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Plant Biosystems, Vol. 141, No. 3, November 2007, pp. 337 – 343
The leaf economics spectrum of Poaceae reflects variation in survival strategies
S. PIERCE1, R. M. CERIANI2, R. DE ANDREIS1, A. LUZZARO1 & B. CERABOLINI1 1
Dipartimento di Biologia Strutturale e Funzionale, Universita` degli Studi dell’Insubria, Italy, and 2Centro Flora Autoctona, Italy
Abstract Leaf morphology reflects a trade-off between maximising resource acquisition and investment in structural/metabolic durability, and continuous variation in such leaf economics is apparent even within traditional plant functional type categories such as ‘grasses’. We hypothesised that the leaf economics spectrum of ‘grasses’ reflects a spectrum of survival strategies, with functional divergence apparent both within and between ecozones. CSR classification and histology of 30 Poaceae with ranges restricted to either the southern Alps or Po plain of Italy demonstrated that alpine species were predominantly stress-tolerators (mean C:S:R ¼ 26.7:46.1:27.2%) but included some competitive ruderals (e.g. Agrostis schraderana). Lowland species were predominantly competitive ruderals but included some stress-tolerators (e.g. Stipa pennata). Functional relationships were confirmed by PCA: PCA1 represented a trade-off between high SLA, high foliar N, rapid phenology (competitive ruderals) and high foliar C:N and dry matter content (stress-tolerators). Stress-tolerance was negatively correlated with the extent of intercellular airspace, and positively with mesophyll, schlerenchyma and vascular tissues (a trade-off between internal conductivity and durability). The leaf economics spectrum of Poaceae reflects a spectrum of whole plant function, but only the overall plant strategy can elucidate the extent to which vegetative or reproductive phases are critical for survival.
Key words: Adaptive radiation, CSR plant strategy theory, functional divergence, grassland communities, plant functional type, Poaceae
Introduction Theophrastus first devised a functional classification system for plants, assigning different species to one of four ‘great groups’ (herbs, sub-shrubs, shrubs and trees). After more than two millennia plants continue to be classified into broad functional categories (plant functional types, PFTs) based on life form (i.e. ‘grasses’, ‘trees’, ‘forbs’ etc.) or photosynthetic modes (C3, C4, CAM). However, functional classification systems face a trade-off between precision and applicability, depending on geographic scale (Grime, 2001), and broad life form-based categories have proven to be too general to be insightful at anything but landscape scales (Aguiar et al., 1996; Craine et al., 1999, 2002; Reich et al., 1999, 2001,
2003; Oleson & Bonan, 2000; Dormann & Woodin, 2002; Wang, 2004; Wright et al., 2004). At finer scales, such as within ecosystems, functional variation within each category produces ‘groups of uncertain ecological significance and coherence’ (Grime, 2001), that differ at most in only a ‘rough sense’ (Reich et al., 1999). Even seemingly clear-cut traits such as photosynthetic pathways are not necessarily well defined, as C4 and CAM plants employ combinations of C3 and C4 carboxylation, the balance of which varies inherently between species, with C4 carboxylation expressed facultatively or extremely weakly by some (e.g. Haslam et al., 2002; Pierce et al., 2002a,b; Ueno, 2004). Even Kranz anatomy is not a prerequisite for C4 photosynthesis (Voznesenskaya et al., 2002; reviewed by
Correspondence: Simon Pierce, Dipartimento di Biologia Strutturale e Funzionale, Universita` degli Studi dell’Insubria, Via J.H. Dunant 3, I-21100 Varese, Italy. E-mail:
[email protected] ISSN 1126-3504 print/ISSN 1724-5575 online ª 2007 Societa` Botanica Italiana DOI: 10.1080/11263500701627695
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Sage, 2004). Crucially, flexibility in carboxylation may occasionally thwart the use of photosynthetic modes to delimit PFTs in grassland habitats (Wang, 2004). Thus, distinctions between life forms are typically blurred by the presence of a continuum of functional traits, and plant ecology would benefit from more precise functional classification systems than the approach of Theophrastus – ideally a method of expressing variation in whole plant function as a continuous spectrum, rather than as discrete categories (Wright et al., 2004). Spectra of plant functional types and associated leaf economics are predicted by plant strategy theories, with leaf morphology essentially reflecting a trade-off between resource acquisition and structural/metabolic durability (Grime, 1979, 2001). Several independent research groups have