Ecology, 91(1), 2010, pp. 166–179
Ó 2010 by the Ecological Society of America
Optimal partitioning theory revisited: Nonstructural carbohydrates
dominate root mass responses to nitrogen
RICHARD K. KOBE,1 MEERA IYER,
AND
MICHAEL B. WALTERS
Department of Forestry, 126 Natural Resources Building, Michigan State University, East Lansing, Michigan 48824-1222 USA
Abstract. Under optimal partitioning theory (OPT), plants preferentially allocate biomass
to acquire the resource that most limits growth. Within this framework, higher root mass
under low nutrients is often assumed to reflect an allocation response to build more absorptive
surface. However, higher root mass also could result from increased storage of total
nonstructural carbohydrates (TNC) without an increase in non-storage mass or root surface
area. To test the relative contributions of TNC and non-storage mass as components of root
mass responses to resources, we grew seedlings of seven northern hardwood tree species
(black, red, and white oak, sugar and red maple, American beech, and black cherry) in a
factorial light 3 nitrogen (N) greenhouse experiment. Because root mass is a coarse metric of
absorptive surface, we also examined treatment effects on fine-root surface area (FRSA).
Consistent with OPT, total root mass as a proportion of whole-plant mass generally was
greater in low vs. high N. However, changes in root mass were influenced by TNC mass in all
seven species and were especially strong in the three oak species. In contrast, non-storage mass
contributed to increased total root mass under low N in three of the seven species. Root
morphology also responded, with higher fine-root surface area (normalized to root mass)
under low vs. high N in four species. Although biomass partitioning responses to resources
were consistent with OPT, our results challenge the implicit assumption that increases in root
mass under low nutrient levels primarily reflect allocation shifts to build more root surface
area. Rather, root responses to low N included increases in: TNC, non-storage mass and fineroot surface area, with increases in TNC being the largest and most consistent of these
responses. The greatest TNC accumulation occurred when C was abundant relative to N.
Total nonstructural carbohydrates storage could provide seedlings a carbon buffer when
respiratory or growth demands are not synchronized with photosynthesis, flexibility in
responding to uncertain and fluctuating abiotic and biotic conditions, and increased access to
soil resources by providing an energy source for mycorrhizae, decomposers in the rhizosphere,
or root uptake of nutrients.
Key words: biomass allocation; biomass partitioning; fine roots; fine root surface area; optimal
partitioning theory; soil resources; storage; total nonstructural carbohydrates.
INTRODUCTION
Under optimal partitioning theory (OPT), plants
should allocate additional biomass to the organ that
takes up the resource that most limits growth (Bloom et
al. 1985). For example, under low light, most plant
species increase leaf area through some combination of
increasing biomass allocation to leaves and producing
thinner leaves (e.g., Grubb et al. 1996, Portsmuth and
Niinemets 2007). Similarly, increased root biomass
under low soil nutrients has been interpreted as evidence
of a plastic allocation response to capture limiting soil
resources (Fichtner and Schultze 1992, Walters and
Reich 2000, Shipley and Meziane 2002, Portsmuth and
Niinemets 2007). Optimal biomass partitioning is a
critical assumption in theories of plant community
dynamics, including the resource ratio hypothesis of
Manuscript received 7 January 2009; revised 16 April 2009;
accepted 27 April 2009. Corresponding Editor: L. A. Donovan.
1 E-mail: [email protected]
166
plant succession (Tilman 1990) and the competitor–
ruderal–stress tolerator life history strategies proposed
by Grime (1979).
Increased root mass under low nutrients could reflect
increased partitioning to root absorptive surface area, as
implicitly assumed in OPT (e.g., Bloom et al. 1985), but
empirical evidence is scant and conclusions can depend
on approach. For example, apparently counter to OPT,
fine-root production can increase with nitrogen (N)
availability (Pregitzer et al. 1993, van Vuuren et al. 1996,
Espeleta and Donovan 2002); however, because of fineroot mortality, production does not directly translate to
standing root surface area. Furthermore, these studies
were not explicitly focused on biomass partitioning and
whether root mass as a fraction of whole-plant mass
(WPM) changed with N. Nonlinear allometric relationships between organ and WPM also have to be taken
into account because treatments erroneously could be
interpreted as differing in biomass partitioning when in
fact all treatments follow a common nonlinear allome-
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OPTIMAL PARTITIONING THEORY REVISITED
tric trajectory but simply travel at different rates along
that trajectory (McConnaughay and Coleman 1999,
Weiner 2004).
