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.

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

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.

Geoscience Australia carried out a marine survey on Carnarvon shelf (WA) in 2008 (SOL4769) to map seabed bathymetry and characterise benthic environments through co-located sampling of surface sediments 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 Australian Institute of Marine Science (AIMS) Research Vessel Solander. Bathymetric mapping, sampling and video transects were completed in three survey areas that extended seaward from Ningaloo Reef to the shelf edge, including: Mandu Creek (80 sq km); Point Cloates (281 sq km), and; Gnaraloo (321 sq km). Additional bathymetric mapping (but no sampling or video) was completed between Mandu creek and Point Cloates, covering 277 sq km and north of Mandu Creek, covering 79 sq km. Two oceanographic moorings were deployed in the Point Cloates survey area. The survey also mapped and sampled an area to the northeast of the Muiron Islands covering 52 sq km. This is a folder of the images derived from benthic samples taken on cruise Sol4769 aboard RV Solander. Subfolders house images of Echinodermata, Mollusca, Polychaete, images taken of fresh material during cruise, and various categories of Crustacea, denoted by a C_ prefix in the folder name. Images of fresh material were made using a Canon EOS 40D camera on a rostrum in the wet lab of the ship. Images of preserved material were made using a Nikon Coolpix camera mounted on a Macroscope in the benthic lab at GA. These images formed the first point of reference in identifying subsequent specimens to save wear and tear on the specimens put aside as reference material.

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.

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.

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.

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 dataset contains species-level identifications of polychaetes collected during survey SOL5117 (R.V. Solander 30 July - 27 August, 2010). Animals were collected from the Joseph Bonaparte Gulf with a Smith McIntyre grab, with a few specimens from a benthic sled. Species-level identifications were undertaken by Chris Glasby and Charlotte Watson at the Museum and Art Gallery of the Northern Territory (MAGNT) and were delivered to Geoscience Australia on the 6 June 2013. See GA Record 2011/08 for further details on survey methods and specimen acquisition. Data is presented here as delivered by the taxonomist, and Geoscience Australia is unable to verify the accuracy of the taxonomic identifications. The data file contains two spreadsheets: - 'species list' includes all polychaete species as identified at the MAGNT, including family, abundance, and comments from the taxonomists. It also contains phyla-level identifications for non-polychaete specimens that were mistakenly sent to the MAGNT with the polychaete samples. CG = Chris Glasby; CW = Charlotte Watson - 'Stations' includes location and depth for each station at which grabs and sleds were deployed.

Conservation planning requires spatial information on biodiversity within a region of interest and its patterns of association with physical environmental features. Such information, however, is often unavailable, and spatial planning is reliant upon proxies based on assumed relationships between species and environmental features have been used as the basis of spatial planning. Here we evaluate the effectiveness of a set of key ecological features (KEF) used in the design of Australia's network of Commonwealth Marine Reserves for representing key marine macrobenthos in a large and biodiverse but data-sparse region in the Oceanic Shoals Commonwealth Marine Reserve (CMR), Timor Sea. Predictive spatial models of the distributions of four key habitat-forming macrobenthic taxa including hard corals, soft corals, gorgonians and sponges, were built using 10 geophysical variables and Boosted Regression Trees. We identified the extent to which KEFs captured the distributions of each taxon, and whether models derived from the western region of the CMR could predict well the distribution of the same taxon in the eastern CMR. All four taxa showed similar habitat preferences, occurring on the tops of raised geomorphic features with hard substrata and while absent from deeper habitats with soft substrata. However, high variability in the biodiversity observed among similar features indicated that factors other than geomorphology alone influence spatial patterns in the distribution of macrobenthos in the region. Overall, models derived from the western region performed reasonably well predicting distribution patterns in eastern region. Transferability of models among sites increased with greater model precision accuracy (higher deviance explained), and all models predicted taxon absence better than presence.