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Exploring the structure of the oceanic environment: A classification approach Edward Gregr Karin Bodtker Andrew Trites Marine Mammal Research Unit Fisheries.

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Presentation on theme: "Exploring the structure of the oceanic environment: A classification approach Edward Gregr Karin Bodtker Andrew Trites Marine Mammal Research Unit Fisheries."— Presentation transcript:

1 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

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

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

7 Classification method H - means clustering algorithm 1 Sea surface salinity Sea surface temperature o C Identify initial clusters Assign pixels to nearest cluster based on maximum likelihood Iterate until stable 1 Hartigan, J. A Clustering Algorithms. John Wiley & Sons, New York.

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

9 Results: correspond to domains Summer,

10 Results: seasonal variability

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

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

13 Results: biological relevance Chl- a, mg/L 1 Summer, Andrew 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... Funding: NOAA, the North Pacific Marine Science Foundation, and the North Pacific Universities Marine Mammal Research Consortium. 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. Intellectual: Ian Perry, Mike Foreman, Stephen Ban, the MMRU lab, and the attendees of numerous earlier presentations of this work.

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17 Map comparisons Higher score, more similar Seasons more similar between regimes than consecutive seasons within each regime. Summer, Fall, KIA = 0.39 AMI = 2.2 Spring, Spring, KIA = 0.49 AMI = 2.4

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

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


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