Plant, Cell and Environment (2005) 28, 697–708

© 2005 Blackwell Publishing Ltd 697

Blackwell Science, LtdOxford, UKPCEPlant, Cell and Environment0016-8025Blackwell Science Ltd 2005? 2005

28?697708Original Article

Physiological QTL in ArabidopsisT. E. Juenger et al.

Correspondence: Thomas E. Juenger. E-mail: tjuenger@mail.utexas.edu

Identification and characterization of QTL underlying whole-plant physiology in Arabidopsis thaliana: dddd13C, stomatal conductance and transpiration efficiency

THOMAS E. JUENGER1*, JOHN K. MCKAY2,6, NEIL HAUSMANN3, JOOST J. B. KEURENTJES4, SÁUNAK SEN5, KIRK A. STOWE3, TODD E. DAWSON3, ELLEN L. SIMMS3 & JAMES H. RICHARDS6

1University of Texas at Austin, Section of Integrative Biology & Institute for Cellular and Molecular Biology, 141 Patterson, # C0930, Austin, TX 78712 USA, 2University of California, Davis, Agronomy and Range Science, One Shields Avenue, Davis, CA 95616 USA, 3University of California, Berkeley, Department of Integrative Biology, 3060 Valley Life Sciences Building, Berkeley, CA 94720 USA, 4Wageningen University, Laboratory of Genetics, Arboretumlaan 4, 6703 BD Wageningen, The Netherlands, 5University of California, San Francisco, Department of Epidemiology and Biostatistics, San Francisco, CA 94143 USA and 6University of California, Davis, Land, Air and Water Resources, One Shields Avenue, Davis, CA 95616 USA

ABSTRACT

Water limitation is one of the most important factors lim-iting crop productivity world-wide and has likely been animportant selective regime influencing the evolution ofplant physiology. Understanding the genetic and physiolog-ical basis of drought adaptation is therefore important forimproving crops as well as for understanding the evolutionof wild species. Here, results are presented from quantita-tive trait loci (QTL) mapping of flowering time (a droughtescape mechanism) and carbon stable isotope ratio (dddd13C)(a drought-avoidance mechanism) in Arabidopsis thaliana.Whole-genome scans were performed using multiple-QTLmodels for both additive and epistatic QTL effects. Wemapped five QTL affecting flowering time and five QTLaffecting dddd13C, but two genomic regions contained QTL witheffects on both traits, suggesting a potential pleiotropicrelationship. In addition, we observed QTL–QTL interac-tion for both traits. Two dddd13C QTL were captured in near-isogenic lines to further characterize their physiologicalbasis. These experiments revealed allelic effects on dddd13Cthrough the upstream trait of stomatal conductance withsubsequent consequences for whole plant transpiration effi-ciency and water loss. Our findings document considerablenatural genetic variation in whole-plant, drought resistancephysiology of Arabidopsis and highlight the value of quan-titative genetic approaches for exploring functional rela-tionships regulating physiology.

Key-words: Arabidopsis; drought; quantitative trait loci;stable carbon isotopes; water deficit; water-use efficiency.

INTRODUCTION

A central question in plant biology is the role and impor-tance of physiological traits in plant adaptation and perfor-mance under diverse environmental conditions. Waterlimitation is a fundamental determinant of plant distribu-tion and abundance, is one of the most important factorslimiting crop productivity world-wide, and has probablybeen an important selective regime influencing the evolu-tion of plant morphology, development, and physiology.Nearly all terrestrial plants are exposed to drought stressat varying times and to different degrees during their lifecycle as water is fundamental to almost all aspects of plantphysiology (Stebbins 1952; Bohnert, Nelson & Jensen 1995;Bray 1997). Plants are thought to have evolved numerousstrategies for coping with limited water availability includ-ing changes in phenological, developmental, and physiolog-ical traits (Schulze et al. 1987; Ludlow 1989; Ehleringer &Monson 1993; Ingram & Bartels 1996; Passioura 1996;Geber & Dawson 1997; Ackerly et al. 2000; Araus et al.2002). Nonetheless, we know relatively little about the nat-ural genetic variation underlying these strategies or thegenes involved in drought adaptation in nature

For most plants, drought avoidance is achieved primarilythrough regulation of stomatal conductance in response tosoil and atmospheric water deficits (Cohen 1970; Cowan1982; Schulze 1986; Dawson & Ehleringer 1993; Meinzer1993). Stomatal conductance can be optimized over thelong-term through developmental changes in stomata den-sity or by short-term changes in stomatal opening and clos-ing. Stomatal closure can serve as a rapid and effectivedrought-avoidance response. However prolonged stomatalclosure is not sustainable as stomatal CO2 uptake is alsoreduced and will ultimately limit photosynthetic assimila-tion and growth (Farquhar & Sharkey 1982; Schulze et al.1987). A high ratio of carbon gained per unit water lostthrough transpiration, namely high water-use efficiency(WUE), is hypothesized to confer a fitness advantage under

698 T. E. Juenger et al.

© 2005 Blackwell Publishing Ltd, Plant, Cell and Environment, 28, 697–708

drought stress (Cohen 1970; Dudley 1996; McKay et al.2001), but may come at a cost of reduced growth rate ordelayed phenology in more favourable environments(Geber & Dawson 1990, 1997; Condon & Hall 1997;McKay, Richards & Mitchell-Olds 2003). Understandingthe genetic basis of WUE is therefore important forimproving crops as well as for understanding the evolutionof wild species.

WUE can be assessed instantaneously by measuring leafgas exchange or, as a time integrated measure, by calculat-ing the stable carbon isotope ratio, d13C, of leaves or sugarsof C3 plants (Farquhar, Ehleringer & Hubick 1989). Varia-tion in d13C reflects differences in the partial pressure ofCO2 inside the leaf and can be generated by variation ineither photosynthetic biochemistry or stomatal constraintson diffusion of CO2 to the leaf interior and chloroplast(Farquhar et al. 1989). As such, d13C has been successfullyused to investigate the role of WUE in drought adaptationin a growing number of studies (Comstock & Ehleringer1992; Ehleringer, Hall & Farquhar 1993; Virgona & Farqu-har 1996; Martin, Taur & Kin 1999; reviewed in Dawsonet al. 2002). There is also a great deal of interest in improv-ing crop performance through direct selection on d13C(Rebetzke et al. 2002; Araus et al. 2002). For example, arti-ficial selection on d13C in wheat has resulted in higher yield-ing cultivars (Rebetzke et al. 2002).

A variety of molecular approaches have identified can-didate genes that might influence drought avoidance andWUE in crop and model plant systems (Ingram & Bartels1996; Cushman & Bohnert 2000; Haake et al. 2002; Sekiet al. 2002). However, many of these candidate loci involveloss-of-function alleles (e.g. inability to synthesize ABA)resulting in impaired growth and reproduction even undervery benign growth chamber conditions. Many candidategenes have also been identified through differential geneexpression studies where experimentally imposed cellularwater-deficit results in up- and down-regulation of geneexpression (Seki et al. 2002). Unfortunately, the functionsof many of these genes are unknown and not all desicca-tion-induced genes are expected to be involved in adaptiveor tolerance responses (Bray 2002). It is unclear the degreeto which loci detected through these methods will also beresponsible for naturally occurring variation (McKay et al.2003) and knowledge of loci underlying natural variation indrought-related whole-plant traits like WUE and d13Cwould be valuable for breeding applications.

