1. Andrew Hansen and Linda Phillips Montana State University Curt Flather Colorado State University Biophysical and Land-use Controls on Biodiversity: Regional to Continental Scales Joint Workshop on NASA Biodiversity, Terrestrial Ecology, and Related Applied Sciences May 1-2, 2008

2. Human Land Use (Land use, Home density) Current Biodiversity Value Biophysical Potential (i.e. Energy, Habitat structure) Conservation Priority/Strategies Research Questions: 1: Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use? 2: How are these patterns of biodiversity modified due to land use? 3: What geographic areas are highest priorities for conservation based on biodiversity modification resulting from land use change?

3. Ecosystem Energy as a Framework for Conservation? Hawkins et al. 2003 Key Hypothesis Primary productivity, and the factors that drive it (climate, soils, topography), ultimately influence:  disturbance and succession  resources for organisms  species distributions and demographies  community diversity  responses to habitat fragmentation, land use, exotics  effectiveness of conservation

4. Conservation Category Low Energy Medium Energy High Energy Conservation Zones Protect high energy placesProtect more natural areasProtect low energy places Disturbance Use fire, flooding, logging judiciously in hotspots Similar to “ Descending ”  Use disturbance to break competitive dominance  Use shifting mosaic harvest pattern  Maintain structural complexity Landscape Pattern Maintain connectivity due to migrations Manage for patch size and edge Sensitive Species Many species with large home ranges and low population sizes due to energy limitations Forest interior species Exotics High exotics likely due to productivity and high land use Protected Area Size LargeSmaller Land Use Low overallHigh overallModerate overall Focused on hot spots Emphasize “ backyard ” conservation More random across landscape Plan development outside of hotspots Apply restoration Framework for Classifying Ecosystems for Conservation

5. Focus of This Talk 1.Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use? Which MODIS energy products best explain patterns of bird diversity across North America? Does the relationship between birds and energy (slope and sign) differ between places of low, medium, and high energy?

6. History of Predictor Variables Used to Explain Species Energy Patterns NDVI = (NIR - red) / (NIR + red) Latitude (MacArthur 1972) Evapotranspiration (Currie 1987, Hawkins et al 2003) Ambient temperature (Acevedo and Currie, 2003) Water/Energy Balance (Hawkins et al 2003) 1960’s 1970’s 1980’s Remote Sensing advances 1990’s 1999 present MODIS Land Surface Product Development NDVIEVI GPP (simulated from fpar, climate, cover type) NPP AVHRR Thematic Mapper Precipitation (Chown et al., 2003) --

7. Phillips, L.B., Hansen, A.J. & Flather, C.H. (in press), Remote Sensing of Environment Not complete vegetation cover (backscatter) Dense vegetation (saturation) Does NDVI have limitations that higher order products address? GPP NPP

8. What is the shape of the species energy relationship?

9. What is the shape of the relationship? Why? energy richness Hypothesis: More individuals hypothesis (Wright, 1983, Preston, 1962; MacArthur & Wilson, 1963, 1967) Hypothesis: Competitive exclusion (MacArthur and Levins, 1964, 1967; Grime, 1973 1979, Rosenzweig 1992)

10. Energy as a framework for conservation Identify and manage hotspots judiciously Protect harsh places But most of landscape is high in diversity, so more options for multiple use such as shifting mosaic approach to forest management; If slope and sign vary among energy levels, conservation strategies should differ among low, intermediate, and high energy places.

11. Response data  Bird richness from BBS data for years 2000-2005, estimated richness using COMDYN  Subset of routes (1838) to represent terrestrial natural routes (exclude human dominated land uses, water impacted) Methods 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 Water birds, hawks, owls, and nonnative species excluded in this analysis

12. Predictor data Calculate both breeding season averages for NDVI, EVI and GPP and annual averages of NDVI, EVI, and GPP, NPP Calculate both breeding season averages for NDVI, EVI and GPP and annual averages of NDVI, EVI, and GPP, NPP Methods Annual Average MODIS GPP NDVI Enhanced Vegetation Index Gross Primary Production Net Primary Production MODIS products used

13. Statistical analysis Stratify BBS routes by vegetation life from and density (MODIS VCF) Stratify BBS routes by vegetation life from and density (MODIS VCF) Perform correlation analyses between predictors across vegetative strata and regression analysis between predictor and response variables across strata. Methods

14. Statistical analysis Perform regression analysis with linear, polynomial, spline and breakpoint spline models Perform regression analysis with linear, polynomial, spline and breakpoint spline models Perform simple linear regression analysis of four quartiles of GPP to determine slopes and significance Perform simple linear regression analysis of four quartiles of GPP to determine slopes and significance Assess and control for effects of spatial correlation on significance levels and coefficients using generalized least squares analyses. Assess and control for effects of spatial correlation on significance levels and coefficients using generalized least squares analyses. Methods