Alternatively, increased root mass under low nutrients
could reflect increased partitioning to sugars and starch
(i.e., total nonstructural carbohydrates, TNC). Total
nonstructural carbohydrates can constitute 10–40% of
total root dry mass (Nguyen et al. 1990, Kobe 1997,
Canham et al. 1999) and generally increase with higher
irradiance (Mooney et al. 1995, Naidu and DeLucia
1997, Gansert and Sprick 1998) and lower nutrient
availability. Increased TNC in roots under low nutrient
conditions could be consistent with OPT as it may
increase access to limiting nutrients by providing a cue
for mycorrhizal colonization (Same et al. 1983, Graham
et al. 1997), rhizosphere exudates to mycorrhizae and
microbial decomposers (Cardon et al. 2002, Phillips and
Fahey 2005, Dijkstra and Cheng 2007), and energy for
nutrient uptake and assimilation (Rothstein et al. 2000).
Nitrate is a more energetically expensive form of N than
ammonium, arising from both the costs of nitrate
reduction to ammonium and ion movement across
membranes (Cannell and Thornley 2000); thus, species
with a preference for nitrate may partition greater root
mass to TNC.
Total nonstructural carbohydrates may accumulate
under low fertility (McDonald et al. 1986, Mooney et al.
1995, Rothstein et al. 2000) because C is less limiting
than other resources; whether TNC storage is consistent
with OPT depends on where it ultimately is allocated.
Total nonstructural carbohydrates storage provides a
carbon buffer when respiratory, growth, or other
physiological demands are not synchronized with
photosynthesis. In seasonal environments, TNC reserves
built up during past growing seasons are a carbon source
for respiration during dormancy, endow frost tolerance
(Kozlowski 1992), and are used for the construction of
leaves and fine roots at the start of new growing seasons
(Chapin et al. 1990, Loescher et al. 1990, Kozlowski
1992, Gaucher et al. 2005). Total nonstructural carbohydrates also enable recovery from tissue loss due to
herbivory (Marquis et al. 1997) and fire (Kruger and
Reich 1997, Langley et al. 2002, Hoffmann et al. 2003).
Biomass partitioning to TNC storage rather than
absorptive root surface area could convey a more nimble
flexibility for plant interactions with the biotic and
abiotic environment during the growing season, which
would enhance carbon balance and survivorship under
uncertain and fluctuating environments. For example,
TNC could continue to be stored or be mobilized to
support mycorrhizae or other microbial symbionts,
increased uptake kinetics, or rapid construction of fine
roots (e.g., Gaudinski et al. 2009) when soil resource
levels increased (e.g., Volder et al. 2005). In contrast,
biomass partitioning to absorptive root surface area,
regardless of whether the proximal carbon source was
from storage or current photosynthate, locks in one
strategy of nutrient acquisition.
167
Although this study focuses on within-species variation in TNC in response to resources, interspecific
variation in TNC could be an important basis of
variation in species life history strategies (Kobe 1997,
Kruger and Reich 1997, Canham et al. 1999, Myers and
Kitajima 2007). Thus, interspecific variation in partitioning to TNC could have important implications for
community processes and the distribution of species
across soil resource gradients; we focus more explicitly
on species differences in mass partitioning to TNC and
community-level implications in another paper (R. K.
Kobe et al., unpublished manuscript).
To understand root mass responses to resources,
distinguishing TNC from non-storage components is
crucial because each is involved in different functions.
We define non-storage root mass as: total root mass TNC root mass (Canham et al. 1999). Partitioning to
non-storage implies increased investment in defense
(e.g., lignins and phenolics), structure to access soil
resources (i.e., fine roots), and/or metabolic compounds
(e.g., lipids and proteins) that enhance nutrient uptake
(Villar et al. 2006). Although non-storage mass is not a
physiological entity, it is a closer approximation to
structural mass involved in resource uptake than total
root mass.
To test resource effects on biomass partitioning, we
adopt a whole-plant perspective that incorporates effects
of plant size and the potential for nonlinear allometric
trajectories (McConnaughay and Coleman 1999, Weiner
2004). Specifically, we developed species- and resourcespecific models of root (total, TNC, and non-storage),
leaf, and stem mass, and fine-root surface area (FRSA)
based on a greenhouse experiment with seedlings of seven
temperate tree species. The models were used to test
effects of resources per the following hypotheses: (1)
partitioning of biomass to leaves and stems increases
under high N and low light; and (2) partitioning of
biomass to roots increases under low N and high light.
To understand root mass responses, we also tested three
alternative but non-mutually exclusive hypotheses. Resource-dependent increases in partitioning to root mass
arise from increases in: (3) TNC and/or (4) non-storage
root mass. Given that root mass alone is a crude metric of
absorptive potential, (5) root surface area per unit nonstorage root mass increases under low N availability.
