Interactions of drought and shade effects on seedlings of four Quercus species: physiological and structural leaf responses


  • José Luis Quero,

    1. Grupo de Ecología Terrestre, Departamento de Ecología, Facultad de Ciencias, Universidad de Granada, E−18071 Granada, Spain;
    2. Área de Ecología, Facultad de Ciencias, Universidad de Córdoba, E−14071 Córdoba, Spain;
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  • Rafael Villar,

    1. Área de Ecología, Facultad de Ciencias, Universidad de Córdoba, E−14071 Córdoba, Spain;
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  • Teodoro Marañón,

    1. Instituto de Recursos Naturales y Agrobiología, CSIC, PO Box 1052, E-41080 Sevilla, Spain
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  • Regino Zamora

    1. Grupo de Ecología Terrestre, Departamento de Ecología, Facultad de Ciencias, Universidad de Granada, E−18071 Granada, Spain;
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Author for correspondence: José Luis Quero Tel: +34 958 243242 Fax: +34 958 243238 Email:


  • • Here, we investigated the physiological and structural leaf responses of seedlings of two evergreen and two deciduous Quercus species, grown in a glasshouse and subjected to contrasted conditions of light (low, medium and high irradiance) and water (continuous watering vs 2-months drought).
  • • The impact of drought on photosynthetic rate was strongest in high irradiance, while the impact of shade on photosynthetic rate was strongest with high water supply, contradicting the hypothesis of allocation trade-off.
  • • Multivariate causal models were evaluated using d-sep method. The model that best fitted the dataset proposed that the variation in specific leaf area affects photosynthetic rate and leaf nitrogen concentration, and this trait determines stomatal conductance, which also affects photosynthetic rate.
  • • Shade conditions seemed to ameliorate, or at least not aggravate, the drought impact on oak seedlings, therefore, the drought response on leaf performance depended on the light environment.


Light and water are main resources affecting leaf traits, regulating plant growth and survival, and determining the distribution of plants at global scale. The functional response of seedlings to the combination of shade and drought involves biochemical, physiological, and structural changes at the leaf and whole-plant level (Holmgren, 2000; Sack & Grubb, 2002; Sack, 2004; Aranda et al., 2005). Some hypotheses predict that under limiting light availability (primary limitation), the shortage of another resource such as water should have less impact on plant performance (Canham et al., 1996). In addition, shade by the tree canopy has indirect effects, such as reducing leaf and air temperatures, vapour pressure deficit and oxidative stress, that would alleviate the drought impact on seedlings in the understorey (Holmgren, 2000). Empirical evidence of facilitation effects of shrubs and trees on seedlings in the understorey in Mediterranean environments has been widely documented (Castro et al., 2004a; Gómez-Aparicio et al., 2004). A contrary hypothesis predicts that deep shade will aggravate the stress imposed by drought, based on the proposed trade-off mechanism that shaded plants allocate more to shoot, and to leaf area, than to root, thereby diminishing the ability to capture water (Smith & Huston, 1989). In fact, some studies have found a higher impact of water stress on shaded plants (Abrams & Mostoller, 1995; Valladares & Pearcy, 2002). A third group of hypotheses posits that the effects of shade and water-shortage are independent, that is, their impacts are orthogonal (Sack & Grubb, 2002; Sack, 2004).

In woody species, there is a suite of leaf traits associated to leaf life span. Deciduous species tend to achieve higher photosynthetic and respiration rates and higher stomatal conductance, and have higher nitrogen (N) concentration in the leaf, compared with related evergreen species (Reich et al., 1992, 1997; Villar et al., 1995; Takashima et al., 2004; Wright et al., 2004). In Mediterranean environments, deciduous species tend to be more abundant in habitats with greater availability of water and nutrients, where the overstorey canopy is denser. Hence, it would be expected that seedlings of deciduous species are more shade-tolerant and water-demanding. By contrast, evergreen species tend to dominate in habitats that are drier and poorer in nutrients, where the overstorey canopy is sparse. We would therefore expect that seedlings of evergreen species are more tolerant to drought but not necessarily to shade.

One way to understand plants function is to explore leaf-trait relationships in different environmental conditions; however, most studies have discussed simple bivariate relationships. In order to develop a quantitative model of plant functioning relating to gas exchange, it would be necessary to move to multivariate relationships to be investigated by causal model (Meziane & Shipley, 2001). These authors proposed a model in which SLA was the forcing variable directly affecting both leaf N and net photosynthetic rate. Leaf N then directly affects photosynthetic rate, which in turn affects stomatal conductance. This model was found to agree with several datasets (Meziane & Shipley, 2001). To date, these models have not been applied to datasets with limiting light and water conditions, as are typical of Mediterranean forest.

We have designed an experiment with controlled conditions of light and water to investigate the physiological and structural leaf traits responses of tree seedlings to six combinations of light (three levels) and water (two levels). Four species of the same genus (Quercus) differing in leaf life span, were selected: two evergreens and two deciduous. Thus, we compared deciduous and evergreen species under the same genus, including the phylogeny in the design and data analysis.

There are some specific questions to investigate plant responses to different light–water scenarios: Are shade and drought impacts on seedlings positive, negative or independent? Do species or functional groups (evergreen vs deciduous) respond differently? Which physiological and structural leaf traits are most affected by the combined stress? What are the functional relationships among those variables? The answers to these questions would help to understand the functioning of plants and their implications for the species distribution in nature.

Materials and Methods

Experimental design

Acorns of four oak species, major components of Mediterranean forest –Quercus suber L., Quercus ilex ssp. ballota (Desf.) Samp. (evergreen), Quercus canariensis Willd. and Quercus pyrenaica Willd. (deciduous) – were collected in the south of Spain. At landscape scale, the evergreen species tend to occupy drier habitats than the deciduous species at each site, although the regional ranges overlap (see Table 1 for more details). Single acorns were weighted individually and sown (in December 2002) in cylindrical pots of 3.9-l volume (50 cm high × 10 cm diameter), thereby avoiding as much as possible interference during root growth. Pots contained a mixed soil of 2 : 3 sand and 1 : 3 peat. Ten grams of a slow-release fertilizer (Plantacote, Pluss NPK: 14 : 9 : 15, Aglukon, Valencia, Spain) were added at the middle of the experiment. The experiment was carried out in a glasshouse of the University of Córdoba (Spain, 37°51′ N, 4°48′ W; at an altitude of 100 m) with an automatic irrigation system and regulation of air temperature.

Table 1.  Oak species included in the experiment (nomenclature follows Amaral, 1990), their leaf life span, frequency in southern Spain (calculated from 12572 records in the National Forest Inventory) and range of precipitation where they were recorded
SpeciesOrigin of seedsFunctional groupFrequency in southern Spain (%)Precipitation (mm)
  1. Data from the National Meteorological Institute; Urbieta et al. (2004).

