Predictions of 27 Arctic pelagic seabird distributions using public environmental variables, assessed with colony data:
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ARCTIC OCEAN DIVERSITY SYNTHESIS
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 Falk Huettmann & Yuri Artukhin & Olivier Gilg & Grant Humphries
Received: 10 May 2010 / Revised: 6 January 2011 / Accepted: 7 January 2011 / Published online: 3 March 2011 # Senckenberg, Gesellschaft für Naturforschung and Springer 2011
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 This article belongs to the special issue "Arctic Ocean Diversity Synthesis" Electronic supplementary material The online version of this article (doi:10.1007/s12526-011-0083-2) contains supplementary material, which is available to authorized users. F. Huettmann (*) : G. Humphries EWHALE lab, Institute of Arctic Biology, Biology & Wildlife Department, University of Alaska, 419 Irving I, Fairbanks, AK 99775, USA e-mail: [email protected] Y. Artukhin Laboratory of Ornithology, Kamchatka Branch of Pacific Inst. of Geography, Russian Academy of Science, Rybakov pr. 19a, Petropavlovsk-Kamchatsky 683024, Russia O. Gilg Laboratoire Biogéosciences, UMR CNRS 5561, Equipe Ecologie Evolutive, Université de Bourgogne, 6 Boulevard Gabriel, 21000 Dijon, France O. Gilg Department of Biological and Environmental Sciences, Division of Population Biology, University of Helsinki, P.O. Box 65, Helsinki 00014, Finland
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,
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