METHODS
Experiment
To evaluate generality of responses, the study
included seven species that vary in shade tolerance and
association with site fertility (Table 1). Paper birch also
was planted but only eight seedlings survived (all high
light, three in low, and five in high N) and limited results
are reported. Seed (Sheffield Seed, Locke, New York,
USA) was stratified and germinated in perlite. In
February 2002, germinants were planted in polyethylene-coated 10.2 3 10.2 3 27 cm3 cardboard containers
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RICHARD K. KOBE ET AL.
Ecology, Vol. 91, No. 1
TABLE 1. Soil fertility association, shade tolerance, and harvest schedule for the species used in this study.
Species
Acer rubrum
Acer saccharum
Quercus alba
Quercus velutina
Quercus rubra
Prunus serotina
Fagus grandifolia
Betula papyrifera
Common
name
Abbreviation
red maple
sugar maple
white oak
black oak
red oak
black cherry
American beech
paper birch
RM
SM
WO
BO
RO
BC
AB
PB
Shade tolerance
category
tolerant
very tolerant
intolerant
intolerant
intermediate
intolerant
very tolerant
intolerant
Age at harvest (d)
Soil fertility
affinity
1
2
3
4
intermediate–high
high
low–intermediate
low
low–high
intermediate–high
intermediate–high
intermediate–high
10
14
25
29
34
37
37
40
53
52
75
86
76–77
84
85
86
84–85
105
106
105
106
106
105
122
Notes: Soil fertility affinities are based on species relative abundance as a canopy species by habitat type according to a
classification system based on understory vegetation (Burger and Kotar 2003). Shade tolerance categories are based on Burns and
Honkala (1990).
filled with a 10:9:1 by volume silica sand : perlite : field
soil mix. For each species–nutrient treatment, we
planted 30–36 high-light seedlings and, to compensate
for expected shade mortality, 36–45 low-light seedlings.
High sand and perlite content facilitated recovery of fine
roots during harvests and provided a nutrient-poor
medium in which nutrient additions could be controlled.
Field soil was obtained from a mesic beech–maple–oak
forest at the Michigan State University Tree Research
Center, East Lansing, Michigan, USA.
The experiment was designed as a factorial with two
levels of light (2% and 22% full sun) and N availability
(0.5 mg N/L and 50 mg N/L in a modified Hoagland’s
solution added every three days). To prevent buildup of
salts, containers were flushed weekly with deionized
water. Light levels mimicked understory environments
beneath closed canopies (2% full sun) and in large tree
fall gaps (22% full sun) (Schreeg et al. 2005), and N
treatments approximated minimum and maximum KClextractable soil N concentrations in northern Michigan
(Zak et al. 1986; R. K. Kobe, unpublished data). To
achieve light levels, we used an inner layer of black
shade cloth and, to minimize heat build-up, an outer
layer of reflective knitted poly-aluminum shade cloth.
Mean daytime temperatures, monitored with Hobo
Dataloggers (Onset Computer, Bourne, Massachusetts,
USA), were 23.628 6 0.048C and did not differ between
light treatments (t test, P . 0.95).
To examine allometric trajectories, we harvested six
seedlings/species–nutrient–light combination at regular
intervals to ;100 d (Table 1). Poor survivorship in some
low-light treatments precluded some harvests. To
minimize diurnal variation in TNC, all harvests were
initiated 2.5 h after sunrise and were completed within
3.5 h. Seedlings were washed in deionized water;
separated into leaves, stems, and roots; freeze-dried for
2 d; and weighed. Dried roots were pulverized with a
ball mill (Kinetic Laboratory Equipment, Visalia,
California, USA).
Root and TNC analyses
Washed fresh roots were scanned and the digital
images analyzed for surface area with WinRhizo
(Regent Instruments, Blain, Quebec, Canada). To
measure TNC, we first extracted and analyzed soluble
sugars and then analyzed extraction residues for starch.
We measured TNC in roots of all harvested seedlings
and in stems for a subset of final harvest seedlings to test
whether root and stem TNC were correlated. We did not
analyze foliar TNC because it is a short-term pool that
fluctuates diurnally (Kozlowski 1992).
Soluble carbohydrates in a 20-mg sample were
extracted three times in 80% ethanol and centrifuged,
and concentrations (as glucose equivalents) in the
supernatant were measured with a phenol-sulfuric acid
colorimetric assay (Dubois et al. 1956). To measure
sugar alcohols (e.g., sorbitol) in black cherry, extracts
were derivatized (following Sweeley et al. 1963) and
analyzed using gas chromatography (Roper et al. 1988).
