Andrew Hansen and Linda Phillips Montana State University Ruth DeFries University of Maryland

1. Testing Biophysical and Land Use Controls on Biodiversity using MODIS and AMSR-E Products Andrew Hansen and Linda Phillips Montana State University Ruth DeFries University of Maryland NASA Biodiversity and Ecological Forecasting Team Meeting August 29-31, 2005

2. Long Standing Question How are species distributed across space and what controls this distribution? • Difficult to answer because fine-scale detection of species is required over large areas. • Progress is enabled by convergence of: • theory on controls of biodiversity • satellite data for continental scale monitoring and analysis • continental-scale field monitoring

3. Vegetation Structure and Biodiversity Habitat Structure – The vertical and horizontal distribution of plant biomass. Habitat structure is known to influence biodiversity at landscape scales and current management is based on this.

4. Energy and Species Richness • Biodiversity is often strongly correlated with energy. • Energy • Heat – e.g., temperature, potential evapotranspiration • Ecological productivity – e.g., NPP • Why? • Abundant food resources or warmer thermal conditions allow higher survival and reproduction of individuals within a population, and larger population sizes reduce the chance of species extinctions (Wright 1983). “Measures of energy (heat, primary productivity)…[and water balance]…explain spatial variation in richness better than other… variables in 82 of 85 cases”, Hawkins et al. 2003.

5. Energy and Species Richness - Hurlbert and Haskell (2003) - North America - 140 km x 140 km - Terrestrial breeding (BBS) and wintering (CBC) birds - AVHRR NDVI mean monthly (UNEP 1993)

6. MODIS and AQUA Satellite data and products relevant to the question have improved substantially with the MODIS and AQUA sensors.

7. MODIS and AMSR-E Predictors

8. MODIS and AQUA Satellite data and products relevant to the question have improved substantially with the MODIS and AQUA sensors. • Finer spatial resolution for NDVI (250 m vs 1 km) • Higher radiometric and spectral resolution • Addition of EVI, more sensitive to high biomass • Addition of vegetation composition products (land cover and continuous fields) and soil moisture.

9. Questions • 1. Do MODIS products better explain bird species richness than earlier technologies? • Do MODIS products allow separation of the effects of vegetation productivity and vegetation structure? • How much variation in bird species richness is explained by MODIS?

10. USGS Breeding Bird Survey • Survey unit is a roadside route • 39.4 km in length • 50 stops at 0.8 km intervals • Birds tallied within 0.4 km • 3 minute sampling period • One survey per year • 1966 to 2004, or fewer years • Water birds, hawks, owls, and nonnative species excluded in this analysis

11. BBS Issues: Sampling Biases • Road-side survey. • Nonrandom habitats? • Road-side bird species? • Observer effects. • New observers tend to miss species. • Species detectability. • Ability to detect a bird differs among species and habitats. • Representation of species richness. • Average annual or cumulative. Uniqueness of data set outweighs bias Mark recapture statistical methods (estimated but not used here) Average annual

12. BBS Issues: Land Use Effects Cropland Urban and built up Cropland and natural vegetation Previous analyses have included all regions, regardless of land use. We used BBS routes >50% natural vegetation.

13. Statistical Techniques • Model selection techniques based on AIC (distance between specified model and reality). • Coefficient of determination for amount of variance explained. • Bird species richness transformed (log+1) to improve normality. • Mixed models will be used to control for spatial and temporal autocorrelations, but not done yet.

14. Landbird Species Richness

15. Predictors: MODIS vs Earlier Technologies MODIS NDVI better than AVHRR NDVI because: narrower red and NIR bands (which is thought to result in better detection of photosynthetic activity), higher radiometric resolution, improved compositing algorithm?

