Origin of high density seabed pockmark fields and their use in inferring bottom currents

Some of the highest density pockmark fields in the world have been observed on the northwest Australian continental shelf (>700/km2) where they occur in muddy, organic-rich sediment around carbonate banks and paleochannels. Here we developed a semi-automated method to map and quantify the form and density of these pockmark fields (~220,000 pockmarks) and characterise their geochemical, sedimentological and biological properties to provide insight into their formative processes. These data indicate that pockmarks formed due to the release of gas derived from the breakdown of near-surface organic material, with gas accumulation aided by the sealing properties of the sediments. Sources of organic matter include adjacent carbonate banks and buried paleochannels. Polychaetes biodiversity appears to be affected negatively by the conditions surrounding dense pockmark fields since higher biodiversity is associated with low density fields. While regional bi-directionality of pockmark scours corresponds to modelled tidal flow, localised scattering around banks suggests turbulence. This multi-scale information therefore suggests that pockmark scours can act as proxy for bottom currents, which could help to inform modelling of benthic biodiversity pattern

Predictive modelling of seabed sediment parameters using multibeam acoustic data: a case study on the Carnarvon Shelf, Western Australia

Seabed sediment textural parameters such as mud, sand and gravel content can be useful surrogates for predicting patterns of benthic biodiversity. Multibeam sonar mapping can provide near-complete spatial coverage of high-resolution bathymetry and backscatter data that are useful in predicting sediment parameters. Multibeam acoustic data collected across a ∼1000 km2 area of the Carnarvon Shelf, Western Australia, were used in a predictive modelling approach to map eight seabed sediment parameters. Four\ud machine learning models were used for the predictive modelling: boosted decision tree, random forest decision tree, support vector machine and generalised regression neural network. The results indicate overall satisfactory statistical performance, especially for %Mud, %Sand, Sorting, Skewness and Mean Grain Size. The study also demonstrates that predictive modelling using the combination of machine learning models has provided the ability to generate prediction uncertainty maps. However, the single models were shown to have overall better prediction performance than the combined models.\ud Another important finding was that choosing an appropriate set of explanatory variables, through a manual feature selection process, was a critical step for optimising\ud model performance. In addition, machine learning models were able to identify important explanatory variables, which are useful in identifying underlying environmental processes and checking predictions against the existing knowledge of the study area.\ud The sediment prediction maps obtained in this study provide reliable coverage of key physical variables that will be incorporated into the analysis of covariance of physical\ud and biological data for this area

Including biogeochemical factors and a temporal component in benthic habitat maps: Influences on infaunal diversity in a temperate embayment

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 sedimentbiogeochemistry-based habitat maps. August and February surveys were undertaken in Jervis Bay, NSW, Australia, to collect samples for physical (% mud, sorting, % carbonate), biogeochemical (chlorophyll a, sulfur, sediment metabolism, bioavailable elements) and infaunal analyses. Boosted decision tree and cokriging models generated spatially continuous data layers. Habitat maps were made from classified layers using geographic information system (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; 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. © 2011 CSIRO