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. 2010 Aug 3;106(4):637–645. doi: 10.1093/aob/mcq154

Plant resource-use strategies: the importance of phenotypic plasticity in response to a productivity gradient for two subalpine species

Fabrice Grassein 1,*, Irène Till-Bottraud 1, Sandra Lavorel 1
PMCID: PMC2944977  PMID: 20682576

Abstract

Background and Aims

Functional traits are indicators of plant interactions with their environment and the resource-use strategies of species can be defined through some key functional traits. The importance of genetic variability and phenotypic plasticity in trait variations in response to a common environmental change was investigated in two subalpine species.

Methods

Two species with contrasted resource-use strategies, Dactylis glomerata and Festuca paniculata, were grown along a productivity gradient in a greenhouse experiment. Functional traits of different genotypes were measured to estimate the relative roles of phenotypic plasticity and genetic variability, and to compare their levels of phenotypic plasticity.

Key Results

Trait variability in the field for the two species is more likely to be the result of phenotypic plasticity rather than of genetic differentiation between populations. The exploitative species D. glomerata expressed an overall higher level of phenotypic plasticity compared with the conservative species F. paniculata. In addition to different amplitudes of phenotypic plasticity, the two species differed in their pattern of response for three functional traits relevant to resource use (specific leaf area, leaf dry matter content and leaf nitrogen content).

Conclusions

Functional trait variability was mainly the result of phenotypic plasticity, with the exploitative species showing greater variability. In addition to average trait values, two species with different resource-use strategies differed in their plastic responses to productivity.

Keywords: Plant functional traits, genetic variability, Dactylis glomerata, Festuca paniculata, subalpine grasslands

INTRODUCTION

To understand the relationships between species diversity and environmental processes, some ecological analyses simplify the existing diversity by grouping species into homogeneous groups called ‘functional groups’ according to their structural and functional characteristics (traits) (Gitay and Noble, 1997). Taking a different perspective, evolutionary biology shows that the keystone of evolution is diversity, and that species are extremely variable for many traits (Darwin, 1859; West-Eberhard, 1989). Far from being exclusive, these ecological and evolutionary concepts can be used together for the study of functional traits to develop new insights into functional ecology. This study contributes to this endeavour by studying the response abilities of plant species to environmental gradients in terms of their functional traits.

Functional traits are indicators of plant performance responses to environmental factors (Violle et al., 2007). Species can be grouped depending on similarities in their response to given environmental changes (Lavorel et al., 1997) and traits can be used to predict the consequences of global changes (Chapin, 2003). Important efforts have been devoted to documenting the relationships between functional traits and environmental factors, leading to the identification of several axes of variation, of which a major one is related to the resource-use strategy and leaf life span of species (Chapin et al., 1980; Reich et al., 2003; Diaz et al., 2004; Wright et al., 2004). At one end of this axis are species dominant in resource-rich habitats, characterized by a short leaf life span with fast tissue turnover, high resource capture and fast-growth abilities. This resource exploitation strategy is characterized by functional traits related to leaf life span and to the ability to acquire resources, such as a high specific leaf area (SLA), photosynthetic capacity (Amass) related to photosynthetic efficiency, or leaf nitrogen content (LNC) correlated with plant growth rate (Reich et al., 1997, 1999; Wright et al., 2004). At the other end of this axis, species have slower tissue turnover with longer leaf life span and are characterized by lower SLA and LNC (Reich et al., 1992). Their conservative resource-use strategy involves other traits that enhance nutrient conservation (Chapin et al., 1980; Poorter and Garnier, 1999). One example is a large investment in high density tissues for which leaf dry matter content (LDMC) is a good estimator (Ryser and Urbas, 2000). The importance and the relevance of these three leaf traits referred to as ‘functional markers’ (LNC, SLA and LDMC; Garnier et al., 2004) in the characterization of the two strategies has been demonstrated both globally and locally (Garnier et al., 2001; Diaz et al., 2004; Wright et al., 2004).

