Evidence of the ‘plant economics spectrum’ in a subarctic flora

Abstract

Summary1. 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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Evidence of the ‘plant economics spectrum’ in a
subarctic flora
Gre ´ goire T. Freschet*, Johannes H. C. Cornelissen, Richard S. P. van Logtestijn and
Rien Aerts
Department of Systems Ecology, Institute of Ecological Science, Faculty of Earth and Life Sciences, VU University,
De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands
Summary
1. A fundamental trade-off among vascular plants between traits inferring rapid resource acquisi-
tion 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
consistentbetweenorgans.
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
mainvascularhighertaxaandgrowthformsofasubarcticflora.Thetraitswerechosentohavecon-
sistent relevance for plant defence and growth across organs and environments: carbon, nitrogen,
phosphorus,lignin,drymattercontent,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
localleafeconomicsspectrum andthe root (r = 0.64),stem(r = 0.78) and whole-plant(r = 0.93)
economicsspectra.
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’closelyrelatedtothelocalsubarctic‘leafeconomicsspectrum’. Extendingthatconceptto
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-
groundplantfunctionsfromtheabove-groundcomponentsalone.
Key-words: dry matter content, growth form, nutrient content, phylogeny, plant trait,
specific leaf area, terrestrial and aquatic environments, trade-off, vegetative organs
Introduction
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; Dı´az et al.
2004)classifyplantsaccordingtomeaningfulaxesofplantspe-
cialization. Each of these axes represents a trade-off that limits
possible investments of resources to different parts of cells, dif-
ferent 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
plantsbetweenrapidacquisitionandconservationofresources
(Grime et al. 1997; Reich, Walters & Ellsworth 1997; Dı´az
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
ratioreflecttheresourceconservationstrategy.
This trade-off, described as the ‘world-wide leaf economics
spectrum’ (Wright et al. 2004), has sofar not been extended to
theentireplant.Thisisduepartlytothedifficultyofmeasuring
attributes of other plant parts, especially below-ground. Con-
sequently, it is still highly uncertain whether or where traits of
other plant components such as stems or roots will fit on this
*Correspondence author. E-mail: gregoire.freschet@falw.vu.nl
Journal of Ecology 2010, 98, 362–373doi: 10.1111/j.1365-2745.2009.01615.x
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Page 2

axis of specialization. In other words, do all organs of a plant
species support either a more resource conservative or acquisi-
tive 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
alongthisgradientoftraitsrelatedtotheacquisitionorconser-
vation of resources is a high research priority, because varia-
tion 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 interspe-
cific 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-Dı´ez et al.
1998; Wright et al. 2006; Chave et al. 2009) and roots alone
(e.g. Ryser & Lambers 1995; Reich et al. 1998; Roumet, Urce-
lay & Dı´az 2006), the role of integrated interspecific variation
in leaf, stem and root traits still needs to be tested comprehen-
sively(Westoby&Wright2006).Sofar,fewstudieshaveinves-
tigated 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 corre-
lated 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; Tjoel-
ker 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 transportcapacity. 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. Con-
sistency in trait relationships and differential investments
between organs should also be tested across different environ-
ments,andwiderrangesofplantfunctionaltypesandclades.
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 organsinto one theoretical frame-
work. 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 envi-
ronmental 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 diver-
sityneedstobetestedfurther(Wrightet al.2005).
Focusing on plant traits representative of the acquisition–
conservationtrade-offacrossspecies,weheretestthehypothe-
ses that (i) interspecific trait variation of non-leaf plant organs
iscorrelatedwiththatofleaftraitsacrossenvironments,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 environmen-
tal 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’asdefinedbywhole-planttraitcoordination.
We addressed these hypotheses by measuring suites of simi-
lar plant traits from the main vegetative organs, i.e. leaves,
stems and roots, for a subarctic flora representing the key spe-
cies from aquatic, riparian and terrestrial environments and
covering the main vascular higher taxa and growth forms in
thisregion.
Materials and methods
STUDY AREA, SPECIES TYPES AND SAMPLING
The study was carried out around the Abisko Research Station,
North Sweden (68?21¢ N, 18?49¢ E), at low altitude (350–400 m
a.s.l.), below the tree line. Climatic data from the recent decade
(1999–2008) showed a mean annual rainfall of 352 mm and mean
JanuaryandJulytemperaturesof)9.7and12.3 ?C,respectively,with
temperatures ranging from )39.0 to 21.3 ?C (meteorological data,
Abisko Research Station). The forested area, which was the focus of
thisstudy,featuresPodsolsoils(Sjo ¨ gersten&Wookey2002)andcov-
ers most of the landscape below 700–800 m a.s.l. except for occa-
sional swamps and peatlands. The three most distinct ecosystem
types within the chosen forested sites were: upland dry birch forest,
riparian birch forest and forested freshwater systems (ponds and
streams). Seven sampling sites (c. 20-m transects) each including all
three ecosystem types were used to identify the dominant species
(roughly 80–90% of total vascular plant biomass) of each of the eco-
systems (see Cornelissen et al. 2003). These included 15 species from
the dry forest, 18 from the riparian forest and 7 from aquatic systems
(see Table S1 in Supporting Information for the species list and char-
acteristics; or the try online data base http://www.try-db.org for trait
data). When present in two or more ecosystem types, species were
sampled only from the ecosystem where they occurred most abun-
dantly. Within each ecosystem type, species were collected from the
samplingsitewheretheywerethemostabundant.Specieswereidenti-
fied according to Mossberg, Stenberg & Ericsson (1992). Among
these species, seven groups of plant types (woody evergreens (4),
A ‘plant economics spectrum’363
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Table 1. Overviewofmulti-speciesstudiesinvestigatingsimilarplanttraitsacrossleaves,stemsandroots
Source
Trait
Leaves versus stems
Leaves versus roots
Stems versus roots
Clades
Plant types
r
P-value
No.
species
R, r or q*
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 deciduousWoody 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
SLA, specific leaf area; SRL, specific root length; Rmass, mass-based respiraton; NA, non-available data.
The goodness of relationships were found in the literature either as linear regression coefficient (R), Pearson’s correlation coefficient (r), or Spearman’s correlation coefficient (q), the latter being
notified by an asterisk. P is significance of the relationship.
364G. T. Freschet et al.