The pellet remaining after ethanol extraction was
analyzed for starch. To gelatinize the starch, we
autoclaved and then incubated samples with 10 units
of amyloglucosidase (Roche Diagnostics, Indianapolis,
Indiana, USA) at 558C for 16 h. Because sample
processing can introduce trace amounts of monomers
from structural carbohydrates, the extractant was
analyzed colorimetrically using glucose-specific trinder
reagent (Sigma Chemical, St. Louis, Missouri, USA)
(Roper et al. 1988). Total nonstructural carbohydrates
concentration was calculated as the sum of glucose
equivalents of soluble sugars and starch normalized to
total organ mass. Total nonstructural carbohydrates
pool sizes were calculated as (concentration 3 root
mass). In black cherry and sugar maple, low-light
seedlings had inadequate mass for TNC analysis and
were combined into one or more composite samples in a
given treatment. See Iyer (2006) for details.
Models and data analysis
Resources could affect component mass and root
surface area directly or be mediated through effects on
plant mass. Thus, for each species treatment, we
modeled the response variable as a function of mass.
We modeled mass of leaf, stem, and root total, TNC,
and non-storage as functions of WPM and root surface
area as a function of non-storage root mass. The tested
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OPTIMAL PARTITIONING THEORY REVISITED
models included: (1) simple linear with zero intercept (y
¼ ax); (2) exponential (y ¼ exp(a 3 x) – 1); (3) power (y ¼
a 3 (xb)); and (4) sigmoid (y ¼ a 3 exp(b/x)), where a
and b are estimated parameters in each model. Linear
models with nonzero y intercepts (a general case of
model 1) were tested but are not reported because y
intercepts generally were not different from zero. The
model with the strongest empirical support was chosen
based on Akaike’s Information Criteria with a correction for small sample size (AICc). For each species–
treatment case, we chose the simplest model within two
AIC units of minimum AIC (Burnham and Anderson
2002) with significant parameter estimates (i.e., 95%
support did not encompass zero).
The nonlinear models are robust for characterizing
relationships but a common model is needed to
statistically compare across experimental treatments. To
test effects of light and N availability, we compared
treatment slope estimates for linear zero-intercept models, which provided good fits to most cases. Treatment
effects were considered significant (P , 0.05) when slope
estimates had ,29% overlap in 95% support (Austin and
Hux 2002). When compared treatments had minimal
overlap in the independent variable but apparently
different slope estimates, we graphically assessed treatment differences in the region closest to overlap to
minimize the possibility that treatments followed a
common nonlinear allometric trajectory, but occupied
different segments of that trajectory. All models (linear
and nonlinear) were fit using Gauss-Newton nonlinear
estimation in Systat 12 (Chicago, Illinois, USA), based
on a loss function with normal error, which was
supported by probability plots and G tests.
RESULTS
Gross mass partitioning to leaves, stems, and roots
Throughout the results, treatment comparisons are
based on differences in mass partitioning to different
plant organs (i.e., organ mass as a function of WPM) and
are not based on absolute differences in organ mass.
Supporting hypothesis 1, mass partitioning to leaves was
greater under low irradiance for all species 3 N
combinations, except black oak at both N levels and
red maple at high N (Fig. 1; also see Appendix A). Even
though WPM ranges did not overlap between low- and
high-light seedlings, trajectories of leaf mass as a function
of WPM clearly separated in all species (Appendix B) for
which linear model slopes differed. Linear functions
provided the best fits for 17 of 28 cases. High N also led
to higher leaf mass under high light but not in the three
oak species (Fig. 1). Under low light, N had negligible
effects with slight but significantly higher leaf mass per
unit WPM for black cherry (0.55 in high vs. 0.48 in low
N) and black oak (0.37 in high vs. 0.31 in low N). Stem
mass was higher under low vs. high irradiance for all
species, within N level. Nitrogen generally did not
influence mass partitioning to stems (Fig. 1).
169
Consistent with OPT and hypothesis 2, root mass as a
function of WPM was greater at low vs. high N under at
least one light level in all species (Fig. 2; Appendix A).
Nitrogen had no effect on biomass partitioning to roots
in black and red oak under high light and sugar maple
and white oak under low light. Biomass partitioning to
roots was greater under high than low irradiance at both
N levels for all species (Appendix A), but these results
must be interpreted cautiously because of minimal
overlap in WPM between low- and high-light seedlings
(Fig. 2). We cannot exclude the possibility that seedlings
from different light treatments followed the same
nonlinear allometric trajectory, with seedlings from
low light having low WPM, which was associated with
disproportionately lower root mass. Supporting this
interpretation, root mass from low- and high-light
treatments were similar for each species at overlapping
low WPM (Fig. 2) and root mass increased disproportionately with WPM in 13 of 27 cases (best fits to
nonlinear models; Appendix C).
Sums of leaf, stem, and root vs. WPM slopes were
close to the expected value of one (within 0.06) in all but
three species-treatment cases, supporting the validity of
this approach. The exceptions were beech (low light,
high N) and red maple (low light, both high and low N),
where model fits were poor for at least one organ
component (Appendix A).