recently demonstrated this leaf economics spectrum in the world flora: i.e. a ‘quick to slow return on investments of nutrients and dry mass’ (Wright et al., 2004; see also Reich et al., 1999; Dı´az et al., 2004). Indeed, inherent growth rate is limited mainly by leaf traits as denser leaves include a greater investment in storage and structural tissues at the expense of intercellular airspace, imposing greater internal resistance to CO2 diffusion (e.g. Atkinson & Farrar, 1983; Garnier, 1992; Lambers & Poorter, 1992; Maxwell et al., 1997; Atkin & Lambers, 1998; but see the caveat of Shipley, 2002; Villar et al., 2005). However, the economics of leaves are typically studied separately from the economics and survival strategy of the whole plant (e.g. Wright et al., 2004). Additionally, despite being frequently used as a single PFT, groups such as ‘grasses’ include taxa of radically different ecology (e.g. the genera Anomocloa, Olyra, Erharta, Bromus, Arundo, Eragrostis and Tripsacum) and plant function potentially varies even at finer scales between closely related species in the same habitat – the true extent of functional variability is rarely quantified, even for groups as important as the grasses. We hypothesised that (1) the leaf economics spectrum of ‘grasses’ reflects a spectrum of overall plant function, and (2) grasses exhibit functional divergence in situ both within and between ecozones (alpine and lowland). Leaf functional traits such as specific leaf area (SLA) and leaf dry matter content (LDMC) are used in the calculation of CSR strategies, and thus a correlation between CSR strategies and these aspects of leaf economy is inevitable. Thus we used histological techniques to directly determine the extent of leaf tissues that govern internal diffusion resistances (mesophyll and intercellular airspace) and leaf durability (schlerenchyma and vascular tissues), and correlated this with CSR strategies for 30 grass species restricted to either alpine or lowland ecozones.
Materials and methods CSR plant strategies of species were calculated using the methodology and custom-written Excel spreadsheet detailed extensively in the original validation of the technique (Hodgson et al., 1999), with CSR coordinates adapted for ternary plots as described and validated in our previous work (Caccianiga et al., 2006). To summarise, seven main traits were measured either in situ (canopy height, lateral spread), from material collected in situ and transported immediately to the laboratory (leaf dry weight, leaf dry matter content, specific leaf area: from the same individuals as the canopy height/ lateral spread measurements) or were based on field observations (flowering time, flowering start). These traits were determined for 30 species (each represented by 10 replicate individuals; nomenclature follows the Flora of Italy; Pignatti, 1982). Laboratory measurements followed the standardised methodologies detailed in Cornelissen et al. (2003): for the determination of leaf fresh weight (LFW) and leaf area (LA; i.e. the mean surface area of fullyexpanded leaves) leaf material was stored at 48C overnight to obtain full turgidity. LA was determined using a digital leaf area meter (Delta-T Image Analysis System; Delta-T Devices, Burwell, Cambridgeshire, UK). Leaf dry weight (LDW) was then determined following drying for 24 h at 1058C, and parameters such as specific leaf area (SLA; i.e. LA divided by LDW) were calculated. Adaptive trends were confirmed from a matrix of 30 species 6 9 traits (the seven traits used in CSR classification plus foliar N contents and C:N ratio; determined by CHN-analyzer; NA-2000 N-Protein; Fisons Instruments S.p.A., Rodano (MI), Italy). Multivariate space was represented in two-dimensional space by PCA projection (centred by standard deviation, produced using Syntax 5.2 software; J. Podani, Department of Plant Taxonomy & Ecology, L. Eo¨tvo¨s University, Budapest, Hungary; see Podani, 1994). Principal components of the data, and thus principal adaptive trends within the community, were identified from axes with the highest eigenvector scores, and statistically significant correlations between PCA axis and plant traits were identified using Pearson’s correlation coefficient. CSR strategies were overlaid on the PCA projection by representing the CSR ternary triplet coordinates (e.g. C:S:R ¼ 10:70:20%) by colour combinations determined from equivalent Red, Green, Blue ternary triplets (i.e. the system used to create colour combinations by the human eye, cathode ray tubes, LCDs and graphics software packages). Thus the corresponding RGB triplet for the above CSR triplet is 26,178,51 (i.e. pure green (0, 255,0) represents an exclusively S strategy).
