During the ADBEX III voyage, many samples were taken of the sea ice and snow. These samples were analysed to determine water density, with the results recorded in a physical note book that is archived at the Australian Antarctic Division. Logbook(s): - Glaciology ADBEX III Water Density Results - Glaciology ADBEX III Oxygen Isotope Sample Record

A geomorphology map of the Australasian seafloor was created as a Geographic Information System layer for the study described in Torres, Leigh G., et al. "From exploitation to conservation: habitat models using whaling data predict distribution patterns and threat exposure of an endangered whale." Diversity and Distributions 19.9 (2013): 1138-1152. The geomorphology map was generated using parameters derived from the General Bathymetric Chart of the World (GEBCO 2008, http://www.gebco.net/), with 30 arc-second grid resolution. Geomorphology features were delineated manually with a consistent spatial resolution. Each feature was assigned a primary attribute of depth zone and a secondary attribute of morphological feature. The following feature classes are defined: shelf, slope, rise, plain, valley, trench, trough, basin, hills(s), mountains(s), ridges(s), plateau, seamount. Further information (methods, definitions and an illustration of the geomorphology map) is provided in Appendix S2 of the paper which is available for download (see related URLs).

Metadata record for data from ASAC Project 1119 See the link below for public details on this project. A marked bend in the Hawaiian-Emperor seamount chain supposedly resulted from a recent major reorganization of the plate-mantle system there 50 million years ago. Although alternative mantle-driven and plate-shifting hypotheses have been proposed, no contemporaneous circum-Pacific plate events have been identified. We report reconstructions for Australia and Antarctica that reveal a major plate reorganization between 50 and 53 million years ago. Revised Pacific Ocean sea-floor reconstructions suggest that subduction of the Pacific-Izanagi spreading ridge and subsequent Marianas/Tonga-Kermadec subduction initiation may have been the ultimate causes of these events. Thus, these plate reconstructions solve long-standing continental fit problems and improve constraints on the motion between East and West Antarctica and global plate circuit closure.

Metadata record for data from ASAC Project 545 See the link below for public details on this project. From the abstract of the referenced paper: Blood was collected for haematological, red cell enzyme and red cell metabolic intermediate studies from 20 Southern elephant seals Mirounga leonina. Mean haematological values were: haemoglobin (Hb) 22.4 plus or minus 1.4 g/dl, packed cell volume (PCV) 54.2 plus or minus 3.8%, mean cell volume (MCV) 213 plus or minus 5 fl and red cell count (RCC) 2.5 x 10 to power 12 / l. Red cell morphology was unremarkable. Most of the red cell enzymes showed low activity in comparison with human red cells. Haemoglobin electrophoresis showed a typical pinniped pattern, ie two major components. Total leucocyte counts, platelet counts, and coagulation studies were within expected mammalian limits. Eosinophil counts varied from 0.5 x 10 to power 9 / l (5%-49%), and there was a very wide variation in erythrocyte sedimentation rates, from 3 to 60mm/h.

This line shapefile represents the following features of the Antarctic Circumpolar Current: Subtropical Front (STF); Subantarctic Front (SAF); Southern Antarctic Circumpolar Current Front (sACCf); Polar Front (PF); Southern Boundary of the Antarctic Circumpolar Current as described in Alejandro H. Orsi, Thomas Whitworth III, and Worth D. Nowlin Jr (1995) On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep-Sea Research 42 (5), 641-673. The shapefile was created from data provided by lead author Alejandro Orsi to the Australian Antarctic Data Centre in August 2001. The data in the files from Alejandro Orsi was also combined in a csv file. The data available for download includes the original data, the shapefile and the csv file.

Overview The aim of this project was to investigate the genetic connectivity and diversity of Antarctic benthic amphipods over fine (100's of m's), intermediate (10's of km's) and large (1000's of km's) scales, using highly variable molecular markers. To achieve this, we developed seven microsatellite markers specific to the common Antarctic amphipod species Orchomenella franklini. A total of 718 specimens of O. franklini were collected from East Antarctica. Approximately 30 specimens were collected from each site, and sites were spatially hierarchically nested - i.e. sites (separated by 100m) were nested within locations (separated by 1-30km), which were nested within 2 broad regions (separated by approx. 1400km). Each amphipod sample was genotyped for all seven microsatellite loci (although occasionally a locus would not amplify in a given sample). This dataset provides all the resultant genetic data - that is, the size of the two alleles that were amplified for each microsatellite locus, in each of 718 amphipod specimens. Data collection and analysis Please refer to the associated publication (see below) for all relevant methodology. Explanation of worksheet Sample ID- a unique code given to identify each amphipod sample (the code itself has no actual meaning). Region- the broad region of the Antarctic coast from which each sample was collected. The two regions (Casey and Davis station) are separated by approx. 1400km. Location- the locations (within a region) from which each sample was collected. The names of each location reflect actual names registered by the Australian Antarctic Division and therefore their coordinates can be pinpointed on maps held by the Australian Antarctic Division Data Centre. Locations (and corresponding sites) written in italicised typeface are considered polluted (see publication for more information on this classification). Site- the sites sampled within each location. Sites are named simply by a two -letter abbreviation of the location they are from, followed by a lowercase 'a', 'b', 'c' or 'd' representing site 1, 2, 3 etc. Microsatellite data - this provides all the microsatellite genetic data generated for each amphipod specimen. Data are presented as the allele sizes (in number of base pairs) recorded for each of the seven microsatellite loci amplified. The seven microsatellite loci are called Orcfra3, Orcfra4, Orcfra5, Orcfra6, Orcfra12, Orcfra13, Orcfra26. As O. franklini is a diploid organism, each microsatellite locus has two allele sizes (hence why there are two columns underneath each locus). A '0' signifies that a particular locus did not amplify successfully in the corresponding organism (after at least two attempts). Samples were collected from Casey station between January 2009 and March 2009, and from Davis station between November 2009 and April 2010. Genetic data was generated and analysed between April 2009 and November 2009, and between May 2010 and April 2011. Genetic data obtained from the common Antarctic amphipod species Orchomenella franklini - Genetic data obtained from the common Antarctic amphipod species Orchomenella franklini. A total of 718 specimens were collected from sites within 20 km of Casey station or Davis station. Collection dates ranged from 2009 to 2010. Each amphipod sample was genotyped for seven microsatellite loci (although occasionally a locus would not amplify in a given sample).

