Denis Vile
email: denis.vile@supagro.inra.fr

Marjorie Pervent

Michaël Belluau

François Vasseur

Justine Bresson

Bertrand Muller

Christine Granier

Thierry Simonneau

Arabidopsis growth under prolonged high temperature and water deficit: independent or interactive effects?p ce_2445 702..718

Keywords: Arabidopsis thaliana, biomass allocation, hyponasty, leaf morphology, multistress, phenology, stomatal density

High temperature (HT) and water deficit (WD) are frequent environmental constraints restricting plant growth and productivity. These stresses often occur simultaneously in the field, but little is known about their combined impacts on plant growth, development and physiology. We evaluated the responses of 10 Arabidopsis thaliana natural accessions to prolonged elevated air temperature (30 °C) and soil WD applied separately or in combination. Plant growth was significantly reduced under both stresses and their combination was even more detrimental to plant performance. The effects of the two stresses were globally additive, but some traits responded specifically to one but not the other stress. Root allocation increased in response to WD, while reproductive allocation, hyponasty and specific leaf area increased under HT. All the traits that varied in response to combined stresses also responded to at least one of them. Tolerance to WD was higher in small-sized accessions under control temperature and HT and in accessions with high biomass allocation to root under control conditions. Accessions that originate from sites with higher temperature have less stomatal density and allocate less biomass to the roots when cultivated under HT. Independence and interaction between stresses as well as the relationships between traits and stress responses are discussed.

INTRODUCTION

High temperature (HT) and water deficit (WD) are two important environmental constraints restricting plant growth and productivity in many areas of the world [START_REF] Boyer | Plant productivity and environment[END_REF][START_REF] Ciais | Europe-wide reduction in primary productivity caused by the heat and drought in[END_REF]. Global climate change will presumably increase the occurrence and extend the distribution of these constraints, leading to further reduction of productivity and shifts in biodiversity [START_REF] Chaves | How plants cope with water stress in the field. Photosynthesis and growth[END_REF][START_REF] Lobell | Climate and management contributions to recent trends in US agricultural yields[END_REF]Porter 2005;[START_REF] Thuiller | Climate change threats to plant diversity in Europe[END_REF]IPCC 2007). The two stresses often occur simultaneously in the field, but little is known about their combined effects on plant growth, development and physiology [START_REF] Machado | Combined effects of drought and high temperature on water relations of wheat and sorghum[END_REF][START_REF] Zhang | Water deficits and heat shock effects on photosynthesis of a transgenic Arabidopsis thaliana constitutively expressing ABP9, a bZIP transcription factor[END_REF].

Different mechanisms have been identified as ensuring plant survival and growth under elevated temperatures or water shortage. They include long-term evolutionary phenological and morphological adaptations and short-term avoidance or acclimation mechanisms. Even moderate increases in air temperature [START_REF] Lafta | Effect of high temperature on plant growth and carbohydrate metabolism in potato[END_REF][START_REF] Loveys | Growth temperature influences the underlying components of relative growth rate: an investigation using inherently fast-and slow-growing plant species[END_REF] or decreases in soil water availability (Passioura 1996) are responsible for impaired plant growth. Many elementary biological processes and morphological traits underlying plant growth are sensitive to temperature, and their responses repeatedly resemble a bell-shaped curve. As temperature rises above a particular threshold, processes such as net photosynthetic rate are negatively affected [START_REF] Körner | Significance of temperature in plant life[END_REF]Sage & Kubien 2007;Parent et al. 2010), ultimately leading to a decline in plant performance. Temperature is also the main determinant of plant phenology (Ritchie & NeSmith 1991), and moderate increases in air temperature generally accelerate the rate of developmental processes leading to early flowering in most wild and cultivated species [START_REF] Johnson | Temperature-dependence of plant and crop processes[END_REF]. Whereas the effects of WD on phenology remain elusive, delayed timing of reproduction is often observed in crop species [START_REF] Mcmaster | Simulating crop phenological responses to water deficits[END_REF]. The effects of these stresses also depend on the phenological stage at which they occur (Prasad, Staggenborg & Ristic 2008). For instance, HT has greater impacts on seed yield during the reproductive phase (Jenks & Wood 2010). Therefore, accelerated reproduction in response to HT is generally viewed as an escape mechanism.

HT and WD have contrasted effects on patterns of biomass allocation to organs and tissues. For instance, allocation to roots rapidly increases in response to moderate soil WD [START_REF] Boyer | Water transport[END_REF], whereas leaf relative water content and specific leaf area (SLA) decline in plants subjected to water stress (Poorter et al. 2009). Leaf structure is also affected by temperature, but, in contrast to WD, higher temperature often leads to the production of thinner leaves with higher SLA [START_REF] Boese | Effect of growth temperature and temperature shifts on spinach leaf morphology and photosynthesis[END_REF][START_REF] Loveys | Growth temperature influences the underlying components of relative growth rate: an investigation using inherently fast-and slow-growing plant species[END_REF][START_REF] Luomala | Stomatal density, anatomy and nutrient concentrations of Scots pine needles are affected by elevated CO2 and temperature[END_REF]Poorter et al. 2009). These morphological changes are accompanied by changes in leaf anatomy. Leaves developed under WD have generally smaller cells in the parenchyma and the epidermis [START_REF] Lecoeur | Expansion of pea leaves subjected to short water deficit: cell number and cell size are sensitive to stress at different periods of leaf development[END_REF]) and higher stomatal density [START_REF] Vile | RD20, a stress-inducible caleosin, participates in stomatal control, transpiration and drought tolerance in Arabidopsis thaliana[END_REF][START_REF] Tisne | Keep on growing under drought: genetic and developmental bases of the response of rosette area using a recombinant inbred line population[END_REF]. [START_REF] Wahid | Heat tolerance in plants: an overview[END_REF] reported similar effects of HT and WD on cell density, but limited data are available on changes in leaf anatomy in response to HT.

The effects of WD, particularly osmotic stresses or watering deprivation, and HT, particularly short periods of acute heat stress, have been mostly analysed separately. There is, however, strong evidence that HT and WD interact to influence plant functioning (Rizhsky, Liang & Mittler 2002;Rizhsky et al. 2004). For instance, WD induces stomatal closure and reduces transpiration fluxes [START_REF] Hsaio | Plant responses to water stress[END_REF]. This in turn can cause an increase in leaf temperature by reducing transpirational cooling [START_REF] Cook | Transpiration: its effects on plant leaf temperature[END_REF], and potentially enhances plant susceptibility to higher air temperature. Increase in leaf temperature can also raise plant water loss through transpiration [START_REF] Lafta | Effect of high temperature on plant growth and carbohydrate metabolism in potato[END_REF], and decrease root growth [START_REF] Kuroyanagi | Mediation of hightemperature injury by roots and shoots during reproductive growth of wheat[END_REF], thus increasing plant susceptibility to water shortage. By contrast, changes in leaf orientation in response to elevated temperature [START_REF] Fu | Heliotropic leaf movements in common beans controlled by air temperature[END_REF]) such as hyponasty [START_REF] Koini | High temperature-mediated adaptations in plant architecture require the bHLH transcription factor PIF4[END_REF][START_REF] Van Zanten | Hormone-and light-mediated regulation of heat-induced differential petiole growth in Arabidopsis thaliana[END_REF]) modify the leaf energy balance and could contribute to water saving by limiting rises in leaf temperature and evaporative demand. Hyponasty could also increase water consumption if associated with increased transpiration. Lastly, effects of HT on growth could lead to reduced leaf area, limiting plant water losses and thus mitigating the effects of WD.

In the face of the multiplicity of interacting, sometimes opposite effects between these two stresses, it appears difficult to predict plant responses to combined HT and WD. The aim of this study was therefore to evaluate the responses to both isolated and combined HT and WD in natural accessions of the model plant Arabidopsis thaliana. The following questions were addressed: (1) How do HT and WD interact on traits related to plant growth, morphology and development and to what extent do their combined effects differ from those of isolated stresses? (2) Is the variability of responses to isolated and combined HT and WD related to the climatic conditions at the accessions collection sites? (3) To what extent are these responses related to trait values exhibited in control conditions? A set of 10 Arabidopsis accessions spanning nearly the entirety of the latitudinal range of this species was selected to identify common responses and explore the natural variation of Arabidopsis tolerance to both stresses. Controlled environmental conditions were applied in full factorial experiments and maintained constant from the seedling to the reproductive stage. Control air temperature (CT) was set to 20 °C, as in most experimental studies [START_REF] Balasubramanian | Potent induction of Arabidopsis thaliana flowering by elevated growth temperature[END_REF]Saidi, Finka & Goloubinoff 2011), whereas HT was set to 30 °C. This HT level has been identified to be the basal thermotolerance, that is the highest temperature tolerated by a plant that has never encountered previous HT, of the Arabidopsis accession Col-0 (Ludwig-Muller, [START_REF] Ludwig-Muller | A glucosinolate mutant of Arabidopsis is thermosensitive and defective in cytosolic Hsp90 expression after heat stress[END_REF]. Soil WD was maintained constant at a level previously shown to significantly decrease leaf water potential and impair plant growth, resulting in reduced plant size of Col-0 by half [START_REF] Aguirrezábal | Plasticity to soil water deficit in Arabidopsis thaliana: dissection of leaf development into underlying growth dynamic and cellular variables reveals invisible phenotypes[END_REF].

