A range of physical descriptors of the seabed can potentially be used as surrogates for defining patterns of benthic marine biodiversity, including bathymetry, geomorphology and sediment type. These variables can be mapped, described and sampled across spatial scales that are of value to the management of the marine estate by providing a template for monitoring benthic ecosystems. As part of a four-year program (2007-2010) funded by the Australian Government, Geoscience Australia led marine surveys designed to collect robust datasets for the analysis of surrogacy relationships between a suite of physical variables and benthic biota in select areas of the Australian continental shelf. This paper focuses on results of the 2008 Carnarvon shelf survey, located within a Commonwealth Marine Park and adjacent to the World Heritage-listed Ningaloo Reef (Western Australia). High resolution multibeam sonar mapping, underwater video and benthic sampling revealed a complex geomorphology of ridges, mounds and sandy bedforms. The largest ridge extends 15 km alongshore is 20 m high and interpreted as a drowned forereef. Smaller ridges are ~1 km long, oriented northeast and preserve the form of aeolian dunes. Mounds are up to 5 m high and form extensive fields surrounded by flat sandy seabed. These ridges and mounds provide hardground habitat for diverse coral and sponge communities, whereas the surrounding sandy seafloor is characterised by few sessile benthic organisms. Multivariate analysis of these relationships is used to develop predictive models of benthic habitats, demonstrating the utility of high resolution physical data for informing management of these ecosystems.

The identification of marine habitats based on physical parameters is increasingly important for marine reserve design, allowing characterisation of habitat types over much wider areas than is possible from often patchy biological data. Marine management zones often contain a wide array of physical environments, which may not be captured in the biological sampling effort. The mismatch between biological and physical information leads to uncertainty in the application of bio-physical relationships at the broader management scale. In this study, a case study from northern Australia is used to demonstrate a methodology for defining uncertainties which result from the extrapolation of bio-physical associations across areas where detailed biological data is absent. In addition, uncertainties relating to the interpolation of physical data sets and that resulting from the cluster analysis applied to the physical data are calculated and mapped, providing marine managers with greater robustness in their analysis of habitat distributions.

The East Antarctic continental shelf has had very few studies examining the macrobenthos structure or relating biological communities to the abiotic environment. In this study, we apply a hierarchical method of benthic habitat mapping to Geomorphic Unit and Biotope levels at the local (10s of kilometers) scale across the George V Shelf between longitudes 1421E and 1461E. We conducted a multi-disciplinary analysis of seismic profiles, multibeam sonar, oceanographic data and the results of sediment sampling to define geomorphology, surficial sediment and near-seabed water mass boundaries.

Geoscience Australia carried out marine surveys in Jervis Bay (NSW) in 2007, 2008 and 2009 (GA303, GA305, GA309, GA312) to map seabed bathymetry and characterise benthic environments through colocated sampling of surface sediments (for textural and biogeochemical analysis) and infauna, observation of benthic habitats using underwater towed video and stills photography, and measurement of ocean tides and wavegenerated currents. Data and samples were acquired using the Defence Science and Technology Organisation (DSTO) Research Vessel Kimbla. Bathymetric mapping, sampling and tide/wave measurement were concentrated in a 3x5 km survey grid (named Darling Road Grid, DRG) within the southern part of the Jervis Bay, incorporating the bay entrance. Additional sampling and stills photography plus bathymetric mapping along transits was undertaken at representative habitat types outside the DRG. This 81 sample dataset comprises acidextractable concentrations of trace elements (Fe, Mn, Co, Ni, Cu, Zn, Ge, As, Cd and Pb) in surface seabed sediments (~0 to 2 cm) from Jervis Bay.

Geoscience Australia carried out marine surveys in Jervis Bay (NSW) in 2007, 2008 and 2009 (GA303, GA305, GA309, GA312) to map seabed bathymetry and characterise benthic environments through colocated sampling of surface sediments (for textural and biogeochemical analysis) and infauna, observation of benthic habitats using underwater towed video and stills photography, and measurement of ocean tides and wavegenerated currents. Data and samples were acquired using the Defence Science and Technology Organisation (DSTO) Research Vessel Kimbla. Bathymetric mapping, sampling and tide/wave measurement were concentrated in a 3x5 km survey grid (named Darling Road Grid, DRG) within the southern part of the Jervis Bay, incorporating the bay entrance. Additional sampling and stills photography plus bathymetric mapping along transits was undertaken at representative habitat types outside the DRG. This 126 sample data set comprises TCO2 flux and pool data for surface seabed sediments (~0 to 2 cm).

Geoscience Australia carried out marine surveys in Jervis Bay (NSW) in 2007, 2008 and 2009 (GA303, GA305, GA309, GA312) to map seabed bathymetry and characterise benthic environments through co-located sampling of surface sediments (for textural and biogeochemical analysis) and infauna, observation of benthic habitats using underwater towed video and stills photography, and measurement of ocean tides and wave-generated currents. Data and samples were acquired using the Defence Science and Technology Organisation (DSTO) Research Vessel Kimbla. Bathymetric mapping, sampling and tide/wave measurement were concentrated in a 3x5 km survey grid (named Darling Road Grid, DRG) within the southern part of the Jervis Bay, incorporating the bay entrance. Additional sampling and stills photography plus bathymetric mapping along transits was undertaken at representative habitat types outside the DRG. This 110 sample data-set comprises salinity, dissolved oxygen, water temperature and light attenuation (KD) measurements from Jervis Bay.

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.

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.

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).

Dense coral-sponge communities on the upper continental slope (570 - 950 m) off George V Land, east Antarctica have been identified as Vulnerable Marine Ecosystems. We propose three main factors governing their distribution on this margin: 1) their depth in relation to iceberg scouring; 2) the flow of organic-rich bottom waters; and 3) their location at the head of shelf cutting canyons. Icebergs scour to 500 m in this region and the lack of such disturbance is a likely factor allowing the growth of rich benthic ecosystems. In addition, the richest communities are found in the heads of canyons which receive descending plumes of Antarctic Bottom Water formed on the George V shelf, which could entrain abundant food for the benthos. The canyons harbouring rich benthos are also those that cut the shelf break. Such canyons are known sites of high productivity in other areas due to strong current flow and increased mixing with shelf waters, and the abrupt, complex topography.