Subtropical to boreal convergence of tree growth responses
to atmospheric carbon dioxide
Yan Xu1*, Zhihong Xu1*, Xuemei Shao2, Graham Farquhar3, Thomas Drouet4,
Timothy J. Blumfield1, Putranto Nugroho1, Valérie Penninckx4, Sune Linder5,
Chengyuan Xu1, Kevin Lee Griffin6, William Schuster7 & Huijun Zhao8
1Environmental
Futures Centre & School of Biomolecular and Physical Sciences, Griffith University, Nathan,
Queensland 4111, Australia. 2Institute of Geographical Sciences and Natural Resources Research, Chinese
Academy of Sciences, Beijing 100101, China. 3Research School of Biology, Australian National University,
Canberra, ACT 2601, Australia. 4Laboratoire d'Ecologie végétale et Biogéochimie, Université Libre de Bruxelles,
Brussels, Belgium. 5Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, SE230 53, Alnarp, Sweden. 6Lamont-Doherty Earth Observatory of Columbia University, 61 Route 9W, 6 Biology,
Palisades, NY 10964 – 8000, USA. 7Black Rock Forest Consortium, 65 Reservoir Road, Cornwell, NY 12518,
USA.
8Environmental
Futures Centre & Griffith School of Environment, Griffith University, Gold Coast,
Queensland 4222, Australia.
*These authors contributed equally to this work.
Tree water-use efficiency (WUE) has increased in the past century under climate
change1-8, but this has not translated into globally enhanced tree growth4-5, 7-11.
The impact of climate change, particularly complex atmospheric carbon dioxide
(CO2), temperature and water interactions, on plant photosynthesis, WUE and
tree growth remains elusive1-13. Plant photosynthesis is an important biological
process, subject to either CO2 or water limitation globally, and there is a
subtropical to boreal convergence of tree-leaf temperatures14. Here we advance
an atmospheric CO2-water limitation model, which predicts enhanced tree WUE
with both rising atmospheric CO2 and water limitation, and show that tree
1
growth of beech and oak in four temperate forest ecosystems of Belgium
responded non-linearly to rising atmospheric CO2 and water limitation due to
increasing mean annual temperature and decreasing summer rainfall during
1840s–1990s. It is the CO2 limitation and then CO2-induced water limitation, not
rising temperature itself, that regulate tree growth under climate change. This
model has been tested in other forest ecosystems of Australia, China, Sweden
and USA, highlighting the subtropical to boreal convergence of non-linear
responses of tree growth to rising atmospheric CO2 although the sensitivity and
magnitude of tree growth responses varied with both biotic (e.g. tree species and
aging) and abiotic factors (e.g. water and nitrogen availability). The tipping
points15-16 of atmospheric CO2 (range: 324–378 ppm) for these forest ecosystems
have occurred in the past 10–50 years, and thereafter tree growth has decreased
with rising atmospheric CO2. This results in a positive feedback to atmospheric
CO2, leading to accelerated global warming and increasing water limitation in
central Europe and elsewhere4-5, 7, 9-11.
Climate change, particularly rising atmospheric CO2 and global warming, has been
well established and this pattern is projected to intensify in the coming decades17-22.
However, the effects of complex atmospheric CO2, temperature and water interactions
on tree WUE and growth at both spatial and temporal scale are still not well
understood1-13, 23-26. Atmospheric CO2 is the single most important climate-relevant
greenhouse gas22, accounting for about 80% of non-condensed greenhouse gases in
the atmosphere at current temperatures, and makes a significant contribution to
current global warming17-22. Much progress has been made in our understanding of
the physical mechanisms governing the closely coupled atmospheric CO2 and
2
temperature relationship17-22. However, the relationships between atmospheric CO2
and water availability and between atmospheric temperature and precipitation are far
less clear and deserve much greater attention23-26. There is increasing evidence of
more climate extremes13, 23-27.
Recent research finding14 has highlighted that there is a subtropical to boreal
convergence of tree-leaf temperature at 21.4oC. Moreover, there is also a global
convergence in temperature sensitivity of respiration in terrestrial ecosystems28-29.
Despite the temperature convergence for plant photosynthesis at the leaf level 14 and
for respiration at the ecosystem level28-29, there is a strong relationship between
photosynthesis and respiration in terrestrial ecosystems28. This highlights the
importance of plant photosynthesis in driving the dynamic CO2 exchange between the
atmosphere and the terrestrial ecosystems, and hence the feedback between the
changing climate and the terrestrial carbon cycle at both local and global scale.
Here we present a new conceptual framework of the atmospheric CO2-water
limitation model that regulates plant photosynthesis and tree growth during a growing
season under climate change as shown in Fig. 1. We hypothesize that despite the
complex atmospheric CO2, temperature and water interactions, it is directly the rising
atmospheric CO2 in the first CO2 limitation phase and then indirectly the CO2-induced
water limitation, resulting from the CO2-coupled warming and decreasing water
availability associated with the warming, that regulate tree growth. Based on this
model, the atmospheric CO2-driven plant photosynthesis and hence tree growth would
be largely independent of the increases in atmospheric mean annual temperature or
mean growing season temperature under global warming. The atmospheric CO2-water
limitation model also predicts that there is a critical point or tipping point of
atmospheric CO2 when tree growth peaks (Fig. 1). Before the tipping point of
3
atmospheric CO2 is reached, the CO2 limitation controls tree growth which increases
with rising CO2. However, this CO2 fertilization effect decreases with increasing
water limitation since rising CO2 results in increasing temperature (positive feedback)
and decreasing water availability (negative feedback). The increasing water limitation
(particularly seasonal drying) then decreases tree growth and hence increases the
atmospheric CO2 (positive feedback).
