Balance Ice Fluxes for the Antarctic ice sheet. These ice fluxes (in km^2/yr)represent the (hypothetical) distribution of ice flux that would keep the Antarctic ice sheet in its present shape (i.e. surface topography), under the influence of a prescribed accumulation distribution. The present fluxes were computed using computer code BalanceV2 (by Warner) (outlined in Budd and Warner 1996, and detailed in Fricker, Warner and Allison 2000), using the surface accumulation dataset of Vaughan et al (1999), and the ice sheet surface elevation dataset distributed by BEDMAP (attributed to Liu et al 1999). This ice flux dataset represents the (hypothetical) distribution of ice flux that would keep the ice sheet topography in its present shape, under the influence of the given accumulation distribution.

Balance Ice Velocities for the Antarctic ice sheet. These ice velocities (in m/yr) represent the (hypothetical) distribution of depth-averaged ice velocities that would keep the Antarctic ice sheet in its present shape (i.e. surface topography and thickness), under the influence of a prescribed accumulation distribution. The present fluxes were computed using computer code BalanceV2 (by Warner) (outlined in Budd and Warner 1996, and detailed in Fricker, Warner and Allison 2000), using the surface accumulation dataset of Vaughan et al (1999), the ice sheet surface elevation dataset distributed by BEDMAP (attributed to Liu et al 1999), and the ice sheet thickness compilation distributed by the BEDMAP consortium (Lythe et al 2001).

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.

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.

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.

The AA4528 corridor dataset contains the Matlab scripts for the corridor algorithm, ice shelf locations and file extensions. The corridor algorithm is designed to calculate the parts of the ocean which can directly propagate swell into an exposed ice shelf. The algorithm achieves this as an expansion of the coastal exposure algorithm (Reid and Massom, 2021), with the details of the inner working of the algorithm work presented in the paper attached with this dataset. Corridors can be used to calculate the frequency of swell reaching an ice shelf per year and can be combined with hindcasts to extract relevant wave data to an ice shelf for modelling or data analysis purposes. The corridor algorithm requires sea ice concentration data, which was provided by the NSIDC Sea ice concentrations from the Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1 (https://nsidc.org/data/nsidc-0051). Ice shelf coordinates were extracted from the gfsc_25s.msk that come with the sea ice data, with the aid of Antarctic Mapping Toolbox (Greene et al., 2017), and were attached separately to make editing more consistent. As this is designed to use daily sea ice data from the 1st of January 1979 onwards, I’ve also attached the sea ice files for the off-days when the sea-ice data was taken every 2nd day. Th file extensions script was also included to be able to switch through off-day files and changes that occur with the NSIDC file format. The ocean hindcast that the corridor algorithm was built around is the CAWCR Wave Hindcast – Aggregated Collection (https://data.csiro.au/collections/collection/CI39819v005). The corridor algorithm uses daily data to make it consistent with the sea ice data and calculated the maximum significant wave height for each cell present in the hindcast. Data that was extracted from it was the maximum daily significant wave height recorded in the corridor and the direction of that cell. Data was taken from 01/09/1979 to 31/08/2019 giving 40 years of data which accounts for seasonality of corridors. The excel spreadsheet attached contains relevant corridor data for each ice shelf with an area greater than 500 km^2. Area was determined by either the supplementary files from Rignot et. al., 2013, or ice shelf areas from the Antarctic mapping toolbox (Greene et al., 2017). Angle1 and Angle2 were the ones used in the direction filter, and there should be a comment in the filter with how it handles if Angle 1 is greater than Angle 2 or vice versa. Ac is the corridor area, PA is potential corridor area (i.e. the absolute max it could be with the settings we used, Ac_max is the maximum corridor area, D_cor is the days that corridors were present, Hs is significant wave height and LW (large waves) is counting days per year when significant wave heights greater than or equal to 6 m (Morim et al., 2021). Refs: Greene, C. A., Gwyther, D. E. and Blankenship, D. D. (2017) ‘Antarctic Mapping Tools for MATLAB’, Computers and Geosciences, 104, pp. 151–157. doi: 10.1016/j.cageo.2016.08.003. Morim, J. et al. (2021) ‘Global-scale changes to extreme ocean wave events due to anthropogenic warming’, Environmental Research Letters, 16(7), p. 074056. doi: 10.1088/1748-9326/ac1013. Reid, P. and Massom, R. (2021) ‘Change and Variability in Antarctic Coastal Exposure , 1979-2020’. In pre-print (https://assets.researchsquare.com/files/rs-636839/v1/02002d0b-2c6c-402b-8e14-7f77075d8f90.pdf?c=1631885736) Rignot, E. et al. (2013) ‘Ice-shelf melting around antarctica’, Science, 341(6143), pp. 266–270. doi: 10.1126/science.1235798.

