REGULAR ARTICLE

Effect of species, root branching order and seasonon the root traits of 13 perennial grass species

Catherine Picon-Cochard & Rémi Pilon &

Emilie Tarroux & Loïc Pagès & Jean Robertson &

Lorna Dawson

Received: 19 January 2011 /Accepted: 22 September 2011 /Published online: 15 October 2011# Springer Science+Business Media B.V. 2011

AbstractAims Inter-specific comparisons of plant traits may varydepending on intra-specific variation. Here we examinethe impact of root branching order and season on keyfunctional root traits for grass species. We also compareroot traits among co-existing grass species as a steptowards defining root trait syndromes.Methods Monocultures of 13 grass species, grownunder field conditions and subjected to intensivemanagement, were used to record root trait valuesfor coarse roots (1st order, >0.3 mm), fine roots (2nd

and 3rd orders, <0.2 mm) and mixed root samplesover three growing seasons.Results Branching order and species had a significanteffect on root trait values, whereas season showed amarginal effect. The diameter of coarse roots was morevariable than that of fine roots and, as expected, coarseroots had higher tissue density and lower specific rootlength values than fine roots. Principal componentanalysis run on eight root traits provided evidence fortwo trait syndromes related to resource acquisition andconservation strategies across grass species.Conclusions Our data show that root branching orderis the main determinant of root trait variation amongspecies. This highlights the necessity to include theproportion of fine vs coarse roots when measuringtraits of mixed root samples.

Keywords Grassland . Root morphology . Rootarchitecture . Root respiration . Root trait syndromes

Introduction

The current development of plant trait databases isimportant to increase our knowledge of plant func-tional groups and strategies based on trait syndromes.These databases also help to understand and predictspecies distributions in present and future environ-ments (Grime et al. 1988; Fitter and Peat 1994;Westoby 1998; Kühn et al. 2004; Kleyer et al. 2008).In order to generalise the use of trait values at the

Plant Soil (2012) 353:47–57DOI 10.1007/s11104-011-1007-4

Responsible Editor: Peter J. Gregory.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s11104-011-1007-4) contains supplemen-tary material, which is available to authorized users.

C. Picon-Cochard (*) : R. Pilon : E. TarrouxINRA, UR874, Grassland Ecosystem Research Team,234 Avenue du Brézet,63100 Clermont-Ferrand, Francee-mail: picon@clermont.inra.fr

L. PagèsINRA, UR1115, Plantes et Systèmes de Culture Horticoles,Domaine Saint-Paul,Site Agroparc,84914 Avignon, France

J. Robertson : L. DawsonThe James Hutton Institute,Craigiebuckler,Aberdeen AB15 8QH, UK

species level, i.e. in any terrestrial biome, it isimportant to know the main sources of trait variation,both above and belowground. Nevertheless the intra-specific variability among traits is often neglectedespecially in the case of root traits which are less wellstudied than leaf traits (Kattge et al. 2011).

A number of root traits have been identified thatreflect root functioning, such as specific root length(SRL) and its components root diameter and roottissue density. The SRL characterises the root lengthas the quantity of invested biomass, and is thusconsidered as an index of root benefit to root cost(Ryser 1998; Fitter 2002). Some studies also reportedthe role of these root functional traits in ecosystemprocesses, such as above- and below-ground fluxes(Personeni and Loiseau 2005; Klumpp and Soussana2009). However we are still a long way from beingable to determine generalised root traits patternsbecause of the limited data available.

Numerous factors affect root trait values amongspecies and cause problems for classifying rootsystems and comparing species grown in naturalecosystems. Firstly, roots grow in a physically andbiologically heterogeneous environment (Fitter 2002).Secondly, although some attempts have been made tostandardise measurements of root traits (Bouma et al.2000; Cornelissen et al. 2003; Ryser 2006), there isstill a lack of consistency in the methods used toobserve and measure roots growing in naturalconditions. Furthermore, in the last 10 years, rootsystem architecture and branching order have beenrecognised as important determinants of root mor-phology variation in woody and herbaceous species(Pregitzer et al. 1997; Comas et al. 2002; Craine et al.2002; Waisel and Eishel 2002; Drouet et al. 2005;Guo et al. 2008; Comas and Eissenstat 2009; Zobel2011). Fine roots play an important role in resourceacquisition, as well as carbon and nutrient cyclingthrough their turnover and decomposition (Eissenstatand Yanai 1997). Coarse roots are more important foranchorage and deep soil colonisation, and it has beensuggested that they have a greater longevity than fineroots (Canadell et al. 1996; Pregitzer et al. 1997; Gilland Jackson 2000; Gill et al. 2002). Thus, traitvariation among the root system orders will affectthe plant’s capacity to cope with complex environ-ments (Fitter 1987). In addition very few of thenumerous roots in a root system are exposed to thesame conditions (physical and chemical) in micro

sites within the rooting volume. Consequently, thereis a need for greater focus on distinguishing physio-logical traits among root orders (Waisel and Eishel2002; Zobel 2003; Pierret et al. 2007). Thus,measuring root traits at the whole root system level, i.e.by considering all branching orders together, can affectour understanding of root trait variation among species,as we do not know the range of variation within-speciesto compare with variation between species.

The date roots were harvested could be anothersource of root trait variation among species grown infield conditions. In fact, the seasonality of root growthobserved in perennial grasses increases the proportionof new roots with an intact cortex and which are moreactive than older roots (Garwood 1967; Matthew et al.1991; Garcia-Pausas et al. 2010). If the proportion ofolder vs younger roots changes seasonally, we maythen expect that the root morphology is also affectedas suggested by Craine et al. (2003) and Mokany andAsh (2008).

