Understanding marine biodiversity has received much attention from an ecological and conservation management perspectives. The Australian Government's Department of the Environment, Water, Heritage and the Arts has initiated the Commonwealth Environment Research Facilities (CERF) initiative to enhance the understanding of Australia's natural environment for policy making. One part of the CERF initiative through the marine biodiversity hub was to predict biodivesity from expansive physical variables. This talk presents some of the work arising from this area.

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.

Geoscience Australia carried out a marine survey on Lord Howe Island shelf (NSW) in 2008 (SS06_2008) 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 wave generated 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. The "2461_ss062008" folder contains raw multibeam backscatter data of the Lord Howe Rise. The raw multibeam backscatter data were collected along survey lines using SIMRAD EM300 from aboard RV Southern Surveyor

This introductory chapter provides an overview of the book's contents and definitions of key concepts including benthic habitat, potential habitat and seafloor geomorphology. The chapter concludes with a summary of commonly used habitat mapping technologies. Benthic (seafloor) habitats are physically distinct areas of seabed that are associated with particular species, communities or assemblages that consistently occur together. Benthic habitat maps are spatial representations of physically distinct areas of seabed that are associated with particular groups of plants and animals. Habitat maps can illustrate the nature, distribution and extent of distinct physical environments present and importantly they can predict the distribution of the associated species and communities.

This study tested the performance of 16 species models in predicting the distribution of sponges on the Australian continental shelf using a common set of environmental variables. The models included traditional regression and more recently developed machine learning models. The results demonstrate that the spatial distributions of sponge as a species group can be successfully predicted. A new method of deriving pseudo-absence data (weighted pseudo-absence) was compared with random pseudo-absence data - the new data were able to improve modelling performance for all the models both in terms of statistics (~10%) and in the predicted spatial distributions. Overall, machine learning models achieved the best prediction performance. The direct variable of bottom water temperature and the resource variables that describe bottom water nutrient status were found to be useful surrogates for sponge distribution at the broad regional scale. This study demonstrates that predictive modelling techniques can enhance our understanding of processes that influence spatial patterns of benthic marine biodiversity. Ecological Informatics

Marine physical and geochemical data can be valuable in predicting the potential distributions and assemblages of marine species, acting as surrogate measures of biodiversity. The results of surrogacy analysis can also be useful for identifying ecological processes that link physical environmental attributes to the distribution of seabed biota. This paper reports the results of a surrogacy study in Jervis Bay, a shallow-water, sandy marine embayment in south-eastern Australia. A wide range of high-resolution co-located physical and biological data were employed, including multibeam bathymetry and backscatter data and their derivatives, parameters that describe seabed sediment and water column physical characteristics, seabed exposure, and infauna species. The study applied three decision tree models and a robust model selection process. The results show that the model performance for three diversity indices and seven out of eight infauna species range from acceptable to good. Important surrogates for infauna diversity and species distributions within the mapped area are broad-scale habitat type, seabed exposure, sediment nutrient status, and seabed rugosity and heterogeneity. The results demonstrate that abiotic environmental parameters of a sandy embayment can be used to effectively predict infauna species distributions and biodiversity patterns. International Journal of Geographical Information Science

This study investigated the surrogacy relationships between marine physical variables and the distribution of marine infauna species and measures of benthic biodiversity across the continental shelf offshore from Ningaloo Reef, Western Australia. The three study areas are located at Mandu Creek, Point Cloates and Gnaraloo covering a combined area of 1038 km2. The physical variables include morphometric variables derived from multibeam bathymetry data, texture measures derived from acoustic backscatter data, sediment variables from 265 samples, seabed exposure estimates and geomorphic feature types. Together, these data were used to model total abundance and species richness, and 10 individual infauna species using a Random Forest Decision Tree. The key findings are: - Generally, the surrogacy relationships are stronger at Gnaraloo than at Mandu and Point Cloates. This is likely due to the fact that Gnaraloo is dominated by soft sediment and Point Cloates and Mandu have larger areas of hard substrates which preclude infauna. - At Gnaraloo, the most important physical surrogates were the sediment variables. - At Point Cloates, the most important physical surrogates were the bathymetry-derived parameters including seabed heterogeneity, morphological position, and slope. - At Mandu, the most important physical surrogates were the mixture of the bathymetry- derived parameters including morphological position and geomorphic features, and the sediment variables including gravel content, and backscatter derived texture measures. - Seabed exposure was not a useful physical surrogate for the infauna distribution in this study. The likely reasons are not clear, but could be a function of the grid resolution (150 m) of the hydrodynamic model used to generate the exposure variable relative to infaunal patterns; or that the infauna species are protected by the sediment from seabed disturbance.

