Multi-scale model of Hendra virus dynamics

These are the environmental data needed to run the function to estimate Hendra virus prevalence (see https://github.com/hanlab-ecol/BatOneHealth for more information on the functions and workflow). All of the items representing environmental variables should be downloaded from this collection (bat roost predictions, maximum temperature, minimum temperature, temperature difference, precipitation, precipitation anomaly, NDVI, soil moisture, morning vapor pressure, solar exposure, potential evapotranspiration, land cover, ONI, SOI, and SAM) otherwise you will receive an error message when running the function. After downloading, all the items should be unzipped and placed in a folder named AustralianClimateVariables to be placed within your working directory (where you want the output to be placed). Note that you will also have to place two model objects and one tabular dataset of model results (found in the Github repository detailed above) in your working directory for the functions to work, so please read the README there.

All of these environmental variables were used to train the model components using in estimating Hendra virus prevalence based on bat stress. Each of these items contains a series of raster files at 5 km resolution using a CRS of GDA2020 (EPSG: 8059). Except for land cover, soil moisture, and potential evapotranspiration, all variables are presented monthly from 1996 to 2021. Several of the items include a series of rasters showing the recent history of a variable that take into account the amount of change over a period of time. These histories are typically averaged for 2, 3, 6, 9, 12, 15, 18, 21, and 24 months and are often measuring the standard deviation across this time period except in the case of precipitation and solar exposure which are sums of all values within the lagged period of time.