From the abstract of some of the referenced papers: An expert system is being developed which will apply knowledge-based techniques to the automated interpretation of remotely sensed sea-ice images taken over East Antarctica by the NOAA series of meteorological satellites. It is capable of accepting satellite images, deriving characteristic features from them and then performing knowledge-based reasoning to identify regions of cloud, land, open water and various categories of sea-ice. XXXXXXXXXXXXX This paper describes the system design of SPARTEX, a system developed to use information from remote sensing and geographic information systems linked to expert systems. It aims to automate the process of classifying information about the actual or potential use of part of the earth's surface. See the link below for public details on this project.

This data set contains the results from a study of the behaviour of Weddell seals (Leptonychotes weddelli) at the Vestfold Hills, Prydz Bay, Antarctica. Three satellite transmitters were deployed on tagged female Weddell seals at the Vestfold Hills mid-winter (June) 1999. The transmitters were recovered in December, late in the pupping season. In total, the three transmitters were deployed and active 170 days, 175 days and 180 days. I used the first two classes of data to get fixes with a standard deviation less than 1 km. Most seal holes were more that 1 km apart (see Entry: wed_survey) so at this resolution we can distinguish between haul-out sites. We examine the number and range of locations used by the individual seals. We use all data collectively to look at diurnal and seasonal changes in haul-out bouts. None of the seals were located at sites outside the area of fast ice at the Vestfold Hills, although one seal was sighted on new fast-ice (20 - 40 cm thick). Considering the long bouts in the water, and that we only tracked haul-out locations, the results do not eliminate the possibility that the seals made long trips at sea. The original data are stored by the Australian Antarctic Division in the ARGOS system on the mainframe Alpha. The transmitter numbers are 23453, 7074 and 7075.

The GEBCO_2021 Grid provides global coverage of elevation data in meters on a 15 arc-second grid of 43200 rows x 86400 columns, giving 3,732,480,000 data points. The GEBCO 2021 grid is reformatted as a Cloud Optimised GeoTIFF suitable for online requests and republished for use by science software. Original GEBCO grid was obtained from https://www.gebco.net/data_and_products/gridded_bathymetry_data/gebco_2021/

