1. Remote Sensing and Avian Biodiversity Patterns in the United States Volker C. Radeloff 1, Anna M. Pidgeon 1, Curtis H. Flather 2, Patrick Culbert 1, Veronique St-Louis 1, and Murray K. Clayton 3 1 Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Wisconsin; 2 U.S.Forest Service Rocky Mountain Research Station, Fort Collins, Colorado 3 Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin

2. Introduction We know generally what affects biodiversity –MacArthur’s big three –Habitat loss and fragmentation –Human threats –History –… The “machinery controlling species diversity” after MacArthur (1972)

3. Introduction Less clear are relative importance, interactions, and regional variability of these factors Basic science question –How can we explain observed spatial patterns of biodiversity? Applied science needs –Landscape level biodiversity maps

4. Questions Can remote sensing measures of habitat structure predict avian biodiversity patterns? Can measures of human threats to habitat predict biodiversity?

5. Questions Can remote sensing measures of habitat structure predict avian biodiversity patterns? Can measures of human threats to habitat predict biodiversity?

6. Questions And how do relationships differ among Species Guilds, and Ecoregions?

7. Habitat Structure Local measures of habitat structure (e.g., foliage height diversity) are among the strongest predictors of biodiversity More structure means more ecological niches, and generally higher biodiversity The challenge is to measure vegetation structure with remote sensing

8. Habitat Structure – New Mexico 600 m Mesquite Sandsage 600 m St-Louis et al. 2006. Remote Sensing of Environment

9. High texture Low texture Habitat Structure – New Mexico Many texture measures available Can image texture capture fine- scale habitat structure?

10. Habitat Structure – New Mexico R 2 = 0.50 p < 0.001

11. Habitat Structure – New Mexico R 2 = 34%R 2 = 66% R 2 = 73% NIR SWIR NDVI Image texture captures fine-scale habitat structure in semi-deserts Good predictor of species richness St-Louis et al. 2008. Ecography

12. Habitat Structure – Wisconsin

13. 3x3 Window Standard Deviation TM4

14. Habitat Structure – Wisconsin R 2 = 0.31 p < 0.001 Multivariate model R 2 = 0.56 BUT – relationship is negative

15. Habitat Structure – Wisconsin 3x3 Window Standard Deviation TM4

16. Habitat Structure Phenology affects texture measures Both problem and opportunity

17. Habitat Structure Satellite image texture is a good measure of fine-scale habitat structure The scale of texture is between point measurements and landscape indices Texture captures structure within a land cover class Texture predicts avian biodiversity well

18. Human Threats Habitat loss Habitat fragmentation Habitat modification These threats may interact

19. Human Threats

20. Barren Transitional Urban/Developed Water Grassland Agriculture Forest Wetland Shrubland National Land Cover Database

23. Northern Wisconsin Madison, Wisconsin Human Threats

24. Proportion (%) of possible occurrences in models Pidgeon et al 2007. Ecological Applications Human Threats

25. Habitat loss is the major predictor Habitat fragmentation is important Habitat modification (i.e., housing development) just as important Threats interact: multivariate models predict best

26. Conclusions Can remote sensing measures of habitat structure predict avian biodiversity patterns? YES! Can measures of human threats to habitat predict biodiversity? YES! Our ability to explain, and thus to predict avian biodiversity patterns is increasing

27. Thank You!