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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).

  • Metadata record for data from ASAC Project 2535 See the link below for public details on this project. Project 2535 'Variability and stability of Antarctic Bottom Water (AABW)' Metadata description (1) Model analysis of natural AABW variability:- We have assessed the interannual to multi-decadal variability of AABW in a global coupled climate model, focussing on variations in bottom water formation rates, T-S changes on AABW neutral surfaces, and the physical mechanisms controlling this variability. The global coupled climate model used is the CSIRO Mark 3 Coupled Climate Model, which incorporates sub-models of the ocean, atmosphere, sea-ice, and land-surface. The experiments were run over a global grid at approximate resolution of 1.9 degrees x 1.9 degrees x 18 levels in the atmosphere, and 1.875 degrees x 0.94 degrees x 31 levels in the ocean. Variables analysed include oceanic temperature, salinity and circulation on AABW density layers, sea-ice extent and thickness, atmospheric sealevel pressure, temperature, and winds. The model integration considered was run with steady CO2 levels for two hundred years in a quasi-steady state mode. Full details of the CSIRO Mark 3 Coupled Climate Model can be found in Gordon et al. (2002). Gordon, H.B., Rotstayn, L.D., McGregor J.L., Dix M.R., Kowalczyk E.A., O'Farrell S.P., 2002: The CSIRO Mk3 Climate System Model. CSIRO Division of Atmospheric Research Technical Paper, No. 60. 130pp. (2) Model simulations of CO2-induced change in AABW: We also ran simulations of climate change within the Canadian University of Victoria Earth System Climate Model of Intermediate Complexity at a global longitude x latitude resolution of 3.6 degrees x 1.8 degrees. The model includes a primitive equation three-dimensional, 19 level ocean model, a sea-ice model, a simple land and river model and a two dimensional energy-moisture balance atmospheric model. A number of sensitivity experiments on ocean mixing parameters and the sea-ice model were conducted to optimise the Southern Hemisphere climatology for the control experiment. The control case (CTRL) was integrated for 3100 years starting from idealised initial conditions. Three climate change experiments were conducted, in which atmospheric carbon dioxide concentrations are changed to 450 ppm, 750 ppm and 1000 ppm from a pre-industrial level of 280 ppm, over different temporal regimes. Full model experiment descriptions appear in Bates, Sijp, and England (2005).

  • Access database containing biological and environmental data collected by the Australian Antarctic Division, Human Impacts Benthic Biodiversity group.

  • This parameter set was developed to provide a plausible implementation for the ecological model described in Bates, M., S Bengtson Nash, D.W. Hawker, J. Norbury, J.S. Stark and R. A. Cropp. 2015. Construction of a trophically complex near-shore Antarctic food web model using the Conservative Normal framework with structural coexistence. Journal of Marine Systems. 145: 1-14. The ecosystem model used in this paper was designed to have the property of structural coexistence. This means that the functional forms used to describe population interactions in the equations were chosen to ensure that the boundary eigenvalues of every population were all always positive, ensuring that no population in the model can ever become extinct. This property is appropriate for models such as this that are implemented to model typical seasonal variations rather than changes over time. The actual parameter values were determined by searching a parameter space for parameter sets that resulted in a plausible distribution of biomass among the trophic levels. The search was implemented using the Boundary Eigenvalue Nudging - Genetic Algorithm (BENGA) method and was constrained by measured values where these were available. This parameter set is provided as an indicative set that is appropriate for studying the partitioning of Persistent Organic Pollutants in coastal Antarctic ecosystems. It should not be used for predictive population modelling without independent calibration and validation.

  • Scanned copy of an acoustics log from Casey Station. Data were collected during 1997. There is no accompanying information to go with the log.

  • These are phytoplankton pigment datasets collected on the BROKE voyage of the Aurora Australis during the 1995-1996 summer season. The readme file in the data download states: Data supplied by Dr Simon Wright. Details phytoplankton pigment data from BROKE. "BROKEPIGDBase.xls Contains 5 worksheets. 'Notes' repeats the information presented here. 'Key' describes the column headings, chemical names. 'Raw_Data' is the exact spreadsheet receieved from Dr Wright. 'Standard_sample_source' contains all the phyto-chemical data as taken from the CTD programme. 'Non_standard_sample_source' contains phyto-chemical data that seems to have been collected opportunistically, to test some assumptions. The details of the locations of the opportunistic samples are detailed in the column 'Sample_source'. Note- it is unsure whether the numbers in the CTD column describe the Station Number. This has to be verified. Converted into a MS Access database- 'BROKE_phytoplankton.mdb' by Natalie Kelly. This database contains 3 tables. One is a description of the column names, chemical etc. The other two contain both the Standard and Non-Standard Sample source phytochemical data. Natalie Kelly 19 November 2005"

  • CAWCR Hindcast* and ECMWF ERA-5** model predictions of wave spectral properties (wave height and period) and corresponding observed data from ACE. Observations are mapped to model grids. Quality control is applied, i.e. cells with a number of points less than 5 and/or with high data variation (Standard Deviation/Mean greater than 0.2) are eliminated. Files are named as follows: WaMoS_vs_CAWCR_Hs.mat WaMoS_vs_CAWCR_Tm.mat WaMoS_vs_ERA5_Hs.mat WaMoS_vs_ERA5_Tp.mat In each file, columns show Latitude (deg.), Longitude (deg.), Time (number of days from January 0, 0000), Model Parameters (Hs, Tp or Tm) and Observed Parameters (Hs, Tp or Tm), respectively. Hs denotes significant wave height in meters, Tp is peak wave period in seconds and Tm is mean wave period based on the first moment of wave spectrum in seconds. The MATLAB file, WaMoSvsModel_FigurePlot.m, can be used to visualise the results. The files dscatter.m and polyfix.m are functions used in the MATLAB script. A sample figure (SampleFigure.png) is also included for users’ reference. * Durrant, T., Greenslade, D., Hemer, M. and Trenham, C., 2014. A global wave hindcast focussed on the Central and South Pacific (Vol. 40, No. 9, pp. 1917-1941). ** Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Dec. 12, 2018.