Public summary for project 2128: The aim of this study is to relate the foraging behaviour of Antarctic fur seals breeding on the Kerguelen Plateau at Iles Kerguelen and Heard Island, to the distribution of prey species at sea. Specifically this project seeks to examine the relationship between predators and prey, and how their locations at sea vary according to the position of major productive zones, such as the Antarctic Polar Frontal Zone. This project will provide important data on the relationship between predators and their prey and the developing commercial fisheries in the region. These data are central to improved conservation and management of marine resources on the Kerguelen Plateau. Variations made to the work plan The original comparative aspects of the program planned for the 1999/00 season, where fur seals from Iles Kerguelen and Heard Island were to be satellite tracked simultaneously could not be undertaken because of original 1999/00 field season to Heard Island was re-scheduled to 2000/01. Fortunately the project collaborator Dr Christophe Guinet (French CEBC-CNRS) agreed to extend the work program at Iles Kerguelen another season, and the comparative and integrated fur seal-prey-fisheries study over the Kerguelen Plateau was undertaken the following season (2000/01). Details of this study are presented in ASAC project 1251 (CI - Goldsworthy)and 1085 (CI-Robertson). Significant findings: The distribution of the foraging activity of Antarctic fur seal females was investigated at Cap Noir (49 degrees 07 S, 70 degrees 45E), Kerguelen Island in February 1998. Eleven females were fitted with a satellite transmitter and Time Depth recorder. The two sets of data were combined to locate spatially the diving activity of the seals. The fish component of the fur seal diet was determined by the occurrence of otolotihs found in 55 scats collected during the study period at the breeding colony. Oceanographic parameters were obtained simultaneously through direct sampling and satellite imagery. The mesopelagic fish community was sampled on 20 stations along four transects where epipelagic trawls were conducted at night at 50 meters of depth. We then investigated, using geographic information systems, the relationship between the spatial distribution of the diving activity of the fur seals and oceanographic factors that included sea surface temperature, surface chlorophyll concentration, prey distribution and bathymetry obtained at the same spatio-temporal scale as the spatial distribution of the diving activity of our study animals. An inverse relationship was found between the main fish species preyed by fur seal and those sampled in trawl nets. However, the diving activity of Antarctic fur seal females was found to be significantly related to oceanographic conditions, fish-prey distribution and to the distance from the colony but these relationships changed with the spatial scale investigated. A probabilistic model of the Kerguelen Plateau was developed that predicted where females should concentrate their foraging activity according to the oceanographic conditions of the year, and the locations of their breeding colonies. Maternal allocation in growth of the pup was measured in Antarctic fur seals (Arctocephalus gazella) at Iles Kerguelen during the 1997 austral summer. Absolute mass gain of pups following a maternal foraging trip was independent of the sex of the pup but was positively related to the foraging trip duration and to maternal length. However, daily mass gain, i.e. the absolute mass gain of the pup divided by the foraging trip duration, decreased with increasing foraging trip duration but increased with maternal length. While fasting, the daily mass loss of the pup was related to the sex of the pup and initial body mass, with both heavier pups and female pups losing more mass per day than lighter pups and male pups. The mass specific rate of mass loss was significantly higher in female pups than in male pups. Over the study period, the mean growth rate was zero with no difference between female and male pups. The growth rate in mass of the pup was positively related to maternal length but not maternal condition, negatively related to the foraging trip duration of the mother and the initial mass of the pup. This indicated that during the study period heavier pups grew more slowly due to their higher rate of daily mass loss during periods of fasting . Interestingly, for a given maternal length, the mean mass of the pup during the study period was higher for male than for female pups, despite the same rate of daily mass gain. Such differences are likely to result from sex differences in the mass specific rate of mass loss. As female pups lose a greater proportion of their mass per day, a zero growth rate i.e. mass gain only compensates for mass loss, is reached at a lower mass in female pups compared to male pups. Our results indicate that there are no differences in maternal allocation according to the sex of the pup but suggest that both sexes follow a different growth strategy. Results are in line with the objectives of the project. animal_id (identifier of the individual animal) location_class (the Argos location class quality, 0-3) latitude (decimal degrees) longitude (decimal degrees) observation_date (the date of observation, in ISO8601 format yyyy-mm-ddTHH:MM:SSZ. This information is also separated into the year, month, day, etc components) observation_date_year (the year of the observation date) observation_date_month (the month of the observation date) observation_date_day (the day of the observation date) observation_date_hour (the hour of the observation date) observation_date_minute (the minute of the observation date) observation_date_time_zone (the time zone of the observation date) deployment_longitude (location that the tracker was deployed, decimal longitude) deployment_latitude (location that the tracker was deployed, decimal latitude) trip (the identifier of the trip made by this animal) at_sea (whether this point was at sea (1) or on land (0)) complete (was this trip complete - i.e. did the animal return to the colony) scientific_name (scientific name of the tracked animal)

