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  • PANGAEA  (24)
  • Dartmouth, Canada  (1)
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  • 1
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    Unknown
    PANGAEA
    In:  Supplement to: Degen, Renate; Jørgensen, Lis Lindal; Ljubin, Pavel; Ellingsen, Ingrid H; Pehlke, Hendrik; Brey, Thomas (2016): Patterns and drivers of megabenthic secondary production on the Barents Sea shelf. Marine Ecology Progress Series, 546, 1-16, https://doi.org/10.3354/meps11662
    Publication Date: 2023-03-02
    Description: Megabenthos plays a major role in the overall energy flow on Arctic shelves, but information on megabenthic secondary production on large spatial scales is scarce. Here, we estimated for the first time megabenthic secondary production for the entire Barents Sea shelf by applying a species-based empirical model to an extensive dataset from the joint Norwegian- Russian ecosystem survey. Spatial patterns and relationships were analyzed within a GIS. The environmental drivers behind the observed production pattern were identified by applying an ordinary least squares regression model. Geographically weighted regression (GWR) was used to examine the varying relationship of secondary production and the environment on a shelfwide scale. Significantly higher megabenthic secondary production was found in the northeastern, seasonally ice-covered regions of the Barents Sea than in the permanently ice-free southwest. The environmental parameters that significantly relate to the observed pattern are bottom temperature and salinity, sea ice cover, new primary production, trawling pressure, and bottom current speed. The GWR proved to be a versatile tool for analyzing the regionally varying relationships of benthic secondary production and its environmental drivers (R² = 0.73). The observed pattern indicates tight pelagic- benthic coupling in the realm of the productive marginal ice zone. Ongoing decrease of winter sea ice extent and the associated poleward movement of the seasonal ice edge point towards a distinct decline of benthic secondary production in the northeastern Barents Sea in the future.
    Keywords: 2008-GS-140; 2008-GS-144; 2008-GS-147; 2008-GS-151; 2008-GS-152; 2008-GS-175; 2008-GS-178; 2008-GS-183; 2008-GS-186; 2008-GS-190; 2008-GS-193; 2008-GS-194; 2008-GS-196; 2008-GS-199; 2008-GS-200; 2008-GS-260; 2008-GS-285; 2008-GS-286; 2008-GS-311; 2008-GS-312; 2008-GS-313; 2008-GS-314; 2008-GS-315; 2008-GS-318; 2008-GS-319; 2008-GS-320; 2008-GS-321; 2008-GS-322; 2008-GS-323; 2008-GS-324; 2008-GS-325; 2008-GS-326; 2008-GS-327; 2008-GS-328; 2008-GS-329; 2008-GS-330; 2008-GS-331; 2008-GS-332; 2008-GS-333; 2008-GS-334; 2008-GS-335; 2008-GS-336; 2008-JH-322; 2008-JH-323; 2008-JH-324; 2008-JH-325; 2008-JH-326; 2008-JH-327; 2008-JH-328; 2008-JH-383; 2008-JH-386; 2008-JH-391; 2008-JH-393; 2008-JH-394; 2008-JH-398; 2008-JH-401; 2008-JH-402; 2008-JH-403; 2008-JH-410; 2008-JH-411; 2008-JH-414; 2008-JH-418; 2008-VY-003; 2008-VY-006; 2008-VY-008; 2008-VY-010; 2008-VY-012; 2008-VY-014; 2008-VY-016; 2008-VY-018; 2008-VY-020; 2008-VY-022; 2008-VY-024; 2008-VY-026; 2008-VY-028; 2008-VY-033; 2008-VY-035; 2008-VY-037; 2008-VY-039; 2008-VY-041; 2008-VY-043; 2008-VY-045; 2008-VY-047; 2008-VY-049; 2008-VY-051; 2008-VY-053; 2008-VY-055; 2008-VY-057; 2008-VY-059; 2008-VY-061; 2008-VY-063; 2008-VY-065; 2008-VY-067; 2008-VY-069; 2008-VY-071; 2008-VY-073; 2008-VY-075; 2008-VY-076; 2008-VY-077; 