Umina Coastal Sands Woodland Survey for nomination before Sci Comm. The UMINA(Umina Coastal Sands Woodland Survey for nomination before Sci Comm) Survey is part of the Vegetation Information System Survey Program of New South Wales which is a series of systematic vegetation surveys conducted across the state between 1970 and the present.

Goulburn River National Park (Lisa Hill/Steve Bell 1998). The GOULRIV(Goulburn River National Park (Lisa Hill/Steve Bell 1998)) Survey is part of the Vegetation Information System Survey Program of New South Wales which is a series of systematic vegetation surveys conducted across the state between 1970 and the present.

Vegetation Survey and Mapping of Koorawatha, Dananbilla, Gungewalla and Illunie Nature Reserves. The COWRA(Vegetation Survey and Mapping of Koorawatha, Dananbilla, Gungewalla and Illunie Nature Reserves) Survey is part of the Vegetation Information System Survey Program of New South Wales which is a series of systematic vegetation surveys conducted across the state between 1970 and the present.

The digital 3-dimenional (3D) mineral mapping suite of Queensland comprises ~20 “standardized” products at the spectral resolution of the ASTER (Advanced Space-borne Thermal Emission and Reflection Radiometer) sensor and generated from publicly-available satellite, airborne, field and drill core spectral data spanning the visible near infrared (VNIR; 0.4 to 1.0 µm), shortwave infrared (SWIR; 1.0 to 2.5 µm) and thermal infrared (TIR; 7.5 to 12.0 µm) wavelength regions, including: 1. Satellite ASTER maps at both 30 m and 90 m pixel resolution with complete coverage of the state of Queensland, i.e. 1.853 million km²; 2. Airborne HyMap maps at ~5 m pixel resolution with a coverage of ~25,000 km2 from areas across north Queensland; 3. Field point samples (~300) from the National Geochemical Survey of Australia (NGSA) collected from a depth of 0-10 cm of flood overbank sediments; 4. Drill-core profiles (~20) of the National Virtual Core Library (NVCL) selected from the area around the Georgetown seismic line (07GA-IG2). Key to the processing of the remote sensing data-sets (ASTER and HyMap) was the implementation of unmixing methods to remove the effects dry and green vegetation. This unmixing was not applied to the Australian ASTER geoscience maps released in 2012 (called here Version 1 or V1) resulting in extensive areas with little/no mineral information because of the need to apply masks. The vegetation unmixing methods used in the Version 2 (V2) processing of the ASTER and HyMap imagery has resulted in very few areas without coherent mineral information. The resultant V2 “mineral group” products were designed to measure mineral information potentially useful for mapping: (i) primary rock composition; (ii) superimposed alteration effects; and (iii) regolith cover. These V2 products may assist in mapping soil properties and groundwater conditions. However their relatively low spectral resolution (based on ASTER’s 14 VNIR-SWIR-TIR bands) means that they do not provide the high level of mineralogical detail available from hyperspectral systems (>100 spectral bands), like HyMap and the HyLogger. Nevertheless, the relatively low spectral resolution of ASTER means that all other sensor data can be spectrally resampled to that resolution. Furthermore, the ASTER global data archive, which now spans entire Earth’s land surface <80degrees latitude, means that it can be used as global base-map for integrating all other spectral data.

The Revised Universal Soil Loss Equation (RUSLE) estimates the annual soil loss that is due to erosion using a factor-based approach with rainfall, soil erodibility, slope length, slope steepness and cover management and conservation practices as inputs. The collection is (i) a set of maps that represent the RUSLE factors, (ii) a map of the RUSLE estimates of soil erosion in Australia and (iii) a map of the uncertainty in the estimates of erosion.

