1 / 26

Joseph Maina 1 Valentijn Venus 2

Modelling ecological susceptibility of coral reefs to environmental stress using remote sensing, GIS and in situ observations: A case study in the Western Indian Ocean. Joseph Maina 1 Valentijn Venus 2. Ecological Modelling, in Review. 1 Mombasa, Kenya 2 .ITC, Enschede, The Netherlands.

meadow
Download Presentation

Joseph Maina 1 Valentijn Venus 2

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Modelling ecological susceptibility of coral reefs to environmental stress using remote sensing, GIS and in situ observations: A case study in the Western Indian Ocean Joseph Maina1 Valentijn Venus2 Ecological Modelling, in Review 1Mombasa, Kenya 2.ITC, Enschede, The Netherlands

  2. Coral Reef Ecosystems • Most diverse marine ecosystems • Economic value • Geophysical value

  3. Problems • Ecological shift • Decline in coral cover • Loss of live livelihood Source: Gardner et al., 2003

  4. Climate change and coral bleaching • Climate models forecast: • SST increased by 1oC for last 100 yrs • Current increase 1-2 oC per century • Corals near their thermal threshold • Increased frequency and intensity of coral bleaching

  5. Case study: Western Indian Ocean

  6. Main objectives • Relative importance of environmental variables -spatial pattern of coral bleaching • Identify specific areas likely to be resilient • Suitability of low-moderate spatial resolution remote sensors

  7. Methods: research approach 2 3 1 4 5

  8. Methods: satellite data **28 Derived variables: long term and short term ≈ 5000 images

  9. Satellite-in situ comparison Unpublished in situ data by Dr.Tim McClanahan, WCS

  10. Methods: bleaching observation data • 33405 colonies sampled from 66 reefs • (WCS) • 216 bleaching occurrence & severity point data • (www.reefbase.org)

  11. Statistical Analysis: selected Results • Bleaching as a function of environmental variables Short term conditions Historical conditions

  12. Reef base data: Mean against observed bleaching

  13. Modeling Susceptibility – concept High Low High Low High Low Resistance + Tolerance + Recovery = Resilience Adopted from Obura 2005

  14. Methods: Long term conditions

  15. Methods: Fuzzy logic functions

  16. Methods: Modeling Susceptibility Normalized parameters using fuzzy logic Susceptibility from Wind velocity

  17. Integration of parameters – model 1 Spatial Principal Component Analysis

  18. Integration of parameters: model 2 Number of layers Pixels within a each layer

  19. Integration of parameters (2) Cosine amplitude – pair wise relation strength

  20. Results: Susceptibility Models Kappa statistic = 0.7

  21. Evaluating SM: Mortality from 1998 ENSO Adj R2 = 0.22 P = 0.03 Adj R2 = 0.17 P = 0.06 unpublished data mortality data by Mebrahtu Ateweberhan, PhD

  22. Results (2): management implications • More than half IUCN category I& II • Marine Protected Areas located in moderate to high

  23. Key Findings: summary • Long term and short term environmental conditions predicted coral bleaching • Good correlation between susceptibility and mortality • More than half IUCN no take zones located in moderate-highly susceptible areas • Moderate resolution data suitable for meso-scale studies

  24. RS data/model limitations • Uncertainties: spatial and temporal • boundaries • Assumes strong connectivity – interpolation • of data to coastal areas • Bulky data - processing time • Delivery formats - (AMIS, ASI?) • Uncertainty: expert knowledge & ecological data

  25. Recommendations • Long time series data • Moderate to high resolution data for local scale studies – hierarchical modeling (AMIS, ASI) • Simplify data access methods/conventional formats (AMIS, ASI) • Closed area management should review status of MPA’s

  26. ‘All Models Are Wrong’ Thank you Acknowledgements: EU Erasmus Mundus program Consortium Directors: Prof’s: Peter Atkinson, Peter Pilesjo, Katarzyna Dabrowska, and Andrew Skidmore Mr. Valentijn Venus, ITC, The Netherlands Dr. Chris Marnnaettes, ITC Dr. Colette Robertson, NOCS, Southampton, UK Mr. Bas Beistos, ITC Mr. Aditya Singh, UoF, USA Dr. Tim McClanahan, WCS, NY, USA Dr. Jay Herman, NASA, USA Mr. John Gunn, Earth and Space Research, USA Mr. Ruben van Hooidonk, Purdue University, USA Dr. Mebrahtu Ateweberhan, GEF-World bank project, Mombasa, Kenya Dr. Ruby Moothien-Pillay, MOI, Mauritius Dr. Graham Quartley, NOCS, Southampton, UK Dr. Valborg Byfield, NOCS, Southampton, UK

More Related