This layer is a circumpolar, pelagic regionalisation of the Southern Ocean south of 40 degrees S, based on sea surface temperature, depth, and sea ice information. The results show a series of latitudinal bands in open ocean areas, consistent with the oceanic fronts. Around islands and continents, the spatial scale of the patterns is finer, and is driven by variations in depth and sea ice. The processing methods follow those of Grant et al. (2006) and the CCAMLR Bioregionalisation Workshop (SC-CAMLR-XXVI 2007). Briefly, a non-hierarchical clustering algorithm was used to reduce the full set of grid cells to 250 clusters. These 250 clusters were then further refined using a hierarchical (UPGMA) clustering algorithm. The first, non-hierarchical, clustering step is an efficient way of reducing the large number of grid cells, so that the subsequent hierarchical clustering step is tractable. The hierarchical clustering algorithm produces a dendrogram, which can be used to guide the clustering process (e.g. choices of data layers and number of clusters) but is difficult to use with large data sets. Analyses were conducted in Matlab (Mathworks, Natick MA, 2011) and R (R Foundation for Statistical Computing, Vienna 2009). Three variables were used for the pelagic regionalisation: sea surface temperature (SST), depth, and sea ice cover. Sea surface temperature was used as a general indicator of water masses and of Southern Ocean fronts (Moore et al. 1999, Kostianoy et al. 2004). Sea surface height (SSH) from satellite altimetry is also commonly used for this purpose (e.g. Sokolov and Rintoul 2009), and may give front positions that better match those from subsurface hydrography than does SST. However, SSH data has incomplete coverage in some near-coastal areas (particularly in the Weddell and Ross seas) and so in the interests of completeness, SST was used here. During the hierarchical clustering step, singleton clusters (clusters comprised of only one datum) were merged back into their parent cluster (5 instances, in cluster groups 2, 3, 8, and 13). Additionally, two branches of the dendrogram relating to temperate shelf areas (around South America, New Zealand, and Tasmania) were merged to reduce detail in these areas (since such detail is largely irrelevant in the broader Southern Ocean context).

From the abstract of the referenced paper: Satellite telemetry data are a key source of animal distribution information for marine ecosystem management and conservation activities. We used two decades of telemetry data from the East Antarctic sector of the Southern Ocean. Habitat utilization models for the spring/summer period were developed for six highly abundant, wide-ranging meso- and top-predator species: Adelie, Pygoscelis adeliae and emperor, Aptenodytes forsteri penguins, light-mantled albatross, Phoebetria palpebrata, Antarctic fur seals, Arctocephalus gazella, southern elephant seals, Mirounga leonina, and Weddell seals, Leptonychotes weddellii. The regional predictions from these models were combined to identify areas utilized by multiple species, and therefore likely to be of particular ecological significance. These areas were distributed across the longitudinal breadth of the East Antarctic sector, and were characterized by proximity to breeding colonies, both on the Antarctic continent and on subantarctic islands to the north, and by sea-ice dynamics, particularly locations of winter polynyas. These areas of important habitat were also congruent with many of the areas reported to be showing the strongest regional trends in sea ice seasonality. The results emphasize the importance of on-shore and sea-ice processes to Antarctic marine ecosystems. Our study provides ocean-basin-scale predictions of predator habitat utilization, an assessment of contemporary habitat use against which future changes can be assessed, and is of direct relevance to current conservation planning and spatial management efforts. The data files provided here comprise the model predictions of the preferred habitat for each of the six species listed above, as well as the overlap results obtained by combining these six sets of results. See the paper for methods used to generate the model predictions and to combine the individual species results. File names for individual species are of the form results_SPP_TYPE.asc, where SPP is one of "afs" (Antarctic fur seal), "ap" (Adelie penguin), "ep" (emperor penguin), "lma" (light-mantled albatross), "ses" (southern elephant seal), or "ws" (Weddell seal. TYPE is either "mean" (mean estimate of habitat preference) or "iqr" (inter-quartile range of uncertainty in the estimate; see paper for details). Data values for individual species results are percentiles of the study area, so that values of 90% or higher are pixels corresponding to the most important 10% of habitat for that species, values of 80% or greater are the top 20% of habitat, and so on. The overlap results files are named overlay_results_mean.asc and overlay_results_iqr.asc. Values in these files represent the average of the top four individual species results in a given pixel (see paper for details).