No environment can be considered constant and plants have to cope with spatial and/or temporal environmental heterogeneity leading to phenotypic variability (Schlichting, 1986; West-Eberhard, 1989; Via et al., 1995). This variability is produced by two mechanisms. The first one is genetic variability: different genotypes produce different phenotypes and as a consequence are selected in contrasted environments depending on their trait values. Natural selection, shaping the association between genotype and environment by selecting the genotype expressing the most appropriate phenotype, can lead to the creation of different ecotypes in different environments over small scales (Linhart and Grant, 1996; Joshi et al., 2001). The second mechanism, phenotypic plasticity, is the ability of one genotype to express different phenotypes depending on environmental conditions and can allow a genotype to be present in different environments (Bradshaw, 1965; Schlichting, 1986; Pigliucci, 2001; De Witt and Scheiner, 2004; Sultan, 2004). Both mechanisms are important. Natural selection is acting on existing genetic variability and the time lag between the cue and the response can be long, especially for long-lived species. Conversely phenotypic plasticity does not require genetic variation and allows a more flexible and quicker response to environmental change, but is usually considered as unsuitable to support extreme phenotypic changes (De Witt et al., 1998; Pigliucci, 2001; Valladares et al., 2007). These two mechanisms thus appear as complementary responses of species to environmental heterogeneity.

Ecological studies of resource-use strategies often do not consider, and sometimes even avoid, intraspecific trait variability so as to allow for a better constancy of species rankings (Garnier et al., 2001; see Albert et al., 2010b). However, exploitative species are dominant in favourable environments due to their ability to respond plastically to nutrient availability (Grime, 1977; Chapin, 1980). They have been hypothesized to be more phenotypically variable and specifically to express more phenotypic plasticity than conservative species in order to be able to exploit the varying supply of nutrients (Crick and Grime, 1987; Lavorel et al., 2009). Genetic variability is less likely because the same individuals are faced with rapidly changing environments. Indeed, phenotypic plasticity in exploitative species is considered as an adaptive mechanism to respond to a variety of environmental conditions but could also be the result of the specialization of a genotype for a particular environment and being maladapted for other environmental conditions that would result in phenotypic variations (Taylor and Aarssen, 1988; Lortie and Aarssen, 1996). Overall it is not yet possible to assess the validity of this hypothesis given the paucity of evidence for intraspecific trait variation in the field or under experimental conditions (Albert et al., 2010a), let alone in relation to species resource-use strategies (for a review of SLA, see Poorter et al., 2009).

In a greenhouse experiment, the phenotypic response to a productivity gradient of two coexisting species of subalpine grasslands of the French Alps was studied. In alpine grasslands a large diversity of habitats can occur over short distances, mainly in relation to altitudinal gradients with diversified environmental conditions, together with human activities influencing the distribution of grassland types and their species composition (Körner, 2003). Albert et al. (2010a) indeed observed strong intraspecific variation in functional traits of 13 species over a mountain catchment of which approx. 70 % could be attributed to inter-site differences for their environmental conditions. Community-level traits also varied with land use in subalpine grasslands (Quétier et al., 2007), with some of that variation due to changes in species composition, but also significant intraspecific trait variation (Lavorel et al., 2009). Plant life span is very long in these habitats (several decades), thus probably much longer than the rapid recent land-use changes, and different habitats with differing management form a fine-grained landscape mosaic over time and space. In such heterogeneous environments, it is expected that phenotypic plasticity will be the most important source of variation for functional traits. Genetic differentiation between populations is less likely but could also occur in contrasted habitats if natural selection is strong enough to counter the homogenizing effects of gene flow over the short distances concerned in this study. The patterns of response and the sources of trait variation (genetic variability and phenotypic plasticity) were compared for two species with contrasted resource-use strategies: one exploitative, Dactylis glomerata, and one conservative, Festuca paniculata, for which Albert et al. (2010b) documented strong intraspecific trait variation. More specifically, the following predictions were tested. (1) The response of functional traits to the environment can be mediated by different sources of variation: (1a) little genetic differentiation is present between close populations under contrasted management and (1b) most of the phenotypic variability for functional traits will be the result of phenotypic plasticity rather than genetic variability. (2) The two species differ in their level of variability, with greater variability present for the exploitative species. Trait values and responses to a productivity gradient are discussed for both species in relation to their resource-use strategies.