Quantitative trait loci (QTL) mapping is a complemen-tary approach useful for identifying the genes underlyingnaturally occurring variation in traits such as those confer-ring drought adaptation. QTL mapping allows one to sta-tistically identify chromosomal regions containing geneticfactors contributing to variation in a polygenic trait (Lynch& Walsh 1998; Alonso-Blanco & Koornneef 2000). SeveralQTL that affect d13C have been identified in tomato, rice,barley and soybean (Martin et al. 1989; Nienhuis et al. 1994;Specht et al. 2001; Teulat et al. 2002; Price et al. 2002). In A.thaliana, we recently reported significant genetic variationin d13C among accessions (McKay et al. 2003), mapped pre-

liminary QTL for d13C (Hausmann et al. 2004), and showedthat two loci responsible for natural variation in floweringtime (FRIGIDA and FLOWERING LOCUS C) also havepleiotropic effects on d13C (McKay et al. 2003). We havealso found an association between ecotype d13C values andthe climatic conditions of their native habitats, suggestingthat natural variation in d13C may reflect adaptation to dif-ferent climates.

Here, we confirm and extend our earlier studies withadditional QTL mapping. Furthermore, we fine-map twod13C QTL using near-isogenic lines to further characterizetheir physiological effect and to explore the functionalrelationships regulating whole-plant, drought resistancephysiology.

MATERIALS AND METHODS

Plant material – recombinant inbred lines

We used 162 recombinant inbred lines (RILs) generatedfrom a reciprocal cross between Landsberg erecta (Ler) andCape Verde Islands (Cvi) accessions (Alonso-Blanco et al.1998b) to map QTL. The parental accessions for this map-ping population are derived from climatically divergentlocations (Northern Europe and Tropical Cape VerdeIsland) and differ in both flowering time and d13C – the Cviaccession flowers slightly later and has a lower (more neg-ative) d13C (lower WUE) when compared with the Leraccession. These lines are available from the Arabidopsisstock centre (http://arabidopsis.org) as accession numbercs22000. We constructed a linkage map using 111 geneticmarkers. The RIL genotype at each marker locus wasobtained from the published data available from the Ara-bidopsis stock centre. The linkage map was constructedusing markers genotyped in at least 80% of the sampledlines. The map position of each marker (d cM) was esti-mated from the observed recombination frequencies (r)using the Kosambi mapping function as implemented bythe software MAPMAKER 3.0 (Lander et al. 1987). Theseanalyses provided a unique position for each marker thatdid not differ in order from the published Arabidopsis RILlinkage maps.

Plant material – near-isogenic lines

Two QTL regions were investigated with near-isogeniclines (NILs) that captured Cvi alleles in a homogeneous Lerbackground. RIL Cvl-134 from the Ler/Cvi RIL populationwas chosen for generating a NIL for the QTL detected onthe bottom of chromosome II [delta-2.1]. This RIL con-tained a Cvi introgression at the lower arm of chromosomeII (11.2 Mbp – end) along with three additional introgres-sions on chromosomes I and IV. Following procedures sim-ilar to those of Alonso-Blanco et al. (2003) and Bentsinket al. (2003 and references therein) using backcrosses toLer, further selfed generations, and marker-assisted selec-tion, a homozygous line containing a single Cvi introgres-sion on the end of chromosome II (~17.9 Mbp – end) wasselected. We refer to this NIL as ‘NIL delta-2.1’.

Physiological QTL in Arabidopsis 699

© 2005 Blackwell Publishing Ltd, Plant, Cell and Environment, 28, 697–708

A NIL previously developed from RIL Cvl-49 and Lerby Bentsink et al. (2003) (NIL26) captured the d13C QTLdetected on chromosome III [delta-3.1]. This NIL containsa single Cvi introgression at the top of chromosome III(end- approximately 0.79 Mbp) and for the purposes of thispaper, we refer to it as ‘NIL delta-3.1’.

Plant growth conditions – QTLmapping experiment

Replicate plants were grown under standard glasshouseconditions using Promix BT™ potting soil and 164-mLConetainer™ pots (Stuewe & Sons, Corvallis, OR). Indi-vidual Conetainers™ were racked in 61 ¥ 30-cm trays athalf the possible density (49 plants per tray – skipping everyother position). Long-day photoperiod conditions (16 hlight/8 h dark) were obtained with supplemental high-intensity discharge lamps and during daytime PPFD wasmaintained at ≥1000 mmol m-2 s-1. Glasshouse temperaturewas approximately 18–21 ∞C. Several seeds were initiallyplanted in each Conetainer™, cold/wet stratified for 7 d at4 ∞C, and subsequently thinned to a single replicate individ-ual at the first true leaf stage. Plants were kept well-watered, such that the soil surface of each pot wascontinuously damp and soil conditions were optimal for A.thaliana growth.

We used a randomized incomplete block design includingthe two parental lines and 162 RILs derived from theLer ¥ Cvi mapping population. Twelve replicate plants fromeach of the 164 lines were randomly arrayed across individ-ual Conetainer™ trays that each contained 49 plants – weconsidered each tray an incomplete block (164 lines ¥ 12replicates = 1968 plants). Flowering time was recorded bydaily inspection of the plants and was recorded as the num-ber of days since flowering began in the mapping popula-tion. This experiment was conducted from June to August2000.

QTL analyses

We mapped QTL using the Pseudomarker multiple-QTLframework presented by Sen & Churchill (2001). Thismethodology relies on a Monte Carlo imputation algorithmto simulate multiple versions of complete genotype infor-mation on a dense genome-wide grid. The authors refer tothese imputed genotypes as ‘pseudomarkers’. Thispseudomarker grid is scanned using both one and two QTLmodels at each position across the genome and evidencefor a QTL or a QTL–QTL (epistatic) interaction is deter-mined using robust one- and two-dimensional permutationtests.

We used a series of 256 Monte Carlo imputations toestimate the missing marker data at genotyped locations aswell as to infer the genotype of ‘pseudomarkers’ at 2 cM

intervals across the A. thaliana genome. This fine-scaleanalysis was implemented because the Ler ¥ Cvi linkagemap contained several modest gaps on chromosome V.Here, the residual sum of squares corresponding to a par-

ticular model was calculated by averaging the residual sumof squares over the imputations. For technical reasons, theaverage is not a simple arithmetic mean (see Appendices Cand F in Sen & Churchill 2001). Additional details of ourmodel selection and QTL mapping strategy were presentedin an earlier paper (Juenger et al. 2005).

