Estimate of food web relations based on dietary studies, estimates of production, biomass and productire per biomass and ecotrophic efficiency, time series of fisheries catch, effort and landings data, time series of abundance and biomass data for some taxa, environmental time series (eg. SST, wind stress). Data ranging from 1990-2012 for the Spencer Gulf, South Australia.

Metadata record for data from ASAC Project 2584 See the link below for public details on this project. The Southern Ocean plays a significant role in the biogeochemical cycling of sulphur due to high spring-summer fluxes of dimethylsulfide (DMS), particularly south of 60 degrees S. Recent DMS flux perturbation simulations have recently highlighted the key role of the SO between 50-70 degrees S in the DMS-climate feedback hypothesis [Gabric et al., 2003; Gabric et al., 2004]. This project examines the interactions and feedback between marine polar plankton and global climate through the use of biogeochemical and global climate models, and explores the sensitivity of climate to the current and future biogenic production of dimethylsulphide at polar latitudes. This was a modelling project, and as such did not collect any data of its own. Taken from the abstracts of the referenced papers: The global climate is intimately connected to changes in the polar oceans. The variability of sea ice coverage affects deep-water formations and large-scale thermohaline circulation patterns. The polar radiative budget is sensitive to sea-ice loss and consequent surface albedo changes. Aerosols and polar cloud microphysics are crucial players in the radioactive energy balance of the Arctic Ocean. The main biogenic source of sulfate aerosols to the atmosphere above remote seas is dimethylsulfide (DMS). Recent research suggests the flux of DMS to the Arctic atmosphere may change markedly under global warming. This paper describes climate data and DMS production (based on the five years from 1998 to 2002) in the region of the Barents Sea (30-35 degrees E and 70-80 degrees N). A DMS model is introduced together with an updated calibration method. A genetic algorithm is used to calibrate the chlorophyll-a (CHL) measurements (based on satellite SeaWiFS data) and DMS content (determined from cruise data collected in the Arctic). Significant interannual variation of the CHL amount leads to significant interannual variability in the observed and modelled production of DMS in the study region. Strong DMS production in 1998 could have been caused by a large amount of ice algae being released in the southern region. Forcings from a general circulation model (CSIRO Mk3) were applied to the calibrated DMS model to predict the zonal mean sea-to-air flux of DMS for contemporary and enhanced greenhouse conditions at 70-80 degrees N. It was found that significantly decreasing ice coverage, increasing sea surface temperature and decreasing mixed-layer depth could lead to annual DMS flux increases of more than 100% by the time of equivalent CO2 tripling (the year 2080). This significant perturbation in the aerosol climate could have a large impact on the regional Arctic heat budget and consequences for global warming. The response of oceanic phytoplankton to climate forcing in the Arctic Ocean has attracted increasing attention due to its special geographical position and potential susceptibility to global warming. Here, we examine the relationship between satellite derived (sea-viewing wide field-of-view sensor, SeaWiFS) surface chlorophyll-a (CHL) distribution and climatic conditions in the Barents Sea (30-35 degrees E, 70-80 degrees N) for the period 1998-2002. We separately examined the regions north and south of the Polar Front (~76 degrees N). Although field data are rather limited, the satellite CHL distribution was generally consistent with cruise observations. The temporal and spatial distribution of CHL was strongly influenced by the light regime, mixed layer depth, wind speed and ice cover. Maximum CHL values were found in the marginal sea-ice zone (72-73 degrees N) and not in the ice-free region further south (70-71 degrees N). This indicates that melt-water is an important contributor to higher CHL production. The vernal phytoplankton bloom generally started in late March, reaching its peak in late April. A second, smaller CHL peak occurred regularly in