Quercus canariensis Willd.Sierra del Aljibe (SE Spain)Deciduous 2.41073628–1338
Quercus ilex ssp. ballota (Desf.) SampSierra Nevada (SW Spain)Evergreen50.8 668268–1366
Quercus pyrenaica Willd.Sierra de Cardeña (S Spain)Deciduous 0.4 773604–990
Quercus suber L.Sierra del Aljibe (SE Spain)Evergreen15.8 839489–1366

Oak seedlings were subjected to three light levels: (1) high-irradiance treatment (HI), receiving available radiation inside the glasshouse; (2) medium-irradiance treatment (MI), covered by a light-green screen (27% of available radiation); and (3) deep-shade or low-irradiance treatment (LI), covered by a dense green cloth (3% of available radiation). Each light treatment was imposed using a shade frame (150 × 120 × 200 cm) and replicated four times; therefore, there were 12 shade frames in total. Each of the four species and the two levels of watering were set up within each shade frame, each by one plant in a single pot. The experimental light treatments simulated the field conditions in the forest understorey, distinguishing three types of microhabitat: open (HI), under single tree cover (MI), and under shrub and tree cover (LI) (Marañón et al., 2004). The mean ± SE of the photosynthetic active radiation measured (with EMS7, canopy transmission meter, PP-system, UK) at midday on May 28, 2003, for each light treatment was 760 ± 150, 187 ± 27 and 23 ± 2 µmol of photons m−2 s−1 in HI, MI and LI respectively. Light quality (red:far red (R : FR) ratio, measured with sensor SKR 110; Skye Instruments, Llandrindod Wells, UK) was different from 1 only in LI, but this value (0.25 ± 0.004) was similar to that for dense forest microhabitat (0.28 ± 0.03, t-test, P = 0.31).

Pots were watered weekly during the first stage of the experiment. Once the seedlings emerged (January–February, 2003), a drip-irrigation system was inserted in the pots. Four months after sowing (at the end of April 2003), half of the pots stopped receiving any watering (low-water treatment, LW) while the other half was kept continuously moist (high-water treatment, HW). Low-water treatment simulated a typical Mediterranean-climate situation of seasonal drought, compared with a continuously moist one (HW) with reduced or no drought. During the experiment, we measured soil moisture (in volumetric water content, VWC), measured along the first 20 cm depth (with a TDR mod 100; Spectrum Technologies, Inc., Plainfield, IL, USA) each c. 3 d, in a subsample of five pots under different light and water treatments. Pots under LW decrease their water content similarly for the three light treatments (Table 2a; repeated measures anova, P = 0.17). At the same time of photosynthetic measurements (end of July 2003, c. 2 months after stopping irrigation), we measured VWC of each pot. For each water treatment, there were no differences in water content between the pots of different species or between the three different light treatments at the end of the experiment (Table 2b). The mean ± SE values in July 2003, were 13.20 ± 0.20% (for HW treatment) and 2.96 ± 0.13% (for LW). The latter value was very similar to those found under field conditions at the end of the drought period (Gómez-Aparicio et al., 2005).

Table 2.  (a) Soil water content (measured with TDR) at the beginning, middle and end of the experiment (mean ± SE) in a subsample of pots under the six light and water combinations; (b) results of the three-way anova for the effects of water supply (W), irradiance treatments (I), and species (S), and their interactions at the end of the experiment for all pots where photosynthetic measurements were done
 Time (d)Combined treatments
High waterLow water
  1. HI, high irradiance; MI, medium irradiance; LI, low irradiance (see the Materials and Methods section for details).

Soil water content (%)013.8 ± 0.612.4 ± 0.612.0 ± 0.613.1 ± 0.610.3 ± 0.611.1 ± 0.6
3011.8 ± 0.511.0 ± 0.511.0 ± 0.5 6.6 ± 0.5 5.2 ± 0.5 3.6 ± 0.5
6012.8 ± 0.413.2 ± 0.413.2 ± 0.4 3.2 ± 0.4 2.4 ± 0.1 2.2 ± 0.1
3-way anova resultsFactordfMean squaresP   
Water (W)  13053< 0.001   
Irradiance (I)  269.020.408   
Species (S)  366.140.461   
W × I  216.700.804   
W × S  3 6.980.965   
I × S  632.960.856   
I × W × S  6 7.630.996   

Physiological and structural measurements

Photosynthesis response to irradiance was measured in mid-height fully expanded leaf of, in general, six plants per species and treatment combination. The measurements were done in the four different shade frames (replicates) for each light treatment to avoid pseudoreplication. We used a gas-exchange portable analyser (Ciras-2; PP-System, Hitchin, UK). The instrument was adjusted to have constant conditions of CO2 concentration (360 p.p.m), flow (150 cm3 min−1), and leaf temperature (25°C) inside the leaf chamber. Photosynthetic rate was measured at 10 light intensities of PAR obtained by using a quartz halogen light unit coupled to leaf chamber following the order 1000, 1300, 1500, 800, 600, 400, 200, 100, 50 and 0 µmol m−2 s−1 (Fig. 1), to reduce the equilibrium time required for stomatal opening and photosynthesis induction (Kubiske & Pregitzer, 1996). Each leaf was kept for 1 min at the same light intensity into the leaf chamber; net assimilation rate, transpiration rate and intercellular CO2 concentration were recorded three times, and the average value at each light intensity was calculated. Net CO2 assimilation rates (A) were plotted against incident PAR, and the resulting curve was fitted by the nonrectangular hyperbola model of Thornley (1976):

Figure 1.

Evolution of the photosynthetic rate (mean values and SE bars) with increasing irradiance (light curves), for seedlings (e.g. Quercus pyrenaica) cultivated in high (100%, a), medium (27%, b), and low (3%, c) irradiance, respectively. In each Figure (of light conditions), seedlings under continuous irrigation treatment (closed circles, high water treatment) are distinguished from seedlings subjected to drought (open circles, low water treatment).