We estimated the additive effect of each QTL as half thedifference in the phenotypic means for the two homozy-gous genotypes at a locus. The sign of the additive effectcorresponds to the direction of the effect of alleles from theCvi parent: positive values indicate that alleles from Cviincreased trait values while negative values indicate thatCvi alleles decreased trait values. We estimated the propor-tion of the total genetic variance (%VG) explained by eachQTL by dividing the sums of squares for each significantmarker by the total corrected model sums of squares fromfull QTL models in PROC GLM in SAS (SAS Institute1997). We estimated the proportion of genetic varianceexplained by interacting QTL as the difference in theadjusted r2 of additive GLM models versus those incorpo-rating interactions. We plotted the posterior probability dis-tribution of the QTL locations under the final model tolocate the genomic position of QTL. In the case of linkedor interacting QTL, we plotted the two-dimensional poste-rior probability distribution under a multiple-QTL model.Under complex genetic models (e.g. multiple-linked QTL,epistasis) strict confidence intervals are difficult to define.As a result, the confidence intervals presented for severalQTL (delta-1.1, delta-5.1, delta-5.2) should be taken asapproximations.

We fit models of the main and interactive effects of allsignificant markers detected in the Pseudomarker analysis(Lynch & Walsh 1998) using SAS Proc Mixed (SAS Insti-tute 1997). In this framework, marker–marker interactionrepresents epistasis (Hausmann et al. 2004; Juenger et al.2005). We also tested for nuclear–cytoplasmic interactionsfor all QTL and QTL pairs identified with Pseudomarkerusing the above approach. We estimated genetic correla-tions (rG) among traits as the standard Pearson product-moment pairwise correlation between the breeding valuesfor each trait. The significance of each genetic correlationwas determined using a t-test after a Z transformation ofthe correlation coefficient.

NIL analyses

We used a randomized complete block design with six rep-licates of each NIL and the recurrent parent Ler in a studyof whole-plant physiology (three blocks, each with tworeplicates of each genotype). Plants were grown in Cone-tainers™ filled with a 1 : 1 mixture of fritted clay (TurfacePro-League®; Profile Products LLC, Buffalo Grove, IL,USA) and peat-based potting mix. These Conetainers™were capped by modified, sealed, 50-mL centrifuge tubesto adapt to the cuvette used for gas exchange measure-ments. Seeds were planted into moist soil in a small hole inthe top of the centrifuge tube cap and then cold stratifiedin the dark for 7 d at 4 ∞C. The plants germinated and

700 T. E. Juenger et al.

© 2005 Blackwell Publishing Ltd, Plant, Cell and Environment, 28, 697–708

grew in a high light growth chamber (370 mmol m-2 s-1

PPFD). Photoperiod was 12 h light/12 h dark, where thelights ramped up and down for the first and last hour oflight and the temperature cycled 23/13 ∞C light/dark. Ger-mination was scored daily and used to schedule gasexchange measurements on plants of exactly equal age.After 21 d, we began measuring photosynthesis (A) andtranspiration (E) during the middle of the light periodusing a custom-made whole-plant cuvette with the LiCor6400 photosynthesis system (LiCor Inc., Lincoln, NE,USA) as described by McKay et al. (2001). In the cuvettethe leaf temperature was 26 ± 1 ∞C and PPFD was315 mmol m-2 s-1. Input CO2 was constant at 400 mmolmol-1, so depending on the plant, the sample CO2 rangedfrom 378 to 393 mmol mol-1 and VPD ranged from 1.65 to2.0 kPa. Each replicate plant was measured at least 10times on each of two measurement periods over a 6-dperiod. Thus, a minimum of 20 repeated measures wereaveraged to obtain a single phenotypic value per plant. Atthe time of each measurement a digital photo was taken ofeach plant and analysed for projected leaf area using aseries of macros in PHOTOSHOP (Adobe, Seattle, WA,USA) and SCION IMAGE (Scion, Frederick, MD, USA).Stomatal conductance (gs) was calculated according to theLiCor 6400 manual (LiCor Inc. 2003) and expressed on aprojected leaf area basis (Donahue, Poulson & Edwards1997). Leaf temperature was measured on one leaf of eachrosette during gas exchange measurements with a fine wirethermocouple and boundary layer resistance was keptminimal by vigorous mixing of cuvette air. After the sec-ond set of measures, plants in two blocks were harvestedand total leaf area and dry mass were obtained for theseplants. The entire rosette of each harvested plant was thenused for carbon isotope analysis (see below). Phenotypicdata from this experiment were analysed with standardGLM in SAS incorporating genotype and blocking factors(SAS Institute 1997). When significant genotype effectswere detected, the pattern was further explored usingDunnett one-tailed post hoc analyses.

To further evaluate water use in the NILs, we conducteda whole-plant transpiration efficiency (TE) experiment.Individual plants were grown in 250-mL plastic cups, eachfilled with a standard mass of 1 : 1 fritted clay and PromixBT™ potting soil mix. We measured field capacity of thesoil mix following a 24-h gravitational drain of saturatedsoil. Each cup was covered with parafilm and sealed with aplastic lid that had a 6-mm diameter hole for the plant.Eight replicates of each NIL and Ler were planted in TEcontainers and cold stratified in the dark for 7 d at 4 ∞C. Theplants germinated and grew in a growth chamber(200 mmol m-2 s-1 PPFD) in a randomized block design.Photoperiod was 16 h light/8 h dark and the temperaturecycled 23/18 ∞C (light/dark). We weighed each containerevery 2 d during the experiment – then additional waterwas added with a syringe to bring the soil in each containerback to 90% of field capacity. Total water use was summedfor the 35-d growing period and all plants were floweringand setting fruit at the experiment’s completion, when

above-ground dry biomass and d13C were measured. Weassessed evaporative loss from TE containers using ‘blanks’lacking an Arabidopsis plant. Total evaporation from blankcontainers was < 4% of the average total water lost frompots in the experiment. Transpiration efficiency of eachplant was calculated as mg dry above-ground biomassdivided by ml of water transpired. As evaporative losseswere extremely low, we did not correct estimates of tran-spired water for evaporative loss. These data were analysedusing a randomized block ANOVA with GLM in SAS incor-porating genotype and blocking factors (SAS Institute1997).

To confirm differences in stomatal conductance, we alsoexamined the rate of water loss from detached rosettes.Whole rosettes of 20-day-old-plants prior to bolting werecut off from their root system, weighed immediately forfresh weight, and then weighed every 20 min for 3.7 h. Tenreplicates of each NIL and 20 replicates of Ler werescreened. Plants had grown in a randomized block designin 5-cm-square pots with Promix BT™ soil using the sameprotocol and growing conditions as for the TE experiment,except plants were watered as needed to ensure optimalArabidopsis growth.

For growth rate assessments we used a randomized com-plete block design with 24 replicates of each NIL and therecurrent parent (two blocks, each with 12 replicates ofeach genotype). Plants were grown using the same protocoland conditions as for the water loss experiments. Pots wereshuffled within blocks each morning to homogenize envi-ronmental variation. We scored date of seed germinationand collected digital images of growing rosettes on days 12,14, 16, 18, and 20 of the experiment. These images wereanalysed for projected leaf area as described for the gasexchange measurements. All plants were harvested on day21, rosettes were weighed for fresh mass, leaves were sep-arated, and total leaf area was estimated using a flat-bedscanner.

Univariate repeated measure ANOVAs were used to anal-yse both the water loss and growth rate experiments usingthe GLM procedure of SAS (SAS Institute 1997).