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woody deciduous (12), fern allies (3), club mosses (1), graminoids (4),
terrestrial forbs (12) and aquatic forbs (4)) and six clades (core eudi-
cots(26), basal eudicots(2), monocots(6), gymnosperms (2), pterido-
phytes (3) and lycophytes (1)) were identified following the APG II
(2003) classification. Each species was sampled for living leaves, fine
stems (< 3 mm diameter) and fine roots (< 2 mm diameter). To
ensure a fair comparison of root types in terms of structure and func-
tion, only the finest root branch order visible to the naked eye was
considered for each species. Similarly, twigs of woody species
(< 3 mm diameter) were thought more closely related to stems of
forbs and herbs in terms of function and physiological activity. In
total, 40 species were sampled for leaves, 39 for stems and 36 for fine
roots. Leaves, stems and roots were sampled from distinct individu-
als. A minimum of 10 different plant individuals (up to 50 for some
species)wereusedforeachspeciesandorgantoensuretherepresenta-
tiveness of the pool collected. For each organ, one part of the collec-
tion was placed in a paper bag and air-dried for the purpose of
chemical analyses while the other was immediately placed in a closed
plastic bagtobeanalysedforDMC–and SLAforleaves–within 5 h
(see Cornelissen et al. 2003, for more details). For root sampling,
plant individuals were excavated and brought to the laboratory. Soil
and alien material were washed off the root system before living and
undamaged roots were collected. Large mycorrhizal rhizomorphs
were brushed off the roots. For all organs, parts with obvious symp-
tomsofdamage,infectionorherbivoreactivitywereavoided.Petioles
and rachides were included as partof the leaf. To avoid effects of sea-
sonalvariation,allleaveswerecollectedwhilefullymatureandbefore
the onset of senescence (see Quested et al. 2003), i.e. between 28 July
and 3 August 2008. Accordingly, stems were all sampled between 4
and 10 August 2008. Roots were collected partly in August 2007 and
partlyinAugust2008owingtothelabour-intensiveprocessinvolved.
PLANT TRAIT MEASUREMENTS
All collected plant material was measured for C, N, P and lignin con-
tent. For that purpose, air-dried subsamples of each material were
ground and subsequently oven-dried for 24 h at 60 ?C. Carbon and
N concentrations were measured by dry combustion on a NA 1500
elementalanalyser(CarloErba,Rodana,Italy).ForP,50 mgofsam-
ple was digested in 1 mL of a 1 : 4 mixture of 37% (v⁄v) HCl and
65% (v⁄v) HNO3in a closed Teflon cylinder for 6 h at 140 ?C. Sam-
ples were then diluted with 4 mL demineralized water and total P
concentration was quantified by spectrophotometry, using the
ammonium molybdate method (Murphy & Riley 1962). Lignin con-
centration was determined as described in Poorter & Villar (1997).
Briefly, the ground material underwent several polar (water, metha-
nol) and non-polar (chloroform) extraction steps, to extract soluble
sugars, soluble phenols and lipids. Acid hydrolysis removed starch,
fructan,pectin and partofthehemi-cellulose.Apart fromsomerecal-
citrant hemi-cellulose, proteins and possibly silicates, the residue
should contain almost only lignin and cellulose. The mass of the resi-
due, corrected for ash content (including silicates), and its C and N
concentrations, were measured. The lignin concentration was thus
calculatedbasedonthedifferenceinCcontent oflignin andcellulose,
aftercorrectionforremainingproteins.
Each sample was also measured for pH by shaking 0.15 mL
ground sample with 1.2 mL demineralized water in an Eppendorf
tube for 1 h at 250 rpm. After centrifugation at 9000 · g for 5 min,
pHofthesupernatantsolutionwasmeasured.
For DMC the samples (10–50 pooled leaves per species, 10–20
stems and 10–50 fine roots) were immersed in tap water over-
night (leaves, fine roots and non-ligneous stems) or for 3 days
(woody stems), then wiped gently and measured for their water-
saturated weight. Subsequently dry weight was measured after
drying for 48 or 72 h (woody stems) at 60 ?C. DMC was
expressed as the ratio between dry weight (mg) and water-satu-
rated weight (g).
For SLA measurements, 10 random fresh leaves per species were
scanned individually using an Area Meter (Delta-T; Burwell, Cam-
bridge, UK), then oven-dried (60 ?C, 48 h) and weighed separately.
SLA of each leaf was then expressed as the ratio between leaf area
(m2) and leaf dry mass (kg). The mean of all 10 ratios was used as
speciesSLA.
ENVIRONMENTAL VARIABLES
Three distinct environments, upland dry birch forest, riparian birch
forest and forestedfreshwatersystem weremeasured for soil Cand N
and soil litter temperature and humidity during the growing season.
Soil litter temperature and humidity were measured from mid-May
to mid-September with one automatic data logger (Hobo Weather
Station, Onset Computer Corporation, Cape Cod, MA, USA) per
environment. Temperature (?C) of the litter was measured with four
probes per station and four probes were used in terrestrial and ripar-
ian environments to measure litter water content (m3water m)3lit-
ter). Probes for water content (Hobo Soil Moisture Smart Sensor)
were calibrated for each litter substrate at the start of the experiment.
Aquatic environments were assumed to have litter water content of
1 m3m)3. For soil nutrient measurement, 12 soil samples were taken
randomlyfromthe0–10 cmsoillayerofeachecosystemusingametal
corer. Coarse litter was brushed off the soil gently prior to sampling.
The 12 samples were pooled, dried at air temperature and homoge-
nized. Living roots and gravel were extracted manually, weighed and
discarded. Remaining samples were weighed and a subsample per
environment was then ground and subsequently used for C and N
analyses following the same methods as for plant material (see
above). The weight percentages of gravel were then used to correct
totalaveragesoilCandNcontent(%)ofeachenvironment.
DATA ANALYSIS
For each trait, cross-species Pearson’s correlations were performed
between the different plant parts. Some data (leaf C:N and P; stem
DMC, N and P; root N, C:N and P) needed log transformation to
correct for deviations from normality. Biplots of leaf–stem and leaf–
root relationships were constructed for DMC, lignin, pH and N.