This project used computer-based modelling and existing field data to analyse the production and cycling of dimethylsulphide (DMS) and predicted its role in climate regulation in the Antarctic Southern Ocean. From the Final Report: Aims (i) To calibrate an existing dimethylsulphide (DMS) production model in a section of the Antarctic Southern Ocean. (ii) To use the calibrated model to investigate the effect of GCM-predicted climate change on the production and sea-to-air flux of DMS under current and enhanced greenhouse climatic conditions. (iii) To provide regional assessments of the sign and strength of the DMS-climate feedback in the Southern Ocean. Characteristics of Study Region: Our study region extends from 60-65 degrees S, 123-145 degrees E in the Antarctic Southern Ocean, and was the site of a major biological study in the austral summer of 1996 (Wright and van den Enden, 2000). Field observations show that a short-lived spring-summer bloom event is typical of these waters (El-Sayed, 1988, Skerratt et al. 1995); however there can be high interannual variability in the timing and magnitude of the bloom (Marchant and Murphy, 1994). The phytoplankton community structure has been described by Wright and van den Enden (2000), who report maximum chlorophyll (Chl) concentrations during January-March in the range (1.0-3.4) microgL-1. During this survey, macronutrients did not limit phytoplankton growth. Thermal stratification of the mixed layer was strongly correlated with high algal densities, with strong subsurface Chl maxima (at the pycnocline) observed. The mixed layer depth determined both phytoplankton community composition and maximum algal biomass. Coccolithophorids (noted DMS producers) were favoured by deep mixed layers, with diatoms dominating the more strongly stratified waters. Pycnocline depth varied from 20-50 m in open water. Algal abundance appeared to be controlled by salp and krill grazing. Field data support the existence of seasonal DMS production in the Antarctic region. However, a large range in DMS concentrations has been reported in the open ocean , reflecting both seasonal and spatial variability (Gibson et al., 1990, Berresheim, 1987; Fogelqvist, 1991). Blooms of the coccolithophores, and prymnesiophytes such as Phaeocystis, form a significant fraction (~23%) of the algal biomass (Waters et al 2000). Concentrations of DMS in sea ice are reported to be very high (Turner et al. 1995) and may be responsible for elevated water concentrations during release from melt water (Inomata et al. 1997). Field measurements of dissolved DMS made in the study region have been summarised by Curran et al. (1998). DMS concentrations were variable in the open ocean during spring and summer (range: 0-22 nM), with the higher values recorded in the seasonal ice zone and close to the Antarctic continent. Zonal average monthly mean DMS in the study region have been estimated by Kettle et al. (1999). (See downloadable full report for reference list). A copy of the referenced publication is also available for download by AAD staff. It contains the modelling information.

This dataset comprises a table and set of maps of all geographic sites of ice-free land along the East Antarctica coastline between longitudes 37°E and 160°E. Each geographic site comprises a discrete area of ice-free land and includes islands within 100 km of the coast and outcrops of ice free continental rock within 1 km of the coast. The geographic sites were identified in a geographic information system using polygons sourced from the AAT Coastline 2003 dataset produced by Geoscience Australia and the Australian Antarctic Division, and exposed rock polygons sourced from the Antarctic Digital Database version 4.0 produced for the Scientific Committee on Antarctic Research. The maps are grouped into sub-regions and regions, with multiple maps in most sub-regions. The maps were designed to be of a scale that could be used in the field to identify sites by their shape and location. This dataset has previously been used in the specific context of potential breeding habitat for Adelie penguins (doi:10.4225/15/5758F4EC91665) but has potential for broader use in a wide range of ecological and environmental studies. 2021-06-30 - an updated copy of the spatial reference system spreadsheet was uploaded. The update was only minor.