Carbon isotope analyses

Above-ground biomass was analysed for carbon isotoperatio (d13C) of all RIL. As d13C of large numbers of sam-ples is an expensive phenotype to obtain, we used a pool-ing scheme to derive estimates of d13C for eachexperimental line. For the initial QTL analysis, five repli-cate plants were randomly chosen from each inbred line.The above-ground portions of these plants were pooledand ground for approximately 1 min in a Krups Type 203Household Coffee Grinder to reach a flour-like consis-tency. A subsample was taken from this homogeneousmixture and placed in a screw-type capsule with one stain-less steel ball pestle and ground to a fine powder by shak-ing for 2 min in a Wig-L-Bug™ (Dentsply International,Surrey, UK). Five milligrams of powder from each linewere placed in a tin capsule and d13C determined at the

Physiological QTL in Arabidopsis 701

© 2005 Blackwell Publishing Ltd, Plant, Cell and Environment, 28, 697–708

Center for Stable Isotope Biogeochemistry at the Univer-sity of California, Berkeley. For the NIL experiments, weharvested all leaves from four of the replicates. These werefreeze dried and ground to a fine powder in centrifugetubes with ball-bearing pestles. Two milligrams of powderfrom each sample were loaded into a tin capsule and anal-ysed at the UC Davis Stable Isotope Facility. Data arepresented as carbon isotope ratios relative to the V-PDBstandard (RPDB), where d13C (‰) = (RS/RPDB - 1) ¥ 1000.These values are expressed per mil (‰). As the d13C ofCO2 in the air in the various experiments, especially thosein the growth chambers, was not assured to be constantand equal to -8‰, we chose to use the isotopic composi-tion of the samples (d13C) rather than discrimination, D13C(Farquhar et al. 1989).

RESULTS

Quantitative genetic analysis

The Ler ¥ Cvi RIL population displayed considerabletransgressive segregation for both d13C and flowering time.There were approximately 4- and 17-fold increases in vari-ation for flowering time and d13C in the RIL populationcompared with the differences between the parents (Fig. 1).We observed a 4.65‰ difference in d13C and a 26 d differ-ence in flowering time between the extreme RILs, indicat-ing very large differences in whole-plant physiology amongthe RILs. Broad sense heritability for flowering time wasestimated as 0.86. We were unable to estimate the broadsense heritability of d13C as we lacked data on its within-RIL variance since we used biomass pooled from all repli-cates per line to obtain a single d13C value for each RIL.We found a significant positive genetic correlation (rG)between d13C and flowering time (rG = 0.47). Early flower-ing genotypes have decreased and late flowering genotypesincreased d13C (Fig. 2).

QTL analyses

A variety of methods have been used to localize thegenomic regions underlying complex polygenic variation(see Broman 2001 for a recent review). Here, we implementa novel Bayesian QTL mapping strategy that incorporatesmultiple-QTL models and genome-wide scans for bothadditive and epistatic QTL effects.

Table 1 lists the QTL detected in this experiment. Eachd13C QTL is designated by the identifier ‘delta’ followed bythe chromosome and a unique number corresponding tothe order of the QTL along the linkage group. Several

Figure 1. Frequency distributions of δ13C and flowering time for the Ler × Cvi RIL population. Arrows correspond to the parental line means and flowering time is expressed as days after flowering was first observed in the population.

Flowering time (d after flowering began)

0 10 20 30

Co

un

t

0

20

40

60

Pro

po

rtio

n p

er b

ar

0.0

0.1

0.2

0.3

0.4

1313C (‰d )-33 -32 -31 -30 -29

Co

un

t

0

20

40

Pro

po

rtio

n p

er b

ar

0.0

0.1

0.2

0.3

Ler

LerCvi

Cvi

Figure 2. Bivariate relationship between δ13C and flowering time in 162 Ler × Cvi RILs. Flowering time is expressed as days after flowering was first observed in the population.

Flowering time (d after flowering began)

0 10 20 30

1313C

(‰

d)

-33

-32

-31

-30

-29

702 T. E. Juenger et al.

© 2005 Blackwell Publishing Ltd, Plant, Cell and Environment, 28, 697–708

flowering time and d13C QTL detected in this study overlapwith those identified previously (Alonso-Blanco et al.1998a, b; Ungerer et al. 2003; Hausmann et al. 2004; Juengeret al. 2005) – we use the published nomenclature for theflowering time QTL but acknowledge the difficulty of com-paring QTL results across experiments. One unique flow-ering time QTL was identified and is designated by theidentifier ‘FT’ followed by chromosome and ordering indi-cators. QTL presented in Table 1 were significant at theempirically determined threshold value corresponding toP = 0.05 based on permutation.

We detected five QTL affecting flowering time and fiveQTL affecting d13C (Fig. 3). Two flowering time QTL over-lapped in genomic position with two d13C QTL (Table 1).This co-localization may result from pleiotropic relation-ships between these two traits or be due to physical linkage.The proportion of the total genetic variance explained byadditive QTL (%VG) ranged from 1 to 29%, and averaged9.2%. Additive effects (2a) corresponded, on average, to2.67 d in flowering time and 0.38‰ difference in d13C.

We detected significant QTL–QTL interactions forthe traits measured. A very strong interaction occurredbetween two QTL located on chromosome V (FLF ¥FLG), which explained approximately 12% of the totalgenetic variation in flowering time. This QTL interactionhas been observed in a number of studies of the Ler ¥ Cvimapping population (Alonso-Blanco et al. 1998a, b;Ungerer et al. 2003; Hausmann et al. 2004; Juenger et al.2005) – FLF and FLG QTL are likely caused, respectively,by polymorphisms in FLOWERING LOCUS C (FLC) (El-Assal et al. 2003) and AERIAL ROSETTE 1 (ART)(Poduska et al. 2003). We detected interchromosomalepistasis between two d13C QTL (delta-1.1 ¥ delta-5.1),which explained approximately 2.5% of the total geneticvariance in d13C (Fig. 4). We found no cytoplasmic effect ornuclear–cytoplasmic interactions for either flowering timeor d13C phenotypes.

Confidence intervals for delta-5.1 and delta-5.2 QTL arebroadly overlapping. Despite similar localization, support

for two d13C QTL on chromosome V comes from the pat-tern of epistasis observed. In this case, models incorporat-ing QTL interaction are significant for markers near LODpeaks for delta-5.1 but not delta-5.2. Under complexgenetic interactions, the precise positions of QTL are diffi-cult to define and therefore the presence of and localizationof delta-5.1 should be taken as preliminary until confirmedwith additional fine-mapping.

Whole-plant physiological analyses

Genome-wide QTL scans are a quick and relativelystraightforward means for coarse localization of geneticfactors influencing a complex trait. In general, QTL studiesusing the existing RIL mapping populations in A. thalianaassign the positions of QTL to genetic intervals rangingfrom 5 to 50 cM, corresponding (on average) to 1.2–12 Mbof DNA (Koornneef, Alonso-Blanco & Vreugdenhil 2004).The accuracy of the mapping depends primarily on themagnitude of QTL effects, the genetic architecture of QTL(e.g. the occurrence of epistasis or linked QTL), and theheritability of the traits under study.