Iron (Fe) oxide mineralogy in most Australian soils is poorly characterized, even though Fe oxides play an important role in soil function. Fe oxides reflect the conditions of pH, redox potential, moisture, and temperature in the soil environment. The strong pigmenting effect of Fe oxides gives most soils their color, which is largely a reflection of the soil’s Fe mineralogy. Visible-near-infrared (vis-NIR) spectroscopy can be used to identify and measure the abundance of certain Fe oxides in soil, and the visible range can be used to derive tristimuli soil color information. We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer with a wavelength range of 350-2500 nm. We determined the Fe oxide abundance for each sample using the diagnostic absorption features of hematite (near 880 nm) and goethite (near 920 nm) and derived a normalized iron oxide difference index (NIODI) to better discriminate between them. The NIODI was generalized across Australia with its spatial uncertainty using sequential indicator simulation, which resulted in a map of the probability of the occurrence of hematite and goethite. We also derived soil RGB color from the spectra and mapped its distribution and uncertainty across the country using sequential Gaussian simulations. The simulated RGB color values were made into a composite true color image and were also converted to Munsell hue, value, and chroma. These color maps were compared to the map of the NIODI, and both were used to interpret our results. The maps were validated by randomly splitting the data into training and test data sets, as well as by comparing our results to existing studies on the distribution of Fe oxides in Australian soils. Attributes: Units of measurement: 1. Munsell Hue; 2. Munsell Chroma; 3. Munsell value; 4. NIODI; 5. NIODI uncertainty. For details please see Viscarra Rossel et al. (2010). Data Type: Float Grid. Map projection: Lambert Conformal Conic. Datum: GDA94. Map units: Decimal degrees. Resolution: 10,000 metres. File Header Information: ncols 392; nrows 361; xllcorner -2032461.3; yllcorner -4936305.3; cellsize 10000; NODATA_value -9999; byteorder LSBFIRST.

Clay minerals are the most reactive inorganic components of soils. They help to determine soil properties and largely govern their behaviors and functions. Clay minerals also play important roles in biogeochemical cycling and interact with the environment to affect geomorphic processes such as weathering, erosion and deposition. This data provides new spatially explicit clay mineralogy information for Australia that will help to improve our understanding of soils and their role in the functioning of landscapes and ecosystems. I measured the abundances of kaolinite, illite and smectite in Australian soils using near infrared (NIR) spectroscopy. Using a model-tree algorithm, I built rule-based models for each mineral at two depths (0-20 cm, 60-80 cm) as a function of predictors that represent the soil-forming factors (climate, parent material, relief, vegetation and time), their processes and the scales at which they vary. The results show that climate, parent material and soil type exert the largest influence on the abundance and spatial distribution of the clay minerals; relief and vegetation have more local effects. I digitally mapped each mineral on a 3 arc-second grid. The maps show the relative abundances and distributions of kaolinite, illite and smectite in Australian soils. Kaolinite occurs in a range of climates but dominates in deeply weathered soils, in soils of higher landscapes and in regions with more rain. Illite is present in varied landscapes and may be representative of colder, more arid climates, but may also be present in warmer and wetter soil environments. Smectite is often an authigenic mineral, formed from the weathering of basalt, but it also occurs on sediments and calcareous substrates. It occurs predominantly in drier climates and in landscapes with low relief. These new clay mineral maps fill a significant gap in the availability of soil mineralogical information. They provide data to for example, assist with research into soil fertility and food production, carbon sequestration, land degradation, dust and climate modeling and paleoclimatic change. Attributes: Units of measurement: 1. Abundance of kaolin (0 - 1) for the 0-20 cm and 60-80 cm depths; 2. Abundance of illite (0 - 1) for the 0-20 cm and 60-80 cm depths; 3. Abundance of smectite (0 - 1) for the 0-20 cm and 60-80 cm depths; 4. Ternary RGB image of mineral composition for the 0-20 cm and 60-80 cm depths. For details please see Viscarra Rossel (2011). Data Type: Float Grid. Kaolinite, illite, smectite composite maps in GEOTIFF format. Map projections: Geographic. Datum: GDA94 Map units: Decimal degrees. Resolution: 0.00083333333 degrees. File Header Information: ncols 48874; nrows 40373; xllcorner 112.91246795654; yllcorner -43.642475129116; cellsize 0.00083333333333333; NODATA_value -9999; byteorder LSBFIRST.