Root TNC mass could be viewed as an index of
whole-plant partitioning to TNC because root and stem
TNC concentrations were highly correlated for each
species (r ¼ 0.62–0.85), TNC concentrations were
greater in roots than stems in all six tested species
(Appendix D), and, on average, root mass was ;2.8
times greater than stem mass (Fig. 1; Appendix A). In
the rest of the paper, we focus on root mass
partitioning between TNC and non-storage pools and
fine-root surface area as a function of non-storage root
mass.
Root TNC mass
Supporting hypothesis 3, root TNC mass was greater
under low vs. high N at a given WPM for all species
under at least one light level (Fig. 3). Assuming a linear
relationship between TNC mass and WPM, TNC mass
ranged from 10% to 233% higher in low than high N
(Fig. 1) among the 12 pairs of light 3 species treatment
combinations that could be tested (Fig. 3). Only black
oak under high light and cherry under low light did not
differ in TNC between N levels. Small sample sizes
precluded comparisons for sugar and red maple under
low light.
Root TNC mass was greater under high vs. low
irradiance for all tested species (Fig. 1). However, at
similar WPM, root TNC mass was comparable in high
and low light for most species, except black cherry (Fig.
3). Thus, we cannot distinguish whether seedlings from
different light treatments followed different allometric
trajectories or occupied different regions on the same
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RICHARD K. KOBE ET AL.
Ecology, Vol. 91, No. 1
FIG. 1. Mass proportions based on linear model slope estimates for root total nonstructural carbohydrates (TNC), root nonstorage, stems, and leaves for seven tree species under differing light and nutrient regimes. Note that for red and sugar maple under
low light, low mass and sample sizes precluded separate analysis of TNC and non-storage root mass; total root mass is reported.
Different lowercase letters designate significant differences among resource treatments in a given mass component within species.
See Appendix A for parameter estimates and statistics. Treatment abbreviations are: HLHN, high light, high nitrogen; HLLN, high
light, low nitrogen; LLHN, low light, high nitrogen; LLLN, low light, low nitrogen.
allometric trajectory. In support of the latter, power and
sigmoid functions provided the best fit in 12 of 24 cases
(Fig. 3, Appendix C), which implies disproportionately
less root TNC mass at low WPM, regardless of light
treatment.
Starch generally dominated TNC mass and was 15–30
times greater than soluble sugars (including sorbitol and
myoinositol) in black cherry (high light) and 1.5–8 times
greater in most other species. Paper birch had higher
soluble sugar than starch concentrations (Appendix E).
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OPTIMAL PARTITIONING THEORY REVISITED
171
FIG. 2. Root mass as a function of whole-plant mass (WPM; note different scales) for the different light and nutrient
treatments. Best-fit equations are graphed; see Appendices A and C, respectively, for linear and nonlinear model parameter
estimates and statistics. In some cases, only one line is visible because treatment slope estimates were very similar. For clarity, insets
are shown in cases in which it was difficult to distinguish low WPM data points (generally low-light seedlings). See Fig. 1 for an
explanation of treatment abbreviations.
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RICHARD K. KOBE ET AL.
Ecology, Vol. 91, No. 1
FIG. 3. Root total nonstructural carbohydrates (TNC) mass as a function of whole-plant mass (WPM; note different scales).
Best-fit equations are graphed; see Appendices A and C, respectively, for linear and nonlinear model parameter estimates and
statistics. For clarity, insets are shown in cases in which it was difficult to distinguish low WPM data points (generally low-light
seedlings). See Fig. 1 for an explanation of treatment abbreviations.
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OPTIMAL PARTITIONING THEORY REVISITED
Root non-storage mass
Contrary to expectation under hypothesis 4, resource
effects on non-storage root mass were negligible to
modest for most species and in all cases were weaker
than for TNC. Based on the linear model results, root
non-storage mass responses to lower N ranged from a
(nonsignificant) 10% decrease to a 71% increase, with
most changes close to zero (Fig. 1). There were no
resource effects on non-storage mass for the three oaks
or sugar maple. Non-storage mass was a constant
proportion of WPM among treatments for the oak
species: ;0.4 in red and white oak and 0.38 in black oak
(Fig. 4; Appendix A). Similarly for sugar maple, nonstorage mass followed a single allometric trajectory for
combined treatments (non-storage ¼ 0.39WPM1.18, r 2 ¼
0.98; Fig. 4).
Based on slope estimates of the linear models, nonstorage mass at a given WPM was higher under low vs.
high N in black cherry and American beech (under both
low and high light) and red maple (under high light;
Figs. 1 and 4). Also for beech and cherry, non-storage
root mass was higher under high vs. low irradiance (Fig.
1). However, apparent differences between irradiance
levels could have been artifacts of fitting models to
different regions of WPM; in overlapping regions at
lower WPM, low- and high-light non-storage mass
values were similar (Fig. 4).
