Baseline maps of U.S. mature and old-growth forests for conservation and management

Abstract Mature and old-growth (MOG) forests hold significant ecological and societal value in the United States. In 2022, a presidential executive order directed the U.S. Forest Service to conduct a MOG forest inventory, and regional definitions were developed using the Forest Inventory and Analysis (FIA) system. However, the sparse forest inventory network limited estimates to coarse area-level summaries, and efforts to include specific MOG forest management practices in National Forest management plans were abandoned. National-scale MOG forest mapping at high spatial resolution is essential for balancing and harmonizing MOG conservation, stewardship and management directives, and several recent studies have produced such maps. These approaches either apply alternative definitions, interpolate MOG information without incorporating empirical data at prediction locations, or define MOG characteristics from remote sensing products rather than inventory data. Thus, an enhanced MOG forest inventory that leverages both the national FIA MOG classifications and empirical remote sensing data for predictions remains a pressing need. Here, we developed a spatial Bayesian modeling framework that uses remotely sensed predictor variables to operationalize the FIA plot-level MOG definitions into spatially continuous, fine-scale inference across the conterminous U.S. Our models produce posterior distributions of MOG class presence at 1 ha resolution, enabling probability surfaces and aggregated estimates with transparent uncertainty at policy and ecologically relevant scales. Cross-validation revealed minimal bias nationally but moderate over-prediction in some strata. We estimate that, on all lands, 154.83 million ha contain mature forest and 25.17 million ha contain old-growth—respectively 43.74% and 8.57% of forest area. Our work advances spatial modeling techniques for integrating inventory plot and remote sensing data to characterize complex, context-dependent forest attributes consistently across large geographic extents. This study produces publicly available, spatially continuous MOG information based on the national definitions, providing a foundational resource to inform MOG forest management and conservation.