2008-VY-078; 2008-VY-079; 2008-VY-081; 2008-VY-082; 2008-VY-083; 2008-VY-085; 2008-VY-087; 2008-VY-089; 2008-VY-091; 2008-VY-093; 2008-VY-095; 2008-VY-097; 2008-VY-099; 2008-VY-101; 2008-VY-103; 2008-VY-105; 2008-VY-107; 2008-VY-109; 2008-VY-111; 2008-VY-113; 2008-VY-114; 2008-VY-116; 2008-VY-118; 2008-VY-120; 2008-VY-123; 2008-VY-126; 2008-VY-128; 2008-VY-130; 2008-VY-132; 2008-VY-134; 2008-VY-136; 2008-VY-138; 2008-VY-140; 2008-VY-142; 2008-VY-144; 2008-VY-146; 2008-VY-148; 2008-VY-153; 2008-VY-155; 2008-VY-157; 2008-VY-158; 2008-VY-160; 2008-VY-162; 2008-VY-164; 2008-VY-166; 2008-VY-168; 2008-VY-170; 2008-VY-172; 2008-VY-174; 2008-VY-176; 2008-VY-178; 2008-VY-180; 2008-VY-182; 2008-VY-184; 2008-VY-186; 2008-VY-188; 2008-VY-190; 2008-VY-192; 2008-VY-194; 2008-VY-196; 2008-VY-198; 2008-VY-200; 2008-VY-202; 2008-VY-204; 2008-VY-206; 2008-VY-208; 2008-VY-210; 2008-VY-212; 2008-VY-214; 2008-VY-216; 2008-VY-218; 2008-VY-220; 2008-VY-222; 2008-VY-224; 2008-VY-226; 2008-VY-228; 2008-VY-229; 2008-VY-232; 2008-VY-234; 2008-VY-236; 2008-VY-238; 2008-VY-240; 2008-VY-243; 2008-VY-244; 2008-VY-245; 2008-VY-246; 2008-VY-248; 2008-VY-251; 2008-VY-253; 2008-VY-254; 2008-VY-255; 2008-VY-256; 2008-VY-257; 2008-VY-258; 2008-VY-259; 2008-VY-260; 2008-VY-261; 2008-VY-262; 2008-VY-264; 2008-VY-265; 2008-VY-267; 2008-VY-268; 2008-VY-269; 2008-VY-271; 2008-VY-272; 2008-VY-273; 2008-VY-275; 2008-VY-277; 2008-VY-278; 2008-VY-279; 2008-VY-280; 2008-VY-281; 2008-VY-282; 2008-VY-283; 2008-VY-284; 2008-VY-285; 2008-VY-288; 2008-VY-290; 2008-VY-291; 2008-VY-292; 2008-VY-293; 2008-VY-294; 2008-VY-296; 2009-GS-142; 2009-GS-143; 2009-GS-146; 2009-GS-154; 2009-GS-155; 2009-GS-158; 2009-GS-159; 2009-GS-162; 2009-GS-163; 2009-GS-166; 2009-GS-167; 2009-GS-170; 2009-GS-171; 2009-GS-174; 2009-GS-175; 2009-GS-178; 2009-GS-179; 2009-GS-182; 2009-GS-184; 2009-GS-187; 2009-GS-188; 2009-GS-191; 2009-GS-192; 2009-GS-195; 2009-GS-196; 2009-GS-203; 2009-GS-204; 2009-GS-207; 2009-GS-208; 2009-GS-211; 2009-JH-282; 2009-JH-284; 2009-JH-286; 2009-JH-288; 2009-JH-290; 2009-JH-292; 2009-JH-294; 2009-JH-296; 2009-JH-298; 2009-JH-305; 2009-JH-307; 2009-JH-311; 2009-JH-313; 2009-JH-318; 2009-JH-325; 2009-JH-327; 2009-JH-333; 2009-JH-335; 2009-JH-337; 2009-JH-339; 2009-JH-341; 2009-JH-345; 2009-JH-347; 2009-JH-350; 2009-JH-353; 2009-JH-356; 2009-JH-362; 2009-JH-365; 2009-JH-368; 2009-JH-371; 2009-JH-373; 2009-JH-375; 2009-JH-377; 2009-JH-379; 2009-JH-383; 2009-JH-385; 2009-JH-390; 2009-JH-392; 2009-JH-395; 2009-JH-398; 2009-JH-400; 2009-JH-403; 2009-JH-405; 2009-JH-407; 2009-JH-410; 2009-JH-412; 2009-JH-417; 2009-JH-422; 2009-JH-424; 2009-JH-427; 2009-JH-429; 2009-JH-431; 2009-JH-433; 2009-JH-436; 2009-JH-438; 2009-JH-442; 2009-JH-445; 2009-JH-447; 2009-JH-449; 2009-JH-452; 2009-JH-454; 2009-JH-456; 2009-JH-461; 2009-JH-463; 2009-JH-465; 2009-JH-468; 2009-JH-470; 2009-JH-472; 2009-JH-475; 2009-JH-478; 2009-JH-480; 2009-JH-482; 2009-JH-484; 2009-JH-486; 2009-JH-488; 2009-JH-490; 2009-JH-492; 2009-JH-494; 2009-JH-496; 2009-JH-497; 2009-JH-500; 2009-JH-502; 2009-JH-504; 2009-JH-506; 2009-JM-491; 2009-JM-495; 2009-JM-497; 2009-JM-499; 2009-JM-506; 2009-JM-509; 2009-JM-519; 2009-JM-522; 2009-JM-527; 2009-JM-528; 2009-JM-532; 2009-JM-541; 2009-JM-543; 2009-JM-544; 2009-JM-549; 2009-JM-550; 2009-JM-555; 2009-JM-557; 2009-JM-559; 2009-JM-560; 2009-JM-561; 2009-JM-563; 2009-JM-565; 2009-JM-566; 2009-JM-568; 2009-JM-572; 2009-JM-574; 2009-JM-578; 2009-JM-582; 2009-JM-586; 2009-JM-587; 