Data gathered in the field during the sample collection phase of the National Geochemical Survey of Australia (NGSA) has been used to compile the Preliminary Soil pH map of Australia. The map, which was completed in late 2009, offers a first-order estimate of where acid or alkaline soil conditions are likely to be expected. It provides fundamental datasets that can be used for mineral exploration and resource potential evaluation, environmental monitoring, landuse policy development, and geomedical studies into the health of humans, animals and plants.

A key component of Geoscience Australia's marine program involves developing products that contain spatial information about the seabed for Australia's marine jurisdiction. This spatial information is derived from sparse or unevenly distributed samples collected over a number of years using many different sampling methods. Spatial interpolation methods are used for generating spatially continuous information from the point samples. These methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Machine learning methods, like random forest (RF) and support vector machine (SVM), have proven to be among the most accurate methods in disciplines such as bioinformatics and terrestrial ecology. However, they have been rarely previously applied to the spatial interpolation of environmental variables using point samples. To improve the accuracy of spatial interpolations to better represent the seabed environment for a variety of applications, including prediction of biodiversity and surrogacy research, Geoscience Australia has conducted two simulation experiments to compare the performance of 14 mathematical and statistical methods to predict seabed mud content for three regions (i.e., Southwest, North, Northeast) of Australia's marine jurisdiction Since 2008. This study confirms the effectiveness of applying machine learning methods to spatial data interpolation, especially in combination with OK or IDS, and also confirms the effectiveness of averaging the predictions of these combined methods. Moreover, an alternative source of methods for spatial interpolation of both marine and terrestrial environmental properties using point survey samples has been identified, with associated improvements in accuracy over commonly used methods.

Explaining spatial variation and habitat complexity of benthic habitats from underwater video through the use of maps. Different methodologies currently used to process and analyse percent cover of benthic organisms from underwater video will be addressed and reviewed.

OzCoasts is a web-based database and information system managed by Geoscience Australia that draws together a diverse range of data and information on Australia's coasts and estuaries. Maps, images, reports and data can be downloaded and there are tools to assist with coastal science, monitoring, management and policy. A Tropical Rivers module is the newest major feature of the website and was developed in partnership with the Griffith University node of the Tropical Rivers and Coastal Knowledge (TRaCK) consortium and Boab Interactive. The module contains the Australian Riverine Landscape Classifier (AURICL) and provides links to the TRaCK Digital Atlas. AURICL will assist researchers and policy makers make better decisions about riverine landscapes. It is a dynamic and flexible system (i.e. can be updated as new data layers become available) for classifying and comparing tropical catchments and their rivers based on the similarity, or dissimilarity, of a wide range of parameters. Importantly, AURICL provides researchers with: (i) data-sets to link stream segments from the National Catchment Boundaries database to estuary point locations for north Australia; (ii) a collection of riverine attribute data that sum their upstream contributions to an estuary; and (iii) an amalgamation of inputs for estuaries with multiple contributing streams. To date, researchers have only had access to very general data on the catchments that feed estuaries (e.g. catchment areas). The Mangroves and Coastal Saltmarsh of Victoria: Distribution, Condition, Threats and Management report is new to the Habitat Mapping module, and constitutes the first State-wide assessment of Victoria's coastal wetlands. The 514 page report, led by Prof. Paul Boon (Victoria University), examines the diversity of wetland types and plant communities along the Victorian coast and provides analysis of the ecological condition and major threats to coastal wetlands in Victoria. OzCoasts will also soon deliver the Coastal Eutrophication Risk Assessment Tool (CERAT) for the NSW Office of Environment and Heritage. CERAT will help identify and prioritise land use planning decisions to protect and preserve the health of NSW estuaries. A partnership between OzCoasts and the coastal facility of the TERN (Terrestrial Ecosystem Research Network) is also currently under negotiation.

pH is one of the more fundamental soil properties governing nutrient availability, metal mobility, elemental toxicity, microbial activity and plant growth. The field pH of topsoil (0-10 cm depth) and subsoil (~60-80 cm depth) was measured on floodplain soils collected near the outlet of 1186 catchments covering over 6 M km2 or ~80% of Australia. Field pH duplicate data, obtained at 124 randomly selected sites, indicates a precision of 0.5 pH unit (or 7%) and mapped pH patterns are consistent and meaningful. The median topsoil pH is 6.5, while the subsoil pH has a median pH of 7 but is strongly bimodal (6-6.5 and 8-8.5). In most cases (64%) the topsoil and subsoil pH values are similar, whilst, among the sites exhibiting a pH contrast, those with more acidic topsoils are more common (28%) than those with more alkaline topsoils (7%). The distribution of soil pH at the national scale indicates the strong controls exerted by precipitation and ensuing leaching (e.g., low pH along the coastal fringe, high pH in the dry centre), aridity (e.g., high pH where calcrete is common in the regolith), vegetation (e.g., low pH reflecting abundant soil organic matter), and subsurface lithology (e.g., high pH over limestone bedrock). The new data, together with existing soil pH datasets, can support regional-scale decision-making relating to agricultural, environmental, infrastructural and mineral exploration decisions.