In this study we examined root traits of 13 co-occurring grass species representative of the fertile,productive and disturbed perennial grasslands in theFrench Massif Central (Louault et al. 2005). Theywere grown for 3 to 4 years as monocultures toaccurately collect roots of mature target-speciesplants, and were subjected to intensive managementto mimic fertile and disturbed conditions. At maturity,perennial C3 grasses have a simplified root systemarchitecture: first-order roots emerging at the collar level(shoot-born roots), these are the mother roots of finely-branched second order roots, and third-order roots arethe daughter roots of the second order ones. Thus firstorder roots have a larger diameter than second and thirdorder roots (Kutschera and Lichtenegger 1982). Sevenroot traits (morphology and chemistry) and root masswere measured in three growing seasons and onephysiological root trait (specific root respiration) wasassessed over one season. Morphology was deter-mined for 1st order roots, for a cluster of 2nd and 3rdorders and for mixed root samples including all rootorders. Analysis of all root orders was made for inter-specific comparison and for comparison with data inthe literature.

This study had two main objectives: (1) to identifywhich traits varied the most among species, rootbranching order and season in order to compare inter-specific and intra-specific root trait variation and giverecommendations for the use of root traits in

48 Plant Soil (2012) 353:47–57

experiments and also when using plant databases; (2) tocompare root traits among co-existing grass species as astep towards defining root trait syndromes.

Materials and methods

Site

This field experiment was located in an upland area ofcentral France at Theix (45°43′N, 03°01′E, 870 m a.s.l.) on a granitic brown forest soil (Cambic soil, FAO).In 2001, monocultures of 13 grass species were sownin 2-m length rows set 14 cm apart in 4.2 m2 plotsarranged in a randomised block design, with threeblocks per species (see Pontes et al. 2007 for a fulldescription). The 13 grass species were Arrhenathe-rum elatius (Ae), Anthoxanthum odoratum (Ao),Alopecurus pratensis (Ap), Dactylis glomerata (Dg),Elytrigia repens (Er), Festuca arundinacea (Fa),Festuca rubra (Fr), Holcus lanatus (Hl), Loliumperenne (Lp), Phleum pratense (Php), Poa pratensis(Pp), Poa trivialis (Pt), and Trisetum flavescens (Tf).From spring 2002 to autumn 2006, these monocul-tures were cut six times a year, 6 cm above the stembase, and supplied with nitrogen fertilizer after eachcutting to ensure non-limiting nitrogen nutrition(NH4NO3, 360 kg ha−1 year−1). P (80 kg ha−1 year−1

P2O5) and K (240 kg ha−1 year−1 K2O) were alsoapplied in spring each year. Consequently, harvestedabove-ground vegetation was always green, whateverthe dates. The soil volumetric water content wasmeasured with a TDR probe (TDR, Trime-FM,Medfield, USA). If the soil water content fell below10%, all plots were irrigated. A total of 100 mm of waterwas supplied in five applications during the summers of2003 (between July 7 and August 15) and 2004(between June 28 and August 5; Pontes et al. 2007).

Minirhizotron measurements

In November 2002, 39 clear 50 cm long acrylic tubeswere inserted into the soil at an angle of 45° beneaththe vegetation rank for each of the 13 species. Images(13.5×18 mm) were recorded with a minirhizotronsystem (BTC-2, Bartz Technology, USA). Based onobservations recorded in spring 2003, roots wereclassified by their order, beginning with first-orderroots (the coarsest roots observed in the images) and

then second and third-order roots. A maximum ofthree orders was observed. For second and third-orderroots, only roots with a definite connection weremeasured. Measurements were performed on 1,474first-order, 7,694 second-order and 2,891 third-orderroots. The average diameter was measured withWinRHIZOTronMF software (V2005a, RégentInstruments, Ca) and mean values were calculatedfor each root order from three tubes of each of the 13species. For three species (Ae, Dg, Fr), no third-orderroots were found. The analysis determined the meandiameter of each branching order in the 13 species:1st order: 0.481 mm (0.291–0.878 mm, min-maxrange); 2nd and 3rd order: 0.190 mm (0.173–0.211 mm, min-max range). Thus, regardless ofspecies, we decided that 0.3 mm and 0.2 mm werethe lower and upper limits defining 1st order rootsand clusters of 2nd and 3rd order roots, respectively.

Root collection

In spring (March), summer (July) and autumn(November) 2004, 39 root cores were collected withan auger beneath the vegetation (one core per block,15 cm depth, 8 cm diameter, V=754 cm3) and storedat −18°C before subsequent processing. The coresamples were defrosted, cleaned to clear soil fromroots, sieved through 1 mm and 0.2 mm sieves(Picon-Cochard et al. 2009) with tap water thenoven-dried (48 h at 60°C) and weighed. The root mass,including live and dead roots, was calculated in relationto the soil surface, and expressed as g DW m−2.

Root morphology

After washing, subsamples of fresh roots were stainedwith methylene blue dye (5 g l−1) by soakingovernight at 5°C to increase the contrast for scanning.Four subsamples were scanned separately: two repre-senting all root orders, one representing fine roots(cluster of 2nd and 3rd order roots), and onerepresenting coarse roots (1st order roots, shoot-born). To avoid introducing possible artefacts intodiameter measurements care was taken to ensure thatno cortex sloughing of the sub-sampled roots oc-curred. For each sub-sample, fresh coloured rootswere spread over a mesh screen (50 μm), that wasimmersed in a thin layer of water, allowing contactbetween roots and water. Then roots were transferred

Plant Soil (2012) 353:47–57 49

on a transparent acetate sheet by turning the mesh andimmediately scanned with a transparent light systemat a resolution of 400 dpi (15.8 pixels per mm) (Epson1600XL Pro). This resolution was chosen because itis a good compromise between image size and imagequality. However in the case of fine roots (0–0.1 mm),this resolution may underestimate diameter values andaffect inter-specific comparisons (Bouma et al. 2000).WinRhizoPRO software (V2002c, Régent Instruments,Ca) was used to determine root length (m), average rootdiameter (D, mm), root length and volume (cm3) byclass diameter (10 classes of 0.1 mm-wide incre-ments). The root volume was calculated as the sum ofeach volume by class diameter to avoid bias due to askewed root diameter distribution (Ryser 2006).Thereafter, each of the four sub-samples was oven-dried (48 h at 60°C) and weighed to calculate roottissue density (RTD, g cm−3) and specific root length(SRL, m g−1). For all root order sub-samples, themean of the two sub-samples was calculated and usedas a single value.