This dataset is a collection of marine environmental data layers suitable for use in Southern Ocean species distribution modelling. All environmental layers have been generated at a spatial resolution of 0.1 degrees, covering the Southern Ocean extent (80 degrees S - 45 degrees S, -180 - 180 degrees). The layers include information relating to bathymetry, sea ice, ocean currents, primary production, particulate organic carbon, and other oceanographic data. An example of reading and using these data layers in R can be found at https://australianantarcticdivision.github.io/blueant/articles/SO_SDM_data.html. The following layers are provided: Layer name: depth Description: Bathymetry. Downloaded from GEBCO 2014 (0.0083 degrees = 30sec arcmin resolution) and set at resolution 0.1 degrees. Then completed with the bathymetry layer manually corrected and provided in Fabri-Ruiz et al. (2017) Value range: -8038.722 - 0 Units: m Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ Citation: Fabri-Ruiz S, Saucede T, Danis B and David B (2017). Southern Ocean Echinoids database_An updated version of Antarctic, Sub-Antarctic and cold temperate echinoid database. ZooKeys, (697), 1. Layer name: geomorphology Description: Last update on biodiversity.aq portal. Derived from O'Brien et al. (2009) seafloor geomorphic feature dataset. Mapping based on GEBCO contours, ETOPO2, seismic lines). 27 categories Value range: 27 categories Units: categorical Source: This study. Derived from Australian Antarctic Data Centre URL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Citation: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10 Layer name: sediments Description: Sediment features Value range: 14 categories Units: categorical Source: Griffiths 2014 (unpublished) URL: http://share.biodiversity.aq/GIS/antarctic/ Layer name: slope Description: Seafloor slope derived from bathymetry with the terrain function of raster R package. Computation according to Horn (1981), ie option neighbor=8. The computation was done on the GEBCO bathymetry layer (0.0083 degrees resolution) and the resolution was then changed to 0.1 degrees. Unit set at degrees. Value range: 0.000252378 - 16.94809 Units: degrees Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ Citation: Horn, B.K.P., 1981. Hill shading and the reflectance map. Proceedings of the IEEE 69:14-47 Layer name: roughness Description: Seafloor roughness derived from bathymetry with the terrain function of raster R package. Roughness is the difference between the maximum and the minimum value of a cell and its 8 surrounding cells. The computation was done on the GEBCO bathymetry layer (0.0083 degrees resolution) and the resolution was then changed to 0.1 degrees. Value range: 0 - 5171.278 Units: unitless Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ Layer name: mixed layer depth Description: Summer mixed layer depth climatology from ARGOS data. Regridded from 2-degree grid using nearest neighbour interpolation Value range: 13.79615 - 461.5424 Units: m Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Layer name: seasurface_current_speed Description: Current speed near the surface (2.5m depth), derived from the CAISOM model (Galton-Fenzi et al. 2012, based on ROMS model) Value range: 1.50E-04 - 1.7 Units: m/s Source: This study. Derived from Australian Antarctic Data Centre URL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Citation: see Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031.

Metadata record for data from ASAC Project 1117 See the link below for public details on this project. ---- Public Summary from Project ---- The aim of this project is to determine how feasible it is to regularly sample the pelagic under-ice community during winter at a coastal site near Mawson. Very few attempts have been made to sample the water column under the ice during the winter months and the processes that occur during this period remain critical gaps in our knowledge of the Antarctic marine ecosystem. The pelagic community under the Mawson sea ice was sampled during the winter of 2001 using 'light trap' sampling devices. The 'light traps' were tested at various depths in a range of configurations to determine whether they were an appropriate instrument to sample the winter pelagic community under the ice. Fourteen successful deployments of the light traps were made on seven separate occasions from 12 June to 12 September 2001. The light traps were deployed at three different depths - the underside of the sea ice, mid water, and just above the sea floor. Two different light sources were used to attract the animals, namely fluorescent tubes and cyalume sticks. Two different configurations of the traps were tested to retain the animals inside the trap - one with plastic flaps to trap the animals, the other with no flaps, allowing the animals to move freely inside the trap. The light traps were deployed and retrieved during darkness to avoid any influence of ambient light. The objectives of the project were met and it is assessed that the pelagic community in winter can be effectively sampled using this methodology. A result of particular interest is the success of the traps in capturing Pleuragramma antarctica, a species which has proven difficult to capture using traditional sampling methods such as nets.

