CAREER: Advancing a macrosystems framework for climate-phenology coupling through integrated research and education

物候学--生物事件重复发生的时间--是大自然的日历。季节性事件的时间变化，如叶子和花朵的出现，是气候变化最明显的指纹之一。这种物候变化反过来又可以通过改变碳吸收等关键的地球系统过程来影响气候。因此，气候和物候是紧密联系在一起的，导致了一个具有反馈回路的复杂系统。在这个NSF CAREER项目中，将开发一个新的框架来量化气候-物候耦合中的多个过程，以回答有关气候和物候如何在不同尺度和偏远地区之间相互作用的问题。这项关于气候-物候耦合的研究将产生重大的社会效益，例如促进作物管理和预测花粉爆发，以减轻公共卫生中的空气传播过敏。该项目还将在西班牙裔美国人服务机构加州大学圣克鲁斯实施一项教育计划，以加强气候变化和数据科学教育。科学推广将通过学生使用社交媒体实现倍增和协同效应，将教育接受者转变为气候变化的积极教育者。该项目将在新颖的气候-物候耦合框架的基础上，构建一系列动力学、统计学和计算模型，以三种方式推进生态预测。首先，气候-物候耦合的强度将在全球范围内的空间和时间上系统地量化，并根据植物分布变化和人类活动进行解释。第二，缩放，在宏观系统生物学的基本挑战，将与多尺度数据集从国家生态观测网络（氖）进行测试。识别和量化的跨尺度的相互作用和出现将是发展高度预测物候模型的关键。第三，将通过时空分析探索未充分研究的遥相关领域，这将促进下一代物候模型，从而改进对未来碳、水和能量通量的预测。该项目还将开发可重复的工作流程，以从多个来源传输数据，并进行短期生态预测。最后，在研究计划中获得的气候物候学知识和新的数据科学技能将用于加强大学生和专业人员的数据科学培训计划，加强中学生的体验学习，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准。

Phenology - the timing of recurring biological events - is nature’s calendar. Changes in timing of seasonal events such as leaf and flower emergence are among the most visible fingerprints of climate change. Such phenological changes, in turn, can affect climate by altering critical Earth system processes such as carbon uptake. Thus, climate and phenology are closely coupled, leading to a complex system with feedback loops. In this NSF CAREER project, a novel framework to quantify multiple processes in climate-phenology coupling will be developed to answer questions about how climate and phenology interact with each other across scales and among remote locations. This research on climate-phenology coupling will accrue significant societal benefits, such as facilitation of crop management and forecasting pollen outbreaks to mitigate airborne allergies in public health. This project will also implement an education plan at the University of California Santa Cruz, a Hispanic American-Serving Institution, to enhance climate change and data science education. Scientific outreach will be multiplicative and synergistic through student use of social media, transforming recipients of education into active educators of climate change.Building on the novel climate-phenology coupling framework, the project will construct a series of dynamical, statistical, and computational models to advance ecological forecasting in three ways. First, the strength of climate-phenology coupling will be systematically quantified over space and time on a global scale and interpreted in light of plant distributional shifts and human activities. Second, scaling, a fundamental challenge in macrosystems biology, will be tested with multi-scale datasets from the National Ecological Observatory Network (NEON). The identification and quantification of cross-scale interactions and emergence will be pivotal to developing highly predictive phenology models. Third, the understudied field of teleconnections will be explored through spatiotemporal analyses, which will promote next-generation phenology models leading to improved projections of future carbon, water, and energy fluxes. The project will also develop reproducible workflows to stream data from multiple sources and perform near-term ecological forecasting. Finally, climate-phenology knowledge and novel data science skills gained in the research plans will be used to strengthen a data science training program for university students and professionals, enhance experiential learning for secondary school students, and improve climate change education for the general public.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.