MATERIALS AND METHODS

Study species and origin of plant material

Festuca paniculata (L.) Schinz and Thell and Dactylis glomerata L. are two common species in the French Alps, with different altitudinal ranges but coexisting in subalpine grasslands, such as at the Lautaret Pass (2100 m, 100 km south-east of Grenoble). This site was chosen because past and present grassland management types form a fine-grained mosaic (Quétier, 2006) and the two species are dominant in different grassland sites depending on their management. Festuca paniculata dominates in less fertile, never-ploughed grasslands while D. glomerata dominates sites with moderate-to-high water and nutrient availabilities, usually formerly ploughed and subsequently fertilized sites. Management activities can have a strong effect on grassland processes and can influence species composition. As a consequence, these two species are able to coexist in some sites due to recent management dynamics (Quétier et al., 2007). Both species are tall, caespitous grasses with sexual reproduction and vegetative multiplication by tillering. They differ, however, in their resource-use strategy and associated traits: D. glomerata is described as an exploitative species which expresses high values for SLA and LNC while F. paniculata is a conservative species with high values of LDMC (Gross et al., 2007). Following Gross et al. (2007), this study focuses on these two species that encompass, in the study site, the acquisition–conservation trade-off (Diaz et al., 2004; Wright et al., 2004b), which opposes fast-growing species (low LDMC) characterized by a high response to nutrient addition to slow-growing species (high LDMC) with low or no response to fertilization

Experimental design

During September 2004, two populations for each species were sampled at the Lautaret Pass, at two sites separated by at least 3 km and differing in their management and their dominant species but exposed to similar climatic conditions since they are located on the same mountain slope. Sites for F. paniculata consisted of one site that is currently mowed and has formerly been cultivated and one site that has never been ploughed, mowed until approx. 20 years ago and now under extensive grazing. This last site was also sampled for D. glomerata, and compared with a second site that has formerly been ploughed and which is currently fertilized and mown for at least 50 years. The differences between sites for their management result in different levels of productivity.

For each species and population, ten individual tussocks (at least 2 m apart), considered as genotypes given the caespitous habit of the two species and lack of vegetative propagation through stolons or rhizomes, were collected in the autumn and cultivated in a common garden in Grenoble over winter in order to decrease the contrast between the different environments of origin and to allow vegetative multiplication.

During February 2005, 24 ramets of each genotype were isolated, cut to 3 cm for the aerial parts and 5 cm for the roots and planted in plug trays (35 cells, 6 cm diameter). To further reduce the conditioning effect of the environment of origin, plants were cultivated in a greenhouse in Grenoble with a soil mixture of 1/2 potting compost (Castorama®) and 1/2 perlite, and were watered every other day. After 1 month, ramets were standardized to 5 cm (for aerial parts and roots) and fresh biomass (later used as initial biomass) was measured before planting in 9 × 9 cm pots with a soil mixture composed of 2/3 sand, 1/4 perlite and 1/12 potting compost. This mixture corresponds to the lower range of nutrient availability measured in the study site's grasslands (Gross et al., 2007).

After 2 weeks of acclimation in the greenhouse with watering every 2 or 3 d, three treatments were applied:

  • (1) Low levels of water and nutrients (‘poor’ treatment): 25 mL of tap water twice a week during April and May, increased to 50 mL twice a week during June and July to adjust to the warmer temperatures.

  • (2) High level of water and low level of nutrients (‘water’ treatment): 100 mL of tap water twice a week, 200 mL in June and July.

  • (3) High levels of water and nutrients (‘nutrient’ treatment): 100 mL of KNOP solution [200 mg Ca(NO3)2·4H2O, 200 mg KNO3, 50 mg KH2PO4, 50 mg MgSO4·7H20 and FeCl3 as trace elements per litre of tap water] twice a week, 100 mL of KNOP solution and 100 mL of water during June and July.

These three treatments represented a productivity gradient. The low water and high nutrient modality was not used because the aim of this experiment was not to predict the response of species to water and nutrients, but only to compare the response of species to the environmental gradient observed in the natural conditions where these species occur. This modality is almost equivalent to the poor treatment (Volis et al., 2002), and is moreover not representative of available habitats in natural systems (nutrient availability is usually limited by water availability). Due to time and practical limitations, it was not possible to monitor soil water content during the experiment.

