Workshop: Data-Model Assimilation in Ecology: Techniques and Applications, July 2007

研讨会：生态学中的数据模型同化：技术与应用，2007 年 7 月

• 批准号: 0714142
• 负责人: Yiqi Luo
• 金额: $ 4.8万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-15 至 2009-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0714142&HistoricalAwards=false
• 关键词: Workshop; Data; Model; Assimilation; Ecology

Ecology is being rapidly transformed from a data-poor to data-rich field. With such rich data sources, it is urgent to develop research capability in the area of data-model assimilation so that large volumes of data can be converted into knowledge of ecosystem functioning. The workshop will be a community effort to comprehend the data-model assimilation approach, learn relevant techniques, examine application cases, discuss general issues in ecological applications, and devise paths to prepare the community for the new era of research. The workshop involve case studies of applications, techniques, and issues in applications, and will recommend future directions in applications of data-model assimilation in ecology. Case studies will present some successful applications in biogeochemistry, population, and community ecology and will illustrate a variety of techniques that have been applied to ecological research, such as genetic algorithms, Markov Chain/Monte Carlo approaches, adjoint methods, and Kalman filters. A session on general application issues will discuss information content of data and models, the best strategies to extract information, ill-conditioned inversion, and selection of target variables. This workshop is designed to help prepare the community for a data-rich, NEON-type era of ecological research. As a research community in ecology, expertise is needed to handle large datasets and gain knowledge from analyzing massive databases. It is urgent to train next generations of ecologists with strong quantitative skills who can apply mathematics and statistics to analyze large volumes of ecological data. The workshop will generate strategies for promotion of data-model assimilation, training of next generation scientists, and publications of methods for data-model assimilation and case studies of applications.The workshop will have 25 participants, including 4 graduate students, 7 junior faculty/scientists, 6 women, and 5 international scientists from Australia, UK, Germany, and Canada. The participants will cover multiple research areas, including limnology, population ecology, community ecology, soil science, ecohydrology, isotope inverse analysis, and biogeochemistry. Results will be published and it is anticipated that the workshop will have major impacts on future research directions in ecology.

生态学正在从一个数据贫乏的领域迅速转变为一个数据丰富的领域。有了如此丰富的数据来源，迫切需要发展数据模型同化领域的研究能力，以便将大量数据转化为生态系统功能的知识。研讨会将是一个社区的努力，以理解数据模型同化方法，学习相关技术，审查应用案例，讨论生态应用中的一般问题，并设计途径，使社区为新的研究时代做好准备。讲习班涉及应用、技术和应用中的问题的个案研究，并将建议数据模型同化在生态学中的应用的未来方向。案例研究将展示在生物地球化学、种群和群落生态学中的一些成功应用，并将说明应用于生态研究的各种技术，如遗传算法、马尔可夫链/蒙特卡罗方法、伴随方法和卡尔曼滤波。一般应用问题部分将讨论数据和模型的信息内容、提取信息的最佳策略、病态反演和目标变量的选择。本次研讨会旨在帮助社区为数据丰富、霓虹灯式的生态研究时代做好准备。作为一个生态研究团体，需要专业知识来处理大型数据集，并从分析大量数据库中获取知识。培养具有较强的定量分析能力，能够运用数学和统计学分析大量生态数据的下一代生态学家迫在眉睫。讲习班将制定促进数据模型同化的战略，培训下一代科学家，出版数据模型同化方法和应用案例研究。研讨会将有25名参与者，包括4名研究生，7名初级教师/科学家，6名女性和5名来自澳大利亚，英国，德国和加拿大的国际科学家。与会者将涵盖多个研究领域，包括湖沼学、种群生态学、群落生态学、土壤学、生态水文学、同位素逆分析和生物地球化学。预计此次研讨会将对未来生态学的研究方向产生重大影响。