The data provided here are model iteration objects and rasters needed to run the multi-scale modelling process and predict how the host condition affects probability of Hendra virus shedding. The dataset contains predictions of three proxies for host conditions (including food shortage, rehabilitation admissions and formation of a new roost) across eastern Australia in 2008-2019. The Roost Species Distribution Model (SDM) has predictions of roost suitability. These are monthly, spatially explicit predictions of particular conditions or probability of roost occupations. The model objects are iterations of models that were initially trained on data held in figshare (https://figshare.com/s/ddb5a1584609b20f6596). These data objects are linked with code provided at https://github.com/hanlab-ecol/BatOneHealth to be able to run the models and analyses. This includes comparisons of virus predictions of seven different multiscale model structures to observed Hendra virus shedding in field surveys. The purpose of this study was to determine if quantifying and incorporating host condition into epidemiological models improves predictions of virus shedding in space and time. The data objects relate to the 1,000 iterations run of this process to better able to account for uncertainty. Full details about this dataset can be found at https://doi.org/10.5285/93bb37c6-ef86-4386-945d-c1a3d1e2683c

This dataset contains RGB photographs acquired from drone surveys. There are 741 harvest plots from 38 surveys at 36 sites around the world. Each site was approximately 1 ha in area. Included with the photographic images are the coordinates of ground control markers, biomass, taxonomic and location data for harvest plots and ancillary metadata. The observations can be used to obtain allometric size-biomass models. This work was supported by the Natural Environment Research Council award number NE/R00062X/1 as part of the project 'Do dryland ecosystems control variability and recent trends in the land CO2 sink?' Full details about this dataset can be found at https://doi.org/10.5285/1ec13364-cbc6-4ab5-a147-45a103853424