Functional divergence in the Poaceae Leaf anatomical characteristics were determined from young fully expanded leaves of 10 individuals for each species. Leaves were fixed in 5% formaldehyde for 20 min, followed by dehydration through a graded series of ethanol (50 – 100% over 6 h). Samples were infiltrated for 12 h with a methacrylate resin (Technovit 7100) and 3 mm transverse sections cut via microtome. Sections were then dyed with 0.13% w/v basic fuchsin and mounted using Canada balsam. Digital images of leaf sections were overlaid with a ‘tissue map’ showing the position and extent of each tissue type (mesophyll, epidermis, vascular bundle, sclerenchyma) and intercellular airspace, represented as uniform bold colours to enhance contrast (Paint Shop Pro software v.6; Jasc Software, Eden Prairie, MN, USA). Tissue maps were then converted into shape files using ArcView Geographic Information System (GIS) v 3.1 software (Environmental Systems Research Institute, Redlands, CA, USA), from which the relative area covered by pixels of each colour (and thus each tissue type) was calculated. Results A PFT spectrum was apparent throughout the grasses studied, with variation exhibited even within subgroups of Poaceae (i.e. alpine/lowland species). Lowland grass species were typically competitive/ ruderal strategists (Figure 1), such as Festuca pratensis (C:S:R ¼ 42.7:14.2:43.1%). (Authorities of species names and the plant traits used in CSR classification are shown in Table I) At the opposite end of the spectrum, alpine species were stress-tolerators such
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as Festuca halleri (16.5:57.6:25.9), Oreochloa disticha (23.9:65.3:10.8) and Sesleria varia (24.7:62.4:12.9) (Figure 1). However, lowland strategies ranged from strongly competitive species (e.g. Sorghum halepense; 73.3:3.4:23.3) to strongly ruderal strategists (e.g. Lolium perenne; 25.9:0.0:74.1), and also included a small number of broadly stress-tolerant species (e.g. Chrysopogon gryllus; 37.8:52.4:9.8). Two competitive ruderal species also had alpine distributions: Agrostis shraderana (34.8:0.0:65.2) and Poa supina (40.0:5.5:54.5). Alpine grasses had a significantly lower SLA than lowland counterparts (17.9 + 3.38 mm2 mg71 cf. 28.9 + 3.45 mm2 mg71, respectively; p 0.05) and less epidermal tissues, but did not differ significantly in terms of other leaf traits (Table II). A gradient in CSR strategies, from R to S, is apparent along PCA1, whilst CSR strategies tended towards C along PCA2: this confirms that the ordinations of species within the CSR triangle follow real trends (Figure 1). Indeed, PCA1 (eigenvalue ¼ 38.8%) was correlated positively with stress-tolerance (S; Pearson’s correlation coefficient: p 0.001) and negatively with R (p 0.001) and represented a trade-off between high SLA, high foliar N, rapid phenology vs. high foliar C:N and high leaf dry matter content (Figure 2; Table III). PCA2 (eigenvalue ¼ 27.8 %) correlated positively with C (p 0.001) and negatively with R (p 0.05) and represented a trade-off in whole plant dimensional traits such as canopy height and lateral spread vs. flowering period (Figure 2; Table III). Stresstolerance was correlated negatively with the extent of intercellular airspace (p 0.05) and epidermal tissues (p 0.001) and positively with the extent of mesophyll (p 0.05), schlerenchyma (p 0.01) and vascular tissues (p 0.01; Table IV). R exhibited precisely the opposite correlations: thus leaves of stress-tolerators had the least internal airspace (equivalent to less internal conductance to CO2) and more mesophyll and structural tissues (greater structural and metabolic durability; Table IV). Discussion
Figure 1. The CSR plant strategy spectrum of 30 Poaceae with alpine or lowland distributions in northern Italy. Species numbers are listed in Table I. The smaller triangle (top right) indicates the mean strategies for alpine and lowland species.