In this project a simplified computer model was developed to reflect the variation and influences of sea ice on the atmosphere. The model was incorporated into a global general circulation model. The data set resulting from the project consists of simulated sea ice characteristics (concentration etc.) available on a regular global grid. From the abstracts of some of the referenced papers: An observed ocean-drift data set is used as the basis of a wind-driven coupled ocean-sea-ice-atmosphere model including interaction and feedback. The observed characteristics of the Antarctic sea ice are described including the ice thickness, ice concentration and horizontal advection. The atmospheric model computes heat fluxes, sea-ice growth, changes in concentration and advection. Sensitivity studies show reasonable and stable simulations of the observed sea-ice characteristics for the present mean Antarctic winter climate. The response times and feedbacks of the ice-atmosphere system as represented by the model appear to allow scope for the development of some persistence of anomalies. To assess the sensitivity of the southern hemisphere circulation to changes in the fraction of open water in the sea ice we have conducted four experiments with a July 21-wave General Circulation Model (GCM) with this fraction set to 5, 50, 80 and 100%. The mean surface temperatures and the surface atmospheric temperatures over the sea ice increased as the water fraction increased and the largest changes were simulated adjacent to the coast. Significant anomalies in the surface heat fluxes, particularly those of sensible heat, accompanied the decrease in the sea ice concentration. Substantial atmospheric warming was simulated over and in the vicinity of areas in which leads were considered. In all but one experiment there were anomalous easterlies between about 40 and 60S with westerly anomalies further to the south. The surface pressure at high latitudes appears to change in a consistent fashion with the fraction of open water, with the largest changes occurring in the Weddell and near the Ross Seas. Some of the feedbacks which may enhance the responses here, but which are not included in our model are discussed. We present a simple parameterisation of the effect of open leads in a general circulation model of the atmosphere. We consider only the case where the sea ice distribution is prescribed (ie not alternative) and the fraction of open water in the ice is also prescribed and set at the same value at all points in the Southern Hemisphere and a different value in the Northern Hemisphere. We approximate the distribution of sea ice over a model 'grid box' as a part of the box being covered by solid ice of uniform thickness and the complement of the box consisting of open water at a fixed -1.8 degrees C. Because of the nonlinearity in the flux computations, separate calculations are performed over the solid sea ice and over the open leads. The net fluxes conveyed to the atmosphere over the grid box are determined by performing the appropriate area-weighted average over the two surface types. We report on an experiment designed to assess the sensitivity of the modelled climate to the imposition of a 50% concentration in the winter Antarctic sea ice. Significant warming of up to 6 degrees C takes place in the vicinity of and above the Antarctic sea ice and is associated with significant changes in the zonal wind structure. Pressure reductions are simulated over the sea ice, being particularly marked in the Weddell Sea region, and an anomalous east-west aligned ridge is simulated at about 60S. Very large changes in the sensible heat flux (in excess of 200 W per square metre) are simulated near the coast of Antarctica. Increasingly, many aspects of the study of Antarctica and the high southern latitudes are being aided by various types of numerical models. Among these are the General Circulation Models (GCMs), which are powerful tools that can be used to

