DEPLETION OF NON-STRUCTURAL CARBOHYDRATE RESERVES IN TEMPERATE
TREE SEEDLINGS UNDER STRESS
By
Andrea Joan Maguire

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Plant Biology – Master of Science

2013

ABSTRACT
DEPLETION OF NON-STRUCTURAL CARBOHYDRATE RESERVES IN TEMPERATE
TREE SEEDLINGS UNDER STRESS
By
Andrea Joan Maguire

Plant species that store larger amounts of non-structural carbohydrates (NSC) could use
carbon reserves to sustain respiration during periods of negative carbon balance. I aimed to
establish whether resource-stressed seedlings deplete their carbon reserves over time and if
differences in allocation of NSC to storage are related to species-level differences in stress
tolerance. I tested the effects of stress on NSC reserves in seedlings of five temperate tree species
(Acer rubrum, Betula papyrifera, Fraxinus americana, Quercus rubra, and Quercus velutina) in
a greenhouse experiment. Seedlings were subjected to combinations of three stress types (shade,
drought and defoliation) for a total of eight treatment combinations. I harvested seedlings over a
period of 32 to 97 days and measured biomass and NSC concentrations to estimate depletion
rates. Seedling growth ceased for all species under all stress treatments except for defoliation,
and in some cases biomass decreased. Across species and all treatments except defoliation alone,
NSC accumulation ceased, and in many cases concentrations decreased. Results indicate that
NSC can be mobilized in response to stress, but the response depends on the species and type of
stress. Thus, NSC depletion could depend on a species-specific ability to mobilize NSC under a
given stressor. These results support that NSC depletion plays a role in seedling responses to
common stressors, and that species differences in NSC storage are important for understanding
carbon starvation as a buffer against stress.

For Zubin Modi

iii

ACKNOWLEDGMENTS

First of all I would like to thank my advisor Rich Kobe for helping me through in the ins
and outs of graduate school and for offering advice and support. I also want to thank my other
committee members, Nate Swenson and Mike Walters. I would like to thank my best friend and
partner Zubin Modi for always being there for me. I am also grateful for the support from my
parents, sisters and friends. This research was possible thanks to Paul Bloese and Randy
Klevickas from the MSU Tree Research Center for logistical help and use of facilities and
numerous Kobe lab undergraduates, especially Sydny Landon and Jeff Kinney. I also thank
David Rothstein and Mike Walters for use of their equipment, Wayne Loescher for help on NSC
analyses, and David Minor, Ellen Holste, Scott Stark, Megan Edwards, Yoshiko Iida, and Natalia
Umana for valuable feedback on the manuscript. This work was supported by National Science
Foundation award (DEB 0958943) and a National Science Foundation Graduate Research
Fellowship.

iv

TABLE OF CONTENTS

LIST OF TABLES..……………………………………………………………...…………….…vi
LIST OF FIGURES………………………………………………………...……….……...…...viii
INTRODUCTION………………………………………………………………….…………......1
METHODS………………………………………………………………….…………...……......5
Study Species……….…………………….………………….…………………….………..5
Experimental Design……….…………………….………………….……………………...6
Harvests……….…………………….………………….…………………….………..........7
NSC Analysis.……….……….….……………….………………………...……….…..…...7
Data Analysis……….…………………….………………….…………..……………….…8
RESULTS………………………………………………….……………………….…………....10
Pre-treatment Biomass and NSC concentrations……….…………………………………10
Changes in the control treatment over time……….………………...…….………………12
Effects of stress treatment on biomass over time……….…………………….…………....15
Effects of stress treatment on NSC concentration over time………….………………..….16
DISCUSSION……………………………………………...……………………….…………....18
Overview……….…………………….………………….…………………….…………...18
NSC depletion in response to shade and drought……….…………………….…………...18
Defoliation effects……….…………………….………………….…………………….….20
Starch vs. soluble sugars……….…………………….………………….…………………21
NSC concentrations and species stress tolerance……….…………………….…………...22
Caveats/ Future Directions……….…………………….………………….…………....…23
Summary……….…………………….………………….…………………….………...…24
APPENDIX….………………………………………………..………………………….………25
LITERATURE CITED…………………………………………………………………………..36

v

LIST OF TABLES

Table 1 Study species and their relative shade and drought tolerance, and seed size. Shade and
drought tolerance based on Burns & Honkala (1990)………………………....…….....…5
Table 2 Estimated number of weeks until seedling NSC concentrations of 1% are reached.
Estimates are based off of parameter estimates from linear models for non-structural
carbohydrate (NSC) concentrations (soluble sugars + starch) in the stem and root of
seedlings as a function of time under five treatments: C = control (50% light, wellwatered), D = drought (50% light and no water), S = shade (< 3% light, well-watered),
SD = shade + drought (< 3% light, no water) for five temperate tree species. Models
include seedlings from a non-significant defoliation treatment that is pooled with other
treatments. Note that estimates meant to be used as an index for comparison, not for
prediction. Species abbreviations in Table 1.……………………………………………17
Table 3 Parameter estimates for linear models of non-structural carbohydrate (NSC)
concentrations (soluble sugars + starch) in the stem and root of seedlings as a function of
time under five treatments. C = control (50% light, well-watered), D = drought (50%
light and no water), S = shade (< 3% light, well-watered), SD = shade + drought (< 3%
light, no water) for five temperate tree species. Parameters include a common intercept
with different slopes for each treatment and 95% support intervals (SI), and NSC
concentrations are natural log transformed. Models include seedlings from a nonsignificant defoliation treatment that is pooled with other treatments. Species
abbreviations in Table 1….....……………………………………………………………26
Table 4 Means and standard deviations for mass and non-structural carbohydrate (NSC)
components of seedlings sampled before treatment. a) NSC concentration, b) Starch
concentration, c) Soluble sugar concentration d) Plant mass. Species abbreviations in
Table 1. Values in gray for FA were estimated from linear models because plant mass
was not measured for the first harvest and could not be calculated directly.……....……28
Table 5 Relationship between total plant mass and non-structural carbohydrate (NSC)
concentrations, stem and root NSC concentrations, and seed mass and total mass. Species
abbreviations in Table 1.…............................................................................................…29
Table 6 Parameter estimates for linear models of plant mass as a function of time under five
treatments. C = control (50% light, well-watered), D = drought (50% light and no water),
S = shade (< 3% light, well-watered), SD = shade + drought (< 3% light, no water) for
five temperate tree species. Parameters include a common intercept with different slopes
for each treatment and 95% support intervals (SI), and plant mass is natural log
transformed. Models include seedlings from a non-significant defoliation treatment that is
pooled with other treatments. Species abbreviations in Table 1……………………...…30

vi

Table 7 Parameter estimates for linear models of soluble sugars as a function of time under five
treatments. C = control (50% light, well-watered), D = drought (50% light and no water),
S = shade (< 3% light, well-watered), SD = shade + drought (< 3% light, no water) for
five temperate tree species. Parameters include a common intercept with different slopes
for each treatment and 95% support intervals (SI) and soluble sugar concentrations are
natural log transformed. Models include seedlings from a non-significant defoliation
treatment that is pooled with other treatments. Species abbreviations in Table 1.…....…32
Table 8 Parameter estimates for linear models of starch as a function of time under five
treatments. C = control (50% light, well-watered), D = drought (50% light and no water),
S = shade (< 3% light, well-watered), SD = shade + drought (< 3% light, no water) for
five temperate tree species. Parameters include a common intercept with different slopes
for each treatment and 95% support intervals (SI) and starch concentrations are natural
log transformed. Models include seedlings from a non-significant defoliation treatment
that is pooled with other treatments. Species abbreviations in Table 1.…………………34

vii

LIST OF FIGURES

Figure 1 Species ranking in seedling shade and drought tolerance and their pre-treatment total
non-structural carbohydrate (NSC) concentrations. Tolerance rankings based on Burns &
Honkala, (1990) *For FA, NSC was estimated from linear models because plant mass
was not measured for the first harvest and I could not calculate concentrations directly.
Mean seedlings age ranged from 12 – 18 weeks. Species abbreviations in Table 1. For
interpretation of the references to color in this and all other figures, the reader is referred
to the electronic version of this thesis..…………………………………….………….…11
Figure 2 Relationship between the natural log of stem and root non-structural carbohydrate
(NSC) concentrations for five temperate tree species. Species abbreviations in Table 1…2
Figure 3 Total non-structural carbohydrate (NSC) concentrations in seedlings over time under
five treatments. C = control (50% light, well-watered), D = drought (50% light and no
water), S = shade (< 3% light, well-watered), SD = shade + drought (< 3% light, no
water). Each point represents one seedling. Lines are best-fit linear models for each
treatment with a common intercept and NSC concentrations are natural log transformed.
Models include seedlings from a non-significant defoliation treatment that is pooled with
other treatments……………………………….………………………………….………13
Figure 4 Soluble sugar concentrations in seedlings over time under five treatments. C = control
(50% light, well-watered), D = drought (50% light and no water), S = shade (< 3% light,
well-watered), SD = shade + drought (< 3% light, no water). Each point represents one
seedling. Lines are best-fit linear models for each treatment with a common intercept and
NSC concentrations are natural log transformed. Models include seedlings from a nonsignificant defoliation treatment that is pooled with other treatments.….…..………...…14

viii

INTRODUCTION
Carbon balance is vital to plant performance. Carbon gained through photosynthesis can
be allocated to growth, reproduction, defense and maintenance of metabolic functions.
Assimilated carbon also can be stored as non-structural carbohydrates (NSC), mainly consisting
of starch, sucrose, glucose and fructose (Chapin et al., 1990; Kozlowski, 1992), and used for
future functions. Storage can be a passive or active process. Passive storage results when sink
limitation leads to a build up of carbon and excess NSC is accumulated in response to carbon
supply exceeding demand (Chapin et al., 1990; Körner, 2003; Millard et al., 2007). At the same
time, storage also can be an active carbon sink where NSC reserves are competing with other
sinks and NSC is stored at the expense of growth (Wiley & Helliker, 2012; Chapin et al., 1990).
Active storage could have several adaptive advantages as NSC may be mobilized for future
growth, recovery of lost tissue, and fueling respiratory needs (Chapin et al., 1990; Kozlowski,
1992; Hoch et al., 2003).
NSC is hypothesized to play a role in the tolerance of environmental stress, especially
when carbon gain is limited (McDowell et al., 2008). Thus, NSC depletion could be a
mechanism underlying tree mortality (McDowell et al., 2008; Breshears et al., 2009; Sala et al.,
2012). Tree mortality has been increasing worldwide due to associated environmental stress
including drought, pest-outbreaks, and increases in temperature (Allen et al., 2010; Carnicier et
al., 2011; Choat et al., 2012; Williams et al., 2012). These trends motivate a strong interest in
understanding the mechanisms behind tree mortality and the interactions among different types
of stress that could inform predictions of tree responses to global change in the future.
The role of NSC reserve depletion as a mechanism underlying tree mortality has been
debated for cases of drought stress (McDowell & Sevanto 2010; Sala et al., 2012). A synthesis of

