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  • 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.

  • 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.

  • General overview The following datasets are described by this metadata record, and are available for download from the provided URL. - Raw log files, physical parameters raw log files - Raw excel files, respiration/PAM chamber raw excel spreadsheets - Processed and cleaned excel files, respiration chamber biomass data - Raw rapid light curve excel files (this is duplicated from Raw log files), combined dataset pH, temperature, oxygen, salinity, velocity for experiment - Associated R script file for pump cycles of respirations chambers #### Physical parameters raw log files Raw log files 1) DATE= 2) Time= UTC+11 3) PROG=Automated program to control sensors and collect data 4) BAT=Amount of battery remaining 5) STEP=check aquation manual 6) SPIES=check aquation manual 7) PAR=Photoactive radiation 8) Levels=check aquation manual 9) Pumps= program for pumps 10) WQM=check aquation manual #### Respiration/PAM chamber raw excel spreadsheets Abbreviations in headers of datasets Note: Two data sets are provided in different formats. Raw and cleaned (adj). These are the same data with the PAR column moved over to PAR.all for analysis. All headers are the same. The cleaned (adj) dataframe will work with the R syntax below, alternative add code to do cleaning in R. Date: ISO 1986 - Check Time:UTC+11 unless otherwise stated DATETIME: UTC+11 unless otherwise stated ID (of instrument in respiration chambers) ID43=Pulse amplitude fluoresence measurement of control ID44=Pulse amplitude fluoresence measurement of acidified chamber ID=1 Dissolved oxygen ID=2 Dissolved oxygen ID3= PAR ID4= PAR PAR=Photo active radiation umols F0=minimal florescence from PAM Fm=Maximum fluorescence from PAM Yield=(F0 – Fm)/Fm rChl=an estimate of chlorophyll (Note this is uncalibrated and is an estimate only) Temp=Temperature degrees C PAR=Photo active radiation PAR2= Photo active radiation2 DO=Dissolved oxygen %Sat= Saturation of dissolved oxygen Notes=This is the program of the underwater submersible logger with the following abreviations: Notes-1) PAM= Notes-2) PAM=Gain level set (see aquation manual for more detail) Notes-3) Acclimatisation= Program of slowly introducing treatment water into chamber Notes-4) Shutter start up 2 sensors+sample…= Shutter PAMs automatic set up procedure (see aquation manual) Notes-5) Yield step 2=PAM yield measurement and calculation of control Notes-6) Yield step 5= PAM yield measurement and calculation of acidified Notes-7) Abatus respiration DO and PAR step 1= Program to measure dissolved oxygen and PAR (see aquation manual). Steps 1-4 are different stages of this program including pump cycles, DO and PAR measurements. 8) Rapid light curve data Pre LC: A yield measurement prior to the following measurement After 10.0 sec at 0.5% to 8%: Level of each of the 8 steps of the rapid light curve Odessey PAR (only in some deployments): An extra measure of PAR (umols) using an Odessey data logger Dataflow PAR: An extra measure of PAR (umols) using a Dataflow sensor. PAM PAR: This is copied from the PAR or PAR2 column PAR all: This is the complete PAR file and should be used Deployment: Identifying which deployment the data came from #### Respiration chamber biomass data The data is chlorophyll a biomass from cores from the respiration chambers. The headers are: Depth (mm) Treat (Acidified or control) Chl a (pigment and indicator of biomass) Core (5 cores were collected from each chamber, three were analysed for chl a), these are psudoreplicates/subsamples from the chambers and should not be treated as replicates. #### Associated R script file for pump cycles of respirations chambers Associated respiration chamber data to determine the times when respiration chamber pumps delivered treatment water to chambers. Determined from Aquation log files (see associated files). Use the chamber cut times to determine net production rates. Note: Users need to avoid the times when the respiration chambers are delivering water as this will give incorrect results. The headers that get used in the attached/associated R file are start regression and end regression. The remaining headers are not used unless called for in the associated R script. The last columns of these datasets (intercept, ElapsedTimeMincoef) are determined from the linear regressions described below. To determine the rate of change of net production, coefficients of the regression of oxygen consumption in discrete 180 minute data blocks were determined. R squared values for fitted regressions of these coefficients were consistently high (greater than 0.9). We make two assumptions with calculation of net production rates: the first is that heterotrophic community members do not change their metabolism under OA; and the second is that the heterotrophic communities are similar between treatments. #### Combined dataset pH, temperature, oxygen, salinity, velocity for experiment This data is rapid light curve data generated from a Shutter PAM fluorimeter. There are eight steps in each rapid light curve. Note: The software component of the Shutter PAM fluorimeter for sensor 44 appeared to be damaged and would not cycle through the PAR cycles. Therefore the rapid light curves and recovery curves should only be used for the control chambers (sensor ID43). The headers are PAR: Photoactive radiation relETR: F0/Fm x PAR Notes: Stage/step of light curve Treatment: Acidified or control The associated light treatments in each stage. Each actinic light intensity is held for 10 seconds, then a saturating pulse is taken (see PAM methods). After 10.0 sec at 0.5% = 1 umols PAR After 10.0 sec at 0.7% = 1 umols PAR After 10.0 sec at 1.1% = 0.96 umols PAR After 10.0 sec at 1.6% = 4.32 umols PAR After 10.0 sec at 2.4% = 4.32 umols PAR After 10.0 sec at 3.6% = 8.31 umols PAR After 10.0 sec at 5.3% =15.78 umols PAR After 10.0 sec at 8.0% = 25.75 umols PAR This dataset appears to be missing data, note D5 rows potentially not useable information See the word document in the download file for more information.

  • 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).

