This study presents new information on the regional geochemical characteristics of deep-sea floor sediments (1300 - 2423 m water depth) on the Lord Howe Rise (deep-sea plateau) and Gifford Guyot (seamount/tablemount), remote areas off eastern Australia. The aim was to provide a coherent synthesis for a suite of geochemical data that can be used to make habitat inferences and to develop surrogates of biodiversity. Sediment characteristics analysed were mineralogy, organic carbon and nitrogen concentrations and isotopic compositions, and concentrations of major and trace elements. We also measured parameters that convey information about the reactivity of organic matter and on the bio-availability of bioactive trace elements (e.g. chlorin indices and acid-extractable elements). Surface sediments from the region were calcareous oozes that were carbon-lean (0.26±0.1%) and had moderate to high chlorin indices (0.62 - 0.97)..

Models of seabed sediment mobilisation by waves and currents over Australia's continental shelf environment are used to examine whether disturbance regimes exist in the context of the intermediate disturbance hypothesis (IDH). Our study shows that it is feasible to model the frequency and magnitude of seabed disturbance in relation to the dominant energy source (wave-dominated shelf, tide-dominated shelf or tropical cyclone dominated shelf). Areas are mapped where the recurrence interval of disturbance events is comparable to the rate of ecological succession, which meets criteria defined for a disturbance regime. We focus our attention on high-energy, patch-clearing events defined as exceeding the Shields (bed shear stress) parameter value of 0.25. Using known rates of ecological succession for different substrate types (gravel, sand, mud), predictions are made of the spatial distribution of a dimensionless ecological disturbance index (ED), given as: ED = FA (ES/RI), where ES is the ecological succession rate for different substrates, RI is the recurrence interval of disturbance events and FA is the fraction of the frame of reference (surface area) disturbed. Maps for the Australian continental shelf show small patches of ED-seafloor distributed around the continent, on both the inner and outer shelf. The patterns are different for wave-dominated (patches on the outer shelf trending parallel to the coast), tide-dominated (patches crossing the middle-shelf trending normal to the coast) and cyclone-dominated (large oval-shaped patches crossing all depths). Only a small portion of the shelf (perhaps ~10%) is characterised by a disturbance regime as defined here. To our knowledge, this is the first time such an analysis has been attempted for any continental shelf on the earth.

The World Summit on Sustainable Development implementation plan requires, by 2012, a representative system of marine protected areas (RSMPA) for the purposes of long-term conservation of marine biodiversity. A great challenge for meeting this goal, particularly in data-poor regions, is to avoid inadvertant failure while giving science the time and resources to provide better knowledge. A staged process is needed for identifying areas in data-poor regions that would enable the objectives to be achieved in the long term. We elaborate a procedure that would satisfy the first stage of identifying a RSMPA, including areas suitable as climate change refugia and as reference areas for monitoring change without direct interference of human activities. The procedure is based on the principles of systematic conservation planning. The first step involves the identification of ecologically-separated provinces along with the physical heterogeneity of habitats within those provinces. Ecological theory is then used to identify the scale and placement of MPAs, aiming to be the minimum spatial requirements that would satisfy the principles for a representative system: comprehensiveness, adequacy and representativeness (CAR). We apply the procedure to eastern Antarctica, a region with spatially-restricted sampling of most biota. We use widely available satellite and model data to identify a number of large areas that are likely to encompass important areas for inclusion in a RSMPA. Three large areas are identified for their pelagic and benthic values as well as their suitability as climate change refugia and reference areas. Four other areas are identified specifically for their benthic values. These areas would need to be managed to maintain these values but we would expect them to be refined over time as more knowledge becomes available on the specific location and spatial extent of those values.

A growing need to manage marine biodiversity at local, regional and global scales cannot be met by applying the limited existing biological data sets. Abiotic surrogacy is increasingly valuable in filling the gaps in our knowledge of biodiversity hotspots, habitats needed by endangered or commercially valuable species and systems or processes important to the sustained provision of ecosystem services. This review examines the utility of abiotic surrogates across spatial scales with particular regard to how abiotic variables are tied to processes which affect biodiversity and how easily those variables can be measured at scales relevant to resource management decisions.

