Crinoids, and especially comatulids as Anthometra adriani, are well represented among the macrofauna from the continental shelf offshore from Terre Adélie and George V Land, East Antarctica. These animals are suspension feeders that depend on the local current regime to feed. Nearly 500 specimens from this species were sampled during the Collaborative East Antarctic Marine Census (CEAMARC) expedition onboard the RV Aurora Australis (December 2007 to January 2008), from 46 of the 87 stations over a 400 km² area. Abiotic environmental factors (such as depth, temperature, salinity, oxygen) were measured at each site. The ecological niche of Anthometra adriani was described using Ecological Niche Factor Analysis (ENFA) and Mahalanobis Distances Factor Analysis (MADIFA). An Environmental Suitability Map (ESM) was developed for this species on the CEAMARC study area. The results show that A. adriani seems to prefer relatively cold and well-oxygenated waters in moderately deep areas. The ESM shows four optimal regions for this species: the eastern side of the George V Basin, the western part of the Mertz Bank, the southern side of the Adélie Bank, and the coastal area between the Astrolabe and Mertz Glaciers.

The CARS2006 database is derived from all available historical subsurface ocean property measurements (Ridgway et al, 2002). The measurements have been collected primarily using research vessel instrument profiles and autonomous profiling buoys. The observations have been collected over approximately 50 years and have been used to provide an estimate at every depth and every location in the world's oceans for each day of the year, but not for any individual year. CARS2006 spans the southern 2/3 of the world's oceans, from 70o S to 26o N, except in the Atlantic where is reaches only to10o N. The six water properties mapped in are temperature (deg C), salinity (PSU), oxygen (ml/litre), nitrate (micromole/litre), silicate (micromole/litre), phosphate (micromole/litre). It comprises historic mean fields and average seasonal cycles, derived from all available historical subsurface ocean property measurements (primarily research vessel instrument casts and autonomous profiling buoys). There are 12 grids in the dataset. Two for each of the six water properties: mean and standard deviation. Please see the metadata for more detailed information.

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.

Geoscience Australia's GEOMACS model was utilised to produce hindcast hourly time series of bed shear stress on the Australian continental shelf on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The effective depth range of the model output is approximately 20 - 150 m (see 'Data Quality Attribute Accuracy' below). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and density-driven circulation. The stability of the seabed sediment surface, which is controlled by seabed shear stress, is likely to influence benthic community structure and species diversity. There are 8 grids in the dataset: geomacs_excee, geomacs_gmean, geomacs_qua25, geomacs_qua50, geomacs_qua75, geomacs_range, geomacs_ratio, and geomacs_tmean. Please see the metadata for further information.

Geoscience Australia carried out a marine survey on Lord Howe Island shelf (NSW) in 2008 (SS062008) to map seabed bathymetry and characterise benthic environments through colocated sampling of surface sediments and infauna, rock coring, observation of benthic habitats using underwater towed video, and measurement of ocean tides and wavegenerated currents. Subbottom profile data was also collected to map sediment thickness and shelf stratigraphy. Data and samples were acquired using the National Facility Research Vessel Southern Surveyor. Bathymetric data from this survey was merged with other preexisting bathymetric data (including LADS) to generate a grid covering 1034 sq km. As part of a separate Geoscience Australia survey in 2007 (TAN0713), an oceanographic mooring was deployed on the northern edge of Lord Howe Island shelf. The mooring was recovered during the 2008 survey following a 6 month deployment. lh_back_8m is a backscatter grid of the Lord Howe survey area produced from the processed EM300 backscatter data of the survey area using the CMST-GA MB Process.

The term 'surrogacy' is used in habitat mapping with reference to the biophysical variables that can be mapped with a quantifiable correspondence to the occurrence of benthic species and communities. Surrogacy research can be defined as an empirical method of determining which easily measured characteristics best describe the species assemblage in a particular space and at a particular time. These characteristics act as predictors (with some known probability and uncertainty) for the occurrence of species assemblages in unexplored areas. Abiotic variables are, in general, more easily and less expensively obtained than biological observations, which is a key driver for surrogacy research. However, the suite of abiotic factors that exert control over the occurrence of species (its niche) is also a scientifically interesting aspect of ecology that provides important insights into a species evolution and biogeography. This chapter provides a review of surrogates used by case study authors and of the methods used to quantify relationships between variables.

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.

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 dataset comprises chlorophyll a and pheophytin a measurements on surface seabed sediments (~0 to 2 cm) from Jervis Bay.

Dense coral-sponge communities on the upper continental slope at 570 - 950 m off George V Land have been identified as a Vulnerable Marine Ecosystem in the Antarctic. The challenge is now to understand their likely distribution. Based on results from the Collaborative East Antarctic Marine Census survey of 2007/2008, we propose some hypotheses to explain their distribution. Icebergs scour to 500 m in this region and the lack of such disturbance is probably a factor allowing growth of rich benthic ecosystems. In addition, the richest communities are found in the heads of canyons. Two possible oceanographic mechanisms may link abundant filter feeder communities and canyon heads. The canyons in which they occur receive descending plumes of Antarctic Bottom Water formed on the George V shelf and these water masses could entrain abundant food for the benthos. Another possibility is that the canyons harbouring rich benthos are those that cut the shelf break. Such canyons are known sites of high productivity in other areas because of a number of oceanographic factors, including strong current flow and increased mixing with shelf waters, and the abrupt, complex topography. These hypotheses provide a framework for the identification of areas where there is a higher likelihood of encountering these Vulnerable Marine Ecosystems.

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).