Linear regressions were used to provide the slopes of the linear
relationships. For N, leaf–stem and leaf–root relationships were
slightly improved by using logarithmic transformations on stem N
and root N; nevertheless, slopes of linear regression were still pro-
vided for additional information. Linear regressions were usedto dis-
play regression lines of each separate environment. Due to the small
number of observations for several clades and plant types, we could
not testtheinfluenceofeachcladeandplant typeonregressionslopes
inarobustway.Wewere,however,abletotestforenvironmentinflu-
ence (‡ 5 observations per group) with standardized major axis
(SMA) tests using SMATR freeware (Warton et al. 2006). Principal
component analyses (PCA) were performed with the total plant trait
set (19 traits), leaf traits only, stem traits only, root traits only and
stem-plus-root traits pooled. Because of the generally high propor-
tions of variance explained by the first PCA axis, these scores were
usedinallsubsequentanalysesasaproxyforthewholeplant orplant
organ economics. To test for significant differences between groups
of species, anovas were carried out – followed by post hoc multiple
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pairwisecomparisons(Tukey’stest)–onthespeciesscoresonthefirst
PCA axis for the complete plant trait set, with either plant environ-
ment, plant type or plant clade as qualitative variable. Linear regres-
sionswere usedtotestthe predictivevalue ofenvironmental variables
(soil C, N and C:N, soil litter temperature and water content) on the
whole-plant PCA first-axis species scores. Finally, Pearson’s correla-
tions were performed between the species scores on each pair of first
axes derived from the PCAs for the different organs. This allowed us
to test the degree of association of the spectrum for each different
plant organ with the whole-plant spectrum and those of each of the
otherorgans.
Results
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 signifi-
cantcorrelations.Phosphoruscontentdid notdisplayanypar-
ticularpattern.
Asseen in Fig. 1for asubset of traits, all– significant– rela-
tionships were linear except for leaf–stem N and leaf–root N
(slightly exponential). Slopes of linear regressions were, how-
ever, 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. Standard-
ized 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
amongorgans.
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, vari-
ables representative of the resource acquisitive strategy, i.e.
SLA, N content and pH, decreased while variables represen-
tative 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 clus-
tered 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 compari-
son 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). Envi-
ronmental variables representative of soil organic matter
quantity (C,R2= 0.55,
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 ever-
greens 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 dif-
ferent from all other groups except fern allies (P = 0.48).
Fern allies, forbs, graminoids and club mosses, occupying a
P < 0.0001),quality(N,
Table 2. Pearsoncorrelations(r)ofspeciestraitsacrossplantparts
Leaves versus stems
(n = 39)
Leaves versus roots
(n = 36)
Stems versus roots
(n = 35)
rP-valuerP-valuerP-value
Carbon (%)
Lignin (%)
DMC (mg g)1)
C⁄N
pH
Nitrogen (%)
Phosphorus (%)
0.81
0.69
0.85
0.59
0.66
0.47
0.21
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.002
NS (0.20)
0.66
0.70
0.48
0.45
0.61
0.35
)0.14
<0.0001
<0.0001
0.003
0.005
<0.0001
0.035
NS (0.43)
0.84
0.82
0.68
0.41
0.27
0.28
0.22
<0.0001
<0.0001
<0.0001
0.014
NS (0.12)
NS (0.11)
NS (0.21)
r is Pearson’s correlation coefficient; P is significance of the correlation, with NS (non-significant P); n is the number of species.
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Page 6

central position on the spectrum, were not significantly differ-
ent 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).
Similarlyto the whole-plantPCA,organPCAsalldisplayed
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 strat-
egyatone endoffirstPCAaxisand variablesrepresentative of
theresourceconservativestrategyattheother.Pearson’scorre-
lations between species first-axis scores of plant trait-based
PCA (leaf, stem and root traits pooled) versus PCAs based on
leaf,stemandroottraits,separately,displayedhighcorrelation
coefficients (r) of 0.93, 0.92 and 0.79, respectively, with
0
100
200
300
400
500
Leaf DMC (mg g–1)
Stem DMC (mg g–1)
Y = –26.53 + 1.18 X
0
100
200
300
400
500
Root DMC (mg g–1)
Y = 143.63 + 0.42 X
0
100
200
300
400
500
Stem DMC (mg g–1)
Root DMC (mg g–1)
Y = 147.71 + 0.38 X
0
10
20
30
40
Leaf lignin (%)
Stem lignin (%)
Y = 1.23 + 1.56 X
0
10
20
30
40
Leaf lignin (%)
Root lignin (%)
Y = 2.41 + 1.53 X
0
10
20
30
40
Stem lignin (%)
Root lignin (%)
Y = 3.43 + 0.81 X
3
4
5
6
7
Leaf pH
Stem pH
Y = 3.18 + 0.41 X
3
4
5
6
7
0
100200 300 400
500
Leaf DMC (mg g–1)
0 100200300400 500
0100 200300 400 500
010 203040
0 10 2030400 10 203040
345673456734567
Leaf pH
Root pH
Y = 3.45 + 0.38 X
3
4
5
6
7
Stem pH
Root pH
–0.5
–0.4
–0.3
–0.2
–0.1
0
0.1
0.2
0.3
0.4
0.5
0
Leaf N (%)
Log [Stem N]
–0.5
–0.4
–0.3
–0.2
–0.1
0
0.1
0.2
0.3
0.4
0.5
Leaf N (%)
Log [Root N]
–0.5
–0.4
–0.3
–0.2
–0.1
0
0.1
0.2
0.3
0.4
0.5
Log [Stem N]
Log [Root N]
( Y = –0.15 + 0.08 X )
( Y = –0.38 + 0.12 X )
0.5
1
1.52 2.53 3.5
0
0.5
–0.5 –0.4 –0.3 –0.2 –0.1 0.1 0.2 0.3 0.4 0.50
1
1.52 2.53 3.5
Fig. 1. Biplots ofleaf, stemand roottrait relationships. TypeI regression lines are shown for the total species pool (—) alongwiththe regression
equations,andforeachenvironment,terrestrial(––),riparian(—)andaquatic(---),whensignificant.Speciesaredistinguishedaccordingtotheir
environment:sTerrestrial;
Riparian;Aquatic.DistributionsofhighertaxaandplanttypesareavailableinFigs S1andS2,respectively.
A ‘plant economics spectrum’367
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Page 7

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 correla-
tions as well (r of 0.81, 0.78, 0.64 and 0.63, respectively, with
P < 0.0001).
