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

Primary Production

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

Primary Production

I R S S

Richard Waring1, Joanne Nightingale1,

Nicholas Coops2 & Weihong Fan3

1 Oregon State University 2 University of British Columbia

3 Richard Stockton College of New Jersey

Richard Waring1, Joanne Nightingale1,

Nicholas Coops2 & Weihong Fan3

1 Oregon State University 2 University of British Columbia

3 Richard Stockton College of New Jersey

Outline

1. Theoretical relationship between productivity and tree species richness

2. Measures of tree species richness & forest productivity – model / satellite

3. Data quality

4. Actual relationships between productivity and tree species richness

Theoretical Relation between Productivity and Tree Diversity

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Relative Productivity

Rel

ativ

e T

ree

Ric

hn

ess

All butlight

limiting No factorentirely limiting

Light limitingfrom competitionwith a few fast-growing species

Theory tested in the Pacific Northwest(Swenson & Waring 2006 Global Ecology & Biogeography)

Model GPP with 3-PG

Recorded Tree richness n = 10 300 CVS plots

Tree richness predictable from modeled GPP

10 ha CVS data per 100 km2

R2 = 0.71

0.5 ha FIA data per 100 km2

R2 = 0.16

Measures of GPP

3-PGSTemperature,

Radiation, VPD,Frost & Rainfall

Available soil water & index of

fertility

MODIS GPP & SPOT NPP

Temperature, Radiation, VPD

MODIS

EVI, NDVI, fPAR

Satellite data Climatic data Soils data

Increasin

g co

mp

lexity

Models of GPP (3-PGS & MODIS GPP)

Quantum / Radiation Use Efficiency

x

PAR

x PAR

GPP

Environmental Modifiers

VPD

Tmin

MODIS Modifiers

Soil Water

RainfallAdditional 3-PGS ModifiersWater Balance ???

Soil Water Sensitivity

Highly sensitive >20%

Not sensitive 5%Moderate sensitivity 5-20%

MODIS GPP ~40% higher than 3-PGS estimates

How “good” is our Soils data anyway??

W. Fan Soil Nitrogen

W. Hargrove Soil Nitrogen

ORNL

EPA Level 1 Ecoregions

South West

Central

East

North West

West

North East

Satellite Index

Annual Average Maximum NDVI

Exp R2 = 0.55

0

10

20

30

40

50

60

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0NDVI

Sp

ecie

s / h

a

NORTH WEST

WEST

SOUTH WEST

CENTRAL

EAST

NORTH EAST

Annual Average Maximum EVI

Exp R2 = 0.680

10

20

30

40

50

60

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0EVI

Sp

ecie

s / h

a

NORTH WEST

WEST

SOUTH WEST

CENTRAL

EAST

NORTH EAST

GPP Models

Annual Average 3-PGS GPP

Polynomial R2 = 0.53

Annual Average MODIS GPP

Power R2 = 0.51

Correlations

GPP / Tree species Richness R2 Form

MODIS NDVI (maximum) 0.55 Exp

MODIS EVI (maximum) 0.68 Exp

MODIS GPP (annual average) 0.51 Pow

MODIS GPP (growing season) 0.64 Exp

3-PGS (annual average) 0.53 Poly

3-PGS (growing season) 0.52 Poly

Note: all models are highly correlated with 3-PGS, R2 ~ 0.7

MODIS EVI (maximum) 0.68

MODIS GPP (growing season) 0.64

3-PGS (annual average ) 0.53 Poly

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