Correlative and Dynamic Species Distribution
PERSPECTIVE Correlative and dynamic species distribution modelling for ecological predictions in the Antarctic: a cross-disciplinary concept Julian Gutt,1 Damaris Zurell,2 Thomas J. Bracegridle,3 William Cheung,4 Melody S. Clark,3 Peter Convey,3 Bruno Danis,5 Bruno David,6 Claude De Broyer,10 Guido di Prisco,7 Huw Griffiths,3 Re´ mi Laffont,6 Lloyd S. Peck,3 Benjamin Pierrat,6 Martin J. Riddle,8 Thomas Sauce` de,6 John Turner,3 Cinzia Verde,7 Zhaomin Wang3 & Volker Grimm9 1 Department of Bentho-pelagic processes, Alfred Wegener Institute, Columbusstr., DE-27568 Bremerhaven, Germany 2 Institute of Earth and Environmental Sciences, University of Potsdam, Karl-Liebknecht-Str. 24-25, DE-14476 Potsdam, Germany 3 British Antarctic Survey, High Cross, Madingley Road, Cambridge CB3 0ET, UK 4 Fisheries Centre, University of British Columbia, 2202 Main Mall Vancouver, BC, Canada 5 Department of Antarctic biodiversity information facility, Royal Belgian Institute of Natural Sciences, rue Vautier, 29, BE-1000 Brussels, Belgium 6 Bioge´ osciences, Laboratory 6282, Centre National de la Recherche Scientifique, Universite´ de Bourgogne, 6 boulevard Gabriel, FR-21000 Dijon, France 7 Institute of Protein Biochemistry, National Research Council, Via Pietro Castellino 111, IT-80131 Naples, Italy 8 Australian Antarctic Division, 203 Channel Highway, Kingston, Tasmania 7050, Australia 9 Department of Ecological Modelling, Helmholtz Centre for Environmental Research, Permoserstr. 15, DE-04318 Leipzig, Germany 10 Department of Invertebrates, Royal Belgian Institute of Natural Sciences, rue Vautier, 29, BE-1000 Brussels, Belgium Keywords Abstract Environmental change; integrative modelling framework; spatially and Developments of future scenarios of Antarctic ecosystems are still in their temporally explicit modelling infancy, whilst predictions of the physical environment are recognized as being macroecology; biodiversity; habitat of global relevance and corresponding models are under continuous develop- suitability models.
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