what’s driving changes in amazon forests? jeffrey q. chambers, liliane m. teixeira, samir g....
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What’s Driving Changes in Amazon Forests?
Jeffrey Q. Chambers, Liliane M. Teixeira, Samir G. Rolim, Joaquim dos Santos, Niro Higuchi, and Susan E. Trumbore
Tulane UniversityINPA
University of California at IrvineInstituto Ambiental da Companhia Vale do Rio Doce
Phillips, O. L., and A. H. Gentry. 1994. Increasing turnover through time in tropical forests. Science 263:954-958. (Also Phillips et al. 2004)
Phillips, O. L et al.. 1998. Changes in the carbon balance of tropical forests: evidence from long-term plots. Science 282:439-442. (Also Baker et al. 2004)
Phillips, O. L. et al. 2002. Increasing dominance of large lianas in Amazonian forests. Nature 418:770-774.
Laurance, W.F. et al. 2004. Pervasive alteration of tree communities in undisturbed Amazonian forests. Nature 428 171-175
Changing Dynamics of Tropical Forests
Relative dominance of lianas/trees has doubled over the past 20 years
Tree biomass has increased in Neotropics since 1980 at about 0.5 Mg C ha-1 yr-1
Forest turnover (recruitment + mortality) doubled from 1975-1990
What is causing this non-equilibrium behavior?
Changes widely cited as driven by increasing atmospheric CO2
Tree
Stand
20 m
Plot0.00
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tive
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stem density (per 20 m2)
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mortality rate (% stems yr-1)
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tive
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ya
Log[growth rate (cm yr-1)]
Modeling Forest Size Structure
Chambers, J. Q. et al. (in press) Response of tree biomass and wood litter to disturbance in a Central Amazon forest. Oecologia.
0
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50 100 150 200
Cou
nt
Maximum DBH (cm)
Tree Species Diversity and Forest Structure
Family Species n Db,max
Burseraceae Protium cf llewelynii Macbr. 206 51.9Burseraceae Protium grandifolium Engl. 178 29.5Euphorbiaceae Micrandropsis scleroxylon (Rodr.) Rodr. 167 48.7Bombacaceae Scleronema micranthum (Ducke) Ducke 126 91.6Olacaceae Minquartia guianensis Aubl. 111 79.1Lecythidaceae Eschweilera cf wachenheimii (R.Ben.) Sandw. 103 44.8Violaceae Rinorea sp. 01 101 32.8Arecaceae Oenocarpus bacaba Mart. 93 20.1Moraceae Naucleopsis caloneura Huber 93 20.0Papilionoideae Swartzia reticulata Ducke 89 42.2
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ma
xim
um tr
unk
diam
ete
r (c
m)
number of individuals
p = 0.01n = 220
Species Matter
0
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80
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
data 319 Mg ha -1
model 425 Mg ha -1
Bio
mas
s (M
g h
a-1)
tree base diameter class (cm)
0
20
40
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10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
data 319 Mg ha -1
model 310 Mg ha -1
Bio
mas
s (M
g h
a-1)
tree base diameter class (cm)
What happens as individual trees reach their species size limits?
without species with species
Forest biomass distribution predicted much better with species information on maximum size
Coarse Litter Decomposition Study
Coarse litter comprises trunks and branches > 10 cm diameter
Mortality records from 21 ha of permanent inventory data stratified by wood density and trunk diameter
155 trees, dead for 3-15 years, remains located in the field
Three cross-sections removed from each dead tree
Calculating Decomposition Rates
t1t0
mass1 = weight - moisturemass0 = r2 h
kd = ln(m1/m0)/(t1-t0)Decomposition rate constant:
Decomposition includes respiration, leaching and fragmentation
]log[163.067.010.1 bd Dk Quantitative coarse litter decomposition model:
Chambers, J. Q., N. Higuchi, L. V. Ferreira, J. M. Melack, and J. P. Schimel. 2000. Decomposition and carbon cycling of dead trees in tropical forests of the central Amazon. Oecologia 122:380-388.
Modeling Coarse Litter Decomposition and Respiration
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tive
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wood density (g cm-3)
80%
20%]log[163.067.010.1 bd Dk
Chambers, J. Q., N. Higuchi, L. V. Ferreira, J. M. Melack, and J. P. Schimel. 2000. Decomposition and carbon cycling of dead trees in tropical forests of the central Amazon. Oecologia 122:380-388.
Chambers, J. Q., J. P. Schimel, and A. D. Nobre. 2001c. Respiration from coarse wood litter in central Amazon forests. Biogeochemistry 52:115-131.
Comparison of Field Data and Model Predictions
Attribute Units Empirical Model
large wood Mg C ha-1
156 164coarse litter Mg C ha
-115 16
growth Mg C ha-1
yr-1
1.7 1.6
mortality Mg C ha-1
yr-1
2.1 1.8
mean D b cm 21.1 20.4
mean age > 10 cm D b years n.d. 175
mean age > 100 cm D b years 425 383maximum age years 1372 1192
Summers, P. M. 1998. Estoque, Decomposição e Nutrients da Liteira Grossa em Floresta de Terra-Firme, na Amazônia Central. MS thesis. Instituto Nacional de Pesquisas da Amazônia, Manaus, Brasil.