MATERIALS AND METHODS

Plant material and growth conditions

Ten accessions of A. thaliana were grown in one to three independent experiments depending on the accession (Table 1). Seeds of all genotypes were stored at 4 °C in the dark ensuring stratification. Five seeds from each genotype were directly sown at the soil surface in 225 mL culture pots filled with a mixture (1:1, v : v) of loamy soil and organic compost (Neuhaus N2). Pots were damped with sprayed deionized water three times a day and placed in two controlled growth chambers in darkness (20 °C, 65% air relative humidity) until germination. After germination, plants were cultivated with a daily cycle of 12 h light supplied from a bank of HQi lamps which provided 175 mmol m -2 s -1 photosynthetic photon flux density (PPFD) at plant height.

Soil WD and HT treatments were applied to half of the pots after emergence of the first two true leaves (stage 1.02 in [START_REF] Boyes | Growth stage-based phenotypic analysis of Arabidopsis: a model for high throughput functional genomics in plants[END_REF]) ensuring a good establishment of the seedlings. In the first growth chamber, CT was set to 20/17 °C day/night, while HT treatment was set to 30/25 °C in the second one. Air relative humidity was adjusted to 65% under CT and 85% under HT in order to maintain equal water vapour pressure deficit (VPD) at 0.9 kPa. This was set up in order to avoid the confounding effect of temperature on transpiration through increased VPD. Soil water content was controlled before sowing to estimate the amount of dry soil and water in each pot. Subsequent changes in pot weight were due to changes in water status. Soil water content was maintained at 0.35 and 0.20 g H2O g -1 dry soil with a modified one-tenth strength Hoagland solution [START_REF] Hoagland | The water-culture method for growing plants without soil[END_REF] in the wellwatered (WW) and WD treatments, respectively. The field capacity of the substrate was 0.78 g H2O g -1 dry soil [START_REF] Granier | PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit[END_REF]; therefore, the WW and WD treatments represented 45 and 25% of the soil field capacity, respectively. Pot weight was precisely adjusted to reach the target soil water content by weighing and watering each individual pot every Monday, Wednesday and Friday. Other days, a standard volume of nutrient solution amounting to the mean volume of previously weighed water applications for each treatment was added to the plants without weighing the pots.

Three consecutive experiments were carried out following the same experimental procedure (see Table 1). In experiments 1 and 2, only one plant per pot was maintained until first silique shattering, while one to three plants were maintained until inflorescence emergence in experiment 3 for photosynthesis measurements and abscisic acid (ABA) content determination. number was determined for each plant at each precise adjustment of soil water content, that is three times a week, only in experiments 2 and 3.

Dynamics of leaf production

For each plant in experiment 2, a sigmoid curve was fitted to the relationship between total number of rosette leaves (LN) and time from stage 1.02 to stage 8.00 by the following four-parameter logistic model:

LN a e d d b = + - - ( ) ( ) 1 0 ( 1
)
where d is the number of days after stage 1.02, a is the maximum vegetative leaf number, d0 is the time when a/2 leaves have developed and b is the inverse of slope factor which refers to the steepness of the curve, and is thus a parameter related to the maximum rate of leaf production.

In order to standardize between genotypes, we used an estimate of leaf production duration (days) as d0b ln(0.05/0.95), that is the time period for vegetative leaf number to increase from 5 to 95% maximum number. The maximum rate of leaf production (Rmax, leaf d -1 ) was calculated from the first derivative of the logistic model at d0 as Rmax = a/(4b).

In experiment 3, since leaf emergence rate is maximal and nearly constant between stage 1.02 and stage 5.01, Rmax was fairly well estimated by the slope of the relationship between LN and time during this period. Rmax varied across genotypes and treatments with highly reproducible results between experiments (r = 0.85, P < 0.001). Most of the plants survived the HT and WD treatments, and reached the reproductive stage. Only a few plants did not survive the combined HT ¥ WD treatment.

Whole plant and leaf traits

In experiment 2, 20 d after germination, tip height, total length and blade length of the youngest fully expanded leaf were measured on each plant with a digital calliper as described in [START_REF] Hopkins | A latitudinal cline and response to vernalization in leaf angle and morphology in Arabidopsis thaliana (Brassicaceae)[END_REF]. At this time, plants had six to 14 leaves depending on the genotype, and inflorescence had not emerged. Measurements were taken in randomized order between 2 and 4 h after lights went on in the chambers to avoid any effects associated with time of the day like endogenous rhythms. The proportion of leaf composed of blade was estimated by the blade ratio, the blade length divided by total leaf length. Leaf insertion angle (degree) was calculated as q = arcsine (leaf tip height/leaf length).

Plants were harvested at stage 8.00, in the morning and after irrigation. Rosettes were cut, inflorescences were detached from the rosettes and their fresh weights (FWs) (milligrams) were determined immediately. Leaf blades were separated from the rosette, and FWs of the sixth and ninth leaves were determined. Mean leaf thickness (LT) of these two leaves was determined with a linear variable displacement transducer (Solartron) connected to a multimeter and previously calibrated with 5 mm accuracy. Depending on the size of the leaf, LT was measured on 6 to 10 points per leaf blade, avoiding the mid-vein. All blades were then stuck on a sheet of paper, arranged by order of emergence on the rosette, and the sheet of paper was scanned for area measurements. Additionally, a transparent imprint of the adaxial epidermis of the sixth leaf was obtained by drying off a varnish coat spread on the surface of the leaf. Imprint was peeled off and then stuck on microscope slides with one-sided adhesive for further measurements. Roots were carefully extracted from the soil and gently washed in deionized water. Leaf blades, petioles, reproductive structures and roots were then separately oven-dried at 65 °C for at least 3 d, and dry masses were determined. Rosette area (cm 2 ) was determined as the sum of individual leaf blade areas measured on the scans with an image analysis software (Bioscan-Optimas 4.10, Edmond, WA, USA). From these measurements, leaf dry matter content (LDMC, the ratio of dry mass to fresh mass, mg g -1 ) and SLA (the ratio of leaf area to leaf dry mass, m 2 kg -1 ) were calculated at the rosette and leaf (for leaves 6 and 9) levels. Biomass allocation was assessed by the ratios of above-ground vegetative, reproductive and below-ground dry masses to total plant dry mass. Root-to-shoot ratio was calculated as the ratio of root to vegetative above-ground masses.

Leaf epidermal anatomy

Epidermal imprints of the sixth leaves were placed under a microscope (Leitz DM RB; Leica, Wetzlar, Germany) coupled to an image analyser. Mean cell and stomatal densities were determined by counting the number of cells and stomata in two 0.12 mm 2 zones in the middle part of the leaf blade distributed on both sides of the mid-vein halfway from the margins. Stomatal index was calculated as 100 ¥ stomatal number/(stomatal number + stomatal number ¥ 2 + epidermal cell number).

Net photosynthetic rate

Net photosynthetic rate was measured using a single leaf chamber designed for Arabidopsis connected to an infrared gas analyser system (CIRAS 2, PP Systems, Amesbury, MA, USA) in experiment 3. Carbon fluxes were determined at steady state (approximately 15 min after light was switched on) under control temperature (20 °C) and HT (30 °C) but only in WW conditions, and under ambient CO2 (390 ppm) and light intensity (175 mmol m -2 s -1 PPFD). Photosynthesis was measured on two to 15 plants at bolting on An-1, Col-0, Cvi-0, Ler, Mt-0 and Sha.

Leaf ABA content

Leaf ABA (ng g -1 FW) was determined by radioimmunoassay (Quarrie et al. 1988) as previously described [START_REF] Barrieu | The monoclonal antibody MAC252 does not react with the (-) enantiomer of abscisic acid[END_REF]. Leaf samples were ground finely under liquid nitrogen, placed in distilled water (5 mL per mg FW) and immediately warmed at 70 °C for 5 min before shaking at 4 °C overnight. Extracts were then centrifuged at 16 000 g for 10 min at 4 °C; the supernatant was conserved at -20 °C and used for radioimmunoassay.

Meteorological data at the geographical origin of the accessions

Meteorological data (temperature, precipitation, relative humidity, diurnal temperature range) at the geographical origin of the accessions were extracted from high-resolution gridded datasets of climate data (New et al. 2002). Mean monthly parameters were calculated for the main period of vegetative growth of A. thaliana from September to May [START_REF] Hoffmann | Biogeography of Arabidopsis thaliana (L.) Heynh. (Brassicaceae)[END_REF].

