Presentation is loading. Please wait.

Presentation is loading. Please wait.

Modelling ecological susceptibility of coral reefs to environmental stress using remote sensing, GIS and in situ observations: A case study in the Western.

Similar presentations


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

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

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

4 Climate change and coral bleaching o Climate models forecast: SST increased by 1 o C for last 100 yrs Current increase 1-2 o C per century o Corals near their thermal threshold o 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

8 Methods: satellite data Data ProductSatellite/Sensor Spatial Resolution Time Scale Sea surface Temperature ( o C) NOAA AVHRR~4 kmMonthly; Chlorophyll a (mg/l)SeaWiFS~9 kmMonthly; PAR (Einstein/m 2 /day) SeaWiFS ~9 km Monthly; Ocean current (m/s) OSCAR: TOPEX/Pseidon;JASON; QuikSCAT 1 o x 1 o Monthly; Wind speed (m/s) SSM/I (Special Sensor Microwave/Imager) 0.25 o x 0.25 o Weekly; 1997 to 2005 UV irradiance (Milliwatts/m 2 /nm ) TOMS1 o x 1 o Daily; 1996 to 2005 ≈ 5000 images**28 Derived variables: long term and short term

9 Satellite-in situ comparison Unpublished in situ data by Dr.Tim McClanahan, WCS

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

11 Statistical Analysis: selected Results Short term conditions Historical conditions R Square F RatioProb > F AIC < Variablet RatioF RatioProb > F SST anomaly Wind speeds anomaly SST Hotspot Currents anomaly UV radiation UV radiation anomaly Surface currents PAR anomaly R Square F RatioProb > FAIC < ` Variablet RatioF RatioProb > F Meridional currents <.0001 UV radiation <.0001 Wind speed <.0001 SST CV SST hotspot o Bleaching as a function of environmental variables

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 Susceptibility from Wind velocity

17 Spatial Principal Component Analysis Selected PC’sIIIIIIIVVVIVII Contribution ratio (%) Cumulative contribution (%) Integration of parameters – model 1

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

19 Cosine amplitude – pair wise relation strength P1P2P3P4P5P6P7P8P9P10P11P12 Eigen vectors/Scores Max SSTP SSTP UVP ChlorophyllP CVP Bleaching modelP Wind speedP PARP Zonal currentsP SST HotspotP Meridional currentsP SlopeP Integration of parameters (2)

20 Results: Susceptibility Models Kappa statistic = 0.7

21 Evaluating SM: Mortality from 1998 ENSO Adj R 2 = 0.22 P = 0.03 Adj R 2 = 0.17 P = 0.06 unpublished data mortality data by Mebrahtu Ateweberhan, PhD

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

23  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 Key Findings: summary

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

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


Download ppt "Modelling ecological susceptibility of coral reefs to environmental stress using remote sensing, GIS and in situ observations: A case study in the Western."

Similar presentations


Ads by Google