Chambers, J. Q., N. Higuchi, and J. P. Schimel. 1998. Ancient trees in Amazonia. Nature 391:135-136.
Chambers, J. Q., T. Van Eldik, J. Southon, and N. Higuchi. 2001. Tree age structure in tropical forests of Central Amazonia. Pages 68-78 in R. O. J. Bierregaard, C. Gascon, T. E. Lovejoy, and R. C. G. Mesquita, editors. Lessons from Amazonia. Yale Univeristy Press, New Haven.
total wood
growth
recruitmentmortality
respiration
fragmentationlive wood wood litter
CO2 CO2
Carbon Cycling Structure of the Model
This stochastic-empirical forest inventory model can be used to explore how changes affecting individual trees influences ecosystem scale carbon cycling and storage.
The carbon sequestration potential of woodModel experiment: how does carbon balance respond to a large increase in productivity?
Productivity increased as a function of the known and expected increase in atmospheric CO2.
The slope of the response evident by 2010-2020 probably represents a large portion of forest long-term carbon sequestration potential
Chambers, J. Q., and W. L. Silver. 2004. Some aspects of ecophysiological and biogeochemical responses of tropical forests to atmospheric change. Philosophical Transactions of the Royal Society of London, Series B 359:463-476.
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tota
l woo
d (
Mg
C h
a-1)
year
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CO
2 con
cen
trat
ion
(ppm
)
year
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1850 1900 1950 2000 2050 2100 2150
25% tree growth increase50% tree growth increase75% tree growth increase100% tree growth increase
tree
bio
ma
ss (
Mg
ha-1
)
calendar year
Tree biomass response to various beta factors
The slope of these responses
from 1980-2020 (generously
corresponding to pan-
Amazonian forest inventory
census) varied from 0.05-0.51
Mg C ha-1 yr-1.
Only a very large , corresponding to a 100% increase in wood productivity with CO2 doubling, agrees with forest inventory data.
)]CO/[]COln([/)1NPP)/NPP(( 22ae ae
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tree
bio
mas
s in
crem
ent
(M
g C
ha
-1 y
r-1)
turnover rate (% yr-1)
r2
adj = 0.25
p < 0.0001
Modeling Carbon Cycling Dynamics Across the Basin
Actual biomass range (green ellipse) considerably lower than model predictions, suggesting productivity/turnover envelope may be too large, or other factors.
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tree
bio
mas
s (M
g C
ha-1
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growth turnover ratio (mm yr-1/%)
Malhi, Y.et al. 2004. The above-ground wood productivity and net primary productivity of 100 neotropical forests. Global Change Biology 10:563-591.
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carb
on s
ink
[198
0 -
2020
] (M
g C
ha
-1 y
r-1)
average tree growth rate (mm diameter yr-1)
Predicted Carbon Fertilization Sink Potential 1980-2020
Based on 100 ha model runs, sink potential showed no correlation with increased growth rate because more productive forests also exhibit greater variability in mortality.
Simulated CO2 fertilization (based on 25% increase in NPP w/ 2xCO2) clearly evident when mortality variability shut off.
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on s
ink
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0] (
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C h
a-1
yr-1
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average tree growth rate (mm diameter yr-1)
Permanent plot data provide information on background mortality
What is the effect of infrequent high mortality years on forest carbon balance?
Little information on frequency and extent
200 m
Severe downburst winds associated with late dry season storms
Catastrophic and Background Mortality
T0
T1
T2
T3
T0T1
T2
T3
Km-22
Km-23
Vicinal ZF-2
Bloco 4
Bloco 2
Bloco 1A
W
N
E
S
T0
T1
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Escala 1: 20.000
Response to Disturbance: BIONTE Logging Experiment
Permanent PlotsT0: control plots (3 ha)T1: 32% basal area extraction (3 ha)T2: 42% basal area extraction (3 ha)T3: 69% basal area extraction (3 ha)Catastrophic mortality results in a large shift in woody biomass from live to dead pools.
It also results in an increase for many years in average growth rates for surviving trees from competitive release.-20
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lute
gro
wth
rat
e in
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se (
%)
percent of biomass loss
Growth Response of Surviving Trees Following Catastrophic Mortality
This response best modeled assuming exponential decline in growth rate back to pre-disturbance rate (1.1 mm yr-1)
Interestingly, the “control” plots showed a similar decline, but with a lower initial growth rate (Go).
10.1207.0 tot eGG
7.1341.1oG
Tree growth rate response to disturbance is large (e.g. 4x w/ 30% biomass loss), with a rapid recovery (-0.207) to pre-disturbance levels.
