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

2. Long Standing Question  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 How are species distributed across space and what controls this distribution?

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

9. Questions 1. Do MODIS products better explain bird species richness than earlier technologies? 2.Do MODIS products allow separation of the effects of vegetation productivity and vegetation structure? 3.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.  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. BBS Issues: Sampling Biases Mark recapture statistical methods (estimated but not used here) Average annual Uniqueness of data set outweighs bias

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.  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. Statistical Techniques

14. Landbird Species Richness

15. Predictors: MODIS vs Earlier Technologies ThemeSensorSpatial resolution PeriodFormulation NDVIMODIS1 kmJune 26 2004Spectral only (ratio of NIR/R); narrower bands than AVHRR NDVIAVHRR8 kmLate June 2003Spectral only (ratio of NIR/R) 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 MODIS NDVI 0.91373 Variable R-Square AIC AVHRR NDVI 0.5187 -5237.6764 MODIS NDVI 0.4869 -5105.8716 AVHRR NDVI vs MODIS NDVI Correlation N=2075 Best Model 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 LAINDVIEVIGPPPSNnetNPP (Annual) LAI.837.776.779.530.345 NDVI.909.835.512.393 EVI.677.315.246 GPP.842.621 PSNnet.624 NPP N=2057 P<.0001

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

20. MODIS Structural Variables LAI

21. MODIS Structural Variables Variable R-Square AIC MODIS NDVI 0.4869 -5105.8716 MODIS EVI 0.4443 -4941.7766 MODIS LAI 0.3939-4756.6805 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. GPP: energy fixed PSNnet: GPP- maintenance respiration NPP: PSNnet – annual respiration costs MODIS Productivity Products 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 R 2 =.43 Bird richness (log) MODIS GPP – June 26 (gC/m 2 /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. Bird richness (log) MODIS NDVI - Late June MODIS Structure vs Productivity R 2 =.43 R 2 =.49 Is NDVI a better predictor because bird richness is driven both by structure and productivity? MODIS GPP – June 26 (gC/m 2 /day)

27. LAI : leaf area per unit area NDVIEVI : canopy volume and photosynthesis GPP: energy fixed PSNnet: GPP- maintenance respiration NPP: PSNnet – annual respiration costs MODIS Products: Vegetation Structure or Productivity? Vegetation structure 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 ThemeDefinition Period Continuous Fields Vegetation Proportion cover of plant lifeform, leaf type, and leaf longevity. 2001 Land Cover17 natural and human cover classes using IGBP scheme. Converted to cover type richness. 2001 Soil MoistureDaily measurements of surface soil moisture (top few millimeters) and vegetation water content, from passive microwave measurements. Not yet included. June 26 2004

30. Best Models Using MODIS Products Observed landbird richness = NDVI LAI GPP NPP NPP 2 VCF VCF 2 R 2 =.58AIC = -4924N=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 NPP 2 VCF R 2 =.82AIC = -111.74N=30 BBS Ecoregion Level

33. Human Land Use (Land use, Home density) Current Biodiversity Value Biophysical Potential (i.e. Energy, Habitat structure) Conservation Priority 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.

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 ThemeSensorSpatial resolution PeriodFormulation NPPMODIS1 kmAnnual average, 2004 Modeled from: Spectral data MODIS products Met data NDVIMODIS1 kmJune 26 2004Spectral only (ratio of NIR/R); narrower bands than AVHRR NDVIAVHRR8 kmLate June 2003Spectral only (ratio of NIR/R) PETMet stations10 km (Canadian) Ave annualEstimated from met data. 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 - 2.0 o x 2.0 o - Native terrestrial breeding birds - Range maps for birds - 6 climate variables including PET (UNEP 1993).

39. MODIS Productivity vs Structure ThemeDefinitionPeriodFormulation NPPsum of the cumulative daily PSNnet less the costs associated with annual maintenance and growth respiration. Annual 2004 Modeled from: Spectral data MODIS products Met data PSNnetequal to GPP less the maintenance respiration. June 26 2004 GPPrate at which light energy is converted to plant biomass. June 26 2004 EVIradiometeic measures of the amount, structure, and condition of vegetation. June 26 2004 Spectral only (based on NIR, Red, Blue) NDVIJune 26 2004 Spectral only (ratio of NIR/R) LAIone sided green leaf area per unit ground area in broadleaf canopies, or as the projected needleleaf per unit ground area in needle canopies. June 26 2004 Surface reflectance (bands 1-7) Land cover Ancillary data (look-up tables)

40. Correlations Among MODIS Products LAINDVIEVIGPPPSNnetNPP (Annual) LAI.837.776.779.530.345 NDVI.909.835.512.393 EVI.677.315.246 GPP.842.621 PSNnet.624 NPP N=2057 P<.0001

41. MODIS Structure vs Productivity R 2 =.43 R 2 =.49 Bird richness (log) MODIS NDVI – June 26 Bird richness (log) MODIS GPP – June 26 (gC/m 2 /day)

42. MODIS Land Cover Heterogeneity NDVI LAI GPP NPP VCF 0.82046 0.82252 0.84082 0.40322 N=1824 P<.0001 Correlation R 2 =.12 Bird richness (log) MODIS Land Cover heterogeneity

43. MODIS Vegetation Continuous Fields R 2 =.49 NDVI LAI GPP NPP VCF 0.82046 0.82252 0.84082 0.40322 N=1824 P<.0001 Correlation Bird richness (log) MODIS Vegetation Continuous Fields - % tree

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

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

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

47. Correlations Among MODIS Products LAINDVIEVIGPPPSNnetNPP (Annual) LAI.837.776.779.530.345 NDVI.909.835.512.393 EVI.677.315.246 GPP.842.621 PSNnet.624 NPP N=2057 P<.0001

48. Energy and Species Richness R 2 =.69 - Currie (1991) - North America - 2.5 o x 2.5-5.0 o - 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.

51. Conclusions  MODIS products in total explain the majority of variation in landbird richness.  Strength of the relationship increases with spatial scale of the analysis.