Discussion
THE LINK BETWEEN PLANT ORGAN ECONOMICS AND
THE CHOSEN TRAITS
The traits involved in this study were chosen to represent the
trade-off between fast acquisition and conservation of
Ca.p
E.f
H.v
M.t
P.a
A.i
A.a
Tro.e
C.r
Co.p
D.c
E.a
F.u
G.s
P.p
Ri.s
Ru.s
S.c
S.l
S.p
Sa.a
So.a
B.n
B.p
C.s
D.f
E.n
E.s
Stems P
J.c
L.a
P.s
P.t
V.m
V.u
V.v
Leaves DMC
SLA
Leaves N
Leaves C / N
Leaves P
Leaves Lignin
Leaves pH
Stems DMC
Stems Lignin
Stems N
Roots pH
Stems C / N
Roots DMC
Roots N
Roots C / N
Roots P
Roots Lignin
Axis 1 (43.3 %)
Axis 2 (11.6 %)
Aquatic
(a)
Riparian
(b)
Terrestrial
(c)
Stems pH
Eudicot (a)
Lyco. (ab)
Gymno. (b)
Monocot (a)
Basal eudicot (a)
Pterido. (a)
(C) Phylogeny
Aquatic
forb (a)Fern ally (ab)
Forb (b)
Graminoid (b)
Woody deciduous
(c)
Woody evergreen
(d)
(B) Plant types
(A) Environment
Club
moss (bcd)
Fig. 2. The plant economics spectrum, from fast-growing (left) to nutrient-conservative (right) species. Variables (plant traits) used for the PCA
are displayed withtheir vector. The plant economics spectrumis represented as a straight horizontal line(axis 1). Species nameabbreviationsare
provided next to their position on the graph; see Table S1, for details of the abbreviations. (A), (B) and (C) are three representations of the same
PCA with different grouping of species (i.e. supplementary qualitative variables). Centroids of each species group – plant environment in (A),
planttypein(B)andplantphylogenyin(C)–aredisplayedwithanindexoftheirstatisticaldifference(a,b,c,d…;resultofpairwisecomparisons
ofaxis1speciesscores).Symbols:(A)
Aquatic;Riparian;sTerrestrial;(B)
Woodydeciduous;sWoodyevergreen;(C)Basaleudicot;dMonocot;DPteridophyte;sCoreeudicot;
Aquaticforb;DGraminoid;Forb;dFernally;hClubmoss;
Lycophyte; Gymnosperm.
–8
–6
–4
–2
0
2
4
6
8
0.40.50.60.70.80.91
Average litter water content during the
growing season (m3m–3)
R2 = 0.52 P < 0.0001
–8
–6
–4
–2
0
2
4
6
8
Soil N (%)
R2 = 0.53 P < 0.0001
Plant PCA first axis scores
Nutrient
conservative
species
Nutrient
acquisitive
species
0 0.51 1.52
Fig. 3. Linearregressionsbetweenenvironmentalvariablesandthespecieseconomicstrategies.
368G. T. Freschet et al.
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Page 8

resources. They are, however, not exhaustive. To get a more
comprehensivepicture,otherimportanttraitsrepresentativeof
growth rate or nutrient uptake could have been used such as
respirationrate,tissuelifespan,specificrootlength,tissueden-
sity or type of mycorrhizal association. The chosen traits,
focusing on structural and chemical investments, are neverthe-
lessfairlyrepresentativeoftheseplanttraitsandfunctions.For
instance, N content relates to respiration (Reich et al. 2008)
and to mycorrhizal type (Cornelissen et al. 2001), while DMC
is representative of tissue density (Wahl & Ryser 2000), life
span and growth rate (Cornelissen, Diez & Hunt 1996; Ryser
1996; Wright & Cannon 2001). Lignin is inversely correlated
with digestibility (Cornelissen et al. 2004). Phosphorus and
pH, representative of plant nutrient content, are part of leaf
growth and defence strategy (Wright et al. 2004; Cornelissen
et al. 2006). While clearly established for leaf economics, the
link between these traits and the root and stem functions has,
however,notyetbeenclearlydemonstratedandremainspartly
speculative. As a result of this coordinated choice of traits, all
traits involved in this study are highly inter-correlated, except
P content (Fig. 2). This is surprising, as leaf and root P con-
tents usually covary with other nutrients (Thompson et al.
1997; Gordon & Jackson 2000), notably N. Moreover, leaf P,
which tends to covary with plant relative growth rate (e.g. Ka-
zakou et al. 2006), is supposed to reflect plant life strategy
(Thompson et al. 1997). In the present work, leaf, stem and
root P contents hardly contribute to the plant resource eco-
nomics axis. We hypothesize that in some other ecosystems of
theworld,withstrongphosphoruslimitationofbiologicalpro-
cesses, e.g. certain (sub)tropical, temperate and boreal forests,
wetlands and drylands (see Wardle, Walker & Bardgett 2004;
Wassen et al. 2005; Lambers et al. 2008), P contents of the dif-
ferentorganswillcorrespondmorecloselytothisaxis.
Independent of the plant part considered, nutrient content
variables – here N and pH (i.e. cation content) – are inversely
correlated with structural variables – DMC, lignin, C:N – con-
firming at the whole-plant level a pattern well-established at
leaflevel(e.g.Cornelissenet al.2004;Garnieret al.2004).Evi-
dence is accumulating that such patterns of trait correlations
represent evolutionary and⁄or biophysical constraints on leaf
structureand function (Reich et al. 2003b). The presentresults
provide strong evidence that interspecific variation in this suite
of traits is consistent not only at the level of individual plant
organsbutalsoatthewhole-plantlevel.
TIGHT ACROSS-SPECIES COORDINATION BETWEEN
LEAF, STEM AND ROOT TRAITS, ACROSS
ENVIRONMENTS, CLADES AND PLANT TYPES
Finding strategic dimensions of trait variation and correlation
is a predominant goal of functional ecology. At present, the
relationship between root and shoot traits is still poorly under-
stood and, for many traits, inconsistent (see Table 1 but also
Wright et al. 2006). The cross-species positive correlations
found in this study between the three main plant parts, leaves,
stems and roots, for any particular trait, are generally good.
Interestingly, traits representative of structural investment,
such as DMC, C and lignin contents, are more strongly corre-
lated across organs than mineral nutrient traits such as pH, N
or P. This contrasts with the world-wide correlations found by
Kerkhoff et al. (2006) for N and P across all organs and
indicates that regional differences exist with respect to those
–6
–4
–2
0
2
4
6
Leaf PCA first axis scores
Stem PCA first axis scores
(a)
r = 0.78
–6 –4–2
0246
–6
–4
–2
0
2
4
6
Leaf PCA first axis scores
Root PCA first axis scores
r = 0.64
(b)
–6 –4–20246
–6
–4
–2
0
2
4
6
Leaf PCA first axis scores
Stem + root PCA first axis scores
r = 0.81
(d)
–6–4–2 0246
–6
–4
–2
0
2
4
6
–6 –4–20246
Stem PCA first axis scores
Root PCA first axis scores
(c)
r = 0.63
Fig. 4. Correlations
spectra, as indicated by PCA first axis score,
ofdifferentplantparts.
between economics
A ‘plant economics spectrum’ 369
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Page 9

relationships. The present results indicate that strong correla-
tions exist also for structural traits, seemingly independent of
phylogenetic or plant-type constraints. However, inconsistent
results found in the literature for the same structural traits
imply that these relationships are influenced by spatial scale or
geographical area of study. Further empirical data and meta-
analysis are still much needed to settle this issue. Kerkhoff
et al. (2006) showed that both the contingencies of evolution-
ary history and some degree of environmental convergence
have led to a common set of rules that constrain the partition-
ing of N and P among plant organs. Similarly, we found here
that, while a trade-off in plant resource investment may exist
across plant organs within individual vascular plants, it is con-
strained within a rather narrow range of variation, especially
intermsofstructuralinvestments.
