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Exploring the structure of the oceanic environment: A classification approach

Exploring the structure of the oceanic environment: A classification approach. Edward Gregr Karin Bodtker Andrew Trites. Marine Mammal Research Unit Fisheries Centre University of British Columbia October 2004. Why classify oceanic structure?. related to biological spatial distributions

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Exploring the structure of the oceanic environment: A classification approach

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  1. Exploring the structure of the oceanic environment: A classification approach Edward GregrKarin BodtkerAndrew Trites Marine Mammal Research Unit Fisheries Centre University of British Columbia October 2004

  2. Why classify oceanic structure? • related to biological spatial distributions • temporal changes (e.g. regime shifts) • Steller sea lion in an ecosystem context

  3. Oceanic structure classified Dodimead et al. 1963

  4. Extending the classification approach • biological perspective • quantitative and repeatable • adaptable • consider temporal variability (seasons, years, regimes) • different spatial scales (zooplankton vs. fish vs. sea lions)

  5. High density Residential Industrial Roads Water Pasture Forest Wetland Grass A quantitative approache.g. classifying landscapes

  6. Wind stress SSH Surface current speed SST SSS Data for oceanic classification 1 degree ROMS output1, interpolated to equal area grid. Seasonal averages,1966-1975 and 1980-1989. 1Yi Chao, Jet Propulsion Lab, California Institute of Technology

  7. Sea surface salinity 33 31 32 34 35 0.0 -0.1 -0.2 -0.3 Sea surface temperature oC -0.4 -0.5 -0.6 -0.7 -0.8 + + + + + Classification methodH - means clustering algorithm1 Identify initial clusters Assign pixels to ‘nearest’ cluster based on maximum likelihood Iterate until stable 1Hartigan, J. A. 1975. Clustering Algorithms. John Wiley & Sons, New York.

  8. 60° 50° 40° 130° 30° 140° 150° 130° 180° 160° 170° 170° 160° 150° 140° Results: summer, 1966-1975

  9. Results: correspond to domains Summer, 1966-1975

  10. Results: seasonal variability

  11. 60° Pre - winter Post - winter 50° 40° 130° 30° 140° 150° 130° 180° 160° 170° 170° 160° 150° 140° Results: regime variability • Alaska gyre: evidence of stronger flow post - 1976 • Transitional domain: boundary shift

  12. Post-76 Pre-76 Winter Spring Summer Fall • Consistency between some seasons differs before and after regime shift Results: map comparisons • Seasons more similar between regimes than consecutive seasons within each regime

  13. 1.38 0.70 1.03 0.41 0.56 Results: biological relevance Chl-a, mg/L1 Summer, 1997-2003 1Andrew Thomas, School of Marine Sciences, University of Maine

  14. Summary • quantitative and adaptable approach • regions correspond to classic domains • temporal differences mapped and quantified • regions have biological relevance

  15. Thanks very much ... Intellectual:Ian Perry, Mike Foreman, Stephen Ban, the MMRU lab, and the attendees of numerous earlier presentations of this work. Data:Yi Chao, Jet Propulsion Lab, California; Mike Foreman, Institute of Ocean Sciences, British Columbia; Al Hermann, PMEL, Washington; Wieslaw Maslowski, Naval Postgraduate School, California; Andy Thomas, University of Maine, Maine. Funding:NOAA, the North Pacific Marine Science Foundation, and the North Pacific Universities Marine Mammal Research Consortium.

  16. Fall, 1980 - 1989 Summer, 1980 - 1989 KIA = 0.39 AMI = 2.2 Spring, 1966 - 1975 Spring, 1980 - 1989 KIA = 0.49 AMI = 2.4 Map comparisons Higher score, more similar Seasons more similar between regimes than consecutive seasons within each regime.

  17. Classification algorithm Selecting the number of clusters to keep Keep 6 or 8 clusters

  18. Oceanic structure classified Biomes and provinces of Longhurst 1998 • variability within not evident • boundaries may shift

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