Our study represents the first attempt to test the atmospheric CO2-water limitation
model. We attempt to quantify the impacts of complex atmospheric CO2, temperature
and water interactions on tree growth, using long-term records of tree growth and
stable isotope compositions (δ13C, δ15N and δ18O) in tree rings of beech and oak in
central and high Belgium (central Europe) in relation to atmospheric CO2 obtained
from East Antartica5, 30, mean annual temperature and summer rainfall during the 150year period between 1840s and 1990s (Supplementary Information). We also test the
atmospheric CO2-water limitation model with tree WUE, tree ring δ18O and growth
data of four different tree species in two temperate forest ecosystems of China. In
addition, the model has been tested with the atmospheric CO2 and long-term tree
growth data in other subtropical, temperate and boreal forest ecosystems of China,
Sweden and USA (Table 1, Table S1, Table S2 and Fig. S1).
Tree WUE for beech at two sites and oak at one site in central Belgium increased
linearly with rising atmospheric CO2 which accounts for 28–29% of the variation in
tree WUE (Fig. 2a, b and c), compared with linear increases in tree WUE from rising
atmospheric CO2 contributing to 35% of the tree WUE variation for beech and 11%
for oak at one site in high Belgium (Fig. 2d). When tree age and other abiotic factors
(particularly water availability as reflected by tree ring δ18O and nitrogen availability
4
as indexed by tree ring δ15N) were included with atmospheric CO2, about 37–82% of
the variation in tree WUE could be accounted for (Table S2).
Tree growth in central Belgium, expressed as basal area increment, responded nonlinearly to rising atmospheric CO2 (Fig. 3a, b and c), with 41%, 66% and 58% of tree
growth explained by the atmospheric CO2 for the three sites respectively. This is
predicted by the atmospheric CO2-water limitation model (Fig. 1). Tree basal area
increment increased initially with rising atmospheric CO2, but peaked as the critical
CO2 was reached at 336–342 ppm (Fig. 3a, b and c). Tree basal area increment
decreased with rising CO2 after the critical CO2 was reached, since water limitation
(particularly seasonal drying) progressed as the growth limiting factor due to the
atmospheric warming coupled with the rising CO2 and decreasing summer or growing
season rainfall during the 1840s–1990s (Supplementary Information). It is important
to note that when both atmospheric CO2 and mean annual temperature were included
in the regression analysis for the three sites in central Belgium, only atmospheric CO 2
was the significant variable and again tree growth responded non-linearly to rising
CO2, with the mean annual temperature being an insignificant variable. However,
mean summer or growing season rainfall or tree ring δ18O was significantly and
positively related to tree growth as basal area increment in central Belgium, in
addition to the atmospheric CO2 as the primary key driving variable.
Basal area increments for both beech and oak trees in high Belgium responded
non-linearly to rising atmospheric CO2 (R2 = 0.422 for the former and R2 = 0.727 for
the later). These peaked at the critical CO2 of 330 and 324 ppm for beech and oak
respectively, and decreased thereafter (Fig. 3d). When both atmospheric CO2 and
mean annual temperature were included in the regression analysis for high Belgium,
only atmospheric CO2 was the significant variable. Again tree growth responded non-
5
linearly to rising CO2, with mean annual temperature shown to be an insignificant
variable.
We have tested the atmospheric CO2-water limitation model with tree growth data
for different species in the other subtropical, temperate and boreal forest ecosystems
of Australia, China, Sweden and USA, and there is a subtropical to boreal
convergence of non-linear responses of tree growth to rising atmospheric CO2 (R2
range: 0.087–0.648) (Table 1) although the sensitivity and magnitude of tree growth
responses varied with both biotic (e.g. tree species and age) and abiotic factors (e.g.
water and nitrogen availability) (Table S2). Tree WUE of four species in two
temperate forest ecosystems of China increased significantly with rising atmospheric
CO2 (R2 range: 0.532–0.816) (Fig. S2). Tree growth of two or three species in each of
the two temperate forest ecosystems of China was non-linearly related to tree WUE,
increasing initially with tree WUE due to the CO2 fertilization and then decreasing
with tree WUE due to the increasing water limitation (Fig. S3).
The significant non-linear relationships between tree growth and mean annual
temperature in central Belgium (Fig. S4a, b and c) are to be expected due to the
closely coupled positive relationship between atmospheric CO2 and mean annual
temperature (R2 = 0.614, n = 23, P < 0.001) or mean growing season temperature (R2
= 0.654, n = 23, P < 0.001) or mean maximum temperature (R2 = 0.615, n = 23, P <
0.001), and the significant negative relationship between mean summer rainfall in
central Belgium and mean growing season temperature (R2 = 0.197, n = 23, P < 0.001)
or mean maximum temperature (R2 = 0.189, n = 23, P = 0.038).
Our results have demonstrated that tree growth of various species in the subtropical,
temperate and boreal forest ecosystems of Australia, China, Europe and USA
responded non-linearly to rising atmospheric CO2 as predicted by the atmospheric
6
CO2-water limitation model (Fig. 1), with the tipping points5, 15-16 of atmospheric CO2
(range: 324–378 ppm with a mean of 351 ppm) which have occurred in the past 10–
50 years. Our research findings are supported by the work5 with four subtropical
forest ecosystems in southern China, with the CO2 tipping points for tree growth of
Masson pine to be 340, 354, 355 and 373 ppm respectively (mean: 356 ppm). The
overall average CO2 tipping point for tree growth among the subtropical, temperate
and boreal forest ecosystems is about 354 ppm, which is just above the critical
planetary boundary for atmospheric CO2 of 350 ppm15. There is recent widespread
tree growth decline in boreal and temperate forest ecosystems despite increasing
atmospheric CO2 and temperature which would be expected to increase tree growth in
these ecosystems4, 7. This unexpected widespread tree growth decline in temperate
and boreal forest ecosystems, following the initial positive CO2 fertilization effect on
tree growth, has been partly attributed to warming induced water stress4, 7.
