R Gavazzi

C Adami

F Durret

J.-C Cuillandre

O Ilbert

A Mazure

R Pelló

M P Ulmer

A weak lensing study of the Coma Cluster ⋆

Keywords: Gravitational Lensing -Galaxies, clusters, individual, Coma

Context. Due to observational constraints, dark matter determinations in nearby clusters based on weak lensing are still extremely rare, in spite of their importance for the determination of cluster properties independent of other methods. Aims. We present a weak lensing study of the Coma cluster (redshift∼ 0.024) based on deep images obtained at the CFHT. Methods. After obtaining photometric redshifts for the galaxies in our field based on deep images in the u * (1×1 deg 2 ), and in the B, V, R and I bands (42'×52'), allowing us to eliminate foreground galaxies, we apply weak lensing calculations on shape measurements performed in the u * image. Results. We derive a map of the mass distribution in Coma, as well as the radial shear profile, and the mass and concentration parameter at various radii. We obtain M 200c = 5.1 +4.3 -2.1 ×10 14 h -1 70 M ⊙ and c 200c = 5.0 +3.2 -2.5 , in good agreement with previous measurements. Conclusions. With deep wide field images it is now possible to analyze nearby clusters with weak lensing techniques, thus opening a broad new field of investigation.

Introduction

Although weak lensing studies are of great importance in cosmology, in particular concerning various aspects of the analysis of large scale structures in the universe (e.g. Mellier 1999;Bartelmann & Schneider 2001;[START_REF] Schneider | Saas-Fee Advanced Course 33: Gravitational Lensing: Strong, Weak and Micro[END_REF]), dark matter determinations in clusters based on weak lensing have been limited to relatively distant objects, usually with redshifts larger than about 0.15. This is mainly due to the fact that the cluster lensing strength is maximized if the angular diameter distance to the foreground cluster is about half that of the background source galaxies, and to the requirement of large field images, in order to cover the full cluster in a single exposure. Very few low redshift clusters have been analyzed until now in detail through weak lensing: Abell 3667 at redshift 0.055 (Joffre et al. 2000), and clusters observed in the shallow Sloan Digital Sky Survey [START_REF] Sheldon | [END_REF], including Coma (Kubo et al. 2007). We present here a weak lensing study of the Coma cluster at z Coma = 0.024 based on deep exposures in several bands obtained with the CFH12k and Megaprime/Megacam large cameras at the Canada France Hawaii Telescope. The J2000 location of the brightest cluster galaxy NGC 4874 is RA=12:59:35.67, DEC=+27:57:33.7 . In the following we as-⋆ Based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This work is also partly based on data products produced at TERAPIX and the Canadian Astronomy Data Centre as part of the Canada-France-Hawaii Telescope Legacy Survey, a collaborative project of NRC and CNRS.

sume the "concordance model" cosmological background with H 0 = 70 km s -1 Mpc -1 , Ω m = 0.3 and Ω Λ = 0.7, for which 1 ′′ on the sky corresponds to a transverse scale of 0.79 kpc. All magnitudes are in the AB system.

Data analysis

Cluster membership & background sources

Multiband imaging for this cluster was obtained at CFHT, both in the inner parts made of two CFH12k camera pointings in BVRI bands resulting in about 0.54 deg 2 coverage and with Megacam (1 × 1 deg 2 fov) in the u * band (see Adami et al. 2006Adami et al. , 2007Adami et al. , 2008, for a detailed description of these data).

We were able to estimate photometric redshifts of all sources down to R 23 in the area covered by the CFH12k images (Adami et al. 2008). This allowed us to separate the population of faint background sources from nearby objects with z phot ≤ 0.04 and to eliminate the bulk of the cluster members that could bias the weak lensing study (e.g. Medezinski et al. 2007). Adami et al. (2008) used the same technique to discriminate between z < 0.2 and z > 0.2 galaxies. Here, we push the limit to z phot = 0.04 to increase the number of background sources although a more conservative cut at 0.2 would not produce noticeable changes given the small number of added objects. This photometric redshift separation is possible only in the innermost 0.54 deg 2 . However we found that none of our results below could be significantly biased due to the extreme prevalence of distant sources in the faint end of the magnitude counts (a factor ∼ 10 -20) despite some incompleteness for u * 24.5 . We thus apply a magnitude selection 23.5 ≤ u * ≤ 25 everywhere in the field but we complement the selection by removing the few objects in the inner regions that are known to be at z phot ≤ 0.04.

A clear advantage of lensing by nearby deflectors is that the geometric distance factor D LS /D S (e.g. Bartelmann & Schneider 2001), which is the ratio of angular distances between deflector and source D LS and to the source D S , is almost constant and close to unity for most of the faint background sources. Given the low redshift of Coma, even a source at z ∼ 0.2 will experience lensing effects with D LS /D S ≃ 0.87. In order to check this small redshift dependance, we used the CFHTLS-deep photometric redshifts (Ilbert et al. 2006) measured in four independent 1 deg 2 fields to infer the redshift distribution of sources having 23.5 ≤ u * ≤ 25. The mean and median photometric redshifts are 0.87 and 0.92 respectively and the sample contains a negligible amount of low redshift (z < 0.04) galaxies. We estimate the mean w = D LS /D S averaged over the whole population of sources to be w ≃ 0.962. This is the value we will consider below for further weak lensing mass calibrations.