We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer. These spectra provide an integrative measure that provides information on the fundamental characteristics and composition of the soil, including colour, iron oxide, clay and carbonate mineralogy, organic matter content and composition, the amount of water present and particle size. This soil information content of the spectra was summarised using a principal component analysis (PCA). We used model trees to derive statistical relationships between the scores of the PCA and 31 predictors that were readily available and we thought might best represent the factors of soil formation (climate, organisms, relief, parent material, time and the soil itself). The models were validated and subsequently used to produce digital maps of the information content of the spectra, as summarised by the PCA, with estimates of prediction error at 3-arc seconds (around 90 m) pixel resolution. The maps might be useful in situations requiring high-resolution, quantitative soil information e.g. in agricultural, environmental and ecologic modelling and for soil mapping and classification. Attributes: Units of measurement: 1. Principal component 1; 2. Principal component 3; 3. Principal component 3. For interpretations please see Viscarra Rossel & Chen (2011). Data Type: Float Grid. Map Projection: Geographic. Datum: GDA94. Map units: Decimal degrees. Resolution: 0.00083333333 degrees. File Header Information: ncols 48874; nrows 40373; xllcorner 112.91246795654; yllcorner -43.642475129116; cellsize 0.00083333333333333; NODATA_value -9999; byteorder LSBFIRST.

Global change poses significant and urgent challenges for biodiversity conservation. Species persistence under a rapidly changing environment ultimately depends on abilities to disperse to favourable habitats or adapt in situ by plastic or evolutionary mechanisms. Conservation strategies preserving endemism and adaptive potential are critical. This study aims to investigate the phylogeographic history of Victorian Alpine plants using high-density genetic markers. Multi-taxa genomic data was compared to determine common phylogeographic patterns and identify evolutionary processes shaping biodiversity. Spatial patterns of genetic structure were used to delineate evolutionary bioregions and refugia of high conservation value. Life-history traits have seldom been explicitly within a landscape genetic framework. Spatial isolation is a key component of genetic structure for sessile organisms. This study demonstrates that life-history traits are primary drivers of inter-population connectivity and genetic structure. Differences across taxa impacted on patterns of genetic structure on fine spatial scales, while common patterns were observed at broad scales regardless of life-history traits. These findings complement other Australian Alpine genetic studies indicate that flora and fauna in Victorian Alps share a common genetic structure and phylogeographic history driven by unique processes. The geomorphology of the Victorian Alps has clearly driven the evolutionary trajectories of the native flora and fauna. This approach could inform evidence based conservation policy. Previously undelineated cryptic species were revealed by this study—highlighting limitations of traditional taxonomy and the utility of new approaches. This project demonstrates how genomic technologies can characterise evolutionary processes at landscape scales, and detect important patterns in at-risk ecosystems. This data is related to the following publication: Bell, N., Griffin, P. C., Hoffmann, A. A., & Miller, A.D. (2018). Spatial patterns of genetic diversity among Australian alpine flora communities revealed by comparative phylogenomics. Journal of Biogeography, 45, 177–189. Published online at https://onlinelibrary.wiley.com/doi/epdf/10.1111/jbi.13120 (free access). DOI: 10.1111/jbi.13120

This weather data package comprises weather data for automatic weather stations situated at 13 sites separated by distances of between 5 and 80 km. The weather stations record temperature and rainfall (in 2010, one weather station was set up so that it also began recording wind speed and direction). The air temperature, rainfall, wind speed and wind direction data are recorded in a data logger housed within the instrument stand. The network program uses a core of 12 sites and aims to quantitatively track long-term shifts in biodiversity and ecological processes in relation to key drivers, including unpredictable rainfall and droughts, fire, feral predators and grazing. A synopsis of related data packages which have been collected as part of the Desert Ecology Plot Network's full program is provided at http://www.ltern.org.au/index.php/ltern-plot-networks/desert-ecology