Monitoring changes in the spatial distribution and health of biotic habitats requires spatially extensive surveys repeated through time. Although a number of habitat distribution mapping methods have been successful in clear, shallow-water coastal environments (e.g. aerial photography and Landsat imagery) and deeper (e.g. multibeam and sidescan sonar) marine environments, these methods fail in highly turbid and shallow environments such as many estuarine ecosystems. To map, model and predict key biotic habitats (seagrasses, green and red macroalgae, polychaete mounds [Ficopamatus enigmaticus] and mussel clumps [Mytilus edulis]) across a range of open and closed estuarine systems on the south-west coast of Western Australia, we integrated post-processed underwater video data with interpolated physical and spatial variables using Random Forest models. Predictive models and associated standard deviation maps were developed from fine-scale habitat cover data. Models performed well for spatial predictions of benthic habitats, with 79-90% of variation explained by depth, latitude, longitude and water quality parameters. The results of this study refine existing baseline maps of estuarine habitats and highlight the importance of biophysical processes driving plant and invertebrate species distribution within estuarine ecosystems. This study also shows that machine-learning techniques, now commonly used in terrestrial systems, also have important applications in coastal marine ecosystems. When applied to video data, these techniques provide a valuable approach to mapping and managing ecosystems that are too turbid for optical methods or too shallow for acoustic methods.

This special issue of Continental Shelf Research presents 13 research papers that contain the latest results in the field of benthic marine environment mapping and seabed characterisation. A total of 10 papers in this special issue were presented as papers and posters at GeoHab conferences in 2007 (Noumea, New Caledonia), 2008 (Sitka, Alaska) and 2009 (Trondheim, Norway). The annual GeoHab conference provides a forum in which marine physical and biological scientists, managers, policy makers, and industry representatives can convene to engage in discussions regarding mapping and characterising the seabed. The papers contained in this special issue build on the work published in Greene and Todd (2005): Mapping the Seafloor for Habitat Characterization, a special publication of the Geological Association of Canada.

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.

A number of terms used in this book are derived from the fields of biogeography and benthic ecology and these are defined in the glossary; the reader is also referred to the works cited at the end of this chapter for further information. Many of the case studies presented in this book refer to habitat classification schemes that have been developed based on principles of biogeography and ecology. For these reasons a brief overview is provided here to explain the concepts of biodiversity, biogeography and benthic ecology that are most relevant to habitat mapping and classification. Of particular relevance is that these concepts underpin classification schemes employed by GeoHab scientists in mapping habitats and other bioregions. A selection of published schemes, from both deep and shallow water environments, are reviewed and their similarities and differences are examined.

In ecology, a common form of statistical analysis relates a biological variable to variables that delineate the physical environment, typically by fitting a regression model or one of its extensions. Unfortunately, the biological data and the physical data are frequently obtained from eparate sources of data. In such cases there is no guarantee that the biological and physical data are co-located and the regression model cannot be used. A common and pragmatic solution is to predict the physical variables at the locations of the biological variables and then to use the predictions as if they were observations.We show that this procedure can cause potentially misleading inferences and we use generalized linear models as an example. We propose a Berkson error model which overcomes the limitations. The differences between using predicted covariates and the Berkson error model are illustrated by using data from the marine environment, and a simulation study based on these data.

This dataset contains species identifications of echinoderms collected during survey GA2476 (R.V. Solander, 12 August - 15 September 2008). Animals were collected from the Western Australian Margin with a BODO sediment grab or rock dredge. Specimens were lodged at Museum of Victoria on the 10 March 2009. Species-level identifications were undertaken by Tim O'Hara at the Museum of Victoria and were delivered to Geoscience Australia on the 24 April 2009. See GA Record 2009/02 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.