As demonstrated by the differences between slopes of leaf–
stem and leaf–root correlations (not always equal to 1; Fig. 1),
this goes in conjunction with a differential investment between
organs. With regard to the intercepts (rather close to zero,
except for pH), this pattern of investment is, however, fairly
similarbetweenleavesandstemsacrossallspeciesstudiedhere,
whichever environment, plant type or clade they belong to.
Conversely, the much larger values of intercepts found for
leaf–root correlations imply differential investments across
species, in relation to their position on the nutrient acquisi-
tion–conservation trade-off. We hypothesize that internal
physiological, ontogenetic and allometric constraints may be
responsible for the reduced range of differential investment
between leaves and stems (see also Niklas 1999; Wright et al.
2006) and for the consistent differential investments between
leavesandroots.
Although the small aquatic data set limits its scope, it is
nevertheless striking to see that aquatic plants seem to fall
into the same pattern of trait coordination among organs as
terrestrial ones. With regard to the contrasted habitats and
abiotic conditions faced by aquatic and terrestrial species,
one would expect major differences between their respective
organs in terms of roles and adaptations (Niklas 1992) and
therefore trade-offs. Physical properties of fluids enveloping
organisms profoundly influence every biological function.
For instance, terrestrial plant organs suffer desiccation, as
opposed to aquatic ones. Aquatic stems and leaves face
much stronger forces than terrestrial organs when exposed
to the same fluid flow due to the respective viscosities of
water and air. Aquatic and terrestrial stems, whose main
function is plant support in both cases, obey furthermore dif-
ferent constraints, the former adapting to pulling forces by a
tensile body while the latter adapt to downward gravity
forces by a compressive body. Besides, the main function of
aquatic roots may be less related to nutrient uptake than to
anchorage and they may thus take a different form com-
pared to terrestrial roots. Although all these (among many
other) shifts in environmental constraints affect plant organ
forms and functions, they do not seem to affect dramatically
the trait coordination between the different organs. Part of
the answer may lie in the similar paths taken by all plant
organs to adapt to their environment. In this regard, all
organs of aquatic species need lower structural investments
and, along with that, higher nutrient contents than their ter-
restrial counterparts.
EVIDENCE OF A ‘PLANT ECONOMICS SPECTRUM’
We identified in the first PCA axis for all organs (Fig. 2), a
coherent ‘plant economics spectrum’ that is the result of mul-
tiple covarying leaf, stem and root traits around which the
species cluster. This axis of specialization is, however, built
mostly out of plant vegetative organ structural and chemical
investment variables and represents therefore only a partial
picture of the plants’ resource economics. With respect to
stems and roots, more confident interpretation of the data
would require further assessment of the linkages between the
present traits and plant economics. This spectrum is never-
theless fully consistent with the current knowledge on the
influence of environment on plant traits and life strategies
(e.g. Diaz, Cabido & Casanoves 1998; Hendricks et al. 2000;
Ackerly & Cornwell 2007). Plant-type clustering (Fig. 2B)
along this spectrum is also concordant with the literature
(e.g. Diaz & Cabido 1997; Grime et al. 1997; Diaz et al. 2004
for the nutrient acquisitive character of aquatic plants), con-
firming further the link with well-established plant strategy
schemes. The novelty of our approach comes from the rigor-
ous characterization of the same traits across all vegetative
organs, thus allowing a consistent comparison of interspecific
trait variation between plant organs. Some non-leaf traits,
mostly allometric and regenerative traits, have previously
been integrated into well-known strategy schemes (e.g. Grime
et al. 1997; Westoby 1998; Craine et al. 2001; Ackerly 2004;
Dı´az et al. 2004), and leaf traits were found to be the stron-
gest representatives of the resource acquisition–conservation
axis. Conversely, leaf, stem and root traits merged here into
one ‘plant economics spectrum’.
Sinceliteratureontherelevanceofplanttraitsacrosstheter-
restrial–aquatic boundary is still scarce, some caution is neces-
sary concerning the position of aquatic plants on this
spectrum. Some evidence exists nevertheless with regard to the
positive role of lignin and C:N ratio on herbivory defence
(Hanley et al. 2007) and the positive correlation of N and P
with plant growth rate (Nielsen et al. 1996). Other traits such
as density might, however, be needed to complete the picture,
since the chosen traits do not reflect adaptations such as the
presence of internal gas spaces, which have an important role
in oxygen and carbon dioxide diffusion (Sorrell & Dromgoole
1989;Raven1996)andtherebyplantgrowth.
LOCAL ENVIRONMENTAL FEATURES PLAY A PIVOTAL
ROLE IN EXPLAINING PLANT FUNCTIONAL TRAIT
VARIATION
This study emphasizes the importance of local environmental
variation as a driver of functional trait diversity. In line with
Wright et al. (2004), we found great variation in species traits
at the local scale. Besides, surrogates for environmental vari-
ablessuchassoilorganicmatterquantity(C),quality(N,C:N)
370G. T. Freschet et al.
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Page 10

and mineralization rate (litter temperature and moisture) dur-
ing the growing season are good predictors of species econom-
ics, as defined by their scores on the plant economics axis.
Below-ground and above-ground systems are under strong
mutual influence through complex feedbacks between plant
community composition and soil fertility (Aerts 1999; Wardle
et al. 2004). The link identified here between plant traits and
ecosystem properties is a direct consequence of those strong
interactions. It reinforces the idea that plant traits can be used
to capture ecosystem properties (e.g. Chapin et al. 1996; Gar-
nier et al. 2004). The spatial changes occurring within and
across environments can thus potentially explain a large part
of the local plant species and plant trait diversity. The fact that
groups of dominant species sorted by environmental features,
all found within 20-m transects, are very significantly different
fromeachotherandshowlittleoverlap(Fig. 2A)alsosupports
that claim. Microenvironmental variation is in general mostly
driven notonlyby heterogeneityin microtopography and con-
sequent surface hydrology, but also by local heterogeneity in
soil depth, biotic factors and successional time (e.g. Grime
2001). These results suggest that this microvariation might be
the missing key to our understanding of the tremendous func-
tional diversity generally found at the local scale (see also
Cornwell&Ackerly2009).