1

mortality mechanisms suggests two non-mutually exclusive hypotheses of drought-induced
mortality: carbon starvation and hydraulic failure (McDowell et al., 2008). Drought can have
direct consequences such as embolism, hydraulic failure, and cell failure (Bréda et al., 2006), but
it can also affect a tree’s carbon balance. Drought can impede photosynthesis through leaf loss
and stomatal closure, causing a tree to rely on its NSC to meet metabolic demands. When carbon
is no longer available to sustain basic functions, a tree may die of carbon starvation (McDowell
et al., 2008; Breshears et al., 2009). Evidence supporting carbon starvation due to drought often
has not been based on direct measurements of NSC (Adams et al., 2009; Breshears et al., 2009;
Hartmann, 2011). When NSC has been measured directly, results have provided mixed support
for the role of NSC reserve mobilization, including examples of NSC depletion (Galiano et al.,
2011), increased NSC storage (Galvez et al., 2011; Anderegg et al., 2012), and no NSC response
(Gruber et al., 2011). Carbon starvation due to other types of stress has similarly conflicting
results. For example, defoliation can deplete NSC (Landhäusser & Lieffers, 2011; Eyles et al.,
2009), but not always (Piper et al., 2009).
Species differences in responding to environmental stress may lead to contradictory
results for the role of NSC in stress tolerance. For drought tolerance, species differences in the
regulation of water loss could result in differences in the risk of carbon starvation. Species that
close stomata and stop photosynthesizing during drought maintain safe water potentials but may
die from carbon starvation. On the other hand, species that continue photosynthesizing may
allow water potentials to decrease close to critical values and are more likely to die from
hydraulic failure, but have a lower risk of carbon starvation (McDowell et al., 2008). Species
differences in seedling adaptations to shade also may affect the role of NSC. Trees exhibit a
growth-survival tradeoff where species that tolerate shade allocate more photosynthate to NSC

2

storage versus those favoring rapid growth that allocate more to structural growth (Kobe, 1997;
Myers & Kitajima, 2007; Poorter & Kitajima, 2007). Similarly, species differences in NSC
storage are related to differences in tolerance to low water availability (Meier & Leuschner,
2008) and tissue loss (Canham et al., 1999; Poorter & Kitajima, 2007). Though changes were not
measured over time, correlations between survival and NSC storage suggest that NSC can act as
a buffer that protects against multiple low-resource stressors (Chapin et al., 1990; Kitajima,
1994; Kobe, 1997; Canham et al., 1999).
While increased NSC storage may confer a general stress tolerance strategy, the cooccurrence of different types of stress can increase the complexity of the NSC response. For
example, one type of stress can increase vulnerability to another stress such as when drought
predisposes a tree to insect attack by decreasing its defense or increasing the production of
volatiles that attract insects (McDowell et al., 2008). In addition, shade or defoliation could
lessen the impact of drought because it decreases the loss of water through stomata (Sack, 2004;
Sánchez-Gómez et al., 2006). Also, there is some evidence that drought and shade tolerance may
be incompatible because the traits that confer tolerance to shade may be disadvantageous to those
that tolerate drought (Valladares & Niinemets, 2008). In this case, NSC may be mobilized to deal
with one stress but not another. If allocation to NSC storage enhances survival during times of
stress, then increased NSC storage could either buffer against multiple stresses or for different
stresses depending on the species’ traits (Chapin et al., 1993). To understand carbon starvation, it
is important to look at the interactions of multiple stressors and how species may respond
differentially.
If stored NSC is driving survivorship differences among and within species under
different types of environmental stress, then NSC should be mobilized under stress. I examined

3

the NSC response over time in seedlings of five temperate tree species under combinations of
defoliation, shade, and drought stress. I hypothesized that resource-stressed seedlings deplete
their stored carbon reserves over time (Hypothesis 1). If allocation to NSC storage enhances
survival during times of stress, then species that allocate more NSC to storage could buffer
against multiple stress types (Hypothesis 2). On the other hand, since species are differentiated in
adaptations to tolerate different types of stress, seedlings of different species may mobilize NSC
in response to one type of stress but not others. In this case, the role of NSC stores in tolerating
stress may differ for different stress- species combinations (Hypothesis 3). I note that if
hypothesis 3 is supported, debate surrounding the question of whether stress tends to decrease or
increase carbon stores (e.g., Sala et al., 2012) may arise from species differences.

4

METHODS
Study Species
This study included five northern hardwood tree species with a range of shade and
drought tolerances and seed sizes: Acer rubrum (AR), Betula papyrifera (BP), Fraxinus
americana (FA), Quercus rubra (QR), and Quercus velutina (QV) (Table 1). These species are
common in deciduous forests throughout eastern North America. The experiment was conducted
in a greenhouse at Michigan State University’s Tree Research Center in East Lansing, MI from
April - November 2010. Seedlings were either started from seed or transplanted from natural
populations. Seed was obtained from a commercial seed source (Sheffield Seed Co., Locke, NY,
USA) for A. rubrum, B. papyrifera, Q. rubra, and Q. velutina. Seedlings for F. americana were
collected from the field as new germinants and transplanted into pots at the greenhouse because
the length of the seed stratification period was prohibitive. The mass of each seed was measured
after removal of accessory structures (e.g., acorn caps and wings) for A. rubrum, Q. rubra, and
Q. velutina.
Table 1
Study species and their relative shade and drought tolerance, and seed size. Shade and drought
tolerance based on Burns & Honkala (1990).
Species

Abbrev.

Common
Name

Shade Tolerance

Drought
Tolerance

Seed
Size

Acer rubrum

AR

Red maple

tolerant

intermediate

small

BP

Paper birch

very intolerant

very intolerant

very
small

FA

White ash

intermediate

intermediate

small

Quercus rubra

QR

Northern red
oak

intermediate

tolerant

large

Quercus
velutina

QV

Black oak

intolerant

very tolerant

large

Betula
papyrifera
Fraxinus
americana

5

Experimental Design
All seeds were planted in individual 660 ml pots under moderately high light levels (~
50% PAR) and watered as needed during the establishment phase before treatments were
applied. Seeds were planted in a commercial mixture that included basic starter nutrients (Fafard
#2, BFG Supply Co., Kalamazoo, MI, USA), plus field soil with a volume ratio of 10: 1
commercial mixture: field soil (Kobe et al., 2010). This combination allowed for ease of harvest
while still including natural soil microbes. Field soil was obtained from the Manistee National
Forest in northern lower Michigan and at the Tree Research Center’s Sandhill Research Forest in
East Lansing, MI. After an initial growth period to allow a sufficient number of seedlings to
establish (12 −22 weeks), seedlings were submitted to a 2 x 2 x 2 factorial experiment: (< 3% vs.
50% PAR) x (complete drought vs. watering as needed) x (50% defoliation vs. no defoliation)
for a total of eight treatment combinations. A. rubrum seedlings had a longer establishment
period than the other species due to low numbers of successful germinants and re-planting. Our
control treatment was the same as pre-stress conditions (50% sun, watering as needed, and no
defoliation). For the shade treatment, benches were covered with two layers of shade cloths each
(80% and 70%), reducing light to less than 3% PAR; light levels were verified with a quantum
sensor. The drought treatment consisted of no watering. In the defoliation treatment I simulated
herbivory by removing whole leaves at the base of the petiole until approximately 50% of the
total leaf area was removed. Seedlings of each species were randomly assigned to each
treatment.

6

Harvests
I began surveying seedlings for survivorship once treatments started; however, there was
no mortality. Before treatments were imposed, 24 seedlings per species were harvested to
establish pre-treatment biomass and NSC levels. Then, three seedlings per species in each
treatment were harvested at several time points over a period of 32 to 97 days for a total of 885
seedlings. The harvest schedule was based on expectations of survival times for the different
species - treatment combinations, as well as logistics of harvesting with the aim of getting six
time points throughout the harvest period. I achieved at least six harvests for all species except A.
rubrum, which were harvested over the shortest time frame and only had five harvests due to
lower seedling numbers from the start. After harvesting, root systems were carefully hand
washed with water and a sieve, and tissue was separated into stems, leaves, and roots. Stem and
root samples were microwaved for 60 seconds at 600 watts to denature enzymes that could
degrade NSC molecules. Tissues were put in drying ovens overnight at 65 °C, and dry mass was
measured for each component.