We distinguished a broad strategy spectrum (from competitive/ruderal species to stress-tolerators) within the ‘grasses’, which was associated with a leaf economics spectrum previously predicted (Grime, 1979, 2001) and detected (Wright et al., 2004) in the world flora. This spectrum of plant function was directly correlated with a leaf economics trade-off: in contrast to faster-growing strategies, the robust leaves of stress tolerators had more extensive schlerenchyma and vascular tissues, greater C:N, and a combination of limited airspace and investment in mesophyll that has previously been
32.9 27.7 39.7 27.8 19.1 24.7 27.7 27.0 35.4 27.7 19.4 23.7 29.4 23.4 21.8 17.8 39.2 18.1 21.8 18.8 16.4 23.9 31.1 36.7 27.3 16.9 17.7 33.1 29.4 48.9
85 + 6.3 270 + 33.6 2535 + 39.7 128 + 4.6 520 + 34.5 261 + 4.8 728 + 32.3 434 + 47.6 567 + 14.4 486 + 24.6 765 + 14.2 176 + 17.8 60 + 6.9 220 + 4.0 355 + 16.9 179 + 18.2 388 + 13.9 561 + 49.5 386 + 19.8 519 + 18.1 212 + 18.0 445 + 19.5 168 + 18.6 101 + 3.0 110 + 13.5 130 + 5.5 406 + 8.7 278 + 11.6 1035 + 22.1 383 + 14.5
Species
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
2 1 2 2 3 3 3 3 2 4 5 2 2 3 4 3 2 3 3 3 8 2 3 2 4 2 5 3 4 2
Flowering period (months) 3 2 5 3 2 2 4 4 3 3 4 4 3 3 3 3 3 2 4 3 3 3 3 3 3 4 3 3 2 3
Lateral spread 2.9 + 0.36 9.6 + 1.93 658.6 + 40.94 6.4 + 0.71 20.4 + 0.69 43.3 + 3.98 52.9 + 6.06 80.0 + 5.91 46.4 + 4.68 41.9 + 3.17 106.5 + 10.40 7.0 + 0.76 1.9 + 0.19 7.1 + 0.88 23.5 + 1.91 5.2 + 0.28 26.7 + 2.68 20.4 + 2.60 13.9 + 1.52 36.9 + 4.03 12.4 + 0.59 12.4 + 1.35 12.7 + 0.74 8.0 + 0.79 10.0 + 1.27 5.0 + 0.35 4.4 + 0.36 21.8 + 1.85 185.9 + 38.64 78.1 + 7.43
Leaf dry weight (mg) 17.6 + 1.20 39.1 + 2.42 12.2 + 0.19 16.9 + 1.33 58.6 + 1.66 19.5 + 0.76 23.4 + 0.57 14.7 + 0.40 13.0 + 1.01 37.8 + 2.49 24.1 + 0.84 35.5 + 0.89 14.3 + 1.65 15.6 + 1.35 28.6 + 0.88 15.6 + 0.51 7.1 + 0.45 42.6 + 4.32 36.1 + 2.23 31.5 + 1.53 48.3 + 1.58 24.0 + 0.93 11.1 + 0.34 8.4 + 0.47 17.0 + 0.73 39.6 + 1.73 56.4 + 1.87 9.8 + 0.77 21.2 + 0.46 5.2 + 0.22
Specific leaf area (mm2 mg71)
1700 – 3600* 1500 – 2800* 0 – 900{ 1800 – 2950* 0 – 1600{ 0 – 1600{ 400 – 1500{ 0 – 1200{ 0 – 1000{ 0 – 1500{ 0 – 800{ 0 – 500{ 2000 – 3400* 1200 – 2400* 0 – 1800{ 0 – 1500{ 1600 – 3000* 0 – 1500{ 0 – 1500{ 0 – 1300{ 0 – 2000{ 0 – 1200{ 1200 – 2600* 2000 – 3300* 1500 – 3600* 1600 – 2800* 0 – 1200{ 1500 – 2600* 0 – 600{ 0 – 800{
Altitudinal range (m a.s.l.)
