Abstract
We present a first compilation, quantification and summary of 27 seabird species presence data for north of the Arctic circle (>66 degrees latitude North) and the ice-free period (summer). For species names, we use several taxonomically valid online databases [Integrated Taxonomic Information System (ITIS), AviBase, 4 letter species codes of the American Ornithological Union (AOU), The British List 2000, taxonomic serial numbers TSNs, World Register of Marine Species (WORMS) and APHIA ID] allowing for a compatible taxonomic species cross-walk, and subsequent applications, e.g., phylogenies. Based on the data mining and machine learning RandomForest algorithm, and 26 environmental publicly available Geographic Information Systems (GIS) layers, we built 27 predictive seabird models based on public open access data archives such as the Global Biodiversity Information Facility (GBIF), North Pacific Pelagic Seabird Database (NPPSD) and PIROP database (in OBIS-Seamap). Model-prediction scenarios using pseudo-absence and expert-derived absence were run; aspatial and spatial model assessment metrics were applied. Further, we used an additional species model performance metric based on the best publicly available Arctic seabird colony location datasets compiled by the authors using digital and literature sources. The obtained models perform reasonably: from poor (only a few coastal species with low samples) to very high (many pelagic species). In compliance with data policies of the International Polar Year (IPY) and similar initiatives, data and models are documented with FGDC NBII metadata and publicly available online for further improvement, sustainability applications, synergy, and intellectual explorations in times of a global biodiversity, ocean and Arctic crisis.
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Acknowledgements
We would like to devote this research to R.G.B. Brown and R. O’Connor for their visions and life-long efforts regarding (arctic) pelagic seabirds, quantitative ecology and modeling .This study and its data are a gift to IPY and the polar and global audience; we hope it leaves a good legacy and can be used with success. We are grateful to IPY and the Arctic Frontiers 2010 conference organizers for encouraging and supporting this project. Special thanks goes to D. Carlson and the IPY team, the GBIF team, Census of Marine Life (COML), J. Ausubel and his team, ArcOD, B. Bluhm, B. Best (R code to harvest GBIF), A. Hahn (for large GBIF query help), the Polar Macroscope crew with CAML, V. Wadley, and H. Beyer (free Hawth’s GIS tools). We acknowledge specifically all data providers in GBIF (see Online Resource 2: THANKS SO MUCH), OBIS-Seamap, and submissions made by the Beringia Seabird Catalogue, NPPD, Bruno Danis at SCAR-MARBIN and other sources mentioned in this publication. We are to blame for potential oversights, data and processing errors. F.H. wishes to thank specifically S. Linke, the EWHALE lab, I. Rutzen for her thesis data work, A. Baltensperger and M. Lindgren for online queries, the AK GAP project, and D. Cairns and A.W. (Tony) Diamond for earlier collaborations and discussions. D. Steinberg from Salford Systems Ltd kindly provided an academic software license. We are very grateful to G.H. for doing most of the actual model-prediction runs. We further thank C. Zoeckler and four anonymous reviewers for input and improvements. M. Gavrilo and others provided friendly discussions, corrections and, some valuable publications. This is EWHALE lab publication #102.
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This article belongs to the special issue "Arctic Ocean Diversity Synthesis"
Electronic supplementary material
Below is the link to the electronic supplementary material.
Online Resource I
List of major sources of bird banding data for the Circumpolar Arctic. (DOC 24 kb)
Online Resource II
Study Area shapefiles (coastline, Arctic Circle polygon and 21 Assessment Areas: Svalbard, Barents Sea, Kara Sea, Wrangel Island, Bering Sea, Franz-Josef-Land, New Siberian Islands, Severnaya Zemlya, Atlantic, West Greenland, North Alaska and Canada, Northeast Greenland, Northeast Canada, Nowaja Zemlya, Central Canadian Arctic, Central Arctic, Eastern Siberian Sea, Western Siberia Sea, Laptev Sea, Canadian Archipelago, East Greenland). (ZIP 371 kb)
Online Resource III
Number of records (by major dataset and provider) in GBIF (data was accessed 15th February 2010) over 66 degrees N latitude (Arctic circle) for the 27 species of this study. The North Pacific Pelagic Seabird Database (NPPSD) was also added externally to these data queries. No relevant online data were found for Aleutian tern, Heuglin’s Gull and Skua. (DOC 496 kb)
Online Resource IV
All presence-only data in CSV (ASCII and jpgs) format for all species listed in Table 1. FGDC NBII Metadata are submitted and available with NBII at the Metadata Clearinghouse website. (ZIP 2846 kb)
Online Resource V
a) All presence/random data as shapefiles (ArcGIS9.3) for all species listed in Table 1 b) All presence/expert-derived absence data as shapefiles (ArcGIS3) for all species listed in Table 1. FGDC NBII Metadata are submitted and available with NBII at the Metadata Clearinghouse website. (ZIP 1318 kb)
Online Resource VI
Individual high-resolution circumpolar seabird prediction maps in ArcGIS (ESRI GRID format) for 27 species of this study. FGDC NBII Metadata are submitted and available with NBII at the Metadata Clearinghouse website. (ZIP 8111 kb)
Online Resource VII
All compiled circumpolar seabird colony locations for the species studied in shapefiles and as jpgs. Data are merged from some available digital databases, and literature references. FGDC NBII Metadata are submitted and available with NBII at the Metadata Clearinghouse website. (ZIP 27245 kb)
Online Resource VIII
List of additional 31 bird species suggested to be included in future marine studies of the circumpolar Arctic. (DOC 23 kb)
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Huettmann, F., Artukhin, Y., Gilg, O. et al. Predictions of 27 Arctic pelagic seabird distributions using public environmental variables, assessed with colony data: a first digital IPY and GBIF open access synthesis platform. Mar Biodiv 41, 141–179 (2011). https://doi.org/10.1007/s12526-011-0083-2
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DOI: https://doi.org/10.1007/s12526-011-0083-2