(A, photosynthetic rate; I, photosynthetic active radiation (PAR); Φ, apparent quantum yield; Amax, maximum light saturated assimilation rate; Rd, dark respiration rate; θ, ‘bending degree’ or curvature). Parameters of the model were calculated by the nonlinear estimation module (statistica version 6.0, Statsoft, Inc., Tulsa, OK, USA). The variance explained by the model was very high (mean r2 values of 0.98 ± 0.03). Despite its methodological importance, this value is rarely given, and comparison with other studies is difficult. Using this formula, by definition, the maximum photosynthetic rate is obtained at the infinite light intensity, and then overestimated. Therefore, we recalculated Amax (hereafter, Aarea) assuming a PAR of 2000 µmol m−2 s−1, the approximate maximum value for that season and latitude (Castro et al., 2004b; Rey-Benayas et al., 2005). The light saturation point (LSP) was calculated as the lowest value of PAR for which photosynthesis reached 90% of Aarea. Water-use efficiency (WUE) values were calculated as Aarea : stomatal conductance per area (gsarea) ratio (Cavender-Bares & Bazzaz, 2000) and photosynthetic N-use efficiency (PNUE) as photosynthetic rate per mass (Amass)/N concentration (Field & Mooney, 1986).

In the same leaves, a chlorophyll index was measured using a CCM-200 (Optic Science, Hudson, NH, USA), which works similarly to SPAD (Minolta) and readings are well correlated with chlorophyll content. Leaves were then collected and scanned, and the area was measured with an image analyser (image pro-plus version 4.5; Media Cybernetic, Inc., Silver Spring, MD, USA). They were oven-dried (at 80°C for at least 48 h) and weighed. The specific leaf area (SLA) was calculated as the ratio between the leaf area and its dry mass. Leaves were ground with liquid N in an agate mortar, and analysed for N and carbon (C) concentration using an elemental analyser (Eurovector EA 3000; EuroVector SpA, Milan, Italy).

The level of response to the variation of each factor (light and water) was estimated by the indices Responselight and Responsewater, respectively, ranging from 0 to 1. The index of response was calculated as the difference between the maximum and the minimum mean values, divided by the maximum mean value. Although other authors called this the plasticity index (PI) (Valladares et al., 2000a), we have preferred the neutral term ‘Response’ first, because in the case of water treatment, the seedlings had to adjust to a seasonal drought and were not acclimatized from the beginning of the experiment, and second, because we did not control possible genetic variability.

Statistical analyses

Mean (± SE) values of the 20 variables of seedling leaf performance, for each Quercus species and irradiance and water treatment, are shown in Appendix 1. To avoid pseudoreplication, we calculate the mean values of the different variables for each light treatment replicates. These mean values were used to test the differences among species and the effects of light and water treatments on each variable by three-way anovas (species, light, and water as source factors) with Type III sums of squares. Previously, ancova was explored considering the seed mass as covariable; seed mass did not significantly affect leaf traits of 6-month-old seedlings (P > 0.05 in all cases); therefore, we present here only the anova results for simplicity. A similar anova procedure was used to explore the differences between deciduous and evergreen species, using leaf habit as factor instead of species. When the difference was significant, a multiple comparison of means test (post hoc Unequal N Tukey's Honestly Significant Difference test) was carried out. Before ancova and anova, data were square-root-, arcsine-, or log-transformed to satisfy the normality and homoscedasticity assumptions (Zar, 1984). Leaf-trait relationships were studied by Pearson's correlation analyses between pairs of variables, separating watered and drought conditions. The program statistica version 6.0 (Statsoft, Inc., Tulsa, OK, USA) was used for statistical analyses.

In order to explain the empirical patterns of direct and indirect covariation between variables, a multivariate analysis was carried out to test for causal models linking changes in main leaf traits (SLA and N content) with physiological performance (photosynthetic rate and stomatal conductance), following Shipley's d-sep method (Shipley, 2000). Significance was fixed at the 0.05 level throughout the study. In order to control the inflation of type I error derived from repeated testing, the false discovery rate (FDR, the expected proportion of tests erroneously declared as significant) criterion was applied to repeated test tables throughout the paper. The FDR was controlled at the 5% level using a standard step-up procedure (see García, 2004). However, when testing multiple path models, we obtained an estimate for the expected number of erroneously accepted null hypotheses (type II errors), while controlling the FDR at the 5% level (Ventura et al., 2004). This approach allowed us to focus the attention on those accepted models which had a low probability of being type II errors.


Combined effects of shade and drought

The reduction in the availability of light and water imposed structural changes in the leaves of oak seedlings and affected their physiological performance (Figs 1 and 2).

Figure 2.

General variation in leaf traits of oak seedlings (means and SE bars for replicates of the four Quercus species) in response to the six combinations of light and water treatments. Light levels are ‘Low irradiance’ (LI, 3%), ‘Moderate irradiance’ (MI, 27%), and ‘High irradiance’ (HI, 100%), and water levels are ‘High water’ (HW, solid line) and ‘Low water’ (LW, dashed line). a) Amass, photosynthetic rate per mass (nmol CO2 g−1 s−1); b) gsarea, stomatal conductance per mass (mmol H2O m−2 s−1); c) SLA, specific leaf area (cm2 g−1); d) Nmass, nitrogen concentration (mg g−1).

Most variables showed strong interactions of light and water effects (as demonstrated by the anovas, Table 3 and Fig. 2), reflecting that the drought impact on the physiological and structural traits of seedlings was highly significant under HI and MI but negligible under LI. Some exceptions were SLA and N concentration (Fig. 2).

Table 3.  Results of the three-way anovas for some structural and physiological leaf traits, according to the factors oak species (S), and light (L) and water (W) treatments
Leaf traitsSpeciesFactorsInteractionsR2
LightWaterL × WS × LS × WS × L × W
  1. The proportion of the explained variance (SSx/SStotal) and the level of significance (*, P < 0.05; **, P < 0.01; ***, P < 0.001) for each factor and the interactions are indicated; those values not remaining significant after controlling the false discovery rate are underlined. R2 is the proportion of total variance absorbed by the model. Φ, quantum yield (no units); θ, curvature (no units); Area, leaf area (cm2); Aarea, photosynthetic rate per area (µmol CO2 m−2 s−1); Amass, photosynthetic rate per mass (nmol CO2 g−1 s−1); Carea, carbon content per area (g C m−2); Cmass, carbon concentration (mg g−1); Chl index, chlorophyll index (no units); Ci/Ca, ratio internal vs external CO2 concentration; gsarea, stomatal conductance per area (mmol H2O m−2 s−1); gsmass, stomatal conductance per mass (mmol H2O g−1 s−1); LCP, light compensation point (µmol photons m−2 s−1); LSP, light saturation point (µmol photons m−2 s−1); Narea, nitrogen content per area (g N m−2); Nmass, nitrogen concentration (mg g−1); Rarea, respiration rate per area (µmol CO2 m−2 s−1); Rmass, respiration rate per mass (nmol CO2 g−1 s−1); PNUE, photosynthetic nitrogen-use efficiency (µmol CO2 (mol N)−1 s−1); SLA, specific leaf area (cm2 g−1); WUE, water-use efficiency (µmol CO2 (mmol H2O)−1).

