Mapping of benthic habitats seldom considers biogeochemical variables or changes across time. We aimed to: (i) develop winter and summer benthic habitat maps for a sandy embayment; and (ii) compare the effectiveness of various maps for differentiating infauna. Patch-types (internally homogeneous areas of seafloor) were constructed using combinations of abiotic parameters, and are presented in sediment-based, biogeochemistry-based and combined sediment/biogeochemistry-based habitat maps. August and February surveys were undertaken in Jervis Bay, Australia, to collect samples for physical (%mud, sorting, %carbonate), biogeochemical (chlorophyll a, sulfur, sediment metabolism, bio-available elements) and infaunal analyses. Boosted Decision Tree and cokriging models generated spatially continuous data-layers. Habitat maps were made from classified layers using GIS overlays, and were interpreted from a biophysical-process perspective. Biogeochemistry and %mud varied spatially and temporally, even in visually homogeneous sediments. Species turnover across patch-types was important for diversity, and the utility of habitat maps for differentiating biological communities varied across months. Diversity patterns were broadly related to reactive carbon and redox which varied temporally. Inclusion of biogeochemical factors and time in habitat maps provides a better framework for differentiating species and interpreting biodiversity patterns than once-off studies based solely on sedimentology or video-analysis.

This study investigated bio-environment relationships in Jervis Bay, a sandy partially enclosed embayment in NSW. Three decision tree models and a robust model selection process were applied to a wide-range of physical data (multibeam bathymetry and backscatter grids and derivatives, parameters that describe seabed sediment and water column physical/geochemical characteristics, seabed exposure) and co-located biological data. The models for selected infaunal species and three diversity indices explained 32-79% of data variance. Patterns of abundance and diversity were statistically related to a wide range of environmental variables, including sediment physical (e.g. mud, CaCO3, gravel) and geochemical properties (e.g. chlorophyll a, total sediment metabolism, total sulphur), seabed morphometric characteristics (e.g. local Moran's I of bathymetry, rugosity), seabed exposure regime and water column light attenuation. The modelled response curves together with results from an earlier habitat mapping study informed the development of a conceptual model that provides a process-based framework for the interpretation of biodiversity patterns in the southern part of the Bay. The conceptual model had three zones which were noted for: (i) fine-sediment resuspension and macroalgae accumulation (leading to anoxia; extreme); (ii) bioturbation (in-between); and (iii) exposure of the seabed to waves (extreme in places). Most bio-environment relationships pointed to complex relationships between multiple biological and physical factors occurring in the different process domains/zones. The combined use of co-located samples and bio-environment and conceptual models enabled a mechanistic understanding of benthic biodiversity patterns in Jervis Bay.

This dataset contains the sea surface temperature data derived from the MODIS Terra sensor, the chlorophyll data derived from the SeaWIFS satellite, and the K490 data derived from the SeaWIFS satellite. Ocean temperature is a useful indicator of the type of marine life that could be found at a particular location. Many marine plants and organisms have a relatively narrow range of tolerance for temperature, and will either perish or be out-competed where temperatures are outside their comfort zone. Chlorophyll a is a plant pigment which provides a measurement of the biomass (or quantity) of plants. In the water column, it is a measure of the suspended (or planktonic) biomass of single-celled microscopic plants. Chlorophyll is a commonly used measure of water quality. K490 indicates the turbidity of the water column; the depth to which the visible light in the blue-green region of the spectrum penetrates the water column. It is directly related to the presence of particles in the water column. Turbidity has consequences for benthic marine life, ranging from the availability of light to the quantity of nutrients in the water column. The datasets contain 6 grids. Two for each variable: mean and standard deviation. Please see the metadata for detailed information.

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 50 sample data set comprises %TOC, %TN, TOC/TN ratios, and carbon and nitrogen isotopic ratios for surface (0.0 to 2.0 cm) sediments from Jervis Bay.

This dataset was created in ArcGIS and provides the direct distance from all grid cell points of the Australian Exclusive Economic Zone (adjacent to the mainland) to the Australian coastline. The distance measured is in decimal degrees and meters. The distance to the coastline is likely to reflect some degree of exposure to a wave/current regime. The proximity to land is also likely to reflect the potential influence of river discharge (sediment and fresh water), wind blown dust, and anthropogenic pollutants.