16. AVHRR NDVI vs MODIS NDVI Correlation AVHRR NDVI MODIS NDVI 0.91373 Best Model Variable R-Square AIC AVHRR NDVI 0.5187 -5237.6764 MODIS NDVI 0.4869 -5105.8716 N=2075 Bird richness (log) AVHRR NDVI: June 26

17. Conclusion • MODIS and AVHRR NDVI were similar in explaining variation in landbird richness. • Why? • 8 km vs 1 km??

18. Structural Variables: MODIS NDVI, EVI, LAI N=2057 P<.0001

19. MODIS Vegetation Indices Dense forests are scaled to low to moderate EVI values. (Value x 1000) NDVI EVI

20. MODIS Structural Variables LAI

21. Variable R-Square AIC MODIS NDVI 0.4869 -5105.8716 MODIS EVI 0.4443 -4941.7766 MODIS LAI 0.3939 -4756.6805 MODIS Structural Variables Bird richness (log) MODIS NDVI – June 26

22. Conclusions MODIS NDVI produces stronger models than EVI or LAI. • NDVI and EVI may be stronger predictors than LAI because they reflect both canopy density and photosynthetic activity, which perhaps birds are responding to. • NDVI may stronger than EVI because birds less sensitive to high vegetation density and more influenced by lower vegetation densities.

23. MODIS Productivity Products GPP: energy fixed PSNnet: GPP- maintenance respiration NPP: PSNnet –annual respiration costs Birds should be better predicted by NPP than PSNnet or GPP because NPP reflects the proportion of the fixed energy that is available to consumers? However, the MODIS formulation of NPP is annual; growing season may better predict breeding birds.

24. MODIS Productivity Products Variable R-Square AIC GPP 0.3307 -4558.9579 PSNnet 0.1049 -3960.8136 NPP(ann) 0.0887 -3923.9422 Bird richness (log) R2 = .43 MODIS GPP – June 26 (gC/m2/day)

25. Conclusion GPP is the best predictor among productivity variables. NPP is expected to be better, but annual formulation may reduce utility for breeding season richness.

26. MODIS Structure vs Productivity Bird richness (log) R2 = .49 Is NDVI a better predictor because bird richness is driven both by structure and productivity? MODIS NDVI - Late June Bird richness (log) R2 = .43 MODIS GPP – June 26 (gC/m2/day)

27. MODIS Products: Vegetation Structure or Productivity? Vegetation structure LAI : leaf area per unit area NDVI EVI : canopy volume and photosynthesis GPP: energy fixed PSNnet: GPP- maintenance respiration NPP: PSNnet – annual respiration costs Energy fixed for consumers

28. Conclusion • The results are consistent with the hypothesis that landbird diversity is related to both vegetation structure and productivity. • However, MODIS structure and productivity measures are correlated. • Radar-based approaches to quantify vegetation structure would seem a means of further testing this hypothesis.

29. Predictors: Additional Products

30. Best Models Using MODIS Products Observed landbird richness = NDVI LAI GPP NPP NPP2 VCF VCF2 R2 = .58 AIC = -4924 N=2058 BBS Route Level

31. Bird Conservation Regions • Defined by North American Bird Conservation Initiative • BBS routes >50% natural vegetation are included • BCRs with >14 routes included.

32. Best Models Using MODIS Products Observed landbird richness = NDVI GPP NPP NPP2 VCF R2 = .82 AIC = -111.74 N=30 BBS Ecoregion Level

33. Overall Study and Next Steps Develop predictors for 2000-04 period, including NDVI and GPP phenology. Add topographic, climatic, and other predictors Resolve treatment of statistical issues on species detectability and spatial autocorrelation. Biophysical Potential (i.e. Energy, Habitat structure) Human Land Use (Land use, Home density) Current Biodiversity Value Conservation Priority

34. Thanks • NASA EOS Program • Woody Turner, Dick Waring, Steve Running • Jim Nichols, John Sauer, and colleagues at Patuxent. • Curt Flather

35. Study Area

36. Predictors: MODIS vs Earlier Technologies MODIS NDVI better than AVHRR NDVI because: narrower red and NIR bands, higher radiometric resolution, improved compositing algorithm?