NSC Analysis
Non-structural carbohydrate concentrations were determined from stem and root samples
for each seedling harvested. Dried samples were homogenized and pulverized to a fine powder
using a ball mill (Kinetic Laboratory Equipment, Visalia, California, USA) and stored at 4° C
until analysis. Small samples were ground by hand using a mortar and pestle. NSC was measured
in two steps modified from Kobe et al., (2010). First, I extracted soluble sugars from 12 to 14 mg
samples three times in 80% ethanol by heating and then centrifuging for 5 minutes at 1900g.
Concentrations in the supernatant were measured using a phenol-sulfuric acid colorimetric assay

7

(Dubois et al., 1956). The remaining pellet was put in a steam bath for one hour to gelatinize the
starch and then incubated with amyloglucosidase at 55° C for 16 hours to digest the starch. The
digested sample was analyzed colorimetrically using a glucose hexokinase assay reagent (Sigma
G3293) and read on an absorbance microplate reader (ELx808 Absorbance Microplate Reader,
BioTek Instruments, Inc., Winooski, VT). This method ensures that structural carbohydrates
introduced during sampling would not be measured. Total NSC concentrations were calculated
as the sum of soluble sugar and starch concentrations derived from the assays. Total NSC pools
were calculated as the product of sample NSC concentration and total mass of the organ.

Data Analysis
The change in root and stem NSC over time was analyzed by fitting linear models of
NSC concentration as a function of time for each species-treatment combination. I used a
maximum likelihood approach to estimate the starting point and rate of change of NSC levels.
Data were transformed (ln (NSC + 1)) prior to analysis in order to normalize the data with
positive numbers. For each species, I tested models for each NSC component: a null model, a
model with only time as a covariate, and ten models with time and different treatment
combinations. To test for the effects of each treatment, I compared models with all eight
treatments (control (C), drought (D), defoliation (H), defoliation + drought (HD), shade (S),
shade + defoliation (SH), shade + drought (SD), shade + defoliation + drought (SHD)) to models
that collapsed the focal treatment into the others. For example, pooling defoliation into the other
treatments results in 4 possibilities (C, D, S, and SD). In all models that included treatments, I
estimated a common intercept that represents the NSC starting point at the first harvest before
treatments were applied. To compare whether treatments had an effect, I used AICc to choose

8

the best-supported model. The best model was considered to be the simplest model within two
units of the minimum AICc. For all species and most NSC components (39 of 45), the best
common model pooled defoliation with other treatments. To facilitate comparisons among
species, I used this model.
Slope estimates represent the rate at which seedlings deplete NSC reserves. Estimates
were considered significant when 95% support intervals did not encompass zero. I also examined
correlations between soluble and starch NSC concentrations in both the stem and root
components to understand stress induced allocation responses. Growth was analyzed by fitting
linear models of biomass change over time. I also compared pre-treatment species means for
NSC and biomass components using ANOVA. For FA, mass for the first harvest was mistakenly
not recorded before seedlings were ground, so it was estimated from the y-intercept (i.e., zero
time). To make comparisons across species in cases of NSC depletion, I used the fit models to
estimate the number of weeks that it would take to deplete NSC concentrations to 1% of plant
mass. I calculated time to depletion estimates for use as an index and not for prediction. I
estimated uncertainty by constructing confidence intervals using the support intervals from the
slope estimates. These patterns were related back to expected growth and survival from the
literature and the different life-history strategies for each species (Table 1). All analyses were
completed in R version 2.12.1 (R Development Core Team), using packages bbmle (Bolker,
2012).

9

RESULTS
Pre-treatment Biomass and NSC concentrations
a

b

b

b

c

Initial mean biomass differed among species (QV > QR > BP > FA > AR ) (Figure
a

1), as did root mass fraction (QV > QR

ab

b

c

d

> FA > AR > BP ). Pre-treatment levels of NSC

varied among species with mean concentrations ranging from 2.13 to 20.38 % dry mass. Mean
concentrations of NSC were highest in the species with the highest root mass fraction, and
a

b

b

b

c

followed the same relative rankings (QV > QR > FA > AR > BP ) (Appendix 2). When
looking at the separate tissues, the rankings differed slightly, but all species had higher
concentrations in the root than in the stem (Appendix 2). Root NSC concentrations were an
average of 1.3 to 4.5 times higher than stem NSC (Figure 2, Appendix 2).
Starch made up most of the NSC with mean starch across species ranging from 74.0 to
98.6 % of the total NSC, with average concentrations from 1.58 to 20.1 % dry mass. Starch
tended to be located mostly in the root, with an average of 1.3 to 4.8 times more starch in roots
than stems across species (Appendix 2). Concentrations of soluble sugars ranged from 0.28 to
0.78 % dry mass. Soluble sugars were highest in FA and BP, the species that had the lowest
starch, and lowest in QV, which had the highest starch. AR, BP and FA had higher soluble sugar
concentrations in the root and QV and QR in the stem, but the differences were not significant.
Soluble sugars tended to be more evenly distributed among stems and roots than starch
(Appendix 2). The initial NSC concentration was correlated with seed size within species for AR
(r = 0.47, P < 0.001) and QV (r = 0.82, P < 0.001), but not QR (r = 0.23, P = 0.343). Initial NSC
was strongly related to seed size even after several months. Among species, the largest seeded
species (QV) had the highest starting mass and NSC concentration, and the smallest seeded

10

species (BP) had the lowest starting point of NSC, though not the lowest mass (Table 1,
Appendix 2).

Figure 1
Species ranking in seedling shade and drought tolerance and their pre-treatment total nonstructural carbohydrate (NSC) concentrations. Tolerance rankings based on Burns & Honkala,
(1990) *For FA, NSC was estimated from linear models because plant mass was not measured
for the first harvest and I could not calculate concentrations directly. Mean seedlings age ranged
from 12 – 18 weeks. Species abbreviations in Table 1. For interpretation of the references to
color in this and all other figures, the reader is referred to the electronic version of this thesis.

NSC

QR

4

Shade Tolerance

20

AR

5

15

FA

3

10

QV

2

5
1

BP

1

2

3

4

5

Drought Tolerance

11

QR
QV

1.5

2.0

2.5

AR
BP
FA

0.0

0.5

1.0

LN Stem NSC

3.0

3.5

Figure 2
Relationship between the natural log of stem and root non-structural carbohydrate (NSC)
concentrations for five temperate tree species. Species abbreviations in Table 1.

0.5

1.0

1.5

2.0

2.5

3.0

3.5

LN Root NSC

Changes in the control treatment over time
The seedlings in the control treatments increased in biomass over time for all species, and
the increase was faster in the roots than stems, with root mass fraction also increasing in all
species. QV had the highest rate of biomass increase and AR had the lowest (Appendix 4). As
biomass increased, controls accumulated NSC (total pool size) in all species. NSC concentrations
based on plant mass (% dry mass) also increased in all species, but was not significant for AR.
Both starch and soluble pools were positively correlated with total mass (Figure 3, Appendix 3),
but species differed in their proportional increases. The rate of increase for NSC concentration
a

b

b

b

was higher in BP than the other species (BP > QR >FA > QV ). The NSC increase was in

12

both the stem and the root for BP, QR and QV, but only significant in the root for FA (Appendix
1).

Figure 3
Total non-structural carbohydrate (NSC) concentrations in seedlings over time under five
treatments. C = control (50% light, well-watered), D = drought (50% light and no water), S =
shade (< 3% light, well-watered), SD = shade + drought (< 3% light, no water). Each point
represents one seedling. Lines are best-fit linear models for each treatment with a common
intercept and NSC concentrations are natural log transformed. Models include seedlings from a
non-significant defoliation treatment that is pooled with other treatments.

0

5

10

15

20

25

2.5
1.5
0.5

2.0

3.0

NSC (LN % dry mass)

BP

1.0

NSC (LN % dry mass)

AR

30

0

20

40

60

30

40

50

60

70

80

2.5
1.5
0.5

3.0
2.0
1.0
20

3.5

QR
NSC (LN % dry mass)

Time in Treatment (days)

FA
NSC (LN % dry mass)

Time in Treatment (days)

0

Time in Treatment (days)

20

40

60

80

Time in Treatment (days)

13

Figure 3 (cont’d)

2.0

3.0

C
D
S
SD

1.0

NSC (LN % dry mass)

QV

0

20

40

60

80

100

Time in Treatment (days)

Figure 4
Soluble sugar concentrations in seedlings over time under five treatments. C = control (50%
light, well-watered), D = drought (50% light and no water), S = shade (< 3% light, wellwatered), SD = shade + drought (< 3% light, no water). Each point represents one seedling.
Lines are best-fit linear models for each treatment with a common intercept and NSC
concentrations are natural log transformed. Models include seedlings from a non-significant
defoliation treatment that is pooled with other treatments.

0

5

10

15

20

25

30

Time in Treatment (days)

0.7
0.5
0.3
0.1

0.4

0.6

0.8

Soluble Sugars (LN % dry mass)

BP

0.2

Soluble Sugars (LN % dry mass)

AR

0

20

40

60

Time in Treatment (days)

14

Figure 4 (cont’d)

20

30

40

50

60

70

80

Time in Treatment (days)

0.8
0.6
0.4
0.2
0.0

0.6

0.8

1.0

1.2

Soluble Sugars (LN % dry mass)

QR

0.4

Soluble Sugars (LN % dry mass)

FA

0

20

40

60

80

Time in Treatment (days)

0.7
0.3

0.5

C
D
S
SD

0.1

Soluble Sugars (LN % dry mass)

QV

0

20

40

60

80

100

Time in Treatment (days)

Effects of stress treatment on biomass over time
In all of the stress treatments, BP and QV had significant decreases in whole plant
biomass. Stress treatments caused no significant changes in biomass for AR, FA, and QR. Root
mass fraction either increased or stayed the same in all the stress treatments. The drought
treatment had increased root mass fraction in all species except for AR, but the shade + drought
treatment only had increases for AR. The shade treatment had increases in root mass fraction for

15

AR, FA and QV. The increased root mass fraction was due more to a loss of leaves than
increases in root biomass; there were no significant increases in root biomass in any of the stress
treatments (Appendix 4).