2009-JM-590; 2009-JM-592; 2009-JM-595; 2009-JM-599; 2009-JM-602; 2009-JM-604; 2009-JM-607; 2009-JM-609; 2009-JM-611; 2009-JM-613; 2009-JM-615; 2009-JM-617; 2009-VY-01; 2009-VY-02; 2009-VY-03; 2009-VY-04; 2009-VY-05; 2009-VY-06; 2009-VY-07; 2009-VY-08; 2009-VY-09; 2009-VY-10; 2009-VY-11; 2009-VY-12; 2009-VY-13; 2009-VY-14; 2009-VY-15; 2009-VY-16; 2009-VY-18; 2009-VY-19; 2009-VY-20; 58GS2008; 58GS2009; 58JH2008; 58JH2009; 58JM2009; 90VY2008; 90VY2009; Arctic Ocean; Barents Sea; Basis of event; Campaign of event; Date/Time of event; Event label; G. O. Sars (2003); Jan Mayen; Johan Hjort (1990); Kara Sea; Latitude of event; Location of event; Longitude of event; North Greenland Sea; Norwegian Sea; Secondary production as carbon; Vilnyus
    Type: Dataset
    Format: text/tab-separated-values, 398 data points
    Location Call Number Expected Availability
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  • 2
    Publication Date: 2023-09-05
    Description: Interests in exploring Cold Water Corals (CWC) ecosystems witnessed a dramatic increase in the last decades, after the realisation that their habitats are threatened by ocean warming and acidification. However, they are still largely overlooked by the scientific community in deep and harsh environments like the Southern Ocean. Recent advances in species distribution models (SDM) have allowed forecasting species distribution patterns and assessing climate change impacts at different spatial scales. Several limitations related to the accuracy of species presences, the lack of reliable absence data and the limited spatial resolution of environmental factors, have restricted the widespread utilisation of these approaches in polar areas. In this work, real presence-absence records of 13 species were gathered from research expeditions and literature and combined with model-generated pseudo-absences, to cover the study area. Moreover, a final set of 14 high-resolution environmental variables was pre-selected and nine species distribution modelling algorithms were merged with means of the ensemble forecasting platform 'biomod2' to model the habitat suitability for azooxanthallate scleractinian corals, in the Weddell Sea. 'Biomod2' is implemented in 'R' and is a freeware, open source package. Response of scleractinian distribution to the future climate change was also investigated, based on two future scenarios of the bottom sea temperature. Present ensemble prediction maps accurately captured the potential ecological niches of the modelled species (good to excellent true skill statistic (TSS) and area under the receiver operating characteristic curve (AUC) evaluation measures). In the Weddell Sea, scleractinian distribution is limited to the continental shelf and slope areas with preference to small scale features (i.e., seamounts), which have been identified as having a high probability of supporting cold-water coral habitat. The most important factors in determining CWC habitat suitability were distance to coast and ice shelves, bathymetry, calcium carbonate and temperature. The response of scleractinian to future climate revealed some changes in small-scale spatial distribution patterns. Under warmer conditions, the CWC will probably expand their distribution range by a total of 6 to 10%, by 2037 and 2150 respectively, compared to the present. This expansion would concern the Filchner Trough and the adjacent continental shelves as well as the eastern side of the Antarctic Peninsula.
