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1. A fundamental trade-off among vascular plants between traits inferring rapid resource acquisition and those leading to conservation of resources has now been accepted broadly, but is based on empirical data with a strong bias towards leaf traits. Here, we test whether interspecific variation in traits of different plant organs obeys this same trade-off and whether within-plant trade-offs are consistent between organs.
2. Thereto, we measured suites of the same chemical and structural traits from the main vegetative organs for a species set representing aquatic, riparian and terrestrial environments including the main vascular higher taxa and growth forms of a subarctic flora. The traits were chosen to have consistent relevance for plant defence and growth across organs and environments: carbon, nitrogen, phosphorus, lignin, dry matter content, pH.
3. Our analysis shows several new trait correlations across leaves, stems and roots and a striking pattern of whole-plant integrative resource economy, leading to tight correspondence between the local leaf economics spectrum and the root (r = 0.64), stem (r = 0.78) and whole-plant (r = 0.93) economics spectra.
4. Synthesis. Our findings strongly suggest that plant resource economics is consistent across species’ organs in a subarctic flora. We provide thus the first evidence for a ‘plant economics spectrum’ closely related to the local subarctic ‘leaf economics spectrum’. Extending that concept to other biomes is, however, necessary before any generalization might be made. In a world facing rapid vegetation change, these results nevertheless bear considerable prospects of predicting below-ground plant functions from the above-ground components alone.
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Functional traits of plants are nowadays widely accepted as potentially powerful indicators of the ecology of species. They are indeed a consistent tool to determine plant strategies world-wide and allow the synthesis of various empirical data from contrasting areas and environments. Plant ecological strategy schemes (e.g. Grime 1977; Westoby 1998; Díaz et al. 2004) classify plants according to meaningful axes of plant specialization. Each of these axes represents a trade-off that limits possible investments of resources to different parts of cells, different tissues and different plant organs. Recent syntheses and reviews have emphasized the existence of one of these axes, which describes a fundamental trade-off among vascular plants between rapid acquisition and conservation of resources (Grime et al. 1997; Reich, Walters & Ellsworth 1997; Díaz et al. 2004; Wright et al. 2004). Sets of plant functional traits are widely recognized as powerful proxies for this trade-off. Thus, for instance, rapid acquisition of resources is generally correlated with high specific leaf area (SLA), leaf nitrogen (N) and phosphorus (P) content or pH of foliar extracts (a proxy for cation content; see Cornelissen et al. 2006), while high leaf dry matter content (DMC), lignin content or carbon (C) to N ratio reflect the resource conservation strategy.
This trade-off, described as the ‘world-wide leaf economics spectrum’ (Wright et al. 2004), has so far not been extended to the entire plant. This is due partly to the difficulty of measuring attributes of other plant parts, especially below-ground. Consequently, it is still highly uncertain whether or where traits of other plant components such as stems or roots will fit on this axis of specialization. In other words, do all organs of a plant species support either a more resource conservative or acquisitive strategy (e.g. Grime 2001), or is it common to find organs supporting resource conservation and organs supporting acquisition within the same plant species (e.g. Tilman 1982)? To understand how the different plant parts are coordinated along this gradient of traits related to the acquisition or conservation of resources is a high research priority, because variation not only in leaf traits but also in stem and root traits may determine important effects of plant species composition on ecosystem processes and services (De Deyn, Cornelissen & Bardgett 2008; Suding et al. 2008). To know whether interspecific variation in leaf traits alone reliably reflects trait variation of other plant organs would thus be a major advance in plant ecology.