Root chemical composition

Unstained, dried root sub-samples including all rootorders were milled to 1 mm (Cyclotec, FOSSTECATOR, Sweden). Total root nitrogen content(RNC,%) was determined by elemental analysis(CarloErba, It). Neutral detergent fibre (NDF,%), aciddetergent fibre (ADF,%) and acid detergent lignin(lignin,%) were measured according to Van Soest andWine (1967). These compounds were also predictedby near-infrared reflectance spectroscopy (Foss 6500,Silver Spring, MD, USA) using a calibration equationdeveloped with 119 samples for ADF and ADL and108 samples for NDF.

Specific root respiration

In June 2005, 39 root cores were collected for the 13species and then transferred to the lab. Roots werecarefully washed then placed in pots filled withsterilised, moistened fine sand to measure root respira-tion in a closed chamber system (Li6400-09, Licor,USA). In addition the sand substrate was checked toensure that it did not contribute to respiration measure-ments (Tarroux 2005). Three measurements of rootrespiration (μmol m−2 s−1) were performed for eachcore and specific root respiration was calculated by

dividing with root dry mass (SRR, nmol g−1 s−1). Theroot morphology of these samples was measuredusing the same protocol as described above.

Statistical analysis

One and two-way ANOVA (GLM model) wereperformed to determine the effects of species,season, and species x season interactions, forroot traits of coarse roots (1st order), fine roots(cluster of 2nd and 3rd order roots) and formixed root-order samples. Species, season androot branching order comparisons were performedby multi-range testing using Fisher’s LSD proce-dure. Effect of season on species ranking wastested using Spearman’s rank coefficients. Sum-mer was the reference season for testing whetherthe species ranking for a given trait wasconserved over time. The coefficient of variationwas calculated to test the importance of root traitvariation among species (CVsp), season (CVs) androot branching order (CVo). A doubly centredprincipal component analysis was performed oncorrelations among root mass and eight root traitsmeasured on mixed roots: acid detergent fibre,diameter, Lignin, neutral detergent fibre, root mass,root N content, root tissue density, specific rootlength, specific root respiration. Species were ordi-nated by their score on components 1 and 2. Whennecessary, variables were log-transformed or thesquare root calculated to normalise the data prior toANOVA. All statistical analysis was performed usingStatgraphics Plus (StatPoint, Inc., USA).

Results

Root trait variation among species

Species was the main factor affecting root massand root traits (Table 1). Variation in morphologyand chemical traits was generally higher amongspecies (CVsp) than season (CVs) (Tables S1 andS2). According to Spearman analysis, species rankingwas conserved among seasons, as indicated bysignificant (P<0.05) correlations between the refer-ence season (summer) and target season (spring orautumn) (Table 2). There was more variation amongmorphological traits than among chemical

50 Plant Soil (2012) 353:47–57

characteristics (Tables S1 and S2). The specific rootlength (SRL) varied the most among species then roottissue density (RTD) and diameter and for chemicaltraits, root nitrogen content (RNC) was more variablethan lignin NDF and ADF.

Irrespective of season, root mass values rangedbetween 119 and 828 gDM m−2 for E. repens and L.perenne, respectively (Fig. 1). For root morphology,P. trivialis showed lower mean diameter values(<0.21 mm) while four species (A. elatius, A.pratensis, F. arundinacea, L. perenne) showed highervalues (>0.30 mm). F. rubra and D. glomeratashowed higher root tissue density (>0.24 gcm−3) thanA. pratensis, L. perenne and P. trivialis (<0.15 gcm−3;Table S1). Chemical composition analysis (RNC, NDF,ADF, lignin) showed that species with high RNC values(>1.6%) (E. repens, L. perenne, P. trivialis) also hadlow NDF and ADF values (Table S2).

Root trait variation among branching orders

In general, first-order roots showed 2.6-fold higherroot tissue density values and thus lower SRLvalues (−94%) than second-plus-third-order roots(P<0.001) (Table 3). The root tissue density of fineand coarse roots was similar for only three species (A.pratensis, F. arundinacea, L. perenne) in spring, andthis dropped to one species in summer (F. arundina-cea) and autumn (A. elatius) (not shown). Variationdue to root branching order (CVo) was much higherthan CVsp for the three morphological traits, diame-ter, tissue density and specific root length (Table 3).In addition, the CVo of SRL was higher than that ofthe root tissue density, with one exception insummer, whereas the CVo of diameter was approx-imately two times smaller than those of SRL and roottissue density.

