Predictive layers of trace elements in soil in the conterminous United States

This dataset provides posterior mean predicted rasters of the distribution of trace elements important to vertebrate health (cobalt, copper, iron, manganese, selenium, and zinc) in the soil across the conterminous United States. Rasters were generated using a Bayesian modeling framework implemented in R with the R-INLA framework (Integrated Nested Laplace Approximation) and the Stochastic Partial Differential Equation (SPDE) approach. Geochemical data were derived from the U.S. Geological Survey survey of soils of the conterminous United States, and spatial modeling incorporated environmental covariates including soil properties (percent clay, organic matter, saturated hydraulic conductivity, pH), topography (elevation, slope), climate (temperature, precipitation), and land cover. The resulting rasters, at approximately 2450 m resolution, can be used to visualize spatial patterns, extract geochemical data for modeling, and support ecological, environmental, or agricultural analyses. The raster files represent posterior mean predicted concentrations and are provided for download.