Towards an acoustic-based coupled observation and modelling system for monitoring and predicting ecosystem dynamics of the open ocean

Author:

Handegard Nils Olav1,Buisson Louis du23,Brehmer Patrice4,Chalmers Stewart J5,De Robertis Alex6,Huse Geir1,Kloser Rudy7,Macaulay Gavin1,Maury Olivier38,Ressler Patrick H6,Stenseth Nils Chr910,Godø Olav Rune1

Affiliation:

1. Institute of Marine Research; PO Box 1870 Nordnes 5817 Bergen Norway

2. Zoology Department; University of Cape Town; Private Bag X3 Rondebosch 7701 Cape Town South Africa

3. Institut de Recherche pour le Développement; UMR EME (IRD-IFREMER-UM2) Avenue Jean Monnet BP 171 34203 Sète Cedex France

4. Institut de Recherche pour le Développement; UMR LEMAR (UBO-CNRS-IRD-IFREMER), BP 70 29280 Plouzané France

5. University of Aberdeen, Oceanlab; Newburgh Aberdeenshire AB41 6AA UK

6. Department of Commerce; Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration; 7600 Sand Point Way N.E. Seattle WA 98115 USA

7. CSIRO Marine and Atmospheric Research; GPO Box 1538 Hobart TAS 7001 Australia

8. Department of Oceanography; University of Cape Town; Private Bag X3 Rondebosch 7701 Cape Town South Africa

9. Centre for Ecological and Evolutionary Synthesis (CEES); Department of Biology, University of Oslo; P.O. Box 1066 Blindern, Oslo Norway

10. Institute of Marine Research, Flødevigen Marine Research Station; 4817 His Norway

Publisher

Wiley

Subject

Management, Monitoring, Policy and Law,Aquatic Science,Ecology, Evolution, Behavior and Systematics,Oceanography

Reference55 articles.

1. The ecology, distribution, and abundance of midwater fishes of the Subarctic Pacific gyres;Beamish;Progress In Oceanography,1999

2. Towards improved understanding of the diversity and abundance patterns of the mid-ocean ridge macro- and megafauna;Bergstad;Deep Sea Research Part II: Topical Studies in Oceanography,2008

3. Global fish production and climate change;Brander;Proceedings of the National Academy of Sciences,2007

4. Maximum likelihood estimation in nonlinear structured fisheries models using survey and catch-at-age data;Brinch;Canadian Journal of Fisheries and Aquatic Sciences,2011

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