  • This indicator is no longer maintained, and is considered OBSOLETE. INDICATOR DEFINITION Measurements of sea surface temperature in the Southern Ocean. Measurements are averaged over latitude bands: 40-50 deg S, 50-60 deg S, 60 deg S-continent. TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: CONDITION RATIONALE FOR INDICATOR SELECTION Australian and Antarctic climate and marine living resources are sensitive to the distribution of ocean temperature. Sea surface values are relatively easy to monitor, and therefore can be used as a relevant indicator of the state of the ocean environment. The information provided by long records of sea surface temperature is needed to detect changes in the Southern Ocean resulting from climate change; to test climate model predictions; to develop an understanding of links between the Ocean and climate variability in Australia; and for sustainable development of marine resources. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: Southern Ocean: 40 deg S to the Antarctic continent Frequency: Monthly averages over summer Measurement technique: Measurements of sea surface temperature from Antarctic supply ships. The best spatial coverage of sea surface temperature is provided by satellites, due to extensive cloud cover in the Southern Ocean and biases in the satellite measurement, in situ observations of sea surface temperature are necessary. RESEARCH ISSUES Sea surface temperature has not been previously used as a spatially averaged environmental indicator. Some experimentation with past data are required to define the most appropriate averaging strategy. New technologies like profiling Argo floats need to be exploited to provide better spatial and temporal coverage of temperature in the Southern Ocean. LINKS TO OTHER INDICATORS Sea ice extent and concentration Chlorophyll concentrations Sea surface salinity

  • The dataset submitted here is 'Sea-ice freeboard derived from airborne laser scanner'. Between 2007 and 2012, the Australian Antarctic program operated a scanning LiDAR system and other scientific instruments for sea-ice geophysical surveys in East Antarctica. For example see Lieser et al. [2013] for the 2012 survey. The dataset here provides the sea-ice freeboard (i.e. elevation above sea level) along various helicopter flight lines of the 2012 survey in the sea-ice zone between 113 degE and 123 degE. The data collection was based on: - Riegl LMS Q240i-60 scanning LiDAR, measuring sea ice elevation above the WGS84 reference ellipsoid; - Hasselblad H3D II 50 camera, taking aerial photographs at about 13 cm resolution every 3-5 seconds (older digital camera used in 2007); - inertial navigation and global positioning system, OxTS RT-4003. The following geophysical corrections were applied to the sea-ice elevations to derive the sea-ice freeboard: - geoid correction (from the EGM2008 Earth gravity model); - mean ocean dynamic topography correction (from the DTU Space model - DTU10MDT); - ocean tide correction (from the Earth and Space Research CATS2008 Antarctic tide model); - atmospheric pressure (inverse barometer effect) correction from ECMWF data (4-year average) and ship-board underway observations. The geophysical corrections have been validated along selected flight lines by extracting ocean surface elevations from leads between ice floes as identified in the aerial photography. Contained in this dataset are the following files: - a netCDF file for 8 selected flights of the 2012 survey containing sea-ice freeboard values; - a postscript file for 4 of the 8 selected flights showing the residuals from the applied geophysical corrections. These 4 flights were selected on the basis of having a good spread of observable leads along the entire flight line that enabled the extraction of ocean surface elevations.

  • This is a parent record for data collected from AAS project 4102. Project 4102 also follows on from ASAC project 2683, "Passive acoustic monitoring of antarctic marine mammals" (see the related metadata record at the provided URL). Public Summary: Half a century ago the Antarctic blue whale was perilously close to extinction. Over 350,000 were killed before the remaining few were fully protected. A decade ago this elusive and poorly understood species was estimated to be less than 5% of its pre-whaling abundance. This multi-national, circumpolar project will develop and apply powerful new techniques to survey these rare whales and gain an insight into their recovery and ecology. The project is the flagship of the Southern Ocean Research Partnership - an International Whaling Commission endorsed collaborative program.

  • The RAN Australian Hydrographic Service conducted hydrographic survey HI290 at Heard Island, February to March 1997. The survey dataset, which includes the Report of Survey, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office and is available for download from a Related URL in this metadata record. The survey was lead by LT R.D.Bowden. The spatial extent given in this metadata record is that of Heard Island as the spatial extent of the survey is unknown to the Australian Antarctic Data Centre. The data are not suitable for navigation.

  • This record contains the source, gridded data used to produce the maps described in the metadata record with the ID "SIPEX_II_NAME". See the provided URL. The UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) is used to model a wide range of atmospheric dispersion events. These data were collected during the SIPEX II voyage of the Aurora Australis, 2012. The use of NAME and the NWP met data was provided by the UK Met Office for free for research purposes. The analysed wind fields used for the running of NAME are calculated using the Met Office's Unified Model (UM). These are calculated by incorporating all observational site data at six hourly intervals into a forecasting system +/- 3 hours of the observation time. This is continuously repeated to produce a 3D analysis of the state of the atmosphere defined by meteorological variables. It is these variables that are incorporated into NAME and are used to calculate wind vectors, particle position, etc. The global resolution for these fields is 25 km. Model Descriptor Inert particles released for two hours each day between 01:00 - 03:00. The lat/lon for the ship was taken at 02:00 every day. The particles were tracked backwards in time for ten days. The NAME output grid comprised of 267 by 165 boxes of 0.5652 degrees longitude and 0.375 degrees latitude. The lat/lon minimum was 60.0,-85.0 and the max was 210,-23. The plots show the daily particle densities in g s m-3 per grid box for the whole of the back run. There are four different types of plots showing surface influence (0-100m), whole troposphere influence (0-16000m) and below the avg boundary layer (BL). The BL heights have also been plotted at the time of release for each of the backruns.

  • Bathymetric contours and height range polygons of approaches to Mawson Station, derived from RAN Fair sheet, Aurora Australis and GEBCO soundings.