To understand the resource economics trade-off at the whole-plant level, several steps still need to be taken. Despite promising advances for stems alone (e.g.Castro-Díez et al. 1998; Wright et al. 2006; Chave et al. 2009) and roots alone (e.g. Ryser & Lambers 1995; Reich et al. 1998; Roumet, Urcelay & Díaz 2006), the role of integrated interspecific variation in leaf, stem and root traits still needs to be tested comprehensively (Westoby & Wright 2006). So far, few studies have investigated trait covariation between above- and below-ground organs. These have revealed promising, if partly inconsistent, relationships (see Table 1, for an overview). Leaf, stem and root N content were found consistently correlated world-wide (Kerkhoff et al. 2006). Mass-based respiration was also correlated between leaves and roots (Tjoelker et al. 2005), which is supported by data by Reich et al. (2008) who showed a strong link between N and respiration within each vegetative organ. SLA and specific root length were strongly correlated for woody species (Reich et al. 1998; Wright & Westoby 1999; Withington et al. 2006) but decoupled when the species pools comprised both woody and herbaceous species (correlations derived from Craine et al. 2001 and Reich et al. 2003a; Tjoelker et al. 2005). According to Ryser (2006), the decoupling between specific root length of herbaceous and woody plants may be a consequence of plant size differences, as taller plants need stronger anchorage and more transport capacity. Specific root length might thus not carry precisely the same meaning across plant types and clades in term of resource economics. More generally, tissue density, organ thickness, lignin content or life span were found either poorly or non-correlated between leaves and roots (Table 1; Craine & Lee 2003; Craine et al. 2005). These relationships need to be synthesized and extended to traits of plant stems. Exploring the differences in resource allocation between organs is another challenge. Consistency in trait relationships and differential investments between organs should also be tested across different environments, and wider ranges of plant functional types and clades.
Table 1. Overview of multi-species studies investigating similar plant traits across leaves, stems and roots
|Source||Trait||Leaves versus stems||Leaves versus roots||Stems versus roots||Clades||Plant types|
|r||P-value||No. species||R, r or ρ*||P-value||No. species||r||P-value||No. species|
|Correlations explicitly inquired in previous studies|
| Reich et al. (1998)||SLA versus SRL|| || || ||0.78||<0.05||9|| || || ||Core eudicot Gymnosperm||Woody deciduous Woody evergreen|
| Wright & Westoby (1999)||SLA versus SRL|| || || ||0.73||<0.001||33|| || || ||Core eudicot||Woody deciduous|
| Craine & Lee (2003)||Nitrogen|| || || ||0.55||<0.001||24|| || || ||Monocot||Graminoid|
|Tissue density|| || || ||0.29||<0.001||24|| || || || || |
| Craine et al. (2005)||Nitrogen|| || || ||0.57||<0.001||90|| || || || || |
|Thickness|| || || ||NA||NS (0.97)||90|| || || || || |
|Tissue density|| || || ||NA||NS (0.49)||90|| || || ||Monocot||Graminoid|
|Lignin|| || || ||NA||NS (0.65)||90|| || || || || |
|Soluble fraction|| || || ||NA||NS (0.97)||90|| || || || || |
| Tjoelker et al. (2005)||Nitrogen|| || || ||0.77||<0.001||31|| || || || || |
|C/N|| || || ||0.70*||<0.001||31|| || || || || |
|SLA versus SRL|| || || ||0.12*||NS (0.50)||33|| || || ||Monocot Core eudicot||Graminoid Forb Woody deciduous|
|Rmass|| || || ||0.53*||0.002||31|| || || || || |
|Life span|| || || ||0.50*||NS (0.07)||14|| || || || || |
| Kerkhoff et al. (2006)||Nitrogen||0.69||<0.05||202||0.62||<0.05||173||0.84||<0.05||146||Monocot Core eudicot||Graminoid Forb|
|Phosphorus||0.62||<0.05||176||0.69||<0.05||123||0.72||<0.05||96||Basal eudicot||Woody deciduous|
|N/P||0.66||<0.05||149||0.59||<0.05||117||0.61||<0.05||91||Gymnosperm Magnoliid||Woody evergreen|
| Withington et al. (2006)||SLA versus SRL|| || || ||0.77||<0.05||11|| || || ||Core eudicot||Woody deciduous|
|Life span|| || || ||−0.12||NS (0.73)||11|| || || ||Gymnosperm||Woody evergreen|
|Correlations derived from published studies with available data sets|
| Craine et al. (2001)||SLA versus SRL|| || || ||0.15||NS (0.21)||76|| || || ||Monocot||Graminoid|
|Tissue density|| || || ||0.02||NS (0.86)||76|| || || ||Core eudicot||Forb|
| Reich et al. (2003a)||SLA versus SRL|| || || ||−0.11||NS (0.56)||30|| || || ||Monocot Core eudicot||Graminoid Forb Woody deciduous|
While ultimately the application of large world-wide data sets on roots and stems (e.g. Reich et al. 2008; Chave et al. 2009) is essential for testing this approach, we analyse here a local data set to bring together a wide range of plant species and traits for different plant organs into one theoretical framework. The choice of the local scale, where the resource economics trade-off is likely to operate most strongly (Wright et al. 2004), is appropriate to test for plant organ coordination of interspecific trait patterns. Furthermore, the reasons for the great variation in species traits occurring at local scale are still poorly understood (Ackerly & Cornwell 2007). In world-wide meta-analyses or large-scale studies, disentangling local environmental variations is generally out of reach and only between-site, macro-climatic variations are thus taken into account. Nevertheless, great differences in soil characteristics, microclimate, successional phase and biotic interactions exist at local scales, the link of which to plant functional trait diversity needs to be tested further (Wright et al. 2005).
