The Coorong, a shallow coastal lagoon at the mouth of the Murray River, has had a significant decline in water quality over the last 15 years because of reduced freshwater inflows. Salinity has increased throughout the lagoon and currently ranges between 60 and 190 psu depending on the proximity to the Murray Mouth and the season. Although nutrient inflow has been negligible in recent years, the lagoon is considered euthrophic. This study aimed to identify the source of nutrients and the biogeochemical processes that transform them. The key findings were: 1. Groundwater discharge is likely to be an important nutrient source 2. Nitrogen appears to be the nutrient limiting primary production 3. Decomposition of organic matter in the sediments is highly seasonal with much higher rates in the summer.

This atlas volume summarises historic geographical knowledge about Australia's soil resources and land use and complements the other environmental and resource topics in the Atlas of Australia Resource Series. The following volumes in this series are also available: <ul><li><a href="https://www.ga.gov.au/products/servlet/controller?event=GEOCAT_DETAILS&amp;catno=60922">Volume 3: Atlas of Australian Resources Third Series - Agriculture (1982 (edition)</a></li> <li><a href="https://www.ga.gov.au/products/servlet/controller?event=GEOCAT_DETAILS&amp;catno=60924">Volume 5: Atlas of Australian Resources Third Series - Geology and Minerals (1988 edition)</a> </li> <li><a href="https://www.ga.gov.au/products/servlet/controller?event=GEOCAT_DETAILS&amp;catno=60925">Volume 6: Atlas of Australian Resources Third Series - Vegetation (1990 edition)</a></li> </ul> <strong>The following PDFs have been reproduced in A3 format, for best results please print on A3 paper (297mm x 420mm).</strong>

At the end of 1945 the area dredged by the Bulolo Gold Dredging Co. Ltd. was 831 hectares. At that time 307 hectares remained to be dredged and of this 227 hectares contained soil which has been shown by soil analysis to be reasonably good. Resoiling would not be practicable on about 70 hectares of this area, as the Company plans to dredge this section twice, first with shallow and then with deep-digging dredges. Similar soil exists on 3-400 hectares classified previously by the Company as marginal ground, but which, since the increase in the Australian price of gold, is now probably quite profitable. This makes the total area that might be suitable for resoiling about 500 hectares compared to 1,281 hectares that either has been dredged or to which for other reasons resoiling is not applicable. Revenue to the Administration from the gold produced at Bulolo is over £1,000 per hectare from royalty alone and as most of the area has now been worked it seems questionable whether resoiling is worth further consideration at this stage. No provision was made for resoiling in the terms of the original mining tenements, but the Company's officials have expressed willingness to co-operate in carrying out the wishes of the administration. It has been considered impracticable now to resoil the areas that have been dredged or to rebuild the existing dredges for mechanical resoiling, but if the Department of Internal Territories considers that the matter should be pursued further, the Company might be asked to consider removing soil from the dredge path by bulldozer or other mechanical means and replacing the soil on the flattened out tailings after the dredge has passed; also to provide details of the economics of carrying out a similar scheme on the areas in which values were previously considered to be marginal.

This record gives a brief account of the conditions encountered in a geological reconnaissance of the south-western portion of the Canning Basin - an area covered mostly by sand and seif dune, interspersed by scattered low rock outcrops.

In a recent paper, Dye (2006) analyzed the distribution of species of macrobenthos and meiobenthos within two geomorphic facies of four small intermittently closed and open estuaries in New South Wales, Australia (colloquially known as ICOLLs). We believe that Dye's (2006) study is not an appropriate test of the Roy et al. (2001) habitat classification, and consequently several of the hypotheses posed by Dye do not follow logically from their model.

The Primary Coastal Sediment Compartment data set represents a regional-scale (1:250 000 - 1:100 000) compartmentalisation of the Australian coastal zone into spatial units within (and between) which sediment movement processes are considered to be significant at scales relevant to coastal management. The Primary and accompanying Secondary Coastal Sediment Compartment data sets were created by a panel of coastal science experts who developed a series of broader scale data sets (Coastal Realms, Regions and Divisions) in order to hierarchically subdivide the coastal zone on the basis of key environmental attributes. Once the regional (1:250 000) scale was reached expert knowledge of coastal geomorphology and processes was used to further refine the sub-division and create both the Primary and Secondary Sediment Compartment data sets. Environmental factors determining the occurrence and extents of these compartments include major geological structures, major geomorphic process boundaries, orientation of the coastline and recurring patterns of landform and geology - these attributes are given in priority order below. 1 - Gross lithological/geological changes (e.g. transition from sedimentary to igneous rocks). 2 - Geomorphic (topographic) features characterising a compartment boundary (often bedrock-controlled) (e.g. peninsulas, headlands, cliffs). 3 - Dominant landform types (e.g. large cuspate foreland, tombolos and extensive sandy beaches versus headland-bound pocket beaches). 4 - Changes in the orientation (aspect) of the shoreline.

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.

This dataset contains species identifications of benthic worms collected during survey TAN0713 (R.V. Tangaroa, 7 Oct - 22 Nov 2007). Animals were collected from the Faust and Capel basins and Gifford Guyot with a boxcore, rock dredge, or epibenthic sled. Specimens were lodged at Museum of Victoria in June 2008. Species-level identifications were undertaken by Robin Wilson at the Museum of Victoria and were delivered to Geoscience Australia on 1 Aug 2008. See GA Record 2009/22 for further details on survey methods and specimen acquisition. Data is presented here exactly as delivered by the taxonomist, and Geoscience Australia is unable to verify the accuracy of the taxonomic identifications.

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.

High element enrichment factors (EFs) are commonly used in the literature to support the hypothesis that a particular suite of elements is of anthropogenic origin. Real-world examples of regional geochemical surveys demonstrate that EFs can be high or low due to a multitude of reasons, of which contamination is but one. This applies to EFs calculated relative to either the crust or some local background (e.g., a deeper soil layer). Results from local studies near industrial centres showing high (and pollution-related) EFs cannot be generalised over large areas or for sample sites far removed (i.e., more than some tens of kilometers) from a likely pollution source. Regional-scale geochemical mapping, on the other hand, facilitates the reliable estimation of the influence of contamination on the measured element concentrations. EFs are strongly influenced by, among other factors, biogeochemical processes that redistribute chemical elements between environmental compartments at the Earth?s surface. Using EFs to detect or 'prove' human influence on element cycles in remote areas should be avoided because, in most cases, high EFs cannot conclusively demonstrate, nor even suggest, such influence.