testing the growth–differentiation balance hypothesis

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Research

Blackwell Publishing Ltd

Testing the growth–differentiation balance hypothesis:

dynamic responses of willows to nutrient availability

Carolyn Glynn

1

, Daniel A. Herms

2

, Colin M. Orians

3

, Robert C. Hansen

4

and Stig Larsson

1

1

Department of Ecology, Swedish University of Agricultural Sciences, Box 7044, SE-750 07 Uppsala, Sweden;

2

Department of Entomology and

4

Department

of Food, Agricultural, and Biological Engineering, The Ohio State University/Ohio Agricultural Research and Development Center, 1680 Madison Ave.,

Wooster, OH 44691, USA;

3

Department of Biology, Tufts University, Medford, MA 02155, USA

Summary

• Here, the growth–differentiation balance hypothesis (GDBH) was tested byquantifying temporal variation in the relative growth rate (RGR), net assimilationrate (NAR), and phenylpropanoid concentrations of two willow species (

Salix sericea

and

Salix eriocephala

) across five fertility levels.• Initially, RGR increased and total phenylpropanoids declined (although everyindividual phenolic did not) as fertility increased, but NAR was unaffected.Subsequently, NAR and phenylpropanoids declined in the low fertility treatment,generating a quadratic response of secondary metabolism across the nutrient gradient.As above- and below-ground growth rates equilibrated, NAR and phenylpropanoidsincreased in the low fertility treatment, re-establishing a negative linear effect offertility on secondary metabolism.• A transient quadratic response of secondary metabolism is predicted when GDBHis integrated with models of optimal phenotypic plasticity, occurring when low NARimposes carbon constraints on secondary metabolism in low nutrient environments.Once plants acclimate to nutrient limitation, the equilibrium allocation state ispredicted to be a negative correlation between growth and secondary metabolism.• Although both willow species generally responded according to GDBH, the com-plexity observed suggests that prediction of the effects of nutrient availabilityon secondary metabolism (and other plastic responses) in specific cases requires

apriori

knowledge of the physiological status of the plant and soil nutrient availability.

Key words:

condensed tannins, growth–differentiation balance hypothesis,optimality theory, phenolic glycosides, phenotypic plasticity, secondary metabolism,

Salix eriocephala

,

Salix sericea

.

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© The Authors (2007). Journal compilation ©

New Phytologist

(2007)

doi

: 10.1111/j.1469-8137.2007.02203.x

Author for correspondence:

Daniel A. HermsTel:

+

1 330 2023506Fax:

+

1 330 2633686Email: [email protected]

Received:

8 April 2007

Accepted:

28 June 2007

Introduction

Phenotypic plasticity can facilitate acclimation of organismsto environments that vary in space and time (Agrawal, 2001).Most studies addressing effects of nutrient availability onconstitutive secondary metabolism in plants have revealedsome plasticity (Kytö

et al

., 1996; Koricheva

et al

., 1998). Theadequacy of theories addressing plasticity in secondary metabolismhas been questioned (Berenbaum, 1995; Hamilton

et al

., 2001).In a recent assessment, Stamp (2003) characterized the growth–

differentiation balance hypothesis (GDBH) (Loomis, 1932;Lorio, 1986) as extended by Herms & Mattson (1992) as the mostmature, but concluded that it has yet to be tested adequately.

GDBH states that there is a physiological trade-off betweengrowth and secondary metabolism imposed by developmentalconstraints in growing cells, and competition betweenprimary and secondary metabolic pathways in mature cells.GDBH integrates this trade-off with responses of net assimi-lation rate (NAR) and relative growth rate (RGR) to resourceavailability to predict that nutrient (or water) availability will

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have a parabolic effect on secondary metabolite concentration(Fig. 1; Herms & Mattson, 1992). NAR reflects the balancebetween carbon gain (via photosynthesis) and losses (viarespiration, exudation, volatilization, and leaching) per unit leafarea per unit time, and thus integrates environmental effects onnet carbon acquisition at the whole-plant level over the specifiedgrowing period. Mathematically, RGR is the product of NARand leaf area ratio (LAR), which is the ratio of total leaf area tototal plant mass (McDonald, 1990; Lambers & Poorter, 1992).

The quadratic response of secondary metabolism predictedby GDBH to occur across a fertility gradient is based on threemain physiological assumptions (Herms & Mattson, 1992):differential investment of photosynthate into new leaf area isthe major determinant of phenotypic variation in RGR (Potter& Jones, 1977; Körner, 1991); NAR (and photosynthesis) is lesssensitive to nutrient availability than is RGR, being reducedonly when nutrient deficiency is severe (McDonald, 1990; Lux-moore, 1991); and secondary metabolism diverts resourcesfrom production of new tissue, and vice versa (Chapin, 1989).

