Collaborative Research: Phenobase: Community, infrastructure, and data for global-scale analyses of plant phenology

合作研究：Phenobase：用于全球范围植物物候分析的社区、基础设施和数据

• 批准号: 2223509
• 负责人: Ellen Denny
• 金额: $ 11.86万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223509&HistoricalAwards=false
• 关键词: Collaborative; Research; Phenobase; Community; infrastructure

Plant phenology – the timing of plant life-cycle events, such as leaf growth, flowering, and fruiting – plays a fundamental role in shaping terrestrial ecosystems. The timing of plant phenology not only affects the fitness of individual plants, it also impacts the fitness and behaviors of organisms dependent on plants, which in terrestrial ecosystems includes nearly all animals, either directly or indirectly. Thus, changes in plant phenology can trigger dramatic, and sometimes devastating, consequences for ecosystems and human economic interests and health. Plant phenological data are therefore indispensable for understanding ecosystem function, detecting ecosystem changes, and predicting the impacts of ongoing climate and land use changes. Given the importance of plant phenology, continuing local, regional and national data collection efforts have generated large volumes of phenological data. However, these data are surprisingly heterogeneous, difficult to integrate, and thus remain largely inaccessible for broader research. At the same time, community science and specimen digitization infrastructure have produced massive, rapidly expanding collections of herbarium specimens and in situ plant photographs, which contain a wealth of virtually untapped historical and contemporary phenological information. This project will use machine learning approaches to extract phenological data from plant photographs and digitized specimens. These data will then be integrated with phenological monitoring resources to create an open access, global plant phenology database – Phenobase. During this project, one postdoctoral researcher and several graduate and undergraduate students will be trained in programming and data science skills. The goal of this project is to support community needs for generating and delivering high-precision, harmonized and semantically integrated plant phenological data at unprecedented taxonomic, geographic, and temporal scales, along with new tools to help scientists and the public engage with these data. To achieve this goal, this project will develop a global, standardized knowledge base by integrating different phenology observation networks around the world; expand this knowledge base by using computer vision (CV) techniques to generate new, high-quality phenological data from the rapidly growing collection of community-submitted plant photographs on iNaturalist and Budburst; add critical historical data by using similar CV techniques on herbarium specimens available through iDigBio and GBIF; develop tools for data query, access, and visualization delivered via the Web and as software packages; and foster compelling, community-driven use cases showcasing the use of Phenobase for new research and for public good. These approaches will not only meet current growth in imaging, but scale to meet continuing, exponential growth into the future. By weaving together phenologically relevant outputs from monitoring projects from around the globe, including the efforts of millions of community scientists, Phenobase will support and empower phenological research that is currently impossible. Results derived from this project can be found at http://plantphenology.org/.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.

植物物候学——植物生命周期事件的时间，如叶片生长、开花和结果——在塑造陆地生态系统中起着根本作用。植物物候发生的时间不仅影响植物个体的适应性，也直接或间接地影响依赖植物的生物的适应性和行为，在陆地生态系统中，这些生物包括几乎所有的动物。因此，植物物候变化会对生态系统、人类经济利益和健康造成巨大的、有时是毁灭性的后果。因此，植物物候数据对于理解生态系统功能、探测生态系统变化以及预测持续的气候和土地利用变化的影响是不可或缺的。鉴于植物物候学的重要性，持续不断的地方、区域和国家数据收集工作产生了大量的物候数据。然而，这些数据令人惊讶地异构，难以整合，因此在很大程度上仍然无法进行更广泛的研究。与此同时，社区科学和标本数字化基础设施已经产生了大量快速扩展的植物标本馆标本和原位植物照片，其中包含了大量几乎未开发的历史和当代物候信息。该项目将使用机器学习方法从植物照片和数字化标本中提取物候数据。然后，这些数据将与物候监测资源相结合，创建一个开放获取的全球植物物候数据库- Phenobase。在这个项目中，一名博士后研究员和几名研究生和本科生将接受编程和数据科学技能的培训。该项目的目标是支持社区在前所未有的分类、地理和时间尺度上生成和提供高精度、协调和语义集成的植物物候数据的需求，以及帮助科学家和公众参与这些数据的新工具。为了实现这一目标，该项目将通过整合世界各地不同的物候观测网络，建立一个全球性的标准化知识库；通过使用计算机视觉（CV）技术从iNaturalist和Budburst网站上快速增长的社区提交的植物照片中生成新的、高质量的物候数据，扩大这一知识库；利用类似的CV技术对iDigBio和GBIF提供的植物标本添加关键的历史数据；开发数据查询、访问和可视化工具，通过网络和软件包交付；并培育引人注目的、社区驱动的用例，展示Phenobase在新研究和公共利益中的应用。这些方法不仅能满足当前成像技术的增长，而且还能满足未来持续的指数级增长。通过整合来自全球各地监测项目的物候相关产出，包括数百万社区科学家的努力，Phenobase将支持和授权目前不可能的物候研究。该项目的成果可以在http://plantphenology.org/.This上找到，该奖项反映了美国国家科学基金会的法定使命，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。