Structural traits
Area53.8***12.1*** 0.0 1.3
SLA29.9***63.7*** 0.0 0.1
Nmass14.5***39.8*** 5.5*** 4.9**
Cmass50.8*** 1.02.8* 0.3 3.46.0*2.266.5
Narea29.0***44.4*** 3.5***1.8*
Carea34.1***59.2*** 0.0 0.1
Chl index31.9*** 5.2* 6.7** 6.5**
Physiological traits
Φ 3.2 7.5*19.6*** 7.3*
θ 4.9 6.6* 1.6 9.3**14.4*
LCP 0.938.2*** 0.2 5.4* 2.77.7*5.460.5
LSP 0.318.1***10.1*** 6.4*
Aarea 5.8*** 5.9***37.0***23.4*** 2.34.0**2.380.7
Rarea 3.218.2***16.7*** 0.2
gsarea 7.4*** 2.0*46.0***24.2*** 0.53.3**1.684.8
Amass21.9***14.2***21.5***14.4*** 1.95.0**2.981.7
Rmass17.9*** 3.412.5*** 0.7
gsmass19.2*** 7.8***30.8***15.6*** 0.24.6**2.1*80.3
WUE 2.4 3.019.9***12.6*** 7.88.6**5.659.9
PNUE17.5*** 6.1**24.1***13.7*** 1.57.6***6.0*76.6
Ci/Ca 2.15.1* 9.0***10.7**13.5***9.0***6.355.7

Photosynthetic rate and stomatal conductance of the four oak species were similar along the three irradiance levels in HW (Fig. 2a,b). However, these traits decreased with irradiance under the LW. WUE (ratio between these traits) showed differences in water treatments, being higher in LW. However, PNUE decreased in LW as whole (Table 3).

Leaves of oak seedlings grown under LI had higher SLA (Fig. 2c) and were richer in N (Fig. 2d).

Differences among Quercus species

Leaf structural traits were characteristic of each species and showed significant differences in the anovas (see species as factor in Table 3; Appendix 1). For example, leaf area varied across the species (54% of variance explained) and SLA showed statistical differences among each of the four Quercus species (30% of variance), with the rank Q. ilex < Q. suber < Q. pyrenaicaQ. canariensis (Fig. 2C).

Fewer physiological features varied across the Quercus species (only 6 out of 13; Table 3). For example, Amass differed among species (22% of variance; deciduous Q. pyrenaica and Q. canariensis had higher values than evergreen Q. ilex and Q. suber) (Fig. 2a). In general, the effects of shade and/or drought on physiological variables were higher than the interspecific variation (for example, LCP was highly affected by light (38% of variance), but varied only slightly across species (1% of variance)) (Table 3).

Differences between functional groups

Leaf traits of seedlings were related to the leaf habit. When the seedlings of deciduous species (Q. pyrenaica and Q. canariensis) were grouped and compared by anovas with the evergreen species (Q. ilex and Q. suber), all seven leaf structural traits showed significant differences (Appendix 1). Seedlings of deciduous species had higher leaf area, SLA (Fig. 2c), and N concentration (Nmass, Fig. 2d), but lower chlorophyll (Chl) index (Appendix 1).

Differences in life span also predicted some variation in seedling physiological performance (significant anovas for 5 out of 13 variables). Seedlings of deciduous species had higher Amass (19% of variance), respiration rate per mass (Rmass) (13% of variance), PNUE (13% of variance), and stomatal conductance (3% of variance) than evergreens. There were no apparent differences between deciduous and evergreen seedlings in WUE.

Responses to variation of light and water

There was a high variation in the degree of response to light vs that to water, for the 20 variables measured (Fig. 3). Results for the four species were averaged to show the general response pattern. The response to light (Responselight) had a mean value of about 0.35 for the 20 variables, with a wide variation among them (Fig. 3). The structural water-induced response of leaf traits was very low (mean Responsewater of 0.07), while the general physiological response was relatively high (mean Responsewater of 0.35) (Fig. 3). Some variables had relatively persistent values even for stressed seedlings (low response traits). Among the variables exhibiting high response, some were highly affected by shade (Responselight > 0.5) but not affected by drought; the most remarkable example is SLA. By contrast, other leaf traits had high response in drought-affected seedlings (Responsewater > 0.5), but were more independent of shade stress; the best example here is the gsarea and Aarea.

Figure 3.

Bivariate diagram of the comparative response to light (Responselight) vs the response to water (Responsewater), for physiological and structural leaf traits, averaged for the four oak species. Response was calculated as (maximum value − minimum value/maximum value). Φ, quantum yield (no units); θ, curvature (no units); Area, leaf area (cm2); Aarea, photosynthetic rate per area (µmol CO2 m−2 s−1); Amass, photosynthetic rate per mass (nmol CO2 g−1 s−1); Carea, carbon content per area (g C m−2); Cmass, carbon concentration (mg g−1); Chl index, chlorophyll index (no units); Ci/Ca, ratio internal vs external CO2 concentration; gsarea, stomatal conductance per area (mmol H2O m−2 s−1); gsmass, stomatal conductance per mass (mmol H2O g−1 s−1); LCP, light compensation point (µmol photons m−2 s−1); LSP, light saturation point (µmol photons m−2 s−1); Narea, nitrogen content per area (g N m−2); Nmass, nitrogen concentration (mg g−1); Rarea, respiration rate per area (µmol CO2 m−2 s−1); Rmass, respiration rate per mass (nmol CO2 g−1 s−1); PNUE, photosynthetic nitrogen-use efficiency (µmol CO2 (mol N)−1 s−1); SLA, specific leaf area (cm2 g−1); WUE, water-use efficiency (µmol CO2 (mmol H2O)−1).

Causal links among leaf structural traits and physiological performance

A diverse correlation patterns were revealed among leaf structural traits and physiological variables. These relationship patterns were similar for the four oak species between different variables shown in the four oak species (test of Homogeneity of slopes model, P > 0.05 for all cases; data not shown). In many cases, correlations between leaf traits differed depending on the water treatment (44% of bivariate relationships were different, Table 4). Amass and Rmass were significantly correlated in both drought and watered conditions (Table 4). Amass was also correlated with gsarea, under drought and water treatments (Fig. 4d).

Table 4.  Pearson's correlation coefficients for some structural and physiological traits. Bold and normal letters represent high- (HW) and low- (LW) water treatments, respectively
  1. The level of significance (*, P < 0.05; **, P < 0.01; ***, P < 0.001) is indicated; those values not remaining significant after controlling the false discovery rate are underlined. Cmass, carbon concentration (mg g−1); gsmass, stomatal conductance per mass (mmol H2O g−1 s−1); LCP, light compensation point (µmol photons m−2 s−1); LSP, light saturation point (µmol photons m−2 s−1); Nmass, nitrogen concentration (mg g−1); Rarea, respiration rate per area (µmol CO2 m−2 s−1); PNUE, photosynthetic nitrogen-use efficiency (µmol CO2 (mol N)−1 s−1); SLA, specific leaf area (cm2 g−1); WUE, water-use efficiency (µmol CO2 (mmol H2O)−1).