Data analysis

Statistical significance of trait variation was tested by three-way multivariate and univariate analyses of variance (manova and anova) with genotype, soil water content and air temperature as fixed factors. Post hoc comparison between treatments was performed with Kruskal-Wallis non-parametric test. Principal component analyses (PCAs) were performed to study the relationships between the traits and the effects of the temperature and soil water treatments. PCAs were performed on data from the experiment where higher number of both traits and genotypes were studied (experiment 2) and on standardized mean trait values by genotype and treatment (n = 36) because traits were measured in very different units. Between-and within-treatment PCAs were performed on mean trait values to test for differences between treatments and focus on genotypic effects, respectively [START_REF] Chessel | The ade4 package-I-One-table methods[END_REF]). The null hypothesis that there is no difference between treatments was tested with a randomization test (randtest.between in the R/ade4 package). The procedure checks that the observed value of the between/ total inertia ratio is higher than expected under the null hypothesis. The distribution of the between/total inertia ratio is obtained by permuting the rows of the data frame, that is means per genotype and treatment (n = 999) and thus changing assignment to treatment group. Response ratios (R) between treated (T) and control (C) groups were calculated as RT|C = mean trait valueT/mean trait valueC to quantify the effects of the treatments for each genotype.

Five values of response ratios were calculated to obtain the response to WD according to the control conditions (WD-20 °C/WW-20 °C), the response to WD at HT (WD-30 °C/ WW-30 °C), the response to HT in WW conditions (WW-30 °C/WW-20 °C), the response to HT in WD conditions (WD-30 °C/WD-20 °C) and the response to the combination of HT and WD compared to the control conditions (WD-30 °C/WW-20 °C). The response ratio quantifies the proportionate change that results from an experimental manipulation [START_REF] Hedges | The meta-analysis of response ratios in experimental ecology[END_REF]. Response ratios were log-transformed in the statistical analyses. We tested the significance of the relationships between traits, response ratios, coordinates of the genotypes of the PCA axes and climatic descriptors with correlation coefficients. All statistical tests were performed using R v.2.10 (R Development Core Team 2009).

RESULTS

Analysis of multiple plant traits reveals significant genotype by environment effects but predominant additive effects of HT and WD

anovas explained from 25 to 85% of the total variance of 16 functional traits related to plant growth, structure and physiology, and the manova explained 58% of the total variance in the multivariate dataset (Table 2). Across traits, there was a highly significant genotypic variability among accessions (18% of variance explained in the manova; from 4 to 47% of variance explained across traits). Additionally, strong genotype by environment (soil water content, temperature or both) interactions were detected for all traits as indicated by highly significant first-and second-order interaction terms, highlighting the large natural phenotypic variability in the responses to both isolated and combined HT and WD. While significant for most of the traits, the effect of WD was not significant at the multivariate level. Interestingly, lack of significant interaction between water regime and temperature at the multivariate level and for most of the traits was indicative of prevailing additive effects of WD and HT (Table 2).

A PCA was performed in order to explore the multivariate pattern of effects of both isolated and combined HT and WD on the studied traits. First, second and third principal components (PC) explained 45, 25 and 9% of the total variance, respectively (Fig. 1; see Supporting Information Table S1 for variable loadings). Size-related traits contributed most to PC1 which opposed large plants with numerous vegetative leaves and high rate of leaf production to plants that had high reproductive mass allocation and thinner, more erect leaves with high SLA (Fig. 1a). Biomass allocation to the roots, epidermal cell density and stomatal density closely and negatively correlated with PC2. LDMC contributed less to this axis but contributed to most of the variation on third axis.

Projection of the accessions (Fig. 1b) showed that the four temperature-by-soil water treatments were significantly discriminated in the first factorial plane (P < 0.001; permutation tests of between-treatments PCA), although the high genotypic variability was distinguishable as indicated by the distance of the accessions from the centroid of each treatment. Along PC1, plants grown under control conditions (20 °C air temperature; 0.35 g H2O g -1 dry soil) were opposed to plants grown under combined HT and WD conditions (30 °C; 0.20 g H2O g -1 dry soil). As indicated by the position of the centroid of each treatment along PC1, all treatments reduced plant performance compared to control conditions, and the combined stress was more detrimental to plants than isolated HT or WD. Isolated HT and WD treatments were significantly separated along PC2, indicating opposite effects of these stresses on traits related to this axis. Specifically, WD led to an increased biomass allocation to roots, a decrease in SLA and higher epidermal cell and stomata densities, whereas HT had opposite effects.

The combination of HT and WD is more detrimental to plant development than isolated effects, but differences between genotypes exist

As shown by the PCA, rosette development dynamics were significantly affected by HT, WD and their combination (Fig. 2; Table 2; Supporting Information Fig. S1). In control conditions, the average of maximum rate of leaf production (Rmax, leaf d -1 ) was 0.95 among genotypes and varied significantly from 0.75 in An-1 to 1.08 in Cvi-0 and Mt-0 (Supporting Information Fig. S2). The three treatments significantly reduced Rmax (Fig. 2a; Table 2). Although the sensitivity of phenology to treatments varied significantly among Arabidopsis accessions, WD was, on average, more detrimental for leaf production (23% mean decrease) than HT (16% mean decrease; but see Lc-0 and Sha in Supporting Information Fig. S2a). Combining HT and WD had greater effects (40% mean decrease among genotypes) on Rmax than isolated treatments (Fig. 2a). The duration of vegetative leaf production, which is highly related to flowering time in A. thaliana, also varied widely among accessions from 21 to 63 d in An-1 and Lc-0, respectively (Supporting Information Fig. S2b). Duration of leaf production and flowering time increased or decreased depending on accession and treatment leading to a highly significant secondorder interaction term in the anova (Table 2). While not significant in all accessions, WD tended to increase the duration of leaf production either at control or HT (nonsignificant water regime by temperature interaction in anova; Table 2; Fig. 2b). By contrast, increasing air temperature tended to shorten the life cycle either in WW or WD conditions. As a result of their effects on plant growth dynamics, HT and WD significantly reduced total plant mass in all accessions but Cvi-0 and Lc-0 (Fig. 3; Table 2). On average, HT and WD similarly reduced total dry mass by twofold. Combining HT and WD (HT ¥ WD) reduced plant size more severely than isolated stresses from 55% in An-1 to 91% in Ct-1 (Fig. 3 and 85% mean decrease). In some genotypes, plant dry mass tended to be less affected by isolated or combined HT and WD (An-1, Lc-0), while in others, it was less reduced only under HT (Cvi-0) or WD (Est-1, Ler). This resulted in weak relationships between response ratios to HT and WD for total dry mass (Supporting Information Fig. S3). However, the response ratio of HT ¥ WD to control conditions (RHT ¥ WD|C) for the total dry mass was close to the sum of the response ratios of WD and HT to control conditions (RWD|C + RHT|C) suggesting nearly additive effects. This was true for all accessions except Cvi-0, Lc-0 and Mt-0. These accessions apart, clear additive effects were indicated by a significant relationship between

Table 2.

Results of the partitioning of phenotypic variation among the natural genotypes grown in a full factorial design of contrasted soil relative water content (W, 0.35 and 0.20 g H2O g -1 dry soil in well-watered and water deficit treatment, respectively) and air temperature (T, 20 °C and 30 °C in control and high temperature treatment, respectively) Trait Genotype (G) Soil water content (W) RHT ¥ WD|C and RWD|C + RHT|C (r = 0.82; P < 0.05) with a slope not significantly different from one. Compared to other accessions, the growth of Mt-0 was less affected by the combination of HT ¥ WD than by WD only (Fig. 3). To further investigate the genetic variability of responses to HT and WD, we analysed the ranking of the genotypes from the PCA performed on trait values. The rankings were well conserved on PC1 and PC2. The Spearman's coefficients of rank correlation varied from 0.58 to 0.92 (Supporting Information Table S2). This indicated that accessions which exhibited higher value of a trait compared to other accessions in control conditions conserved this advantage when stressed.

Temperature (T) W ¥ T G¥ W G¥ T G¥ W ¥ T R 2 (%) d.f.

Biomass allocation to roots increases under WD and reproductive allocation increases at HT

Biomass allocation also changed at the whole plant and leaf levels in response to isolated and combined WD and HT (Table 2; Fig. 3). Interestingly, at the whole-plant level, WD and HT had different effects on allocation to roots and to reproductive structures. WD resulted in a significant increase in biomass allocation to roots, but reproductive allocation did not change significantly (Fig. 4a). The reverse was found under HT where no changes were detected in the biomass allocation to roots, whereas a significant positive effect was observed on reproductive allocation.

WD and HT have different effects on leaf structure

Leaves produced at HT tended to be thinner and had a higher SLA, while in WD, LDMC was increased (Fig. 4b-d; Supporting Information Fig. S2g-i). More precisely, SLA was much affected by HT in WW conditions and was significantly higher in all genotypes with little variation observed in WD, while LDMC tended to increase in response to WD, particularly at HT, and decrease under HT in WW conditions.

HT but not WD induces leaf hyponasty

In all accessions, HT induced a highly significant increase in leaf insertion angle, that is hyponasty, associated with a significant reduction in the proportion of blade compared to petiole length (Fig. 4e,f; Table 2). WD had no significant effect on hyponasty either at control or HT. By contrast, a significant increase in blade ratio was found in response to WD, resulting in significant water by temperature interaction in the anova for this trait (Table 2).