Surface area of fine roots
Fine-root surface area (,1.0 mm diameter) per unit
non-storage root mass responded positively to low N in
four of seven species under high light and with similar
magnitude as root TNC changes (Fig. 5; Appendix F),
providing qualified support for hypothesis 5. Under low
light, differences in FRSA between N levels were
generally small (11% to 24%) and only the difference
for red oak was significant (17%; Appendix F). We
obtained similar results for N effects on FRSA when we
analyzed total FRSA and used total root mass as the
independent variable. Irradiance level had weaker effects
than N on FRSA; under high N for beech and red oak,
FRSA per unit non-storage mass was significantly
higher under low than high light. In most cases, FRSA
increased as a simple linear proportion of root nonstorage mass (Fig. 5). For red and sugar maple under
high light and high N, FRSA increased less than
proportionately with root non-storage mass, as indicated by empirical support for a power function with
exponent ,1.
DISCUSSION
Root mass responses to N were influenced strongly
by TNC and moderately by non-storage mass
Consistent with optimal partitioning theory (OPT)
(Bloom et al. 1985, Shipley and Meziane 2002) and
hypothesis 2, seedlings of all species had higher root
mass under low N. However, counter to hypothesis 4,
173
higher root mass did not simply arise from increased
partitioning to non-storage root mass to build more
absorptive surface area, as implicitly assumed under
OPT. Rather, increased TNC under low N was a large
contributor to root mass changes (supporting hypothesis
3), occurred in all species, and increased more on a
percentage basis than non-storage mass in all cases (Fig.
1).
These results pose the question: could higher TNC
mass under reduced N, documented here and in other
woody (Rothstein et al. 2000) and herbaceous species
(Mooney et al. 1995, Stitt and Krapp 1999), promote
greater access to soil N? Although we did not address
this question in our study, we speculate that there are at
least three non-mutually exclusive mechanisms through
which root TNC would promote greater access to soil
N. First, root TNC cues and supports mycorrhizal
colonization (Same et al. 1983, Graham et al. 1997). We
did not assess mycorrhizae, but decreased mycorrhizal
colonization under N fertilization in sugar maple and
red oak plantations (Phillips and Fahey 2007) is
consistent with this mechanism. Second, root TNC
could be the source of rhizosphere carbon exudates
(Schwab et al. 1991), which prime populations of
microbial decomposers and support mycorrhizae (Dakora and Phillips 2002, Dijkstra and Cheng 2007). The
lower levels of root TNC under high N reported here
parallels decreased respiration by rhizosphere microbes
under N fertilization (Phillips and Fahey 2007). Paper
birch had the highest soluble sugar among species here,
consistent with its high rates of rhizodeposition (Bradley
and Fyles 1995). Third, root TNC could provide energy
for mineral N uptake/reduction and carbon skeletons to
assimilate N, supported by a positive correlation
between N uptake and root TNC in aspen (Rothstein
et al. 2000). From an interspecific perspective, species
that preferentially take up energetically expensive
nitrate over ammonium (Cannell and Thornley 2000)
might maintain higher levels of root TNC, but this
speculation cannot be evaluated due to the paucity of
information on species N form preference. In support of
this idea, beech, which prefers NO3, had higher TNC
than sugar maple, which has strong preference for NH4þ
(Rothstein et al. 1996, Templer and Dawson 2004).
However, red oak has the highest TNC levels and
apparently has no N form preference (Templer and
Dawson 2004).
The highest levels of root TNC at high light and low
nutrients and the lowest at low light and high nutrients
are consistent with the carbon–nitrogen balance hypothesis (Lerdau and Coley 2002), which previously has
been applied to defenses but also could apply more
generally to the storage of non-limiting resources. In
high light and low N, relatively abundant C may
accumulate because other elements limit construction
of new tissue. Conservative allocation strategies in which
non-storage mass and FRSA do not respond to low N,
as observed for black and white oak here, likely
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Ecology, Vol. 91, No. 1
FIG. 4. Non-storage root mass as a function of whole-plant mass (WPM; note different scales). Note that red maple and sugar
maple have data for high light only. Best-fit equations are graphed; see Fig. 1 and Appendix C, respectively, for linear and
nonlinear model parameter estimates and statistics. For clarity, insets are shown in cases in which it was difficult to distinguish low
WPM points (generally low-light seedlings). See Fig. 1 for an explanation of treatment abbreviations.
exacerbate accumulation of carbon and favor forms that
later can be mobilized (TNC). Although accumulation
of carbon due to carbon–nutrient imbalances may have
contributed to observed patterns, our results also
support that TNC storage does not arise solely from
resource imbalances. Under conditions that presumably
would be least favorable for TNC accumulation, low
light and high N, TNC concentrations still attained 20–
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175
FIG. 5. Fine-root (,1 mm diameter) surface area as a function of non-storage root mass. Red maple and sugar maple have data
for high light only, and results were not significant for black cherry under low light. Best-fit equations are graphed; see Appendix F
for parameter estimates and statistics. For clarity, insets are shown in cases in which it was difficult to distinguish low root mass
points (generally low-light seedlings). See Fig. 1 for an explanation of treatment abbreviations.
