Collaborative Proposal: MRA: Quantifying phenological coherence and seasonal predictability across NEON and USA-NPN monitoring sites

合作提案：MRA：量化 NEON 和 USA-NPN 监测站点的物候一致性和季节可预测性

• 批准号: 2017815
• 负责人: Toby Ault
• 金额: $ 30万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017815&HistoricalAwards=false
• 关键词: Collaborative; Proposal; MRA; Quantifying; phenological

The seasonal timing, or phenology, of many North American plant species is in flux with accelerating environmental change. The influence of factors such as temperature and precipitation on plant growth, as weather patterns change from spring to summer, vary from place-to-place, month-to-month, and for different plant species. An improved understanding of these relationships revealed by this research will support development of more accurate and diverse models of spring plant growth stages. These models may then be able to predict which trees and shrubs will be favored in different regions with future environmental change, to the benefit of many types of societal planning. Further, the project will implement national-scale, long-lead forecasts for new measures representing the spring season, which will also be relevant for annual agricultural, horticultural, and forestry management planning. Additional broader impacts of this research include: (1) developing junior high school materials and programming for students to engage with phenological observations and real-time weather data; and (2) enlisting tens of thousands of non-scientist volunteer observers to broaden participation in citizen science spring phenology data collection campaigns through the USA-National Phenology Network (NPN) Nature’s Notebook program.This research is guided by three fundamental questions that span spatial and temporal scales: (1) Which species exhibit variation in phenological response across latitude, elevation, or other gradients? (2) Does the influence of variables that drive phenological events (such as accumulated warmth and light intensity) increase or decrease as the season progresses, and does this vary geographically, with the onset of spring’s progression poleward and upslope? (3) At what lead times (days, weeks, months, or seasons) can climate forecasts reliably predict phenological behavior at monitoring sites? By combining recently developed state-of-the-art weather and climate forecasts and robust statistical post-processing techniques with rich ground-based phenological data resources from the National Ecological Observatory Network (NEON) and other large-scale networks, this project will: (1) develop and refine models of spring plant phenological activity for dozens of species; (2) evaluate the influence of changes in climate driver variables over the course of the spring season—resulting from changes in the structure of large-scale circulation patterns—on predicting phenological events; (3) determine which species exhibit varying phenological response across spatial gradients, and therefore should be accounted for in predictive models; and (4) assess the potential predictability of those models on seasonal to decadal time horizons to operationalize long-term forecasting by the USA-NPN. Finally, research knowledge gained from this project will provide valuable insights toward enhanced understanding of the much more challenging plant-climate interactions in autumn.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.

许多北美植物物种的季节时间或物候随着环境变化的加速而变化。随着春季到夏季天气模式的变化，温度和降水等因素对植物生长的影响因地而异。这项研究揭示的对这些关系的更好理解将支持开发更准确和多样化的春季植物生长阶段模型，然后这些模型可以预测哪些树木和灌木。随着未来环境的变化，将受到不同地区的青睐，此外，该项目还将对代表春季的新措施进行全国范围的长期预测，这也将与年度农业、园艺和林业管理规划相关。这项研究的内容包括：(1) 为学生开发初中材料和编程，以参与物候观测和实时天气数据；(2) 招募数以万计的非科学家志愿者观察员，扩大公民科学春季活动的参与范围；物候数据收集活动通过美国国家物候网络 (NPN) 自然笔记本计划。这项研究以跨越空间和时间尺度的三个基本问题为指导：(1) 哪些物种在纬度、海拔或其他梯度上表现出物候反应的变化？ ）随着季节的推移，驱动物候事件（例如累积的温暖和光强度）的变量的影响会增加还是减少，并且随着春季向极地和上坡的开始，这种影响是否会因地理位置而变化（3）在什么时候？通过将最近开发的最先进的天气和气候预报以及强大的统计后处理技术与丰富的地面数据相结合，气候预报能否可靠地预测监测点的物候行为？该项目将利用来自国家生态观测网络（NEON）和其他大型网络的物候数据资源：（1）开发和完善数十个物种的春季植物物候活动模型（2）评估气候变化的影响；驱动变量在春季过程中，由于大规模环流模式结构的变化，预测物候事件；（3）确定哪些物种在空间梯度上表现出不同的物候响应，因此应在预测模型中予以考虑； (4) 评估这些模型在季节性到十年时间范围内的潜在可预测性，以实施 USA-NPN 的长期预测。最后，从该项目中获得的研究知识将为增强对更具挑战性的理解提供宝贵的见解。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。