This chapter presents a broad synthesis and overview based on the 57 case studies included in Part 2 of this book, and on questionnaires completed by the authors. The case studies covered areas of seafloor ranging from 0.15 to over 1,000,000 km2 (average of 26,600 km2) and a broad range of geomorphic feature types. The mean depths of the study areas ranged from 8 to 2,375 m, with about half of the studies on the shelf (depth <120 m) and half on the slope and at greater depths. Mapping resolution ranged from 0.1 to 170 m (mean of 13 m). There is a relatively equal distribution of studies among the four naturalness categories: near-pristine (n=17), largely unmodified (n = 16), modified (n=13) and extensively modified (n=10). In terms of threats to habitats, most authors identified fishing (n=46) as the most significant threat, followed by pollution (n=12), oil and gas development (n=7) and aggregate mining (n=7). Anthropogenic climate change was viewed as an immediate threat to benthic habitats by only three authors (n=3). Water depth was found to be the most useful surrogate for benthic communities in the most studies (n=17), followed by substrate/sediment type (n=14), acoustic backscatter (n=12), wave-current exposure (n=10), grain size (n=10), seabed rugosity (n=9) and BPI/TPI (n=8). Water properties (temperature, salinity) and seabed slope are less useful surrogates. A range of analytical methods were used to identify surrogates, with ARC GIS being by far the most popular method (23 out of 44 studies that specified a methodology).

In ecology, a common form of statistical analysis relates a biological variable to variables that delineate the physical environment, typically by fitting a regression model or one of its extensions. Unfortunately, the biological data and the physical data are frequently obtained from eparate sources of data. In such cases there is no guarantee that the biological and physical data are co-located and the regression model cannot be used. A common and pragmatic solution is to predict the physical variables at the locations of the biological variables and then to use the predictions as if they were observations.We show that this procedure can cause potentially misleading inferences and we use generalized linear models as an example. We propose a Berkson error model which overcomes the limitations. The differences between using predicted covariates and the Berkson error model are illustrated by using data from the marine environment, and a simulation study based on these data.

Publicly available bathymetry and geophysical data can be used to map geomorphic features of the Antarctic continental margin and adjoining ocean basins at scales of 1:1-5 million. These data can also be used to map likely locations for some Vulnerable Marine Ecosystems. Seamounts over a certain size are readily identified and submarine canyons and mid ocean ridge central valleys which harbour hydrothermal vents can be located. Geomorphic features and their properties can be related to major habitat characteristics such as sea floor type (hard versus soft), ice keel scouring, sediment deposition or erosion and current regimes. Where more detailed data are available, shelf geomorphology can be shown to provide a guide to the distribution in the area of the shelf benthic communities recognised by Gutt (2007). The geomorphic mapping method presented here provides a layer to add to benthic bioregionalistion using readily available data.

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.

Deep sea environments occupy much of the sea floor, yet little is known about diversity patterns of biological assemblages from these environments. Physical mapping technologies and their availability are increasing rapidly. Sampling deep-sea biota over vast areas of the deep sea, however, is time consuming, difficult, and costly. Consequently, the growing need to manage and conserve marine resources, particularly deep sea areas that are sensitive to anthropogenic disturbance and change, is leading the promotion of physical data as surrogates to predict biological assemblages. However, few studies have directly examined the predictive ability of these surrogates. The physical environment and biological assemblages were surveyed for two adjacent areas - the western flank of Lord Howe Rise (LHR) and the Gifford Guyot - spanning combined water depths of 250 to 2,200 m depth on the northern part of the LHR, in the southern Pacific Ocean. Multibeam acoustic surveys were used to generate large-scale geomorphic classification maps that were superimposed over the study area. Forty two towed-video stations were deployed across the area capturing 32 hours of seabed video, 6,229 still photographs, that generated 3,413 seabed characterisations of physical and biological variables. In addition, sediment and biological samples were collected from 36 stations across the area. The northern Lord Howe Rise was characterised by diverse but sparsely distributed faunas for both the vast soft-sediment environments as well as the discrete rock outcrops. Substratum type and depth were the main variables correlated with benthic assemblage composition. Soft-sediments were characterised by low to moderate levels of bioturbation, while rocky outcrops supported diverse but sparse assemblages of suspension feeding invertebrates, such as cold water corals and sponges which in turn supported epifauna, dominated by ophiuroids and crinoids. While deep environments of the LHR flank .