Exciting Publication Co-authored by FEP staff in Journal of Ecology

  • Phenology Trail Monitoring

Last week, a paper was published in the Journal of Ecology co-authored by FEP staff documenting the advancement of spring in plants.
Around 7 years ago, Conrad discovered a unique treasure trove of 19th century records from around New York State documenting the timing of the seasonal life cycle events (phenology) of many different plants and animals. When were the apple trees in flower? Or the lilacs in leaf? All of these observations were carefully documented as part of an early meteorological network that initially was comprised of the academies (19th century high school equivalents) before expanding more widely. The network collected data from 1826-1871. 
We digitized and standardized the resulting data set (available through a searchable browser here) and partnered with Kerissa Battle of Community Greenways Collaborative and the New York Phenology Project to explore how this historical data set might be paired with recent records—from a current phenology network of citizen scientists—to inform our understanding of the effects of climate change on seasonal plant cycles.
This week that analysis, spearheaded by Kerissa, and with a great team of co-authors, has been published as a paper in the Journal of Ecology: it is freely accessible here. If you want a short teaser, below is the title and brief abstract sharing what we found:
Citizen science across two centuries reveals phenological change among plant species and functional groups in the Northeastern US
Citizen science observations across two centuries reveal a dramatic, climate-driven shift to earlier leaf out and flowering, which varies across settings, species and functional groups. Plants in urban areas, insect pollinated trees and early-season species show the greatest rate of advancement overall. This unprecedented comparison of historical-modern network observations illustrates how long-term monitoring and citizen science efforts are invaluable for ecological forecasting and discovery.