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.