Focusing on plant traits representative of the acquisition–conservation trade-off across species, we here test the hypotheses that (i) interspecific trait variation of non-leaf plant organs is correlated with that of leaf traits across environments, clades and plant types; (ii) trait values for leaves, stems and roots of the same species generally occupy the same position on the acquisition–conservation trade-off axis; (iii) local environmental features explain a significant part of the variance in plant functional trait variations; and that (iv) the ‘leaf economics spectrum’ is an adequate predictor of the ‘plant economics spectrum’ as defined by whole-plant trait coordination.
We addressed these hypotheses by measuring suites of similar plant traits from the main vegetative organs, i.e. leaves, stems and roots, for a subarctic flora representing the key species from aquatic, riparian and terrestrial environments and covering the main vascular higher taxa and growth forms in this region.
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When comparing plant functional traits of different vegetative organs – leaves, stems and roots – across multiple subarctic species representing a broad spectrum of vascular plant taxa, growth forms and habitats, significant positive correlations were found in most cases (Table 2). Lignin and C content, DMC and C:N all showed robust significant correlations between the different plant parts. Significant correlations were also found for N and pH between leaves and stems and leaves and roots, but stem and root N and pH did not show significant correlations. Phosphorus content did not display any particular pattern.
Table 2. Pearson correlations (r) of species traits across plant parts
| ||Leaves versus stems (n = 39)||Leaves versus roots (n = 36)||Stems versus roots (n = 35)|
|DMC (mg g−1)||0.85||<0.0001||0.48||0.003||0.68||<0.0001|
|Nitrogen (%)||0.47||<0.002||0.35||0.035||0.28||NS (0.11)|
|Phosphorus (%)||0.21||NS (0.20)||−0.14||NS (0.43)||0.22||NS (0.21)|
As seen in Fig. 1 for a subset of traits, all – significant – relationships were linear except for leaf–stem N and leaf–root N (slightly exponential). Slopes of linear regressions were, however, not always close to 1, revealing different structural and chemical allocations across organs. Thus, DMC displayed a narrower range of variation and was generally lower in roots than in leaves and stems. Lignin content of roots and stems was similar but higher than in leaves. Leaf pH reached lower values in leaves than in stems and roots. Nitrogen content was highest in leaves, intermediate in roots and lowest in stems. Visual inspection showed no phylogenetic or plant type (Figs S1 and S2, respectively) group clustering away from the general regression lines, whichever trait or organ relationship was considered. In other words, although a few outliers were observed, no plant type or clade seemed to consistently offset either slope or intercept of organ trait relationships. Standardized major axis tests on environments (Fig. 1) demonstrated also that terrestrial, riparian and aquatic groups of species did not display any significant difference in slope or intercept for any trait. All clades, plant types and environments fitted thus consistently in the emerging patterns of trait covariation among organs.
Figure 1. Biplots of leaf, stem and root trait relationships. Type I regression lines are shown for the total species pool (—) along with the regression equations, and for each environment, terrestrial (––), riparian (—) and aquatic (- - -), when significant. Species are distinguished according to their environment: ○ Terrestrial; Riparian; Aquatic. Distributions of higher taxa and plant types are available in Figs S1 and S2, respectively.