One clone from each genotype was planted in a randomly assigned position in one of six (D. glomerata) or five (F. paniculata) glasshouse blocks in a randomized complete block design. The difference in the number of blocks was due to a lower survival of F. paniculata after transplantation. Each experimental block included the three treatments in a split plot design, each treatment (main plot) containing one clone of each genotype (subplot) (Underwood, 1997). All genotype × treatment combinations were present only once in each block, and so replicated five or six times for F. paniculata and D. glomerata, respectively. A total of 360 and 300 plants were cultivated for D. glomerata and F. paniculata, respectively (20 individuals × 3 treatments × 5 or 6 blocks). The experiment ran from mid-April to mid-July 2005 (13 weeks). The two species were grown in separate areas of the glasshouse, but did not experience different growth conditions.

Measurements and analyses

At the end of the experiment, several functional traits were measured on each plant following standardized methods (Cornelissen et al., 2003). At the whole organism level (individual level), vegetative height, number of leaves, above- and below-ground biomass were measured to calculate the shoot : root ratio and total biomass. For the last mature leaf (leaf-level traits), length and width of the leaf, leaf dry matter content (LDMC), specific leaf area (SLA) and leaf nitrogen content (LNC) were measured, the last using a CHONS microanalyser (Carlo Erba 1500).

Statistical analyses were performed using JMP 5 (SAS Institute). When necessary, data were log- or arcsine square-root transformed to conform to the assumptions of normality and homogeneity of variance. Initial biomass was used as a covariate.

To compare levels of phenotypic plasticity, a relative distance plasticity index (RDPI) ranging from 0 (no plasticity) to 1 (maximal plasticity) can be obtained for each genotype as RDPI = Σ[dij → i′j′/(xd + xd)]/n where n is the total number of distances. For a trait x, phenotypic plasticity is considered as a random variable, each realization being described by the absolute distance between two randomly selected replicates (j and j′) of the same genotype belonging to different treatments (i and i′, where i is always different from i′, as replicates were grown in different treatments). Therefore, relative distances rdd→d′d′ are defined as dd→d′d′/(xd′d′ + xd) for all pairs of replicates of a given genotype grown in different treatments. This index gives an unbiased estimation of the levels of phenotypic variation and allows the exploration of plasticity with strong statistical power to test for differences in plasticity between genotypes and species (Valladares et al., 2006). One value of RDPI was calculated for each trait and genotype across treatments, and one-way ANOVAs were performed to compare RDPI between species. Because RDPI ranges from 0 (no plasticity) to 1 (maximal plasticity), the index was arcsin-square root transformed prior to analysis.

A two-way analysis of variance (ANOVA) was used to test the effect of species, treatment and their interaction on functional traits. Type III ANOVAs were used to separate genetic and environmental (i.e. plasticity) effects on phenotypic variation for functional traits, and to detect potential population differentiation. The effects of block, treatment, population and genotype nested within population and all their possible interactions were tested. Genotype and block were considered as random factors, and error terms were corrected accordingly. The population effect was tested in order to detect a genetic adaptation of genotypes to their environment of origin (maternal effects and local adaptation).

Broad-sense heritabilities were calculated as H2 = VG/VP, where VG and VP are the genetic and total components variance, respectively, estimated using an ANOVA with all previous effects except population factor, in order to estimate only the differences between genotypes as the genetic effect (Falconer and Mackay, 1996). The broad-sense heritabilities were used to estimate the part of the phenotypic variability due to differences between genotypes without influences of treatment and block.

RESULTS

Mean values for all traits except height were significantly different between the two species (Table 1). Dactylis glomerata had a greater number of leaves, greater biomass and longer and wider leaves. Dactylis glomerata also had higher values of LNC and SLA and lower values of LDMC compared with F. paniculata (Fig. 1).

Table 1.