CONCLUSION: EVIDENCE OF A STRONG LINK BETWEEN
THE ‘LEAF ECONOMICS SPECTRUM’ AND THE ‘PLANT
ECONOMICS SPECTRUM’ AT HIGHER LATITUDES
Lookingforconsistencyofspeciesrankingbetweenthespectra
for the different plant organs, we have unravelled promising
relationships, the mostclosely coordinated organs being leaves
and stems, while roots exhibit a slightly less tight but still coor-
dinated pattern. The similarity of the species ranking for all
threeplantpartsinmultivariatespace(Fig. 4a–c)supportsour
hypothesis that leaves,stems and roots occur at the same posi-
tion on the resource acquisition versus conservation trade-off.
This similarity is based on the strong coherence between plant
traitsofthedifferentorgans,acrossclades,planttypesorplant
environments represented in this study. Moreover, the wide
spread of eudicots and monocots along the plant economics
spectrum demonstrates the spectrum’s overall existence not
only between but also within large clades. The non-coordi-
nated part of the trait variation between leaves and roots (see
Table 1; Fig. 4b) and stem and roots (Fig. 4c) might stem not
only from the somewhat different meanings plant traits carry
depending on the environmental context but also across plant
types and clades (Ryser 2006; Lusk et al. 2008). The subarctic
leaf economics spectrum is representative of the rest of the
plant (stem and root PCA, Fig. 4d) and reflects remarkably
well the plant economics spectrum (leaf, stem and root PCA,
r = 0.93; data not shown). These findings support the view
that evolution works on the entire plant rather than just iso-
latedplanttraits(e.g.Reichet al.2003b;Kerkhoffet al.2006),
andtheyshednewlightonthefunctionofplantsasconnectors
of above-ground and below-ground systems (see Wardle et al.
2004).However,sinceourworkisbasedondatafromhigh-lat-
itude ecosystems only, with several important traits to com-
plete the picture lacking, it needs to be extended to other plant
traits and further tested on other floras and biomes across the
world. These results, if widely applicable,would imply promis-
ingperspectivesforfunctionalecology,astheystronglysuggest
that plant resource economics can to some significant degree
be studied through the prism of leaves. In the present context
of rapid human-induced changes of ecosystems world-wide
(Millennium Ecosystem Assessment 2005), being able to use
leaf trait changes to predict whole-plant functional trait
changes, including trait changes below-ground, would indeed
be of great help for better forecasting ecosystem consequences
ofchangesinvegetationcomposition.
Acknowledgements
We are grateful to the Abisko Scientific Research Station (ANS) and its staff
for their kind assistance and hospitality. G.T.F. was supported by EU Marie
Curie host fellowship grant MEST-CT-2005-MULTIARC 021143; R.A. and
R.S.P.L. by EU ATANS grant Fp6 506004; and J.H.C.C. by grants
047.017.010 and 047.018.003 of The Netherlands Organisation for Scientific
Research (NWO). We are thankful to anonymous referees who provided con-
structivecommentsonthemanuscript.
References
Ackerly, D. (2004) Functional strategies of chaparral shrubs in relation to sea-
sonalwaterdeficitanddisturbance.EcologicalMonographs,74,25–44.
Ackerly, D.D. & Cornwell, W.K. (2007) A trait-based approach to community
assembly: partitioning of species trait values into within- and among-com-
munitycomponents.EcologyLetters,10,135–145.
Aerts,R.(1999)Interspecificcompetitioninnaturalplantcommunities:mecha-
nisms, trade-offs and plant-soil feedbacks. Journal of Experimental Botany,
50,29–37.
APG II (2003) An update of the Angiosperm Phylogeny Group classification
for the orders and families of flowering plants: APG II. Botanical Journal of
theLinneanSociety,141,399–436.
Castro-Dı´ez, P., Puyravaud, J.P., Cornelissen, J.H.C. & Villar-Salvador, P.
(1998)Stem anatomy and relative growth rate in seedlings ofa wide range of
woodyplantspeciesandtypes.Oecologia,116,57–66.
Chapin, F.S., III, Bret-Harte, M.S., Hobbie, S.E. & Zhong, H. (1996) Plant
functional types as predictors of transient responses of arctic vegetation to
globalchange.JournalofVegetationScience,7,347–358.
Chave, J., Coomes, D., Jansen, S., Lewis, S.L., Swenson, N.G. & Zanne, A.E.
(2009)Towardsaworldwidewoodeconomicsspectrum.EcologyLetters,12,
351–366.
Cornelissen, J.H.C., Diez, P.C. & Hunt, R. (1996) Seedling growth, allocation
and leaf attributes in a wide range of woody plant species and types. Journal
ofEcology,84,755–765.
Cornelissen, J.H.C., Aerts, R., Cerabolini, B., Werger, M. & van der Heijden,
M. (2001) Carbon cycling traits of plant species are linked with mycorrhizal
strategy.Oecologia,129,611–619.
Cornelissen,J.H.C.,Lavorel,S.,Garnier,E.,Diaz,S.,Buchmann,N.,Gurvich,
D.E.,Reich,P.B.,Steege,H.t.,Morgan,H.D.,Heijden,M.G.A.v.d.,Pausas,
J.G. & Poorter, H. (2003) A handbook of protocols for standardised and
easymeasurement ofplant functional traits worldwide.AustralianJournalof
Botany,51,335–380.
Cornelissen, J.H.C.,Quested,H.M., Gwynn-Jones, D., VanLogtestijn, R.S.P.,
De Beus, M.A.H., Kondratchuk, A., Callaghan, T.V. & Aerts, R. (2004)
Leaf digestibility and litter decomposability are related in a wide range of
subarcticplantspeciesandtypes.FunctionalEcology,18,779–786.
Cornelissen, J.H.C., Quested, H.M., van Logtestijn, R.S.P., Pe ´ rez-Harguinde-
guy, N., Gwynn-Jones, D., Dı´az, S., Callaghan, T.V., Press, M.C. & Aerts,
R. (2006) Foliar pH as a new plant trait: can it explain variation in foliar
chemistry and carbon cycling processes among subarctic plant species and
types?Oecologia,147,315–326.
Cornwell, W.K. & Ackerly, D.D. (2009) Community assembly and shifts in
plant trait distributions across an environmental gradient in coastal Califor-
nia.EcologicalMonographs,79,109–126.