Fine-scale localization and, ultimately, identification ofthe molecular variants accounting for natural variation rep-resent major challenges. One step toward molecular iden-tification of QTL is the development of NILs that captureparticular QTL in an isolated homogeneous background.NILs facilitate detailed phenotyping as they are immortal,easily replicated, and provide a well-controlled geneticbackground. These features increase the statistical powerof detecting subtle QTL effects and evaluating traits thatare particularly difficult to phenotype.

We utilized NILs containing Cvi alleles at two d13C QTL[delta-2.1 and delta-3.1] introgressed into a homogeneousLer nuclear and cytoplasmic background. NIL delta-2.1 andNIL delta-3.1 contain approximately 1.8 and 0.79 Mb of theCvi genome, respectively. This span of genomic DNA cor-responds to several hundred genes in each interval. Wescreened replicates of each NIL and Ler (the recurrent

Table 1. Results of QTL analyses in the Ler ¥ Cvi population using two-dimensional genome scans

QTL Trait ChromosomePosition(cM) Marker

Additive effect2a (SE) %VG

EDI –cry2* flowering time I 9 AXR-1 -2.30 (0.75) 4.7delta-1.1 d13C I 24 GD.86C -0.36 (0.08) 6.5delta-2.1 d13C II 78 EC.235 L-Col/247C -0.42 (0.8) 8.9FT-3.1 flowering time III 0 DF.77C +0.66 (0.54) <1delta-3.1 d13C III 0 DF.77C -0.41 (0.08) 8.3FLF – FLC* flowering time V 18 BH.180C +5.08 (0.59) 29delta-5.1 d13C V 34 DF.184 L-Col +0.35 (0.11) 4.0FLG* flowering time V 39 GH.121 L-Col +4.12 (0.58) 23delta-5.2 d13C V 46 AD.129 L-Col +0.31 (0.10) 3.4FLH* flowering time V 107 DF.119 L +1.21 (0.53) 2.3

The sign of the additive effects indicate the direction effect of Cvi alleles. For each QTL, the chromosome on which it resides, the estimatedposition, the associated genetic marker, the additive genotypic effect (2a) with its SE, and the proportion of the total genetic variance itexplained in the full QTL model (%VG) is given. *QTL detected in this study that overlap with previously described QTL using thenomenclature of Alonso-Blanco et al. (1998a).

Physiological QTL in Arabidopsis 703

© 2005 Blackwell Publishing Ltd, Plant, Cell and Environment, 28, 697–708

parent) for d13C, whole-plant gas-exchange rates, plantwater loss rates, and growth characteristics in growth cham-ber experiments. We detected significant differencesbetween each NIL and Ler for d13C (P < 0.05), confirming

and further localizing these QTL (Fig. 5). Additive QTLeffects (2a) were estimated at – 0.83 and – 0.72‰ for delta-2.1 and delta-3.1, respectively.

We used a custom-made whole-plant gas exchangecuvette (McKay et al. 2001) and the LiCor 6400 to evaluatethe physiological impact of Cvi alleles at delta-2.1 anddelta-3.1. There was a significant difference between eachNIL and Ler for gs (P < 0.05; Fig. 5), but no differences forA (P > 0.50). These results support the hypothesis thatdelta-2.1 and delta-3.1 alleles affect d13C through changesin stomatal control of CO2 diffusion to the leaf interior,although internal conductance differences or feed-backs,not assessed with the gas exchange measurements, cannotbe excluded. Additive QTL effects for gs were estimated at0.17 mol H2O m-2 s-1 for both NIL lines.

To evaluate the impact of gs differences on plant perfor-mance, we conducted a transpiration efficiency (TE) exper-iment using a closed-container growth system. Again, wefound significant differences between each NIL and Ler inTE (P < 0.0001, Fig. 5). We observed a 16.2 and 22.5%decrease in TE for NIL delta-2.1 and NIL delta-3.1 relativeto Ler, respectively. We found a significant positive relation-

Figure 3. QTL positions for δ13C and flowering time on each of the Arabidopsis linkage groups. Each bar represents the 95% confidence interval surrounding a QTL position as determined from the area under the posterior probability density of QTL locations. Solid black bars represent approximate positions of NIL introgressions for NIL delta-2.1 and NIL delta-3.1 intervals. Genomic positions are presented in cM units at each of the genetic markers used in the QTL mapping study.

Figure 4. Epistatic interaction between delta-1.1 and delta-5.1 QTL.

Allele at QTL delta-5.1

1313C

(‰

d)

-32

-31 Ler 1.1 Cvi 1.1

Ler Cvi

704 T. E. Juenger et al.

© 2005 Blackwell Publishing Ltd, Plant, Cell and Environment, 28, 697–708

ship between d13C and TE among replicate plants within theTE experiment (Fig. 6).

To further characterize the NILs, we measured water lossrates from whole rosettes over time after they were severedfrom their root systems. Variation in the rate of water losson excised plants is likely the result of constitutive differ-ences in initial gs (prior to stomatal closure), the sensitivityof stomatal closure to water deficit, and cuticular conduc-tance. We detected strikingly different rates of water lossfrom detached rosettes for each NIL when compared to Ler(Fig. 7). We present the results of the repeated measuresanalysis of variance for delta-2.1 in Table 2 (the pattern ofsignificant is virtually identical for delta-3.1 and so is notincluded). At the end of the 220-min experiment, detachedrosettes from Ler, NIL delta-2.1, and NIL delta-3.1 had lost

approximately 56, 68 and 70% of their initial fresh weight,respectively. In general, the NILs exhibit a more rapid rateof immediate water loss (within 100 min) followed by par-allel responses through the later time intervals.

Finally, we investigated the impact of these QTL onoverall plant growth. We measured the growth rate of rep-

Figure 5. Phenotypic effects of NIL delta-2.1 and NIL delta-3.1 for δ13C, stomatal conductance, and transpiration efficiency. Each bar represents the mean ± 1 SE (n = 5–8) for each genetic line. In each case, both NILs are significantly different from the recurrent parent Ler (P < 0.05) in the expected direction.

Ler -WT NIL delta 2.1 NIL delta 3.1

1313C

(‰

d)

-34.5

-34.0

-33.5

-33.0

Ler -WT NIL delta 2.1 NIL delta 3.1

gs (m

ol H

20 m

-2 s

-1)

0.0

0.2

0.4

0.6

Ler -WT NIL delta 2.1 NIL delta 3.1

TE

(m

g r

ose

tte/

g H

2O)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Figure 6. Transpiration efficiency (TE) and δ13C among seven replicates each of NIL delta-2.1, NIL delta-3.1, and Ler. As expected the two traits are positively correlated (r = 0.8, P < 0.00000005).

Transpiration efficiency (mg ml–1 H2O)

2.0 2.5 3.0

13C

(‰

)d

-35.0

-34.5

-34.0

-33.5NILdelta 2.1NIL delta 3.1Ler WT

Figure 7. Time course of water loss from detached rosettes of NIL delta-2.1, NIL delta-3.1, and Ler. Each point represents the mean ± 1 SE (n = 10 for each NIL, n = 20 for Ler) at 20 min intervals.

