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

Yegorova, Svetlana12; Dobrowski, Solomon1; Parks, Sean1; Davis, Kimberley34; Metlen, Kerry5; Haugo, Ryan5; Timberlake, Thomas6; Hoecker, Tyler1; Kemp, Kerry6; Wahlberg, Max6; Naficy, Cameron7; Jeronimo, Sean8; Fitzgerald, Katherine9; Wijayratne, Upekala6

Published Dec 16, 2025 on Dryad. https://doi.org/10.5061/dryad.pzgmsbd20

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Dec 16, 2025 version files 1.23 GB

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Abstract

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.