a The six-point classification (for graminoids) of Hodgson et al. (1999) is used for lateral spread, where: 1, plant short-lived; 2, loosely tufted ramets radiating about a single axis, no thickened rootstock; 3, compactly tufted ramets appressed to each other at base; 4, shortly creeping, 540 mm between ramets; 5, creeping, 40 – 79 mm between ramets; 6, widely creeping, 479 mm between ramets. Leaf characters represent the means (+1 S.E., where appropriate) of 10 replicates (n ¼ 5 for canopy height). Altitudinal range of each species within Italy (and nomenclature) follows Pignatti (1982). *denotes a predominantly alpine distribution, and {denotes a predominantly lowland distribution.
Agrostis rupestris All. Agrostis schraderana Becherer Arundo donax L. Avenula versicolor (Vill.) Lainz Brachypodium sylvaticum (Hudson) Beauv. Bromus erectus Hudson Calamagrostis arundinacea (L.) Roth. Calamagrostis epigejos (L.) Roth. Chrysopogon gryllus (L.) Trin. Digitaria sanguinalis (L.) Scop. Echinochloa crus-galli (L.) Beauv. Eleusine indica (L.) Gaertner Festuca halleri All. Festuca nigrescens Lam. Festuca pratensis Hudson Festuca tenuifolia Sibth. Festuca varia Haenke Holcus lanatus L. Holcus mollis L. Lolium multiflorum Lam. Lolium perenne L. Melica ciliata L. Nardus stricta L. Oreochloa disticha (Wulfen) Link Poa alpina L. Poa supina Schrader Poa trivialis L. Sesleria varia (Jacq.) Wettst. Sorghum halepense (L.) Pers. Stipa pennata L.
Leaf dry matter content (%)
Canopy height (mm)
Table I. Whole-plant and leaf functional traits used for the CSR classification of 30 species of Poaceae from either alpine or lowland habitats in northern Italy.a
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Table II. Leaf functional characteristics for 30 species of Poaceae of northern Italy.a
n N (number content of taxa) (%) Alpine Lowland Significance
11 19
1.8 2.0 n.s.
C:N 25.5 + 2.23 22.2 + 1.63 n.s.
The proportion of transverse leaf sections consisting of each Leaf dry tissue type (%) matter content SLA Intercellular Vascular (%) (mm2 mg71) Epidermis airspace Mesophyll Sclerenchyma bundle 17.9 + 3.38 28.9 + 3.45 *
29.6 25.2 n.s.
18.1 23.9 **
14.4 13.3 n.s.
54.7 50.2 n.s.
5.4 5.3 n.s.
7.1 8.3 n.s.
a
Species are divided according to altitudinal distribution. *denotes significance at the p 0.05 level, as determined by Student’s t-test; **p 0.01; ***p 0.001; n.s., no significance, SLA, specific leaf area. Proportion data were arcsine transformed prior to statistical analysis.
t prin r o line no f Mo lour on co
Figure 2. Principal components analysis (PCA) biplot (axes 1 and 2) for a species 6 traits matrix. (CAN H, canopy height; LAT S, lateral spread; SLA, specific leaf area; LDW, leaf dry weight; LDMC, leaf dry matter content; FLO ST, flowering start; FLO P, flowering period). CSR strategies (i.e. CSR ternary triplet coordinates) are represented by RGB (red, green, blue) ternary triplet values – i.e. pure colours present pure strategies, with mixed colours representing intermediate strategies (e.g. blue/green ¼ SR strategy; see text for details). Eigenvalues are as follows: PCA 1 ¼ 38.8%; PCA 2 ¼ 27.8%; PCA 3 ¼ 12.6%). For a colour version of this figure, please visit the Plant Biosystems website: http://www.informaworld.com/TPLB Table III. Pearson’s correlation coefficients between PCA axes and CSR strategies.a Strategy C S R
PCA 1
PCA 2
PCA 3
70.253 n.s. 0.879 *** 70.884 ***
0.575 *** 70.040 n.s. 70.318 *
0.184 n.s. 70.143 n.s. 0.053 n.s.
a
*p 0.05, **p 0.01, ***p 0.001, n.s., no significance.
implicated in decreased internal CO2 conductance and lower inherent growth rates for a number of grass species (Atkinson & Farrar, 1983; Garnier, 1992; Atkin & Lambers, 1998).