METHODS SUMMARY
All tree growth data for various species in subtropical, temperate and boreal forest
ecosystems were obtained by either collecting tree discs (hoop pine in the subtropical
forest ecosystems of Australia and beech & oak in the temperate forest ecosystems of
Belgium) or tree ring cores for 3–23 trees of each species (Table S1 and Fig. S1).
Ring width with 0.01 mm or 0.001 cm precision was measured. Ring widths were
converted to annual basal area increments (BAI): BAI = π (Rn2 – Rn–12) = π (Rw2 + 2
Rn–1 Rw), where Rw and Rn–1 are the tree ring width between Rn and Rn–1, and radius
for the n–1 year prior to the tree ring formation respectively. For each disc or tree ring
core, wood samples representing 5-year growth intervals were cut off. All ice core
CO25, 30 and weather data were arranged to coincide with the corresponding 5-year
7
intervals of individual tree discs or tree ring cores. Samples were analysed for tree
ring isotope compositions (δ13C, δ15N and δ18O) for each of the 3–6 trees per species
in temperate forest ecosystem of Belgium (δ13C, δ15N and δ18O) and China (δ13C and
δ18O). Tree WUE was calculated as reported previously1-5, 7. All tree ring isotopic and
growth data were checked for the normality and multiple regressions were performed,
using Statistix software (Version 8.0).
Full Methods and any associated references are available in the online
version of the paper.
1.
Bert, D., Leavitt, S.W. & Dupouey, J.L. Variations of wood δ13C and water-use efficiency of Abies
alba during the last century. Ecology 78, 1588–1596 (1997).
2.
Duquesnay, A., Bréda, N., Stievenard, M. & Dupouey, J.L. Changes of tree-ring 13C and water-use
efficiency of beech (Fagus sylvatica L.) in north-eastern France during the past century. Plant Cell
Environ. 21, 565–572 (1998).
3.
Saurer, M., Siegwolf, R.T.W. & Schweingruber, F.H. Carbon isotope discrimination indicates
improving water-use efficiency of trees in northern Eurasia over the last 100 years. Glob. Change
Biol. 10, 2109–2120 (2004).
4.
Silva, L.C.R., Anand, M. & Leithead, M.D. Recent tree growth decline despite increasing
atmospheric CO2. PLoS ONE 5, doi: 10.1317/journal.pone.0011543 (2010).
5.
Sun, F.F. et al. Long-term tree growth rate, water use efficiency and tree ring nitrogen isotope
composition of Pinus massoniana L. in response to global climate change and local nitrogen
deposition in Southern China. J. Soils Sediments 10, 1453–1465 (2010).
6.
Loader, N.J. et al. Recent trends in the intrinsic water-use efficiency of ringless rainforest trees in
Borneo. Phil. Trans. R. Soc. B 366, 3330–3339 (2011).
7.
Gómez-Guerrero, A. et al. Growth decline and divergent tree ring isotopic composition (13C and
18O) contradict predictions of CO2 stimulation in high altitude forests. Glob. Change Biol. 19, 1748–
1758 (2013).
8.
Keenan, T.F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide
concentrations rise. Nature 499, 324–327 (2013).
8
9.
Johnson, S.E. & Abrams, M.D. Age class, longevity and growth rate relationships: protracted growth
increases in old trees in the eastern United States. Tree Physiol. 29, 1317–1328 (2009).
10. Xu, Z.H., Chen, C.R., He, J.Z. & Liu, J.X. Trends and challenges in soil research 2009: linking
global climate change to local long-term forest productivity. J. Soils Sediments 9, 83–88 (2009).
11. Zhao, M.S. & Running, S.W. Drought-induced reduction in global terrestrial net primary production
from 2000 through 2009. Science 329, 940–943 (2010).
12. Heimann, M. & Reichstein, M. Terrestrial ecosystem carbon dynamics and climate feedbacks.
Nature 451, 289–292 (2008).
13. Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).
14. Helliker, B.R. & Richter, S.L. Subtropical to boreal convergence of tree-leaf temperatures. Nature
454, 511–514 (2008).
15. Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).
16. Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).
17. Mann, M.E., Bradley, R.S. & Hughes, M.K. Global-scale temperature patterns and climate forcing
over the past six centuries. Nature 392, 779–787 (1998).
18. Hegerl, G.C., Crowley, T.J., Hyde, W.T. & Frame, D.J. Climate sensitivity constrained by
temperature reconstructions over the past seven centuries. Nature 440, 1029–1032 (2006).
19. Allen, M.R. et al. Warming caused by cumulative carbon emissions towards the trillionth tonne.
Nature 458, 1163–1166 (2009).
20. Matthews, H.R., Gillett, N.P., Stott, P.A. & Zickfeld, K. The proportionality of global warming to
cumulative carbon emissions. Nature 459, 829–832 (2009).
21. Frank, D.C. et al. Ensemble reconstruction constraints on the global carbon cycle sensitivity to
climate. Nature 463, 527–530 (2010).
22. Lacis, A.A., Schmidt, G.A., Rind, D. & Ruedy, R.A. Atmospheric CO 2: Principle control knob
governing earth’s temperature. Science 330, 356–359 (2010).
23. Treydte, K.S. et al. The twentieth century was the wettest period in northern Pakistan over the past
millennium. Nature 440, 1179–1182 (2006).
24. Engelbrecht, B.M.J. et al. Drought sensitivity shapes species distribution patterns in tropical forests.
Nature 447, 80–82 (2007).
25. Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture
supply. Nature 467, 951–954 (2010).
26. Schiermeier, Q. The real holes in climate science. Nature 463, 284–287 (2010).
27. Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755
(2012).
