this project include primary producers at the base of the food web, as well as bacteria and a variety of small grazers that turn over rapidly and provide links that transfer energy to higher trophic levels, such as fish, seabirds, and marine mammals, helping to maintain healthy ecosystems. Mounting recognition points to the societal value of not just timely information about the status and change of marine ecosystems but how well we can model and predict them. While existing and emerging observation systems are poised to contribute, there is a large need for computationally-enabled marine biodiversity data and information systems. Despite recognition as system-level science, the way specialists and non-specialists think about ecosystems must move from reducing the problem spaces into specialized and finer partitions to a significantly more integrative and predictive one. This research effort brings together computational and information scientists, oceanographers and microbiologists to develop a Marine Biodiversity Virtual Laboratory (MBVL). In addition to research investigations of marine ecosystems, the Virtual Laboratory provides a platform for education via student diversity programs at the three institutions. The important learning opportunities will be two-fold for students: (1) to learn about, model, and make predictions for biodiversity in natural systems, and (2) to be exposed to working in an interdisciplinary team that includes both natural scientists and computer scientists. The project aims to foster a new generation of data scientists with skills that can cross disciplines. There will be capabilities to trace, verify and trust the original data and model sources that comprise key indicators of diversity as part of ecosystem health; this has become a requirement for technical (and societal) implementations. MBVL will contain a knowledge base for such indicators. While this is essential for research, the MBVL will also enhance education as students will truly probe, question, explore and experiment with the health, risks, and possible changes in specific marine environments. MBVL will address multi-scale, heterogeneous data challenges with informatics solutions that enable the cyber-generation and documentation of biodiversity indicators, providing the traceability between data and information to be used as a basis for sustainable ecosystem-based management and needed policy decisions. Thus researchers and students will be further engaged to think and act at the system-level. The research will have impacts beyond ocean sciences, i.e. more broadly in management and policymaking regarding marine resources and ecosystem services via key links with fisheries managers enabled by collaborations external to this project and with industry partners to evaluate and disseminate solutions to a much wider user base.

海洋生态系统是由相互作用的生物组成的复杂而多变的网络，支持着全球大约一半的生产力，在维持全球进程方面发挥着重要作用。该项目的重点是低营养级，包括食物网底层的初级生产者，以及细菌和各种小型食草动物，它们迅速转换并提供将能量转移到高营养级的联系，如鱼类，海鸟和海洋哺乳动物，帮助维持健康的生态系统。越来越多的人认识到，不仅要及时了解海洋生态系统的状况和变化，而且要了解我们如何对海洋生态系统进行建模和预测。虽然现有的和新出现的观测系统已准备好作出贡献，但对计算支持的海洋生物多样性数据和信息系统的需求很大。尽管被认为是系统级科学，但专家和非专家思考生态系统的方式必须从将问题空间缩小到专业化和更精细的分区，转变为更具综合性和预测性的分区。这项研究工作汇集了计算和信息科学家，海洋学家和微生物学家，以开发海洋生物多样性虚拟实验室（MBVL）。除了对海洋生态系统的研究调查外，虚拟实验室还通过这三个机构的学生多样性计划提供了一个教育平台。对学生来说，重要的学习机会是双重的：（1）学习，建模和预测自然系统中的生物多样性，（2）接触到包括自然科学家和计算机科学家在内的跨学科团队的工作。该项目旨在培养具有跨学科技能的新一代数据科学家。将有能力追踪、核实和信任原始数据和模型来源，这些数据和模型来源构成了作为生态系统健康一部分的多样性关键指标;这已成为技术（和社会）实施的一项要求。 MBVL将包含此类指标的知识库。虽然这对研究至关重要，但MBVL也将加强教育，因为学生将真正探索，质疑，探索和实验特定海洋环境的健康，风险和可能的变化。MBVL将通过信息学解决方案解决多尺度、异构数据的挑战，使生物多样性指标的网络生成和记录成为可能，提供数据和信息之间的可追溯性，作为基于生态系统的可持续管理和所需决策的基础。因此，研究人员和学生将进一步参与系统层面的思考和行动。该研究将产生超出海洋科学的影响，即通过与渔业管理人员的关键联系，在海洋资源和生态系统服务的管理和决策方面产生更广泛的影响，渔业管理人员通过本项目外部的合作以及与行业伙伴的合作来评估和传播解决方案，以更广泛的用户群。