Figure 4.

Correlations between leaf traits. (a) Nitrogen concentration (Nmass) and (b) photosynthetic rate on mass basis (Amass) vs specific leaf area (SLA); (c) stomatal conductance on area basis (gsarea) vs Nmass; (d) Amass vs gsarea. Circles, Quercus ilex ssp. ballota; diamonds, Q. canariensis; squares, Q. suber; triangles, Q. pyrenaica. Values for seedlings grown under high-water conditions (closed symbols) are distinguished from those grown under low-water conditions (open symbols).

In some cases, leaf structural traits can be used as predictors of physiological performance. Nmass was a good predictor of gsarea; but only for drought-affected seedlings (Fig. 4c). The specific leaf area (SLA) was a good predictor for several physiological activities. Seedlings of higher SLA tended to have higher photosynthetic rate (Fig. 4B), higher Nmass (Fig. 4a), and lower LCP and LSP (Table 4). The instantaneous water-use efficiency (WUE) was negatively correlated with the instantaneous photosynthetic N-use efficiency (PNUE) for watered seedlings, but not when affected by drought (Table 4). The SLA of drought-affected seedlings (unlike watered ones) was significantly correlated with PNUE. The WUE was not correlated with SLA for either of the water treatments.

The results of the multivariate analyses (d-sep test) of causal models linking leaf traits (SLA and Nmass) and physiological functions (Amass and gsmass) are shown in Table 5 and Fig. 5. Model D was accepted by the whole dataset and most of the different light and water treatments. According to this model, the variation in SLA affects Amass and Nmass, and this trait determines gsmass, which also affects photosynthetic rate. Model F, which best fitted the datasets in the study by Meziane & Shipley (2001), was also accepted by most datasets in this experiment but did not fit the data of LI, and hence it was rejected for the combined dataset (Table 5).

Table 5.  Probabilities under the null hypothesis that the data accord with each of the six proposed models, for each of the treatment conditions and combinations using the d-sep method
TreatmentModel type
  1. HI, high irradiance; MI, medium irradiance; LI, low irradiance or deep shade; HW, high water; LW, nonwatered (drought). Models that would be rejected at the 5% level on a per-test basis are shown in bold type. However those whose values remained significant after controlling the False Discovery Rate (FDR) at the 5% level, following the Ventura et al. (2004) criteria, are underlined.

HI HW0.022580.014060.664640.015950.818010.36896
HI LW0.668770.413940.744900.487240.075260.50847
MI HW0.460330.525700.795990.341950.724780.67319
MI LW0.455700.459800.571110.665890.648030.32076
LI HW0.007450.010840.093450.239750.168120.11715
LI LW0.014140.002510.077070.422380.062430.02955
HI + MI0.446740.091570.467290.354930.000010.22299
Figure 5.

Alternative multivariate models linking the specific leaf area (SLA), leaf nitrogen content on mass basis (N), net photosynthetic rate on mass basis (A), and stomatal conductance on mass basis (gs). Model D (framed) was the best fitted to the dataset of Quercus seedling responses to water and light treatments.


Are the impacts of shade and drought on seedlings, positive, negative or independent?

Most leaf traits showed strong interactions in their responses to light and water treatments (Table 3; Fig. 2), and hence their variation was not independent. We did find that oak seedlings grown under deep shade increased their SLA, but they did not necessarily have a lower physiological performance, in terms of net photosynthetic rate, stomatal conductance or WUE when subjected to drought, as would be expected from the trade-off hypothesis (Smith & Huston, 1989). By contrast, under similar drought conditions, deep-shaded seedlings were able to achieve higher photosynthetic rate, stomatal conductance, and N concentration than seedlings under full light (Fig. 2). Moreover, under drought conditions, seedlings with higher SLA had higher Aarea while lower Rarea, indicating a higher positive C balance in these leaves (Table 4). The apparent alleviation of drought impact for seedlings growing in shade, demonstrated here under experimental conditions, could explain the pattern of higher seedling survival under shade of shrubs and trees (facilitation effect), commonly observed in Mediterranean forests (Castro et al., 2004b; Gómez-Aparicio et al., 2004; Marañón et al., 2004).

Other studies have also found structural and physiological evidence supporting the hypothesis of shade as lessening the drought stress on seedlings of woody species (Holmgren, 2000; Prider & Facelli, 2004; Duan et al., 2005). Conversely, plants under high irradiance, when subjected to water stress, suffer a more drastic reduction in net photosynthesis, and can be more predisposed to photo-inhibition, in comparison with plants in the shade (see References in Holmgren, 2000); although sunflecks can cause severe photoinhibition in shaded leaves (Valladares & Pearcy, 2002). However, Sack & Grubb (2002) and Sack (2004) found that the effect of shade and drought showed orthogonal impacts (no interactions) on final dry mass, relative growth rate, and biomass allocation on seedlings of different species. The authors proposed that seedlings are able to tolerate both shade and drought by developing plant features conferring reduced demand for light and/or water (see References in Sack & Grubb, 2002).

By contrast, there are studies showing negative responses to combined shade and drought conditions for Quercus species. In a controlled experiment, Q. suber seedlings grown in shade were less efficient in developing physiological mechanisms of water tolerance, in particular, osmotic adjustment and effective control of water loss (Aranda et al., 2005). This has been found in field studies with other woody species (Valladares & Pearcy, 2002).

These contrasting results indicate that, physiological and structural mechanisms involved in the integrated responses of the tree seedlings to shade and drought strongly depend on plant functional type.

Do species or functional groups (evergreen vs deciduous) respond differently?

Seedlings of the deciduous species here (Q. pyrenaica and Q. canariensis) differed in leaf structure (higher values for leaf area, SLA and N, but lower chlorophyll concentrations) and in physiological activities (higher values of photosynthetic and respiration rates, stomatal conductance and PNUE) compared with seedlings of evergreen oaks (Q. ilex and Q. suber) subjected to the same conditions of light and water. A similar trend in structural and physiological differences between seedlings, associated with the contrasted leaf habit (deciduous vs evergreen) of adults, has been documented for other Mediterranean species (Villar et al., 1995; Villar & Merino, 2001). Within the same genus Quercus, Takashima et al. (2004) found that the PNUE in evergreen species was lower than in deciduous ones; in evergreen oak seedlings the allocation of N to photosynthesis was smaller, while that to cell walls was greater, in order to acquire leaf toughness.