Functional variation was detected both within and between alpine and lowland ecozones. Lowland species exhibited greater functional diversity, possibly reflecting a greater range of available niches encompassing competitive ruderalism to competitive stress-tolerance. The prevalence of competitive ability in lowland species suggests that abiotic niches are not as limiting as those apparent in the alpine zone, but that this is tempered by disturbance. In alpine zones, the grasses studied exhibited primarily conservative, defensive survival strategies characterised by significantly lower SLA (i.e. investment in durable leaf tissues). As grass form and function differs markedly within and between ecozones the general life form of Poaceae cannot represent a coherent functional type. However, grasses share sufficiently similar forms and features to be classified as a single taxonomic group – how can they exhibit differences in function? Poaceae are defined principally by the caryopsis (a fruit comprised of a fused ovule and ovary), and by features including lodicules (modified petals), the hollow, cylindrical stem and distinctive phytoliths (silica bodies; Kellogg, 2001). These features are functional: lodicules swell at anthesis to open the lemma that protects developing flowers (Heslop-Harrison & Heslop-Harrison, 1996), the hollow cylindrical stem provides effective and economic support for reproductive organs (Niklas, 1992) and characteristic phytoliths defend against grazing herbivores by abrading teeth. As these features define the family their functions would undoubtedly have been critical to the survival of the earliest grasses, grazed by titanosaur sauropods in Late Cretaceous conifer/cycad woodlands (Prasad et al., 2005). (The apical meristem of grasses is concealed from herbivores at the base of the leaves, but this is a common feature within the monocots rather than a distinguishing feature of Poaceae.) Pollen evidence indicates that grassland ecosystems are unlikely to have existed until the early-to-mid Miocene (20 – 10 Ma b.p.; Jacobs et al., 1999), the development of which facilitated diversification into the contemporary *10,000 species (Mathews et al., 2000). Crucially, diversification is essentially a
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Table IV. Pearson’s correlation coefficients between C, S and R strategies and the extent of different leaf tissues (the proportion of the leaf existing as different tissue types).a Leaf tissue type
C
S
Epidermis Intercellular airspace Mesophyll Schlerenchyma Vascular bundle
0.475 ** 70.016 n.s.
70.772 *** 70.413 *
0.617 *** 0.501 **
70.136 n.s. 70.206 n.s. 70.050 n.s.
0.392 * 0.500 ** 0.446 **
70.380 * 70.464 ** 70.499 **
R
picture of how plants survive. Currently the only plant strategy theory that realistically incorporates leaf economics and differential investment in vegetative vs. reproductive phases of the life cycle is CSR theory. Acknowledgements
a
*p 0.05, **p 0.01, ***p 0.001; n.s., no significance.