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28. Mahecha, M.D. et al. Global convergence in the temperature sensitivity of respiration at ecosystem
level. Science 329, 838–840 (2010).
29. Yvon-Durocher, G. et al. Reconciling the temperature dependence of respiration across timescales
and ecosystem types. Nature 487, 472–476 (2012).
30. Etheridge, D.M. et al. Historical CO2 records from the Law Dome DE08, DE08-2 and DSS ice cores.
Carbon Dioxide Information Analysis Centre, Oak Ridge National Laboratory, US Department of
Energy, Oak Ridge, USA (1998).
Supplementary Information is available in the online version of the paper.
Acknowledgements We thank the Australian Research Council, Griffith University, Fonds National de
la Recherche Scientifique (FNRS, Belgium), National Basic Research Program of China, Chinese
Academy of Sciences (CAS), … for supporting this research. The weather data were provided by the
Institut Royal de Météorologie, Belgium. Drs Yong Zhang and Yang Wang of CAS Institute of
Geographical Sciences and Natural Resources Research, Beijing, China, assisted in sampling. Drs
Fangfang Sun and Ling Ma of CAS South China Botanical Garden, Guangzhou, China, and Ms Wenjing
Li and Mr Rene Diocares of Griffith University, assisted in tree ring isotope analyses. Drs Chengrong
Chen and Nina Prasolova of Griffith University, and Drs Zhengguo Shi and Xiaodong Yan of CAS in
Beijing, assisted in the data analyses and presentation.
Author Contributions Z.H.X. & Y.X. coordinated this study, data analyses and writing in close
consultation with X.M.S, G.F., T.D., T.J.B., P.N., V.P., S.L., C.Y.X., K.L.G., W.S. & H.J.Z. Y.X., X.M.S.,
T.D., T.J.B., P.N., V.P., C.Y.X., K.L.G. & W.S. contributed to the collection of tree growth and tree ring
isotopic and climatic data. G.F., S.L. and H.J.Z. contributed to the writing. All authors discussed the
results and commented on the manuscript.
Author Information The authors declare no competing financial interests. Reprints and permissions
information is available at www.nature.com/reprints. Correspondence and requests for materials should
be addressed to Z.H.X. (zhihong.xu@griffith.edu.au).
10
Table 1 | Tree growth responses to atmospheric carbon dioxide concentration ([CO 2],
ppm) in subtropical, temperate and boreal forest ecosystems of Australia, China,
Sweden and USA
Sites
Species*
Regression equations*
Imbil,
HP
BAI = - 633.2 + 3.80 [CO2] – 5.28  10-3 [CO2]2 (R2 = 0.466, n = 55, P < 0.001)
Australia
BP
BAI = - 550.5 + 3.30 [CO2] – 4.85  10-3 [CO2]2 (R2 = 0.321, n = 45, P < 0.001)
Yarraman,
HP
BAI = - 2446.2 + 14.4 [CO2] – 0.0210 [CO2]2 (R2 = 0.337, n = 58, P < 0.001)
Australia
BP
BAI = - 893.8 + 5.09 [CO2] – 7.08  10-3 [CO2]2 (R2 = 0.481, n = 35, P < 0.001)
Tiantong,
MP
BAI = - 709.4 + 4.44 [CO2] – 6.71  10-3 [CO2]2 (R2 = 0.166, n = 133, P < 0.001)
Baishan,
NF
BAI = - 409.1 + 2.32 [CO2] – 3.16  10-3 [CO2]2 (R2 = 0.591, n = 90, P < 0.001)
China
LS
BAI = - 665.9 + 4.05 [CO2] – 5.93  10-3 [CO2]2 (R2 = 0.305, n = 172, P < 0.001)
KP
BAI = - 831.5 + 4.90 [CO2] – 6.88  10-3 [CO2]2 (R2 = 0.617, n = 156, P < 0.001)
KL
BAI = - 273.3 + 1.74 [CO2] – 2.49  10-3 [CO2]2 (R2 = 0.125, n = 155, P < 0.001)
Jingsong,
NF
BAI = - 800.0 + 4.38 [CO2] – 5.80  10-3 [CO2]2 (R2 = 0.648, n = 65, P < 0.001)
China
LS
BAI = - 678.1 + 3.87 [CO2] – 5.30  10-3 [CO2]2 (R2 = 0.495, n = 136, P < 0.001)
KP
BAI = - 293.3 + 1.88 [CO2] – 2.77  10-3 [CO2]2 (R2 = 0.0874, n = 176, P < 0.001)
KL
BAI = - 310.3 + 1.84 [CO2] – 2.51  10-3 [CO2]2 (R2 = 0.307, n = 113, P < 0.001)
Oak*
BAI = - 438.8 + 2.47 [CO2] – 3.44  10-3 [CO2]2 (R2 = 0.223, n = 81, P < 0.001) (A1)
Subtropical
China
Temperate
Hudson
highlands,
BAI = - 425.2 + 2.45 [CO2] – 3.42  10-3 [CO2]2 (R2 = 0.246, n = 145, P < 0.001) (A2)
USA
BAI = - 807.8 + 4.68 [CO2] – 6.62  10-3 [CO2]2 (R2 = 0.437, n = 386, P < 0.001) (A3)
BAI = - 759.5 + 4.66 [CO2] – 6.88  10-3 [CO2]2 (R2 = 0.215, n = 268, P < 0.001) (A4)
Boreal
Kulbäcklidens,
SP
BAI = - 51.3 + 0.306 [CO2] – 4.06  10-4 [CO2]2 (R2 = 0.237, n = 252, P < 0.001)
Sweden
NS*
BAI = - 272.9 + 1.70 [CO2] – 2.53  10-3 [CO2]2 (R2 = 0.475, n = 90, P < 0.001) (S1)
BAI = - 65.8 + 0.398 [CO2] – 5.73  10-4 [CO2]2 (R2 = 0.588, n = 146, P < 0.001) (S2)
*HP: Hoop pine (Araucaria cunninghamii); BP: Bunya pine (Araucaria bidwillii); MP: Masson pine (Pinus
massoniana); NF: Needle fir (Abies nephrolepis); LS: Little-seed spruce (Picea jezoensis); KP: Korean
pine (Pinus koraiensis); KL: Korean larch (Larix olgensis); Oak: Quercus rubra; SP: Scots pine (Pinus
sylvestris); NS: Norway spruce (Picea abies); BAI: basal area increment (cm2 yr-1); A1: age 35–40 years;
11
A2: age 64–72 years; A3: age 86–93 years; A4: age 130–145 years; S1: lower slope; and S2: upper
slope.