Growing meristems, which are strong photosynthetic sinks,are provisioned by carbon sources that include neighboring,mature leaves (Marcelis, 1996). If increased nutrient availabilityhas little effect on the net carbon gain of source leaves (e.g.Luxmoore, 1991; Ericsson, 1995), it will not increase theircarbon budget, and production of new leaves can be supportedonly if source leaves export a greater proportion of photosyn-thate to growing meristems, retaining less to support otherprocesses, including secondary metabolism (Körner, 1991).Thus, rapidly growing plants are predicted by GDBH to havelow secondary metabolite concentrations (Fig. 1). Moderatenutrient deficiency imposes limitations on the growth of sinktissues with little effect on NAR (sink limitation,

sensu

Patrick,1988), causing carbohydrates that otherwise would be exportedto growing meristems to accumulate in source leaves (Geiger

et al

., 1996), where they may support increased secondary

metabolism (Fig. 1; Waterman & Mole, 1989). Resourcelimitation severe enough to limit NAR is predicted to decreaseboth growth and secondary metabolism (Herms & Mattson,1992) because all functions are carbon limited (sourcelimitation,

sensu

Patrick, 1988) (Fig. 1).These responses can have a temporal component. For

example, as plants acclimate to nutrient limitation, initialconstraints on photosynthesis can relax, which can increaseNAR (Ingestad, 1982; Ingestad & Ågren, 1992), and possiblysecondary metabolism, which may generate a negative ratherthan a quadratic relationship between nutrient availabilityand secondary metabolite production.

Although many studies have used GDBH as a frameworkfor examining environmental effects on secondary metabo-lism, Stamp (2003, 2004) concluded that most experimentalprotocols were not aligned closely enough with assumptionsand predictions of GDBH to provide informative tests. Fewstudies employed the three or more fertility levels necessary todocument a parabolic response, and none of these measuredboth RGR and NAR (or their surrogates or components, suchas plant size and photosynthesis rate), the interrelationships ofwhich are assumed by GDBH to be key determinants ofsecondary metabolite concentrations.

Our objective was to test GDBH by quantifying the RGR,NAR, and secondary metabolite production of the NorthAmerican shrub willows

Salix eriocephala

and

Salix sericea

across a gradient of five fertility levels ranging from very lowto optimal. Phenylpropanoids are the major pool of secondarycompounds in both species, with foliar concentrationsexceeding 20% of dry leaf mass (Orians

et al

., 2000):

Salixeriocephala

produces condensed tannins but no phenolicglycosides, while

S. sericea

is rich in phenolic glycosides withlower concentrations of condensed tannins (Nichols-Orians

et al

., 1993; Orians & Fritz, 1995). Variation in the phenyl-propanoid concentration has been shown to mediate interac-tions between willows and herbivores (Rowell-Rahier, 1984;Tahvanainen

et al

., 1985; Fritz

et al

., 2001).

Methods and Materials

Experimental plant material

On 4 May 2001, approx. 200 stem cuttings were taken fromeight individuals of each species (one

Salix sericea

Marshalland one

Salix eriocephala

Michx. full-sibling family). Stemswere cut into 10-cm sections, dipped in 1% indole-3-butyricacid (IBA) and 0.5% 1-napthalene acetic acid (NAA; Dip ’NGrow Inducing Concentrate

®

, Clackamas, OR, USA), and placedin a mist bed for 20 d. On 24 May, rooted cuttings weretransferred to small (11.5 cm top diameter

×

12 cm deep)plastic pots containing commercial potting medium (Pro-Mix‘GSX’

®

, Premier Horticulture, Inc., Quakertown, PA, USA),and maintained in a glasshouse for

c

. 3 wk. On 18 June, plantswere transplanted to larger plastic pots (27 cm top diameter

×

Fig. 1 Responses of relative growth rate, net assimilation rate, and constitutive secondary metabolism across a gradient of nutrient availability as predicted by the growth–differentiation balance hypothesis. In source-limited plants a positive correlation is predicted between growth and secondary metabolism, while in sink-limited plants the correlation is predicted to be negative (modified from fig. 1 of Herms & Mattson, 1992).


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24 cm deep) containing the same growing medium, and placedoutdoors under shade cloth (25%) to acclimate for 7 d. Plantswere not fertilized before the initiation of the experiment.

Experimental design and treatments

The experiment was designed as a randomized completeblock, with five fertility levels. For each species, each fertilitylevel (assigned randomly) was replicated three times withineach block. On 24 June, the 152 plants of each species withthe most uniform growth were divided into eight blocks (19plants per species per block) based on stem diameter, with thelargest plants assigned to one block, the next largest to another,and so on. From each block, four plants of each species werethen randomly harvested to determine initial leaf area, andabove- and below-ground biomass (as described in ‘Destructiveplant harvests’). The remaining 120 plants of each species wererelocated to a nursery, where they were arranged in eight blocks(15 plants per species per block) on a gravel bed exposed to ambientweather conditions. Each block consisted of five rows of six plants,with 1.0 m between pots in a row and 1.5 m between rows,which was sufficient to prevent plants from shading each other.

