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Predicting tree diversity across the U.S.A. as a function of Gross Primary Production

I R S S. Predicting tree diversity across the U.S.A. as a function of Gross Primary Production. Richard Waring 1 , Joanne Nightingale 1 , Nicholas Coops 2 & Weihong Fan 3 1 Oregon State University 2 University of British Columbia 3 Richard Stockton College of New Jersey.

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Predicting tree diversity across the U.S.A. as a function of Gross Primary Production

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  1. I R S S Predicting tree diversity across the U.S.A. as a function of Gross Primary Production Richard Waring1, Joanne Nightingale1, Nicholas Coops2 & Weihong Fan3 1 Oregon State University 2 University of British Columbia 3 Richard Stockton College of New Jersey

  2. Outline • Theoretical relationship between productivity and tree species richness • Measures of tree species richness & forest productivity – model / satellite • Data quality • Actual relationships between productivity and tree species richness

  3. Theoretical Relation between Productivity and Tree Diversity Light limiting from competition with a few fast-growing species All but light limiting No factor entirely limiting

  4. Model GPP with 3-PG Recorded Tree richness n = 10 300 CVS plots Theory tested in the Pacific Northwest(Swenson & Waring 2006 Global Ecology & Biogeography)

  5. 10 ha CVS data per 100 km2 R2 = 0.71 0.5 ha FIA data per 100 km2 R2 = 0.16 Tree richness predictable from modeled GPP

  6. Measures of GPP Increasing complexity Satellite data Climatic data Soils data

  7. Quantum / Radiation Use Efficiency x x PAR PAR Environmental Modifiers VPD MODIS Modifiers Tmin GPP Soil Water Additional 3-PGS Modifiers Water Balance ??? Rainfall Models of GPP (3-PGS & MODIS GPP)

  8. MODIS GPP ~40% higher than 3-PGS estimates Highly sensitive >20% Moderate sensitivity 5-20% Not sensitive 5% Soil Water Sensitivity

  9. W. Hargrove Soil Nitrogen ORNL W. Fan Soil Nitrogen How “good” is our Soils data anyway??

  10. North West North East West Central East South West EPA Level 1 Ecoregions

  11. Annual Average Maximum NDVI Exp R2 = 0.55 Annual Average Maximum EVI Exp R2 = 0.68 Satellite Index

  12. Annual Average MODIS GPP Power R2 = 0.51 Annual Average 3-PGS GPP Polynomial R2 = 0.53 GPP Models

  13. Correlations MODIS EVI (maximum) 0.68 MODIS GPP (growing season) 0.64 3-PGS (annual average ) 0.53 Poly Note: all models are highly correlated with 3-PGS, R2 ~ 0.7

  14. Conclusions • NDVI & EVI saturate at high levels of productivity (GPP >15 tC/ha/yr) • MODIS GPP (& SPOT NPP) in error with drought • 3-PGS limited by soil & climate – but estimates full range of forest productivity across the USA • If vegetation indices match changes predicted by more complex models, climate change may be inferred

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