12
Figure legends
Figure 1 | Conceptual framework of atmospheric carbon dioxide concentration
(CO2), temperature (T) and water (H2O) interactions and feedbacks in
controlling tree growth index (P) under climate change. Critical point of
atmospheric CO2 (CPCO2) is the CO2 when P peaks or when CO2 limitation index
(LCO2: 0 to 1) equals to H2O limitation index (LH2O: 0 to 1), where P = k (LCO2 LH2O)2 and k is the biochemical reaction constant for plant photosynthesis under the
optimum temperature during tree growing season.
Figure 2 | Relationships between atmospheric carbon dioxide concentration [CO 2]
and tree water-use efficiency (WUE) in temperate forest ecosystems of Belgium
for beech at MES (a); oak at NOY (b); beech at ODE (c); and beech and oak at POUR,
respectively (d).
Figure 3 | Relationships between atmospheric carbon dioxide concentration [CO 2]
and tree growth as basal area increment (BAI) in temperate forest ecosystems of
Belgium for beech at MES (a); oak at NOY (b); beech at ODE (c); and beech and oak
at POUR, respectively (d).
13
METHODS
All tree growth data for various species in subtropical, temperate and boreal forest
ecosystems were obtained by either collecting tree discs (hoop pine in the subtropical
forest ecosystems of Australia and beech & oak in the temperate forest ecosystems of
Belgium) or tree ring cores for 3–23 trees of each species (Table S1 and Fig. S1). Two
cores were taken at 1.3 m above the ground with 10 mm diameter increment borers
(Haglöf Increment Borer, Sweden) for each sampling tree. Ring width with 0.01 mm
or 0.001 cm precision was measured, using a semi-automatic device LINTABTM 6
High Resolution digital positioning table (RinntechICT. International Pty. Ltd,
Germany) and TSAP-Win Scientific Software (Rinntech/ICT International Pty. Ltd,
Germany). Ring widths were converted to annual basal area increments (BAI): BAI =
π (Rn2 – Rn–12) = π (Rw2 + 2 Rn–1 Rw), where Rw and Rn–1 are the tree ring width
between Rn and Rn–1, and radius for the n–1 year prior to the tree ring formation
respectively. For each disc or tree ring core, wood samples representing 5-year growth
intervals were cut off. All ice core CO25,
30
and weather data were arranged to
coincide with the corresponding 5-year intervals of individual tree discs or tree ring
cores. Samples were analysed for tree ring isotope compositions (δ13C, δ15N and δ18O)
for each of the 3–6 trees per species in temperate forest ecosystem of Belgium (δ13C,
δ15N and δ18O) and China (δ13C and δ18O). Tree WUE was calculated as reported
previously1-5, 7. All tree ring isotopic and growth data were checked for the normality
and multiple regressions were performed, using Statistix software (Version 8.0).
Site description and tree growth data
Four sites in temperate forest ecosystems were selected (Table S1), with three (MES,
ODE, NOY) in central Belgium and one (POUR) in high Belgium. Information on the
site characteristics has been reported previously31-32. Beech trees (~130–160 years old)
14
were randomly sampled at the MES, ODE and POUR sites (five, six, and five boles,
respectively), and oak trees (~160 years) at the NOY (five boles) and POUR (five
boles), between 1995 and 1997. The stands of central Belgium treated as even-aged
forests, offer homogeneous sampling conditions, since all beech trees were planted
during the same period and are genetically similar. Discs of about 20 cm in thickness
were cut from the top of the bole (30 m and 20 m height off the ground in central and
high Belgium, respectively). Ring width with 0.01 mm or 0.001 cm precision was
measured along four radii, selected so as to avoid growth anomalies. For each disc,
wood samples representing 5-year growth intervals were cut off. Temperature and
precipitation for high Belgium were collected at St Hubert, ca 25.2 km from the
POUR site, while temperature and precipitation for central Belgium were collected at
Uccle, ca. 2–6 km from the MES, ODE, NOY sites.
Hoop pine tree discs of 20 cm in thickness were cut off about 30 cm above the
ground from four trees in each of the two subtropical hoop pine plantations grown in
Imbil and Yarraman, Queensland, Australia (Table S1). Bunya pine tree ring cores
were collected from three trees at 1.3 m above the ground in each of the two
subtropical Bunya pine plantations nearby the two hoop pine plantations in Imbil and
Yarraman, Queensland, Australia (Table S1). Masson pine tree ring cores were
collected from five trees at 1.3 m above the ground in a secondary subtropical forest
ecosystem of mixed species in Tiantong, China (Table S1).
Tree ring cores were collected from 3–7 trees at 1.3 m above the ground for each
of the four species in two temperate forest ecosystems at Baishan and Jingsong of
China (Table S1). For each tree ring core, wood samples representing 5-year growth
intervals were cut off.