In general, leaf traits of seedlings of deciduous species allow them to achieve a higher relative growth rate than that of seedlings of congeneric, evergreen species (Antunez et al., 2001; Ruiz-Robleto & Villar, 2005).

Which physiological and structural leaf traits are most affected by the combined stress?

Leaf response to irradiance was very variable, in both structural and physiological traits (Fig. 3). For example, shade induced a relatively high variation in the key leaf trait SLA for all four oak species (mean Reponselight of 0.6), similar to the light-induced plasticity values found for evergreen tropical shrubs (16 Psychotria species, mean of 0.4; Valladares et al., 2000b). The ability to respond to light by modifying leaf structural traits may confer shade tolerance by increasing light-capture efficiency (Valladares et al., 2002b). At the same time, the relatively high responsiveness of leaf physiology may also indicate a tolerance to high irradiance (Valladares et al., 2002a).

Drought induced a relatively low response in structural leaf traits but a high one in physiological traits (Fig. 3). In this experiment, we have simulated the Mediterranean climate seasonal drought that predictably occurs a few months after seedling emergence. When drought stress becomes more severe, first-year seedlings, grown under varied irradiance conditions, have already completed their growth, and therefore have low ability to modify structural leaf traits, which usually have a large ontogenetic component. However, they show a high physiological responsiveness to optimize photosynthesis : transpiration ratios under drought conditions.

What are the functional relationships among variables?

Because bivariate relationships are unsuccessful for making causal inferences, we tested several causal models of multivariate links among structural (SLA and Nmass) and physiological (Amass and gsmass) leaf traits (Fig. 5) and accepted one of them (model D) as the best fit to the experiment results. According to this model, there is a direct causal relationship of SLA with dry mass concentration of cytoplasmic constituents, including N, which in turn affects stomatal conductance. Assuming that stomatal behaviour is regulated to maximize WUE, then the passive process of gas exchange across the stomata would result in the net photosynthetic rate (Meziane & Shipley, 2001). In addition, the model proposes a direct causal relationship of SLA with A, not mediated by leaf N. One explanation is that the accumulation of nonstructural carbohydrates will decrease SLA and reduce photosynthesis (Meziane & Shipley, 2001). Another explanation is that self-shading of chloroplasts in the lower part of thicker leaves (with lower SLA) will decrease the net C fixation on a leaf-mass basis (Reich et al., 1999). Thus, there is a complex multivariate link among these three leaf traits: the ratio of leaf area to mass (SLA) is balanced with the amount of organic leaf N per mass (Nmass) to maximize photosynthesis rate (Amass) mediated by stomatal conductance (gsmass), hence optimizing loss of water by transpiration, which is so important in Mediterranean environments.

Ecological significance

The four Mediterranean oak species studied here share a general syndrome of leaf traits that can be suited to a ‘reduced demand for resources’ (Sack et al., 2003), as well as part of a ‘conservative resource-use strategy’ (Valladares et al., 2000a). Although in the physiological literature these traits are usually considered adaptations to the dry Mediterranean climate, most probably they are ancestral traits of Tertiary subtropical oaks, which allowed them to be sorted in when the climatic change imposing the seasonal drought typical of Mediterranean climate became established c. 3.5 million years ago (Herrera, 1992).

Within that general ‘Mediterranean oak syndrome’, there are interspecific differences in the seedling responses to light and water. The changes in structural leaf traits of leaf area, SLA, and concentrations of N and C, and the physiological performance of photosynthetic and respiration rates, and N efficiency (PNUE), were the most affected by the species factor in this experiment. These leaf traits are associated with the plant's physiological response to the abundance of resources, and determine their growth and survivorship (Lambers & Poorter, 1992; Wright et al., 2004). For example, the seedlings of Q. pyrenaica showed the highest values of Aarea, Amass gsarea, gsmass, leaf area and PNUE compared with the other three oak species. These leaf traits would favour seedling growth in nutrient-rich and mesic habitats, but they may confer less tolerance to drought (see species distribution in Table 1).

Mediterranean drought, at all levels of light, is a problem for the seedling in terms of avoiding water loss and maintaining C uptake, and therefore of biomass gain. On the other hand, deep shade in the closed forest understorey environment, independently of water availability, can be a limiting factor in maintaining a positive C balance. In this experiment, the shade conditions seemed to ameliorate, or at least not aggravate, the drought impact on oak seedlings therefore drought response on leaf performance depend of light environment.


We thank the glasshouse staff of the University of Córdoba for their advice and Miguel Ángel Calero, Carlos Casimiro, Loles Bejarano, Ana Murillo, Juan Rubio, Francisco Conde, Francisco J. Morilla and Miguel A. Nuñez for their help during the experiment. We thank Lawren Sack, Fernando Valladares and Steve Long for their comments on a previous version of the manuscript, Luis V. García for his help with numerical analysis and Esteban Alcántara for his help with chlorophyll determinations. We thank three anonymous referees for comments and improvements on the manuscript. This study was supported by the grant FPI-MEC to J.L.Q. (BES-2003-1716), and by the coordinated Spanish CICYT project HETEROMED (REN2002-04041). This research is part of the REDBOME network on forest ecology (