The demographic performance of high level antarctic predators is ultimately determined by the oceanic processes that influence the spatial and temporal distribution of primary productivity. This study will quantify the links between the foraging performance of southern elephant seals and a range of oceanographic parameters, including sea surface temperature, productivity and bathymetry. These data are a crucial component in understanding how antarctic predators will respond to changes in the distribution of marine and will be an important contribution to our understanding of the on-going decline in elephant seal numbers. Data were originally collected on Time Depth Recorders (TDRs), and stored in hexadecimal format. Hexadecimal files can be read using 'Instrument Helper', a free download from Wildlife Computers (see the URL given below). However, these data have been replaced by an Access Database version, and have also been loaded into the Australian Antarctic Data Centre's ARGOS tracking database. The database can be accessed at the provided URLs.

The foraging ecology of three fulmarine petrels including Cape petrels, Southern fulmars and Antarctic petrels were investigated at Hop Island during the 2015/16 austral summer. Two datasets were generated: 1) tracking data from Fulmarine petrels, and 2) stable isotope analysis of blood, feathers and egg shells. Tracking data were collected using Ecotone GPS trackers attached to the birds back feathers with tape. Location data has been interpolated using great circle distance to a time step of 15 minutes and include a record of whether the bird dived during that time period or not. Each location point was assigned a breeding stage (incubation or chick rearing) based on individual nest activities. Stable isotope ratios of carbon (13C/12C) and nitrogen (15N/14N) were determined by analysing 1 mg aliquots through continuous flow - elemental analysis - isotope ratio mass spectrometry (CF-EA-IRMS). Isotopic values of blood reflect approximately the last 52 days before sampling and thus the incubation period of all three species. Egg membranes and feathers remain metabolically inert after formation, and hence reflect the trophic niche during the pre-laying and moult period, respectively. We collected moult feathers during the chick-rearing period and therefore assumed that these were formed one year prior to the collection date and thus represent the trophic niche of the chick-rearing period one year earlier (austral summer 2014-15).