The nutrient treatment was initiated on 25 June, whenfive fertility levels were applied via a computer-controlledfertigation (combined fertilization and irrigation) system: 0,25, 50, 150 and 200 ppm nitrogen (N), with N:P

2

O:K

2

Osupplied in a ratio of 3 : 1 : 2. Sources of nutrients werecalcium nitrate, potassium nitrate, and mono-potassiumphosphate. The irrigation water contained sulfur, magnesium,and sodium at 30, 17, and 47 ppm, respectively. Mineralizationof the composted softwood bark in the potting medium (30–35% by volume) also provided essential macro- and micro-nutrients, and, with the irrigation water, was the only source ofnutrients for plants in the 0 ppm N treatment. No plantsexhibited foliar necrosis, chlorosis, discoloration, or otherpathological symptoms of nutrient deficiency.

Precise fertility levels were maintained automatically byapplying nutrients with each irrigation event (1000 ml perevent) that were scheduled to maintain optimal moisturelevels (between 75 and 100% container holding capacity)(White & Mastalerz, 1966). Fertigation events were triggeredwhen container moisture levels reached 75% of capacity asestimated by an evapotranspiration model calculated fromweather data collected on site (

Q

-

COM

GEM

3 software system,Q-COM Corperation, Irvine, CA, USA) (Lee

et al

., 2000).Targeted moisture levels were maintained as plants grew, andpots therefore dried more quickly, by increasing the leaf areaterm of the model. The leaf area term was increased morequickly in the higher fertility treatments to increase the fre-quency of irrigation events in direct proportion to plant growthrate, thereby maintaining nearly constant levels of nutrientavailability in each treatment. To confirm that targeted water levelswere maintained through the experiment, a computer-monitoredtensiometer was inserted to a depth of 8 cm in one pot per

treatment (selected randomly) in each block. Nutrient solu-tions were delivered from tanks via five output lines connectedto the fertigator, and dispensed via an emitter in each pot. Solu-ble salt concentrations in container leachate were monitoredthroughout the study, and did not reach excessive values.

Destructive plant harvests

To quantify plant biomass and leaf area at the beginning of theexperiment, four plants of each species from each block wereharvested on the day before initiation of fertility treatments.To document ontogenetic variation in plant response to thenutrient gradient, plants were destructively harvested 23, 40,and 85 d after initiation of the fertility treatments (on 17 July,3 August, and 17 September, respectively). One of the threereplicate plants per block (selected randomly) was harvestedon each sampling date, for a total of eight plants per fertilitytreatment per harvest for each species (80 plants total for eachharvest). Thus, a total of 304 plants were destructively harvested(240 treatment plants, and 64 pretreatment plants).

RGR, NAR, and biomass allocation

Immediately before each destructive harvest, leaf mass perunit area (LMA; g m

–2

) was estimated from a representativesample of 12 fully expanded leaves from each plant. The areaof individual leaves was measured using a computerized imageanalyzer (Ci-400 Computer Image Analysis System and software;CID Inc., Vancouver, WA, USA), after which leaves were dried(60

°

C for 48 h) and weighed (to the nearest 0.01 mg). LMA wasthen calculated as the quotient of the mass and area of the leafsample. At harvest, plants were partitioned into leaf, stem, androot fractions. After the shoots had been severed, roots wereextracted by submerging the pot and carefully washing awaythe container medium. Each plant fraction was dried at 60

°

Cfor 48 h, and then weighed (nearest 0.1 mg). The mass of the12 representative leaves was added to the harvested leaf massto quantify the total leaf dry mass. Indices of plant growth andallocation were then calculated from dry mass and leaf areameasurements according to the following equations:

Total plant mass (g)

=

total leaf mass

+

total stem mass

+

total root mass

RGR (g g

–1

d

–1

)

=

[ln(final total mass) – ln(initial total mass)]/time

Total leaf area (m

2

)

=

total leaf mass/LMA

LAR (cm

2

g

–1

)

=

total leaf area/total plant mass

NAR (g m

–2

d

–1

)

=

RGR/LAR

SWR (g g

–1

)

=

stem mass/total plant mass

RWR (g g

–1

)

=

root mass/total plant mass

(SWR, stem weight ratio; RWR, root weight ratio.)

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The initial mass of each plant used to calculate growth,RGR, and NAR over each harvest interval was estimated asthe treatment mean for each block at the beginning of eachharvest interval.

Phytochemistry

Just before a plant was harvested, foliage of two age cohorts(referred to as immature and mature foliage, respectively) wassampled for analysis of secondary metabolites (condensedtannins in both species, and phenolic glycosides in

S. sericea

).Immature leaves were defined as the youngest, fully expandedleaves (i.e. still lighter in color than older leaves). Their plasticronposition varied from 5 to 7 (with the youngest apical leaf atleast 2 cm long designated as leaf 1) depending on the fertilitytreatment. Mature leaves were sampled from positions 14 to 16.

