Public Description of the Project This project will assess the importance of the trace micro-nutrient element iron to Antarctic sea-ice algal communities during the International Polar Year (2007-2009). We will investigate the biogeochemistry of iron, including a comprehensive examination of its distribution, speciation, cycling and role in fuelling ice-edge phytoplankton blooms. A significant part of this research will concentrate on the the influence of organic exopolysaccharides on iron solubility, complexation and bioavailability, both within the ice and in surrounding snow and surface seawater. This innovative research will improve our understanding of key processes that control the productivity of the climatically-important Antarctic sea-ice zone. Project objectives: This project will assess the importance of the trace element iron (Fe) as a micro-nutrient to seasonal sea-ice algal communities in the Australian sector of Antarctica during the International Polar Year (2007-09). We will investigate the biogeochemistry of Fe, including a comprehensive examination of its distribution, speciation, cycling and role in fuelling ice-edge phytoplankton blooms. A significant part of this research will concentrate on the influence of organic exopolysaccharides (EPS) on Fe solubility and complexation (and hence bioavailability), both within the ice and in surrounding surface waters. This innovative research will improve our understanding of key processes that control the productivity of the climatically-important Antarctic sea-ice zone. This metadata record describes data collected at Casey Station as part of project 3026. Collected data from the time series experiment in sea ice near Casey station Antarctica (66 degrees 13 minutes 07 seconds S, 110 degrees 39 minutes 02 seconds E). Measurements were made at the same location during seven consecutive study days between 10 November and 2 December 2009. Variables measured were pFe (particulate Fe), TDFe (total dissolvable Fe), dFe (dissolved Fe), plFe (particulate leachable Fe), PON (particulate organic nitrogen), POC (particulate organic carbon), Chl a (Chlorophyll a), salinity, ice temperature, vb/v (brine volume fraction), mean daily air temperature, and max daily air temperature. Measurements were taken on each study day of the snow directly overlying the sea ice (SNOW), a shallow and a deep brine (B- and B+, respectively), three sections of the sea ice core at median depths 3, 33, and 73 centimeters (SI1, SI2, and SI3, respectively) as well as two consecutive sections in the lower most basal ice (SI4 and SI5). Finally, four samples were taken of the underlying seawater at 0, 5, 10 and 15 m (SW0, SW5, SW10 and SW15, respectively).

