Remote-sensing and biodiversity in a changing climate

1. Remote-sensing and biodiversity in a changing climate Catherine Graham SUNY-Stony Brook Robert Hijmans, UC-Berkeley Lianrong Zhai, SUNY-Stony Brook Sassan Saatchi, JPL/UCLA Tom Smith, UCLA

2. Research Program (NIP) • Integrate remote-sensing data into species distributional modeling • Determine remote-sensing correlates of species richness across multiple taxonomic groups and spatial scales • Integrate remote-sensing data with patterns of evolutionary diversification • Predict future species distributions • Train Latin America and US scientists and conservationists

3. Research Program (NIP) • Integrate remote-sensing data into species distributional modeling • Determine remote-sensing correlates of species richness across multiple taxonomic groups and spatial scales • Integrate remote-sensing data with patterns of evolutionary diversification • Predict future species distributions • Train Latin America and US scientists and conservationists

4. Species Distributional Models 2) Make statistical model describing distribution in envirnomental space; 1) Extract environmental data for point localities; 3) Project this model in geographic space to create a map. Annual Temperature Annual Rainfall

5. Possible environmental datasets • Remote sensing • Indirect measurements • High resolution • Global coverage • Recent coverage • Climate • Direct measurements • Low resolution • Extrapolations • Global coverage • Long term coverage

6. Remote-sensing data: issues for species distributional modeling • Age of point locality data • Spatial accuracy of point locality data

7. Remote-sensing data: issues for species distributional modeling Remote- sensing Climate

8. A solution • Use all point locality data with climate surfaces (museum and accurate recent survey data) • Use only “accurate” point locality data with remote-sensing layers (modis tree)

10. CLIMATE ONLY

11. CLIMATE & REMOTE-SENSING

12. CLIMATE & REMOTE-SENSING WITH ACCURATE POINTS

13. Research program • Integrating remote-sensing data into species distributional modeling • Determining remote-sensing correlates of species richness across multiple taxonomic groups and spatial scales • Integrating remote-sensing data with patterns of evolutionary diversification • Predicting future species distributions • Training programs in Latin America and the US

14. Extinction Risk from Climate Change • (Thomas et al. 2004; Nature) • Predict 18 to 35% of species ’committed to extinction’ by 2050 • Global warming major threat to biodiversity

15. Potential Problem • with Niche Modeling and Climate Change • Future climates will not be completely analogous to current. • => Will models predict lower probabilities (model artifact)? • =>Validity of models should be tested using experimental approaches, historical evidence, physiological models and internal consistency.

16. Environmental space and climate change Future Current Species environmental requirements

17. Approach Compare results from physiology-based models (mechanistic models) with species distribution models Assume mechanistic models are “correct”

18. Experimental Design Compare

19. Species distribution (niche models) used • BIOCLIM – envelop (boxcar) method • DOMAIN – based on similarity statistics • GAM – Non-linear regression • MAXENT – machine learning/maximum entropy

20. Variation in range size and location predicted by models

21. Errors

22. Environmental space and climate change GAMS MAXENT Future BIOCLIM DOMAIN Current Species environmental requirements

23. Modeling species distributions across climates • Species distributional modeling can provide similar results to mechanistic models. • Performance of species distributional models varies • Next? Incorporate climate change with land use patterns to evaluate extinction risk for a suite of species