Minutes since rosette excision 0 50 100 150 200 250

Per

cen

t o

f in

itia

l wat

er c

on

ten

t

20

40

60

80

100

NIL delta 2.1 NIL delta 3.1 Ler WT

Physiological QTL in Arabidopsis 705

© 2005 Blackwell Publishing Ltd, Plant, Cell and Environment, 28, 697–708

licate plants for each NIL and Ler in terms of leaf areaproduction over 3 weeks of vegetative growth. We foundno significant difference in leaf area relative growth ratesamong NIL delta-2.1, NIL delta-3.1, or Ler when plantswere grown under well-watered conditions (in all cases,P > 0.25).

DISCUSSION

Most physiological traits, like most phenotypes of ecologi-cal and agricultural significance, are quantitative and gen-erally influenced by multiple genes and environmentalvariation. In the past, the segregation of multiple genes,environmental effects, and their interactions preventedsimple determination of genotype from phenotypic data(Falconer & Mackay 1996; Lynch & Walsh 1998). Statisticalgenetic studies have focused, alternatively, on populationlevel patterns of genetic variance for polygenic traits. Thisapproach has been useful for evolutionary biology and cropbreeding, but does not offer a functional characterizationof the actual genes underlying polygenic variation. Untilrecently, geneticists simply lacked adequate methods forevaluating the effect of polygenes. Therefore we know rel-atively little about the genetic details of these traits or theirimpact on plant physiology (Ehleringer & Monson 1993;Arntz & Delph 2001). This study is based on an integratedinvestigation of physiology and quantitative genetics inArabidopsis thaliana using a set of RILs derived fromgenetically diverse parental ecotypes. Our goal is to char-acterize QTL underlying constitutive differences in physi-ology between natural accessions of Arabidopsis that haveevolved under conditions differing in water availability.

We found significant genetic variation for both floweringtime and d13C in the Ler ¥ Cvi mapping population andidentified 10 genomic regions affecting these traits. Ourmapping identified five QTL with effects on d13C [delta-1.1,delta-2.1, delta-3.1, delta-5.1, delta-5.2]. Cvi alleles fromthree of the five QTL made d13C values more negative,indicating decreased WUE relative to the Ler ecotype. Thefact that both the Cvi and Ler parental ecotypes containedplus and minus alleles for d13C provides a general explana-tion for the observed transgressive segregation in the RILpopulation. Three of the d13C QTL detected in this study(delta-2.1, delta-3.1, delta-5.1) were previously identified in

similar experiments utilizing both wet and dry experimentalconditions (Hausmann et al. 2004). Hausmann et al. (2004)detected delta-2.1, delta-3.1, delta-5.1 and a novel QTL onchromosome III at approximately 22.2 cM. Interestingly,the QTL corresponding to delta-5.1 was detected onlyunder wet conditions and through its interaction with thenovel QTL on chromosome III. Differences between ourprevious experiments and those presented in this studycould reflect either difference in statistical power, differingenvironmental conditions in each experiment, or the pres-ence of QTL–environment interaction. Additional experi-ments will be needed to further explore the role ofenvironmental conditions and QTL–environment interac-tions on d13C.

Interestingly, in this study, two d13C QTL were locatedwithin the genomic position of flowering time QTL. Thismay indicate a functional or pleiotropic relationshipbetween these traits. A number of studies have reported apositive genetic correlation between d13C and flowering inC3 annuals, suggesting a genetically based tradeoff betweenavoidance of and escape from drought (McKay et al. 2003).These patterns are further supported by a remarkablystrong genetic correlation (rG = 0.98) between date of firstflowering and d13C among 39 Arabidopsis ecotypes grownin a common garden (McKay et al. 2003). Further charac-terization of loci affecting both traits, as well as those withunique effects on d13C, will provide further insight into themechanisms driving the strong correlation observed innature.

We utilized NILs to confirm and characterize two of thed13C QTL identified in our study. Here, a small interval ofthe Cvi genome capturing either delta-2.1 or delta-3.1 wasintrogressed into a homogeneous Ler genomic background.In both cases, NIL studies verify lower d13C values for gen-otypes with Cvi alleles at either QTL. Furthermore, eachNIL exhibited significantly higher gs, as measured withwhole-plant gas exchange. Cvi alleles result in less resis-tance to the escape of water vapour from plant leavesthrough stomata. The effect is consistent with increased gs

leading to increased ci with a subsequent decrease in d13Cvalues. Using the photosynthesis model of Farquhar et al.(1980) and assuming the NILs have the photosyntheticproperties of Ler, the calculated magnitude of change ind13C is very close to what would be expected if gs is the only

Table 2. Univariate repeated measures ANOVA of the rate of water loss in detached rosettes of NIL delta-2.1 compared to Ler

Source DF Type III SS Mean square F-value P-value

Between subjectsLine 1 0.63 0.63 31.27 < 0.0001Block 1 0.07 0.07 3.33 0.0858Error 17 0.34 0.02

Within subjectsTime 12 7.09 0.59 1839 < 0.0001Time × Line 12 0.10 0.01 25.82 < 0.0001Time × Block 12 0.01 0.00 2.73 0.0913Error (Time) 204 0.07 0.00

706 T. E. Juenger et al.

© 2005 Blackwell Publishing Ltd, Plant, Cell and Environment, 28, 697–708

limit on internal CO2 partial pressure. At a whole-plantlevel, we detected a corresponding decrease in TE in bothNILs relative to Ler. Finally, we observed increased ratesof water loss from NIL genotypes compared to Ler inrosettes detached from their root system. NIL containingCvi alleles at two d13C QTL exhibit increased gs, decreasedTE, and altered ability to limit the rate of water loss. Theseresults suggest that genes underlying the observed physio-logical effects may play important roles in controlling sto-matal closure during leaf dehydration as well as during gasexchange under well-watered conditions.

It is reasonable to expect some effects of these (or other)d13C QTL to be due to differences in A, given the linkbetween A, gs, and ci. Increased gs is expected to increaseci and increased A is expected to lower ci (Farquhar &Sharkey 1982). Consideration of indirect (feedback) influ-ences through A or internal conductance differences maycomplicate our interpretations. For example, experimentalevidence suggests that A can affect gs as well as ci (Wong,Cowan & Farquhar 1979). We plan to further characterizecausal relationships between whole-plant physiologicalparameters and genetic variation in d13C with more com-prehensive gas exchange measurements and analysis of A–ci curves, fluorescence, and limitations due to internal con-ductances (Bernacchi et al. 2002).

We clearly detected whole-plant physiological effects(d13C, gs, TE) of the identified genomic regions captured bythe NILs. Although no differences were detected in growthrate under well-watered conditions, this is not surprising.Typically advantages of increased WUE are seen in dryenvironments (Dudley 1996; Rebetzke et al. 2002; Condonet al. 2004). Conversely, increased gs may be detrimentalunder increased soil or atmospheric water deficit. Forexample, constitutive increases in gs through eitherincreased stomatal density or stomatal opening may resultin reduced survivorship or growth rates in the face of waterdeficit. We are currently exploring the TE and competitiveability of these NILs under a variety of soil moisture andcompetitive conditions. These efforts will shed some lighton the ecological impacts of genetic variation in whole-plant physiology in A. thaliana.