15
Oak tree ring cores were collected at 1.3 m above the ground for each of the four
age groups in a temperate forest ecosystem of Hudson Highlands of USA: 10 trees for
age group 35–40 years, 11 trees for age group 64–72 years, 23 trees for age group 86–
93 years and 13 trees for age group 130–145 years (Table S1). More detailed
information on this site has been published previously33-34.
Tree ring cores were collected at 1.3 m above the ground from five trees for each
of Scots pine and Norway spruce in a boreal forest ecosystem of Kulbäcklidens in
Sweden (Table S1).
Stable isotope composition analysis
Stable isotope ratios were analysed on the isotope ratio mass spectrometer at Griffith
University, Queensland, Australia, expressed as carbon (δ13C), oxygen (δ18O) and
nitrogen (δ15N) and reported according to: δ13C, δ18O and δ15N = [(Rsample / Rstandard) –
1]  1000, where Rsample was the isotopic ratio for the sample and Rstandard the isotopic
ratio of the standard35-36.
31. Penninckx, V., Meerts, P., Herbauts, J. & Gruber, W. Ring width and element concentrations in
beech (Fagus sylvatica L.) from a periurban forest in central Belgium. For. Ecol. Manage. 113, 23–
33 (1999).
32. Drouet, T., Herbauts, J. & Demaiffe, D. Long-term records of strontium isotope composition in tree
rings suggest changes in forest calcium sources in the early 20 th century. Glob. Change Biol. 11,
1926–1940 (2005).
33. Schuster, W.S. et al. Changes in composition, structure and aboveground biomass over seventy-six
years (1930–2006) in the Black Rock Forest, Hudson Highlands, southeastern New York State.
Tree Physiol. 28, 537–549 (2008).
34. Xu, C.Y. et al. Age-related decline of stand biomass accumulation is primarily due to mortality and
not to reduction in NPP associated with individual tree physiology, tree growth or stand structure in
Quercus-dominated forest. J. Ecol. 100, 428–440 (2011).
16
35. Xu, Z.H. et al. Carbon isotope discrimination and oxygen isotope composition in clones of the F1
hybrid between slash pine (Pinus elliottii) and Caribbean pine (P. caribaea) in relation to tree growth,
water-use efficiency and foliar nutrient concentration in subtropical Australia. Tree Physiol. 20, 1209–
1217 (2000).
36. Xu, Z.H., Prasolova, N.V., Lundkvist, K., Beadle, C. & Leaman, T. Genetic variation in carbon and
nitrogen isotope composition and nutrient concentration in the foliage of 10-year-old hoop pine
families in relation to tree growth in subtropical Australia. For. Ecol. Manage. 186, 359–371 (2003).
17
Figure 1 (Xu et al., submitted)
18
Figure 2 (Xu et al., submitted)
19
Figure 3 (Xu et al., submitted)
20
Supplementary Information
Subtropical to boreal convergence of tree growth responses
to atmospheric carbon dioxide
Yan Xu1*, Zhihong Xu1*, Xuemei Shao2, Graham Farquhar3, Thomas Drouet4,
Timothy J. Blumfield1, Putranto Nugroho1, Valérie Penninckx4, Sune Linder5,
Chengyuan Xu1, Kevin Lee Griffin6, William Schuster7 & Huijun Zhao8
1Environmental
Futures Centre & School of Biomolecular and Physical Sciences, Griffith University, Nathan,
Queensland 4111, Australia. 2Institute of Geographical Sciences and Natural Resources Research, Chinese
Academy of Sciences, Beijing 100101, China. 3Research School of Biology, Australian National University,
Canberra, ACT 2601, Australia. 4Laboratoire d'Ecologie végétale et Biogéochimie, Université Libre de Bruxelles,
Brussels, Belgium. 5Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, SE230 53, Alnarp, Sweden. 6Lamont-Doherty Earth Observatory of Columbia University, 61 Route 9W, 6 Biology,
Palisades, NY 10964 – 8000, USA. 7Black Rock Forest Consortium, 65 Reservoir Road, Cornwell, NY 12518,
USA.
8Environmental
Futures Centre & Griffith School of Environment, Griffith University, Gold Coast,
Queensland 4222, Australia.
This PDF file includes:
Supplementary Information Text
Supplementary Tables
Supplementary Figures
21
Supplementary Information Text
Four sites of temperate forest ecosystems in Belgium were selected (Table S1), with
three (MES, ODE, NOY) in central Belgium and one (POUR) in high Belgium. We
use detailed records of monthly rainfall (1833–1995) and temperature (1901–1995) at
Uccle, about 2–6 km from the central Belgium sites, together with CO2 from East
Antarctica5, 30 to evaluate the relationships between CO2 and local temperature, and
between local temperature and rainfall for central Belgium. Rising atmospheric CO2 is
coupled with the linearly increasing mean annual temperature (MAT) (R2 = 0.614, n =
23, P < 0.001), mean growing season temperature (R2 = 0.654, n = 23, P < 0.001) and
mean maximum temperature (R2 = 0.615, n = 23, P < 0.001), but is unrelated to mean
minimum temperature (P = 0.452). It is interesting to note that summer rainfall in
central Belgium is negatively related to mean growing season temperature (R2 = 0.197,
n = 23, P < 0.001) and mean maximum temperature (R2 = 0.189, n = 23, P = 0.038),
but is unrelated to either MAT (P = 0.174) or mean minimum temperature (P = 0.580).