Effects of stress treatment on NSC concentration over time
The defoliation treatment had little effect on NSC concentrations, with results similar to
the control; thus all treatments that included defoliation were grouped with other treatments.
Collapsing the defoliation treatment was supported by model comparisons; in a majority of
cases, the best model by AICc included three treatments (drought, shade, shade + drought) and
the control. All results are reported for these groupings. As predicted, NSC concentrations in the
stress treatments either stopped increasing or decreased over time (Figure 1, Appendix 1).
Seedlings also ceased growth in every stress treatment, and lost biomass for all treatments in BP
and QV.
NSC decreased most sharply in the shade + drought treatment for all species, though it
was not always significantly less than drought or shade. In the shade treatment, NSC decreased
only for AR and FA, suggesting that NSC is mobilized in these species as a response to shade
stress. Under drought, NSC decreased for all species except AR, suggesting that all species
except for AR mobilize NSC to tolerate drought. While both stem starch and root starch
decreased in many cases, in shade, the significant decreases were only seen in the stem for FA
and QR, and were faster in the stem for AR. In drought, starch decreased only in the root for AR,
BP, and QR, but in both the stem and root for QV and FA. In the shade + drought treatment,
decreases were seen only in the root for BP, and were the same in the stem and root for AR, FA,
QR and QV (Appendix 6).

16

Soluble sugars did not follow the same trends as total NSC. There were increases in
soluble sugars in the drought treatment for AR, QR and QV, and the shade + drought treatment
for QV. There were decreases in the drought and the shade treatment for BP, and the shade +
drought treatment for AR and BP. Whenever soluble sugars increased, the increase was in both
stem and root tissue (Appendix 5)
Despite several instances of decreasing NSC concentrations, the decrease was not enough
for complete depletion. When significant, across species the decrease from the starting value
after seven weeks ranged from 27.0 to 62.1 % in the shade + drought treatment, 21.6 to 33.8 % in
the drought treatment, and 17.2 to 55.1 % in the shade treatment. The largest decrease was in the
shade + drought treatment in AR, which after seven weeks lost 62.1% of its initial NSC
concentration. Assuming the same rate of decrease, it would take an average of 34 weeks for AR
to reach a concentration of 1 % NSC. These projections were as high as 182 weeks for FA in
shade and 149 weeks for QV in drought (Table 2).

Table 2
Estimated number of weeks until seedling NSC concentrations of 1% are reached. Estimates are
based off of parameter estimates from linear models for non-structural carbohydrate (NSC)
concentrations (soluble sugars + starch) in the stem and root of seedlings as a function of time
under five treatments: C = control (50% light, well-watered), D = drought (50% light and no
water), S = shade (< 3% light, well-watered), SD = shade + drought (< 3% light, no water) for
five temperate tree species. Models include seedlings from a non-significant defoliation
treatment that is pooled with other treatments. Note that estimates meant to be used as an index
for comparison, not for prediction. Species abbreviations in Table 1.

Number of weeks until a NSC concentration of 1% is reached
AR
BP
FA
QR
D
-83 (56, 163)
83 (59,142)
134 (75, 622)
S 41 (25,114)
-182 (100, 1023)
-SD 34 (20,116) 72(51, 122)
50 (40, 67)
62 (46, 94)

17

QV
149 (92, 377)
-88 (66, 133)

DISCUSSION
Overview
Our results support the hypothesis that NSC storage decreases when tree seedlings are
under stress, suggesting that seedlings rely on their NSC reserves to overcome the negative
carbon balance imposed by stress. Furthermore, species had different patterns of NSC depletion
depending on the stress treatment. In the control seedlings, NSC concentrations accumulated in
stem and root tissues over time as biomass increased, which is consistent with other studies
showing increases with both tree age and size (Piper & Fajardo, 2011; Sala et al., 2011). All
species initially responded to stress treatments by stopping growth, and over time, concentrations
of total NSC decreased in most treatments, but at varying rates. The drought tolerant oaks
depleted NSC under drought but not shade stress. Our most shade tolerant species, A. rubrum
depleted reserves in shade, but was the only species that did not deplete reserves in drought. Our
estimations of time to NSC depletion, based on species initial NSC concentrations and their
depletion rates, suggest that susceptibility to carbon starvation depends both on the species and
the type of stress, consistent with our third hypothesis. These results provide evidence that NSC
depletion plays a role in species response to common stress types, and that species differences in
NSC storage may be important for understanding carbon starvation as a buffer against shade- and
drought- induced mortality.

NSC depletion in response to shade and drought
We saw NSC decreases in our drought treatment for all five species. The decrease was
significant for all species except for AR. This is counter to several studies that show increases in
NSC during drought (Galvez et al., 2011; Anderegg et al., 2012). One explanation could be that

18

a drought-adapted species may be more likely to rely on NSC reserves for survival during
drought. This adaptation could result from either greater allocation to storage or a greater ability
to mobilize stored reserves. In our study BP, the least drought-tolerant species was able to
mobilize NSC in response to drought, but had very low initial NSC concentrations. However, if
BP relies on NSC to survive drought, then the low NSC concentrations could be the cause of its
drought intolerance. Alternatively, a drought-adapted species may be better at down regulating
other carbon sinks such as respiration. This was suggested in a study with two Nothofagus
species, the more drought susceptible N. nitida experienced decreases in NSC, while there were
increases in the more drought resistant N. dombeyi (Piper, 2011). If a species is able to continue
producing more carbon than is necessary for sink demands, then accumulation could occur
(McDowell et al., 2008). For drought, growth is thought to be more sensitive than photosynthesis
(Körner, 2003); which could be the case in our study given that no species had biomass increases
during drought, however, we do not have photosynthesis measurements. Carbon balance under
stress and whether a species increases or decreases NSC likely depends on multiple factors
relating to the amount of carbohydrates stored, the ability to mobilize and use the carbohydrates,
and also whether photosynthesis is able to continue during the drought.
The shade treatment overall had some slight decreases in NSC, but the decrease was only
significant in shade tolerant AR. When looking at organs separately, there was a significant
decrease in the stems of AR, FA and QV, which could be due to stem NSC being the first to
decrease in shade. Although shade had an effect when combined with drought, it is likely that the
shade treatment was not strong enough on its own to significantly deplete carbohydrates, and it
could mean that photosynthesis was still possible. In addition, depletion could be counteracted
by a plastic response, such as increased photosynthesis or an active shift from growth to storage

19

(Smith & Stitt 2007; Gibon et al., 2009). All of the species in this study stopped growing after
the stress was imposed either because it was not physiologically possible, or short-term storage
could be favored to buffer against future stress (Sala et al., 2012). In contrast to the control and
defoliation treatments (combined in the analysis), which showed increases in both stem and root
NSC, there were no cases of total NSC increasing in any of the other stress treatments.
For all species, in our harshest treatment of shade + drought, NSC tended to decrease at a
greater rate than shade or drought. This is in contrast to our prediction that shade would help to
lessen the drought effect, because in shade there is a lower evaporative demand. The sharper
decline in NSC in the shade + drought treatment, even for species in which only a single
treatment significantly influenced NSC, suggests that new photosynthate was produced under
only one stress. For example, in AR, it would take 41 weeks for NSC to deplete under shade, and
since NSC was not significantly influenced by drought, we would not expect depletion to speed
up in the combined treatment. However, the fact that it takes only 34 weeks to deplete NSC
under both shade and drought suggests that drought is preventing photosynthetic inputs (Table
2). While these estimates of weeks to depletion assume a constant rate, which may be unrealistic,
they are still useful to make comparisons among the species.

Defoliation effects
While the defoliation treatment was not significantly different from the control for any
species, there was a non-significant tendency for defoliated seedlings to have a slower increase
in both biomass and NSC. Regardless, 50% defoliation had weak effects. One explanation is that
compensatory responses are possible when 50% of leaves remain on the tree. This is consistent
with Canham et al., (1999) who show negligible effects of 50% leaf removal, but stronger effects

20

with complete defoliation. However, other studies have found decreases in NSC after defoliation.
For instance, root soluble sugars decreased in large and small eucalyptus trees after partial
defoliation (Eyles et al., 2009; Quentin et al., 2011), and root NSC decreased following insect
defoliation in mature aspen (Landhäusser et al., 2011). In Van Der Heyden et al., (1996),
defoliation initially decreased NSC, but then it was replenished to levels greater than the control
in high light. NSC depletion is likely dependent on both the level of defoliation and the
ontogenetic stage of the tree, as trees tend to store more carbohydrates as they get larger.

Starch vs. soluble sugars
These patterns of NSC depletion were driven by starch reserves, which made up a
majority of total NSC. When looking only at soluble sugars, different patterns emerged. The
drought treatment had greater increases in soluble sugars than the control for four species, and in
the drought + shade treatment for the oaks. During drought, soluble carbohydrates may actively
increase to regulate the osmotic pressure in the plant and can also help to prevent and reverse
embolism (Nardini et al., 2011; Secchi & Zwieniecki 2011). Soluble sugars are also used for
many basic cellular functions and a certain level of these are needed for the plant to stay alive,
thus making them unavailable for growth and respiration (McDowell & Sevanto 2010, Sala et
al., 2010). However, this may not always be the case, and in our study, BP experienced
decreased soluble sugars in shade, as did both AR and BP in shade + drought. This highlights the
importance of looking at both starch and soluble sugars separately.