In January 2005 a multi-parametric international experiment was conducted that encompassed both Deception Island and its surrounding waters. This experiment used as main platforms the Spanish Oceanographic vessel 'Hesperides', the Spanish Scientific Antarctic base 'Gabriel de Castilla' at Deception Island and four temporary camps deployed on the volcanic island. This experiment allowed us to record active seismic signals on a large network of seismic stations that were deployed both on land and on the seafloor. In addition other geophysical data were acquired, such as: bathymetric high precision multi-beam data, and gravimetric and magnetic profiles. During the whole period of the experiment a multi-beam sounding EM120 was used to perform bathymetric surveys. The characteristic of this sensor permitted to reach up to 11.000 m b.s.l. In table 2 we provide some of its main characteristics. During the experiment different bathymetric profiles were performed with this equipment outside of Port Foster. Some of these images already have provide an accurate vision of the region, and were used to estimate the real size of the water column locate below each shoot. Additional information of these data could be found in the Lamont-Doherty Earth Observatory at IEDA Marine Geoscience Data System (http://www.marine-geo.org/). It is possible to access the summary of downloads that were made of these data and documents at http://www.marine-geo.org/about/downloadreport/person/Ibanez_Jesus/2016A.

From the abstract of some of the referenced papers: An expert system is being developed which will apply knowledge-based techniques to the automated interpretation of remotely sensed sea-ice images taken over East Antarctica by the NOAA series of meteorological satellites. It is capable of accepting satellite images, deriving characteristic features from them and then performing knowledge-based reasoning to identify regions of cloud, land, open water and various categories of sea-ice. XXXXXXXXXXXXX This paper describes the system design of SPARTEX, a system developed to use information from remote sensing and geographic information systems linked to expert systems. It aims to automate the process of classifying information about the actual or potential use of part of the earth's surface. See the link below for public details on this project.

These are the scanned electronic copies of field and lab books used at Davis Station and Kingston between 2009 and 2012 as part of ASAC (AAS) project 3134 - Vulnerability of Antarctic marine benthos to increased temperatures and ocean acidification associated with climate change.

Our understanding of how environmental change in the Southern Ocean will affect marine diversity,habitats and distribution remain limited. The habitats and distributions of Southern Ocean cephalopods are generally poorly understood, and yet such knowledge is necessary for research and conservation management purposes, as well as for assessing the potential impacts of environmental change. We used net-catch data to develop habitat suitability models for 15 of the most common cephalopods in the Southern Ocean. Full details of the methodology are provided in the paper (Xavier et al. (2015)). Briefly, occurrence data were taken from the SCAR Biogeographic Atlas of the Southern Ocean. This compilation was based upon Xavier et al. (1999), with additional data drawn from the Ocean Biogeographic Information System, biodiversity.aq, the Australian Antarctic Data Centre, and the National Institute of Water and Atmospheric Research. The habitat suitability modelling was conducted using the Maxent software package (v3.3.3k, Phillips et al., 2006). Maxent allows for nonlinear model terms by formulating a series of features from the predictor variables. Due to relatively limited sample sizes, we constrained the complexity of most models by considering only linear, quadratic, and product features. A multiplier of 3.0 was used on automatic regularization parameters to discourage overfitting; otherwise, default Maxent settings were used. Predictor variables were chosen from a collection of Southern Ocean layers. These variables were selected as indicators of ecosystem structure and processes including water mass properties, sea ice dynamics, and productivity. A 10-fold cross-validation procedure was used to assess model performance (using the area under the receiver-operating curve) and variable permutation importance, with values averaged over the 10 fitted models. The final predicted distribution for each species was based on a single model fitted using all data: these are the predictions included in this data set. The individual habitat suitability models were overlaid to generate a 'hotspot' index of species richness. The predicted habitat suitability for each species was converted to a binary presence/absence layer by applying a threshold, such that habitat suitability values above the threshold were converted to presences. The threshold used for each species was the average of the thresholds (for each of the 10 training models) chosen to maximize the test area under the receiver-operating curve. The binary layers were then summed to give the number of species estimated to be present in each pixel in the study region.