On each harvest date, a representative sample of 12 leavesof each age cohort was collected from several shoots through-out the canopy. Leaves were removed from the stem with thepetiole intact, placed in paper envelopes and immediatelyflash-frozen in liquid nitrogen, placed under dry ice in acooler, and transported to a freezer within 30 min ofsampling. Samples were stored at –80

°

C until they were driedwithout thawing at –4

°

C (Orians, 1995) in a tray lyophylizer,after which they were weighed (to 0.01 mg). The mass ofthese samples was then added to the total leaf mass for eachplant. Dried leaf samples were stored in airtight desiccators at–18

°

C before and after they were milled (40 mesh) until theywere analyzed for phenylpropanoid concentration.

Condensed tannins were analyzed using standard techniques(Hagerman & Butler, 1989; Orians, 1995; see Albrectsen

et al

., 2004 for details). Concentrations of foliar condensedtannins (mg g

–1

dry weight) were calculated from purifiedwillow tannin standards (0.2–2.0 mg ml

–1

) prepared in asimilar manner. Foliar phenolic glycoside concentrations of

S. sericea

were also analyzed using standard high performancethin layer chromatography (HPTLC) techniques (Lindroth& Koss, 1996; see Albrectsen

et al

., 2007 for details). Totalfoliar phenylpropanoid concentrations of

S. sericea

are defined asthe sum of the concentrations of the two individual phenolicglycosides and condensed tannins.

Statistical analyses

The effects of nutrient level on plant responses were assessedfor each harvest date by analysis of variance (

PROC

GLM

, TypeIII sums of squares; SAS Institute, 1999), with data reportedas least square means

±

1 standard error (SE). Because thefertility treatment is a quantitative factor, relationships amongmeans were examined using orthogonal contrasts to test forlinear, quadratic (means of intermediate fertility levels aregreater or less than means of lowest and highest levels), orasymptotic (mean of highest or lowest fertility level is greateror less than means for the other four treatments, which do not

differ) treatment effects (Chew, 1976). Coefficients for theunequally spaced treatment levels were calculated accordingto Robson (1959). Tests for normality of residuals andhomogeneity of variances revealed that data transformationswere not required.

Results

RGR, NAR, and biomass allocation

Nutrient availability had a positive linear effect on the totalplant mass and total leaf area of both species on all threeharvest dates (Table 1, Fig. 2). The RGR of both species alsoincreased with increasing nutrient availability during the firsttwo harvest intervals, with the most dramatic effect observedbetween days 23 and 40 (Fig. 3). However, nutrient availabilityhad no effect on the RGR of either species between days 40and 85, although total plant mass and leaf area continued toincrease in all treatments (Fig. 2).

The effects of nutrient availability on NAR were alsodynamic, with the two species responding in a similar mannerover time (Fig. 3). Nutrient availability had no effect on theNAR of either species through day 23, but did have significantasymptotic effects on the NAR of both species during thesecond harvest interval. Between days 23 and 40, NAR wassignificantly lower in the 0 ppm N treatment relative to thefour higher fertility treatments, which did not differ. Thispattern changed between days 40 and 85, when the NAR ofboth species increased substantially in the 0 ppm N treatment.Over this time period, the NAR of

S. eriocephala

was actuallygreater in the 0 ppm N treatment than in the four other treat-ments, which did not differ, while in

S. sericea

nutrient avail-ability had a negative linear effect on NAR, which declinedgradually as nutrient availability increased.

The two species displayed striking correspondence in theirallocation responses to nutrient availability over the 85-dgrowing period (Table 1, Fig. 4). Nutrient availability had apositive linear effect on the LAR of both species on all harvestdates, with treatment effects being strongest in the early stagesof the experiment. Relative to their initial state, the LAR ofboth species was much lower in the two lowest fertilitytreatments at day 23, while the LAR of

S. eriocephala

remainedconstant in the three highest fertility treatments. LAR in

S. sericea

also remained constant at the intermediate nutrientlevel (50 ppm N), but increased in the two highest treatments(150 and 200 ppm N) (Fig. 4). The LAR of both speciespeaked at day 40 and was lowest at day 85 in all treatments (aswas variation among the fertility levels). LMA was not affectedby nutrient availability in either willow species (Table 1).

Effects of nutrient availability on below-ground growthwere generally opposite to those observed on LAR (Table 1,Fig. 4). Linear contrasts for effects of nutrient availability werehighly significant for all three harvests, with RWR decreasingas nutrient availability increased. Temporal patterns of

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Table 1

F

-values for orthogonal contrasts from analysis of variance (ANOVA) for tests of linear, quadratic, and asymptotic effects of five nutrient levels on growth and biomass allocation of

