Changing climate may drive large shifts in vegetation zones of Oregon, USA

Svetlana Yegorova^1,2*Solomon Dobrowski¹Sean Parks³Kimberley Davis⁴Kerry Metlen⁵Ryan Haugo⁶Thomas Timberlake⁷Tyler Hoecker¹Kerry Kemp⁸Max Wahlberg^10,9Cameron E Naficy¹¹Sean Jeronimo¹²Katherine Fitzgerald¹³Upekala Wijayratne¹⁴

• ¹University of Montana, Missoula, United States
• ²Wilfrid Laurier University, Waterloo, Canada
• ³Aldo Leopold Wilderness Research Institute, USDA Rocky Mountain Research Station, Missoula, United States
• ⁴Rocky Mountain Research Station Missoula Fire Sciences Laboratory, Missoula, United States
• ⁵The Nature Conservancy - Oregon, Ashland, United States
• ⁶The Nature Conservancy - Oregon, Portland, United States
• ⁷USDA Forest Service Pacific Northwest Research Station, Portland, United States
• ⁸US Forest Service, Wenatchee, United States
• ⁹Universidad de Sevilla, Seville, Spain
• ¹⁰US Forest Service, Bend, United States
• ¹¹Oregon State University, Corvallis, United States
• ¹²Resilient Forestry LLC, Seattle, United States
• ¹³US Fish and Wildlife Service, Olympia, United States
• ¹⁴US Forest Service, Sandy, United States

Anticipating plausible future ecosystem states is necessary for effective ecosystem management. We use climate analog-based impact models and a co-production process with land managers to project future vegetation changes for the state of Oregon, United States, (2041-2070, RCP 8.5) at a management-relevant spatial resolution (270-meters). We explored multiple analog-based methodologies, evaluated analog model performance with contemporary validation, and leveraged climate analogs to assess projection uncertainty by quantifying areas where multiple vegetation trajectories are plausible under a single climate scenario. We find that analog-based models performed well at reproducing landscape-level vegetation composition, and moderately well at reproducing vegetation at the pixel level. Our results suggest that 64% of the study area will experience future climate conditions that support different potential natural vegetation types and 59% will experience climates corresponding with different potential plant physiognomic types, compared to reference-period conditions. We project a 60% reduction of mesic conifer-dominated forests with transitions to mixed evergreen forest types. We also project losses to dry forests, cold forests and parklands, with commensurate expansions of shrublands, grasslands, and geographic redistribution of dry forest types. We find that in many areas, several vegetation trajectories are plausible under a single climate scenario. Finally, we provide guidance for using future vegetation projections and uncertainty outputs in management decisions using the Resist-Accept-Direct (RAD) adaptation framework.