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

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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.