This layer was produced by the Organic Soils Mapping (OSM) project, a project undertaken by DPIPWE Natural Values Conservation Branch between 2019 and 2021. This project received grant funding from the Australian Government through the Australian Heritage Grants Program, funding from 2017 Tasmanian Bushfire Mitigation fund and The Tasmanian Wilderness World Heritage Area fund. This project has used existing (legacy) data from previous Tasmanian land resource assessment studies, spatial data sets, expert desktop inputs, and newly captured site data from the AHG grant project activities. This project has collated these data to produce this and a further 6 spatial layers that show (presence /absence sites) and predict the likely distribution and characteristics of Organic Soils in Tasmania. The project was focused on the Tasmanian Wilderness World Heritage Area (TWWHA), with modelling applied state-wide to assist in organic soil identification elsewhere. The Organic Soil Mapping was undertaken to specifically identify organic soil extent in the TWWHA and to increases the understanding of the organic soil landscapes and characteristics in greater Tasmania. Organic soil areas are recognised globally as ecologically significant. These landscapes support a diverse range of ecosystems. They also provide and control ecosystem services/processes and provide highly valued carbon and water storage functions. These project outputs aim to improve and assist conservation and management of these important soil landscapes. The mapping was performed using a combination of 30m resolution modelling (Digital Soil Mapping (DSM)), expert digitising, with TASVEG, soil mapping and wetlands datasets as masks. A DSM approach was chosen due to the sparse and limited nature of existing and new field data due to the remote and difficult access. DSM involves using soil site data, intersected with a range of spatial environmental predictor datasets (covariates) to develop a series of landscape models, showing the variation in various soil properties between calibration points through interpolation and extrapolation (Kidd et al 2015). A similar process was used to create e a map of OSM drainage as a continuous index, where values correspond to the Australian Soil and Land Survey Field Handbook (https://www.soilscienceaustralia.org.au/about/what-we-do/standards/).

Position Accuracy: 30m resolution. Attribute Accuracy: Each Raster cell is attributed by multiple soil models to arrive at a likelihood of organic soil rating. The accuracy is therefore limited by the precision of the input models. The result of the validation diagnostics can be made available for each surface on request. The input soil models validated to at least a reasonable level with the standard error within acceptable ranges. Logical Consistency: All raster cell data are labelled with an approximate organic soil likelihood rating. There are no duplicates. Completeness: The spatial layers cover the entire state of Tasmania, excluding Macquarie Island. Additional: Component layers of Digital Soil Mapping components are available upon request. Statement A presence-absence organic soil extent map was developed using a binary calibration set of site data (1 = Organic Soil Presence, 0 = Organic Soil Absence) from the newly collected data, and any legacy data points classed as an Organosol (Australian Soil Classification) or containing an organic or peat surface layer(s). Expert desktop digitising was undertaken identifying sites highly likely to be non-organic, mainly targeting mountainous bare-earth ridge tops in access-constrained areas, Categorical modelling was initially tested using a decision Tree (See5) approach, however, better results were used when modelling the presence-absence data as a continuous (1 to 0) index, where the modelled surface shows values closer to 1 as having a higher likelihood of being organic (i.e. meeting all the environmental conditions that indicate the presence of an organic soil). A similar process was used to create maps for a drainage index using recorded field site data to produce a continuous surface, which was aligned according to the Australian Soil and Land Survey Field Handbook (https://www.soilscienceaustralia.org.au/about/what-we-do/standards/). 1 = Very Poorly Drained 2 = Poorly Drained 3 = Imperfectly Drained 4 = Moderately Well Drained 5 = Well Drained 6 = Rapidly Drained Again, an RT modelling approach was used, intersecting the soil site drainage data with the standard suite of covariates, and averaging 10 k-fold cross validations. The resulting Drainage Index (1 ÃÃâ 6) map was then classified and simplified to two classes for legend simplicity, where; Wetter Organic Areas < 3 Drier Organic Areas > 3 Further reading: Kidd, D., Malone, B., McBratney, A., Minasny, B., Odgers, N., Webb, M., Searle, R., 2014a. A New Digital Soil Resource for Tasmania, Australia, 20th WORLD CONGRESS OF SOIL SCIENCE, pp. 612-613; Kidd, D., Webb, M., Malone, B., Minasny, B., McBratney, A., 2015a. 80-metre Resolution 3D Soil Attribute Maps for Tasmania, Australia. Soil Research; Kidd, D., Webb, M., Malone, B., Minasny, B., McBratney, A., 2015b. Digital soil assessment of agricultural suitability, versatility and capital in Tasmania, Australia. Geoderma Regional 6, 7-21; Kidd, D.B., Malone, B.P., McBratney, A.B., Minasny, B., Webb, M.A., 2014b. Digital mapping of a soil drainage index for irrigated enterprise suitability in Tasmania, Australia. Soil Research 52, 107-119;