    Keywords: File content; File format; File name; File size; Uniform resource locator/link to file; Weddell_Sea
    Type: Dataset
    Format: text/tab-separated-values, 10 data points
    Location Call Number Expected Availability
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  • 3
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Jerosch, Kerstin; Scharf, Frauke Katharina; Deregibus, Dolores; Campana, Gabriela L; Zacher-Aued, Katharina; Pehlke, Hendrik; Abele, Doris; Quartino, Maria Liliana (in prep.): The potential macroalgae habitat shifts in an Antarctic Peninsula fjord due to climate change.
    Publication Date: 2024-02-16
    Description: Species distribution models (SDM) predict species occurrence based on statistical relationships with environmental conditions. The R-package biomod2 which includes 10 different SDM techniques and 10 different evaluation methods was used in this study. Macroalgae are the main biomass producers in Potter Cove, King George Island (Isla 25 de Mayo), Antarctica, and they are sensitive to climate change factors such as suspended particulate matter (SPM). Macroalgae presence and absence data were used to test SDMs suitability and, simultaneously, to assess the environmental response of macroalgae as well as to model four scenarios of distribution shifts by varying SPM conditions due to climate change. According to the averaged evaluation scores of Relative Operating Characteristics (ROC) and True scale statistics (TSS) by models, those methods based on a multitude of decision trees such as Random Forest and Classification Tree Analysis, reached the highest predictive power followed by generalized boosted models (GBM) and maximum-entropy approaches (Maxent). The final ensemble model used 135 of 200 calculated models (TSS 〉 0.7) and identified hard substrate and SPM as the most influencing parameters followed by distance to glacier, total organic carbon (TOC), bathymetry and slope. The climate change scenarios show an invasive reaction of the macroalgae in case of less SPM and a retreat of the macroalgae in case of higher assumed SPM values.
    Keywords: IMCOAST/IMCONet; Impact of climate induced glacier melt on marine coastal systems, Antarctica; Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; SPP1158
    Type: Dataset
    Format: application/zip, 2 datasets
    Location Call Number Expected Availability
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  • 4
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Jerosch, Kerstin; Scharf, Frauke Katharina; Pehlke, Hendrik; Weber, Lukas; Abele, Doris (in prep.): Explanation of the spatial distribution of physiochemical properties of Potter Cove, Antarctica, by classification of Potter Cove, Antarctica, via k means clustering, canonical-correlation analysis and multidimensional scaling.
    Publication Date: 2024-02-16
    Description: This study subdivides the Potter Cove, King George Island, 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 includes in total 42 different environmental variables, interpolated based on samples taken during Australian summer seasons 2010/2011 and 2011/2012. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared and the most reasonable method has been applied. 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 and 4, 7, 10 as well as 12 were identified as reasonable numbers for clustering the Potter Cove. Especially the results of 10 and 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.
    Keywords: Carlini/Jubany Station; IMCOAST/IMCONet; Impact of climate induced glacier melt on marine coastal systems, Antarctica; Jubany_Dallmann; MULT; Multiple investigations; PotterCove; Potter Cove, King George Island, Antarctic Peninsula
    Type: Dataset
    Format: application/zip, 101.5 MBytes
    Location Call Number Expected Availability
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  • 5
    facet.materialart.
    Unknown
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven | Supplement to: Jerosch, Kerstin; Pehlke, Hendrik; Weber, Lukas; Teschke, Katharina; Heidemann, Teresa; Scharf, Frauke Katharina (in prep.): Comparing the surface and the bottom of the Southern Ocean using multivariate cluster analysis: regional effects of environmental parameters.
    Publication Date: 2024-02-16
    Description: 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.
    Keywords: File format; File name; File size; Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; SPP1158; Uniform resource locator/link to file; Weddell_Sea
    Type: Dataset
    Format: text/tab-separated-values, 16 data points
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  • 6
    Publication Date: 2024-02-16
    Keywords: Carlini/Jubany Station; IMCOAST/IMCONet; Impact of climate induced glacier melt on marine coastal systems, Antarctica; Jubany_Dallmann; MULT; Multiple investigations; PotterCove; Potter Cove, King George Island, Antarctic Peninsula; Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; SPP1158
    Type: Dataset
    Format: application/zip, 4 MBytes
    Location Call Number Expected Availability
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  • 7
    Publication Date: 2024-02-16
    Description: The bathymetry raster with a resolution of 5 m x 5 m was processed from unpublished single beam data from the Argentine Antarctica Institute (IAA, 2010) and multibeam data from the United Kingdom Hydrographic Office (UKHO, 2012) with a cell size of 5 m x 5 m. A coastline digitized from a satellite image (DigitalGlobe, 2014) supplemented the interpolation process. The 'Topo to Raster' tool in ArcMap 10.3 was used to merge the three data sets, while the coastline represented the 0-m-contour to the interpolation process ('contour type option').
