Spatial Downscaling of Pollen-Based Vegetation Reconstructions into Paleo Land Cover Maps

Abstract. Past land cover dynamics provide important context for understanding long-term ecosystem responses to climatic change, but paleoecological records are typically sparse, irregular, and site-based. Here, we present a spatial downscaling framework that transforms pollen-derived vegetation reconstructions into gridded paleo land cover maps. The pipeline first translates pollen-based vegetation composition into land cover class frequencies, then distributes these classes within pollen source areas using environmental similarity, and finally applies a U-Net-based interpolation approach to generate spatially continuous reconstructions. We apply the framework to Alaska and western Canada, producing 300 m land cover reconstructions across 15 land cover classes and 119 time slices. The source-area product preserves the direct link to pollen records and contains approximately 2.56 billion reconstructed pixels, while the U-Net-based product extends these reconstructions to continuous spatial coverage across the full study region. Reconstructed land cover trends show broad stability during the late Holocene, with stronger changes around 10 ka BP, including declining evergreen needle-leaved forest cover toward older time slices and corresponding changes in sparse vegetation and shrubland. Relative uncertainty estimates indicate higher uncertainty in forested regions and increasing uncertainty with age, reflecting both class ambiguity and reduced paleo-record support. An example analysis of elevational forest dynamics demonstrates the potential of the dataset for spatially explicit paleoecological applications. The framework provides a bridge between site-based pollen records and landscape-scale analyses, while emphasizing that reconstructed maps represent model-based estimates rather than direct observations of past environments.