WD and HT have opposite but additive effects on leaf epidermis anatomy

WD and HT had opposite effects on the cellular anatomy of leaf epidermis, but there was no water by temperature interaction as shown in the anova (Table 2) indicating that the effects were globally additive. Across genotypes, cell and nine Arabidopsis accessions grown under control (CT, 20/17 °C day/night) and high temperature (HT, 30/25 °C day/night), and in well-watered (WW, 0.35 H2O g -1 dry soil) and water deficit (WD, 0.20 H2O g -1 dry soil) conditions. HT and WD treatments were applied after emergence of the first two true leaves and plants were harvested at first visible pod. Only the first two axes are shown. (a) Representation of the variables; LDMC, leaf dry matter content; SLA, specific leaf area. (b) Representation of the accessions with centres of gravity and lines connected to each accession shown for each condition. CT ¥ WW (circles), CT ¥ WD (squares), HT ¥ WW (triangles) and HT ¥ WD (upside-down triangles). Ellipses represent inertia ellipses of each treatment. Each inertia ellipse is centred on the means, its width and height are given by 1.5 times the standard deviation of the coordinates on axes, and the covariance sets the slope of the main axis [START_REF] Thioulouse | ADE-4: a multivariate analysis and graphical display software[END_REF].

stomata densities increased in response to WD both at control temperature and HT, whereas these traits tended to decrease in response to HT (Fig. 5). Stomatal index exhibited much less variation, but genotype and treatment effects were detected (Table 2; Supporting Information Fig. S2l-n). HT resulted in lower stomatal index (Fig. 5c). On the contrary, stomatal index tended to increase in response to WD, but the effect of this treatment was not detectable in several genotypes.

Photosynthesis is reduced at HT and ABA content increases under WD and HT

In WW conditions, net photosynthetic rate was significantly reduced by HT from 3.95 Ϯ 0.73 at 20 °C to 3.30 Ϯ 0.56 mmol CO2 s -1 m -2 at 30 °C (Fig. 6a; Table 2). No significant genotype by temperature interaction was detected (P = 0.29; Table 2). Across all genotypes, leaf ABA content was significantly increased under WD and HT, and it was even more increased in response to the combination of the two stresses WD and HT (Fig. 6b).

Do responses to HT and WD relate to accessions climatic origin?

Beyond mean responses to single or combined treatments, the accessions studied herein displayed a range of sensitivities for their different traits. We explored whether any part of the responses of the accessions was related to the climatic conditions at geographical origin of the populations in which they were collected. The data from the PCA were used in order to reduce the number of comparisons and therefore the risk of type I error.

For each treatment, no trend was observed between accessions coordinates on PC1 from the PCA on trait values and mean monthly temperature at geographical origin of the populations. However, for plants grown under HT in WW conditions, a positive trend was found between coordinates on PC2 and temperature of origin (Fig. 7a). Inspection of Fig. 7 revealed that the accession from Cape Verde Island (Cvi-0) had a contrasted response compared to the other accessions. When excluding Cvi-0 from the analysis, the correlation was high and significant (r = 0.80; P < 0.01; Fig. 7a). The collection site of this accession presents the higher temperature, although it was reported that Cvi-0 has been collected at 1200 m asl [START_REF] Tonsor | Heat shock protein 101 effects in A. thaliana: genetic variation, fitness and pleiotropy in controlled temperature conditions[END_REF], thus possibly encountering lower temperatures. As seen earlier, PC2 was negatively correlated to stomatal and cell density and biomass allocation to roots. Therefore, the accessions that originate from sites with higher temperature tend to have less stomata per unit leaf surface, and to allocate less biomass to the roots than accessions from colder sites when cultivated under HT (Fig. 7b,c). Positive trends were also found between the coordinates on PC2 from the PCA on trait values and mean monthly precipitation from September to May in all treatments (r = 0.40 to 0.73). While not statistically significant, this corresponded to a stronger reduction in stomatal density under WD, HT or both for accessions originating from sites with high precipitations (r = -0.36, -0.51 and -0.56, respectively).

Relationships between plant traits and tolerance to HT and WD

We explored the relationships between plant traits as measured in controlled conditions and accessions response to HT and WD. A negative correlation was found between absolute plant size in controlled conditions and the response ratio of plant size to the treatments. This trend was significant in response to WD (r = -0.73; P = 0.03; Fig. 8a) but not to HT (r = -0.27; P = 0.48) or the combination of HT and WD (r = -0.50; P = 0.17). Thus, stunted accessions (e.g. An-1) tend to be more tolerant to WD. Furthermore, the root-to-shoot ratio in controlled conditions was positively correlated with the response ratio of plant size to WD (r = 0.68; P = 0.04; Fig. 8b) and with the response ratio of leaf production rate under combined HT ¥ WD (r = 0.72; P = 0.04). Thus, accessions with bigger root compartment relative to shoot tended to better maintain growth under WD, and to keep producing leaves at the same rate as control under combined stresses.

DISCUSSION

WD and HT: independent or interacting responses?

Complex interactive responses can occur in plants experiencing multiple environmental stresses [START_REF] Mittler | Abiotic stress, the field environment and stress combination[END_REF]). Here, we report the single or combined effects of soil WD and HT on a large set of plant traits from the cellular to the whole-plant levels in a collection of accessions of the model plant A. thaliana. Plant growth was significantly reduced under HT and WD, and their combination was more detrimental to plant performance as also described in previous studies [START_REF] Xu | Combined effects of water stress and high temperature on photosynthesis, nitrogen metabolism and lipid peroxidation in a perenial grass Leymus chinensis[END_REF]Prasad et al. 2008). Interestingly, single trait as well as multiple traits analyses revealed that the combined effects of these two stresses were globally additive. This held true for traits responding in the same (e.g. plant mass) or reverse (e.g. stomatal density) directions to the two stresses and suggests a certain degree of independency between the mechanisms involved in the responses to WD and HT applied herein. Some traits were specific of the response to either WD or HT. This was the case for biomass allocation to roots which increased in response to WD, and conversely for reproductive allocation, leaf insertion angle and SLA which significantly increased in response to HT [START_REF] Xu | Combined effects of water stress and high temperature on photosynthesis, nitrogen metabolism and lipid peroxidation in a perenial grass Leymus chinensis[END_REF]. However, among the large number of traits investigated, no single trait was affected only by the combination of HT and WD. The impact of the combined stresses has been rarely studied. In wheat and sorghum, [START_REF] Machado | Combined effects of drought and high temperature on water relations of wheat and sorghum[END_REF] found that plant water status in response to HT was highly dependent on soil water availability. The work by Rizhsky and collaborators showed that some molecular responses were specific to the combination of heat and drought compared to either stress alone (Rizhsky et al. 2002(Rizhsky et al. , 2004). Yet our study is, to our knowledge, the first addressing this issue in different ecotypes and using a broad range of growth, developmental and physiological traits, and the lack of Different letters indicate significant differences following Kruskal-Wallis test (P < 0.05). Net photosynthetic rate was not measured in WD conditions. FW, fresh weight.

HT ¥ WD interaction is the rule for most of them, at least for the moderate levels of stresses applied during the whole plant cycle. As generally found, plant growth dynamics (leaf production and leaf expansion) were significantly impaired in response to HT [START_REF] Loveys | Growth temperature influences the underlying components of relative growth rate: an investigation using inherently fast-and slow-growing plant species[END_REF] and WD [START_REF] Granier | PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit[END_REF][START_REF] Hummel | Arabidopsis thaliana plants acclimate to water deficit at low cost through changes of C usage; an integrated perspective using growth, metabolite, enzyme and gene expression analysis[END_REF], leading to reduced plant size at reproductive stage and therefore reduced seed production [START_REF] Aarssen | Genotypic variation in fecundity allocation in Arabidopsis thaliana[END_REF]. However, the two stresses had contrasting effects onto the timing of reproduction.As commonly found in natural and crop species [START_REF] Mcmaster | Simulating crop phenological responses to water deficits[END_REF], WD delayed reproduction, but contrasted effects on final leaf number were found across accessions. By contrast, under HT, fewer leaves were produced when early reproduction occurred. Early reproduction following a moderate increase in temperature has been previously reported in A. thaliana [START_REF] Balasubramanian | Potent induction of Arabidopsis thaliana flowering by elevated growth temperature[END_REF]) and other species [START_REF] Barnabas | The effects of drought and heat stress on reproductive processes in cereals[END_REF]. However, very sparse data are available on the combined effects of HT and WD on reproductive phenology in natural species (but see [START_REF] Barnabas | The effects of drought and heat stress on reproductive processes in cereals[END_REF] for a review in cereals). Here, we found that the effects were globally additive in such a way that WD also delayed flowering under HT. Although the majority of plants reached the flowering stage and a significant increase in biomass allocation to the reproductive stem was found under HT, flower abortions were clearly visible on later reproductive stages and very few pods reached maturity (not shown). The fecundity of the plants was particularly impaired under combined stresses. This was not surprising since reproductive structures are particularly sensitive to heat stress [START_REF] Zinn | Temperature stress and plant sexual reproduction: uncovering the weakest links[END_REF] and even more to combinations of heat and drought [START_REF] Barnabas | The effects of drought and heat stress on reproductive processes in cereals[END_REF]. Notably, HTs (31-33 °C) very close to that experienced here (30 °C) have been shown to be sufficient to impair anthers development in non-acclimated plants of A. thaliana (Sakata et al. 2010). Apparently, vegetative acclimation to long-lasting treatments as experienced here did not change this response.