30% of root mass for all three oak species (Appendices
D and E). Foliar nitrate inhibition of gene expression
involved in starch synthesis (Scheible et al. 1997) is one
process that could regulate TNC.
Fine-root surface area increased under low N
for some species
Fine-root surface area is a better functional metric of
increased partitioning to soil resource uptake than
176
RICHARD K. KOBE ET AL.
undifferentiated root mass. Under low vs. high N, four
of seven species (beech, cherry, red oak, and sugar
maple) increased FRSA per unit non-storage mass, thus
more efficiently deploying non-storage root mass as
potential absorptive surface area and providing some
support for hypothesis 5. From a whole-plant perspective, absolute FRSA also could increase through
increased WPM (not observed in any species) or
partitioning to root mass (observed in cherry, red maple,
and beech). Thus all species but white and black oak had
higher absolute FRSA under low N.
Leaf and stem mass
Consistent with OPT and hypothesis 2, partitioning of
biomass increased to both carbon-capturing leaves and
stems under low light. Greater partitioning to stem mass
likely reflects favoring height growth as a plastic
response to perceived light competition (Walters et al.
1993, Delagrange et al. 2004). In all species but the oaks,
N influenced mass partitioning to leaves under high but
not low light, suggesting that leaf production under high
light may be N limited. Similarly, woody growth in red
oak, American beech, and red maple saplings was
limited by foliar N under high but not low irradiance
(Kobe 2006). Together, these results suggest that under
high N, red oak growth responses are mediated through
increased foliar N concentrations, while beech and red
maple saplings increase both leaf mass and foliar N.
Caveats
Our snapshot TNC measurements do not estimate
total allocation to TNC because these pools, especially
soluble sugars, cycle rapidly (Cardon et al. 2002, Phillips
and Fahey 2005). Starch, primarily a storage compound,
turns over less rapidly than sugars, but can be converted
to sugars. Soluble sugar results should be interpreted
cautiously.
Differences between light treatments were less ambiguous for leaf and stem than root mass. A single
allometric function would not have provided a good fit
to leaf or stem mass from both low- and high-light
treatments, supporting real differences between light
levels. In contrast, mass partitioning to roots in low vs.
high light could have arisen from a common nonlinear
allometric trajectory with treatments occupying different
trajectory segments rather than truly differing (e.g.,
Weiner 2004). Given that all three biomass components
were modeled as functions of WPM, increased partitioning to leaf and stem mass under low light
necessitates that root mass decreased. Thus, evidence
supports real rather than ‘‘allometric’’ effects of
irradiance on mass partitioning to roots.
Yet irradiance results should be interpreted with
caution because our light treatments may have been
less effective for smaller seedlings. High-light seedlings
that had low mass were harvested early in the
experiment (Table 1), and there may not have been
adequate time for light effects to fully manifest. Previous
Ecology, Vol. 91, No. 1
studies provide conflicting results on irradiance effects
on TNC in juvenile trees. In similar species, Canham et
al. (1999) found negligible light effects on root TNC in
2-year-old field seedlings. But TNC concentration
increased with irradiance in roots of red oak saplings
(Naidu and DeLucia 1997) and European beech
(Gansert and Sprick 1998).
Seed-derived TNC and nutrients could have carried
over to the young seedlings in our experiment and
influenced results. However, two lines of evidence suggest
that seed influence was negligible and that TNC was
largely derived from seedling photosynthate. First, TNC
mass increased with WPM in all treatments and species.
If measured TNC were largely from seed reserves, then
TNC mass would have decreased with WPM as TNC
was mobilized for structural growth and respiration.
Second, mean seedling mass from harvests 3 and 4 under
high light was 714% greater than typical species seed
mass (USDA Forest Service 1974) in all species (range ¼
43% in white oak in high light, low N to 3700% in red
maple in high light, high N), except paper birch, which
increased mass .5000-fold in high N and .1000-fold in
low N. White oak was the only species under high light
with decreased mass from seed to seedling.
Some aspects of our methodology may have underestimated the response of FRSA to low N levels. High N
mobility in the sand/perlite potting medium may have
prevented N depletion in the rhizosphere, precluding
benefits of fine-root proliferation. On the other hand, N
treatments strongly influenced WPM and mass partitioning and would be expected to affect FRSA as well.
