Environmental and meteorological data from: A habitat suitability analysis for three Culicoides species implicated in bluetongue virus transmission in the Southeastern United States
Data files
Jan 10, 2025 version files 3.79 MB
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aoi_wgs84_stack_c.tif
3.79 MB
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README.md
2.23 KB
Abstract
Culicoides biting midges adversely impact animal health through transmission of multiple orbiviruses, such as bluetongue virus (BTV). This study used light trapping data collected in the Southeastern United States for three Culicoides midge species that are confirmed or suspected BTV vectors: Culicoides insignis, Culicoides stellifer and Culicoides venustus. Midge presence datasets were combined with meteorological data and ecological data to model habitat suitability for each species. Logistic regression and machine learning models were used to generate individual species distribution models (SDMs). Results for each SDM method were combined in an ensemble model to create a distribution model for each midge species. Based on overlay analyses of livestock populations and midge suitable habitat, there is extensive overlap of cattle and goat populations and suitable habitat for C. insignis in Florida. Suitable habitat for C. stellifer intersects with cattle and goat populations in various counties in Alabama, Arkansas, the Carolinas, Florida, Georgia, Louisiana and Tennessee; and suitable habitat for C. venustus intersects with cattle and goat populations in the same states as C. stellifer, except for Florida. It is critical for orbivirus and midge surveillance to continue in the Southeastern United States as the habitat of all three midge species intersect with livestock populations.
README: Environmental and meteorological data from: A habitat suitability analysis for three Culicoides species implicated in bluetongue virus transmission in the Southeastern United States
https://doi.org/10.5061/dryad.dv41ns289
Description of the data and file structure
File Type:
The file is stored in a .tif format.
Data can be read in (we used R studio and R software) using software that can read .tif and raster data
Description:
The .tif file is a raster stack of twelve layers. These layers are environmental and meteorological variables used in our analysis. Each layer is the mean values taken across the study period from 2008-2020.
When the data is first accessed, each layer is named "aoi_wgs84_stack_c_#", where # corresponds to the layers in numerical order (aoi_wgs84_stack_c_1, aoi_wgs84_stack_c_2, aoi_wgs84_stack_c_3...) from 1-12.
In order of 1-12, the layers are maximum temperature (Celsius), minimum temperature (Celsius), maximum relative humidity (%), minimum relative humidity (%), precipitation (mm), wind speed (m/s), mean normalized difference vegetation index (NDVI), maximum NDVI, minimum NDVI, organic carbon soil content, soil pH, and soil sand fraction.
Files and variables
File: aoi_wgs84_stack_c.tif
Description: This is the raster stack that includes all environmental and meteorological variables used in the analysis.
Each layer is a variable, with layers described as 1-12, and in order: maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, precipitation, wind speed, mean NDVI, maximum NDVI, minimum NDVI, soil organic carbon content, soil pH, and soil sand fraction.
Code:
Access information
Other publicly accessible locations of the data:
- Data can be pulled from gridMET, ISRIC, and MODIS.
Data was derived from the following sources:
- Data was derived from gridMET, ISRIC, and MODIS.
Methods
Data were obtained for each of the environmental and meteoroloical variables using the following sources: meteorological data from GridMET (gridMET – Climatology Lab) and soil and NDVI from International Soil Reference and Information Centre (ISRIC) (ISRIC—World Soil Information) and Moderate Resolution Imaging Spectroradiometer (MODIS). We used the mean values of ecological and meteorological variables to create rasters across the study period from 2008-2020. All data processing was completed using R Version 4.2.1 (R Core Team, 2022).