Two-way ANOVA for functional traits in the greenhouse experiment

Source d.f. F P F P F P
Height Leaf length Leaf width
Species 1 1·38 0·2415 204·16 <0·001 1963·12 <0·001
Treatment 2 92·11 <0·001 378·21 <0·001 114·29 <0·001
S × T 2 0·24 0·7865 110·55 <0·001 44·19 <0·001
Number of leaves Total biomass Shoot : root ratio
Species 1 339·81 <0·001 187·64 <0·001 223·24 <0·001
Treatment 2 290·67 <0·001 299·32 <0·001 88·76 <0·001
S × T 2 146·54 <0·001 102·79 <0·001 0·49 0·6124
SLA LDMC LNC
Species 1 433·15 <0·001 72·84 <0·001 99·03 <0·001
Treatment 2 6·43 0·0018 16·28 <0·001 15·40 <0·001
S × T 2 2·85 0·0588 9·40 <0·001 23·12 <0·001
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Species, treatment effects and their interaction (S × T) were considered as fixed effects.

The F ratio (F) and P-values (P) are presented for each factor.

d.f., Degrees of freedom.

Fig. 1.

Fig. 1.

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Norms of reaction for the two populations of Dactylis glomerata and Festuca paniculata in the three treatments. Mean and s.e. are presented for each treatment. Only population 2 is the same location for both species.

Sources of phenotypic variation

Only a few traits expressed a significant difference between populations. No trait was significantly different between populations for F. paniculata (Table 2). For D. glomerata, only leaf length and the shoot : root ratio expressed a significant population differentiation. There was a significant population × treatment interaction in D. glomerata for total biomass only.

Table 2.

Effects of environmental and genetic (genotype and population) factors and block on traits measured for the two species (Dactylis glomerata and Festuca paniculata)

d.f. F P F P F P
Height Leaf length Leaf width
Dactylis glomerata
 Initial biomass 1 0·01 0·9394 16·18 <.0001 9·38 0·0025
 Bloc (B) 5 1·09 0·4224 0·37 0·858 1·29 0·3418
 Treatment (T) 2 12·48 0·0011 77·06 <.0001 48·57 <.0001
 B × T 10 2·91 0·0019 7·68 <.0001 4·21 <.0001
 Population (P) 1 3·66 0·072 11·99 0·003 1·33 0·2656
 P × T 2 0·61 0·5519 8·04 0·002 0·25 0·7803
 Genotype (population) (G) 16 1·26 0·2814 3·22 0·002 3·63 0·0009
 G × T 32 1·46 0·0603 1·26 0·168 1·74 0·0111
Festuca paniculata
 Initial biomass 1 3·13 0·0798 0·92 0·3394 1·09 0·2996
 Bloc 4 0·23 0·9143 0·93 0·4918 0·35 0·8356
 Treatment 2 20·42 0·0004 18·19 <.0001 12·31 0·0045
 B × T 8 2·76 0·0085 1·83 0·0811 1·36 0·2246
 Population 1 0·31 0·588 1·10 0·3143 4·30 0·0571
 P × T 2 0·59 0·5614 0·63 0·5388 0·15 0·8628
 Genotype (population) 11 4·06 0·0021 2·49 0·0312 3·44 0·0054
 G × T 22 1·36 0·1518 2·47 0·0013 1·06 0·4056
  Number of leaves Total biomass Shoot : root ratio
Dactylis glomerata
 Initial biomass 1 2·29 0·1316 22·57 <.0001 6·08 0·0144
 Bloc 5 0·28 0·9159 0·86 0·5375 0·60 0·6987
 Treatment 2 75·87 <.0001 73·66 <.0001 57·51 <.0001
 B × T 10 9·64 <.0001 13·36 <.0001 3·04 0·0012
 Population 1 0·05 0·8305 0·02 0·8821 9·80 0·0062
 P × T 2 0·15 0·8616 9·18 0·0007 2·52 0·0959
 Genotype (population) 16 3·72 0·0007 3·21 0·0022 6·51 <.0001
 G × T 32 3·44 <.0001 1·63 0·0226 0·52 0·9862
Festuca paniculata
 Initial biomass 1 2·61 0·1097 11·25 0·0011 8·75 0·0039
 Bloc 4 0·42 0·7888 0·36 0·8337 0·20 0·9287
 Treatment 2 12·13 0·0005 27·50 <.0001 10·39 0·0032
 B × T 8 0·61 0·7687 2·04 0·0495 2·33 0·0249
 Population 1 1·30 0·2743 0·60 0·4525 4·37 0·0553
 P × T 2 0·28 0·7602 0·66 0·5248 0·31 0·7386
 Genotype (population) 11 0·96 0·5044 2·42 0·0365 2·41 0·0352
 G × T 22 4·69 <.0001 3·47 <.0001 1·54 0·0796
SLA LDMC LNC
Dactylis glomerata
 Initial biomass 1 0·01 0·9211 0·87 0·3511 4·59 0·0332
 Bloc 5 0·98 0·4729 1·11 0·4133 0·20 0·9541
 Treatment 2 4·92 0·0303 10·86 0·0028 0·02 0·9788
 B × T 10 2·80 0·0027 3·50 0·0003 40·40 <.0001
 Population 1 1·76 0·2029 2·42 0·1389 0·40 0·5348
 P × T 2 0·26 0·7759 0·80 0·4592 2·99 0·0643
 Genotype (population) 16 3·36 0·0015 3·71 0·0007 3·32 0·0016
 G × T 32 1·19 0·2365 1·13 0·2951 0·69 0·8969
Festuca paniculata
 Initial biomass 1 6·51 0·0123 6·61 0·0116 0·73 0·394
 Bloc 4 3·40 0·0655 1·78 0·2253 2·66 0·1102
 Treatment 2 0·34 0·7184 0·51 0·6135 42·51 <.0001
 B × T 8 2·21 0·0327 1·61 0·1306 1·02 0·4288
 Population 1 2·73 0·1183 1·68 0·2146 0·37 0·5498
 P × T 2 1·28 0·2964 1·62 0·218 1·27 0·298
 Genotype (population) 11 1·55 0·1802 1·84 0·1046 2·14 0·0581
 G × T 22 1·58 0·0673 1·59 0·0628 1·30 0·1876
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The F ratio (F) and P-values (P) are presented for each trait.