Is genetic variability of responses related to the climate of origin?

In our study, except the young seedling stage (before the emergence of the firsts true leaves), plants developed entirely under HT, WD or both. This may have led to acclimation processes possibly reinforcing plant tolerance to these stresses. Applying steady-state contrasted temperatures would also have produced different responses than those identified in the case of acute increase of temperature applied at a particular developmental stage as it is largely found in the literature. Nevertheless, a high genotypic variability in traits values was observed in the different growing conditions, and a significant genotype by environment interaction was found. This is not surprising given that the chosen accessions originated from a wide range of environments with varying temperature and drought constraints. A high variability of traits related to growth and phenology has been identified in natural populations of A. thaliana [START_REF] Montesinos-Navarro | Arabidopsis thaliana populations show clinal variation in a climatic gradient associated with altitude[END_REF]. And genotypic variability among natural accessions has previously been identified for traits related to adaptation to WD [START_REF] Mckay | Genetics of drought adaptation in Arabidopsis thaliana: I. Pleiotropy contributes to genetic correlations among ecological traits[END_REF] and temperature [START_REF] Tonsor | Heat shock protein 101 effects in A. thaliana: genetic variation, fitness and pleiotropy in controlled temperature conditions[END_REF].

Here, we applied a HT level within the physiological range of A. thaliana and close to the basal thermotolerance of the accession Col-0 [START_REF] Ludwig-Muller | A glucosinolate mutant of Arabidopsis is thermosensitive and defective in cytosolic Hsp90 expression after heat stress[END_REF]. Unfortunately, as far as we know, basal thermotolerance has not been consistently evaluated for other accessions than Col-0. Therefore, we cannot exclude that the variability of responses to HT observed here between the accessions was related to contrasted basal thermotolerance, which could also depend on the environment encountered in their habitat of origin. Few relationships between plant tolerance to HT and the climatic environment at the collection site of the accessions were found in this study.This is in accordance with [START_REF] Loveys | Growth temperature influences the underlying components of relative growth rate: an investigation using inherently fast-and slow-growing plant species[END_REF] who found no relationship between thermal origin of the accessions and the production of dry matter in response to increasing temperature at the interspecific level. However, a lack of association could arise from the small number of accessions considered in our study. In a more geographically restricted study but including a large set of Arabidopsis natural populations, [START_REF] Montesinos-Navarro | Arabidopsis thaliana populations show clinal variation in a climatic gradient associated with altitude[END_REF] showed that the variation of traits exhibited in controlled conditions was consistent with the temperature and water constraints encountered at the collection sites along an altitudinal gradient, pointing towards a likely adaptive differentiation of the populations to the environmental conditions. Here, we found that accessions that originate from sites with higher mean temperature during the vegetative growth tend to have less stomata per unit leaf surface, and to allocate less biomass to the roots than accessions from colder sites when grown under HT.

Stomatal density and plant response to HT and WD

Despite the prevailing opinion that stomatal density would increase in response to HT [START_REF] Wahid | Heat tolerance in plants: an overview[END_REF], data from the literature are not unanimous (see [START_REF] Luomala | Stomatal density, anatomy and nutrient concentrations of Scots pine needles are affected by elevated CO2 and temperature[END_REF]. Indeed, it is most likely that stomatal density depends on tight interactions between plant water balance (water status and transpiration) and the environmental conditions, particularly relative humidity and VPD (VPDair) encountered by the plant during leaf growth [START_REF] Lake | Response of stomatal numbers to CO2 and humidity: control by transpiration rate and abscisic acid[END_REF]. Assuming that conditions favouring expansion dilute stomata at the leaf surface, increases in humidity in the vicinity of the plant are expected to reduce stomatal density. In this study, the possible effects of VPDair on stomatal density at HT were excluded since VPDair was maintained equal between the control (20 °C) and the HT (30 °C) treatment. In order to fulfil this condition of constant VPDair, air relative humidity was maintained higher under HT (85%) than under control (65%) conditions, possibly favouring the development of leaves with lower stomatal density at HT compared to control temperature. This was observed despite the significantly higher transpiration rate under HT compared to control temperature (Supporting Information Fig. S4). In addition, our results unequivocally show that soil WD led to increases in stomatal densities either at control or HT, thus counteracting the effects of HT. The same trend of decreasing and increasing stomatal density in response to HT and WD, respectively, was found in almost all genotypes. Despite the fact that VPDair was maintained equal between the two temperature treatments, accelerated depletion of soil water or lower leaf water potential may have interfered with plant responses at HT due to higher rates of transpiration [START_REF] Machado | Combined effects of drought and high temperature on water relations of wheat and sorghum[END_REF]; Supporting Information Fig. S4). Interestingly, relationships were found between stomatal density and meteorological conditions at the collection sites. Stomatal density was lower in accessions collected in warmer sites and/or sites with higher amount of precipitations, particularly when considering the responses to HT and WD.

Contrary to what was suggested by [START_REF] Lake | Response of stomatal numbers to CO2 and humidity: control by transpiration rate and abscisic acid[END_REF], we found no relationship between ABA content in the rosette leaves and stomatal density. We cannot exclude a differential response of abaxial versus adaxial leaf epidermis in our experiments (see [START_REF] Luomala | Stomatal density, anatomy and nutrient concentrations of Scots pine needles are affected by elevated CO2 and temperature[END_REF]; however, we observed that stomatal densities of both sides of the leaves are correlated either under WW or WD conditions (Vile & Pervent, unpublished results).

Similarities between responses to HT and low light

It is noteworthy that some of the specific responses to HT were also characteristic of responses to low light intensity. For instance, it is well known that SLA increases and LT decreases in response to low light (Poorter et al. 2009), and that shade leaves have higher SLA and are thinner than leaves exposed to direct sun light [START_REF] Mcmillen | Dependence of photosynthetic rates on leaf density thickness in deciduous woody plants grown in sun and shade[END_REF]. [START_REF] Chabot | Effects of light and temperature on leaf anatomy and photosynthesis in Fragaria vesca[END_REF] reported that decreasing light and moderately elevated temperature had similar effects on thickness. In Arabidopsis, a clear similarity between the responses to light and HT resides also in hyponastic growth, that is the increase in leaf insertion angle [START_REF] Van Zanten | Hormone-and light-mediated regulation of heat-induced differential petiole growth in Arabidopsis thaliana[END_REF]). These authors reported very similar trends of variation in leaf angle in response to HT and low light, and we have recently shown that the hyponastic response to HT can be reversed by increasing light intensity [START_REF] Vasseur | Changes in light intensity reveal a major role for carbon balance in Arabidopsis responses to high temperature[END_REF]. Taken together, these results suggest that part of the responses to a moderate heat stress could be associated to a defect in carbon acquisition through photosynthesis, which is impaired under HT, and/or an increased competition for carbon use due to enhanced physicochemical processes and increased protection mechanisms (notably heat shock proteins; [START_REF] Heckathorn | Nitrogen availability alters patterns of accumulation of heat stress-induced proteins in plants[END_REF].Accordingly, tolerance to warm temperatures is increased at high CO2 concentration in C3 plants [START_REF] Huxman | Gas exchange and chlorophyll fluorescence responses of three south-western Yucca species to elevated CO2 and high temperature[END_REF][START_REF] Taub | Growth in elevated CO2 protects photosynthesis against high-temperature damage[END_REF], and decreased at low nitrogen supply due to a limited production of nitrogen-costly heat shock proteins [START_REF] Heckathorn | Nitrogen availability alters patterns of accumulation of heat stress-induced proteins in plants[END_REF]. The interactive effects of HT and light on plant functioning were analysed here under lower light than encountered in natural conditions. To test whether our results would hold under higher light conditions as found in the nature, especially at HT, experiments should be performed at higher light intensities. Interactions between WD, HT and light also remain to be investigated [START_REF] Vasseur | Changes in light intensity reveal a major role for carbon balance in Arabidopsis responses to high temperature[END_REF].