Appendix 1

Table 6.  Mean ± SE values of structural and physiological leaf traits analysed for Quercus seedlings in different light and water treatments. In general, there were four replicates per treatments, exceptions are indicated in parentheses
Quercus suber (evergreen)
Structural traits
Area   9.2 ± 0.6  10.8 ± 0.6  12.6 ± 0.6  12.1 ± 0.8   8.3 ± 0.5   9.9 ± 0.8
SLA 90.32 ± 6.61101.62 ± 3.38137.59 ± 3.17131.01 ± 5.71246.85 ± 6.84259.54 ± 4.81
Nmass 15.66 ± 1.09 17.21 ± 0.88 15.68 ± 0.67 16.54 ± 0.54 22.13 ± 1.05 26.34 ± 1.92
Cmass 480.3 ± 4.2 446.1 ± 5.7 468.9 ± 4.4 455.9 ± 6.7 470.1 ± 3.3 456.7 ± 13.1
Narea  1.77 ± 0.19  1.71 ± 0.13  1.15 ± 0.06  1.27 ± 0.06  0.90 ± 0.04  1.02 ± 0.08
Carea  5.52 ± 0.58  4.40 ± 0.11  3.43 ± 0.12  3.50 ± 0.11  1.91 ± 0.06  1.76 ± 0.06
Chl index  19.2 ± 2.1  26.5 ± 2.9  24.4 ± 0.5  29.1 ± 1.8  24.4 ± 1.9  29.4 ± 2.5
Physiological traits
Φ0.0274 ± 0.01180.0470 ± 0.00290.0312 ± 0.0090.0531 ± 0.0030.0438 ± 0.00440.0450 ± 0.004
θ 0.545 ± 0.273 0.931 ± 0.023 0.766 ± 0.124 0.727 ± 0.107 0.927 ± 0.034 0.827 ± 0.089
LCP  45.2 ± 1.2  15.1 ± 3.7  27.0 ± 8.0  13.4 ± 1.5   6.5 ± 1.3  10.0 ± 1.7
LSP 735.0 ± 155.8 395.2 ± 74.4 337.2 ± 100.4 517.3 ± 129.3 155.1 ± 27.1 227.7 ± 74.0
Aarea  0.94 ± 0.25 12.72 ± 0.97  1.08 ± 0.32  10.5 ± 1.80  4.48 ± 0.40  4.29 ± 0.51
Rarea  0.56 ± 0.10  0.73 ± 0.19  0.37 ± 0.06  0.69 ± 0.06  0.26 ± 0.04  0.42 ± 0.05
gsarea  14.3 ± 4.3 166.0 ± 29.4  11.4 ± 2.3 179.7 ± 20.9  51.6 ± 11.9  56.1 ± 19.6
Amass   6.9 ± 1.5 126.8 ± 9.2  14.8 ± 4.2 115.7 ± 13.7 111.1 ± 11.4 121.6 ± 23.7
Rmass   4.6 ± 1.1   7.7 ± 2.2   5.1 ± 0.9   8.5 ± 0.6   6.6 ± 1.1  10.7 ± 1.0
gsmass  0.10 ± 0.02  1.78 ± 0.42  0.16 ± 0.03  2.07 ± 0.15  1.28 ± 0.31  1.72 ± 0.78
WUE  76.6 ± 8.2  80.1 ± 7.0  10.6 ± 2.3  57.8 ± 7.0  96.5 ± 11.8  93.7 ± 13.9
PNUE  5.97 ± 1.26104.68 ± 13.04 13.58 ± 4.20 98.72 ± 13.87 70.89 ± 5.52 67.50 ± 17.00
Ci/Ca  0.62 ± 0.03  0.52 ± 0.02  0.49 ± 0.01  0.63 ± 0.04  0.51 ± 0.05  0.53 ± 0.05
Quercus ilex ssp. ballota (evergreen)
Structural traits
Area   7.9 ± 0.8   8.4 ± 0.38.6 ± 0.98.0 ± 0.37.3 ± 1.15.5 ± 0.4
SLA 61.00 ± 1.27 64.64 ± 8.9891.68 ± 5.0186.91 ± 1.35142.74 ± 6.64166.74 ± 8.86
Nmass 13.65 ± 1.31 16.57 ± 0.3315.68 ± 1.2515.74 ± 1.5823.31 ± 0.4223.60 ± 2.04
Cmass 481.8 ± 1.9 473.8 ± 5.5477.6 ± 2.1469.6 ± 5.3460.4 ± 3.4462.8 ± 5.3
Narea  2.27 ± 0.24  2.72 ± 0.351.71 ± 0.111.82 ± 0.191.65 ± 0.081.45 ± 0.13
Carea  7.98 ± 0.16  7.88 ± 1.135.26 ± 0.295.42 ± 0.073.25 ± 0.172.84 ± 0.17
Chl index  35.6 ± 3.0  34.5 ± 2.732.2 ± 1.936.2 ± 4.239.7 ± 3.832.5 ± 2.6
Physiological traits
Φ0.0163 ± 0.00510.0470 ± 0.00810.0413 ± 0.00330.0524 ± 0.00540.0502 ± 0.00250.0404 ± 0.0018
θ 0.805 ± 0.157 0.892 ± 0.0650.906 ± 0.0530.874 ± 0.0530.801 ± 0.0490.896 ± 0.025
LCP  24.3 ± 1.90  27.2 ± 2.919.0 ± 4.515.1 ± 4.16.4 ± 1.214.6 ± 4.0
LSP 325.0 ± 65.1 508.0 ± 122.0158.8 ± 41.4497.3 ± 99.0294.7 ± 36.2233.3 ± 33
Aarea  1.98 ± 0.25  9.39 ± 1.503.56 ± 1.339.23 ± 1.156.10 ± 0.654.43 ± 0.27
Rarea  0.46 ± 0.18  1.20 ± 0.260.76 ± 0.170.79 ± 0.190.31 ± 0.050.57 ± 0.15
gsarea  25.7 ± 4.3 170.1 ± 12.338.4 ± 15.5152.2 ± 8.485.8 ± 8.861.8 ± 13.5
Amass  12.7 ± 1.9  59.4 ± 9.230.7 ± 10.480.5 ± 10.787.5 ± 11.281.0 ± 16.2
Rmass   2.9 ± 1.2   8.1 ± 2.87.1 ± 1.96.8 ± 1.64.5 ± 0.78.9 ± 2.6
gsmass  0.16 ± 0.03  1.03 ± 0.150.33 ± 0.121.3 ± 0.081.22 ± 0.141.19 ± 0.33
WUE  86.8 ± 18.1  58.2 ± 10.089.6 ± 7.767.1 ± 3.971.9 ± 4.683.7 ± 14.0
PNUE 14.01 ± 3.55 49.63 ± 6.8028.83 ± 10.7174.49 ± 13.7552.70 ± 6.6247.53 ± 8.7
Ci/Ca  0.56 ± 0.08  0.64 ± 0.050.54 ± 0.040.60 ± 0.020.60 ± 0.020.56 ± 0.06
LW (n = 3)HW (n = 3)LWHW (n = 3)LW (n = 3)HW
Quercus canariensis (deciduous)
Structural traits
Area11.2 ± 0.69.5 ± 0.812.6 ± 1.813.6 ± 5.411.7 ± 0.59.1 ± 1.2
SLA127.81 ± 13.07111.57 ± 11.98154.83 ± 9.98145.76 ± 5.69339.72 ± 23.48281.32 ± 8.23
Nmass16.57 ± 3.3725.26 ± 4.9518.38 ± 1.9021.91 ± 1.5629.08 ± 3.9224.60 ± 1.66
Cmass451.4 ± 7.6445.9 ± 11.4451.4 ± 2.4443.12 ± 7.14445.1 ± 5.3436.8 ± 9.1
Narea1.34 ± 0.42.24 ± 0.21.18 ± 0.071.50 ± 0.050.85 ± 0.050.88 ± 0.08
Carea3.58 ± 0.434.03 ± 0.332.9 ± 0.213.04 ± 0.071.32 ± 0.071.59 ± 0.09
Chl index16.5 ± 0.930.1 ± 2.225.6 ± 1.428.0 ± 1.424.1 ± 1.424.2 ± 3.9
Physiological traits
Φ0.0342 ± 0.01040.0436 ± 0.00250.0325 ± 0.00480.0466 ± 0.0320.0478 ± 0.00200.0448 ± 0.0062
θ0.308 ± 0.3080.744 ± 0.1440.927 ± 0.0290.760 ± 0.1050.929 ± 0.0360.896 ± 0.027
LCP29.7 ± 4.620.5 ± 4.216.4 ± 4.316.0 ± 1.86.6 ± 1.59.1 ± 4.1
LSP651.8 ± 256.3619.7 ± 129.5240.5 ± 49.2430.4 ± 0.1167.03 ± 47.7222.9 ± 38.3
Aarea3.71 ± 2.5810.54 ± 1.865.14 ± 0.747.45 ± 1.754.85 ± 0.775.08 ± 0.43
Rarea0.77 ± 0.280.84 ± 0.100.43 ± 0.060.63 ± 0.020.31 ± 0.070.33 ± 0.09
gsarea41.7 ± 22.4174.3 ± 21.046.5 ± 11.4125.4 ± 54.364.1 ± 16.066.8 ± 5.2
Amass60.8 ± 40.996.1 ± 3.179.6 ± 13.0105.0 ± 34.4155.0 ± 25.0143.7 ± 8.8
Rmass11.1 ± 4.210.7 ± 2.96.8 ± 1.39.0 ± 0.211.8 ± 3.79.1 ± 2.9
gsmass0.59 ± 0.391.71 ± 0.180.73 ± 0.211.78 ± 0.941.97 ± 0.481.89 ± 0.09
WUE68.8 ± 16.260.6 ± 2.8105.5 ± 13.969.3 ± 13.278.8 ± 7.078.4 ± 3.0
PNUE46.22 ± 25.1855.08 ± 9.0560.35 ± 5.4365.66 ± 17.4976.17 ± 10.6581.93 ± 6.22
Ci/Ca0.64 ± 0.090.62 ± 0.020.46 ± 0.060.60 ± 0.040.59 ± 0.030.59 ± 0.01
  1. Light treatments: HI, high irradiance, MI, medium irradiance; LI, low irradiance. Water treatments: LW, low water; HW, high water.