Salix eriocephala

and

Salix sericea

at 23, 40, and 85 d following initiation of treatments

Plant response variable

Day 23 Day 40 Day 85

Lineareffect

Quadraticeffect

Asymptoticeffect

Lineareffect

Quadraticeffect

Asymptoticeffect

Lineareffect

Quadraticeffect

Asymptoticeffect

Salix eriocephalaTotal plant biomass (g) 10.0** 0.5 5.5* 105.4**** 4.5* 35.9**** 343.8**** 25.1**** 149.0****Total leaf area (m2) 47.4**** 3.5 20.7**** 140.1**** 2.5 82.9**** 313.3**** 25.7**** 134.4****Leaf area ratio (LAR; m2 g–1) 63.0**** 3.0 29.0**** 56.1**** 1.9 28.5**** 18.1*** 0.0 15.6***Leaf mass per unit area (LMA; g m–2) 2.4 1.4 0.5 1.8 0.1 1.6 0.0 2.0 3.2Stem weight ratio (SWR; g g–1) 8.2** 0.1 6.4* 1.7 0.4 3.1 25.0**** 0.0 21.9****Root weight ratio (RWR; g g–1) 23.0**** 3.1 6.3* 54.3**** 1.4 29.0**** 53.3**** 1.0 34.5****

Salix sericeaTotal plant biomass (g) 9.0** 0.2 5.3* 159.5**** 3.7 92.8**** 97.5**** 0.8 65.4****Total leaf area (m2) 30.5**** 1.5 14.5*** 108.8**** 3.2 60.6**** 96.9**** 3.2 53.9****Leaf area ratio (LAR; m2 g–1) 31.8**** 2.5 12.8** 15.3*** 0.5 7.8** 15.5*** 2.8 5.3*Leaf mass per unit area (LMA; g m–2) 0.0 3.5 3.4 0.0 0.6 0.3 3.1 0.4 1.1Stem weight ratio (SWR; g g–1) 9.6** 0.0 9.6** 20.6*** 0.1 16.4*** 32.9**** 0.7 19.9***Root weight ratio (RWR; g g–1) 91.3**** 1.0 58.0**** 137.0**** 5.1* 71.8**** 344.3**** 31.2**** 150.1****

*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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variation in RWR in response to nutrient availability werecomplex, but strikingly similar in the two species. Relative totheir initial state, the RWR of both species increased in thetwo lowest fertility treatments at day 23, and then decreasedsharply at day 40. Conversely, the RWR of plants receiving thethree highest nutrient regimes steadily decreased until day 40.The RWR of both species stabilized between days 40 and 85,remaining nearly constant for the duration of the experiment,although at a higher level in the low than in the high fertilitytreatment. On the final harvest date, plants in the lowestfertility treatment had allocated 40% of their biomass belowground, compared with 25% for plants in the high fertilitytreatments.

Phytochemistry

The effects of nutrient availability on foliar secondary metaboliteconcentrations were also similar in the two species (Table 2,Fig. 5). On day 23, condensed tannin concentrations ofS. eriocephala declined in both immature and mature foliage

as nutrient availability increased. The same pattern wasobserved for total phenylpropanoid concentrations in matureand immature foliage of S. sericea. This pattern changed onday 40, as a positive quadratic response of condensed tanninconcentrations to nutrient availability was observed in bothimmature and mature foliage of S. eriocephala, and a positivequadratic response of total phenylpropanoid concentrationswas also observed in immature foliage of S. sericea. Nutrientavailability continued to have a negative linear effect on totalphenylpropanoid concentrations in mature foliage of S. sericea.The quadratic effects observed on day 40 reverted on day 85to negative linear effects of nutrient availability on condensedtannin concentrations in mature foliage of S. eriocephala, aswell as total phenylpropanoid concentrations in immatureand mature foliage of S. sericea. There remained a marginallysignificant positive quadratic effect of nutrient availability(P = 0.082) on tannin concentration in immature foliage ofS. eriocephala on day 85.

Effects of nutrient availability on individual phenoliccompounds of S. sericea did not always correspond with

Fig. 2 Total plant biomass and leaf area of Salix eriocephala and Salix sericea in response to five levels of nutrient availability at 23, 40, and 85 d following initiation of treatments. Insets provide enhanced resolution of plant responses on days 23 and 40. See Table 1 for probability of significance of treatment effects. Data are least square means ± 1 standard error. N, nitrogen.

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Fig. 3 Relative growth rate (RGR) and net assimilation rate (NAR) of Salix eriocephala and Salix sericea in response to five levels of nutrient availability over three harvest intervals (days 1–23, 23–40, and 40–85, respectively) following initiation of treatments. Data are least square means ± 1 standard error. N, nitrogen; NS, not significant.

Fig. 4 Leaf area ratio and root weight ratio of Salix eriocephala and Salix sericea in response to five levels of nutrient availability at 23, 40, and 85 d following initiation of treatments. See Table 1 for probability of significance of treatment effects. Data are least square means ± 1 standard error. N, nitrogen.