A ‘plant economics spectrum’ 371
? 2009 The Authors. Journal compilation ? 2009 British Ecological Society, Journal of Ecology, 98, 362–373

Page 11

Craine, J.M. & Lee, W.G. (2003) Covariation in leaf and root traits for native
and non-native grasses along an altitudinal gradient in New Zealand. Oeco-
logia,134,471–478.
Craine, J.M., Froehle, J., Tilman, D.G., Wedin, D.A. & Chapin, F.S., III
(2001) The relationships among root and leaf traits of 76 grassland species
and relative abundance along fertility and disturbance gradients. Oikos, 93,
274–285.
Craine, J.M., Lee, W.G., Bond, W.J., Williams, R.J. & Johnson, L.C. (2005)
Environmental constraints on a global relationship among leaf and root
traitsofgrasses.Ecology,86,12–19.
De Deyn, G.B., Cornelissen, J.H.C. & Bardgett, R.D. (2008) Plant functional
traits and soil carbon sequestration in contrasting biomes. Ecology Letters,
11,516–531.
Diaz, S. & Cabido, M. (1997) Plant functional types and ecosystem function in
relationtoglobalchange.JournalofVegetationScience,8,463–474.
Diaz, S., Cabido, M. & Casanoves, F. (1998) Plant functional traits and envi-
ronmental filters at a regional scale. Journal of Vegetation Science, 9, 113–
122.
Diaz,S.,Hodgson,J.G.,Thompson,K.,Cabido,M.,Cornelissen,J.H.C.,Jalil-
i, A. et al. (2004) The plant traits that drive ecosystems: evidence from three
continents.JournalofVegetationScience,15,295–304.
Garnier, E., Cortez, J., Billes, G., Navas, M.-L., Roumet, C., Debussche, M.,
Laurent, G.,Blanchard, A., Aubry, D., Bellmann, A., Neill, C.& Toussaint,
J.-P. (2004) Plant functional markers capture ecosystem properties during
secondarysuccession.Ecology,85,2630–2637.
Gordon, W.S. & Jackson, R.B. (2000) Nutrient concentrations in fine roots.
Ecology,81,275–280.
Grime, J.P. (1977) Evidence for the existence of three primary strategies in
plants and its relevanceto ecological and evolutionarytheory.The American
Naturalist,111,1169–1194.
Grime, J.P. (2001) Plant Strategies, Vegetation Processes, and Ecosystem Prop-
erties.Wiley,NewYork.
Grime,J.P., Thompson,K., Hunt,R., Hodgson, J.G.,Cornelissen,J.H.C.,Ro-
rison, I.H. et al. (1997) Integrated screening validates primary axes of spe-
cialisationinplants.Oikos,79,259–281.
Hanley, M.E., Lamont, B.B., Fairbanks, M.M. & Rafferty, C.M. (2007) Plant
structuraltraitsand their roleinanti-herbivoredefence. Perspectives inPlant
Ecology,EvolutionandSystematics,8,157–178.
Hendricks, J.J., Aber, J.D., Nadelhoffer, K.J. & Hallett, R.D. (2000) Nitrogen
controls on fine root substrate quality in temperate forest ecosystems. Eco-
systems,3,57–69.
Kazakou, E., Vile, D., Shipley, B., Gallet, C. & Garnier, E. (2006) Co-
variations in litter decomposition, leaf traits and plant growth in spe-
cies from a Mediterranean old-field succession. Functional Ecology, 20,
21–30.
Kerkhoff, A.J., Fagan, W.F., Elser, J.J. & Enquist, B.J. (2006) Phylogenetic
and growth form variation in the scaling of nitrogen and phosphorus in the
seedplants.TheAmericanNaturalist,168,E103–E122.
Lambers, H., Raven, J.A., Shaver, G.R. & Smith, S.E. (2008) Plant nutrient-
acquisition strategies change with soil age. Trends in Ecology & Evolution,
23,95–103.
Lusk,C.H.,Reich,P.B.,Montgomery,R.A.,Ackerly,D.D.&Cavender-Bares,
J. (2008) Why are evergreen leaves so contrary about shade? Trends in Ecol-
ogy&Evolution,23,299–303.
Millennium Ecosystem Assessment (2005) Living Beyond Our Means: Natural
AssetsandHumanWell-being.MillenniumEcosystemAssessment.
Mossberg, B., Stenberg, L. & Ericsson, S. (1992) Den Nordiska Floran.
Wahlstro ¨ m&Widstrand,Stockholm,Sweden.
Murphy,J.&Riley,J.P.(1962)Amodifiedsinglemethodforthedetermination
ofphosphateinnaturalwaters.AnalyticaChimicaActa,27,31–36.
Nielsen, S.L., Enriquez, S., Duarte, C.M. & Sand-Jensen, K. (1996) Scaling
maximum growth rates across photosynthetic organisms. Functional Ecol-
ogy,10,167–175.
Niklas, K.J. (1992) Plant Biomechanics: An Engineering Approach to Plant
FormandFunction.UniversityofChicagoPress,Chicago,IL,USA.
Niklas, K.J.(1999)A mechanicalperspectiveonfoliage leafform and function.
NewPhytologist,143,19–31.
Poorter, H. & Villar, R. (1997) The fate of acquired carbon in plants: chemical
composition and construction costs. Plant Resource Allocation (eds F.A.
Bazzaz&J.Grace),pp.39–72.AcademicPress,SanDiego,CA.
Quested, H.M., Cornelissen, J.H.C., Press, M.C., Callaghan, T.V., Aerts,
R., Trosien, F., Riemann, P., Gwynn-Jones, D., Kondratchuk, A. &
Jonasson, S.E. (2003) Decomposition of sub-arctic plants with differing
nitrogen economies: a functional role for hemiparasites. Ecology, 84,
3209–3221.
Raven, J.A. (1996) Into the voids: the distribution, function, development and
maintenanceofgasspacesinplants.AnnalsofBotany,78,137–142.
Reich, P.B., Walters, M.B. & Ellsworth, D.S. (1997) From tropics to tundra:
global convergence in plant functioning. Proceedings of the National Acad-
emyofSciencesoftheUnitedStatesofAmerica,94,13730–13734.
Reich, P.B., Tjoelker, M.G., Walters, M.B., Vanderklein, D.W. & Buschena,
C. (1998) Close association of RGR, leaf and root morphology, seed mass
andshadetoleranceinseedlingsofnineborealtreespeciesgrowninhighand
lowlight.FunctionalEcology,12,327–338.
Reich,P.B., Buschena,C., Tjoelker,M.G.,Wrage,K., Knops, J., Tilman, D.&
Machado, J.L. (2003a) Variation in growth rate and ecophysiology among
34 grassland and savanna species under contrasting N supply: a test of func-
tionalgroupdifferences.NewPhytologist,157,617–631.