21

NSC concentrations and species stress tolerance
We found that in some cases NSC mobilization in response to a stress is more likely in a
species that is adapted to that particular stress. The shade tolerant AR mobilized NSC in shade,
and the drought tolerant QR and QV in drought. A similar result was found when comparing
genotypes with different drought tolerances (Regier et al., 2009). However, the pattern was
different for the most drought intolerant BP and moderately drought tolerant FA. These species
not only mobilize NSC in drought, but at a faster rate than the more drought tolerant oaks (Table
2). Lower starting NSC concentrations and faster rates of depletion could explain the relative
drought intolerance of BP despite its ability to mobilize reserves. BP is also the least shade
tolerant species, but it did not mobilize its NSC in response to shade.
Generally, initial NSC reserves at the start of the experiment followed the drought
tolerance rankings. The amount of NSC was greater for the slower growing and more drought
tolerant oaks, and lower in faster growing and drought intolerant BP (Figure 4). The patterns
were not as clear for shade tolerance; while the least shade tolerant BP had the lowest NSC, the
most shade tolerant AR had intermediate NSC. Interestingly, of the oaks, the less drought
tolerant QR experienced a faster reduction in NSC than the more drought tolerant QV. For
species that do mobilize and deplete carbon reserves during drought, the species with high
allocation to reserves could theoretically rely on carbon reserves the longest. However, for
species that do not to mobilize their reserves, then the amount of stored carbon may not matter as
much. Species that did not deplete NSC could lack the ability to mobilize NSC during a
particular stress, or they could be good at down regulating sinks such as respiration rates.
Similarly, species that can continue to photosynthesize during drought may not have to rely on

22

reserves. It is important to point out that with only five species in this study, it is hard to make
generalizations for comparisons of species stress tolerance.

Caveats/ Future Directions
My results suggest that NSC plays a role in surviving carbon-limiting stress, but while
treatments were severe and seedlings were presumably close to experiencing mortality, none was
observed. It also is important to point out that there were no instances of complete reserve
depletion in any of the treatments, despite NSC concentrations reaching very low levels (0.25%
for BP in the shade + drought treatment). NSC dynamics observed here are consistent with other
studies where carbohydrate reserves are mobilized, but not completely depleted (Millard &
Grelet, 2007; Piper et al., 2011). Our snapshot of seedling NSC may not be generalizable to adult
trees where mortality after drought can take decades (Sala et al., 2010). In addition, species
stress tolerances and patterns of NSC storage and depletion could vary depending on ontogenetic
stage as well as genotype. The size of seedlings in this experiment could have influenced the
amount of stress experienced from each treatment. For example, the A. rubrum had the lowest
biomass, and may have experienced a less severe drought as a result. Lastly, to get a full picture
of carbon balance, respiration rates and other sinks need to be tested concurrently. This is
especially important because increased drought is usually accompanied by an increase in
temperature, which has been shown to increase respiration rates (Hartley et al., 2006). Long-term
studies that examine NSC dynamics along with physiology and that link with mortality events
are needed.

23

Summary
I show evidence of carbon depletion in five species of temperate deciduous tree
seedlings, consistent with the idea that non-structural carbohydrate storage buffers against
resource stress. Using seedlings, I was able to construct a whole plant view of carbohydrates and
show how NSC pools, concentrations and total biomass change. I also show that species
differences in NSC storage may be important for understanding the role of NSC in shade and
drought tolerance among species. Understanding species differences in NSC storage and
mobilization under stress will contribute further to our ability to predict how forests will react to
climate change in the future. Seedling responses are important for understanding patterns of
regeneration under climate change and in predicting composition of future forests.

24

APPENDIX

25

Table 3 Parameter estimates for linear models of non-structural carbohydrate (NSC) concentrations (soluble sugars + starch) in the
stem and root of seedlings as a function of time under five treatments. C = control (50% light, well-watered), D = drought (50% light
and no water), S = shade (< 3% light, well-watered), SD = shade + drought (< 3% light, no water) for five temperate tree species.
Parameters include a common intercept with different slopes for each treatment and 95% support intervals (SI), and NSC
concentrations are natural log transformed. Models include seedlings from a non-significant defoliation treatment that is pooled with
other treatments. Species abbreviations in Table 1.
Total NSC (% dry mass)
N

param.

r

2

Acer rubrum
B0
0.36
47
C
29
D
28
S
25
SD
sigma
Betula papyrifera
B0
0.84
59
C
43
D
36
S
39
SD
sigma

Stem NSC (% dry mass)

Root NSC (% dry mass)

estimate

SI

r

2

estimate

SI

r

2

estimate

SI

2.4220
0.0033
-0.0125
-0.0164
-0.0198
0.4678

(2.2737, 2.5706)
(-0.0071, 0.0138)
(-0.026, 0.001)
(-0.0268, -0.0059)
(-0.0338, -0.0058)
(0.4159, 0.5316)

0.43

1.9206
0.0048
-0.0025
-0.0212
-0.0219
0.4821

(1.7684, 2.0728)
(-0.0056, 0.0152)
(-0.0164, 0.0113)
(-0.0316, -0.0108)
(-0.0358, -0.0079)
(0.4295, 0.5464)

0.38

2.6338
0.0036
-0.0144
-0.0157
-0.0239
0.4840

(2.4827, 2.7848)
(-0.0071, 0.0143)
(-0.0282, -0.0005)
(-0.0265, -0.005)
(-0.0377, -0.0102)
(0.4312, 0.5485)

0.9340
0.0183
-0.0055
0.0009
-0.0064
0.3264

(0.8437, 1.0259)
(0.0159, 0.0208)
(-0.0083, -0.0028)
(-0.0016, 0.0033)
(-0.0091, -0.0038)
(0.2948, 0.364)

0.79

0.5784
0.0150
-0.0012
0.0004
-0.0029
0.2808

(0.5003, 0.657)
(0.0129, 0.0171)
(-0.0035, 0.0012)
(-0.0017, 0.0025)
(-0.0051, -0.0006)
(0.2538, 0.313)

0.78

1.3160
0.0190
-0.0087
0.0019
-0.0103
0.4577

(1.1912, 1.4411)
(0.0156, 0.0224)
(-0.0125, -0.0049)
(-0.0015, 0.0053)
(-0.014, -0.0066)
(0.4139, 0.5099)

26

Table 3 (cont’d)
Total NSC (% dry mass)
N

Param.

r

2

Fraxinus americana
B0
0.79
41
C
41
D
42
S
39
SD
sigma
Quercus rubra
B0
0.69
56
C
36
D
37
S
38
SD
sigma
Quercus velutina
B0
0.67
57
C
37
D
39
S
40
SD
sigma

Stem NSC (% dry mass)

Root NSC (% dry mass)

estimate

SI

r

2

estimate

SI

r

2

estimate

2.5970
0.0070
-0.0084
-0.0038
-0.0139
0.3599

(2.419, 2.7748)
(0.0038, 0.0101)
(-0.0119, -0.0049)
(-0.007, -0.0007)
(-0.0174, -0.0104)
(0.3238, 0.4032)

0.80

2.4098
0.0019
-0.0132
-0.0090
-0.0184
0.3529

(2 .2337, 2.5825)
(-0.0012, 0.005)
(-0.0166, -0.0097)
(-0.0121, -0.0059)
(-0.0219, -0.0149)
(0.3177, 0.3953)

0.76

2.6539
0.0081
-0.0064
-0.0023
-0.0123
0.3913

(2.4601, 2.8466)
(0.0047, 0.0116)
(-0.0102, -0.0026)
(-0.0057, 0.0011)
(-0.0162, -0.0085)
(0.3521, 0.4382)

2.3561
0.0113
-0.0050
-0.0008
-0.0108
0.5035

(2.1905, 2.5216)
(0.0078, 0.0147)
(-0.0089, -0.0011)
(-0.0043, 0.0027)
(-0.0146, -0.0071)
(0.454, 0.5627)

0.62

1.6755
0.0120
-0.0022
0.0026
-0.0052
0.4990

(1.5194, 1.8316)
(0.0086, 0.0153)
(-0.006, 0.0016)
(-0.0008, 0.006)
(-0.0088, -0.0015)
(0.4505, 0.557)

0.67

2.4735
0.0110
-0.0047
-0.0014
-0.0118
0.5401

(2.3006, 2.6464)
(0.0074, 0.0146)
(-0.0087, -0.0007)
(-0.0051, 0.0022)
(-0.0157, -0.0078)
(0.4883, 0.6019)

2.8570
0.0061
-0.0050
-0.0022
-0.0084
0.3957

(2.7191, 2.9936)
(0.0037, 0.0085)
(-0.0079, -0.002)
(-0.0047, 0.0003)
(-0.0112, -0.0055)
(0.357, 0.442)

0.66

1.9500
0.0043
-0.0039
-0.0024
-0.0112
0.4013

(1.8251, 2.0747)
(0.002, 0.0066)
(-0.0067, -0.001)
(-0.0048, 0)
(-0.0139, -0.0085)
(0.3633, 0.4465)

0.58

2.9911
0.0057
-0.0047
-0.0024
-0.0075
0.4558

(2.8334, 3.1488)
(0.0029, 0.0085)
(-0.0081, -0.0013)
(-0.0052, 0.0005)
(-0.0108, -0.0042)
(0.4117, 0.5084)

27

SI

Table 4 Means and standard deviations for mass and non-structural carbohydrate (NSC)
components of seedlings sampled before treatment. a) NSC concentration, b) Starch
concentration, c) Soluble sugar concentration d) Plant mass. Species abbreviations in Table 1.
Values in gray for FA were estimated from linear models because plant mass was not measured
for the first harvest and could not be calculated directly.
a) NSC (% dry mass)
total
stem
root
mean
sd
mean
sd
mean
sd
AR 11.12
5.52
7.13
4.27
13.92
6.78
BP
2.13
0.75
1.06
0.70
4.02
1.75
FA 11.32 3.006822 8.926342 3.80 11.98287 3.94
QR 11.44
3.84
4.87
2.24
13.43
4.62
QV 20.38
3.95
5.31
1.85
23.93
4.38
b) Starch (% dry mass)
total
stem
root
mean
sd
mean
sd
mean
sd
AR 10.68
5.46
6.74
4.23
13.43
6.72
BP
1.58
0.71
0.52
0.65
3.45
1.73
FA 10.53
2.975
8.54
3.87
11.38
3.81
QR 11.05
3.81
4.45
2.27
13.06
4.58
QV 20.10
4.00
4.89
1.89
23.68
4.45
c) Soluble Sugar (% dry mass)
total
stem
root
mean
sd
mean
sd
mean
sd
AR
0.44
0.10
0.39
0.14
0.50
0.13
BP
0.55
0.13
0.54
0.18
0.57
0.12
FA
0.79
-0.61
0.15
0.86
0.17
QR
0.39
0.09
0.42
0.10
0.38
0.10
QV 0.29
0.09
0.42
0.11
0.26
0.10
d) Mass and Age
root mass
mass
fraction
age
mean
sd
mean
sd
mean
sd
AR 1989
1377
0.307
0.111
131
15
BP 3530
832
0.201
0.031
101
0
FA 3370
-0.500
-87
1
QR 3802
1772
0.559
0.110
87
3
QV 5026
1131
0.606
0.083
98
1