Table 1 One-way and two-way ANOVA of root mass (gDMm−2), root diameter (mm), root tissue density (g cm−3), specificroot length (m g−1), and root chemical composition (rootnitrogen content, RNC,%, neutral detergent fibre, NDF,%, aciddetergent fibre, ADF,% and Lignin,%), for 13 monoculture-grown grass species subjected to six cuts per year and Nfertilisation (360 kg Nha−1 year−1). For each variable, the root

branching order was also considered: all-roots, 1st order andcluster of 2nd and 3rd order roots. The significance of species,season and their interaction is indicated for the differentvariables and root branching order; ns: P>0.05; *: P<0.05;**: P<0.01; ***: P<0.001. Degree freedom of species, season,and species × season were 12, 2, and 24, respectively

Variables Root branching order Species Season Species × season

F P F P F P

Root mass all 5.2 *** 89.5 *** 1.8 *

all 32.5 *** 16.9 *** 1.4 ns

Root diameter 1st order 75.7 *** 1.3 ns 3.3 ***

2nd and 3rd orders 11.5 *** 19.2 *** 2.9 ***

all 14.6 *** 2.3 ns 0.8 ns

Root tissue density 1st order 21.6 *** 3.5 * 2.2 **

2nd and 3rd orders 12.1 *** 28.0 *** 3.2 ***

all 73.7 *** 33.5 *** 1.9 *

Specific root length 1st order 45.5 *** 1.5 ns 2.0 *

2nd and 3rd orders 20 *** 33.5 *** 1.8 *

RNC all 25.1 *** 21.6 *** 2.9 ***

NDF all 27.9 *** 15.1 *** 3.3 ***

ADF all 22.6 *** 1.9 ns 1.6 ns

Lignin all 22.2 *** 5.5 *** 1.4 ns

Plant Soil (2012) 353:47–57 51

Seasonal root trait variation

Significant species × season interactions were ob-served for the root mass and important morphologicaltraits of 1st order and clusters of 2nd and 3rd orderroots. This same effect was also observed for SRLand two chemical traits (RNC, NDF) measured for allroot orders (Table 1). Two further traits, the diameterof all root orders and lignin, also varied with season,however no species x season interaction was observedfor these characteristics. The coefficient of variationamong seasons was higher for SRL (mean CVs =0.24) than for root tissue density (CVs = 0.16) anddiameter (CVs = 0.09) (Table S1). F. rubra and H.lanatus showed higher CVs of SRL (0.4 and 0.3,respectively) than P. trivialis and E. repens (0.16 and0.17, respectively). For root chemistry, RNC and

lignin showed higher CVs than NDF and ADF(Table S2). In the case of lignin, E. repens and P.pratensis showed higher values of CVs (0.15 and 0.13,respectively) and F. arundinacea and T. flavescens thelowest (0.03).

Root trait correlations

Principle component analysis (PCA) showed that com-ponents 1 and 2 contributed to 71% of the total variance(Fig. 2). Component 1 explained 46% of the varianceand differentiates between species with high valuesfor ADF, diameter, lignin, NDF and Rmass (A.pratensis, L. perenne, F. rubra, F. arundinacea, D.glomerata) from those with high RNC and SRLvalues (E. repens, P. trivialis, A. odoratum, H.lanatus, P. pratensis). Component 2 explained 25% ofthe variance and discriminates species with high roottissue density values (F. rubra, D. glomerata, E.repens) from those with high specific root respirationvalues (L. perenne, P. trivialis, A. odoratum, A.pratensis) (Table 4). Furthermore, root tissue densitywas significantly (P<0.05) and negatively correlatedwith specific root respiration (SRR) (Fig. 3), but notwith any other root morphological trait measured(Fig. 2).

SpeciesAe Ao Ap Dg Er Fa Fr Hl Lp Php Pp Pt Tf

Roo

t mas

s (g

m-2

)

0

200

400

600

800

1000springsummerautumn

Fig. 1 Spring, summer and autumn root mass (gDW m−2, 0–15 cm depth) of 13 monoculture-grown grass species subjectedto six cuts per year and N fertilisation (360 kg Nha−1 year−1).Vertical bars correspond to the mean standard error. n=3. Ae:Arrhenatherum elatius, Ao: Anthoxanthum odoratum, Ap:Alopecurus pratensis, Dg: Dactylis glomerata, Er: Elytrigiarepens, Fa: Festuca arundinacea, Fr: Festuca rubra, Hl: Holcuslanatus, Lp: Lolium perenne, Php: Phleum pratense, Pp: Poapratensis, Pt: Poa trivialis, Tf: Trisetum flavescens

Table 2 Spearman species ranking coefficients for root traitsmeasured during spring, summer and autumn. For each speciesof 13 monoculture grasses subjected to six cuts per year and Nfertilisation (360 kg Nha−1 year−1), data included in the tableare root mass (gDM m−2), root diameter (mm), root tissuedensity (g cm−3), specific root length (m g−1), and rootchemical composition (see Table 1 legend for details). Foreach variable, the root branching order was also considered, i.e.all-roots, 1st order, and cluster of 2nd and 3rd order roots. Thereference ranking is summer. Correlations marked in bold aresignificant at P<0.05. For each correlation n=39

Variables Root branching order Spring Autumn

Root mass all 0.63 0.21

all 0.84 0.86

Root diameter 1st order 0.95 0.82

2nd and 3rd orders 0.90 0.90

all 0.90 0.90

Root tissue density 1st order 0.94 0.87

2nd and 3rd orders 0.35 0.79

all 0.86 0.98

Specific root length 1st order 0.84 0.86

2nd and 3rd orders 0.86 0.98

RNC all 0.61 0.95

NDF all 0.79 0.85

ADF all 0.92 0.83

Lignin all 0.95 0.91

52 Plant Soil (2012) 353:47–57

Discussion

Inter-specific variation of root traits

Species was the most significant factor affecting root traitvalues, as previously observed for leaf traits by Pontes etal. (2007) on the same set of species and under thesame experimental conditions. This effect was espe-cially clear in the case of coarse root diameter but notfor fine root diameter (eight-fold higher CVsp value,Table 3). This correlation mirrors the relationshipbetween plant size and maximum root diameter forgrass species: taller (Ap, Dg, Fa) species have biggerroots than shorter (Ao, Pt, Tf) species (Ryser 1998).In addition, higher mean diameter values are mainlythe result of anatomical differences, i.e. wider rootshave a higher proportion of xylem (Wahl and Ryser