Metadata record for data from ASAC Project 2677 Data on the sensitivity of Antarctic marine organisms to contaminants is limited, and is essential to understanding the risks contaminants pose to the Antarctic environment. The use of traditional toxicity assessment approaches, to collect high quality sensitivity data for a range of species, is a time consuming and difficult process, especially in remote and hostile environments like Antarctica. In this project, we used a rapid toxicity test approach (described by Kefford et al. 2005) to determine the approximate sensitivity of a large and representative sample of Antarctic marine invertebrates to three common metals (cadmium, copper, zinc). Sensitivity estimates generated via this method are likely to be less precise than those derived from traditional toxicity test methods (due to lower replication and fewer exposure concentrations), but a much larger number of estimates for a wider and more representative range of taxa are able to be produced (under equivalent resourcing). This is advantageous for subsequent Species Sensitivity Distribution (SSD) models, which will include more species and will be more robust, producing protective concentration values that represent a greater proportion of the biodiversity of the region. In this study, a total of 88 different taxa were tested during the 2005/06 Austral summer at Casey station; specimens were collected from a wide range of intertidal and shallow sub-tidal marine sites, providing good representation of the nearshore marine invertebrate community as a whole for this region. Tests were of 10 day duration, with a water change at 4 days. Sensitivity estimates were modelled (LCx; concentrations lethal to x% of the test populations) at 4 and 10 days of exposure, calculated using measured metal concentrations. A series of SSDs were constructed using LC50 values, each one including sensitivity estimates for up to 87 taxa. SSDs were constructed using the Kaplan-Meier function (results provided here) and a log-likelihood based method (available via Kefford et al submitted 2018), both of which allowed inclusion of right- and interval-censored sensitivity data. The results of this work provides a basis for estimating the risk of exposure to three common metal contaminants to Antarctic marine invertebrates. Files: Four files are attached to this record: 1. ASAC_2677-1-Supplementary-Tables.xlsx Excel file containing: 1) LC50 values for all taxa tested, for 4 and 10 d exposure durations. Both modelled and non-modelled estimates are provided. 2) Taxonomic details for all taxa tested. 3) Hazardous concentrations (HCy) to 1%, 5%, 10%, 20% and 50% of the taxa tested (HC1, HC5, HC10, HC20 and HC50, respectively) in μg/L measured on various subgroups calculated from log-normal distributions. 2. AAS_2677-2-CaseyRapidTests_Modelled LCx.xlsx Excel file containing sensitivity estimate values. See ‘FileInfo’ worksheet for description of fields. 3. AAS_2677-3-CaseyRapidTests_Figs-Kaplan-Meier.docx Word document containing Species Sensitivity Distribution model plots, generated using the Kaplan-Meier function. Data are provided for cadmium, copper and zinc based on 4 day and 10 day LC50 values for Antarctic marine invertebrates (subgroup comparisons by phyla, Arthropoda order, abundance category), generating using a rapid testing approach. LC50 values used to generate these plots are provided in the Supplementary Information of Kefford et al (submitted 2018). 4. AAS_2677-4-CaseyRapidTests_Tables-Kaplan-Meier.xlsx Excel file containing results modelled using the Kaplan-Meier function. Includes two worksheets: - Table 1: Summary statistics of 4 and 10d LC50 values (µg/L measured) estimated from Kaplan-Meier functions for the taxa tested and various sub-groups. Values in brackets are 95% confidence intervals (CI). Values and CI omitted were not calculable with the data available. See Supplementary Figures S10-S22 for plots of the Kaplan-Meier functions. - Table 2: Hypothesis testing for differences in the Kaplan-Meier functions between SSD models (constructed using LC50 sensitivity estimates) for 3 metal and 2 exposure durations (4 and 10d) on various sub-groups using Log Rank (Mantel-Cox) test. NC = not calculable with the number of species tested.

Particulate impurities in snow such as dust and soot can absorb sunlight. This is important for snow albedo in the Arctic, but probably not in the Antarctic. Snow was collected in plastic bags through the full depth of the snowpack over first-year sea ice at several locations: one location each on Ice Stations 1, 2, 3, 4, 7, upwind of the ship. The snow was melted and the meltwater filtered. The filters will be analysed for light-absorbing impurities in a laboratory spectrophotometer in Seattle. Samples of size ~1 kg were collected at Ice Station 1. The filters were blank, so at Ice Stations 2 and 3 larger samples of size 2-4 kg were collected. The filters were still blank, so at Ice Stations 4 and 7 larger samples of size ~8 kg were collected; these do show a slight darkening visible by eye. No snow samples were collected at Stations 5 and 6. Local (ship) time is UTC+10; Sun time is UTC+8. Filters are labelled with prefix 'AO' for Antarctic Ocean. This dataset currently contains snow density (except for at one ice station) and volumes of melt water that were filtered. No further analysis has been carried out on these samples at this point, however at a later stage the filters will be analysed for spectral absorption and converted to a mixing ratio of black carbon in the snow.

We assembled tracking data from seabirds (n = 12 species) and marine mammals (n = 5 species), collected between 1991 and 2016, from across the Antarctic predator research community. See https://data.aad.gov.au/metadata/records/SCAR_EGBAMM_RAATD_2018_Standardised and https://data.aad.gov.au/metadata/records/SCAR_EGBAMM_RAATD_2018_Filtered for the tracking data. Habitat selectivity modelling was applied to these tracking data in order to identify the environmental characteristics important to each species, and to produce circum-Antarctic predictions of important geographic space for each individual species. The individual species maps were then combined to identify regions important to our full suite of species. This approach enabled us to account for incomplete tracking coverage (i.e., colonies from which no animals have been tracked) and to produce an integrated and spatially explicit assessment of areas of ecological importance across the Southern Ocean. The data attached to this metadata record include the single-species maps for Adelie, emperor, king, macaroni, and royal penguins; Antarctic and white-chinned petrels; black-browed, grey-headed, light-mantled, sooty, and wandering albatross; humpback whales; Antarctic fur seal, southern elephant seals, and crabeater and Weddell seals. The data also include the integrated maps that incorporate all species (weighted by colony size, and unweighted). See the paper and its supplementary information for full details on the modelling process and discussion of the model outputs.