d.f., Degrees of freedom.

For most traits, a significant part of the variation was the result of differences among genotypes, i.e. genetic variability in both species, and more so for D. glomerata (Table 2). Broad-sense heritabilities ranged from 0 to 0·25 depending on species and traits (Table 3). The genotype effect was significant in D. glomerata for all traits except height. Genotypic differentiation in F. paniculata was significant for five traits out of nine (height, leaf length, leaf width, total biomass and shoot : root ratio). The genotype × environment interaction was significant for only four traits: number of leaves and total biomass for both species, and leaf width or leaf length for D. glomerata and F. paniculata, respectively.

Table 3.

Broad-sense heritabilities of functional traits for Dactylis glomerata and Festuca paniculata

Dactylis glomerata Festuca paniculata
Height 3·43 24·89
Leaf length 15·01 20·86
Leaf width 14·53 27·29
Number of leaves 22·01 0
Total biomass 6·92 25·10
Shoot : root ratio 21·60 9·53
SLA 14·1 9·44
LDMC 14·59 10·94
LNC 3·21 11·22
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Heritability values are expressed as the percentage of total phenotypic variance due to differences between genotypes.

All traits varied significantly across treatments (Table 2) except LNC in D. glomerata and SLA and LDMC in F. paniculata. For morphological traits (Fig. 1A–F), the patterns of response were consistent across species, with an increase in trait values along the productivity gradient, although the greatest and most significant changes were observed in response to increased productivity from the water to the nutrient treatment. Species differed only in the magnitude of their response, with a stronger response to productivity for D. glomerata compared with F. paniculata (Fig. 1 and Table 1; significant species × treatment interactions) for all traits except height and the shoot : root ratio. Leaf economics traits showed contrasted patterns across species (Fig. 1G, I). SLA and LDMC did not vary significantly across treatments for F. paniculata but expressed some response for D. glomerata. Conversely, no significant variation was observed for LNC in D. glomerata in response to treatments, while there was a significantly lower value of LNC for F. paniculata in the water treatment compared with the other two treatments.

Levels of phenotypic variation

High values of RDPI for most functional traits were observed (Fig. 2). When comparing the levels of plasticity between traits, all morphological traits except leaf width were more plastic than leaf economics traits (SLA, LNC and LDMC), and this was true for both species (see Fig. 2). Significant interspecific differences in RDPI for all traits were also found (Fig. 2), with all traits except leaf width being more plastic for D. glomerata than for F. paniculata.

