Predicted cheatgrass cover in Great Basin based on low medium and high invasion scenarios

Data represent predicted cheatgrass (Bromus tectorum) cover from a quantile regression model. We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios.The model was developed using cheatgrass cover data collected by the Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) program, paired with covariates representing climate, weather, fire history, and disturbance. Quantile regression estimates different coefficients for each predictor variable at each quantile of interest, allowing a given environmental variable to be more or less important at the high end of the response distribution. The predictions at each statistical quantile of interest can be interpreted as invasion scenarios, as they correspond to low, medium, and high cheatgrass cover for a given set of environmental conditions. This metadata file describes three raster files, which share a geographic extent and resolution and which represent predictions from different quantiles of the same quantile regression model.

[
  "010:12"
]

[
  "geospatial"
]