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Data and code from: Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management - v2

    This dataset includes code and data to recreate analysis from the manuscript "Nauman, T. W., Munson, S. M., Dhital, S., Webb, N. P., & Duniway, M. C. (2023). Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management. Science of The Total Environment (p. 164605). https://doi.org/10.1016/j.scitotenv.2023.164605". This includes R statistical code, aeolian monitoring data and associated soil, land use, and climate explanatory data for each site, and a raster map showing areas modeled to have more sediment transport.

    Data and code from: Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management

      This dataset includes code and data to recreate analysis from the manuscript "Nauman, T. W., Munson, S. M., Dhital, S., Webb, N. P., & Duniway, M. C. (2023). Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management. Science of The Total Environment (p. 164605). https://doi.org/10.1016/j.scitotenv.2023.164605". This includes R statistical code, aeolian monitoring data and associated soil, land use, and climate explanatory data for each site, and a raster map showing areas modeled to have more sediment transport.

      Data from: Predicting spatial-temporal patterns of diet quality and large herbivore performance using satellite time series

        Analysis-ready tabular data from "Predicting spatial-temporal patterns of diet quality and large herbivore performance using satellite time series" in Ecological Applications, Kearney et al., 2021. Data is tabular data only, summarized to the pasture scale. Weight gain data for individual cattle and the STARFM-derived Landsat-MODIS fusion imagery can be made available upon request.

        Little Washita River Experimental Watershed, Oklahoma (Flow)

        NAL Geospatial Catalog
          Over the past five decades, the United States Department of Agriculture-Agricultural Research Service (USDA-ARS) and the United States Geological Survey (USGS) have collected stream flow, reservoir, and groundwater data in the Fort Cobb Reservoir Experimental Watershed (FCREW) and Southern Great Plains Research Watershed (SGPRW), which includes the Little Washita River Experimental Watershed (LWREW) in central Oklahoma.

          NorWeST Stream Temperature Regional Database and Model

            The NorWeST webpage hosts stream temperature data and climate scenarios in a variety of user-friendly digital formats for streams and rivers across the western U.S. Temperature data and model outputs, registered to NHDPlus stream lines, are posted to the website after QA/QC procedures and development of the final temperature model within a river basin.

            The National Stream Internet project

              National Stream Internet (NSI) project was developed as a means of providing a consistent, flexible analytical infrastructure that can be applied with many types of stream data anywhere in the country. A key part of that infrastructure is the NSI network, a digital GIS layer which has a specific topological structure that was designed to work effectively with SSNMs. The NSI network was derived from the National Hydrography Dataset Plus, Version 2 (NHDPlusV2) following technical procedures that ensure compatibility with SSNMs.

              Arctic Peregrine Falcon Abundance on Cliffs Along the Colville River, Alaska, 1981-2002 and Covariate Input Files

                This data set consists of fourteen data files. Rcode_arctic_peregrine_abundance.R contains R code that was used to analyze Arctic peregrine falcon data collected between 1981 and 2002. The code primarily uses the R package "UNMARKED" and is based on the Dail-Madsen model for estimating population abundance. To run this code in an R environment, download the file and open it in an R interpreter (such as RStudio). The remaining files are all covariate matrices that act as inputs to the R code.