Beschreibung: Here we provide four ArcGIS map packages with georeferenced files on the spatial distribution of Antarctic petrels, Adélie penguins (breeders and non-breeders) and Emperor penguins in the wider Weddell Sea (Antarctica), which were created in the context of the development of a marine protected area in the Weddell Sea. Antarctic petrel (Thalassoica antarctica): We approximated potential foraging habitats of T. antarctica according to existing literature by ice coverage from AMSR-E sea ice maps, bathymetric data from the International Bathymetric Chart of the Southern Ocean (IBCSO), and seawater temperature data from the Finite Element Sea Ice - Ocean Model (FESOM) provided by R. Timmermann (AWI). Subsequently, we combined our Antarctic petrel model with the kernel utilization distribution model from Descamps et al. (2016). The authors kindly provided us with shape files showing the kernel utilization summer and winter distribution of Antarctic petrel breeding at Svarthamaren. Breeding locations and estimated number of breeding pairs were taken from van Franeker et al. (1999). Favourable habitat conditions for Antarctic petrels were predicted for the Lazarev Sea and along the eastern coast of the Weddell Sea, particularly for the area off the Fimbul Ice Shelf and along the coast between approx. 15°E to 10°W within a water depth range from approx. 500 m to 2500 m. Breeding Adélie penguins (Pygoscelis adeliae): The map of potential foraging habitats of breeding P. adeliae is based on British Antarctic Survey (BAS) Inventory data from Phil Trathan (ID 754) and Mike Dunn and P. Trathan (ID 764, 773, 779), a dataset from BAS (P. Trathan) and Instituto Antártico Argentino (Mercedes Santos) (ID 753) and a dataset from the US AMLR Program from Jefferson Hinke and Wayne Trivelpiece (NOAA) (ID 910), which are stored in the Birdlife International's Seabird Tracking Database (data request: 20-10-2015). Suitable foraging habitats for breeding Adélies from colonies from which no tracking data were not available were approximated by a 50 km buffer and a 50-100 km ring buffer around each colony according to the recommendations of a CCAMLR MPA planning workshop. Breeding locations and estimated abundance of breeding pairs were taken from Lynch and LaRue (2014). The tracking data were processed with a state-space model described by Johnson et al. (2008) and were implemented in the R package crawl (Johnson 2011). Jefferson Hinke (NOAA) kindly provided us with support running the R script. Highly suitable foraging habitats occurred about 50 km away from the colonies on King Georg Island, the colony in Hope Bay (Graham Land) and the colonies on the South Orkney Islands. Non-breeding Adélie penguins (Pygoscelis adeliae): The map of potential foraging habitats of non-breeding P. adeliae is based on British Antarctic Survey (BAS) Inventory data from Phil Trathan (ID 754) and Mike Dunn and P. Trathan (ID 773, 779), a dataset from BAS (P. Trathan) and Instituto Antártico Argentino (Mercedes Santos) (ID 753) and a dataset from the US AMLR Program from Jefferson Hinke and Wayne Trivelpiece (NOAA) (ID 910), which are stored in the Birdlife International's Seabird Tracking Database (data request: 20-10-2015). The tracking data were processed with a state-space model described by Johnson et al. (2008) and were implemented in the R package crawl (Johnson 2011). Jefferson Hinke (NOAA) kindly provided us with support running the R script. Highest habitat utilisation was concentrated in relative small areas (e.g., close to King Georg Island). However, the non-breeding Adélies seemed to roam through large parts of the Weddell Sea. Emperor penguins (Aptenodytes forsteri): The probability map of A. forsteri occurrence was developed as a function of distance to colony and colony size from Fretwell et al. (2012, 2014) as well as from sea ice concentration from AMSR-E sea ice maps. Our model of emperor penguin foraging distribution during breeding season showed that the probability of occurrence is highest at the Halley and Dawson colony near Brunt Ice Shelf and at the Atka colony near Ekstrøm Ice Shelf. More information on the spatial analysis is given in working paper WG-EMM-16/03 and WG-SAM-17/30 (for T. antarctica) submitted to the CCAMLR Working Group on Ecosystem Monitoring and Management (EMM) and the CCAMLR Working Group on Statistics, Assessments and Modelling (SAM), respectively (available at https://www.ccamlr.org/en/wg-emm-16 and https://www.ccamlr.org/en/wg-sam-17).

Beschreibung: This study subdivides the Weddell Sea, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis uses 28 environmental variables for the sea surface, 25 variables for the seabed and 9 variables for the analysis between surface and bottom variables. The data were taken during the years 1983-2013. Some data were interpolated. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared for the identification of the most reasonable method. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested. For the seabed 8 and 12 clusters were identified as reasonable numbers for clustering the Weddell Sea. For the sea surface the numbers 8 and 13 and for the top/bottom analysis 8 and 3 were identified, respectively. Additionally, the results of 20 clusters are presented for the three alternatives offering the first small scale environmental regionalization of the Weddell Sea. Especially the results of 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.