Inherent trait variation and plant tolerance to HT and WD

Ecological research has engaged major efforts to identify plant traits, as measured in controlled or natural conditions, that could be good predictors of plant responses to changes in their environment [START_REF] Grime | Plant Strategies, Vegetation Processes and Ecosystem Properties[END_REF][START_REF] Vile | A structural equation model to integrate changes in functional strategies during oldfield succession[END_REF][START_REF] Violle | Let the concept of trait be functional![END_REF]). Here, we found a trade-off between plant size in control conditions and tolerance to WD. A similar negative relationship between plant size and plant tolerance to WD was found in an analysis of 20 accessions capturing much of the genetic variation of A. thaliana worldwide [START_REF] Clark | Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana[END_REF] and a new collection of 88 accessions from Europe and Asia (Bouteillé et al., unpublished results; r = -0.54 and -0.25; P = 0.013 and 0.022, respectively). A re-analysis of the data from [START_REF] Bouchabke | Natural variation in Arabidopsis thaliana as a tool for highlighting differential drought responses[END_REF] also showed a significant negative relationship between total leaf area in WW conditions and its response to a mild WD applied for 10 d (r = -0.49; P = 0.014). Interestingly, we found a similar ranking of responses to WD for the six common accessions (but Sha to a lesser extent) between [START_REF] Bouchabke | Natural variation in Arabidopsis thaliana as a tool for highlighting differential drought responses[END_REF] and our study. Such a trade-off between plant size and the response ratio to WD was also found in a re-analysis of the data of a recent study on stress-related specific mutants of Arabidopsis [START_REF] Skirycz | Survival and growth of Arabidopsis plants given limited water are not equal[END_REF], although plant size variation between lines was weak (r = -0.43; P = 0.014). These authors report that growth reduction caused by stress was independent of plant size under control conditions, but they used the relative response of mutants compared to the wild type, not the response ratio for each line. A first explanation for this trade-off would reside in the fact that large plants consume more water and therefore experience greater water shortage. However, the experimental procedure used in the present study as well as in [START_REF] Bouchabke | Natural variation in Arabidopsis thaliana as a tool for highlighting differential drought responses[END_REF] and in [START_REF] Skirycz | Survival and growth of Arabidopsis plants given limited water are not equal[END_REF], that is a daily irrigation to adjust the soil water content, is unlikely to have favoured small plants that consume less water. A trade-off between plant size and plant tolerance to WD is in accordance with the results of [START_REF] He | Is there a risk to living large? Large size correlates with reduced growth when stressed for knapweed populations[END_REF] that populations of Centaurea stoebe with inherently bigger plant size are more susceptible to stressing (water and nutrient) conditions. In contrast to these authors, who did not observe any relationship with other traits than plant size, here, we found a positive relationship between the root-to-shoot ratio and plant tolerance to WD which could give a proportionate advantage under inherent water shortage.

On the other hand, the negative trend between plant size and Arabidopsis tolerance to HT was weaker and not significant. No single trait was identified as a good predictor of plant response to HT. Some elements suggest that changes in leaf inclination could participate to thermotolerance adjustments by reducing intercepted light and hence tissue temperature (Salvucci & Crafts-Brandner 2004). Although leaf insertion angle increased in response to HT and this response varied between accessions, in our data, hyponasty was not related to thermotolerance. Furthermore, in contrast to the results of [START_REF] Van Zanten | Hormone-and light-mediated regulation of heat-induced differential petiole growth in Arabidopsis thaliana[END_REF], no relationship was observed between the change in leaf angle in response to HT and the diurnal temperature range at the geographical origin of the accessions. This discrepancy could in part be explained by the higher but shorter temperature treatment experienced in Van Zanten et al. (38 °C during 7 h) compared to our study (30 °C during c. 15 d).

Finally, plant tolerance to WD under HT, in terms of plant size reduction, was also related to plant size in WW and control temperature conditions albeit the relationship was weaker than for WD under control temperature. Thus, inherent plant size would participate in soil-water-plant relationships to a larger extent than to the response to increasing temperature.

CONCLUSION

Despite the likely interactive processes involved in plant response to HT and WD, here, we showed that at least moderate levels of these two stresses have additive effects on a large set of plant traits related to growth and development in the model species A. thaliana. This would have important consequences for modelling plant growth under combined stresses. Some traits were affected only by one or the other stress, highlighting the specific sensitivity of some processes such as reproduction in response to HT and resources allocation for a better water acquisition in response to water deprivation. In natural environments, variation in temperature and water availability can act together or independently on co-varying traits and on the distribution of plant species. It was therefore not surprising to find a significant natural variation in Arabidopsis tolerance to HT and WD applied separately or in combination. Genetic variability in the responses of several traits to the different stresses accompanied this natural range of tolerances and was in good correspondence with some characteristics of the climatic origin of the natural populations. This opens several avenues to explore the underlying physiological processes shaping the distribution of this and other species.

  growth and final size, and biomass allocation Rmax (leaf d -1 percentage of variance explained (SSx/SStotal) and the level of significance (*P < 0.05; **P < 0.01; ***P < 0.001) for each factor and the interactions are indicated. Values are from the full model including all interactions.All non-significant terms are reported, but were removed from the final model. Hypothesis testing was based on Pillai-Bartlett statistic in the multivariate analysis of variance (manova) and on F-ratios from type III mean squares for all anovas. R 2 is the proportion of total variance absorbed by the final model. All traits but Rmax and leaf blade ratio were ln-transformed to fulfil anova requirements. Net photosynthetic rate was not measured in WD conditions. a Data are not available for Bay-0 and traits are not included in the manova.FW, fresh weight.

Figure 1 .

 1 Figure 1. Principal component analysis on traits measured on

Figure 2 .

 2 Figure 2. Dynamics of leaf production under control (CT, 20/17 °C day/night) and high temperature (HT, 30/25°C day/night), and in well-watered (WW, 0.35 H2O g -1 dry soil) and water deficit (WD, 0.20 H2O g -1 dry soil) conditions. Maximum rate of leaf production (Rmax) (a), duration of leaf production (b) and total leaf number (c). Bars are means + SE of nine accessions. Different letters indicate significant differences following Kruskal-Wallis test (P < 0.05).

Figure 3 .

 3 Figure 3. Plant dry mass under control (CT, 20/17 °C day/night) and high temperature (HT, 30/25 °C day/night), and in well-watered (WW, 0.35 g H2O g -1 dry soil) and water deficit (WD, 0.20 g H2O g -1 dry soil) conditions. Bars are means Ϯ SE (n = 4 to 9) for the roots (below), vegetative leaves (intermediate) and reproductive stems (top) of 10 Arabidopsis accessions.

Figure 4 .

 4 Figure 4. Biomass allocation and leaf morphology under control (CT, 20/17 °C day/night) and high temperature (HT, 30/25 °C day/night), and in well-watered (WW, 0.35 g H2O g -1 dry soil) and water deficit (WD, 0.20 g H2O g -1 dry soil) conditions. Dry mass allocation to the roots (below), vegetative leaves (intermediate) and reproductive stems (top) (a), leaf thickness (b), specific leaf area (c), leaf dry matter content (d), leaf insertion angle (e) and blade ratio (f). Bars are means Ϯ SE of nine accessions. Different letters indicate significant differences following Kruskal-Wallis test (P < 0.05).

Figure 5 .

 5 Figure 5. Leaf epidermal anatomy under control (CT, 20/17 °C day/night) and high temperature (HT, 30/25 °C day/night), and in well-watered (WW, 0.35 g H2O g -1 dry soil) and water deficit (WD, 0.20 g H2O g -1 dry soil) conditions. Cell density (a), stomatal density (b) and stomatal index (c). Bars are means + SE of nine accessions. Different letters indicate significant differences following Kruskal-Wallis test (P < 0.05).

Figure 6 .

 6 Figure 6. Net photosynthetic rate (a) and abscisic acid (ABA)content (b) under control (CT, 20/17 °C day/night) and high temperature (HT, 30/25 °C day/night), and in well-watered (WW, 0.35 g H2O g -1 dry soil) and water deficit (WD, 0.20 g H2O g -1 dry soil) conditions. Bars represent means + SE of nine accessions. Different letters indicate significant differences following Kruskal-Wallis test (P < 0.05). Net photosynthetic rate was not measured in WD conditions. FW, fresh weight.

Figure 7 .Figure 8 .

 78 Figure 7. Relationships between mean temperature at the collection sites of nine Arabidopsis accessions and (a) PC2 coordinates (Fig. 1), (b) stomatal density and (c) root-to-shoot ratio under high temperature (30 °C) but well-watered (0.35 g H2O g -1 dry soil) conditions.

Table 1 .

 1 Origin of the accessions studied and climate at the collection sites

		Exp. 3	X	X	X	X		X	X	X	X	X
		Exp. 2	X	X	X	X		X	X	X	X	X
		Exp. 1				X	X		X	X
	Relative	humidity	74.4	62.0	73.5	59.5	77.0	82.2	86.3	83.1	88.5	84.3
	Diurnal temperature	range	5.52	7.57	8.73	13.16	8.58	7.74	7.59	7.63	5.83	7.68
	Mean autumn-spring	temperature (°C)	21.8	15.2	12.3	8.2	2.99	5.96	3.96	2.6	4.1	-2.1
	Mean autumn-spring	precipitations (mm)	0.36	52.2	61.19	53.1	29	64.2	52.9	38.2	161.0	48.7
		Accession Latitude (°N) Longitude (°E) Country	Cvi-0 15 -23.4 Cape Verde Island	Mt-0 32.6 22.8 Libya	Ct-1 37 15 Italy	Sha 38 68 Tadjikistan	Bay-0 49 Germany	An-1 51.2 4.4 Belgium	Col-0 53 10 Poland	Ler 53 16 Poland	Lc-0 58 -5 UK	Est-1 59 28 Russia	X indicates the experiments in which accessions were studied.