Researchers studied persistent organohalogen contaminants (POCs) in the eastern Antarctic sector. Samples were collected during January and February 2006 and originated from 12 sampling stations. They were analysed for greater than 100 organohalogen compounds including chlorinated pesticides, polychlorinated biphenyls (PCBs), polybrominated organic compounds and polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs). The suspected naturally occurring organohalogen, 2,4,6-tribromoanisole (TBA) as well as delta-HCH; o,p'- DDE; o,p'-DDD; p,p'-DDD; p,p'-DDT; penta-chlorobenzene (PeCB); HCB; heptachlor-exoepoxide; heptachlor; trans-nonachlor, mirex and toxaphene congeners Tox-26 (B8-1413), Tox-40+41 (B8-1414+ B8-1945) and Tox-50 (B8-2229) were quantified in all samples analysed whilst PCB-101, gamma-HCH, p,p'-DDE cis-nonachlor, Tox-42a (B8-806) and Tox-44 (B8-2229) were quantified in greater than or equal to 75% of samples analysed. Organochlorine pesticides dominated measured krill contaminant burdens with hexachlorobenzene (HCB) as the single most abundant compound quantified: 4.37 ng/glw (lipid weight) or 0.2 ng/gww (wet weight). HCB concentrations were comparable to those detected at this trophic level in both the Arctic and temperate northwest Atlantic, lending support to the hypothesis that HCB will approach global equilibrium at a faster rate than other POCs. Para, para'-dichlorodiphenylethene (p,p'- DDE) was detected at notable concentrations: 2.6 ng/glw 0.13 ng/gww. In contrast to the Arctic, PCBs did not feature prominently in contaminant burdens of Antarctic krill: 1.2 ng g- 1 lw and 0.05 ng/gww., dominant PCB congeners were PCB-18, PCB-28, PCB-31 and PCB- 153. The major commercial polybrominated diphenyl ether (PBDE) congeners -99 and -47 were quantified at low background levels (0.67 ng/glw , 0.03 ng/gww and 0.35 ng/glw, 0.007 ng/gww respectively) with clear concentration spikes observed at around 70 degrees E , in the vicinity of modern, active research stations. The suspected naturally occurring brominated organic compound, 2,4,6-tribromoanisole (TBA), was a ubiquitous contaminant in all samples 49 whereas the only PCDD/Fs quantifiable were trace levels of octachlorodibenzo-p-dioxin (OCDD) and 1,2,3,4,7,8/1,2,3,4,7,9-hexachlorodibenzofuran (HxCDF). This work has been incorporated in AAS project 3115 (ASAC_3115), Persistent Organic Pollutants and Emerging Contaminants of Concern; System Input From Local and Distant Contamination Sources.

These are the scanned electronic copies of field and lab books used at Casey Station between 1997 and 2012 as part of ASAC (AAS) project 2385 - Development and application of DGT devices for passive sampling of contaminated waters in the Antarctic environment.