    Keywords: Carlini/Jubany Station; File content; File name; File size; IMCOAST/IMCONet; Impact of climate induced glacier melt on marine coastal systems, Antarctica; Jubany_Dallmann; MULT; Multiple investigations; PotterCove; Potter Cove, King George Island, Antarctic Peninsula; Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; SPP1158; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 36 data points
    Location Call Number Expected Availability
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  • 8
    Publication Date: 2024-04-20
    Description: Macroalgae is a central part of marine shelf ecosystems in the Arctic, both as primary producers and as habitat builders and may contribute substantially to the carbon export into the deep sea. In Kongsfjorden we quantified the zonation of visually dominant macroalgal taxa and of detached macroalgae from underwater videos taken in summer 2009 at six transects between 2 to 138 m water depth. Four transects were located at the south shore along the length axis of the fjord (Kongsfjordneset, Brandal, Prince Heinrich Island, Tyskahytta). Two further transects investigated the steep bedrock of Hansneset with a west-east orientation 50 m apart from each other: Hansneset 1 (north) and Hansneset 2 (south). The georeferenced data (date, depth, coordinates) of all transects were linked to the timecode of the video and imported into a geographic coordinate system (GIS). Presence/absence and cover data of macroalgae along the transects was collated into the GIS. The resulting shape files provide useful information for further investigations of macroalgae in the fjord and the geographical information may enhance the repeatability of the investigation in the future.
    Keywords: Binary Object; Binary Object (File Size); Brandal_ROV; Event label; Hansneset_north_ROV; Hansneset_south_ROV; Kongsfjorden, Spitsbergen, Arctic; Kongsfjordneset_ROV; Prince_Heinrich_Island_ROV; Remote operated vehicle; ROV; Tyskahytta_ROV
    Type: Dataset
    Format: text/tab-separated-values, 7 data points
    Location Call Number Expected Availability
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  • 9
    Publication Date: 2024-06-12
    Description: Here we provide an ArcGIS map package on the pelagic regionalisation in the wider Weddell Sea (Antarctica), which were created in the context of the development of a marine protected area (MPA) in the Weddell Sea. For the pelagic regionalisation following parameters were incorporated: (i) ice coverage from AMSR-E sea ice maps, (ii) bathymetric data from the International Bathymetric Chart of the Southern Ocean (IBCSO), and (iii) seawater temperature and salinity data from the Finite Element Sea Ice - Ocean Model (FESOM) provided by R. Timmermann (AWI). To classify different pelagic areas we have applied K-means clustering algorithm and 'clusGap' function from R package 'cluster'. Coastal polynyas mainly occurred east and west of the Prime Meridian (between 20°W to 30°E) as well as around the tip of Antarctic Peninsula, whereas the inner Weddell Sea was characterised by perennial ice-coverage. The largest area proportion of the wider Weddell Sea were classified by above average large water depths and relative high probabilities of ice-free days. More information on the spatial analysis is given in working paper WG-EMM-16/03 submitted to the CCAMLR Working Group on Ecosystem Monitoring and Management (available at https://www.ccamlr.org/en/wg-emm-16).
    Keywords: AWI_FuncEco; Development of a CCAMLR Marine Protected Area in the Antarctic Weddell Sea; File content; File format; File name; File size; Functional Ecology @ AWI; Model; Uniform resource locator/link to file; Wider_Weddell_Sea_Antarctica_pelagic_regionalisati; WSMPA
    Type: Dataset
    Format: text/tab-separated-values, 15 data points
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  • 10
    Publication Date: 2024-06-12
    Description: 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).
    Keywords: Antarctica; Aptenodytes forsteri; AWI_FuncEco; Development of a CCAMLR Marine Protected Area in the Antarctic Weddell Sea; File content; File format; File name; File size; Functional Ecology @ AWI; Marine Protected Area (MPA); Model; Pygoscelis adeliae; Uniform resource locator/link to file; Weddell Sea; Wider_Weddell_Sea_Antarctica_Penguins; WSMPA
    Type: Dataset
    Format: text/tab-separated-values, 30 data points
    Location Call Number Expected Availability
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