In summary, we have identified and characterized severalgenomic regions that contribute to flowering time andwhole-plant physiological differences observed betweenLer and Cvi. Genome-wide screens for d13C followed bydetailed phenotyping of NILs provide some of the first dataevaluating the natural genetic variation regulating physiol-ogy in A. thaliana. A concerted effort integrating quantita-tive genetics, whole-plant physiology, and genomicapproaches hold promise for the positional cloning of theseloci. Ultimately, these efforts will help to elucidate the fac-tors affecting plant performance under environmentalstress in both crop and ecological settings.

ACKNOWLEDGMENTS

We thank the Oxford Greenhouse staff at UC Berkeley, theUC Davis Controlled Environment Facilities, and the UT

Brackenridge Field Laboratory greenhouse for help withplant material and our experiments. D. Zhao, J. Povich, S.Schultz, G. Ladd, N. Milan and H.M. Easlon providedexcellent help with plant growth and data collection. Wethank L. Bentsink and M. Koornneef for sharing the chro-mosome II NIL material. We thank K. Shaw for manufac-ture of the whole plant gas-exchange cuvette, and A. Bloomand S. Loriaux (LiCor Inc.) for advice on whole plant gasexchange. This work was supported by a Miller postdoc-toral fellowship (T.E.J.), a Packard Foundation postdoc-toral fellowship (J.K.M.), a NRC Ford Foundationpostdoctoral fellowship (K.A.S.); and grants from Nether-lands Organization for Scientific Research, ProgramGenomics (050-10-029; J.K.) and NSF DEB 981550(E.L.S.), NSF DEB 042011 (T.E.J.), DEB 0419969 (J.H.R.and J.K.M.), and the California Agricultural ExperimentStation (J.H.R.).

REFERENCES

Ackerly D.D., Dudley S.A., Sultan S.E., et al. (2000) The evolutionof plant ecophysiological traits: recent advances and futuredirections. Bioscience 50, 979–995.

Alonso-Blanco C. & Koornneef M. (2000) Naturally occurringvariation in Arabidopsis: an underexploited resource for plantgenetics. Trends in Plant Science 5, 22–29.

Alonso-Blanco C., Bentsink L., Hanhart C.J., Blankestijn-de VriesH. & Koornneef M. (2003) Analysis of natural allelic variationat seed dormancy loci of Arabidopsis thaliana. Genetics 164,711–729.

Alonso-Blanco C., El-Assal S.E.-D., Coupland G. & KoornneefM. (1998a) Analysis of natural allelic variation at flowering timeloci in the Landsberg erecta and Cape Verde Island ecotypes ofArabidopsis thaliana. Genetics 149, 749–764.

Alonso-Blanco C., Peeters A.J.M., Koornneef M., Lister C., DeanC., van den Bosch N., Pot J. & Kuiper M.T.R. (1998b) Devel-opment of an AFLP based linkage map of Ler, Col, and CviArabidopsis thaliana ecotypes and construction of a Ler/Cvirecombinant inbred line population. Plant Journal 14, 259–271.

Araus J.L., Slafer G.A., Reynolds M.P. & Royo C. (2002) Plantbreeding and drought in C3 cereals: what should we breed for?Annals of Botany 89, 925–940.

Arntz A.M. & Delph L.F .(2001) Pattern and process: evidence forthe evolution of photosynthetic traits in natural populations.Oecologica 127, 455–467.

Bentsink L., Yuan K., Koornneef M. & Vreugdenhil D. (2003) Thegenetics of phytate and phosphate accumulation in seeds andleaves of Arabidopsis thaliana, using natural variation. Theoret-ical and Applied Genetics 106, 1234–1243.

Bernacchi C.J., Portis A.R., Nakano H., von Caemmerer S. &Long S.P. (2002) Temperature response of mesophyll conduc-tance. Implications for the determination of Rublisco enzymekinetics and for limitations to photosynthesis in vivo. Plant Phys-iology 130, 1992–1998.

Bohnert H.J., Nelson D.E. & Jensen R.G. (1995) Adaptations toenvironmental stresses. Plant Cell 7, 1099–1111.

Bray E.A. (1997) Plant responses to water deficit. Trends in PlantScience 2, 48–54.

Bray E.A. (2002) Classification of genes differentially expressedduring water-deficit stress in Arabidopsis thaliana: an analysisusing microarray and differential expression data. Annals ofBotany 89, 803–811.

Broman K.W. (2001) Review of statistical methods for QTL map-ping in experimental crosses. Laboratory Animal 30, 44–52.

Physiological QTL in Arabidopsis 707

© 2005 Blackwell Publishing Ltd, Plant, Cell and Environment, 28, 697–708

Cohen D. (1970) The expected efficiency of water utilization inplants under different competition and selection regimes. IsraelJournal of Botany 19, 50–54.

Comstock J.P. & Ehleringer J.R. (1992) Genetic variation inwater-use efficiency correlates with climatic variation in evapo-rative demand. Proceedings of the National Academy of Sciencesof the USA 89, 7747–7751.

Condon A.G. & Hall A.E. (1997) Adaptation to diverse environ-ments: Variation in water-use efficiency within crop species. InEcology in Agriculture (ed. L.E. Jackson), pp. 79–143. AcademicPress, San Diego, CA, USA.

Condon A.G., Richards R.A., Rebetzke G.J. & Farquhar G.D.(2004) Breeding for high water-use efficiency. Journal of Exper-imental Botany 55, 2447–2460.

Cowan I.R. (1982) Regulation of water use in relation to carbongain in higher plants. Encyclopedia of Plant Physiology 12B,489–613.

Cushman J.C. & Bohnert H.J. (2000) Genomics approaches toplant stress tolerance. Current Opinions in Plant Biology 3, 117–124.

Dawson T.E. & Ehleringer J.R. (1993) Gender-specific physiologycarbon isotope discrimination and habitat distribution in box-elder Acer negundo. Ecology 74, 798–815.

Dawson T.E., Mambelli S., Plamboeck A.H., Templer P.H. & TuK.P. (2002) Stable isotopes in plant ecology. Annual Review ofEcology and Systematics 33, 507–559.

Donahue R.A., Poulson M.E. & Edwards G.E. (1997) A methodfor measuring whole plant photosynthesis in Arabidopsisthaliana. Photosynthesis Research 52, 263–269.

Dudley S.A. (1996) Differing selection on plant physiological traitsin responses to environmental water availability: a test of adap-tive hypotheses. Evolution 50, 92–102.

Ehleringer J.R. & Monson R.K. (1993) Evolutionary and ecolog-ical aspects of photosynthetic pathway variation. Annual Reviewof Ecology and Systematics 24, 411–439.

Ehleringer J.R., Hall A.E. & Farquhar G.D. (eds) (1993) StableIsotopes and Plant Carbon-water Relations. Academic Press,New York, USA.

El-Assal S.E.-D., Alonso-Blanco C., Peeters A.J.M., WagemakerC., Weller J.L. & Koornneef M. (2003) The role of crypto-chrome 2 in flowering in Arabidopsis. Plant Physiology 133,1504–1516.

Falconer D.S. & Mackay T.F.C. (1996) Introduction to QuantitativeGenetics, 4th edn. Addison-Wesley Longman, Harlow, Essex.

Farquhar G.D. & Sharkey T. (1982) Stomatal conductance andphotosynthesis. Annual Review of Plant Physiology 33, 317–345.