A summary of landfast sea ice coverage and the changes in the distance between the penguin colony at Point Geologie and the nearest span of open water on the Adelie Land coast in East Antarctica. The data were derived from cloud-free NOAA Advanced Very High Resolution Radiometer (AVHRR) data acquired between 1-Jan-1992 and 31-Dec-1999. The areal extent and variability of fast ice along the Adelie Land coast were mapped using time series of NOAA AVHRR visible and thermal infrared (TIR) satellite images collected at Casey Station (66.28 degrees S, 110.53 degrees E). The AVHRR sensor is a 5-channel scanning radiometer with a best ground resolution of 1.1 km at nadir (Cracknell 1997, Kidwell 1997). The period covered began in 1992 due to a lack of sufficient AVHRR scans of the region of interest prior to this date and ended in 1999 (work is underway to extend the analysis forward in time). While cloud cover is a limiting factor for visible-TIR data, enough data passes were acquired to provide sufficient cloud-free images to resolve synoptic-scale formation and break-up events. Of 10,297 AVHRR images processed, 881 were selected for fast ice analysis, these being the best for each clear (cloud-free) day. The aim was to analyse as many cloud-free images as possible to resolve synoptic-scale variability in fast ice distribution. In addition, a smaller set of cloud-free images were obtained from the Arctic and Antarctic Research Center (AARC) at Scripps Institution of Oceanography, comprising 227 Defense Meteorological Satellite Program (DMSP) Operational Linescan Imager (OLS) images (2.7 km resolution) and 94 NOAA AVHRR images at 4 km resolution. The analysis also included 2 images (spatial resolution 140 m) from the US Argon surveillance satellite programme, originally acquired in 1963 and obtained from the USGS EROS Data Center (available at: edcsns17.cr.usgs.gov/EarthExplorer/). Initial image processing was carried out using the Common AVHRR Processing System (CAPS) (Hill 2000). This initially produces 3 brightness temperature (TB) bands (AVHRR channels 3 to 5) to create an Ice Surface Temperature (IST) map (after Key 2002) and to enable cloud clearing (after Key 2002 and Williams et al. 2002). Fast ice area was then calculated from these data through a multi-step process involving user intervention. The first step involved correcting for anomalously warm pixels at the coast due to adiabatic warming by seaward-flowing katabatic winds. This was achieved by interpolating IST values to fast ice at a distance of 15 pixels to the North/South and East/ West. The coastline for ice sheet (land) masking was obtained from Lorenzin (2000). Step 2 involved detecting open water and thin sea ice areas by their thermal signatures. Following this, old ice (as opposed to newly-formed ice) was identified using 2 rules: the difference between the IST and TB (band 4, 10.3 to 11.3 microns) for a given pixel is plus or minus 1 K and the IST is less than 250 K. The final step, i.e. determination of the fast ice area, initially applied a Sobel edge-detection algorithm (Gonzalez and Woods 1992) to identify all pixels adjacent to the coast. A segmentation algorithm then assigned a unique value to each old ice area. Finally, all pixels adjacent to the coast were examined using both the segmented and edge-detected images. If a pixel had a value (i.e. it was segmented old ice), then this segment was assumed to be attached to the coast. This segment's value was noted and every pixel with the same value was classified as fast ice. The area was then the product of the number of fast ice pixels and the resolution of each pixel. A number of factors affect the accuracy of this technique. Poorly navigated images and large sensor scan angles detrimentally impact image segmentation, and every effort was taken to circumvent this. Moreover, sub-pixel scale clouds and leads remain unresolved and, together with water vapour from leads and polynyas, can contaminate the TB. In spite of these potential shortcomings, the algorithm gives reasonable and consistent results. The accuracy of the AVHRR-derived fast ice extent retrievals was tested by comparison with near- contemporary results from higher resolution satellite microwave data, i.e. from the Radarsat-1 ScanSAR (spatial resolution 100 m over a 500 km swath) obtained from the Alaska Satellite Facility. The latter were derived from a 'snapshot' study of East Antarctic fast ice by Giles et al. (2008) using 4 SAR images averaged over the period 2 to 18 November 1997. This gave an areal extent of approximately 24,700 km2. The comparative AVHRR-derived extent was approximately 22,240 km2 (average for 3 to 14 November 1997). This is approximately 10% less than the SAR estimate, although the estimates (images) were not exactly contemporary. Time series of ScanSAR images, in combination with bathymetric data derived from Porter-Smith (2003), were also used to determine the distribution of grounded icebergs. At the 5.3 GHz frequency (? = 5.6 cm) of the ScanSAR, icebergs can be resolved as high backscatter (bright) targets that are, in general, readily distinguishable from sea ice under cold conditions (Willis et al. 1996). In addition, an estimate was made from the AVHRR derived fast ice extent product of the direct-path distance between the colony at Point Geologie and the nearest open water or thin ice. This represented the shortest distance that the penguins would have to travel across consolidated fast ice in order to reach foraging grounds. A caveat is that small leads and breaks in the fast ice remain unresolved in this satellite analysis, but may be used by the penguins. We examine possible relationships between variability in fast ice extent and the extent and characteristics of the surrounding pack ice (including the Mertz Glacier polynya to the immediate east) using both AVHRR data and daily sea ice concentration data from the DMSP Special Sensor Microwave/Imager (SSM/I) for the sector 135 to 145 degrees E. The latter were obtained from the US National Snow and Ice Data Center for the period 1992 to 1999 inclusive (Comiso 1995, 2002). The effect of variable atmospheric forcing on fast ice variability was determined using meteorological data from the French coastal station Dumont d'Urville (66.66 degrees S, 140.02 degrees E, WMO #89642, elevation 43 m above mean sea level), obtained from the SCAR READER project ( www.antarctica.ac.uk/met/READER/). Synoptic- scale circulation patterns were examined using analyses from the Australian Bureau of Meteorology Global Assimilation and Prediction System, or GASP (Seaman et al. 1995).

An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Merrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157 Cape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011 Ivanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 Please refer to the Seabird Conservation Team Data Sharing Policy for use, acknowledgement and availability of data prior to downloading data.