Adopting a whole-plant perspective, our goal was to
assess how effectively non-storage root mass was
deployed as FRSA and thus we did not distinguish
between fine- and coarse-root mass, which could
underestimate effective FRSA if responses to N treatments were dominated by coarse root mass. This is
unlikely because the proportion of root surface area
contributed by roots ,2.0 mm ranged from 0.70 (white
oak in low light and low N) to 1.0 (several species–
treatment combinations), with an overall mean of 0.88.
We acknowledge that fine-root rank may be more
indicative of function than root diameter (Pregitzer et al.
2002), but we lack rank data. Furthermore, our results
superficially contradict observed increases in fine-root
mass in response to fertilization, either at the stand level
or in finer scale nutrient hot spot studies (e.g., Pregitzer
et al. 1993, van Vuuren 1996). However, these studies
examine phenomenon at different scales than our study,
are not focused on OPT, and thus did not take wholeplant changes into account.
Conclusions
Consistent with OPT, root mass as a proportion of
whole-plant mass increased under low N availability.
However, the increase in root mass was most strongly
influenced by TNC, which could improve plant N status
through cuing mycorrhizal colonization, as carbon
January 2010
OPTIMAL PARTITIONING THEORY REVISITED
exudates that enhance microbial decomposition of
organic matter and as energy and assimilation substrate
for increased N uptake. For some species, the increase in
root mass under low N partly arose from increased
biomass partitioning to non-storage root mass, which
could increase absorptive surface area. Some species
deployed non-storage root mass more efficiently as
FRSA under low N. Generally consistent with these
results, N fertilizations in the field had weaker effects on
fine-root mass and stronger impacts on the TNC-related
processes of rhizosphere microbial respiration and
mycorhizal colonization (Phillips and Fahey 2007).
Our results argue for an expanded optimal partitioning theory that goes beyond undifferentiated root mass
responses to low fertility and that encompasses how root
mass is partitioned to TNC and non-storage and how
non-storage mass is deployed as FRSA. Distinguishing
among these pools has important implications for plant
function. Under low nutrient availability, partitioning
mass to build absorptive surface area locks in one major
strategy to acquire nutrients necessary for seedling
growth and survival. In contrast, partitioning mass to
TNC provides flexibility to respond to temporal
variation in the local environment; stored TNC could
continue to be stored or be mobilized to support
mycorrhizae, prime microbial decomposition, or rapidly
build fine roots and support metabolic costs of nutrient
uptake when soil resources become more available.
Storing photosynthate for future deployment when
environmental conditions were favorable, rather than
immediately constructing fine roots, also would minimize respiratory costs of maintaining living tissue (e.g.,
Kobe 1997). Furthermore, evaluating OPT by biomass
partitioning alone could be misleading since the location
of TNC storage is not necessarily the location for the
ultimate deployment of TNC. Storage of TNC also
could buffer temporary resource shortages (e.g.,
drought) and loss of tissue to herbivory or physical
damage. Through this flexibility and buffering, mass
partitioning to TNC may enable a more optimal match
between seedling performance (i.e., survivorship and
growth) and temporal variation in both the biotic (e.g.,
mycorrhizae, herbivores) and abiotic environments.
ACKNOWLEDGMENTS
We thank Wayne Loescher, Zhifang Gao, and Mercy
Olmstead (TNC analysis), Paul Bloese and Randy Klevickas
(MSU Tree Research Center), and Jeff Eickwort, Jason Vokits,
and Brent Troost (greenhouse and laboratory) for help. The
manuscript benefited from critiques by Tom Baribault, Sarah
McCarthy-Neumann, and Steve Hart’s root discussion group at
Northern Arizona University (NAU). R. K. Kobe thanks NAU
(Biology and Forestry) and The Arboretum at Flagstaff for
hosting sabbatical. McIntire-Stennis funded this project. R. K.
Kobe and M. B. Walters were supported by NSF (DEB
0075472 and IBN 0111037) while contributing to this research.
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APPENDIX A
Parameter estimates of biomass partitioning to total root, root total nonstructural carbohydrates (TNC), root non-storage, leaf,
and stem mass components (Ecological Archives E091-015-A1).
APPENDIX B
Graphs of leaf mass as a function of whole-plant mass by species and resource treatment (Ecological Archives E091-015-A2).
APPENDIX C
Parameter estimates of biomass partitioning for best-supported models that were nonlinear (Ecological Archives E091-015-A3).
APPENDIX D
Graphs of stem vs. root total nonstructural carbohydrates (TNC) concentration (Ecological Archives E091-015-A4).
APPENDIX E
Graphs of soluble sugar and starch concentrations (Ecological Archives E091-015-A5).
APPENDIX F
Parameter estimates for models relating fine-root surface area to non-storage root mass (Ecological Archives E091-015-A6).