Demands are being made of the marine environment that threaten to erode the natural, social and economic benefits that human society derives from the oceans. Expanding populations ensure a continuing increase in the variety and complexity of marine based activities - fishing, power generation, tourism, mineral extraction, shipping etc. The two most commonly acknowledged purposes for habitat mapping in the case studies contained in this book are to support government spatial marine planning, management and decision-making and to support and underpin the design of marine protected areas (MPAs; see Ch. 64).

Spatial interpolation methods for generating spatially continuous data from point locations of environmental variables are essential for ecosystem management and biodiversity conservation. They can be classified into three groups (Li and Heap 2008): 1) non-geostatistical methods (e.g., inverse distance weighting), 2) geostatistical methods (e.g., ordinary kriging: OK) and 3) combined methods (e.g. regression kriging). Machine learning methods, like random forest (RF) and support vector machine (SVM), have shown their robustness in data mining fields. However, they have not been applied to the spatial prediction of environmental variables (Li and Heap 2008). Given that none of the existing spatial interpolation methods is superior to the others, several questions remain, namely: 1) could machine learning methods be applied to the spatial prediction of environmental variables; 2) how reliable are their predictions; 3) could the combination of these methods with the existing interpolation methods improve the predictions; and 4) what contributes to their accuracy? To address these questions, we conducted a simulation experiment to compare the predictions of several methods for mud content on the southwest Australian marine margin. In this study, we discuss results derived from this experiment, visually examine the spatial predictions, and compare the results with the findings in previous publications. The outcomes of this study have both practical and theoretical importance and can be applied to the spatial prediction of a range of environmental variables for informed decision making in environmental management. This study reveals a new direction in and provides alternative methods for spatial interpolation in environmental sciences.

Catchment outlet sediments (0-10 cm depth, sieved to <2 mm) collected at a very low density over most of the Australian continent have been analysed using the Mobile Metal Ion (MMI®) partial extraction technique. Of the 54 elements analysed, eight are generally regarded as essential nutrients for plant growth: Ca, Cu, Fe, K, Mg, Mn, P and Zn. For these, 'bioavailability', defined here as the ratio of the partial digest concentration to the total concentration, has been investigated. This estimation of 'bioavailability' gives results comparable with standard agricultural measurements. Average 'bioavailability' ranges from 15.0% for Ca to 0.1% for Fe. Smoothed (kriged) colour contour maps for continental Australia have been produced for these eight nutrients and interpreted in terms of lithology (e.g., presence of carbonates in the MMI® Ca map), mineralization (e.g., well known and possibly less known mineral districts in the Cu, P and Zn maps), environmental processes (e.g., salinity in K map, weathering and acid generation in Fe map) and agricultural practices (e.g., application of fertilizers in P and Zn maps). This first application of a partial extraction technique at the scale of a continent has yielded meaningful, coherent and interpretable results.

Inland sulfidic soils have recently formed throughout wetlands of the Murray River floodplain associated with increased salinity and river regulation (Lamontagne et al., 2006). Sulfides have the potential to cause widespread environmental degradation both within sulfidic soils and down stream depending on the amount of carbonate available to neutralise acidity (Dent, 1986). Sulfate reduction is facilitated by organic carbon decomposition, however, little is known about the sources of sedimentary organic carbon and carbonate or the process of sulfide accumulation within inland sulfidic wetlands. This investigation uses stable isotopes from organic carbon (13C and 15N), inorganic sulfur (34S) and carbonate (13C and 18O) to elucidate the sources and cycling of sulfur and carbon within sulfidic soils of the Loveday Disposal Basin.

Physical sedimentological processes such as the mobilisation and transport of shelf sediments during extreme storm events give rise to disturbances that characterise many shelf ecosystems. The intermediate disturbance hypothesis predicts that biodiversity is controlled by the frequency of disturbance events, their spatial extent and the amount of time required for ecological succession. A review of available literature suggests that periods of ecological succession in shelf environments range from 1 to over 10 years. Physical sedimentological processes operating on continental shelves having this same return frequency include synoptic storms, eddies shed from intruding ocean currents and extreme storm events (cyclones, typhoons and hurricanes). Modelling studies that characterise the Australian continental shelf in terms of bed stress due to tides, waves and ocean currents were used here to create a map of ecological disturbance, defined as occurring when the Shield's parameter exceeds a threshold of 0.25. We also define a dimensionless ecological disturbance ratio (ED) as the rate of ecological succession divided by the recurrence interval of disturbance events. The results illustrate that on the outer part of Australia's southern, wave-dominated shelf the mean number of days between threshold events that the Shield's parameter exceeds 0.25 is several hundred days.