2000; Hummel et al. 2007). Thus our results confirmthat the diameter of coarse roots is a good trait to useto discriminate grass species; however, we cannot ruleout that the resolution used for image scanning mayhave affected diameter measurements for fine roots.Although it was considered by Ryser (1998) to beambiguous when comparing ecologically contrastingspecies, our data indicated that SRL was the mostvariable trait among species. Ostonen et al. (2007)also argued that it is a good indicator of environmen-tal change for tree species. In fact, high SRL valuesreflect an increased potential to explore the soil andacquire resources but this pattern depends on its twodeterminants, diameter and tissue density. On the contrary,root tissue density is considered as a pivotal trait for theecological behaviour of a species and may also reflectanatomical differences between species (Ryser 1998;

Table 3 Minimum, maximum, mean, median and coefficientof variation (CVsp) for root diameter (mm), root tissue density(g cm−3) and specific root length (m g−1) of 1st order and of

2nd and 3rd order roots determined for the 13 grass species.CVo corresponds to trait variation among branching order, foreach season

Season Root traits Branching order Minimum Maximum Mean Median CVsp CVo

Spring diameter 1st order 0.258 1.019 0.481 0.433 0.37

2nd and 3rd order 0.171 0.216 0.190 0.189 0.05 0.57

root tissue density 1st order 0.072 0.443 0.186 0.165 0.43

2nd and 3rd order 0.048 0.198 0.091 0.086 0.36 0.92

specific root length 1st order 8.4 187.7 39.1 33.7 0.69

2nd and 3rd order 226.7 1266.9 608.4 567.7 0.41 1.21

Summer diameter 1st order 0.225 1.036 0.460 0.418 0.46

2nd and 3rd order 0.168 0.228 0.183 0.181 0.06 0.55

root tissue density 1st order 0.079 0.433 0.213 0.224 0.43

2nd and 3rd order 0.030 0.121 0.069 0.067 0.33 1.62

specific root length 1st order 8.4 187.7 40.3 35.8 0.80

2nd and 3rd order 288.1 1218.9 664.1 609.9 0.39 1.26

Autumn diameter 1st order 0.239 0.953 0.461 0.398 0.45

2nd and 3rd order 0.168 0.203 0.182 0.180 0.04 0.55

root tissue density 1st order 0.075 0.373 0.204 0.185 0.40

2nd and 3rd order 0.034 0.117 0.067 0.066 0.29 0.66

specific root length 1st order 10.0 101.2 40.4 34.6 0.67

2nd and 3rd order 330.9 1266.9 692.4 673.0 0.36 1.27

Plant Soil (2012) 353:47–57 53

Wahl and Ryser 2000). It has been linked to the rootcross-sectional area and the proportion of xylem (Hummelet al. 2007). A large proportion of xylem vessels in thecross-sectional area could increase the root’s capacityto transport water and result in high tensile strength,which might protect roots against mechanical stressesand increase root longevity (Wahl and Ryser 2000).From allometric rules, the root tissue density is expectedto be associated with both the SRL and diameter (Ryserand Lambers 1995), but we did not observe suchcorrelations in our work, which is also consistent withresults obtained in woody species (Comas et al. 2002;Comas and Eissenstat 2004, 2009). This unexpectedrelationship between root tissue density and diameter andSRL could be due to allometric changes between rootlength and diameter or perhaps reflects the difficulty inassessing root volume using image analysis (Ryser 2006).

Intra-specific variation in root traits

This study clearly established that the root branching orderis the most important determinant (CVo/CVsp=2) of root

morphological trait variation in grass species—afactor that has seldom been demonstrated for herba-ceous species, in comparison with woody species(Pregitzer et al. 1997; Comas et al. 2002; Craine et al.2002; Drouet et al. 2005; Guo et al. 2008; Comas andEissenstat 2009). Fine roots exhibited very low tissuedensity, below 0.05 gcm−3 for some species, andcoarse roots had tissue density values of up to 0.4 gcm−3 (Table 3). However this pattern could not begeneralised for all 13 grass species because in threespecies (A. pratensis, F. arundinacea, L. perenne), asimilar tissue density was observed between coarseand fine roots. Nevertheless, we can infer that byproducing low-tissue-density roots a plant can explorea larger soil volume with a relatively low investmentin dry matter, permitting a high relative growth rate,which is a common characteristic of fast-growingspecies (Garnier 1992; Ryser and Lambers 1995).This finding may suggest contrasts between rootfunctioning within the root system of perennial grassspecies, as root tissue density has been associatedwith various processes such as growth rate (Eissenstatand Yanai 1997; Ryser 1998; Wahl and Ryser 2000).The root tissue density is related to the proportion ofstele at the surface because this tissue has a higherproportion of sclerophyllic elements and lignin thanin the cortex (Wahl and Ryser 2000; Hummel et al.2007). Therefore our data suggest that fine roots havea shorter lifespan than coarse roots, as roots with hightissue density values should live longer than roots

Fig. 2 Principal component analysis combining eight root traitsand the root mass of 13 monoculture-grown grass species subjectedto six cuts per year and N fertilisation (360 kg Nha−1 year−1), datameasured in all seasons was combined. The first and secondaxes accounted for 46% and 25% of explained variance,respectively. ADF: acid detergent fibre, D: diameter, NDF:neutral detergent fibre, Rmass: root mass, RNC: root N content,RTD: root tissue density, SRL: specific root length, SRR:specific root respiration. Species acronyms are indicated anddescribed in the Fig. 1 caption

Table 4 Component weights of a principal component analysiscombining eight root traits and the root mass of 13monoculture-grown grass species subjected to six cuts per yearand N fertilisation (360 kg Nha−1 year−1). ADF acid detergentfibre, D diameter, Lignin, NDF neutral detergent fibre, Rmassroot mass, RNC root N content, RTD root tissue density, SRLspecific root length, SRR specific root respiration

Variable Component 1 Component 2 Component 3

ADF 0.46 0.18 0.16

D 0.36 −0.02 −0.55Lignin 0.41 −0.03 0.16

NDF 0.36 −0.10 0.49

Rmass 0.40 −0.21 −0.12RNC −0.30 −0.29 −0.31RTD −0.12 0.58 0.08

SRL −0.29 −0.34 0.53

SRR 0.10 −0.62 −0.05

54 Plant Soil (2012) 353:47–57

characterised by low tissue density (Ryser 1996;Schläpfer and Ryser 1996).

