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.

海洋生态系统是相互作用生物体的复杂且可变的网络，可支持全球生产力的一半，并在维持全球过程中起重要作用。该项目的重点的较低营养水平包括食物网基底部的主要生产者，以及细菌和各种小放牧者，它们迅速翻转，并提供将能量转移到较高营养水平的链接，例如鱼类，海鸟和海洋哺乳动物，有助于维持健康的生态系统。安装识别表明，不仅及时有关海洋生态系统的状态和变化的信息的社会价值，而且还可以如何建模和预测它们。尽管现有和新兴的观察系统有望做出贡献，但对启用计算的海洋生物多样性数据和信息系统的需求很大。尽管承认是系统级科学，但专家和非专家认为生态系统的方式必须从将问题空间减少到专业和更精细的分区转变为更加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，虚拟实验室还通过三个机构的学生多样性计划提供了一个教育平台。对于学生来说，重要的学习机会将是两个方面：（1）学习，建模并为自然系统中的生物多样性做出预测，以及（2）在包括自然科学家和计算机科学家在内的跨学科团队中工作。该项目旨在培养新一代的数据科学家，具有可以跨学科的技能。将有能力追踪，验证和信任原始数据和模型来源，这些数据和模型来源包括多样性的关键指标，这是生态系统健康的一部分；这已成为技术（和社会）实施的要求。 MBVL将包含有关此类指标的知识库。尽管这对于研究至关重要，但MBVL也将增强教育，因为学生将真正探究，提问，探索和实验特定海洋环境的健康，风险和可能的变化。 MBVL将通过信息解决方案来解决多尺度的，异构的数据挑战，这些解决方案能够实现生物多样性指标的网络生成和文档，从而提供数据和信息之间的可追溯性，以作为可持续生态系统基于可持续生态的基于基于生态系统的基础的基础和所需的政策决策。因此，研究人员和学生将进一步互动，以在系统级别进行思考和行动。这项研究将在海洋科学以外的影响，即，通过与该项目外部的协作以及与行业合作伙伴启用的渔业经理联系，在管理和决策方面更广泛地在有关海洋资源和生态系统服务的政策中，并与较广泛的用户群评估和传播解决方案。