28

Table 5 Relationship between total plant mass and non-structural carbohydrate (NSC)
concentrations, stem and root NSC concentrations, and seed mass and total mass. Species
abbreviations in Table 1.
Species Treatment

AR

BP

FA

QR

QV

all
C
D
S
SD
all
C
D
S
SD
all
C
D
S
SD
all
C
D
S
SD
all
C
D
S
SD

Correlations
LN Mass and LN NSC
LN Stem and LN Root
r
CI
r
CI
0.858 (0.804, 0.898) 0.690 (0.587, 0.771)
0.875 (0.783, 0.929) 0.601 (0.376, 0.759)
0.760 (0.545, 0.881) 0.636 (0.351, 0.813)
0.925 (0.843, 0.965) 0.742 (0.511, 0.874)
0.834 (0.654, 0.924) 0.666 (0.368, 0.84)
0.909 (0.879, 0.931) 0.666 (0.574, 0.741)
0.953 (0.922, 0.972) 0.555 (0.348, 0.71)
0.574 (0.327, 0.748) 0.283 (-0.023, 0.54)
0.692 (0.471, 0.832) 0.567 (0.293, 0.755)
0.280 (-0.039, 0.547) 0.172 (-0.152, 0.462)
0.743 (0.665, 0.806) 0.773 (0.703, 0.829)
0.938 (0.884, 0.967) 0.278 (-0.042, 0.545)
0.501 (0.224, 0.703) 0.670 (0.453, 0.812)
0.818 (0.685, 0.899) 0.315 (0.012, 0.565)
0.488 (0.203, 0.696) 0.654 (0.426, 0.804)
0.737 (0.66, 0.799) 0.706 (0.622, 0.775)
0.905 (0.842, 0.943) 0.667 (0.49, 0.791)
0.359 (0.035, 0.615) 0.526 (0.238, 0.728)
0.801 (0.644, 0.893) 0.635 (0.392, 0.796)
0.299 (-0.023, 0.565) 0.607 (0.356, 0.776)
0.833 (0.781, 0.874) 0.440 (0.309, 0.554)
0.921 (0.869, 0.953) 0.464 (0.229, 0.648)
0.524 (0.236, 0.727) 0.103 (-0.233, 0.418)
0.545 (0.269, 0.739) -0.090 (-0.402, 0.241)
0.340 (0.032, 0.589) 0.452 (0.164, 0.669)

29

Table 6 Parameter estimates for linear models of plant mass as a function of time under five treatments. C = control (50% light, wellwatered), D = drought (50% light and no water), S = shade (< 3% light, well-watered), SD = shade + drought (< 3% light, no water)
for five temperate tree species. Parameters include a common intercept with different slopes for each treatment and 95% support
intervals (SI), and plant mass is natural log transformed. Models include seedlings from a non-significant defoliation treatment that is
pooled with other treatments. Species abbreviations in Table 1.
Total Mass
N

param.

r

2

Acer rubrum
B0
0.25
50
C
29
D
31
S
30
SD
sigma
Betula papyrifera
B0
0.91
59
C
43
D
39
S
43
SD
sigma

Stem Mass

Root Mass

estimate

SI

r

2

estimate

SI

r

2

estimate

SI

7.2388
0.0222
0.0092
-0.0085
-0.0011
0.9088

(6.9576, 7.5199)
(0.0028, 0.0415)
(-0.0167, 0.0352)
(-0.0277, 0.0107)
(-0.0262, 0.0239)
(0.8113, 1.0276)

0.25

5.7317
0.0351
0.0209
-0.0012
0.0126
0.9759

(5.4303, 6.0331)
(0.0143, 0.0558)
(-0.0069, 0.0488)
(-0.0217, 0.0194)
(-0.0143, 0.0395)
(0.8716, 1.1029)

0.29

6.0792
0.0358
0.0152
0.0045
0.0152
0.9510

(5.7896, 6.3689)
(0.0156, 0.056)
(-0.0117, 0.0422)
(-0.0155, 0.0244)
(-0.0109, 0.0413)
(0.85, 1.0738)

8.1644
0.0243
-0.0085
-0.0033
-0.0055
0.2969

(8.0834, 8.2455)
(0.0221, 0.0265)
(-0.0109, -0.006)
(-0.0055, -0.0011)
(-0.0079, -0.0032)
(0.2686, 0.3306)

0.91

7.1200
0.0244
0.0016
-0.0011
-0.0016
0.2255

(7.0585, 7.1816)
(0.0227, 0.0261)
(-0.0002, 0.0035)
(-0.0028, 0.0006)
(-0.0034, 0.0002)
(0.204, 0.251)

0.92

6.4648
0.0284
-0.0014
-0.0028
-0.0059
0.2993

(6.3834, 6.5468)
(0.0262, 0.0306)
(-0.0039, 0.0011)
(-0.0051, -0.0006)
(-0.0082, -0.0035)
(0.2707, 0.3331)

30

Table 6 (cont’d)
Total Mass
N

param.

r

2

Fraxinus americana
B0
0.41
42
C
41
D
42
S
40
SD
sigma
Quercus rubra
B0
0.48
57
C
40
D
39
S
40
SD
sigma
Quercus velutina
B0
0.74
58
C
39
D
42
S
42
SD
sigma

Stem Mass

estimate

SI

r

2

estimate

8.1229
0.0046
-0.0045
-0.0046
-0.0032
0.5393

(7.8566, 8.3892)
(-0.0001, 0.0093)
(-0.0097, 0.0007)
(-0.0094, 0.0001)
(-0.0085, 0.0021)
(0.4855, 0.6038)

0.41

6.5716
0.0051
0.0039
-0.0015
0.0003
0.5139

8.1190
0.0102
-0.0021
-0.0003
-0.0043
0.6054

(7.9252, 8.3128)
(0.0062, 0.0143)
(-0.0066, 0.0024)
(-0.0043, 0.0038)
(-0.0087, 0.0002)
(0.5473, 0.6747)

0.48

8.5733
0.0071
-0.0056
-0.0024
-0.0030
0.2919

(8.4724, 8.6741) 0.74
(0.0054, 0.0089)
(-0.0078, -0.0035)
(-0.0042, -0.0006)
(-0.005, -0.0009)
(0.2639, 0.3252)

Root Mass
2

estimate

SI

(6.3178, 6.8254) 0.46
(0.0006, 0.0096)
(-0.0011, 0.0089)
(-0.006, 0.003)
(-0.0048, 0.0053)
(0.4627, 0.5754)

7.4204
0.0081
-0.0009
-0.0029
-0.0027
0.5526

(7.1475, 7.6933)
(0.0033, 0.013)
(-0.0062, 0.0044)
(-0.0078, 0.0019)
(-0.0081, 0.0027)
(0.4975, 0.6188)

6.3469
0.0087
0.0023
0.0026
0.0007
0.6660

(6.1436, 6.5502) 0.57
(0.0043, 0.0131)
(-0.0026, 0.0071)
(-0.0018, 0.007)
(-0.0041, 0.0055)
(0.6027, 0.7412)

7.4365
0.0139
0.0009
0.0007
-0.0061
0.6302

(7.2347, 7.6382)
(0.0097, 0.0181)
(-0.0038, 0.0056)
(-0.0035, 0.005)
(-0.0107, -0.0015)
(0.5699, 0.702)

6.7163
0.0022
-0.0026
-0.0017
-0.0018
0.3713

(6.6011, 6.8315) 0.78
(0.0001, 0.0044)
(-0.0052, 0)
(-0.0039, 0.0005)
(-0.0043, 0.0007)
(0.3365, 0.4126)

7.9240
0.0111
-0.0023
-0.0011
-0.0032
0.3195

(7.8136, 8.0343)
(0.0092, 0.0131)
(-0.0046, 0.0001)
(-0.0031, 0.0009)
(-0.0055, -0.001)
(0.2889, 0.3558)

31

SI

r

Table 7 Parameter estimates for linear models of soluble sugars as a function of time under five treatments. C = control (50% light,
well-watered), D = drought (50% light and no water), S = shade (< 3% light, well-watered), SD = shade + drought (< 3% light, no
water) for five temperate tree species. Parameters include a common intercept with different slopes for each treatment and 95%
support intervals (SI) and soluble sugar concentrations are natural log transformed. Models include seedlings from a non-significant
defoliation treatment that is pooled with other treatments. Species abbreviations in Table 1.
Total Soluble Sugars (% dry mass)
N

param.

r

2

Acer rubrum
B0
0.43
48
C
29
D
29
S
25
SD
sigma
Betula papyrifera
B0
0.57
59
C
43
D
36
S
39
SD
sigma

Stem Soluble Sugars (% dry mass)

Root Soluble Sugars (% dry mass)

estimate

SI

r

2

estimate

SI

r

2

estimate

SI

0.4474
0.0015
0.0052
-0.0010
-0.0052
0.1199

(0.4097, 0.4852)
(-0.0012, 0.0041)
(0.0018, 0.0087)
(-0.0037, 0.0017)
(-0.0088, -0.0017)
(0.1067, 0.1361)

0.49

0.3924
0.0001
0.0060
-0.0034
-0.0059
0.1241

(0.354, 0.4307)
(-0.0025, 0.0027)
(0.0025, 0.0096)
(-0.006, -0.0007)
(-0.0095, -0.0023)
(0.1107, 0.1404)

0.34

0.4842
0.0020
0.0049
-0.0005
-0.0047
0.1473

(0.4386, 0.5298)
(-0.0013, 0.0052)
(0.0007, 0.0091)
(-0.0037, 0.0027)
(-0.0088, -0.0005)
(0.1313, 0.1668)

0.4006
0.0009
-0.0008
-0.0014
-0.0032
0.1091

(0.3701, 0.431)
(0.0001, 0.0018)
(-0.0017, 0.0001)
(-0.0022, -0.0006)
(-0.0041, -0.0023)
(0.0986, 0.1216)

0.54

0.3771
0.0011
-0.0007
-0.0012
-0.0029
0.1169

(0.3445, 0.4097)
(0.0003, 0.002)
(-0.0016, 0.0003)
(-0.0021, -0.0003)
(-0.0039, -0.002)
(0.1057, 0.1302)

0.54

0.4381
0.0006
-0.0007
-0.0016
-0.0039
0.1348

(0.4013, 0.4749)
(-0.0004, 0.0016)
(-0.0018, 0.0003)
(-0.0026, -0.0006)
(-0.005, -0.0028)
(0.1219, 0.15)

32

Table 7 (cont’d)
Total Soluble Sugars (% dry mass)
N

param.