The seasonal root trait variation was assessed inorder to examine whether the date of root collectionis an important factor to consider when comparingspecies or using traits from the plant database. Thevariation in root mass we measured between springand summer mirrors the seasonal root growthpattern previously observed in grass species(Fig. 1) (Steinaker et al. 2010). Thus from spring tosummer we expected a higher proportion of new roots inthe collected soil cores than in spring or autumn. The rootsof grass plants generally have a long lifespan and maypersist for up to 3 years (Eissenstat and Yanai 1997; Gilland Jackson 2000). Thus when collecting soil coresunder mature perennial grasses, we analyse a mixtureof roots ranging from some weeks up to 3 years old.In this case, it is therefore logical to expect changes inroot morphology with season, because as tissuematures, cell wall polysaccharide deposition increases(Stone 2006), leading to a higher proportion ofsclerenchymatous tissue in the stele, which is a majordeterminant of root tissue density (Wahl and Ryser2000; Hummel et al. 2007). For coarse roots, the slightincrease in tissue density could be ascribed to ageing,while diameter values were stable. For the fine roots, aconcomitant decrease in diameter and tissue density couldbe linked either to phenotypic plasticity due to declining

soil nutrient availability (Ryser 1998), corresponding toa period of higher N plant demand (Pontes et al.2007); or to an increasing proportion of new fineroots presenting younger and chemically differenttissue. Nevertheless, we have shown for the first timethat, throughout the growing season, there is a clear trendin species ranking with conservation of most of the roottraits (Table 2). The lower CV due to season incomparison with CV due to branching order andspecies confirmed that season had the least effect onroot trait variation. These results confirm those previ-ously published on species comparison of leaf traits ofherbaceous and woody species (Garnier et al. 2001;Pontes et al. 2007). Although, season showed theleast root trait variation, the date of collection of rootsas well as the proportion of root branching order,which changes markedly depending on the season,should be indicated when root traits are measured.

Defining root trait syndromes in grass species

This study opens the opportunity to assess root traitsyndromes related with resource use strategies in grassspecies, already observed for leaf traits (Wright et al.2004; Pontes et al. 2007) but rarely assessed for roottraits (Soussana et al. 2005; Roumet et al. 2006;Maire et al. 2009). Morphological and chemical roottraits, measured in a root order mixture, i.e. withoutconsidering root branching order, showed that roots withhigh diameter values also had high values for cell wallcomposition (NDF, ADF, lignin) and low values for rootnitrogen content and SRL (Fig. 2). This trait syndromeis generally observed for species exhibiting resourceconservation strategy, whereas the opposite traitsyndrome corresponds to a rapid acquisition strategy,as defined by Grime et al. (1997). Species employingrapid resource acquisition strategies are considered to befast-growing and exhibit higher SRL, RNC and respi-ration values than slow-growing species (Poorter et al.1991; Ryser and Eek 2000; Craine et al. 2001; Nicotraet al. 2002; Comas and Eissenstat 2004). In our study,an expected result was that specific root respiration wasnegatively correlated with root tissue density as highrespiration rates are generally associated with low tissuedensity, i.e. cells with high water content. But it isunexpected to observe that this trend is not associatedwithRNC as evidenced for 39 grassland and savannah species(Tjoelker et al. 2005). This classification correspondsto a primary axis of adaptive specialisation

Root tissue density (g cm-3)0.00 0.05 0.10 0.15 0.20 0.25 0.30

Spe

cific

roo

t res

pira

tion

(nm

ol g

-1 s

-1)

0

2

4

6

8

10

12

14

16

ApAo

AeDg

Er

Fa

Fr

Hl

Lp

PhpPp

Pt

Tf

r2=0.31

Fig. 3 Relationship between specific root respiration(nmol g−1 s−1) and root tissue density (g cm−3) of 13monoculture-grown grass species subjected to six cuts per yearand N fertilisation (360 kg Nha−1 year−1). Measurements wereperformed in June 2005. Mean values are shown, n=3. See theFig. 1 caption for species acronyms

Plant Soil (2012) 353:47–57 55

demonstrated in contrasting habitats and with differ-ent botanical families (Grime et al. 1997). Based onour analysis of root trait syndromes, we hypothesisethat the simultaneous presence of different plantstrategies with contrasting root traits and thus oppo-site effects on root N release and soil N mineralizationrates could be crucial for sustaining the productivity,nutrient use efficiency and increasing soil C and Nsequestration in grasslands (Fornara et al. 2009).

Conclusions

Our study showed that grass species grown in fertile andintensively managed conditions have contrasting root traitvalues. The role of root branching order as the maindeterminant of intra-specific variation of root traits amongperennial grass species was highlighted. The proportion offine vs coarse roots should be calculated whenmeasuringtraits of mixed roots. In addition, the high level ofdiversity in root morphology within grass root systemsraises the question of how this contributes to the co-existence of grass species growing in common habitats(mesic semi-natural grasslands) where above- andbelow-ground plant-plant interactions are critical.