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ACKNOWLEDGMENTS

We thank J.-J. Thioux, M. Dauzat, A. Bédiée and G. Rolland for the technical support during the experiments. We thank A. Skirycz for providing data, and two anonymous referees for improvements to the manuscript.

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SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this article: S1. Loadings of the variables included in the PCA on mean trait values. Table S2. Correlations between genotypes coordinates on first and second principal components from the PCA performed on trait values.

Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.


Thomas Maitre

Elie Bretin

Laurent Mahieu-Williame

Michaël Sdika

Nicolas Ducros

HYBRID SINGLE-PIXEL CAMERA FOR DYNAMIC HYPERSPECTRAL IMAGING

Keywords: Image reconstruction, motion compensation, computational optics, single-pixel imaging

Single-pixel imaging recovers an image from a sequence of point measurements that correspond to inner products between the image of the scene and some light patterns. This technique is particularly suited to hyperspectral imaging which has a wide range of biomedical applications, in particular fluorescence-guided neurosurgery where high spectral resolution is required to improve tumor resection. However, the sequential nature of the acquisition leads to strong artifacts when a dynamic scene is considered. This paper proposes a simple yet efficient strategy for imaging such dynamic scenes. Our method relies on a hybrid device that combines a standard imaging arm with a hyperspectral single-pixel imaging arm. By estimating the motion from the standard imaging arm, we show how to compensate the motion perceived by the hyperspectral arm. We demonstrate the efficiency of the proposed strategy on simulated and experimental data.

INTRODUCTION

Single-pixel imaging (SPI) acquires an image by measuring a series of inner products between some light patterns and the scene [START_REF] Marco F Duarte | Single-pixel imaging via compressive sampling[END_REF], where the light patterns are typically obtained using a digital micro-mirror device (DMD) and only a single point detector is required. The concept of SPI easily generalizes to hyperspectral imaging by replacing the point detector with a spectrometer, thus acquiring a hypercube with thousands of spectral channels in approximately ten seconds [START_REF] Martins | OpenSpyrit: an ecosystem for open single-pixel hyperspectral imaging[END_REF]. Hyperspectral imaging is particularly relevant for biomedical applications as it provides a non-contact and non-ionizing sensing technique for disease diagnosis and image-guided surgery. Numerous studies have used hyperspectral imaging to detect cancers, cardiac disease, ischemic tissue, skin burn, retinal pathologies, diabetes, kidney disease, and more [START_REF] Lu | Medical hyperspectral imaging: a review[END_REF]. We are particularly interested in fluorescence-guided neurosurgery This work was supported by the French National Research Agency (ANR), under Grant ANR-22-CE19-0030-01 (ULHYB Project).

where gliomas can be distinguished from healthy tissue by the injection of a contrast agent (e.g. protoporphyrin-IX [START_REF] Bravo | Hyperspectral data processing improves ppix contrast during fluorescence guided surgery of human brain tumors[END_REF]), provided a high spectral resolution is available.

Single-pixel acquisitions lead to an inverse problem where the image of the scene needs to be reconstructed from the measurements. While most reconstruction methods assume the scene is static [START_REF] Matthew P Edgar | Principles and prospects for single-pixel imaging[END_REF], fast-moving dynamic scenes lead to strong blurring artifacts. A widespread strategy to tackle this issue in medical imaging is motion compensation (e.g., [START_REF] Cian | Robust non-rigid motion compensation of free-breathing myocardial perfusion mri data[END_REF], [START_REF] Rit | Comparison of analytic and algebraic methods for motioncompensated cone-beam ct reconstruction of the thorax[END_REF], [START_REF] Jiao | Direct parametric reconstruction with joint motion estimation/correction for dynamic brain pet data[END_REF]). In SPI, [START_REF] Aswin C Sankaranarayanan | Video compressive sensing for spatial multiplexing cameras using motion-flow models[END_REF] estimates the motion from a low-resolution video to add an optical-flow constraint during the reconstruction. In [START_REF] Jiao | Motion estimation and quality enhancement for a single image in dynamic single-pixel imaging[END_REF], a dynamic reconstruction method is proposed assuming prior knowledge of a constant volume deformation. The ideas of [START_REF] Aswin C Sankaranarayanan | Video compressive sensing for spatial multiplexing cameras using motion-flow models[END_REF] and [START_REF] Jiao | Motion estimation and quality enhancement for a single image in dynamic single-pixel imaging[END_REF] are combined in [START_REF] Sagi Monin | Single-pixel imaging of dynamic objects using multiframe motion estimation[END_REF]. In [START_REF] Guo | Fast localization and single-pixel imaging of the moving object using time-division multiplexing[END_REF], specific patterns are inserted in the sequence to estimate the motion. The work presented in [START_REF] Lorente Mur | Recurrent neural networks for compressive video reconstruction[END_REF] investigates the use of deep-learning for video reconstruction. Motion compensation strategies, however, often come with important restrictions: the videos reconstructed in [START_REF] Aswin C Sankaranarayanan | Video compressive sensing for spatial multiplexing cameras using motion-flow models[END_REF] and [START_REF] Sagi Monin | Single-pixel imaging of dynamic objects using multiframe motion estimation[END_REF] have a smaller frame rate than the number of measurements, while in [START_REF] Guo | Fast localization and single-pixel imaging of the moving object using time-division multiplexing[END_REF], we consider a rigid object and a static background.

In this paper, we propose a simple yet efficient strategy for imaging dynamic scenes that requires less restrictive hypotheses. Our method relies on a hybrid device that combines a standard imaging arm with a hyperspectral single-pixel imaging arm. By estimating the motion from the standard arm, we show how to compensate the motion perceived by the singlepixel arm. In Section 2, we model dynamic SPI and static reconstruction. In Section 3, we present the proposed motioncompensated reconstruction strategy that we evaluate using the simulations and experimental measurements described in Section 4. We report and discuss our results in Section 5.

SINGLE-PIXEL IMAGING

Forward model

We model the single-pixel camera (SPC) acquisition as a series of scalar products between the unknown scene and some light patterns h k , 1 ≤ k ≤ K, that are sequentially loaded on a DMD. We consider a dynamic scene f (x, t) that varies Fig. 1. Setup used for the acquisitions (left hand-side) and acquisition process between two CMOS frames (right hand-side). Light is emitted from the scene and projected onto the DMD. Depending on the orientation of the DMD, light rays are either directed towards the CMOS camera for motion estimation of the scene, or are summed on the single-pixel detector thanks to a converging lens. The single-pixel camera is then linked to a spectrometer for hyperspectral imaging. during the acquisition. Assuming that the k-th DMD pattern is displayed during the time interval [t k , t k+1 ], the k-th measurement (for all spectral channels) is given by

m k = X h k (x)f k (x) dx, (1)
where

f k (x) = t k+1 t k f (x, t
) dt represents the k-th frame of the scene. Our goal is to reconstruct all the frames of the dynamic scene given a set of K measurements. This is a very challenging problem where K functions need to be estimated from K scalar measurements.

The static case

In the case of a static scene f (x, t) = f ref (x), all frames are identical and the forward problem can be discretized as

m = Hf ref , (2)
where m ∈ R K is the measurements vector, f ref ∈ R N is the discrete (unknown) image of N pixels and H ∈ R K×N is the measurement matrix that contains the DMD patterns. In the presence of noise and/or when K ≤ N , a good estimate of f ref can be obtained by optimization of a hand-crafted objective min

f η 2 ∥Hf ref -m∥ 2 2 + R(f ), (3)
where η is a penalty parameter and R is a user-defined regularizer (e.g., ∥ • ∥ 2 2 (L2) or total-variation (TV)). For high noise level and subsampling ratios, powerful alternatives include deep learning-based reconstruction [START_REF] Lorente Mur | Single-pixel image reconstruction from experimental data using neural networks[END_REF][START_REF] Lorente Mur | Deep expectation-maximization for single-pixel image reconstruction with signal-dependent noise[END_REF].

As shown in Section 5, however, the use of the static forward model given by ( 2) results in strong artifacts in the case of dynamic scenes.

PROPOSED DYNAMIC RECONSTRUCTION

Motion compensation

We assume that the scene can be motion-compensated, i.e.

f k (v k (z)) = f ref (z), (4)
where

f ref ∈ L 2 (Z k
) represent a reference static image and v k : Z k → X represents the deformation field of the scene between the k-th frame and the reference frame.

Remark. Given f k (v k (z)) = f ref (z)
, where v k : Z k → X is bijective, the measurement vector can be written as

m = H dyn f ref , (5)
where H K×N dyn represents the dynamic measurement matrix and f ref ∈ R N is the discrete reference image.

