optimal partitioning theory revisited: nonstructural ...kobe/docs/kobe iyer walters 2010... · 2010...

Ecology, 91(1), 2010, pp. 166–179� 2010 by the Ecological Society of America

Optimal partitioning theory revisited: Nonstructural carbohydratesdominate 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 biomassto acquire the resource that most limits growth. Within this framework, higher root massunder low nutrients is often assumed to reflect an allocation response to build more absorptivesurface. However, higher root mass also could result from increased storage of totalnonstructural carbohydrates (TNC) without an increase in non-storage mass or root surfacearea. To test the relative contributions of TNC and non-storage mass as components of rootmass 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 afactorial light 3 nitrogen (N) greenhouse experiment. Because root mass is a coarse metric ofabsorptive 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 wasgreater in low vs. high N. However, changes in root mass were influenced by TNC mass in allseven species and were especially strong in the three oak species. In contrast, non-storage masscontributed to increased total root mass under low N in three of the seven species. Rootmorphology 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 resourceswere consistent with OPT, our results challenge the implicit assumption that increases in rootmass under low nutrient levels primarily reflect allocation shifts to build more root surfacearea. Rather, root responses to low N included increases in: TNC, non-storage mass and fine-root surface area, with increases in TNC being the largest and most consistent of theseresponses. The greatest TNC accumulation occurred when C was abundant relative to N.Total nonstructural carbohydrates storage could provide seedlings a carbon buffer whenrespiratory or growth demands are not synchronized with photosynthesis, flexibility inresponding to uncertain and fluctuating abiotic and biotic conditions, and increased access tosoil 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; optimalpartitioning 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

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 fine-

root 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 relation-

ships 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-

Manuscript received 7 January 2009; revised 16 April 2009;accepted 27 April 2009. Corresponding Editor: L. A. Donovan.

1 E-mail: [email protected]

166

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 carbo-

hydrates 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.

Although this study focuses on within-species varia-

tion 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 parti-

tioning 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 resource-

specific 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. Re-

source-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 non-

storage 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 polyethyl-

ene-coated 10.2 3 10.2 3 27 cm3 cardboard containers

January 2010 167OPTIMAL PARTITIONING THEORY REVISITED

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 KCl-

extractable 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

TABLE 1. Soil fertility association, shade tolerance, and harvest schedule for the species used in this study.

SpeciesCommonname Abbreviation

Shade tolerancecategory

Soil fertilityaffinity

Age at harvest (d)

1 2 3 4

Acer rubrum red maple RM tolerant intermediate–high �� 34 75 105Acer saccharum sugar maple SM very tolerant high �� 37 86 ��Quercus alba white oak WO intolerant low–intermediate 10 37 76–77 106Quercus velutina black oak BO intolerant low �� 84 105Quercus rubra red oak RO intermediate low–high 14 40 85 106Prunus serotina black cherry BC intolerant intermediate–high 25 53 86 106Fagus grandifolia American beech AB very tolerant intermediate–high 29 52 84–85 105Betula papyrifera paper birch PB intolerant intermediate–high �� 122

Notes: Soil fertility affinities are based on species relative abundance as a canopy species by habitat type according to aclassification system based on understory vegetation (Burger and Kotar 2003). Shade tolerance categories are based on Burns andHonkala (1990).

RICHARD K. KOBE ET AL.168 Ecology, Vol. 91, No. 1

models included: (1) simple linear with zero intercept (y

¼ax); (2) exponential (y¼ exp(a3x) – 1); (3) power (y¼a 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 correc-

tion 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 mod-

els, 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 treat-

ment 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).

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 dispropor-

tionately 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


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).

FIG. 1. Mass proportions based on linear model slope estimates for root total nonstructural carbohydrates (TNC), root non-storage, stems, and leaves for seven tree species under differing light and nutrient regimes. Note that for red and sugar maple underlow 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, highlight, low nitrogen; LLHN, low light, high nitrogen; LLLN, low light, low nitrogen.


FIG. 2. Root mass as a function of whole-plant mass (WPM; note different scales) for the different light and nutrienttreatments. Best-fit equations are graphed; see Appendices A and C, respectively, for linear and nonlinear model parameterestimates and statistics. In some cases, only one line is visible because treatment slope estimates were very similar. For clarity, insetsare shown in cases in which it was difficult to distinguish low WPM data points (generally low-light seedlings). See Fig. 1 for anexplanation of treatment abbreviations.


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 andstatistics. For clarity, insets are shown in cases in which it was difficult to distinguish low WPM data points (generally low-lightseedlings). See Fig. 1 for an explanation of treatment abbreviations.


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, non-

storage mass followed a single allometric trajectory for

combined treatments (non-storage¼ 0.39WPM1.18, r2¼0.98; Fig. 4).

Based on slope estimates of the linear models, non-

storage 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 non-

storage 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 indicat-

ed 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,

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 (Da-

kora 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 hy-

pothesis (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


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–

FIG. 4. Non-storage root mass as a function of whole-plant mass (WPM; note different scales). Note that red maple and sugarmaple have data for high light only. Best-fit equations are graphed; see Fig. 1 and Appendix C, respectively, for linear andnonlinear model parameter estimates and statistics. For clarity, insets are shown in cases in which it was difficult to distinguish lowWPM 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

FIG. 5. Fine-root (,1 mm diameter) surface area as a function of non-storage root mass. Red maple and sugar maple have datafor high light only, and results were not significant for black cherry under low light. Best-fit equations are graphed; see Appendix Ffor parameter estimates and statistics. For clarity, insets are shown in cases in which it was difficult to distinguish low root masspoints (generally low-light seedlings). See Fig. 1 for an explanation of treatment abbreviations.


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 perspec-

tive, 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 ambig-

uous 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 parti-

tioning 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

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 (range¼�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 under-

estimated 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 parti-

tioning 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 treat-

ments 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 whole-

plant 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


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 partition-

ing 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 mini-

mize 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 MercyOlmstead (TNC analysis), Paul Bloese and Randy Klevickas(MSU Tree Research Center), and Jeff Eickwort, Jason Vokits,and Brent Troost (greenhouse and laboratory) for help. Themanuscript benefited from critiques by Tom Baribault, SarahMcCarthy-Neumann, and Steve Hart’s root discussion group atNorthern Arizona University (NAU). R. K. Kobe thanks NAU(Biology and Forestry) and The Arboretum at Flagstaff forhosting sabbatical. McIntire-Stennis funded this project. R. K.Kobe and M. B. Walters were supported by NSF (DEB0075472 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).


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