Acknowledgments We thank Sandrine Revaillot and AngéliqueCorrent for root collection, preparation and morphology measure-ments. We also thank Bernard Pons, Sylvie Toillon and Jean-MarieVallée for their technical assistance in the field. We would like tothank Rocio Perrez Gonzalez, Jasmine Ross and Andy Midwoodfrom the James Hutton Research Institute. We are grateful to JulietteBloor for her comments on a previous version of the paper and toLeigh Gebbie for the English improvement of the paper and to threeanonymous reviewers for previous version of the paper. This workwas supported by the French government through GEOTRAITS andGDR UTILITERRES projects and by the INRA EFPA ResearchDepartment (“Utilisation du NIRS pour prédire la proportion etla composition chimique de litières racinaires de graminéesprairiales”). The work of authors Dawson and Robertson isfunded by the Scottish Government's Rural and EnvironmentScience and Analytical Services (RESAS) Division.

References

Bouma TJ, Nielsen KL, Koutstaal B (2000) Sample preparationand scanning protocol for computerised analysis of rootlength and diameter. Plant Soil 218:186–196

Canadell J, Jackson RB, Ehleringer JR, Mooney HA, Sala OE,Schulze ED (1996) Maximum rooting depth of vegetationtypes at the global scale. Oecologia 108:583–595

Comas LH, Bouma TJ, Eissenstat DM (2002) Linking roottraits to potential growth rate in six temperate tree species.Oecologia 132:34–43

Comas LH, Eissenstat DM (2004) Linking fine root traits tomaximum potential growth rate among 11 mature temper-ate tree species. Funct Ecol 18:388–397

Comas LH, Eissenstat DM (2009) Patterns in root trait variationamong 25 co-existing North American forest species. NewPhytol 182:919–928

Cornelissen JHC, Lavorel S, Garnier E et al (2003) Ahandbook of protocols for standardised and easymeasurement of plant functional traits worldwide. AustJ Bot 51:335–380

Craine JM, Froehle J, Tilman DG, Wedin DA, Chapin FS(2001) The relationships among root and leaf traits of 76grassland species and relative abundance along fertilityand disturbance gradients. Oikos 93:274–285

Craine JM, Wedin DA, Chapin FS III, Reich PB (2002)Relationship between the structure of root systems andresource use for 11 North American grassland plants. PlantEcol 165:85–100

Craine JM, Wedin DA, Chapin FS III, Reich PB (2003) Thedependence of root system properties on root systembiomass of 10 North American grassland species. PlantSoil 250:39–47

Drouet JL, Pagès L, Serra V (2005) Dynamics of leaf mass perunit leaf area and root mass per unit root volume of youngmaize plants, Implications for growth models. Eur J Agron22:185–193

Eissenstat DM, Yanai RD (1997) The ecology of root lifespan.Adv Ecol Res 27:1–60

Fitter AH (1987) An architectural approach to the comparativeecology of plant root systems. New Phytol 106:61–77

Fitter AH, Peat HJ (1994) The ecological flora database. J Ecol82:415–425

Fitter AH (2002) Characteristics and functions of root systems.In: Waisel Y, Eshel A, Kafkafi U (eds) Plant roots, thehidden half. Dekker M Inc, New York, pp 15–47

Fornara DA, Tilman D, Hobbie SE (2009) Linkages betweenplant functional composition, fine root processes andpotential soil n mineralization rates. J Ecol 97:48–56

Garcia-Pausas J, Casals P, Romanyà J, Vallecillo S, Sebastià M-T(2010) Seasonal patterns of belowground biomass andproductivity in mountain grasslands in the Pyrenees. PlantSoil. doi:10.1007/s11104-010-0601-1

Garnier E (1992) Growth analysis of congeneric annual andperennial grass species. J Ecol 80:665–675

Garnier E, Laurent G, Bellmann A, Debain S, Berthelier P,Ducout B, Roumet C, Navas ML (2001) Consistency ofspecies ranking based on functional leaf traits. New Phytol152:69–83

Garwood EA (1967) Seasonal variation in appearance andgrowth of grass roots. J Br Grass Soc 22:121–130

Gill RA, Jackson RB (2000) Global patterns of root turnoverfor terrestrial ecosystems. New Phytol 147:13–31

Gill RA, Burke IC, Lauenroth WK, Milchunas DG (2002)Longevity and turnover of roots in the shortgrass steppe,influence of diameter and depth. Plant Ecol 159:241–251

Grime JP, Hodgson JG, Hunt R (1988) Comparative plantecology: a functional approach to common British species.Unwin Hyman Ltd, London

56 Plant Soil (2012) 353:47–57

Grime JP, Thompson K, Hunt R, Hodgson JG, CornelissenJHC, Rorison IH, Hendry GAF, Ashenden TW, AskewAP, Band SR et al (1997) Integrated screening validatesprimary axes of specialisation in plants. Oikos 79:259–281

Guo D, Xia M, Wei X, Chang W, Liu Y, Wang Z (2008)Anatomical traits associated with absorption and mycor-rhizal colonization are linked to root branch order in 23Chinese temperate tree species. New Phytol 180:673–683

Hummel I, Vile D, Violle C, Devaux J, Ricci B, Blanchard A,Garnier E, Roumet C (2007) Relating root structure andanatomy to whole-plant functioning in 14 herbaceousMediterranean species. New Phytol 173:313–321

Kattge J, Diaz S, Lavorel S et al (2011) TRY—a globaldatabase of plant traits. Glob Chang Biol 17:2905–2935

Kleyer M, Bekker RM, Knevel IC, Bakker JP, Thompson K,Sonnenschein M, Poschlod P, van Groenendael JM,Klimes L, Klimesová J et al (2008) The LEDA traitbase,a database of life-history traits of the Northwest Europeanflora. J Ecol 96:1266–1274

Klumpp K, Soussana J-F (2009) Using functional traits to predictgrassland ecosystem change: amathematical test of the response-and-effect trait approach. GlobChangBiol 15:2921–2934

Kühn I, DurkaW, Klotz S (2004) Biolflor; a new plant-trait databaseas a tool for plant invasion ecology. Divers Distrib 10:363–365

