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Harvard Forest Research Project 2022

  • Title: The Long-term Forest Demographic Analysis, MACRO system
  • Principal investigator: James Clark (jimclark@duke.edu)
  • Institution: Duke University
  • Primary contact: Jordan Siminitz (jordan.siminitz@duke.edu)
  • Team members: Zoe Davis; christopher Kilner; Jordan Siminitz; Samantha Sutton
  • Abstract:

    Climate change is rapidly transforming forests over much of the globe in ways that are not anticipated by current science. Large-scale forest diebacks, apparently linked to interactions involving drought, warm winters, and other species, are becoming alarmingly frequent. Models of biodiversity and climate have not provided guidance on if/where/when such responses will occur. Instead models often predict potential numbers of extinctions, but these forecasts not are linked in any mechanistic way to the processes that could cause them. Both modeling and field studies rely on aggregate metrics of species presence/absence or relative abundance at regional scales, but climate affects individuals. Aggregation of individual data to the species level hides or even qualitatively changes climate effects. By sampling and analysis at the individual scale across continental variation in climate, this study can link individual scale processes to regional responses. This study will exploit existing research sites and the NEON platform of sites for synthesis of models and data to determine when and where predicting climate impacts on biodiversity is a plausible goal, understand where surprises are likely to occur, and attribute those predictions back to individual tree health and vulnerability to climate risk factors.

    The study will provide climate vulnerability forecasts for forest biodiversity that are directly linked to the process scale. Our goal is provide probabilistic forecasts for the joint distribution of forest responses to climate change, including growth, reproduction, and mortality risk. For scientists, US Forest Service researchers, and policy makers predictions will anticipate combined risks of increasing drought and longer growing seasons. Methods developed under this project will be disseminated through training workshops for postdoctoral associates at other universities and resource managers.