We present Curie depth point (CDP) and geothermal heat flow (GHF) estimations based on spectral analysis of magnetic airborne data for the Transantarctic Mountains (TAM) and Wilkes Subglacial Basin area. The Curie depth point is defined as the depth at which the Curie Temperature of 580 degC is reached. The Curie Temperature describes the temperature at which magnetic minerals lose their ability to generate a strong magnetic field. We use exclusively high resolution magnetic airborne measurements from the ADMAP-2 compilation, where we have removed data with a 15 km blanking distance threshold to reported flight lines to minimise artefact from interpolation. The obtained magnetic dataset is upward continued to a constant station height of 4 km and subdivided into window with a window size of 200 km, 300 km, and 400 km. For each window we calculate the power spectrum and estimate the CDP from the power spectrum. Subsequently we estimate the magnetic data coverage for each window and discard CDP estimates for window below a data coverage threshold of 80%. From the CDP interface GHF is forward calculated assuming constant thermal conductivity for the crust and a constant temperature at the base of the ice sheet representing the pressure melting point. This study is motivated by the need of high resolution GHF models form the Icesheet modelling community especially in marine based Basins like the Wilkes Subglacial Basin as well as by interpretation of the origin of the Transantarctic Mountains. Recent seismic studies have argued that warmer west Antarctic is present beneath the Transantarctic Mountains, which give thermal support to the mountains range. This hypothesis should lead to increased GHF in the area and therefore can be tested against our GHF model. Our results show elevated heat flow in the area of the Transantarctic mountains supporting the idea of thermal support for the mountain range with an independent method. Furthermore, we image elevate heat flow in the central Basin of the Wilks Subglacial Basin and Rennick Graben which have not been imaged before by continent wide GHF models. Funding for this research was provided by NERC through a SENSE CDT studentship (NE/T00939X/1)

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