1. A case study in the Western Indian Ocean
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
1 Mombasa, Kenya
2.ITC, Enschede, The Netherlands

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

3. Problems Decline in coral cover Ecological shift
Loss of live livelihood
Ecological shift
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 approach2 3 1 4 5

8. Methods: satellite data
Data Product
Satellite/Sensor
Spatial
Resolution
Time Scale
Sea surface Temperature (oC)
NOAA AVHRR
~4 km
Monthly;
Chlorophyll a (mg/l)
SeaWiFS
~9 km
Monthly;
PAR (Einstein/m2/day)
Ocean current (m/s)
OSCAR:
TOPEX/Pseidon;JASON;QuikSCAT
1o x 1o
Monthly;
Wind speed (m/s)
SSM/I (Special Sensor
Microwave/Imager)
0.25o x 0.25o
Weekly; 1997 to 2005
UV irradiance
(Milliwatts/m2/nm )
TOMS
Daily; 1996 to 2005
**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Unpublished in situ data by Dr.Tim McClanahan, WCS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 (

11. Statistical Analysis: selected Results
Bleaching as a function of environmental variables
Short term conditions
Historical conditions
R Square
F Ratio
Prob > F
AIC
0.62
11.8
<.0001
329
Variable
t Ratio
SST anomaly
3.7
13.9
0.001
Wind speeds
anomaly
3.6
13.3
SST Hotspot
3.3
11.2
Currents anomaly
-3.1
9.6
0.003
UV radiation
-2.8 8
0.006
2.8
7.8
0.007
Surface currents
1.9
3.8
0.057
PAR anomaly
-1.8
3.2
0.078
R Square
F Ratio
Prob > F
AIC
0.56
18.9
<.0001
350 `
Variable
t Ratio
Meridional
currents
-5.17
26.8 UV
radiation
-4.42
19.6
Wind speed
-3.7
13.7
SST CV
2.46 6
0.02
SST hotspot
2.25
5.1
0.03

12. Reef base data: Mean against observed bleaching

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

14. Methods: Long term conditions

15. Methods: Fuzzy logic functions

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

17. Integration of parameters – model 1
Spatial Principal Component Analysis
Selected PC’s I II
III IV V VI
VII
Contribution ratio (%)
56.5
13.4
8.4
7.7
4.2
3.7
2.7
Cumulative contribution (%)
69.9
78.3
85.9
90.1
93.8
96.49

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

19. Cosine amplitude – pair wise relation strength
Integration of parameters (2)
Cosine amplitude – pair wise relation strength P1 P2 P3 P4 P5 P6 P7 P8 P9
P10
P11
P12
Eigen vectors/Scores
Max SST 1
0.098
SST
0.92
0.092 UV
0.89
0.87
0.091
Chlorophyll
0.51
0.38
0.43
0.08 CV
0.9
0.91
0.59
0.097
Bleaching model
0.21
0.01
0.61
0.11
0.055
Wind speed
0.79
0.83
0.2
PAR
0.7
0.37
0.78
0.02
0.66
0.084
Zonal currents
0.64
0.65
0.69
0.34
0.03
0.63
0.074
SST Hotspot
0.48
0.42
0.39
0.41
0.28
0.081
Meridional currents
0.49
0.55
0.35
0.57
0.24
0.5
0.44
0.073
Slope
0.54
0.62
0.47
0.082

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. Thank you ‘All Models Are Wrong’ 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