15. variabletimemodel overall rank delta aic-R from best overallr2adj r2 GPPannual quadratic 131.6250.53530.5346 NDVIannual quadratic 272.9390.52120.5205 NPPannual quadratic 396.3210.5130.5123 EVIannual quadratic 4180.6540.48240.4816 NDVIBS linear 5288.0950.35610.3556 NDVIBS quadratic 6309.7860.44060.4398 NDVIannual linear 7331.4380.42190.4215 NPPannual linear 8374.890.40350.4031 EVIBS linear 9395.620.42960.4292 EVIBS quadratic 10395.620.39540.3945 EVIannual linear 11410.6940.38780.3874 GPPannual linear 12411.2440.38760.3872 GPPBS linear 13416.290.3760.3756 GPPBS quadratic 14416.290.38630.3854 Results: Best Predictor?

16. Correlation between NDVI and GPP across vegetation classes Results: Best Predictor?

18. GPP NPP Intrepertation: Best Predictor 1.Annual formulation better than breeding season for all predictors 2.Results suggest that GPP better represents primary productivity and bird richness than NDVI in low and high vegetation areas 3.GPP should be used especially in desert areas (bare ground) and dense forests (SE and PNW) 4.Results help explain differences in past studies on predictors and strength of relationships: will depend on vegetation density of samples.

19. Vegetation Coninuous fields Blue gradient - bare ground Red gradient - forest cover Green gradient - herbaceous cover

20. R2 = 0.5346 Results: Slope and Shape?

22. a=0.083 p<.001 a=0.013 p<.001 a=0.005 p<.036 a= - 0.018 p<.001 Results: Slope and Shape?

23. Variable (annual)modeloverall rank delta aic-R from best overallr2adj r2 GPP Spline (cubic) 100.54840.5464 GPP Breakpoint (linear) 226.3040.53840.5371 GPP Quadratic 331.6250.53530.5346 NDVI Quadratic 472.9390.52120.5205 NDVI Spline (cubic) 574.4160.52340.5214 NDVI Breakpoint (linear) 683.3220.5190.5176 NPP Spline (cubic) 791.330.51760.5155 NPP Quadratic 896.3210.5130.5123 NPP Breakpoint (linear) 9101.4760.51260.5112 EVI Spline (cubic) 10180.6090.48540.4832 EVI Quadratic 11180.6540.48240.4816 EVI Breakpoint (linear) 12186.1340.48180.4803 Results: Slope and Shape?

26. energy richness More Individuals Hypothesis Predicts higher habitat heterogeneity in areas of high richness Competitive Exclusion Hypothesis Predicts high canopy cover in overstory and lower habitat heterogeneity Interpretation: Slope and Shape

28. Disturbance Effects and Ecosystem Energy Diversity increases with disturbance under high energy and decreases under low energy. High Low High Diversity Landscape Productivity Intensity of Disturbance Springfield Cle Elum Disturbance Frequency Species Diversity Cle Elum Springfield High Low Site: Springfield R 2 =.16 P-value <.01 High Low Site: Cle Elum R 2 =.30 P-value <.01 High Low Huston 1994. McWethy et al. in review.

29. Human Land Use (Land use, Home density) Current Biodiversity Value Biophysical Potential (i.e. Energy, Habitat structure) Conservation Priority/Strategies Next Steps: 1: Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use? 2: How are these patterns of biodiversity modified due to land use? 3: What geographic areas are highest priorities for conservation based on biodiversity modification resulting from land use change?

30. Human Land Use (Land use, Home density) Current Biodiversity Value Biophysical Potential (i.e. Energy, Habitat structure) Conservation Priority/Strategies Next Steps: 1: Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use? 2: How are these patterns of biodiversity modified due to land use? 3: What geographic areas are highest priorities for conservation based on biodiversity modification resulting from land use change? Vegetation structure from ELVS/GLAS

31. Balmford et al. 2001 Vertebrates and NPP This study. Humans and NPP Next Steps

33. Does the shape of the relationship vary with energy levels (geographically)?

34. Is the negative portion of the unimodal relationship real? Nugget.002 Sill.006 So using GLS, enter (800000,.25)

36. NDVI = (NIR - red) / (NIR + red) Do higher order MODIS products help us answer these questions?

37. Phillips, L.B., Hansen, A.J. & Flather, C.H. (in press), Remote Sensing of Environment Not complete vegetation cover (backscatter) Dense vegetation (saturation) Does NDVI have limitations that higher order products address? GPP NPP Results: Best Predictor?

38. This slide corresponds to green cells in previous slide