Old paradigms grow up: tree species composition, forest productivity and biomass across Amazonia

Tim Baker

Max Planck Institüt für Biogeochemie, Jena, Germany and Earth and Biosphere Institute, School of Geography, University of Leeds, UK

RAINFOR

FORESTINVENTORIES(BOTANICAL ANDSTRUCTURAL)

AIMTo establish if Amazonian

forests vary across regional scales or are changing over time, in structure,

biomass, composition,and dynamics.

Focus on previously establishedsample plots

NOEL KEMPFF 2001TAMBOPATA 2002

YASUNI 2002

IQUITOS 2001

MANAUS2002

CAXIUANA2002

BRAGANCA2002

TAPAJOS 2003

JATUN SACHA 2002

RAINFOR Field Activities 2001-2004

ACRE2003 SINOP 2002

SAN CARLOS2004

JARI 2003

MOCAMBO2003

EL DORADO2004

ANDES TRANSECT 2003

RIO GRANDE2004

AMACAYACU2004

ALTA FLORESTA 2002

Jon Lloyd1, Oliver Phillips2, Yadvinder Malhi3, Samuel Almeida4, Luzmila Arroyo5, Jerome Chave18, Anthony DiFiore6, Terry Erwin16, Rafael Herrera1, Niro Higuchi17, Tim Killeen7, Susan Laurance8, William

Laurance8, Simon Lewis2, Abel Monteagudo9, David Neill10, Sandra Patiño1,11, Nigel Pitman12, Michael Schwarz1, Natalino Silva13,14,

Rodolfo V. Martinez15.

 1. Max Planck Institüt für Biogeochemie, Jena, Germany 2. University of Leeds, UK. 3. University of Edinburgh, UK. 4. Museu Paraense Emilio Goeldi, Belém, Brazil. 5. Museo Noel Kempff Mercado, Santa Cruz,

Bolivia. 6. New York University, USA. 7.Conservation International, Washington DC, USA. 8. Smithsonian Tropical Research Institute, Balboa, Panama. 9. Universidad Nacional San Antonio Abad del Cusco, Peru. 10.

Missouri Botanical Garden, Quito, Ecuador. 11. Alexander von Humboldt Biological Research Institute, Bogota, Colombia. 12. Duke University, Durham, USA. 13. CIFOR, Tapajos, Brazil. 14. EMBRAPA Amazonia

Oriental, Belém, Brazil. 15. Proyecto Flora del Perú, Oxapampa, Perú. 16. Smithsonian Institution, Washington DC, USA. 17. INPA, Manaus, Brazil. 18. CNRS, Toulouse, France.

Site locations Other plot sites

BLUE - < 1 month with less than 100 mm rainfallRED - > 5 months with

less than 100 mm rainfall

All plots are in ‘old-growth’ forest, and are

typically 1 ha.

Data sources: Forest cover - FAO (2001); Climate - UEA Climatic Research Unit global observational climate dataset, 1960-1998.

277 +/- 6 Mg DW ha-1

341 +/- 9 Mg DW ha-1

246 +/- 10 Mg DW ha-1

Baker et al. (2004) Wood density determines spatial patterns in Amazonian forest biomass. Global Change Biology.

Key results: stand biomass

Malhi et al. (2004) The above-ground coarse wood productivity of 104 Neotropical forest plots, Global Change Biology.

Key results: rates of wood production

Is spatial variation in species composition important for understanding variation in ecosystem structure

and function?

• How should variation in species composition be incorporated into models of carbon cycling?

Outline

1. Regional variation in the abundance of different types of tree in Amazonian forests

2. Variation in growth rates between functional groups

3. Implications for regional patterns of biomass and wood production

Regional variation in the abundance of different types of tree in Amazonian forests

Defining different types of tree

Turner (2001) Light demand

Max. size

•Shade tolerant

•Small stature species

•Light demanding

•Large stature species

•Shade tolerant

•Large stature species

•Light demanding

•Small stature species

Defining different types of tree

Light demand

Max. size

Maximum size

• obtained from floras: estimates of maximum height• >1500 species

• Generic or family level means used for stems with no species level trait data or lacking full species determination

Light demand

• quantified using published wood density data• low wood density related to high light demand

• 583 species (Chave et al. in prep >2000 species)

Defining different types of tree

0

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50

60

0 0.2 0.4 0.6 0.8 1 1.2

Wood density / g cm-3

Max

hei

gh

t /

m

Species-level variation in wood density and max height

• Subcanopy 0-20 m; canopy 21-30 m; emergent 31+ m.

