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Matt Oliver Andrew Irwin Oscar Schofield Josh Kohut John Manderson Matt Grossi

Mapping and Using Dynamic Ocean Biogeographic Provinces. Matt Oliver Andrew Irwin Oscar Schofield Josh Kohut John Manderson Matt Grossi. Questions. Can we make objective, dynamic provinces? Do province boundaries reflect hydrography? Do changes in province size reflect real processes?

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Matt Oliver Andrew Irwin Oscar Schofield Josh Kohut John Manderson Matt Grossi

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  1. Mapping and Using Dynamic Ocean Biogeographic Provinces Matt Oliver Andrew Irwin Oscar Schofield Josh Kohut John Manderson Matt Grossi

  2. Questions • Can we make objective, dynamic provinces? • Do province boundaries reflect hydrography? • Do changes in province size reflect real processes? • How can we use provinces for comparative analysis?

  3. Objective unsupervised classification Oliver et al (2004) JGR Oceans SeaWiFSAVHRR SST MODIS/Aqua

  4. Monthly time-series (1998-2007) Maps are easy to make for your own application – see data.mmab.ca Also will be available via http://oceancolor.gsfc.nasa.gov/ soon

  5. ENSO Time Series Blue & green provinces with triangles

  6. ENSO indexes Oliver & Irwin (2008) GRL Province areas (sum of 3) 2003 Jan 2006 Dec

  7. Oligotrophic province area SeaWiFS/AVHRR 2007 Annual

  8. Area of the most oligotrophic province is mostly increasing over time.Combined Desert Areas are Oscillating SeaWiFS & MODIS/Aqua Oliver & Irwin (2009) GRL

  9. PDO & Oligotrophic Province area The total area of the oligotrophic provinces oscillate together with the PDO.

  10. Flavobacteria communities reflect provinces (Sept 2006) Gómez-Pereira et al. 2010, The ISME Journal

  11. Applying Dynamic Biomes to Fisheries Abundance of Longfin squid and Butterfish related to province gradients If Quicktime animation doesn’t automatically open, click this link: play movie

  12. Merging Argo Profiles and Biomes Searching for vertical structure ARGO DATA ASSESSMENT USING QUALITY CONTROL (QC) FLAGS No. of profiles Remaining Downloaded 500253 Missing variables 2000 498253 Failed QC 82962 415291 Profile <10 m 920 414371 Total analyzed: 414371

  13. Modified Holling III Curve All Models are wrong, some are useful – G. Box A=0.08 B=143 C=1027 D=0.005 E=15.7 E A=4.1 B=147 C=1022 D=0.005 E=11.3 D A=7.3 B=137 C=1021 D=0.004 E=1.04 C

  14. Argo Analysis for Biomes of > 1000 profiles

  15. Neural Net Prediction A-E using Wind, Color, SSH, SST

  16. Argo Analysis for desert biome Errors of Profiles

  17. Spatial Distribution of Profile Errors

  18. 3-D density predictions at 4km resolution 2000m Variability > 0.25 kg/m^3

  19. 3-D density predictions at 4km resolution 500m Variability > 0.25 kg/m^3

  20. 3-D density predictions at 4km resolution 100m Variability > 0.25 kg/m^3

  21. Conclusions • Dynamic Provinces reflect hydrography • Dynamic Provinces reflect ENSO and PDO • Provinces reflect Flavobacterial Communities • Province Boundaries are useful for fisheries • Provinces are useful for 3-D predictions of ocean density (moving from biomes to physics)

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