Fig. 2.

Fig. 2.

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Relative distance plasticity index (RDPI) estimated for each species and calculated for each genotype across the three treatments. The figure shows the mean and s.e. for the two species. Different upper-case and lower-case letters indicate significant differences between traits for Dactylis glomerata and Festuca paniculata, respectively (Tukey post hoc, error 5 %). Asterisks above each functional trait indicate a significant difference between the two species. Mtot, Total biomass, SRR; shoot : root ratio; NL, number of leaves; LF, leaf length; H, height; LNC, leaf nitrogen content; WF, leaf width; SLA, specific leaf area, LDMC, leaf dry matter content.

DISCUSSION

Along an experimental productivity gradient, both species expressed a large amount of intraspecific variation for functional traits. For some traits like LDMC for D. glomerata or LNC for F. paniculata, the variability in this experiment reached almost 40 % of the variability observed in the field for the same species (Albert et al., 2010b), although this observation depends on the trait considered. As hypothesized, little genetic differentiation for functional traits between populations was detected, indicating no local adaptation, which is consistent with the long species generation time compared with the rapidity of recent land use and its rapid change from an evolutionary point of view.

Most of the variability observed was the result of phenotypic plasticity rather than genetic variability which never explained >30 % of the variation. Therefore, the different trait values observed in sites with different grassland management (Quétier et al., 2007) are more likely to be the result of phenotypic plasticity than genetic differentiation.

Some genotypic variability for functional traits was still observed, indicating that at least some individuals sampled within populations are distinct genotypes for their functional trait. This confirms that phenotypic plasticity and genetic variability are two factors explaining intraspecific trait variation in heterogeneous environments (Pigliucci, 2001; De Witt and Scheiner, 2004). No genetic data are available for the two species in the study sites, but trait values reveal significant differentiation between individuals at small scales (a few metres) and confirm that the sampled individuals are genetically different. Genetic variability at such small scale has already been demonstrated in similar ecosystems using genetic marker analysis (Booth and Grime, 2003). It would be interesting to compare more ecologically contrasted populations (and over greater distances), since population differentiation in functional traits has been observed previously for other species occurring in more contrasted conditions associated with water availability for example (Pigliucci and Kolodynska, 2002; Volis et al., 2002). A trait like SLA can also express a large variability in response to other factors such as irradiance, temperature or CO2 (Poorter et al., 2009), and these factors are more likely to vary on a large scale while the sites share similar global conditions. However, subalpine grasslands can vary at a fine-grained scale for edaphic conditions (see, for example, Körner, 2003) which could promote local adaptation over small distances (Byars et al., 2007).

Levels of phenotypic plasticity

Plant response to environmental variation includes passive limitation of growth under low resource conditions as well as active developmental plasticity that enhances resource acquisition in each resource environment (Sultan and Bazzaz, 1993; Sultan, 1995). Phenotypic plasticity is not always adaptive (Gould and Lewontin, 1979; van Kleunen and Fischer, 2005; Valladares et al., 2007) and is often associated with costs (De Witt et al., 1998; Winn, 1999) that should be greater in unfavourable environments (Dorn et al., 2000; van Kleunen et al., 2000; Steinger et al., 2003). Phenotypic constancy has been reported as a component of conservative strategies that could be favoured in poor nutrient conditions (Valladares et al., 2000). Phenotypic plasticity is considered to be greater in exploitative species as a way to exploit different environmental conditions or as the consequences of the large environmental range they occupy (Lortie and Arssen, 1996).

