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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
10
20
30
40
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
10
15
20
25
0
5
10
15
20
25
0
5
10
15
20
25
0
5
10
15
20
25
% 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
2
3
4
5
6
7
0
1
2
3
4
5
6
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