process of functional adaptation. Thus the traits that define the grasses are adaptations to bygone ecosystems, and modern grasses represent a functional type only in a rough sense, watered-down over geological timescales by adaptive radiation in distinct ecological situations, so that contemporary species in alpine and lowland habitats differ considerably in their approach to survival. Thus despite being linked by common ancestry, the particular form of each species reflects functional divergence within the family. Can taxa other than the family be considered as functional types? Convergent evolution leads to functionally equivalent forms (e.g. stem-succulent Euphorbiaceae and Cactaceae), and thus fewer functional types exist than taxa. Indeed, the present study demonstrates both functional convergence between genera (Festuca halleri and Agrostis rupestris) and functional diversity within genera (e.g. Festuca halleri cf. F. pratensis; Figure 1). Silvertown et al. (2001) note that ‘‘species can behave idiosyncratically, rendering taxon membership a poor guide to ecological behaviour’’. If taxa cannot be co-opted for use as PFTs, how can useful PFTs be defined? As Grime (2001) points out, this is largely a problem of scale, resolution and intended application. Certainly, if Ecology is to attempt to answer the most basic questions, such as how species coexist within communities or how vegetation is likely to respond to climate change, then life form classification systems reminiscent of Theophrastus’ great groups will be of limited utility. The worldwide leaf economics spectrum is symptomatic of a spectrum of plant vegetative function (i.e. slow-growing conservative survival strategies vs. rapidly-growing strategies), but leaf function alone can only be used to infer vegetative function – the leaf economics spectrum cannot elucidate whether a species depends more on the reproductive or vegetative phase for survival. Thus, although the leaf economics spectrum is undoubtedly a critical indicator of how plants grow, it does not provide a comprehensive
We thank Corinna Arcellaschi and Marzia Bistoletti for assistance in the field and laboratory. S.P. and A.L. were supported by the Centro Flora Autoctona (Native Flora Centre) of Lombardy, via the University of Insubria.
References Aguiar MR, Paruelo JM, Sala OE, Lauenroth WK. 1996. Ecosystem responses to changes in plant functional type composition: An example from the Patagonian steppe. J Vegetation Sci 7:381 – 390. Atkin OK, Lambers H. 1998. Slow-growing alpine and fastgrowing lowland species: A case study of factors associated with variation in growth rate among herbaceous higher plants under natural and controlled conditions. In: Lambers H, Poorter H, Van Vuuren MMI, editors. Inherent variation in growth rate. Leiden: Backhuys. pp 259 – 288. Atkinson CJ, Farrar JF. 1983. Allocation of photosyntheticallyfixed carbon in Festuca ovina L. and Nardus stricta L. N Phytol 95:519 – 531. Caccianiga M, Luzzaro A, Pierce S, Ceriani RM, Cerabolini B. 2006. The functional basis of a primary succession resolved by CSR classification. Oikos 112:10 – 20. Cornelissen JHC, Lavorel S, Garnier E, Diaz S, Buchmann N, Gurvich DE, et al. 2003. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust J Bot 51:335 – 380. Craine JM, Berin DM, Reich PB, Tilman DG, Knops JMH. 1999. Measurement of leaf longevity of 14 species of grasses and forbs using a novel approach. N Phytol 142:475 – 481. Craine JM, Tilman D, Wedin D, Reich P, Tjoelkier M, Knops J. 2002. Functional traits, productivity and effects on nitrogen cycling of 33 grassland species. Funct Ecol 16:563 – 574. Dı´az S, Hodgson JG, Thompson K, Cabido M, Cornelissen JHC, Jalili A, et al. 2004. The plant traits that drive ecosystems: Evidence from three continents. J Vegetation Sci 15:295 – 304. Dormann CF, Woodin SJ. 2002. Climate change in the Arctic: Using plant functional types in a meta-analysis of field experiments. Funct Ecol 16:4 – 17. Garnier E. 1992. Growth analysis of congeneric annual and perennial grass species. J Ecol 80:665 – 675. Grime JP. 1979. Plant strategies and vegetation processes. Chichester: Wiley. Grime JP. 2001. Plant strategies, vegetation processes and ecosystem properties, 2nd ed. Chichester: Wiley. Haslam RP, Borland AM, Griffiths H. 2002. Short-term plasticity of crassulacean acid metabolism expression in the epiphytic bromeliad, Tillandsia usneoides. Funct Plant Biol 29:749 – 756. Heslop-Harrison Y, Heslop-Harrison JS. 1996. Lodicule function and filament extension in the grasses: potassium ion movement and tissue specialization. Ann Bot 77:573 – 582. Hodgson JG, Wilson PJ, Hunt R, Grime JP, Thompson K. 1999. Allocating CSR plant functional types: A soft approach to a hard problem. Oikos 85:282 – 294.