Proof. By substitution of (4) in (1), followed by the change of variable x = v k (z), we obtain:

m k = X h k (x)f ref (v -1 k (x)) dx (6) = Z k h k (v k (z))f ref (z) |det J k (z)| dz (7) = Z k h dyn k (z)f ref (z) dz, (8)
where

h dyn k (z) = h k (v k (z)) |det J k (z)
|, with J k being the Jacobian of v k . Discretization of (8) completes the proof.

Provided that the deformation field v k is known, the dynamic reconstruction problem simplifies to the resolution of a linear system as in the static case. In accordance with [START_REF] Rit | Comparison of analytic and algebraic methods for motioncompensated cone-beam ct reconstruction of the thorax[END_REF], we need to construct the dynamic matrix H dyn from the static matrix H by applying the deformation field in a row-by-row manner. In practise, the dynamic matrix turns out to be illconditioned. This highlights the need to adopt a similar variational approach as in (3) to reconstruct the reference image.

Hybrid estimation of the deformation field

We propose a hybrid approach whereby the deformation field is estimated from the video stream acquired by a CMOS camera that monitors the same scene as the SPC. As shown in Fig. 1, this is achieved by uploading white patterns periodically onto the DMD such that the CMOS camera visualizes the entire scene. The CMOS camera is synchronized with the DMD such that each of the video frames correspond to the display of a white pattern.

The deformation field can be determined by optical-flow methods [START_REF] Zach | A duality based approach for realtime tv-l 1 optical flow[END_REF]. As the deformation field is known only every few patterns, we use linear interpolation to acquire the deformations for all frames. The obtained deformation field, denoted v cmos k , is given in the spatial coordinates of the CMOS camera. We retrieve the deformation field in the spatial coordinate of the single-pixel camera by assuming the existence of a mapping x = G(x cmos ) that maps any point in the CMOS image to its corresponding position in the single-pixel image. For instance, we can choose G as a homography that is calibrated once in a preliminary step with a direct linear transform (DLT) [START_REF] Hartley | Multiple view geometry in computer vision[END_REF]. By definition of the CMOS deformation field, we have v cmos k (z cmos ) = x cmos k . By exploiting the camera mapping, we obtain

v cmos k (G -1 (z)) = G -1 (x k ) and, therefore, G(v cmos k (G -1 (z))) = x k .
We can finally identify the singlepixel deformation field as

v k = G • v cmos k • G -1 . (9)
4. EXPERIMENTS

Numerical simulations

We simulate the dynamic acquisition of a brain surface subject to a periodic affine motion model

v k (x) = c + s(t k ) -1 0 0 s(t k ) (x -c), (10)
where s(t) = a sin( 2πt T ) + 1, a is the motion amplitude and T the motion period and c is the coordinate of the center of the image. We consider the acquisition of K = 128 2 Hadamard patterns and corrupt the measurements by Poisson noise assuming the maximum image intensity is 1000 photons. The

Experimental acquisitions

The experimental setup is composed of a 1024 × 768 DMD (ViALUX GmbH DLP V-700), a spectrometer (Avantes AvaSpec-ULS2048CL-EVO) and an IDS CMOS camera (UI-3880CP-M-GL Rev 2). The CMOS camera has a resolution of 3088 × 2076 from which a 768 × 544 field of view is extracted. We image a diaphragm using a white LED (Thorlabs LIUCWHA) resulting in a bright disk. We consider two dynamic scenarios where i) the diameter of the diaphragm is fixed to 2 mm while its center is translated across the field of view and ii) the center of the diaphragm is fixed while its diameter varies between 1 mm and 10 mm. We consider the acquisition of K = 64 2 Hadamard patterns and the reconstruction of 64 × 64 images. The two datasets, in addition to many more, are available in the SPIHIM collection [START_REF] Martins | OpenSpyrit: an ecosystem for open single-pixel hyperspectral imaging[END_REF].

RESULTS AND DISCUSSION

Simulation study

Reconstruction results for a static scene are presented in Fig. 2, that demonstrate that reconstruction with noise can be effectively handled with TV regularization. Reconstruction results when the brain surface image is subject to the affine motion described in [START_REF] Jiao | Motion estimation and quality enhancement for a single image in dynamic single-pixel imaging[END_REF] are given in Fig. 3 for two motion amplitudes a. The first two rows are obtained loading the patterns in the natural order [START_REF] William K Pratt | Hadamard transform image coding[END_REF] while the third row used the order proposed in [START_REF] Higham | Deep learning for realtime single-pixel video[END_REF] that was found to improve the reconstruction quality for sub-sampled acquisitions. As expected, we observe stronger motion artifacts when reconstructing the brain surface using the static model (see first column of Fig. 3). These artifacts cannot be corrected simply using TV regularization (see second column of Fig. 3). However, one can see that our motion compensation method effectively allows the reconstruction of the reference image with the help of a L2 regularization to cope with the ill-posed nature of the problem (see third column of Fig. 3). Small residual motion artifacts are still visible in Fig. 3g and 3k when motion is too strong. This can be explained by the interpolation of the deformation field which leads to systematic errors. We can attenuate these artifacts using the motioncompensated method with TV regularization (see fourth column of Fig. 3). We note that even though TV regularization is efficient for noise removal, it can erase small blood vessels in the reconstructed brain. Concerning the order for loading the patterns, blur artifacts were reduced for the static reconstructions using the optimized order, as can be seen in Fig. 3i and3j. Dynamic reconstruction of the scene is still achieved in Fig. 3k and 3l even though the residual artifacts are different. Table 1 reports quantitative metrics -peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) -for the different reconstruction methods. These metrics confirm the visual evaluation. The combination of the proposed motion compensation method and TV regularization outperforms all other methods for dynamic scenes. The smaller the motion amplitude, the closer the scores approach those of the static brain surface. The advantage of TV over L2-regularization is illustrated by a systematic enhancement of both PSNR and SSIM. Concerning the order for loading the patterns, improved PSNR scores were obtained for the static and dynamic-TV reconstructions using the optimized order thus supporting its use for the experimental acquisitions.

(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l)

Experimental validation

Reconstructions from experimental data are displayed in Fig. 4. The superposed contour of the conventional reference im- The deformation was estimated with a CMOS camera using an optical-flow algorithm. A handmade contour of the CMOS reference frame is superposed in red. We present, column-wise, the static, the static-TV, the dynamic-L2 and the dynamic-TV reconstructions.

age shows the importance of dynamic reconstructions over static reconstructions. Strong artifacts are present when the motion is not taken into account (first column), which cannot be removed with regularization (second column). On the contrary, the proposed motion compensation strategy allows the blur artifacts to be removed and a clean disk to be recovered as expected (last two columns of Fig. 4).

CONCLUSION

We demonstrate, using numerical simulations and experimental acquisitions, an effective framework for dynamic singlepixel imaging based on a hybrid approach which exploits the images of the scene provided by a conventional camera. The proposed method can be applied to all channels of the hyperspectral arm. A limitation of this work is that the use of TV regularization tends to erase small details, in particular when used on low spatial resolution images. In the future, we will use the high-resolution images from the standard camera to increase the spatial resolution of the SPC using pansharpening techniques.

Fig. 2 .

 2 Fig. 2. Simulation results: reconstruction of a static brain surface. From left to right: the reference image, the static reconstruction, the static TV reconstruction with η = 8•10 -3 .

Fig. 3 .

 3 Fig. 3. Simulation results: brain surface subjected to the motion given in (10) and Poisson noise. Column-wise, the figure represents the static, the static-TV, the dynamic-L2 and the dynamic-TV reconstructions. (a)-(d) consider a motion amplitude a = 0.05, for (e)-(l) a = 0.2. The measurements for (a)-(h) were acquired by loading the patterns in the natural order while (i)-(l) used the optimized order. The penalty term is set to 10 -3 and 8 • 10 -3 for L2 and TV regularization.

Fig. 4 .

 4 Fig. 4. Experimental results: white spot subjected to a rigid motion (first row) and a zooming motion (second row).The deformation was estimated with a CMOS camera using an optical-flow algorithm. A handmade contour of the CMOS reference frame is superposed in red. We present, column-wise, the static, the static-TV, the dynamic-L2 and the dynamic-TV reconstructions.

Table 1 .

 1 Simulation results: PSNR (dB) and SSIM scores of different reconstruction methods for several motion amplitudes and different pattern ordering.

			Static	Static TV	Dynamic L2	Dynamic TV
	a = 0	PSNR	32.57	34.68
	(nat. order [18])	SSIM	0.94	0.96
	a = 0.05	PSNR	20.16	20.39	27.77	30.69
	(nat. order [18])	SSIM	0.54	0.57	0.87	0.91
	a = 0.1	PSNR	18.05	18.20	26.27	28.43
	(nat. order [18])	SSIM	0.39	0.41	0.84	0.89
	a = 0.2	PSNR	16.12	16.20	24.39	25.32
	(nat. order [18])	SSIM	0.27	0.28	0.81	0.86
	a = 0.2	PSNR	17.92	18.09	22.40	26.45
	(opt. order [19])	SSIM	0.30	0.32	0.74	0.83

COMPLIANCE WITH ETHICAL STANDARDS

This is a study for which no ethical approval was required.