Farquhar G.D., Ehleringer J.R. & Hubick K.T. (1989) Carbonisotope discrimination and photosynthesis. Annual Reviewof Plant Physiology and Plant Molecular Biology 40, 503–538.

Farquhar G.D., von Caemmerer S. & Berry J.A. (1980) A bio-chemical model of photosynthetic CO2 assimilation in leaves ofC3 species. Planta 149, 78–90.

Geber M.A. & Dawson T.E. (1990) Genetic-variation in and cov-ariation between leaf gas-exchange, morphology, and develop-ment in Polygonium arenastrum, an annual plant. Oecologia 85,153–158.

Geber M.A. & Dawson T.E. (1997) Genetic variation in stomataland biochemical limitations to photosynthesis in the annualplant, Polygonum arenastrum. Oecologia 109, 535–546.

Haake V., Cook D., Riechmann J.L., Pineda O., Thomashow M.F.& Zhang J.Z. (2002) Transcription factor CBF4 is a regulator ofdrought adaptation in Arabidopsis. Plant Physiology 130, 639–648.

Hausmann N., Juenger T.E., Sen S., Stowe K., Dawson T.E. &

Simms E.L. (2004) Quantitative trait loci (QTL) affecting d13Cand response to differential water availability in Arabidopsisthaliana. Evolution 59, 81–96.

Ingram J. & Bartels D. (1996) The molecular basis of dehydrationtolerance in plants. Annual Review of Plant Physiology andPlant Molecular Biology 47, 377–403.

Juenger T., Sen S., Stowe K. & Simms E. (2005) Epistasis andgenotype–environment interaction for quantitative trait lociaffecting flowering time in Arabidopsis thaliana. Genetica 123,83–101.

Koornneef M., Alonso-Blanco C. & Vreugdenhil D. (2004) Natu-rally occurring genetic variation in Arabidopsis thaliana. AnnualReview of Plant Biology 55, 141–172.

Lander E., Green S., Abrahamson P., Barlow A., Daley M., Lin-coln S. & Newburg L. (1987) MAPMAKER: an interactivecomputer package for constructing primary genetic linkagemaps of experimental and natural populations. Genomics 1,174–181.

Li-Cor Inc. (2003) Using the LI-6400 Portable Photosynthesis Sys-tem, Version 5. Li-Cor, Lincoln, NE, USA.

Ludlow M.M. (1989) Strategies of response to water stress. InStructural and Functional Responses to Environmental Stresses(eds K.H. Kreeb, H. Richter & T.M. Hinkley), pp. 269–281. SPBAcademic, The Hague, The Netherlands.

Lynch M. & Walsh J.B. (1998) Genetics and Analysis of Quantita-tive Traits. Sinauer, Sunderland, MA, USA.

Martin B., Nienhuis J., King G. & Schaefer A. (1989) Restrictionfragment length polymorphisms associates with water use effi-ciency in tomato. Science 243, 1725–1728.

Martin D., Tauer C.G. & Kin R.K. (1999) Carbon-isotope discrim-ination as a tool to improve water-use efficiency in tomato. CropScience 39, 1775–1783.

McKay J.K., Bishop J.G., Lin J.-Z., Sala A., Richards J.H. &Mitchell-Olds T. (2001) Local adaptation across a climatic gra-dient despite small effective population size in the rare sapphirerockcress. Proceedings of the Royal Society of London, Biolog-ical Sciences 268, 1715–1721.

McKay J.K., Richards J.H. & Mitchell-Olds T. (2003) Genetics ofdrought adaptation in Arabidopsis thaliana: pleiotropy contrib-utes to genetic correlations among ecological traits. MolecularEcology 12, 1137–1151.

Meinzer F.C. (1993) Stomatal control of transpiration. Trends inEcology and Evolution 8, 289–294.

Nienhuis J., Sills G.R., Martin B. & King G. (1994) Variance forwater-use efficiency among ecotypes and recombinant inbredlines of Arabidopsis thaliana (Brassicaceae). American Journalof Botany 81, 943–947.

Passioura J.B. (1996) Drought and drought tolerance. PlantGrowth Regulation 20, 79–83.

Poduska B., Humphrey T., Redweik A. & Grbi V. (2003)The synergistic action of FLOWERING LOCUS C byFRIGIDA and a new flowering gene AERIAL ROSETTE 1underlies a novel morphology in Arabidopsis. Genetics 163,1457–1465.

Price A.H., Cairns J.E., Horton P., Jones H.G. & Griffiths H.(2002) Linking drought-resistance mechanisms to droughtavoidance in upland rice using a QTL approach: progress andnew opportunities to integrate stomatal and mesophyllresponses. Journal of Experimental Botany 53, 989–1004.

Rebetzke G.J., Condon A.G., Richards R.A. & Farquhar G.D.(2002) Selection for reduced carbon isotope discriminationincreases aerial biomass and grain yield on rainfed bread wheat.Crop Science 42, 739–745.

SAS Institute (1997) SAS/STAT software: changes and enhance-ments through release 6.12. SAS Institute, Cary, NC, USA.

Schulze E.-D. (1986) Carbon dioxide and water vapor exchange in

¢c

708 T. E. Juenger et al.

© 2005 Blackwell Publishing Ltd, Plant, Cell and Environment, 28, 697–708

response to drought in the atmosphere and soil. Annual Reviewof Plant Physiology 37, 247–274.

Schulze E.D., Robichaux R.H., Grace J., Rundel P.W. &Ehleringer J.R. (1987) Plant water balance. Bioscience 37, 30–37.

Seki M., Narusaka M., Ishida J., et al. (2002) Monitoring theexpression profiles of 7000 Arabidopsis genes under drought,cold and high-salinity stresses using a full-length cDNA microar-ray. Plant Journal 31, 279–292.

Sen S. & Churchill G. (2001) A statistical framework for quantita-tive trait mapping. Genetics 159, 371–387.

Specht J.E., Chase K., Macrander M., Graef G.L., Chung J., Mark-well J.P., Germann M., Orf J.H. & Lark K.G. (2001) Soybeanresponse to water: a QTL analysis of drought tolerance. CropScience 41, 493–509.

Stebbins G.L. (1952) Aridity as a stimulus to plant evolution.American Naturalist 86, 33–44.

Teulat B., Merah O., Sirault X., Borries C., Waugh R. & This D.(2002) QTLs for grain carbon isotope discrimination in field-grown barley. Theoretical and Applied Genetics 106, 118–126.

Ungerer M.C., Halldorsdottir S.S., Purugganan M.D. & MackayT.F.C. (2003) Genotype–environment interactions at quantita-tive trait loci affecting inflorescence development in Arabidopsisthaliana. Genetics 165, 353–365.

Virgona J.M. & Farquhar G.D. (1996) Genotypic variation in rel-ative growth rate and carbon isotope discrimination in sunfloweris related to photosynthetic capacity. Australian Journal of PlantPhysiology 23, 227–236.

Wong S.C., Cowan I.R. & Farquhar G.D. (1979) Stomatal conduc-tance correlates with photosynthetic capacity. Nature 282, 424–426.

Received August 23 2004; received in revised form November 192004; accepted for publication November 19 2004