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: 2223512
• 负责人: Robert Guralnick
• 金额: $ 29.28万
• 依托单位: University of Florida
• 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=2223512&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——植物生命周期事件的时间，如叶子生长、开花和结果——在塑造陆地生态系统中发挥着重要作用。植物物候的时间不仅影响单个植物的适应性，也影响植物的适应性。植物物候的变化可能对生态系统和人类经济利益和健康造成巨大的、有时甚至是毁灭性的后果。因此，物候数据对于了解生态系统功能、检测生态系统变化以及预测当前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，持续的地方、区域和国家数据收集工作已经产生了大量的物候数据。然而，这些数据的异构性令人惊讶，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大量产生并迅速扩大了植物标本和原位植物的收藏。照片，其中包含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，然后将这些数据与物候监测资源相结合，以创建一个开放获取的全球性数据。植物物候数据库 – Phenobase 在该项目中，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生成和提供高精度、协调一致的需求。在前所未有的分类学、地理和时间尺度上语义整合植物物候数据，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，该项目将通过整合不同的物候观测网络来开发一个全球标准化的知识库。世界各地；通过使用计算机视觉 (CV) 技术，从 iNaturalist 和 Budburst 上社区提交的快速增长的植物照片中生成新的高质量物候数据，并使用类似的 CV 技术添加关键历史数据；通过 iDigBio 和 GBIF 提供植物标本；开发通过网络和软件包提供的数据查询、访问和可视化工具；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使用。这些方法不仅可以满足当前成像的增长，而且可以通过将全球监测项目的物候相关输出（包括数百万社区科学家的努力）结合在一起，以满足未来持续的指数增长。将支持和授权目前不可能的物候研究，该项目的结果可在 http://plantphenology.org/ 上找到。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和评估进行评估，被认为值得支持。更广泛的影响审查标准。