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性时间或物候随着环境的加速变化而变化。由于天气模式从春季到夏季的变化，温度和降水等因素对植物生长的影响因地而异，因月而异，因植物种类而异。这项研究揭示了这些关系的进一步理解将支持更准确和多样化的春季植物生长阶段模型的发展。然后，这些模型可能能够预测哪些树木和灌木将在未来环境变化的不同地区受到青睐，从而有利于许多类型的社会规划。此外，该项目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有关。 这项研究的其他更广泛的影响包括：（1）开发初中教材和编程，让学生参与物候观测和实时天气数据;以及（2）通过美国国家物候学网络（NPN）招募数万名非科学家志愿观察员，以扩大对公民科学春季物候数据收集活动的参与这项研究是由三个基本的问题，跨越空间和时间尺度：（1）哪些物种表现出在不同纬度，海拔或其他梯度的物候反应的变化？(2)驱动物候事件的变量（如累积的温暖和光照强度）的影响是否随着季节的进展而增加或减少，这是否在地理上有所不同，随着春天的开始向极地和上坡前进？(3)在什么样的提前期（天、周、月或季节），气候预报可以可靠地预测监测点的物候行为？通过将最新发展的最先进的天气和气候预报以及强有力的统计后处理技术与来自国家生态观测网（氖）和其他大规模网络的丰富的地面物候数据资源相结合，该项目将：（1）开发和改进几十种春季植物物候活动的模型;（2）评估春季期间气候驱动变量的变化--由大尺度环流模式结构的变化引起--对预测物候事件的影响;（3）确定哪些物种在空间梯度上表现出不同的物候反应，因此应该在预测模型中考虑;以及（4）评估这些模式在季节到十年时间范围内的潜在可预报性，以使USA-NPN的长期预报业务化。最后，从这个项目中获得的研究知识将为增强对秋季更具挑战性的植物-气候相互作用的理解提供有价值的见解。这个奖项反映了NSF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。