Stem Soluble Sugars (% dry mass)

2

estimate

SI

r

2

estimate

0.36

0.6556
-0.0008
0.0013
-0.0007
-0.0009
0.1453

(0.5838, 0.7274)
(-0.0021, 0.0005)
(-0.0001, 0.0027)
(-0.002, 0.0006)
(-0.0024, 0.0005)
(0.1307, 0.1627)

0.41

0.5429
-0.0002
0.0019
-0.0006
-0.0003
0.1318

0.47

0.2082
0.0008
0.0033
0.0007
0.0014
0.1298

(0.166, 0.2505)
(0, 0.0017)
(0.0024, 0.0043)
(-0.0002, 0.0016)
(0.0004, 0.0024)
(0.1171, 0.1449)

0.50

0.53

0.2622
0.0009
0.0029
0.0002
0.0021
0.1141

(0.2227, 0.3018)
(0.0002, 0.0016)
(0.002, 0.0037)
(-0.0006, 0.0009)
(0.0013, 0.0029)
(0.103, 0.1274)

0.48

r

SI

Root Soluble Sugars (% dry mass)
2

estimate

SI

(0.4778, 0.6081) 0.34
(-0.0013, 0.001)
(0.0007, 0.0032)
(-0.0018, 0.0005)
(-0.0016, 0.001)
(0.1187, 0.1477)

0.6906
-0.0010
0.0014
-0.0006
-0.0010
0.1641

(0.6095, 0.7716)
(-0.0025, 0.0004)
(-0.0002, 0.003)
(-0.0021, 0.0008)
(-0.0026, 0.0006)
(0.1477, 0.1838)

0.3627
0.0002
0.0022
-0.0004
-0.0004
0.1129

(0.1905, 0.2789)
(0.0009, 0.0028)
(0.003, 0.0051)
(0.0004, 0.0023)
(0.0005, 0.0025)
(0.1287, 0.1589)

0.43

0.2048
0.0006
0.0030
0.0004
0.0012
0.1356

(0.1613, 0.2482)
(-0.0003, 0.0015)
(0.0019, 0.004)
(-0.0005, 0.0013)
(0.0002, 0.0022)
(0.1226, 0.1511)

0.3457
0.0017
0.0035
0.0004
0.0018
0.1201

(0.3084, 0.383) 0.44
(0.001, 0.0024)
(0.0027, 0.0044)
(-0.0003, 0.0011)
(0.001, 0.0026)
(0.1087, 0.1336)

0.2410
0.0008
0.0027
0.0001
0.0020
0.1312

(0.1956, 0.2863)
(0, 0.0016)
(0.0017, 0.0037)
(-0.0008, 0.0009)
(0.0011, 0.0029)
(0.1186, 0.1462)

r

Fraxinus
americana
B0
C
D
S
SD
sigma
Quercus rubra
B0
57
C
36
D
38
S
38
SD
sigma
Quercus velutina
B0
57
C
38
D
39
S
41
SD
sigma
41
41
42
39

33

Table 8 Parameter estimates for linear models of starch as a function of time under five treatments. C = control (50% light, wellwatered), D = drought (50% light and no water), S = shade (< 3% light, well-watered), SD = shade + drought (< 3% light, no water)
for five temperate tree species. Parameters include a common intercept with different slopes for each treatment and 95% support
intervals (SI) and starch concentrations are natural log transformed. Models include seedlings from a non-significant defoliation
treatment that is pooled with other treatments. Species abbreviations in Table 1.
Total Starch (% dry mass)
N

param.

r

2

Acer rubrum
B0
0.39
47
C
29
D
28
S
25
SD
sigma
Betula papyrifera
B0
0.84
59
C
43
D
36
S
39
SD
sigma

Stem Starch (% dry mass)

Root Starch (% dry mass)

estimate

SI

r

2

estimate

SI

r

2

estimate

SI

2.3665
0.0034
-0.0148
-0.0172
-0.0201
0.4940

(2.2097, 2.5232)
(-0.0077, 0.0144)
(-0.029, -0.0005)
(-0.0283, -0.0062)
(-0.0349, -0.0053)
(0.4392, 0.5614)

0.41

1.8397
0.0053
-0.0046
-0.0216
-0.0219
0.5105

(1.6786, 2.0009)
(-0.0057, 0.0163)
(-0.0193, 0.01)
(-0.0326, -0.0106)
(-0.0367, -0.0071)
(0.4548, 0.5786)

0.39

2.5852
0.0035
-0.0166
-0.0180
-0.0246
0.5115

(2.4256, 2.7448)
(-0.0078, 0.0149)
(-0.0313, -0.002)
(-0.0292, -0.0069)
(-0.0392, -0.0101)
(0.4559, 0.5794)

0.7113
0.0206
-0.0063
0.0021
-0.0054
0.3526

(0.6128, 0.8095)
(0.018, 0.0233)
(-0.0091, -0.0034)
(-0.0005, 0.0048)
(-0.0083, -0.0026)
(0.3186, 0.393)

0.80

0.2790
0.0176
-0.0008
0.0017
-0.0007
0.2986

(0.1956, 0.3621)
(0.0153, 0.0198)
(-0.0032, 0.0017)
(-0.0005, 0.004)
(-0.0031, 0.0016)
(0.2701, 0.3327)

0.78

1.1410
0.0207
-0.0100
0.0031
-0.0098
0.4996

(1.0056, 1.2786)
(0.017, 0.0244)
(-0.014, -0.006)
(-0.0006, 0.0068)
(-0.0139, -0.0058)
(0.4524, 0.5567)

34

Table 8 (cont’d)

Total Starch (% dry mass)
N

param.

r

2

Fraxinus americana
B0
0.79
41
C
41
D
42
S
39
SD
sigma
Quercus rubra
B0
0.69
56
C
37
D
37
S
38
SD
sigma
Quercus velutina
B0
0.67
57
C
37
D
40
S
40
SD
sigma

estimate

SI

Stem Starch (% dry mass)
r

2

Root Starch (% dry mass)

estimate

SI

r

2

estimate

SI

2.5297
0.0074
-0.0099
-0.0042
-0.0157
0.3979

(2.332, 2.7252)
0.80
(0.0039, 0.0109)
(-0.0138, -0.0061)
(-0.0076, -0.0007)
(-0.0196, -0.0118)
(0.3579, 0.4458)

2.3503
0.0019
-0.0160
-0.0099
-0.0215
0.4095

(2.148, 2.5526)
(-0.0017, 0.0055)
(-0.0199, -0.012)
(-0.0135, -0.0063)
(-0.0255, -0.0175)
(0.3685, 0.4586)

0.76

2.5810
0.0086
-0.0076
-0.0025
-0.0138
0.4275

(2.3704, 2.7929)
(0.0049, 0.0124)
(-0.0117, -0.0034)
(-0.0063, 0.0012)
(-0.018, -0.0096)
(0.3848, 0.4789)

2.3324
0.0114
-0.0052
-0.0011
-0.0116
0.5258

(2.1596, 2.5053) 0.61
(0.0078, 0.015)
(-0.0093, -0.0012)
(-0.0048, 0.0025)
(-0.0155, -0.0077)
(0.4742, 0.5874)

1.6101
0.0124
-0.0036
0.0024
-0.0061
0.5473

(1.439, 1.7813)
(0.0087, 0.016)
(-0.0077, 0.0005)
(-0.0014, 0.0061)
(-0.0101, -0.0021)
(0.4942, 0.6107)

0.67

2.4559
0.0111
-0.0053
-0.0017
-0.0125
0.5572

(2.2775, 2.6343)
(0.0074, 0.0148)
(-0.0095, -0.0012)
(-0.0055, 0.002)
(-0.0166, -0.0084)
(0.5037, 0.6209)

2.8352
0.0062
-0.0054
-0.0022
-0.0089
0.4089

(2.6928, 2.9764) 0.67
(0.0037, 0.0087)
(-0.0085, -0.0023)
(-0.0047, 0.0004)
(-0.0118, -0.006)
(0.369, 0.4565)

1.8935
0.0040
-0.0055
-0.0031
-0.0135
0.4483

(1.7542, 2.0329)
(0.0014, 0.0066)
(-0.0087, -0.0023)
(-0.0058, -0.0005)
(-0.0165, -0.0105)
(0.406, 0.4987)

0.58

2.9717
0.0058
-0.0050
-0.0025
-0.0079
0.4771

(2.8067, 3.1368)
(0.0029, 0.0087)
(-0.0085, -0.0014)
(-0.0054, 0.0005)
(-0.0113, -0.0044)
(0.431, 0.5322)