37. Species Energy Theory Recent Reviews Waide, R.B., et al. 1999. The relationship between productivity and species richness. 69(2):330-339. Annual Rev. Ecol. Syst. 30:257-300. Mittelbach, G.G., C.F. Steiner, S.M. Scheiner, K.L. Gross, H.L. Reynolds, R.B. Waide, M.R. Willig, S.I. Dodson, L. Gough. 2001. What is the observed relationship between species richness and productivity? Ecology 82: 2381-2396. Hawkins, B. A., R. Field, H.V. Cornell. D.J. Currie, J. Guegan, D.M. Kaurman, J.T. Kerr, G.G. Mittelbach, T Oberdorff. E.M. O’Brian, E.E. Porter, and J.R.G. Turner. 2003a. Energy, water and broad-scale geographic patterns of species richness. Ecology 84(12)3105-3117. Hawkins, B.A., E.E. Porter, and J. A. F. Diniz-Filho. 2003b. Productivity and history as predictors of the latitudinal diversity gradient of terrestrial birds. Ecology 84(6):1608-1623. “Measures of energy (heat, primary productivity)…[and water balance]…explain spatial variation in richness better than other… variables in 82 of 85 cases”, Hawkins et al. 2003.

38. Energy and Species Richness - Hawkins et al. (2003) - North and Central America - 2.0o x 2.0o - Native terrestrial breeding birds - Range maps for birds - 6 climate variables including PET (UNEP 1993).

39. MODIS Productivity vs Structure

40. Correlations Among MODIS Products N=2057 P<.0001

41. MODIS Structure vs Productivity Bird richness (log) R2 = .49 MODIS NDVI – June 26 Bird richness (log) R2 = .43 MODIS GPP – June 26 (gC/m2/day)

42. Bird richness (log) R2 = .12 MODIS Land Cover heterogeneity MODIS Land Cover Heterogeneity Correlation NDVI LAI GPP NPP VCF 0.82046 0.82252 0.84082 0.40322 N=1824 P<.0001

43. MODIS Vegetation Continuous Fields Correlation NDVI LAI GPP NPP VCF 0.82046 0.82252 0.84082 0.40322 N=1824 P<.0001 Bird richness (log) R2 = .49 MODIS Vegetation Continuous Fields - % tree

44. MODIS Products: Vegetation Structure or Productivity? Vegetation structure LAI NDVI EVI GPP PSNnet NPP Energy fixed for consumers

45. MODIS Vegetation Indices NDVI EVI Bird Richness low high Samples high in EVI are also high in NDVI and richness.

46. MODIS Vegetation Indices NDVI EVI Bird Richness low high Samples high in NDVI are high in richness but scattered in EVI.

47. Correlations Among MODIS Products N=2057 P<.0001

48. Energy and Species Richness R2 = .69 - Currie (1991) - North America - 2.5o x 2.5-5.0o - All birds - Range maps for species - 10 climate variables - Climate atlases PET (mm yr-1)

49. Energy and Species Richness • Biodiversity is often strongly correlated with energy. • Energy • Heat – e.g., temperature, potential evapotranspiration • Ecological productivity – e.g., NPP • Why? • Abundant food resources or warmer thermal conditions allow higher survival and reproduction of individuals within a population, and larger population sizes reduce the chance of species extinctions (Wright 1983).

50. Conclusions • The causes of elevated landbird richness at mid latitudes in North America are not fully understood. • MODIS and AVHRR NDVI were similar in explaining variation in landbird richness. • Some MODIS products are derived from each other, are highly correlated, and thus do not represent orthogonal predictors of biodiversity. • MODIS NDVI produces stronger models than EVI or LAI. • GPP is the best predictor among productivity variables. NPP is expected to be better, but annual formulation likely reduces utility for breeding season richness.