Data are the MSLP (Mean Sea Level Pressure) field of the Antarctic Mesoscale Prediction System (AMPS) (http://www2.mmm.ucar.edu/rt/amps/) available to download via www.earthsystemgrid.org. Data are 45km resolution for the domain d001 (lower left lat/lon = -24.72209 N, 38.30463 E, upper right lat/lon = -21.82868 N, -144.07805 E). Data are 3-hourly forecasts (t=0 to t=120) made every 12 hours using the Polar Weather Research and Forecasting (WRF) model. Data has been converted from grib to nc, 45km resolution polar stereographic to a 0.5 degree resolution latlon grid and concatenated into a single continuous dataset using the first 4 forecasts from each 12-hours. Where data was missing forecasts from the previous 12-hours are used. Data available: 28/10/2008 to 31/12/2012. Data were processed in this manner to be usable by the Melbourne University cyclone tracking scheme (Murray, R. J., and I. Simmonds (1991) A numerical scheme for tracking cyclone centres from digital data. Part I: Development and operation of the scheme, Australian Meteorological Magazine, 39, 155-166.) to investigate Antarctic polar lows. Data are 3-hourly forecasts (from t=0 to t=120) made every 12 hours, which have been processed into a continuous 3-hourly dataset using the first 4 forecasts of every 12 hours. Missing data are filled by previous forecasts.

An R data file containing a hierarchical switching state-space model of pygmy blue whale Argos-collected telemetry data using the bsam package (see Jonsen (2016). Joint estimation over multiple individuals improves behavioural state inference from animal movement data. Scientific Reports 6: 20625.) in R. The model estimated location states for each individual at regular 3-h time intervals, accounting for measurement error in the irregularly observed Argos surface locations; and estimated the behavioural state associated with each location. Satellite tags were deployed on pygmy blue whales located in the Bonney Upwelling region, SA, between 7 January and 16 March 2015. File can be opened in R (A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ ) using the code: readRDS('bw_3h_ssm.RDS')

This integrated stock assessment for the Patagonian toothfish (Dissostichus eleginoides) fishery at the Heard Island and the McDonald Islands in CCAMLR Division 58.5.2, with data until end of July 2015, is based on the best available estimates of model parameters, the use of abundance estimates from a random stratified trawl survey (RSTS), longline tag-release data from 2012-2014 and longline tag-recapture data from 2013-2015, and auxiliary commercial composition data to aid with the estimation of year class strength and selectivity functions of the trawl, longline and trap sub-fisheries.All model runs were conducted with CASAL version 2.30-2012-03-21 (Bull et al. 2012). The assessment model leads to an MCMC estimate of the virgin spawning stock biomass B0 = 87 077 tonnes (95% CI: 78 500-97 547 tonnes). Estimated SSB status in 2015 was 0.64 (95% CI: 0.59-0.69). Using this model, a catch limit of 3405 tonnes satisfies the CCAMLR decision rules. Similarly to the 2014 assessment, the projected stock remains above the target level for the entire projection period.

This dataset contains environmental layers used to model the predicted distribution of demersal fish bioregions for the paper: Hill et al. (2020) Determining Marine Bioregions: A comparison of quantitative approaches, Methods in Ecology and Evolution. It contains climatological variables from satellite and modelled data that represent sea floor and sea surface conditions likely to affect the distribution of demersal fish including: depth, slope, seafloor temperatures, seafloor current, seafloor nitrate, sea surface temperature, chlorophyll-a standard deviation and sea surface height standard deviation. Layers are presented at 0.1 degree resolution. "prediction_space" is a Rda file for R that consists of two objects: env_raster: a raster stack of the environmental layers pred_sp: a data.frame version of the env_raster where some variables have been transformed for statistical analysis and bioregion prediction. "Env_data_sources.xlsx" contains a description of each environmental variable and it's source.