Functional divergence in the Poaceae Jacobs BF, Kingston JD, Jacobs LL. 1999. The origin of grassdominated ecosystems. Ann Missouri Bot Garden 86:590 – 643. Kellogg EA. 2001. Evolutionary history of the grasses. Plant Physiol 125:1198 – 1205. Lambers H, Poorter H. 1992. Inherent variation in growth rate between higher plants: A search for physiological causes and ecological consequences. Adv Ecol Res 23:188 – 261. Mathews S, Tsai RC, Kellogg EA. 2000. Phylogenetic structure in the grass family (Poaceae): Evidence from the nuclear gene phytochrome B. Am J Bot 87:96 – 107. Maxwell K, von Caemmerer S, Evans JR. 1997. Is a low internal conductance to CO2 a consequence of succulence in plants with Crassulacean acid metabolism? Aust J Plant Physiol 24:777 – 786. Niklas KJ. 1992. Plant biomechanics: An engineering approach to plant form and function. Chicago: University of Chicago Press. Oleson KW, Bonan GB. 2000. The effects of remotely sensed plant functional type and leaf area index on simulations of boreal forest fluxes by the NCAR land surface model. J Hydrometeorol 1:431 – 446. Pierce S, Winter K, Griffiths H. 2002a. Carbon isotope ratio and the extent of daily CAM use by Bromeliaceae. N Phytol 156:75 – 83. Pierce S, Winter K, Griffiths H. 2002b. The role of CAM in high rainfall cloud forests: An in situ comparison of photosynthetic pathways in Bromeliaceae. Plant Cell Environ 25:1181 – 1189. Pignatti S. 1982. Flora d’Italia. Bologna: Edagricole. Podani J. 1994. Multivariate data analysis in ecology and systematics – a methodological guide to the SYN-TAX 5.0 package. The Hague: SPB Academic Publishing. Prasad V, Stro¨mberg CAE, Alimohammadian H, Sahni A. 2005. Dinosaur coprolites and the early evolution of grasses and grazers. Nature 310:1177 – 1180. Reich PB, Ellsworth DS, Walters MB, Vose JM, Gresham C, Volin JC, et al. 1999. Generality of leaf trait relationships: a test across six biomes. Ecology 80:1955 – 1969.
343
Reich PB, Tilman D, Craine J, Ellsworth D, Tjoelker MG, Knops J, et al. 2001. Do species and functional groups differ in acquisition and use of C, N and water under varying atmospheric CO2 and N availability regimes? A field test with 16 grassland species. N Phytol 150:435 – 448. Reich PB, Wright IJ, Cavender-Bares J, Craine JM, Oleksyn J, Westoby M, et al. 2003. The evolution of plant functional variation: traits, spectra, and strategies. Int J Plant Sci 164:S143 – S164. Sage RF. 2004. The evolution of C4 photosynthesis. N Phytol 161:341 – 370. Shipley B. 2002. Trade-offs between net assimilation rate and specific leaf area in determining relative growth rate: Relationship with daily irradiance. Funct Ecol 16:682 – 689. Silvertown J, Dodd M, Gowing D. 2001. Phylogeny and the niche structure of meadow plant communities. J Ecol 89:428 – 435. Ueno O. 2004. Environmental regulation of photosynthetic metabolism in the amphibious sedge Eleocharis baldwinii and comparisons with related species. Plant Cell Environ 27:627 – 639. Villar R, Maran˜o´n T, Quero JL, Panadero P, Arenas F, Lambers H. 2005. Variation in relative growth rate of 20 Aegilops species (Poaceae) in the field: The importance of net assimilation rate or specific leaf area depends on the time scale. Plant Soil 272:11 – 27. Voznesenskaya EV, Franceschi VR, Kiirats O, Artyusheva EG, Freitag H, Edwards GE. 2002. Proof of C4 photosynthesis without Kranz anatomy in Bienertia cycloptera (Chenopodiaceae). Plant J 31:649 – 662. Wang RZ. 2004. Photosynthetic and morphological functional types from different steppe communities in Inner Mongolia, North China. Photosynthetica 42:493 – 503. Wright IJ, Reich PB, Westoby M, Ackerley DD, Baruch Z, Bongers F, et al. 2004. The worldwide leaf economics spectrum. Nature 428:821 – 827.