The exploitative D. glomerata was significantly more plastic than F. paniculata, except for one trait, leaf width, and individual-level traits were more plastic than leaf-level traits in both species. Plasticity was observed for functional traits such as SLA, LDMC or LNC which are indicators of structural and physiological adjustments related to resource-use strategies. This plasticity may allow the exploitative species to adapt finely to local environments in order to efficiently use the available resources. Plasticity for morphological traits, such as height or biomass, may play a role in competitive interactions (Navas and Moreau Richard, 2005). Indeed, although the relationships between growth rates and functional traits remain correlative (‘soft’ traits; Hodgson et al., 1999), numerous studies indicate that fast growth is associated with specific physiological and structural adjustments in the acquisition and use of resources (Garnier, 1991; Grime et al., 1997; Glimskär and Ericsson, 1999; Poorter and Nagel, 2000; Grime and Mackey, 2002; Wright et al., 2004). As a consequence, the greater flexibility of a competitive species from mesic habitats like D. glomerata is likely to enhance its growth abilities as well as its ability to adapt to a variety of habitats (Jain et al., 1970; Valladares et al., 2002). The opposite trend was observed for the conservative F. paniculata which dominates in less productive environments, where phenotypic stability is more likely to be selected for than phenotypic plasticity (Lortie and Aarssen, 1996; Valladares et al., 2000), according with the present observations of the lack of response to nutrient abundance.

Functional traits as resource-use strategy indicators

Consistently with two previous studies conducted at the same study site (Gross et al., 2007; Quétier et al., 2007), the two species expressed trait values consistent with their ecological strategies: D. glomerata had high values for LNC and SLA and F. paniculata had high values for LDMC. All morphological traits increased along the resource gradient for both species, with a stronger response of D. glomerata to resource availability. However, species differed in their responses for leaf-level traits, with a response to treatment for both SLA and LDMC in D. glomerata and only for LNC in F. paniculata. We therefore hypothesize that exploitative species are characterized by the ability to express high and constant values for LNC, while conservative species are characterized by high and constant values of LDMC across a gradient of resource availability. The present observations for LDMC were confirmed in an inter-site analysis across the French Alps, where three exploitative grasses (Dactylis glomerata, Agrostis capillaris and Phleum alpinum) showed significant variability in LDMC in response to plot-level productivity, while LDMC was invariant in three more conservative grasses (Bromus erectus, Nardus stricta and Sesleria caerulea) (Lavorel et al., 2009).

More plasticity was observed for individual level traits compared with leaf level traits, and some experiments have illustrated different levels for physiological and morphological traits depending on resource-use strategy (van de Vijver et al., 1993; Grime and Mackey, 2002; Valladares et al., 2002). In addition, some differences were observed between species in their variations for SLA, LDMC and LNC, with variability for one species and constancy for the other, indicating that the response of these traits may be a component of species strategies, rather than passive plastic responses to an environmental gradient. SLA, LNC or LDMC are morphological traits measured at the leaf level and reflecting physiological adjustments in leaf anatomy (Ryser and Lambers, 1995; Poorter and Evans, 1998; Reich et al., 1998a, b; Evans and Poorter, 2001). These functional traits have already been highlighted as key functional markers of plant strategies (Garnier et al., 2001), but in addition to comparing mean trait values between species, the strategy of a species could be further defined by screening procedures of trait responses to an experimental resource gradient, or at least be otherwise documented (Grime, 1977; Dyer et al., 2001; Ackerly, 2003). Such questions warrant consideration in the development of standardized trait measurement protocols (e.g. Cornelissen et al., 2003) and should in the future be considered in comparative analyses of trait variation. The variations observed in the present study also do not negate the value of mean traits as strategy indicators, but rather suggest that trait variation could be equally informative about plant response to environment, whether to resource availability as analysed here, or to various disturbances (see also Albert et al., 2010a). The consequences of different responses to the same environmental change for coexisting species with contrasted strategies needs to be clarified (Reich et al., 2003; Griffith and Sultan, 2006).

ACKNOWLEDGEMENTS

F.G. was funded by a PhD scholarship from the Cluster Environment of Région Rhône-Alpes. This work was supported by the ANR-05-BDIV-009-01 QDIV. It contributes to CNRS GDR 2574 Traits and was conducted as part of CNRS Zone Atelier Alpes. We thank Florian Alberto and Marie-Pascale Colace for assistance in plant culture and trait measurements, Geneviève Girard for chemical analyses, the PDC Laboratory for the greenhouse facilities, Nicolas Gross and Pierre Liancourt for technical and statistical advice about the experimental design, Mélanie Burylo and Sébastien Lavergne for their valuable comments on the manuscript.

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