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Table 2 Effects of five nutrient levels on concentrations of individual phenylpropanoid compounds in immature and mature foliage of Salix sericea harvested 23, 40, and 85 d after initiation of treatments, with probability of significance of orthogonal contrasts from analysis of variance (ANOVA) for linear, quadratic, and asymptotic treatment effects

CompoundFoliage age class

Concentration (mg g–1)

P-valueFertility treatment (ppm N)

0 25 50 150 200 Linear effect Quadratic effect Asymptotic effect

Day 23Salicortin Immature 196.0 ± 2.8 191.2 ± 3.4 179.0 ± 3.6 165.6 ± 3.4 162.1 ± 10.3 0.0003 0.2575 0.0082

Mature 155.6 ± 5.6 152.6 ± 6.8 159.4 ± 5.6 144.1 ± 5.8 151.3 ± 5.4 0.2005 0.3447 0.52632′-Cinnamoylsalicortin Immature 37.7 ± 1.2 37.9 ± 1.5 34.1 ± 2.5 30.8 ± 2.0 28.0 ± 2.1 < 0.0001 0.0325 0.0066

Mature 23.9 ± 1.4 21.5 ± 2.0 21.5 ± 1.9 16.5 ± 1.4 15.5 ± 1.4 < 0.0001 0.1328 0.0021Condensed tannin Immature 86.9 ± 4.8 79.8 ± 4.3 63.3 ± 6.4 51.3 ± 2.8 50.3 ± 3.0 < 0.0001 0.2826 < 0.0001

Mature 87.6 ± 5.1 80.6 ± 7.4 58.2 ± 4.0 48.7 ± 1.4 66.0 ± 14.8 0.0190 0.5184 0.0137Day 40Salicortin Immature 171.2 ± 5.1 191.9 ± 11.0 189.0 ± 11.7 176.7 ± 10.3 169.0 ± 10.4 0.4209 0.0379 0.1975

Mature 143.0 ± 17.1 178.4 ± 7.5 165.4 ± 7.6 159.3 ± 12.7 162.8 ± 4.6 0.7735 0.1103 0.06222′-Cinnamoylsalicortin Immature 33.2 ± 1.9 32.1 ± 4.0 34.8 ± 3.2 35.1 ± 2.3 37.7 ± 1.1 0.0463 0.3141 0.2986

Mature 22.4 ± 3.1 24.8 ± 2.6 22.9 ± 2.2 18.8 ± 1.8 18.5 ± 1.9 0.0343 0.0978 0.0653Condensed tannin Immature 49.3 ± 4.8 59.1 ± 8.2 64.8 ± 5.9 44.9 ± 5.5 44.3 ± 3.3 0.0042 0.0001 0.1739

Mature 94.5 ± 4.8 67.2 ± 5.3 59.7 ± 4.8 39.5 ± 9.0 42.6 ± 4.1 0.0002 0.7588 0.0002Day 85Salicortin Immature 167.9 ± 15.6 181.5 ± 20.5 141.2 ± 16.5 136.2 ± 20.2 136.1 ± 21.5 0.1190 0.3987 0.5208

Mature 155.6 ± 7.3 156.3 ± 4.0 136.0 ± 10.8 138.0 ± 7.6 123.5 ± 10.3 0.0234 0.3079 0.16252′-Cinnamoylsalicortin Immature 36.6 ± 2.8 38.5 ± 1.5 36.1 ± 2.0 36.3 ± 1.6 35.8 ± 2.3 0.3578 0.6639 0.6957

Mature 30.3 ± 2.4 30.0 ± 1.6 31.0 ± 1.5 25.0 ± 2.6 24.5 ± 1.7 0.0005 0.1029 0.0236Condensed tannin Immature 99.7 ± 13.9 83.7 ± 6.7 73.2 ± 7.0 78.5 ± 6.2 72.3 ± 9.0 0.2068 0.4421 0.0623

Mature 83.3 ± 8.1 81.2 ± 2.9 77.0 ± 8.6 68.1 ± 5.2 61.8 ± 7.1 0.0060 0.2082 0.1672

Data are expressed as least square means ± 1 standard error.N, nitrogen.


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fertility effects on total phenylpropanoid concentrations(Table 2). On days 23 and 40, nutrient availability had noeffect on salicortin concentration in mature foliage. Rather,the negative linear effects on total phenylpropanoid concen-trations observed on these dates were a result of effects on2′-cinnamoylsalicortin and condensed tannins. However,nutrient availability did have a negative linear effect on salicortinconcentration in immature foliage on day 23, which didcorrespond with effects on total phenylpropanoid concentration.The quadratic effect of nutrient availability on total phe-nylpropanoid concentration in immature foliage observed onday 40 was a result of the response of salicortin and condensedtannins, which counteracted a positive effect of nutrientavailability on 2′-cinnamoylsalicortin. On day 85, the effectof nutrient availability was not significant for any of the threeindividual compounds in immature foliage, despite theoverall significant negative linear effect on total phenylpro-panoid concentrations. However, in mature foliage, nutrientavailability had significant negative linear effects on all threecompounds.

Discussion

Effects of nutrient availability on total phenylpropanoid levelsof both willows largely conformed to predictions of GDBH,which states that secondary metabolite concentrations arecontingent on the relationship between NAR and RGR. Inour study, this relationship varied across fertility treatments, aswell as over time within a treatment, as did above- and below-ground allocation patterns (LAR and RWR). Although complex,these patterns were highly consistent across the two species,which suggests programmed responses to nutrient availabilityrather than random variation.

