Soil temperature and moisture (soil-climate) affect plant growth and microbial metabolism, providing a mechanistic link between climate and growing conditions. Knowledge of soil-climate conditions, especially with spatiotemporal context, improves understanding of climate-ecosystem connections, including drought effects, crop yields, rangeland and forest productivity, wildfire risks, landslide potential, flood risks, dust storm potential, response to climate change, habitat restoration and recovery, and wildlife habitat selection. We developed these products in the western United States, spanning numerous ecosystems. Our study area encompasses the sagebrush biome in western North America, which occurs east of the Sierra Nevada and Cascade Mountain ranges and west of the Great Plains in the United States. This region is geographically, geologically, and hydrologically diverse, including major physiographic regions of the Rocky Mountains, Intermontane Plateaus, and the Pacific Mountains. The sagebrush biome supports important wildlife, including many ungulate and avian sagebrush-associated species, which have been affected by increasing prevalence of non-native annual grasses (for example, Bromus tectorum and Taeniatherum caput-medusae), wildfires, habitat degradation due to land-use, conifer expansion, and extended droughts and weather variability (chapters J-P in Remington et al. 2021). Understanding the distribution, growth, and recovery of vegetation in arid and semi-arid environments is crucial for rangeland managers. Plants of these systems thrive through physiological adaptations that support a range of conditions resulting in irregular distributions. Habitat conservation and management in semi-arid landscapes is challenging due to limiting environmental conditions, especially soil moisture. Lack of mapped soil-climate data can limit research and management applications because of unexplained spatial and temporal heterogeneity; soil texture, climate normals and elevation are typical surrogates. Multiple-use management that sustains habitat for wildlife and domestic livestock in this semi-arid environment is challenging due to variability in limiting conditions that leads to unexplained and undesired outcomes, such as seedling mortality, and understanding the soil-climate environment will reduce unexplained variability in growing conditions (and therefore patterns produced by differences in vegetation). Thus, the purpose of this work was to improve understanding of fundamental ecosystem properties through a soil-climate model to improve spatial and temporal resolution of variables that directly affect ecosystem conditions. The resulting gridded surfaces are intended to improve conservation and habitat management, including but not limited to increasing the understanding of vegetation patterns (restoration effectiveness), spread of invasive species, and wildfire risk. The flexibility to assess dynamic climate conditions (historical, contemporary, or projected) with our spatial implementation of the Newhall simulation model could improve knowledge of changing spatiotemporal biotic patterns, and these methods could be applied to other geographic regions.
