Antarctic Landfast sea ice (fast ice) is important climatologically, biologically and for logistics for short time-scale anomalies. Until recently, there hasn’t been an accurate, high-resolution fast ice extent dataset which can support an analysis on drivers of fast ice and most studies only investigate fast ice on limited regions of Antarctica in a limited time scale. There is a need to extend the spatial and temporal studying coverage to provide detailed information on the Antarctic coast over a longer period. This is the first detailed analysis to identify and quantify correlation between the environmental anomaly and fast ice anomaly mainly in the east Antarctic coast. By examining regional/local fast ice extent in in east Antarctic coast in the context of the broader and/or remote-teleconnected atmospheric circulation/properties using spatial correlation techniques, a strong correlation between NINO3 region and Lützow-Holm Bay fast ice and similar and significant correlation of regional scale factors from Lützow-Holm Bay to Mawson Coast mainly are found. The results of this thesis suggest that the pack ice, atmospheric factors and oceanic factors are important for interpreting fast ice anomalies. To identify and quantify correlation between the pack ice, temperature at 2m, wind at 10m, snow fall anomaly, sea surface temperature anomaly, ocean heat content anomaly and fast ice anomaly, backward multiple linear regression is conducted to demonstrate some predictive fast ice driver information by quantifying the correlation between different drivers and fast ice anomaly. The multiple linear regression also suggests that oceanic influences including pack ice are generally more important than atmospheric influences. Future experiments could be conducted to interpret fast ice anomalies in the context of the ocean mainly.

Voyage IN2019_V04 contributed an additional 29,000 kms2 of seafloor survey data to the Coral Sea knowledge base. From this new bathymetric data individual seamounts have been extracted and have been classified to the Geoscience Australia Geomorphology Classification Scheme. This dataset contains two layers representing the classification layers- 1) Surface (Plain, Slope, Escarpment) and 2) fine scale Geomorphology of the seamount for the Calder Seamount. Ongoing research with this survey data will provide new insights into the detailed geomorphic shape and spatial relationships between adjacent seabed features. This information will be released in future publications to show the potential of how the scale of such seafloor data can be used for predictive habitat modelling when analysed with the biological data overlays.

Voyage IN2019_V04 contributed an additional 29,000 kms2 of seafloor survey data to the Coral Sea knowledge base. From this new bathymetric data individual seamounts have been extracted and have been classified to the Geoscience Australia Geomorphology Classification Scheme. This dataset contains two layers representing the classification layers- 1) Surface (Plain, Slope, Escarpment) and 2) fine scale Geomorphology of the seamount for the Mellish Seamount. Ongoing research with this survey data will provide new insights into the detailed geomorphic shape and spatial relationships between adjacent seabed features. This information will be released in future publications to show the potential of how the scale of such seafloor data can be used for predictive habitat modelling when analysed with the biological data overlays.

Voyage IN2019_V04 contributed an additional 29,000 kms2 of seafloor survey data to the Coral Sea knowledge base. From this new bathymetric data individual seamounts have been extracted and have been classified to the Geoscience Australia Geomorphology Classification Scheme. This dataset contains two layers representing the classification layers- 1) Surface (Plain, Slope, Escarpment) and 2) fine scale Geomorphology of the seamount for the Lexington Seamount. Ongoing research with this survey data will provide new insights into the detailed geomorphic shape and spatial relationships between adjacent seabed features. This information will be released in future publications to show the potential of how the scale of such seafloor data can be used for predictive habitat modelling when analysed with the biological data overlays.

This project will determine the downburst thunderstorms over Tasmania from the Severe Storms Archive, investigate the atmospheric condition during those downburst thunderstorms and determine the probability of meteorological conditions suitable for downburst thunderstorms over Tasmania during 1990-2019. This project will use the recently completed Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) dataset, which offers more than 100 atmospheric model variables at higher resolution in space and time than existing global reanalyses (Jakob et al. 2017). The hourly temporal resolution, 70 levels vertical resolution and 1.5 km horizontal resolution, which has been developed specifically for Tasmania and other three regions, makes it particularly powerful in comparison to larger scale reanalyses for analysis of short-term phenomena like thunderstorms and their environments in Tasmania.

Voyage IN2019_V04 contributed an additional 29,000 kms2 of seafloor survey data to the Coral Sea knowledge base. From this new bathymetric data individual seamounts have been extracted and have been classified to the Geoscience Australia Geomorphology Classification Scheme. This dataset contains two layers representing the classification layers- 1) Surface (Plain, Slope, Escarpment) and 2) fine scale Geomorphology of the seamount for the Fregetta Seamount. Ongoing research with this survey data will provide new insights into the detailed geomorphic shape and spatial relationships between adjacent seabed features. This information will be released in future publications to show the potential of how the scale of such seafloor data can be used for predictive habitat modelling when analysed with the biological data overlays.

Baited remote underwater stereo-video (stereo-BRUV) were used to sample the fish assemblages of the Hunter Marine Park and adjacent Port Stephens - Great Lakes Marine Park at equivalent depths. Stereo-BRUV were deployed in both autumn and spring from spring 2016 to autumn 2018 and at depth of 32-105m. The videos were analyse to all fish species to the lowest taxonmic level and estimate relative abundance (MaxN) and lengths of all species. This information was used to form a baseline of the benthic fish assemblages of the Hunter Marine Park. This study was done as part of the NESP Marine Biodiversity Hub D3 project (https://www.nespmarine.edu.au/project/project-d3-implementing-monitoring-amps-and-status-marine-biodiversity-assets-continental).