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16.1%
21.3%
22.2%
grow
th r
ate
(mm
yr
-1)
time since disturbance (years)
T1
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19.8%
19.2%
18.3%
grow
th r
ate
(mm
yr
-1)
time since disturbance (years)
T20.0
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24.8%
28.4
27.6
grow
th r
ate
(mm
yr
-1)
time since disturbance (years)
T3
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grow
th r
ate
(mm
yr
-1)
time since disturbance (years)
Control
Carbon Balance and High Mortality Event
Model SimulationsA 10% mortality event in 1975 results in an increase in above-ground tree biomass following event at a rate of 0.5-0.7 Mg C ha-1 yr-1(a).
However if total large wood (TLW, b-upper) carbon balance is considered, sum of both live (b-lower) and dead (c), the ecosystem roughly maintained carbon balance throughout the disturbance event
Changes in disturbance frequency can also have large impacts on tree species composition
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bio
ma
ss (
Mg
ha-1
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AGB-mode
a
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biom
ass
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ha
-1)
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biom
ass
(Mg
ha
-1)
year
TLW-mode
c
Legenda - Vegetação
FLORESTA DE TABULEIRO
FLORESTA SECUNDÁRIA DE TABULEIRO
FLORESTA MUSSUNUNGA
FLORESTA CILIAR
BREJO E FLORESTA DE BREJO
NATIVO
SILVICULTURA TROPICAL
NUCLEO DE VISITAÇÃO
HOSPEDAGENS
Espirito Santo, Atlantic Rainforest, Linhares Reserve.
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1978 1983 1986 1989 1992 1993 1994 1995 1997 1999 2000
Years
AG
BT -
Mg
ha-1
Plot 1 Plot 2 Plot 3 Plot 4 Plot 5
020406080
100120140160180200220240260280300320340360
75 77 79 81 83 85 87 89 91 93 95 97 99
Years
Pre
cip
itat
ion
mm
El Niño Drought, Biomass Collapse and Recovery
Rolim S, Nascimento H, Jesus, R. Chambers, J . (in revision) Biomass Change in Atlantic tropical moist forest: the ENSO effect in permanent sample plots over a 22-year period. Oecologia
Tropical forests may be continually changing over time aggrading in biomass and slowly changing in species composition.
Occasional high mortality events (not necessarily linked to El Niño) result in rapid biomass loss followed by slow recovery, with overall carbon balance over large temporal and spatial scales.
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tre
e bi
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calendar year
b
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tre
e bi
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Tree Biomass Response to Increased Turnover, Changing Species Composition, and Elevated Growth Rates
Biomass response to elevated turnover (tree recruitment and mortality) increases of 25%, 50%, and 100% (a).
Combined effects: 50% turnover increase, average wood density of recruits from 0.70 to 0.60 g cm-3, and increased growth rates of 25%, 50%, and 75% (b).
Changes in tropical forest biomass during the 21st century will depend in large part on tree growth rates response to elevated turnover
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0 0.5 1 1.5 2
y = 0.94185 * x^(1.6522) R2= 0.99611
Err
or in
Bio
mas
s G
row
th R
ate
(M
g h
a-1 yr
-1)
Standard Deviation in Average Zero Error (cm)
ln(Biomass)= –0.37 + 0.333·ln(DBH) + 0.933·ln(DBH)2 – 0.122·ln(DBH)3
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tree
mas
s (k
g)
tree base diameter (cm)
Systematic Error Scaling from Diameter to Biomass
Always results in positive error in biomass growth rate estimates – SD of measurement error not well characterized and probably varies among individuals.
Discussion• Higher growth rates in more dynamic forest results in a larger potential CO2
fertilization sink. However, more dynamic forests exhibit higher mortality variability, and CO2 fertilization sink thus buried in more noise.
• Using published variability in turnover and growth rates, a maximum CO2 fertilization sink in Amazonian forests of 0.10 to 0.25 Mg C ha-1 yr-1 is quite difficult to measure directly from 1980-2020. (Unless of course tropical forests are responding physiologically much different that experimental evidence suggests – e.g. Duke FACE experiment)
• A measurable fertilization sink is even more difficult to understand given an observed doubling of turnover rates (~1970-1990) – which acts to strongly drive biomass down.
• A number of other factors can lead to short-term apparent increases in forest biomass including: (1) spatial and temporal variability in tree mortality rates (and subsequent growth response), (2) variability in other driving factors (e.g. light, moisture, temperature, aerosols, etc., (3) measurement errors including unbalanced scaling diameter to biomass.
• Because observed tropical forests changes are likely transitory, and the pan-Amazonian CO2 sink potential is low (or zero), old-growth forest sink does not balance land-use source (although surprises still possible).
• Funding priority to expand and intensify research on forest inventory plot networks such as RAINFOR toward resolving these issues.
Acknowledgments
Instituto Nacional de Pesquisas da Amazônia (INPA)
NASA LBA-ECO
Project Piculus (Pilot Programs of the G7 Nations)
The Smithsonian Institution: Biological Dynamics of Forest Fragments Project
Japanese International Cooperation Agency (JICA)