Total Organic Carbon A 2 g homogenised wet sediment sub-sample from each core was weighed into a pre-combusted crucible and dried at 105 degrees C. The dried sample was reweighed before being analysed for total carbon by mass loss on ignition at 550 degrees C, the sample was placed in the muffle furnace for 4 hours. Samples 56698, 57062, 56837, 57058, and 580792 were analysed in triplicate to assess the reproducibility of the analytical procedure. (Total number of analyses was 117). - TOC - For the 107 samples: - Mean and SD: 3 plus or minus 4 % DMB, range: 0.16-15 %, n=107 - Considering the mean values for the 27 site locations: - Range: 0.33-14 % DMB, mean and SD: 3.3 plus or minus 3.7 % DMB, n=27 - Analytical uncertainty - Analytical precision: 5 samples analysed in triplicate: - RSD = 6 plus or minus 5% range 1-11%, n=5 - Site heterogeneity: reproducibility (RSD) of mean data from site replicate samples was 26% (mean, SD 15%, range 10-57%, n=27) - From the limited data on reproducibility summarised above, it can be concluded that site heterogeneity contributes most to the uncertainty of the TOC data for the site locations. - DMF - For the 107 samples: - Mean and SD: 0.57 plus or minus 0.23 %, range: 0.09-0.85, n=107 - Considering the mean values for the 27 site locations: - Range:0.17-0.83, mean and SD: 0.57 plus or minus 0.22, n=27 - Analytical uncertainty - Analytical precision: 5 samples analysed in triplicate: - RSD = 2 plus or minus 2% range 0.8-5%, n=5 - Site heterogeneity: reproducibility (RSD) of mean data from site replicate samples (mostly quadruplicates) was 10% (mean, SD 10%, range 1-37%, n=27) - From the limited data on reproducibility summarised above, it can be concluded that site heterogeneity contributes most to the uncertainty of the DMF data for the site locations. Collection of sediment cores Sediment for grain size and various chemical analysis were sampled using a core of PVC tubing (15cm long x 5cm diameter) pushed 10cm into the sediment. These cores were kept upright at all times to ensure the stratigraphy remained intact and frozen in the core tube at -20 degrees C. Grain size analysis The outer 5 mm edge of the core was removed with a scalpel blade and placed in a clean, dried preweighed beaker. The sample was weighed and placed in an oven at 45 degrees C to dry. Once dry the sample was reweighed and then sieved through a 2 mm sieve, any residual sediment in the beaker was weighed and the weight recorded. The less than 2 mm fraction and the greater than 2 mm fraction were separately collected and weighed. A 5 g sample of the less than 2 mm fraction was taken for grain size analysis which was carried out using the Mastersizer 2000 Particle Size Analyser by Associate Professor Damian Gore at the Department of Physical Geography, Macquarie University, Sydney.

This dataset contains observations of ice conditions taken from the bridge of the RV Aurora Australis during SIPEX 2012, following the Scientific Committee on Antarctic Research/CliC Antarctic Sea Ice Processes and Climate [ASPeCt] protocols. See aspect.antarctica.gov.au Observations include total and partial concentration, ice type, thickness, floe size, topography, and snow cover in each of three primary ice categories; open water characteristics, and weather summary. The dataset is comprised of the scanned pages of a single logbook, which holds hourly observations taken by observers while the ship was moving through sea-ice zone. The following persons assisted in the collection of these data: Dr R. Massom, AAD, Member of observation team Mr A. Steer, AAD, Member of observation team Prof S. Warren, UW(Seattle), USA, Member of observation team Dr J. Hutchings, IARC, UAF, USA, Member of observation team Dr T. Toyota, Inst Low Temp Science, Japan, Member of observation team Dr T. Tamura, NIPR, Japan, Member of EM observation team Dr G. Dieckmann, AWI, Germany, Member of observation team Dr E. Maksym, WHOI, USA, Member of observation team Mr R. Stevens, IMAS, Trainee on observation team Dr J. Melbourne-Thomas, ACE CRC, Trainee on observation team Dr A. Giles, ACE CRC, Trainee on observation team Ms M. Zhia, IMAS, Trainee on observation team Ms J. Jansens, IMAS, Trainee on observation team Mr R. Humphries, Univ Wollengong, Trainee on observation team Mr C. Sampson, Univ Utah, USA, Trainee on observation team Mr Olivier Lecomte, Univ Catholique, Louvain-la-Neuve, Belgium, Trainee on observation team Mr D. Lubbers, Univ Utah, USA, Trainee on observation team Ms M. Zatko, UW(Seattle), USA, Trainee on observation team Ms C. Gionfriddo, Uni Melbourne, Trainee on observation team Mr K. Nakata, EES, Japan, Trainee on observation team