R code to fit Gaussian process models to white-nose syndrome/Pseudogymnoascus destructans monitoring data across North America from 2006-2022
This code supports the manuscript "Gaussian process forecasts Pseudogymnoascus destructans will cover coterminous United States by 2030." The code is used to fit Gaussian process models to publicly accessible monitoring data on the spread of white-nose syndrome in North America. These models are used to make predictions on a fine spatial grid, giving a forecast (and hindcast) of the spread of white-nose syndrome at any location. Also contained in the code is a retrospective cross validation experiment, producing parameter estimates and model scoring over time. The code also relies on the GRTS grid for model prediction, which is publicly accessible at https://doi.org/10.5066/p9o75ydv. Shapefiles such as administrative boundaries can be used to add to plots are not required for the analysis.
Citation Information
| Publication Year | 2023 |
|---|---|
| Title | R code to fit Gaussian process models to white-nose syndrome/Pseudogymnoascus destructans monitoring data across North America from 2006-2022 |
| DOI | 10.5066/P9ZD9GVZ |
| Authors | Ashton M. Wiens, Wayne E Thogmartin |
| Product Type | Data Release |
| Record Source | USGS Digital Object Identifier Catalog |
| USGS Organization | Upper Midwest Environmental Sciences Center |
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Gaussian process forecasts Pseudogymnoascus destructans will cover coterminous United States by 2030
Ashton Wiens, Ph.D.
Mathematical Statistician
Wayne E Thogmartin, PhD
Research Ecologist
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Gaussian process forecasts Pseudogymnoascus destructans will cover coterminous United States by 2030
White-nose syndrome has been decimating populations of several bat species since its first occurrence in the Northeastern United States in the winter 2006–2007. The spread of the disease has been monitored across the continent through the collaboration of many organizations. Inferring the rate of spread of the disease and predicting its arrival at new locations is critical when assessing the curreAuthorsAshton M. Wiens, Wayne E. Thogmartin - Connect
Ashton Wiens, Ph.D.
Mathematical StatisticianEmailWayne E Thogmartin, PhD
Research EcologistEmailPhone