Similarly, growing season rainfall is negatively related to mean growing season
temperature (R2 = 0.209, n = 23, P = 0.028) and mean maximum temperature (R2 =
0.251, n = 23, P = 0.015), but is unrelated to either MAT (P = 0.240) or mean
minimum temperature (P = 0.705). This highlights that in central Belgium there
would be increasing water limitation for tree growth during the growing season under
climate change, due to increasing evaporation and transpiration with atmospheric
warming from rising CO2 and decreasing summer or growing season rainfall. It is
important to note that mean annual rainfall is unrelated to MAT (P = 0.513), mean
maximum temperature (P = 0.840), mean growing season temperature (P = 0.942)
and mean minimum temperature (P = 0.206). However, winter rainfall is positively
22
related to mean growing season temperature (R2 = 0.209, n = 23, P = 0.028), mean
maximum temperature (R2 = 0.169, n = 23, P = 0.051) and MAT (R2 = 0.250, n = 23,
P = 0.015), but is unrelated to mean minimum temperature (P = 0.316). This
highlights that climate change, particularly rising CO2 and warming, has significant
impacts on rainfall distribution among seasons, with increasing winter rainfall during
non-growing season at the expense of decreasing both summer and growing season
rainfall, although there is no significant change in mean annual rainfall in central
Belgium for the period 1833–1995.
Monthly rainfall and temperature records cover a shorter period (1951–1995) near
the POUR site in high Belgium. There are significant positive relationships between
these rainfall and temperature data in high Belgium and those of central Belgium (R2
= 0.593, 0.531 and 0.475 for annual rainfall, growing season rainfall and summer
rainfall respectively, n = 55, P < 0.001; and R2 = 0.925, 0.929 and 0.921 for maximum,
mean and minimum annual temperature respectively, n = 55, P < 0.001). Hence, it is
reasonable to use the temperature data in central Belgium as estimates of those for
high Belgium due to their longer period (1901–1995), when assessing the
relationships between tree growth rate and MAT for high Belgium.
23
Table S1 | General characteristics of study sites in subtropical, temperate and boreal forest ecosystems of Australia, China, Europe and
USA
Climate zones
Sites
Latitude
Longitude
Elevation
(m a.s.l.)
MAT* (oC)
MAP* (mm)
Tree
Species*
Life span
Number of trees
Subtropical
Imbil, Australia
26°29ʹ S
152°37ʹ E
26°52ʹ S
151°51ʹ E
29°48ʹ N
121°47ʹ E
50°47ʹ13 N
4°27ʹ01 E
50°47ʹ N
4°23ʹ E
50°47ʹ07 N
4°26ʹ58 E
49°47ʹ57 N
5°16ʹ26 E
42°12ʹ N
128°10ʹ E
135
18.6
1179
16.9
817
16.2
1374
9.8
780
9.8
780
9.8
780
7.8
1200
2.8
670
HP
BP
HP
BP
MP
1938–2012
1929–2012
1934–2012
1930–2012
1852–2012
4
3
4
3
5
Beech
1840–1995
5
Oak
1840–1995
5
Beech
1840–1995
6
Beech
Oak
NF
LS
KP
KL
NF
LS
KP
KL
Oak*
1840–1995
1840–1995
1894–2010
1797–2010
1811–2010
1873–2010
1931–2010
1897–2010
1760–2010
1748–2010
1867–2006
Yarraman, Australia
Tiantong, China
Temperate
Mésange (MES),
Belgium
Enfants Noyés
(NOY), Belgium
Bonne Odeur
(ODE), Belgium
Poursumont
(POUR), Belgium
Baishan, China
600
200
115
110
120
410
5
5
1100
5
5
5
4
Jingsong, China
42°22ʹ N
730
2.8
5
128°24ʹ E
670
7
5
3
Hudson Highlands, USA
41°24ʹ N
110–450
10.4
10 (A1); 11 (A2);
74°01ʹ W
1200
23 (A3); 13 ( A4)
Boreal
Kulbäcklidens, Sweden
64°11ʹ N
240–290
2.7
SP
1739–2012
5
19°34ʹ E
572
NS *
1762–2012
2 (S1); 3 (S2)
*MAT: mean annual temperature; MAP: mean annual precipitation (the data were obtained from meteorologic stations closest to the study sites); HP: Hoop pine
(Araucaria cunninghamii); BP: Bunya pine (Araucaria bidwillii); MP: Masson pine (Pinus massoniana); Beech: Fagus sylvatica; Oak: Quercus rubra; NF: Needle fir
(Abies nephrolepis); LS: Little-seed spruce (Picea jezoensis); KP: Korean pine (Pinus koraiensis); KL: Korean larch (Larix olgensis); SP: Scots pine (Pinus sylvestris);
NS: Norway spruce (Picea abies); A1: age 35–40 years; A2: age 64–72 years; A3: age 86–93 years; A4: age 130–145 years; S1: lower slope; and S2: upper slope.
