This dataset contains species identifications of micro-benthic worms collected during survey SOL4934 (R.V. Solander, 27 August - 24 September, 2009). Animals were collected from the Joseph Bonaparte Gulf with a Smith-McIntyre grab. Specimens were lodged at Northern Territory Museum on the 1 February 2010. Species-level identifications were undertaken by Chris Glasby at the Northern Territory Museum and were delivered to Geoscience Australia on the 7 March 2011. See GA Record 2010/09 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.

Lord Howe Island in the southwest Pacific Ocean is surrounded by a shallow (20 - 120 m) sub-tropical carbonate shelf 24 km wide and 36 km long. On the mid shelf a relict coral reef (165 km2) extends around the island in water depths of 30 - 40 m. The relict reef comprises sand sheet, macroalgae and hardground habitats. Inshore of the relict reef a sandy basin (mean depth 45 m) has thick sand deposits. Offshore of the relict reef is a relatively flat outer shelf (mean depth 60 m) with bedrock exposures and sandy habitat. Infauna species abundance and richness were similar for sediment samples collected on the outer shelf and relict reef, while samples from the basin had significantly lower infauna abundance and richness. The irregular shelf morphology appears to determine the distribution and character of sandy substrates and local oceanographic conditions, which in turn influence the distribution of different types of infauna communities.

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.

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 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)..

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 benthic biodiversity can be quantified using a range of sampling methods, including benthic sleds or trawls, grabs, and imaging systems, each of which targets a particular community or habitat. Research studies often incorporate only one of these sampling methods in published results, and the generality of marine biodiversity patterns identified among different sampling methods remains unknown. In this study we use three biological collections obtained during a collaborative survey between Geoscience Australian and the Australian Institute of Marine Science to the Van Diemen Rise in northern Australia: 1) Infauna sampled from a Smith-McIntyre grab, 2) Epifauna sampled from a benthic sled, and 3) Biological communities identified from video. For each dataset, we investigated potential patterns of species richness and community structure in relation to depth, geomorphology, and study area, as well as the relationships between datasets. No gear type yielded data that was strongly correlated with depth, but different patterns were evident among gear types based on study area and geomorphology. Comparisons among datasets indicate that species richness from sleds and grabs are more strongly correlated with each other than with richness from video. Further research is planned to incorporate datasets from other regions and habitats in order to provide a general assessment of sampling methods used in the quantification of benthic marine biodiversity in Australasia.

This dataset contains species identifications of molluscs collected during survey SOL4934 (R.V. Solander, 27 August - 24 September, 2009). Animals were collected from the Joseph Bonaparte Gulf with a benthic sled. Specimens were lodged at Northern Territory Museum on the 8 February 2010. Species-level identifications were undertaken by Richard Willan at the Northern Territory Museum and were delivered to Geoscience Australia on the 15 March 2010. See GA Record 2010/09 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.<p><p>This dataset is not to be used for navigational purposes.

This dataset contains species identifications of molluscs collected during survey SOL4934 (R.V. Solander, 27 August - 24 September, 2009). Animals were collected from the Joseph Bonaparte Gulf with a benthic sled. Specimens were lodged at Northern Territory Museum on the 3 May 2010. Species-level identifications were undertaken by Richard Willan at the Northern Territory Museum and were delivered to Geoscience Australia on the 5 May 2010 (leg 1 only). See GA Record 2010/09 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.