Kutschera L, Lichtenegger E (1982) Wurzelatlas mitteleuro-päischer grünlandpflanzen. Band I, monocotyledoneae.Gustav Fischer Verlag, Stuttgart

Louault F, Pillar VD, Aufrere J, Garnier E, Soussana JF (2005)Plant traits and functional types in response to reduceddisturbance in a semi-natural grassland. J Veg Sci 16:151–160

Maire V, Gross N, Pontes L, Picon-Cochard C, Soussana JF(2009) A trade-off between root N acquisition and shoot Nutilisation across 13 co-occurring pasture grass species.Funct Ecol 23:668–679

Matthew C, Xia JX, Chu ACP, Mackay AD, Hodgson J (1991)Relationship between root production and tiller appear-ance rates in perennial ryegrass (Lolium perenne L.). In:Atkinson D (ed) Plant root growth—an ecological per-spective. Blackwell, Oxford, pp 281–290

Mokany K, Ash JL (2008) Are traits measured on pot grownplants representative of those in natural communities? JVeg Sci 19:119–126

Nicotra AB, Babicka N, Westoby M (2002) Seedling rootanatomy and morphology, an examination of ecologicaldifferentiation with rainfall using phylogenetically inde-pendent contrasts. Oecologia 130:136–145

Ostonen I, Puttsepp U, Biel C et al (2007) Specific root length as anindicatorofenvironmentalchange.PlantBiosystems141:426–442

Personeni E, Loiseau P (2005) Species strategy and N fluxes ingrassland soil—a question of root litter quality orrhizosphere activity? Eur J Agron 22:217–229

Picon-Cochard C, Pilon R, Revaillot S, Jestin M, Dawson L(2009) Use of near-infrared reflectance spectroscopy topredict the percentage of dead versus living grass roots.Plant Soil 317:309–320

Pierret A, Doussan C, Capowiez Y, Bastardie F, Pagès L (2007) Rootfunctional architecture, a framework for modeling the interplaybetween roots and soil. Vadose Zone J 6:269–281

Pontes LDS, Soussana JF, Louault F, Andueza D, Carrère P(2007) Leaf traits affect the above-ground productivity andquality of pasture grasses. Funct Ecol 21:844–853

Poorter H, Van Der Werf A, Atkin OK, Lambers H (1991)Respiratory energy requirements of roots vary with the potentialgrowth rate of a plant species. Physiol Plant 83:469–475

Pregitzer KS, Kubiske ME, Yu CK, Hendrick RL (1997)Relationships among root branch order, carbon, and nitrogenin four temperate species. Oecologia 111:302–308

Roumet C, Urcelay C, Diaz S (2006) Suites of root traits differbetween annual and perennial species growing in the field.New Phytol 170:357–368

Ryser P, Lambers H (1995) Root and leaf attributes accountingfor the performance of fast- and slow-growing grasses atdifferent nutrient supply. Plant Soil 170:251–265

Ryser P (1996) The importance of tissue density for growth andlife span of leaves and roots, a comparison of fiveecologically contrasting grasses. Funct Ecol 10:717–723

Ryser P (1998) Intra- and interspecific variation in root length,root turnover and the underlying parameters. In: LambersH, Poorter H, Van Vuuren MMI (eds) Inherent variation inplant growth. Physiological mechanisms and ecologicalconsequences. Backhuys Publishers, Leiden, pp 441–465

Ryser P, Eek L (2000) Consequences of phenotypic plasticity vs.interspecific differences in leaf and root traits for acquisition ofaboveground and belowground resources.AmJBot 87:402–411

Ryser P (2006) The mysterious root length. Plant Soil 286:1–6Schläpfer B, Ryser P (1996) Leaf and root turnover of three

ecologically contrasting grass species in relation to theirperformance along a productivity gradient. Oikos 75:398–406

Soussana JF, Teyssonneyre F, Picon-Cochard C, Dawson L(2005) A trade-off between nitrogen uptake and useincreases responsiveness to elevated CO2 in infrequentlycut mixed C3 grasses. New Phytol 166:217–230

Steinaker DF, Wilson SD, Peltzer DA (2010) Asynchronicity inroot and shoot phenology in grasses and woody plants.Glob Chang Biol 16:2241–2251

Stone B (2006) Explorations on the cell walls of grasses andcereals. In: Hayashi T (ed) The science and lore of theplant cell wall. Biosynthesis, structure and function.BrownWalker, Boca Raton, pp 132–139

Tarroux E (2005) Comparaison de la respiration sol-racines de13 monocultures de graminées. MSc report, University ofBlaise Pascal, Clermont-Ferrand, p 36

Tjoelker MG, Craine JM, Wedin D, Reich PB, Tilman D (2005)Linking leaf and root trait syndromes among 39 grasslandand savannah species. New Phytol 167:493–508

Van Soest PJ, Wine RH (1967) Use of detergents in the analysisof fibrous feeds. IV. Determination of plant cell-wallconstituents. J Assoc Off Anal Chem 50:50–55

Wahl S, Ryser P (2000) Root tissue structure is linked toecological strategies of grasses. New Phytol 148:459–471

Waisel Y, Eishel A (2002) Functional diversity of variousconstituents of a single root system. In: Waisel Y, Eshel A,Kafkafi U (eds) Plant roots, the hidden half. MarcelDekker, New York, pp 157–174

Westoby M (1998) A leaf-height-seed (LHS) plant ecologystrategy scheme. Plant Soil 199:213–227

Wright IJ, Reich PB, Westoby M et al (2004) The worldwideleaf economics spectrum. Nature 428:821–827

Zobel RW (2003) Fine roots—discarding flawed assumptions.New Phytol 160:276–279

Zobel RW (2011) A developmental genetic basis for definingroot classes. Crop Sci 51:1410–1413

Plant Soil (2012) 353:47–57 57