24
Table S2 | Tree water-use efficiency (WUE) and growth (basal area increment: BAI) in temperate forest ecosystems of Belgium and China in
response to rising atmospheric carbon dioxide concentration ([CO 2], ppm) and other biotic and abiotic factors
Sites
Belgium
Mésange
(MES)
Enfants
Noyés (NOY)
Bonne Odeur
(ODE)
Poursumont
(POUR)
Tree
species*
Beech
Oak
Beech
Beech
Oak
China
Baishan
WUE (µmol mol-1)
Regression equations*
BAI (cm2 yr-1)
WUE = - 1469.6 + 8.88 [CO2] – 0.0124 [CO2]2 – 0.414 ATree
– 0.326 δ18O (R2 = 0.664, n = 91, P < 0.001)
WUE = - 9.77 + 0.250 [CO2] + 0.718 δ18O + 0.816 δ15N
(R2 = 0.430, n = 129, P < 0.001)
WUE = - 964.3 + 5.96 [CO2] – 8.35  10-3 [CO2]2 – 0.231 ATree
(R2 = 0.372, n = 119, P < 0.001)
WUE = - 50.0 + 0.308 [CO2] + 1.20 δ18O
(R2 = 0.317, n = 101, P < 0.001)
WUE = - 2044.0 + 12.3 [CO2] – 0.0177 [CO2]2 + 4.07 δ18O
+ 2.68 δ15N – 0.419 ATree (R2 = 0.820, n = 74, P < 0.001)
BAI = - 751.9 + 4.71 [CO2] – 7.15  10-3 [CO2]2 + 2.75 δ15N
+ 0.261 (δ15N)2 + 0.110 ATree (R2 = 0.587, n = 80, P < 0.001)
BAI = - 796.0 + 4.72 [CO2] – 7.38  10-3 [CO2]2 + 0.330 WUE
+ 0.0373 RSummer – 0.942 δ15N + 0.194 ATree
(R2 = 0.734, n = 108, P < 0.001)
BAI = - 619.4 + 3.75 [CO2] – 5.48  10-3 [CO2]2 – 0.132 WUE
(R2 = 0.602, n = 119, P < 0.001)
BAI = - 1486.4 + 8.99 [CO2] – 0.0136 [CO2]2 + 0.191 WUE
+ 1.75 δ18O (R2 = 0.551, n = 100, P < 0.001)
BAI = - 447.0 + 2.75 [CO2] – 4.24  10-3 [CO2]2 + 0.486 δ18O
+ 0.0325 ATree (R2 = 0.809, n = 73, P < 0.001)
WUE = - 70.8 + 0.405 [CO2] + 0.156 ATree
BAI = - 553.0 + 3.17 [CO2] – 4.34  10-3 [CO2]2 – 0.133 WUE
(R2 = 0.797, n = 77, P < 0.001)
(R2 = 0.594, n = 77, P < 0.001)
-3
2
18
LS
WUE = - 356.2 + 2.65 [CO2] – 3.33  10 [CO2] – 3.63 δ O
BAI = - 506.7 + 3.06 [CO2] – 4.72  10-3 [CO2]2 + 0.377 WUE
(R2 = 0.780, n = 170, P < 0.001)
(R2 = 0.359, n = 170, P < 0.001)
KP
WUE = - 211.8 + 1.50 [CO2] – 1.83  10-3 [CO2]2 + 0.0344 ATree
BAI = - 712.9 + 4.10 [CO2] – 5.89  10-3 [CO2]2 + 0.460 WUE
2
(R = 0.797, n = 155, P < 0.001)
(R2 = 0.651, n =155, P < 0.001)
KL
WUE = 22.6 + 0.181 [CO2] + 0.0401 ATree
BAI = - 733.7 + 4.49 [CO2] – 6.14  10-3 [CO2]2 – 8.85  10-4 ATree2
(R2 = 0.549, n = 153, P < 0.001)
– 1.42 δ18O (R2 = 0.511, n = 153, P < 0.001)
Jingsong
NF
WUE = - 94.9 + 0.502 [CO2]
BAI = - 800.0 + 4.38 [CO2] – 5.80  10-3 [CO2]2
(R2 =0.543, n = 64, P < 0.001)
(R2 = 0.648, n =65, P < 0.001)
LS
WUE = - 56.1 + 0.426 [CO2]
BAI = - 725.3 + 3.85 [CO2] – 5.37  10-3 [CO2]2 + 3.19 δ18O
(R2 = 0.805, n =121, P < 0.001)
(R2 = 0.543, n = 122, P < 0.001)
KP
WUE = 9.64 + 0.208 [CO2] + 0.0506 ATree
BAI = - 425.5 + 2.62 [CO2] – 3.70  10-3 [CO2]2
(R2 = 0.734, n = 176, P < 0.001)
– 2.14  10-3 [CO2]  ATree (R2 = 0.166, n = 176, P < 0.001)
-3
2
18
KL
WUE = - 368.8 + 2.23 [CO2] – 3.02  10 [CO2] + 1.64 δ O
BAI = - 310.3 + 1.84 [CO2] – 2.51  10-4 [CO2]2
2
+ 0.0786 ATree (R = 0.841, n = 113, P < 0.001)
(R2 = 0.307, n = 113, P < 0.001)
*The abbreviations of tree species are the same with Table S1; ATree: relative tree age (yrs) with the first inner ring of each tree core or disc to be age 0 year; δ 15N (‰):
stable nitrogen isotope composition in tree ring wood material; δ 18O (‰): stable oxygen isotope composition in tree ring wood material; R Summer (mm): summer rainfall;
and [CO2]  ATree: interaction between [CO2] and ATree.
NF
25
Supplementary Figure Legends
Figure S1 | Map of geographical locations for the study sites in subtropical,
temperate and boreal forest ecosystems of Australia, China, Europe and USA.
Figure S2 | Relationships between atmospheric carbon dioxide concentration
[CO2] and tree water-use efficiency (WUE) in temperate forest ecosystems of
China for Needle fir at Baishan (a) and Jingsong (b); Little-seed spruce at Baishan (c)
and Jingsong (d); Korean pine at Baishan (e) and Jingsong (f); and Korean larch at
Baishan (g) and Jingsong (h), respectively.
Figure S3 | Relationships between tree water-use efficiency (WUE) and tree
growth as basal area increment (BAI) in temperate forest ecosystems of China
for Needle fir at Baishan (a) and Jingsong (b); Little-seed spruce at Baishan (c) and
Jingsong (d); Korean pine at Baishan (e) and Jingsong (f); and Korean larch at
Baishan (g) and Jingsong (h), respectively.
Figure S4 | Relationships between mean annual temperature (MAT) and tree
growth as basal area increment (BAI) in temperate forest ecosystems of Belgium
for beech at MES (a); oak at NOY (b); beech at ODE (c); and beech and oak at POUR,
respectively (d).
26
Figure S1 (Xu et al., submitted)
27
Figure S2 (Xu et al., submitted)
28
Figure S3 (Xu et al., submitted)
29
Figure S4 (Xu et al., submitted)
30