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机，例如叶片生长，开花和结果 - 在塑造陆地生态系统中起着基本作用。植物物候的时间不仅影响单个植物的适应性，而且还影响了依赖植物的生物的适应性和行为，而在陆地生态系统中，几乎所有动物都直接或间接地包括所有动物。这是对生态系统以及人类经济利益和健康的后果引发戏剧性的，有时甚至是毁灭性的。因此，植物的物候数据是必不可少的，对于了解生态系统功能，检测生态系统的变化以及预测持续气候和土地利用变化的影响。鉴于植物物候的重要性，持续的本地，区域和国家数据收集工作产生了大量的物候数据。但是，这些数据令人惊讶地异构，难以集成，因此对于更广泛的研究仍然无法访问。同时，社区科学和标本数字化基础设施产生了大量，迅速扩大的标本室标本和原位植物摄影的收藏，其中包含大量实际上未开发的历史和现代的地球术信息。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集成，以创建开放访问，全球植物物候数据库 - 苯比酶。在此项目中，一名博士后研究员以及几位研究生和本科生将接受编程和数据科学技能的培训。该项目的目的是支持社区在前所未有的分类学，地理和临时尺度以及帮助科学家和公众参与这些数据的新工具上，以产生和交付高精度，协调和语义整合的植物物候数据。为了实现这一目标，该项目将通过整合世界各地的不同物体观察网络来发展全球，标准化的知识基础；通过使用计算机视觉（CV）技术来扩展这一知识库，从而从社区和Budburst上迅速增长的社区植物植物照片收集中生成新的高质量的物候数据；通过使用Idigbio和Gbif提供的标本室标本上使用类似的CV技术添加关键的历史数据；开发通过网络和作为软件包交付的数据查询，访问和可视化的工具；促进了引人注目的，社区驱动的用例，显示了对新研究和公共物品的使用的使用。这些方法不仅可以达到当前成像的增长，而且还可以达到延续到未来的持续指数增长的规模。通过从全球监测项目（包括数百万个社区科学家的努力）中编织到现场相关的产出，苯比存酶将支持并增强目前不可能的物候研究。可以从http://plantphenolology.org/.this奖中找到从该项目中得出的结果，该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为通过评估被认为是珍贵的支持。