The negative linear response of total phenylpropanoidconcentrations to the fertility gradient observed on day 23 inimmature and mature foliage of both species is predicted byGDBH when RGR increases with no effect on NAR (Fig. 1;sink-limited range of abscissa). It is possible that the highNAR observed in the low nutrient treatments was facilitatedby nutrient reserves acquired before the start of the experiment(Ingestad, 1982). By day 40, however, the NAR of both species

Fig. 5 Phenylpropanoid concentrations of immature and mature foliage of Salix eriocephala (condensed tannins) and Salix sericea (summed concentrations of salicortin, 2′-cinamoylsalicortin, and condensed tannins) in response to five levels of nutrient availability at 23, 40, and 85 d following initiation of treatments. See Table 2 for probability of significance of treatment effects. Data are least square means ± 1 standard error. N, nitrogen.


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had declined substantially in the 0 ppm N nutrient treatment,possibly as a result of low nutrient uptake coupled with dilutionof previously acquired nutrients (Ingestad, 1982). Totalphenylpropanoid concentrations were also lowest in thistreatment. Consequently, the linear effects of nutrient availabilityon secondary metabolism observed on day 23 transitioned toa positive quadratic response in immature and mature foliageof S. eriocephala, and in immature foliage of S. sericea, on day40. These quadratic responses conform to GDBH when bothRGR and NAR are source-limited at the low end of the fertilitygradient and increase as nutrient availability increases, as weobserved (Fig. 1; full range of abscissa).

Between days 40 and 85, the NAR of both species increasedsubstantially in the lowest nutrient treatment. In response toincreased NAR, GDBH predicts that secondary metaboliteconcentrations should also increase as carbon limitationsrelax. Accordingly, total phenylpropanoids increased in the0 ppm N treatment between days 40 and 85, and the quadraticresponse of secondary metabolite concentration to nutrientavailability reverted to a negative linear response in immaturefoliage of S. sericea and mature foliage of S. eriocephala (Fig. 1;sink-limited range of abscissa). Tannin concentrations ofimmature foliage of S. eriocephala also increased, but notsufficiently to generate a negative linear effect, and we continuedto observe a marginally significant quadratic effect of nutrientavailability. Although a quadratic response in mature foliageof S. sericea was never observed, it is possible that a transientparabolic response occurred either before or after day 40.

Integrating GDBH with models of optimal phenotypic plasticity

A parabolic response of secondary metabolism to resourceavailability is a cornerstone prediction of GDBH that has beentested only rarely (Stamp, 2004). Nutrient availability also hadquadratic effects on foliar terpene concentrations in camphorweed(Heterotheca subaxillaris) (Mihaliak & Lincoln, 1985) and onphenolics in tomato (Lycopersicon esculentum; Wilkens et al.,1996), while water availability had a quadratic effect onconstitutive terpene production in grand fir (Abies grandis)stems (Lewinsohn et al., 1993). However, the fleeting natureof the parabolic responses was not predicted by Herms &Mattson (1992). It is proposed that GDBH can be integratedwith models of optimal phenotypic plasticity to predict that theparabolic response represents a temporary state imposed bycarbon stress in extremely low nutrient environments.

Models of optimal phenotypic plasticity predict that plantswill respond to altered nutrient regimes to increase acquisitionof limiting resources, for example by increasing the root:shootratio in response to nutrient limitation (Bloom et al., 1985;Hirose, 1987; Ingestad & Ågren, 1991, 1992; Shipley &Meziane, 2002). Once a plant has acclimated to its respectivenutrient treatment, these models also predict that plantswill achieve an equilibrium allocation state characterized by

equivalent above- and below-ground growth rates (balancedgrowth), resulting in a stable root:shoot ratio. As plantsacclimate to low nutrient conditions and NAR and RGRincrease, GDBH predicts that secondary metabolism shouldalso increase as carbon limitations relax, and the parabolicresponse of secondary metabolism to nutrient availabilityshould transition to an equilibrium state characterized bya negative correlation between growth rate and secondarymetabolism. This acclimation process would be representedin Fig. 1 by a temporal shift along the abscissa to the right asthe plant transitions from a state of source limitation to oneof sink-limited growth (sensu Patrick, 1988).

The temporal variation observed in dry matter allocation(such as LAR and RWR), NAR, RGR, and secondarymetabolism was remarkably similar in the two willows andhighly consistent with these predictions. As nutrient availabilityincreased, LAR increased and RWR decreased, as predicted byoptimality models. Constant RWR between days 40 and 85suggests that plants had achieved an equilibrium state of bal-anced above- and below-ground growth (Ingestad, 1982;Anten et al., 1995). As allocation patterns equilibrated, NARincreased sharply in the low fertility treatments (possibly as aresult of acclimation responses that buffered effects of nutrientlimitation on photosynthesis). Total phenylpropanoidconcentrations also increased in the lowest nutrient treatment,and the quadratic response transitioned to a negative linear effect.

