Abstract. Forest structure and species composition in many western U.S. coniferous forests have been altered through fire exclusion, past and ongoing harvesting practices, and livestock grazing over the 20th century. The effects of these activities have been most pronounced in seasonally dry, low and mid-elevation coniferous forests that once experienced frequent, low to moderate intensity, fire regimes. In this paper, we report the effects of Fire and Fire Surrogate (FFS) forest stand treatments on fuel load profiles, potential fire behavior, and fire severity under three weather scenarios from six western U.S. FFS sites. This replicated, multisite experiment provides a framework for drawing broad generalizations about the effectiveness of prescribed fire and mechanical treatments on surface fuel loads, forest structure, and potential fire severity. Mechanical treatments without fire resulted in combined 1-, 10-, and 100-hour surface fuel loads that were significantly greater than controls at three of five FFS sites. Canopy cover was significantly lower than controls at three of five FFS sites with mechanical-only treatments and at all five FFS sites with the mechanical plus burning treatment; fire-only treatments reduced canopy cover at only one site. For the combined treatment of mechanical plus fire, all five FFS sites with this treatment had a substantially lower likelihood of passive crown fire as indicated by the very high torching indices. FFS sites that experienced significant increases in 1-, 10-, and 100-hour combined surface fuel loads utilized harvest systems that left all activity fuels within experimental units. When mechanical treatments were followed by prescribed burning or pile burning, they were the most effective treatment for reducing crown fire potential and predicted tree mortality because of low surface fuel loads and increased vertical and horizontal canopy separation. Results indicate that mechanical plus fire, fire-only, and mechanical-only treatments using whole-tree harvest systems were all effective at reducing potential fire severity under severe fire weather conditions. Retaining the largest trees within stands also increased fire resistance.

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Innovations in machine learning and cloud‐based computing were merged with historical remote sensing and field data to provide the first moderate resolution, annual, percent cover maps of plant functional types across rangeland ecosystems to effectively and efficiently respond to pressing challenges facing conservation of biodiversity and ecosystem services. We utilized the historical Landsat satellite record, gridded meteorology, abiotic land surface data, and over 30,000 field plots within a Random Forests model to predict per‐pixel percent cover of annual forbs and grasses, perennial forbs and grasses, shrubs, and bare ground over the western United States from 1984 to 2017. Results were validated using three independent collections of plot‐level measurements, and resulting maps display land cover variation in response to changes in climate, disturbance, and management. The maps, which will be updated annually at the end of each year, provide exciting opportunities to expand and improve rangeland conservation, monitoring, and management. The data open new doors for scientific investigation at an unprecedented blend of temporal fidelity, spatial resolution, and geographic scale.