EAGER-NEON: Scaling up terrestrial plant phenology from individuals to Continental scale

植物开花、树木长叶和蚊子孵化的时间是生物体对周围环境反应的最明显的标志。所有人都可以接触到这些标志，让公众参与观察基本的生态关系。该项目结合并比较了不同来源的数据，以确定在特定地点收集的个体物候（或时间）数据如何代表更大规模的模式。例如，在加州的一片草地上，一个物种的提早开花是否代表了区域或大陆尺度上的提早开花？该项目将制作一系列有关植物物候学的视频，分发给东北地区的K-12教师以及隶属于Budburst项目和Nature？的公民科学家。s Notebook.一名本科生将参与该项目，学习对不同类型的数据进行建模。该项目将聘请一名早期职业博士后研究员，他将接受定量建模和项目领导方面的培训。该项目的目标是通过建立一个整合物候和环境数据的分层建模框架，将个体生物的物候观测扩展到社区，区域和大陆尺度。该框架结合了物候建模与贝叶斯方法的新兴技术，可容纳广泛的数据集，并允许传播的不确定性。该项目将评估非生物环境、物种特征、功能类型、群落组成和景观异质性对13个国家生态观测站网络（氖）物候的影响，并将调查各个地点的物候措施在多大程度上可以外推到更大的地区。将根据从哈佛森林收集的长期物候数据对结果进行校准，以验证仅使用氖数据获得的参数估计值和模型输出。由此产生的建模框架将是灵活和全面的，有利于解释不同的物候数据，包括标准化地面观测从氖，近地表冠层物候数据从PhenoCam网络，卫星遥感，长期地面观测在单个站点，从公民科学网络的意见。

The timing of when plants flower, trees leaf out, and mosquitoes hatch provide some of the most obvious signs of how organisms respond to their immediate environments. These signs are accessible to all people, engaging the public in observing fundamental ecological relationships. This project combines and compares different sources of data to determine how well phenological (or timing) data collected for individuals at a particular location represent larger-scale patterns. Is earlier flowering of one species in a meadow in California, for example, representative of earlier flowering at regional or continental scales? The project will produce a series of videos about plant phenology that will be distributed to K-12 teachers through the Northeast as well as with citizen scientists affiliated with Project Budburst and Nature?s Notebook. An undergraduate student will participate in the project, learning to model diverse kinds of data. The project will engage an early-career postdoctoral researcher who will receive training in quantitative modeling and project leadership. The goal of this project is to scale phenology observations of individual organisms to the community, regional, and continental scales by building a hierarchical modeling framework for integrating phenological and environmental data. The framework combines emerging techniques in phenology modeling with a Bayesian approach that accommodates a wide range of datasets and allows for propagation of uncertainty. The project will evaluate the effects of the abiotic environment, species identity, functional type, community composition, and landscape heterogeneity on phenology across 13 National Ecological Observatory Network, or NEON, sites and will investigate the extent to which phenology measures at individual sites can be extrapolated to larger regions. Results will be calibrated against long-term phenological data collected from the Harvard Forest, to verify parameter estimations and model output obtained using NEON data alone. The resulting modeling framework will be flexible and comprehensive, facilitating the interpretation of diverse phenology data, including standardized ground observations from NEON, near-surface canopy phenology data from the PhenoCam network, satellite remote sensing, long-term ground observations at single sites, and observations from citizen science networks.