Reich, P.B., Wright, I.J., Cavender-Bares, J., Craine, J.M., Oleksyn, J., West-
oby, M. & Walters, M.B. (2003b) The evolution of plant functional varia-
tion: traits, spectra, and strategies. International Journal of Plant Sciences,
164,S143–S164.
Reich, P.B., Tjoelker, M.G., Pregitzer, K.S., Wright, I.J., Oleksyn, J. & Mach-
ado, J.-L. (2008) Scaling of respiration to nitrogen in leaves, stems and roots
ofhigherlandplants.EcologyLetters,11,793–801.
Roumet, C., Urcelay, C. & Dı´az, S. (2006) Suites of root traits differ between
annual and perennialspecies growing inthe field.New Phytologist, 170, 357–
368.
Ryser, P. (1996) The importance of tissue density for growth and life span of
leaves and roots: a comparison offive ecologically contrasting grasses. Func-
tionalEcology,10,717–723.
Ryser,P.(2006)Themysteriousrootlength.PlantandSoil,286,1–6.
Ryser,P.&Lambers,H.(1995)Rootandleafattributesaccountingfortheper-
formance of fast- and slow-growing grasses at different nutrient supply.
PlantandSoil,170,251–265.
Sjo ¨ gersten, S. & Wookey, P.A. (2002) Climatic and resource quality controls
on soil respiration across a forest-tundra ecotone in Swedish Lapland. Soil
BiologyandBiochemistry,34,1633–1646.
Sorrell, B.K. & Dromgoole, F.I. (1989) Oxygen diffusion and dark respiration
inaquaticmacrophytes.Plant,CellandEnvironment,12,293–299.
Suding,K.N.,Lavorel, S.,Chapin, F.S., III,Cornelissen,J.H.C., Diaz, S.,Gar-
nier, E., Goldberg, D., Hooper, D.U., Jackson, S.T. & Navas, M.-L. (2008)
Scaling environmental change through the community-level: a trait-based
response-and-effect framework for plants. Global Change Biology, 14, 1125–
1140.
Thompson,K.,Parkinson,J.A.,Band,S.R.&Spencer,R.E.(1997)ACompar-
ative study of leaf nutrient concentrations in a regional herbaceous flora.
NewPhytologist,136,679–689.
Tillman, D. (1982) Resource competition and community structure. Princeton
UniversityPress,Princeton,NJ,USA.
Tjoelker, M.G., Craine, J.M., Wedin, D., Reich, P.B. & Tilman, D. (2005)
Linking leaf and root trait syndromes among 39 grassland and savannah
species.NewPhytologist,167,493–508.
Wahl, S. & Ryser, P. (2000) Root tissue structure is linked to ecological strate-
giesofgrasses.NewPhytologist,148,459–471.
Wardle,D.A., Walker, L.R.&Bardgett,R.D.(2004)Ecosystempropertiesand
forest decline in contrasting long-term chronosequences. Science, 305, 509–
513.
Wardle, D.A., Bardgett, R.D., Klironomos, J.N., Setala, H., Van der Putten,
W.H. & Wall, D.H. (2004) Ecological linkages between aboveground and
belowgroundbiota.Science,304,1629–1633.
Warton, D.I., Wright, I.J., Falster, D.S. & Westoby, M. (2006) Bivariate line-
fittingmethodsforallometry.BiologicalReviews,81,259–291.
Wassen, M.J., Venterink, H.O., Lapshina, E.D. & Tanneberger, F. (2005)
Endangered plants persist under phosphorus limitation. Nature, 437, 547–
550.
Westoby, M. (1998) A leaf-height-seed (LHS) plant ecology strategy scheme.
PlantandSoil,199,213–227.
Westoby, M. & Wright, I.J. (2006) Land-plant ecology on the basis of func-
tionaltraits.TrendsinEcology&Evolution,21,261–268.
Withington,J.M., Reich,P.B.,Oleksyn,J.& Eissenstat,D.M.(2006) Compari-
sons of structure and life span in roots and leaves among temperate trees.
EcologicalMonographs,76,381–397.
Wright,I.J.&Cannon,K.(2001)Relationshipsbetweenleaflifespanandstruc-
tural defences in a low-nutrient, sclerophyll flora. Functional Ecology, 15,
351–359.
Wright, I.J. & Westoby, M. (1999) Differences in seedling growth behav-
iour among species: trait correlations across species, and trait shifts
along nutrient compared to rainfall gradients. Journal of Ecology, 87,
85–97.
372G. T. Freschet et al.
? 2009 The Authors. Journal compilation ? 2009 British Ecological Society, Journal of Ecology, 98, 362–373

Page 12

Wright, I.J., Reich, P.B., Westoby, M., Ackerly, D.D., Baruch, Z., Bongers,
F. et al. (2004) The worldwide leaf economics spectrum. Nature, 428, 821–
827.
Wright, I.J., Reich, P.B., Cornelissen, J.H.C., Falster, D.S., Groom, P.K.,
Hikosaka, K. et al. (2005) Modulation of leaf economic traits and
trait relationships by climate. Global Ecology & Biogeography, 14, 411–
421.
Wright,I.J., Falster,D.S.,Pickup,M.&Westoby, M.(2006)Cross-species pat-
terns in the coordination between leaf and stem traits, and their implications
forplanthydraulics.PhysiologiaPlantarum,127,445–456.
Received11August2009;accepted9November2009
HandlingEditor:RichardBardgett
Supporting Information
Additional Supporting Information may be found in the online ver-
sionofthisarticle:
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 assump-
tion). Species are distinguished according to their higher taxum:
s Core eudicot;
Lycophyte;
Basal eudicot; d Monocot; D Pteridophyte;
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 assump-
tion). Species are distinguished according to their plant type:
sWoodyevergreen;
Woodydeciduous;dFernally;DGraminoid;
Forb; Aquaticforb;hClubmoss.
Table S1. Species list and characteristics. All leaf, stemand root trait
dataisavailablethroughthetrydatabase:http://www.try-db.org.
Table S2. Contributions (%) of organ traits to the construction of
firstandsecondaxesoforganPCAs.
Asaservicetoourauthorsandreaders,thisjournalprovidessupport-
ing information supplied by the authors. Such materials may be reor-
ganized for online delivery, but are not copy-edited or typeset.
Technical support issues arising from supporting information (other
thanmissingfiles)shouldbeaddressedtotheauthors.
A ‘plant economics spectrum’373
? 2009 The Authors. Journal compilation ? 2009 British Ecological Society, Journal of Ecology, 98, 362–373