Effects of nutrient availability on RGR diminished overtime as plastic responses increased RGR in low nutrientenvironments, while allometric increases in stem relative tofoliage biomass (stem weight ratio; Table 1) in high fertilitytreatments may have increased respiratory losses (e.g. Ågren,1985; Poorter & Garnier, 1999). Despite having no effect onRGR over the last 45 d, nutrient availability continued tohave a positive linear effect on absolute growth rate, causingtreatment effects on total leaf area and biomass to widenover time.

An underlying premise of GDBH as extended by Herms &Mattson (1992) is that phenotypic plasticity in secondarymetabolism has been shaped by natural selection. Once plantsare acclimated to low fertility, high concentrations of secondarymetabolites are predicted to enhance resistance to biotic andabiotic stressors when compensatory growth is physiologicallyconstrained (e.g. Chapin, 1991; Chapin et al., 1993). Lowsecondary metabolism in resource-rich environments isconsidered a constraint imposed by high resource demands ofrapid growth selected for by competitors (Herms & Mattson,1992). There is strong evidence that phenotypic plasticity insecondary metabolism is under genetic control (Han &Lincoln, 1997), including that for some of the phenylpropa-noids examined in these willows (Orians et al., 2003). Moreover,variation in secondary metabolism in response to nutrientavailability has been shown to be regulated by gene expression(Bongue-Bartelsman & Phillips, 1995; Scheible et al., 2004),which also suggests a response to selection.


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It is important to note that not all individual phenylpropa-noids conformed to predictions of GDBH. In S. sericea, it wasfound that condensed tannin concentrations were generallymore responsive to nutrient availability than were phenolicglycosides (although salicortin and 2′-cinnamoylsalicortin didrespond in concert with tannins on some sampling dates),reinforcing the contention of Herms & Mattson (1992) thatpredictions of GDBH are more relevant to large pools ofcompounds (such as total concentrations of the phenylpropa-noids measured here) than to individual compounds. Divergentresponses of individual compounds have also been observed inother studies, and can result from internal metabolic trade-offswithin a pathway and/or differing selection pressures on indi-vidual compounds (Herms & Mattson, 1992; Berenbaum, 1995).

Experimental and ecological implications of complex temporal responses

The spatial and temporal variation that was observed in bothwillows has important experimental and ecological implications.Our results reinforce Stamp’s (2004) analysis that conclusionsregarding effects of nutrient availability on secondarymetabolism will depend on treatment levels employed, whenplants are sampled, and the rate at which, and degree towhich, plants acclimate to changes in fertility. For example,nutrient availability declines steadily following a singlefertilizer application, which can result in resource allocationpatterns that shift continuously as plants attempt to acclimateto a moving target (Ingestad & Ågren, 1991, 1992). Any singlemeasure of secondary metabolism may represent a snapshot ofa transient state that never equilibrates (Stamp, 2004).

An important implication of a quadratic response is thatsecondary metabolite concentrations can either increase ordecrease in response to increased resource availability, dependingon the initial physiological status of the plant and the magnitudeof the treatment (Herms & Mattson, 1992). Results of a recentmeta-analysis are consistent with the pattern proposed inFig. 1: correlations between growth and secondary metabo-lism tended to be positive in low nutrient environments andnegative in fertile environments (Koricheva, 2002).

We do not concur with Stamp’s (2004) argument that goodtests of GDBH must employ steady-state nutrient additionrates. Nonequilibrium conditions are ecologically relevantbecause nutrient availability can be highly dynamic undernatural conditions (Attiwill & Adams, 1993). We do, however,agree with her assessment that tests will not be highly inform-ative if they do not quantify variation in RGR and NAR (orhighly correlated indices such as growth and photosynthesis),as well as secondary metabolism (Stamp, 2004).

The dynamic responses that were observed contributeto an increasing body of evidence that temporal responses ofsecondary metabolism to environmental variation may beecologically important (e.g. Crone & Jones, 1999; Wallin &Raffa, 2001). Although our results are generally consistent

with GDBH, the complexity of the responses suggests thatpredicting effects of nutrient availability on secondarymetabolism (and other plastic responses) in specific casesrequires detailed a priori knowledge of the physiological stateof the plant and the nutrient status of the environment.

Acknowledgements

We thank Brian Brannigan, Bryant Chambers, Bob Fritz,Cathy Love, and Ben Marthey for technical assistance, andJessica Prenger for operation of the fertigation system. BobFritz provided stock plants used to propagate the willows.Critical reviews by Dr Richard Norby and two anonymousreferees substantially improved the manuscript. This projectwas supported in part by the USDA National Research Initiative(Award 00-35316-9250 to DAH), National Science Foundation(NSF) (DEB 9981568 to CMO), the Swedish ResearchCouncil for Environment, Agricultural Sciences and SpatialPlanning, and by state and federal funds appropriated to theOhio Agricultural Research and Development Center andThe Ohio State University.

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