35

LITERATURE CITED

36

LITERATURE CITED
Adams HD, Guardiola-Claramonte M, Barron-Gafford GA, Villegas JC, Breshears DD,
Zou CB, Troch PA, Huxman TE. 2009. Temperature sensitivity of drought-induced tree
mortality portends increased regional die-off under global-change-type drought. Proceedings of
the National Academy of Sciences 106: 7063–7066.
Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell NG, Vennetier M,
Kitzberger T, Rigling A, Breshears DD, Hogg EH (Ted), et al. 2010. A global overview of
drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest
Ecology and Management 259: 660–684.
Anderegg WRL, Berry JA, Smith DD, Sperry JS, Anderegg LDL, Field CB. 2012. The roles
of hydraulic and carbon stress in a widespread climate-induced forest die-off. Proceedings of the
National Academy of Sciences 109: 233–237.
Bolker, B. 2012. bbmle R package. Ben Bolker, R Development Core Team. Version 1.0.5.2. R
Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/
Bréda N, Huc R, Granier A, Dreyer E. 2006. Temperate forest trees and stands under severe
drought: a review of ecophysiological responses, adaptation processes and long-term
consequences. Annals of Forest Science 63: 625–644.
Breshears DD, Myers OB, Meyer CW, Barnes FJ, Zou CB, Allen CD, McDowell NG,
Pockman WT. 2009. Tree die-off in response to global change-type drought: mortality insights
from a decade of plant water potential measurements. Frontiers in Ecology and the Environment
7: 185–189.
Burns RM, Honkala BH. 1990. Silvics of North America: Volume 2. Hardwoods. Agriculture
Handbook 654. U.S. Department of Agriculture, Forest Service, Washington, DC., USA.
Canham CD, Kobe RK, Latty EF, Chazdon RL. 1999. Interspecific and intraspecific variation
in tree seedling survival: effects of allocation to roots versus carbohydrate reserves. Oecologia
121: 1–11.
Carnicer J, Coll M, Ninyerola M, Pons X, Sánchez G, Peñuelas J. 2011. Widespread crown
condition decline, food web disruption, and amplified tree mortality with increased climate
change-type drought. Proceedings of the National Academy of Sciences 108: 1474–1478.
Chapin FS, Autumn K, Pugnaire F. 1993. Evolution of Suites of Traits in Response to
Environmental Stress. The American Naturalist 142: S78–S92.
Chapin FS, Schulze E-D, Mooney HA. 1990. The ecology and economics of storage in plants.
Annual Review of Ecology and Systematics 21: 423–447.

37

Choat B, Jansen S, Brodribb TJ, Cochard H, Delzon S, Bhaskar R, Bucci SJ, Feild TS,
Gleason SM, Hacke UG, et al. 2012. Global convergence in the vulnerability of forests to
drought. Nature 491: 752–756.
DuBois M, Gilles KA, Hamilton JK, Rebers PA, Smith FE. 1956. Colorimetric Method for
Determination of Sugars and Related Substances. Analytical Chemistry 28: 350–356.
Eyles A, Pinkard EA, Mohammed C. 2009. Shifts in biomass and resource allocation patterns
following defoliation in Eucalyptus globulus growing with varying water and nutrient supplies.
Tree physiology 29: 753–764.
Galiano L, Martínez-Vilalta J, Lloret F. 2011. Carbon reserves and canopy defoliation
determine the recovery of Scots pine 4 yr after a drought episode. The New Phytologist 190:
750–759.
Galvez DA, Landhäusser SM, Tyree MT. 2011. Root carbon reserve dynamics in aspen
seedlings: does simulated drought induce reserve limitation? Tree physiology 31: 250–7.
Gibon Y, Pyl E-T, Sulpice R, Lunn JE, Höhne M, Günther M, Stitt M. 2009. Adjustment of
growth, starch turnover, protein content and central metabolism to a decrease of the carbon
supply when Arabidopsis is grown in very short photoperiods. Plant, Cell and Environment 32:
859–874.
Gruber A, Pirkebner D, Florian C, Oberhuber W. 2011. No evidence for depletion of
carbohydrate pools in Scots pine (Pinus sylvestris L.) under drought stress. Plant Biology 14:
142–148.
Hartley IP, Armstrong AF, Murthy R, Barron-Gafford G, Ineson P, Atkin OK. 2006. The
dependence of respiration on photosynthetic substrate supply and temperature: integrating leaf,
soil and ecosystem measurements. Global Change Biology 12: 1954–1968.
Hartmann H. 2011. Will a 385 million year-struggle for light become a struggle for water and
for carbon? - How trees may cope with more frequent climate change-type drought events.
Global Change Biology 17: 642–655.
Van Der Heyden F, Stock WD. 1996. Regrowth of a semiarid shrub following simulated
carbon browsing: the role of reserve carbon. Functional Ecology 10: 647–653.
Hoch G, Richter A, Körner C. 2003. Non-structural carbon compounds in temperate forest
trees. Plant, Cell and Environment 26: 1067–1081.
Kitajima K. 1994. Relative importance of photosynthetic traits and allocation patterns as
correlates of seedling shade tolerance of 13 tropical trees. Oecologia 98: 419–428.
Kobe RK. 1997. Carbohydrate Allocation to Storage as a Basis of Interspecific Variation in
Sapling Survivorship and Growth. Oikos 80: 226–233.

38

Kobe RK, Iyer M, Walters MB. 2010. Optimal partitioning theory revisited: Nonstructural
carbohydrates dominate root mass responses to nitrogen. Ecology 91: 166–179.
Körner C. 2003. Carbon limitation in trees. Journal of Ecology 91: 4–17.
Kozlowski TT. 1992. Carbohydrate Sources and Sinks in Woody Plants. Botanical Review 58:
107–222.
Landhäusser SM, Lieffers VJ. 2011. Defoliation increases risk of carbon starvation in root
systems of mature aspen. Trees 26: 653–661.
McDowell NG, Pockman WT, Allen CD, Breshears DD, Cobb N, Kolb T, Plaut J, Sperry
JS, West A, Williams DG, et al. 2008. Mechanisms of plant survival and mortality during
drought: why do some plants survive while others succumb to drought? New Phytologist 178:
719–739.
McDowell NG, Sevanto S. 2010. The mechanisms of carbon starvation: how, when, or does it
even occur at all? New Phytologist 186: 263–264.
Meier IC, Leuschner C. 2008. Belowground drought response of European beech: fine root
biomass and carbon partitioning in 14 mature stands across a precipitation gradient. Global
Change Biology 14: 2081–2095.
Millard P, Sommerkorn M, Grelet G-A. 2007. Environmental change and carbon limitation in
trees: a biochemical, ecophysiological and ecosystem appraisal. The New Phytologist 175: 11–
28.
Myers JA, Kitajima K. 2007. Carbohydrate storage enhances seedling shade and stress
tolerance in a neotropical forest. Journal of Ecology 95: 383–395.
Nardini A, Lo Gullo MA, Salleo S. 2011. Refilling embolized xylem conduits: is it a matter of
phloem unloading? Plant Science 180: 604–611.
Piper FI, Fajardo A. 2011. No evidence of carbon limitation with tree age and height in
Nothofagus pumilio under Mediterranean and temperate climate conditions. Annals of Botany
108: 907–917.
Piper FI, Reyes-Díaz M, Corcuera LJ, Lusk CH. 2009. Carbohydrate storage, survival, and
growth of two evergreen Nothofagus species in two contrasting light environments. Ecological
Research 24: 1233–1241.
Poorter L, Kitajima K. 2007. Carbohydrate storage and light requirements of tropical moist and
dry forest tree species. Ecology 88: 1000–1011.

39

Quentin AG, Beadle CL, O’Grady AP, Pinkard EA. 2011. Effects of partial defoliation on
closed canopy Eucalyptus globulus Labilladière: Growth, biomass allocation and carbohydrates.
Forest Ecology and Management 261: 695–702.
R Development Core Team (2010). R: A language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.
Regier N, Streb S, Cocozza C, Schaub M, Cherubini P, Zeeman SC, Frey B. 2009. Drought
tolerance of two black poplar (Populus nigra L.) clones: contribution of carbohydrates and
oxidative stress defence. Plant, Cell and Environment 32: 1724–1736.
Sack L, Grubb PJ. 2002. The combined impacts of deep shade and drought on the growth and
biomass allocation of shade-tolerant woody seedlings. Oecologia 131: 175–185.
Sala A, Fouts W, Hoch G. 2011. Carbon Storage in Trees: Does Relative Carbon Supply
Decrease with Tree Size? In: Meinzer FC, Lachenbruch B, Dawson TE, eds. Size- and AgeRelated Changes in Tree Structure and Function, Tree Physiology 4. Dordrecht: Springer
Netherlands, 287–306.
Sala A, Piper F, Hoch G. 2010. Physiological mechanisms of drought-induced tree mortality
are far from being resolved. The New Phytologist 186: 274–281.
Sala A, Woodruff DR, Meinzer FC. 2012. Carbon dynamics in trees: feast or famine? Tree
Physiology 32: 764–775.
Secchi F, Zwieniecki MA. 2011. Sensing embolism in xylem vessels: the role of sucrose as a
trigger for refilling. Plant, Cell and Environment 34: 514–524.
Smith AM, Stitt M. 2007. Coordination of carbon supply and plant growth. Plant, Cell and
Environment 30: 1126–1149.
Valladares F, Niinemets Ü. 2008. Shade Tolerance, a Key Plant Feature of Complex Nature and
Consequences. Annual Review of Ecology, Evolution, and Systematics 39: 237–257.
Wiley E, Helliker B. 2012. A re-evaluation of carbon storage in trees lends greater support for
carbon limitation to growth. New Phytologist 195: 285–289.
Williams AP, Allen CD, Millar CI, Swetnam TW, Michaelsen J, Still CJ, Leavitt SW. 2010.
Forest responses to increasing aridity and warmth in the southwestern United States.
Proceedings of the National Academy of Sciences 107: 21289–21294.

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