Rapid toxicity tests (Kefford et al. 2005) were used to test the sensitivity of a wide range of intertidal and shallow sub-tidal marine invertebrates collected off the northern end of Macquarie Island. The tests were 10 days long, with a water change at 4 days. Resulted in the data set are non-modelled LCx (concentrations lethal to x% of the test populations) values for Copper (Cu) 10 days of exposure. Kefford, B.J., Palmer, C.G., Jooste, S., Warne, M.St.J. and Nugegoda, D. (2005). What is it meant by '95% of species'? An argument for the inclusion of rapid tolerance testing. Human and Ecological Risk Assessment 11: 1025-1046. Invertebrates collected from a range of coastal waters off the northern end of Macquarie Island . The columns in the spreadsheet are as follows: Lowest ID = the lowest identification the taxa is ID to (can be species, genus, family, etc.) Group = major taxonomic group the taxa comes from Letter = a convent identifier to split the taxa LC50 discpt = a string description of the10 day LC50 (lethal concentration for 50% of the test population) LC50 point estimate = a point estimate of the 10 day LC50 (lethal concentration for 50% of the test population) Cencor = indicates if the LC50 is right censored (that is greater than the value indicated in the point estimate) Case = a number to identify the record Project Public Summary: Despite pollution concerns in Antarctic and southern oceans, there is little ecotoxicological data and none from the sub-Antarctic. Ecological risk assessments and water quality guidelines should use local data, especially in the polar environment as organisms may respond differently to pollutants. The sub-Antarctic is, however, between Antarctica and the temperate zone and in the absence of local data, it maybe appropriate to use temperate data. This project will assess how the sensitivity to metals of marine invertebrates varies latitudinally and in which region of the Antarctic, if at all, it is appropriate to use temperate data.

A Langrangian free drift model is developed, including a term for geostrophic currents that reproduces the 13 h period signature in the ice motion observed in the data (CLSC_WIIOS_2017; parent data). The calibrated model is shown to provide accurate predictions of the ice drift for up to 2 days, and the calibrated parameters provide estimates of wind and ocean drag for pancake floes under storm conditions. Model setup is described in "Drift of pancake ice floes in the winter Antarctic marginal ice zone during polar cyclones", Alberello et. al [https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JC015418; pre-print https://arxiv.org/pdf/1906.10839.pdf]. The dataset includes model data. Six model outputs are included. (i) "full_t00" includes the full 10 days simulation, with all the forcing switched on (ii) "noge_t00" includes the full 10 days simulation, but the geostrophic current is suppressed (iii) "full_t25_noup" includes the simulation with start at 2.5 days, all the forcing switched on, no update of the drag coefficients (iv) "full_t25_newn" includes the simulation with start at 2.5 days, all the forcing switched on, the drag coefficients are recalibrated (v) "full_t50_noup" includes the simulation with start at 5 days, all the forcing switched on, no update of the drag coefficients (vi) "full_t50_newn" includes the simulation with start at 5 days, all the forcing switched on, the drag coefficients are recalibrated In each file: - rho_a the air density (1.3 kg/m3) - rho_w the water density (1028 kg/m3) - rho_i the ice density (910kg/m3) - C_w the water drag coefficient (calibrated) - C_a the air drag coefficient (calibrated) - turn the turning angle (25 degrees) - Nansen the Nansen number evaluated using C_a and C_w - aalpha a model parameter (proportional to air and ice parameters) - abeta a model parameter (proportional to water and ice parameters) - ag amplitude of the geostrophic current (U_g=0.125m/s) - tg initial phase of the geostrophic current (in radians) - to start time (in matlab format, use "datestr(to)" ), after which model resolution is 60 seconds - wo components of wind in the East and North direction (m/s) - wi components of wind in the East and North direction (m/s) - uo components of modelled ice drift speed in the East and North direction (m/s) - lo longitude and latitude of modelled ice position (degrees) - xo position of modelled ice in the East and North direction (m), given with respect to the initial position (0,0) - wco components in the East and North direction of geostrophic current (m/s)

This dataset compiles spatial environmental data layers from various sources used for predicting distributional patterns of Antarctic seafloor biodiversity (Jansen et al, in prep). All data layers are projected to polar stereographic (EPSG:3031) at a resolution of 2km. Data layers include seafloor depth, slope and topographic position index, distance to the nearest underwater canyon head, ocean surface net primary productivity, seafloor water current speeds, temperature and salinity, and modelled availability of ocean surface-derived food at the seafloor.