  2. Φ, Quantum yield (no units); θ, curvature (no units); Area, leaf area (cm2); Aarea, photosynthetic rate per area (µmol CO2 m−2 s−1); Amass, photosynthetic rate per mass (nmol CO2 g−1 s−1); Carea, carbon content per area (g C m−2); Cmass, carbon concentration (mg g−1); Chl index, chlorophyll index (no units); Ci/Ca, ratio internal vs external CO2 concentration; gsarea, stomatal conductance per area (mmol H2O m−2 s−1); gsmass, stomatal conductance per mass (mmol H2O g−1 s−1); LCP, light compensation point (µmol photons m−2 s−1); LSP, light saturation point (µmol photons m−2 s−1); Narea, nitrogen content per area (g N m−2); Nmass, nitrogen concentration (mg g−1); Rarea, respiration rate per area (µmol CO2 m−2 s−1); Rmass, respiration rate per mass (nmol CO2 g−1 s−1); PNUE, photosynthetic nitrogen-use efficiency (µmol CO2 (mol N)−1 s−1); SLA, specific leaf area (cm2 g−1); WUE, water-use efficiency (µmol CO2 (mmol H2O)−1).

Quercus pyrenaica (deciduous)
Structural traits
Area13.0 ± 2.216.5 ± 2.420.1 ± 2.218.3 ± 1.812.1 ± 1.514.0 ± 2.5
SLA117.54 ± 4.69123.63 ± 7.04160.11 ± 8.96158.63 ± 3.10281.52 ± 29.14255.15 ± 6.29
Nmass17.75 ± 1.2022.54 ± 0.9617.71 ± 0.8122.85 ± 1.2525.95 ± 2.9322.92 ± 0.25
Cmass429.2 ± 11.4439.7 ± 6.3433.8 ± 0.9437.2 ± 11.1438.2 ± 6.8440.6 ± 9.8
Narea1.52 ± 0.071.85 ± 0.121.10 ± 0.071.44 ± 0.080.92 ± 0.020.92 ± 0.02
Carea3.68 ± 0.193.61 ± 0.202.69 ± 0.172.76 ± 0.031.63 ± 0.141.76 ± 0.10
Chl index19.2 ± 2.924.3 ± 1.723.0 ± 1.631.4 ± 1.529.0 ± 3.525.4 ± 3.4
Physiological traits
Φ0.0372 ± 0.0080.0523 ± 0.00070.0310 ± 0.00470.0529 ± 0.00390.0451 ± 0.00590.0643 ± 0.0092
θ0.980 ± 0.0110.824 ± 0.0570.974 ± 0.0230.906 ± 0.0260.797 ± 0.0970.615 ± 0.069
LCP19.7 ± 1.923.4 ± 3.611.6 ± 4.317.9 ± 6.79.6 ± 3.114.5 ± 7.0
LSP293.8 ± 93.3621.8 ± 54.7153.2 ± 53.2434.1 ± 21.8294.1 ± 53.7378.4 ± 46.6
Aarea4.69 ± 2.0415.22 ± 1.274.00 ± 1.3513.76 ± 0.225.51 ± 0.625.36 ± 0.80
Rarea0.55 ± 0.031.19 ± 0.170.32 ± 0.100.85 ± 0.240.38 ± 0.060.85 ± 0.38
gsarea52.7 ± 29.3293.9 ± 10.734.6 ± 18.0274.8 ± 33.486.1 ± 7.7101.2 ± 11.2
Amass57.8 ± 26.8187.2 ± 17.865.3 ± 23.8234.1 ± 18.2163.2 ± 24.8135.6 ± 16.6
Rmass6.6 ± 0.614.7 ± 2.15.3 ± 2.014.1 ± 4.511.3 ± 1.422.0 ± 10.3
gsmass0.67 ± 0.403.67 ± 0.340.58 ± 0.324.49 ± 0.442.53 ± 0.352.56 ± 0.23
WUE108.0 ± 8.652.0 ± 5.0147.2 ± 17.752.2 ± 4.763.8 ± 3.856.4 ± 4.8
PNUE43.87 ± 18.12115.63 ± 7.0761.15 ± 26.96143.31 ± 5.4287.20 ± 9.5887.99 ± 15.83
Ci/Ca0.45 ± 0.040.62 ± 0.030.31 ± 0.060.63 ± 0.020.65 ± 0.020.68 ± 0.03