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The first PCA axis based on 19 traits from all vegetative plant parts accounted for 43% of overall variation, as against only 12% for axis 2 (Fig. 2). All plant traits, except P content of the different plant parts, contributed substantially to the first axis. With increasing scores on the first PCA axis, variables representative of the resource acquisitive strategy, i.e. SLA, N content and pH, decreased while variables representative of plant nutrient conservation strategy, i.e. DMC, lignin content and C:N, increased. Consistent with this plant strategy axis, all terrestrial species – except Cornus suecica, Deschampsia flexuosa and Equisetum sylvaticum– were clustered on the nutrient-conservative side of the spectrum; all aquatic species were grouped at the far end of the acquisitive side of the spectrum; and riparian species were spread in-between these two extremes (Fig. 2A). Pairwise comparison of the first-axis species scores for the plant environment variable confirmed this (P < 0.001 for terrestrial versus riparian and P < 0.002 for riparian versus aquatic). Environmental variables representative of soil organic matter quantity (C, R2 = 0.55, P < 0.0001), quality (N, R2 = 0.53, P < 0.0001; C:N, R2 = 0.37, P < 0.0001) and mineralization rate (average humidity and temperature of the soil litter layer during growing season, R2 = 0.52 and 0.34, respectively, P < 0.0001 and P = 0.0003, respectively) were significant predictors of the plant PCA first-axis scores (see Fig. 3, for N and litter moisture regressions). The more fertile the environment was (high soil N, litter temperature and moisture; low C:N ratio), the more negative the PCA first-axis scores were, i.e. the more nutrient acquisitive the species strategies. The different plant-type groups were ranked from woody evergreens to aquatic forbs (Fig. 2B). Woody evergreens and woody deciduous were significantly different from all other groups except club mosses (P = 0.31 and P = 0.99, respectively). Aquatic forbs were significantly different from all other groups except fern allies (P = 0.48). Fern allies, forbs, graminoids and club mosses, occupying a central position on the spectrum, were not significantly different from each other. As for phylogenetic groups (Fig. 2C), only gymnosperms showed a significant difference from the other groups except for lycophytes (P = 0.73).
Similarly to the whole-plant PCA, organ PCAs all displayed highly informative first axes (47%, 57% and 54% of overall variation explained for root, stem and leaf PCA, respectively), whereas second axes were not consistent (Table S2). Despite different contributions of variables to the root, stem and leaf first PCA axes, a common pattern of organ economy emerged along all three axes. All three organs showed a similar pattern with variables representative of the resource acquisitive strategy at one end of first PCA axis and variables representative of the resource conservative strategy at the other. Pearson’s correlations between species first-axis scores of plant trait-based PCA (leaf, stem and root traits pooled) versus PCAs based on leaf, stem and root traits, separately, displayed high correlation coefficients (r) of 0.93, 0.92 and 0.79, respectively, with P < 0.0001 (data not shown). Correspondingly, Pearson’s correlations performed between species first-axis scores of leaf trait-based PCA versus stem-plus-root trait PCA (Fig. 4d), leaves versus stems (Fig. 4a), leaves versus roots (Fig. 4b) and stems versus roots (Fig. 4c) displayed relatively strong correlations as well (r of 0.81, 0.78, 0.64 and 0.63, respectively, with P < 0.0001).
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Figure S1. Biplots of leaf, stem and root trait relationships. Data are shown without transformation: regression lines are therefore only informative (not always complying with bivariate normality assumption). Species are distinguished according to their higher taxum: ○ Core eudicot; &U25CF; Basal eudicot; &U25CF; Monocot; Δ Pteridophyte; &bsl00066; Lycophyte; &bsl00066; Gymnosperm.
Figure S2. Biplots of leaf, stem and root trait relationships. Data are shown without transformation: regression lines are therefore only informative (not always complying with bivariate normality assumption). Species are distinguished according to their plant type: ○ Woody evergreen; &U25CF; Woody deciduous; &U25CF; Fern ally; Δ Graminoid; &bsl00066; Forb; &bsl00066; Aquatic forb; □ Club moss.
Table S1. Species list and characteristics. All leaf, stem and root trait data is available through the TRY database: http://www.try-db.org.
Table S2. Contributions (%) of organ traits to the construction of first and second axes of organ PCAs.
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