The Marine Biodiversity Hub was funded by the Australian Government Commonwealth Environmental Research Facilities (CERF) between 2007 and 2010. The Hub was developed to improve the scientific knowledge available to support marine bioregional planning and addressed two fundamental questions: 1. How can we predict the distribution of marine biodiversity; and 2. How can we use this improved capability to conserve and manage biodiversity in a multiple-use environment? This talk focuses on the Surrogates Program, one of four research programs in the Hub. The Surrogates Program addressed the above questions by testing and developing physical variables as surrogates of marine biodiversity, with a focus on seabed environments. In the program, we employed a range of marine survey technologies to collect high-quality and co-located benthic physical and biological data at four selected areas in temperate and tropical waters. We also developed advanced spatial and statistical approaches to test the degree of covariance between the physical and biological data, identify ecological processes, and generate prediction maps. During a number of field campaigns, we deployed a range of instruments to collect data including multibeam sonar, sediment grabs, benthic sleds, towed-video/still images and Autonomous Underwater Vehicles. GIS, machine-learning models and the SWAN hydrodynamic model were used to derive and predict a large number of physical variables as potential surrogates. The effectiveness of the surrogacy approaches were examined using multivariate analyses and spatial modelling techniques. In general, we found that using physical surrogates to predict marine biodiversity is a cost-effective approach. The new knowledge of surrogates and seabed ecological processes directly supports the management of the Australian marine estate. Other major outputs of the Surrogates Program include: - Thirty-seven new and updated national-scale marine physical environmental datasets; - High resolution bathymetry of targeted areas, covering almost 2000 km2, plus 171 km of underwater video transects, 402 sediment grab samples and 232 epifauna samples; - New seabed exposure and fetch models/datasets; and - Peer-reviewed reports and papers in scientific journals. The success of the Marine Biodiversity Hub has enabled the Hub to be refunded for a further four years through the new National Environmental Research Program. In this, Geoscience Australia (GA) is collaborating with the University of Tasmania, CSIRO Marine & Atmospheric Research, Australian Institute of Marine Science, Museum of Victoria, University of Western Australia and Charles Darwin University; GA is also leading Theme 3 Project 1 which focuses on identifying the functions and processes of shelf and canyon ecosystems. The project is expected to further advance marine biodiversity research in Australia by investigating the role of large-scale physical features on the shelf in influencing patterns of marine biodiversity.

Field and supplementary environmental data for the Marine Biodiversity Hub Description: The directory contains the following datasets. 1. Multibeam acoustic data (both backscatter and bathymetry) for three field areas: Jervis Bay, Carnarvon Shelf, and Southern Tasmanian Shelf. 2. Marine environmental data at the Australian continental scale. 3. Side scan data for three regions: Fitzroy, Jervis Bay and Keppel Bay. 4. CARS and Ocean Color datasets obtained from CSIRO. 5. AUV data for the Tasmanian survey (October 2008). These datasets were collected from various field surveys and project partners for the research of Marine Biodiversity Hub. Please contact the CERF project team for further information.

The identification of suitable abiotic surrogates for biological diversity requires the collection of both physical and biological data. However, logistical constraints often preclude experimental designs that incorporate spatial and temporal replication. Given the quite limited resources normally available for surveys, the investigation of appropriate surrogates involves a trade-off between overall spatial coverage and replication. We have completed a survey in Jervis Bay in which environmental and infaunal data were collected contemporaneously in order to be combined with similar data from a previous winter survey (survey number GA309) to investigate variation across seasons. Because there will be a certain error in sampling at the exact location as the previous survey, the survey design also required that replicate samples be taken at a set number of stations in order to investigate fine-scale variability (at the scale of metres). We used grabs to collect paired geochemical and biological samples from thirty-two stations in a defined grid near Darling Rd; at eight of these stations we deployed three pairs of grabs to investigate fine-scale variability. Due to good weather and extra ship time available, we also deployed a CTD to investigate vertical temperature and salinity profiles at each station in the Darling Rd grid, as well as at stations throughout the entire bay. Samples are expected to be processed and analysed by late 2009, but preliminary results indicate that most physical variables and infaunal assemblages varied between seasons. In addition, variation among infaunal assemblages seems greater among stations (hundreds of meters) than within replicates at stations (meters).