• Low (<0.5 g cm-3), medium (0.5 - 0.7 g cm-3) and high (>0.7 g cm-3) wood density classes

High Med Low

Emer.

Can.

Subcan

Emergent

High

Subcanopy

Canopy

Medium Low

Max

imum

hei

ght

Wood density

p>0.01

p>0.01

p>0.01

0

5

10

15

20

25

0

5

10

15

20

25

0

5

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25

0

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0

5

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0

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% abundance of all stemsRED - C & E Amazon

BLUE - W Amazon

59 plots; 43,631 trees

Emergent

High

Subcanopy

Canopy

Medium Low

Max

imum

hei

ght

Wood density

% abundance of all stems

C & E Amazonia

W Amazonia

Why?

In Western Amazon….higher rates of extrinsic disturbance ?

higher soil fertility ?

Variation in growth rates between functional groups

Emergent

High

Subcanopy

Canopy

Medium Low

Max

imum

hei

ght

Wood density

Growth rate

High growth rate

Low growth rate

• 341 species with >20 individuals (21,159 trees)

• Most recent census interval of approx. 6 years

• Diameter increment

• Relative diameter increment

• Biomass increment

• Relative biomass increment

• Biomass calculated using a tree-by-tree allometric equation with a correction factor to account for variation in wood specific gravity

Calculating growth rates

0

0.1

0.2

0.3

0.4

0.5

0

0.1

0.2

0.3

0.4

0.5

0

0.1

0.2

0.3

0.4

0.5

Emergent

High

Subcanopy

Canopy

Medium Low

Max

imum

hei

ght

Wood density

Diameter increment / cm yr-1

0.0

0.5

1.0

1.5

2.0

2.5

0.0

0.5

1.0

1.5

2.0

2.5

0.0

0.5

1.0

1.5

2.0

2.5

Emergent

High

Subcanopy

Canopy

Medium Low

Max

imum

hei

ght

Wood density

Relative diameter increment / % yr-1

0

5

10

15

20

0

5

10

15

20

0

5

10

15

20

Emergent

High

Subcanopy

Canopy

Medium Low

Max

imum

hei

ght

Wood density

Biomass increment / kg DW yr-1

0

1

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7

0

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3

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7

8

0

1

2

3

4

5

6

7

8

Emergent

High

Subcanopy

Canopy

Medium Low

Max

imum

hei

ght

Wood density

Relative biomass increment / % yr-1

Emergent

High

Subcanopy

Canopy

Medium Low

Max

imum

hei

ght

Wood density

Summary

•Diameter increment •Rel. dbh increment •Rel. biomass increment

•Biomass increment

Implications for regional patterns of biomass and wood production

Can variation in species composition explain variation in forest biomass and wood productivity?

• Stand-level biomass/wood production estimated using the abundance and mean biomass/biomass increment for each functional group

For each plot, across functional groups…

• (Abundance x mean biomass or mean productivity)

• Compared with stand-level values calculated using tree by tree data

Estimating stand biomass and productivity from functional composition

Significance for stand-level patterns: wood production

1

2

3

4

5

1 2 3 4 5

Estimated wood production from functional composition / Mg C ha-1 yr-1

Ac

tua

l w

oo

d p

rod

uc

tio

n

Mg

C h

a-1

yr-1

Significance for stand-level patterns: biomass

R2 = 0.52

50

75

100

125

150

175

200

50 75 100 125 150 175 200

Estimated biomass from functional composition / Mg C ha -1

Ac

tua

l b

iom

as

s /

Mg

C h

a-1

Conclusions

1. Larger statured, high wood density species favoured in C & E Amazonia; smaller statured, low

wood density species favoured in W Amazonia

2. Low wood density species have higher rates of diameter growth, but similar rates of absolute biomass increment compared to high wood

density species

3. Variation in forest functional composition can explain a substantial proportion of variation in

stand biomass across Amazonia