Geoscience Australia's GEOMACS model was utilised to produce hindcast hourly time series of continental shelf (~20 - 300 m depth) bed shear stress (unit of measure: Pascal, Pa) on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and densitydriven circulation. Included in the parameters that will be calculated to represent the magnitude of the bulk of the data are the quartiles of the distribution; Q25, Q50 and Q75 (i.e. the values for which 25, 50 and 75 percent of the observations fall below). The interquartile range, , of the GEOMACS output takes the observations from between Q25 and Q75 to provide an accurate representation of the spread of observations. The interquartile range was shown to provide a more robust representation of the observations than the standard deviation, which produced highly skewed observations (Hughes & Harris 2008).

The accuracy of spatially continuous environmental data, usually generated from point samples using spatial prediction methods (SPMs), is crucial for evidence-informed environmental management and conservation. Improving the accuracy by identifying the most accurate methods is essential, but also challenging since the accuracy is often data specific and affected by multiple factors. Because of the high predictive accuracy of machine learning methods, especially random forest (RF), they were introduced into spatial statistics by combining them with existing SPMs, which resulted in new hybrid methods with improved accuracy. This development opened an alternative source of methods for spatial prediction. In this study, we introduced these hybrid methods, along with the modelling procedure adopted to develop the final predictive models. These methods were compared with the commonly used SPMs in R using cross-validation techniques based on both marine and terrestrial environmental data. We also addressed the following questions: 1) whether they are data-specific for marine environmental data, 2) whether input predictors affect their performance, and 3) whether they are equally applicable to terrestrial environmental data? This study provides suggestions and guidelines for the application of these hybrid methods to spatial predictive modelling not only in environmental sciences, but also in other relevant disciplines.

Geoscience Australia's GEOMACS model was utilised to produce hindcast hourly time series of continental shelf (~20 - 300 m depth) bed shear stress (unit of measure: Pascal, Pa) on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and densitydriven circulation. Included in the parameters that were calculated to represent the magnitude of the bulk of the data are the quartiles of the distribution; Q25, Q50 and Q75 (i.e. the values for which 25, 50 and 75 percent of the observations fall below). Q75, or the 0.75 Quartile of the Geomacs output, represents the values for which 75% of the observations fall below (Hughes & Harris 2008).

Geoscience Australia's GEOMACS model was utilised to produce hindcast hourly time series of continental shelf (~20 - 300 m depth) bed shear stress (unit of measure: Pascal, Pa) on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and densitydriven circulation. Included in the parameters that represent the magnitude of the bulk of the data are the quartiles of the distribution; Q25, Q50 and Q75 (i.e. the values for which 25, 50 and 75 percent of the observations fall below). Q50, or the 0.50 Quartile of the Geomacs output, represents the values for which 50% of the observations fall below (Hughes & Harris 2008).

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. jb_s3 is an ArcINFO grid of southern part of Jervis Bay survey area (south3 is part of Darling RD grid) produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software

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. jb_s1 is an ArcINFO grid of southern part of Jervis Bay survey area (south1 is part of Darling RD grid) produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software

Geoscience Australia's GEOMACS model was utilised to produce hindcast hourly time series of continental shelf (~20 - 300 m depth) bed shear stress (unit of measure: Pascal, Pa) on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and densitydriven circulation. Included in the parameters that represent the magnitude of the bulk of the data are the quartiles of the distribution; Q25, Q50 and Q75 (i.e. the values for which 25, 50 and 75 percent of the observations fall below). Q25, or the 0.25 Quartile of the Geomacs output, represents the values for which 25% of the observations fall below (Hughes & Harris 2008).

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. jb_s2 is an ArcINFO grid of southern part of Jervis Bay survey area (south2 is part of Darling RD grid) produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software

Geoscience Australia's GEOMACS model was utilised to produce hindcast hourly time series of continental shelf (~20 - 300 m depth) bed shear stress (unit of measure: Pascal, Pa) on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and densitydriven circulation. Ecological data collected from Torres Strait suggests that bed shear stresses exceeding 0.4 Pa are important in determining the species present (Long, Bode, & Pitcher 1997). Although this data may not be representative of other regions or benthic communities, it has been utilised to calculate two parameters for determining the relationship between shear bed stress and the benthic community. One of the parameters, which is denoted by , and is calculated using; represents the stress in excess of 0.4 Pa integrated over time as a proportion of the total stress integrated over time, and is intended to represent the proportion of the total integrated stress that has some control on the benthic community (Hughes & Harris 2008).

Baseline information on biodiversity and habitats is required to manage Australia's northern tropical marine estate. This study aims to develop an improved understanding of seafloor environments of the Timor Sea. Clustering methods were applied to a large dataset comprising physical and geochemical variables which describe organic matter (OM) reactivity/quantity/source and geochemical processes. Infauna data were used to assess different groupings. Clusters based on physical/geochemical data discriminated infauna better than geomorphic features. Major variations amongst clusters included grainsize and a cross-shelf transition in from authigenic-Mn /As enrichments (inner shelf) to authigenic-P enrichment (outer shelf). Groups comprising raised features had the highest reactive OM concentrations (e.g. low chlorin indices and C:N-ratios, and high k) and benthic algal '13C signatures. Surface area normalised OM concentrations higher than continental shelf norms were observed in association with: (i) low -15N, inferring Trichodesmium input; and (ii) pockmarks, which impart bottom-up controls on seabed chemistry and cause inconsistencies between bulk and pigment OM pools. Low Shannon-Wiener diversity occurred in association with low redox and porewater pH and evidence for high energy. Highest beta-diversity was observed at euphotic depths. Geochemical data and clustering methods used here provide insight into ecosystem processes influencing biodiversity patterns in the region.