Shear measurement

In Adami et al. (2009), we presented measurements of the systematic orientation of faint Coma cluster galaxies. The small size of these galaxies required a careful deconvolution of the Point Spread Function (PSF) smearing effect which was done with the same procedure we use here. It is based on the KSB technique (Kaiser et al. 1995) and the pipeline has been applied to ground-based imaging from CFH12k and Megacam cameras (e.g. Gavazzi et al. 2004;Gavazzi & Soucail 2007). We used the u * channel to measure shapes because it is the one with largest spatial coverage, and also the most homogeneous since the images in the other bands are built from two adjacent pointings. The median u * band seeing FWHM is 1. ′′ 02. Despite this relatively poor value, we were able to successfully correct for PSF smearing.

The quality of PSF anisotropy corrections can be seen in the left panels of Fig. 1 in Adami et al. (2009). The good overall image quality is reflected in the raw and corrected ellipticities of stars. The low scatter in the corrected ellipticities, 0.0028 rms, is also a good assessment of the controled systematics.

Based on mock images designed to simulate Megacam data for the CFHTLS survey and kindly made available to us by B. Rowe, we were able to assess the reliability of our shear measurements, especially for the isotropic part of the PSF smearing effect. These simulations are similar to STEP2 simulations (Massey et al. 2007) as they capture the complex shape of sources by the use of shapelet models. Following notations of the STEP shear measurement project (Heymans et al. 2006;Massey et al. 2007), residual additive calibration errors are found to be c ≤ 0.003 and multiplicative m ≤ 0.05, which is significantly smaller than the statistical errors present in our data (see below).

Because redder bands are usually used for weak lensing studies, we checked that PSF-corrected ellipticities of central objects measured in both Megacam-u * and CFH12k-I bands are consistent. This is found to be the case with great accuracy as shown in Fig. 1. We checked that the ellipticity difference e I -e u * (for both components 1 and 2 taken together or considered individually) does not correlate with either e u * , u * magnitude, I magnitude or r h,u * the u * -band half light radius.

Convergence map

From the source catalogue, we can infer the shear field γ(θ) and deduce the associated convergence field κ(θ) which is the projected surface mass density expressed in units of the critical density Σ crit = c 2 (4πG) -1 D S /(D L D LS ). Here D S /D LS is replaced by the source population average inverse 1/w which leads to a critical density Σ crit = 1.69 × 10 10 h 70 M ⊙ / kpc 2 .

Shear and convergence are related by:

κ(θ) = R 2 K(θ -ϑ) * γ(ϑ)d 2 ϑ, (1)
K(θ) = 1 π -1
(θ 1 -iθ 2 ) 2 is a complex convolution kernel (Kaiser & Squires 1993). The shear field is smoothed with a Gaussian G(θ) ∝ exp(-θ 2 2θ 2 s ) with θ s = 170 ′′ ≃ 80 h -1 70 kpc. The convergence field is smoothed by the same filter. The convergence map presents correlated noise properties (van Waerbeke 2000) characterised by n bg ≃ 11 arcmin -2 , the number density of background sources. By bootstrapping the orientation of background sources, we estimated this noise level to be σ κ = 0.0134 with little variation over the field (except near edges and masked bright stars).

The top panel of Fig. 2 shows the reconstructed convergence map 1 × 1 deg 2 field of view with contours showing a > 5σ significance central peak along with various substructures. The high physical resolution of ∼ 80 h -1 70 kpc that is made possible by the vicinity of Coma somewhat balances the low associated lensing efficiency. The position of the brightest cluster galaxies with r ≤ 14 is shown as red crosses. For comparison we also show in the lower panel of Fig. 2 the luminosity-weighted r-band luminosity distribution coming from bright r ≤ 19 cluster members that lie on the Red Sequence defined by the relation 0.72 ≤ (gr) + (r -14) * 0.035 ≤ 0.87. The convergence contours are overlaid. For this latter study we used the more extended SDSS DR7 photometry, which is better suited for investigation of the bright end of the luminosity function 1 . This panel also shows contours of X-ray emissivity in excess of a smooth β-model profile (Neumann et al. 2003). The blue dotted contours show the excess X-ray emission over a smooth β-model X-ray emissivity map (Neumann et al. 2003).

Radial mass profile

We now investigate the azimuthally-averaged tangential shear profile γ t (R) which is simply related to the azimuthally-averaged projected surface mass density profile Σ(R) by the relation

Σ crit γ t (R) ≡ ∆Σ(R) = M(< R)/(πR 2 ) -Σ(R) , (2)
where we have defined the frequently used rescaled shear ∆Σ and M(< R) is the projected mass enclosed by radius R.

We measure the radial shear profile average in circular annuli centered on the peak of the convergence map (α=12:59:39.007, δ=+27:57:55.93) which is only 40 ′′ east of NGC 4874, i.e. in the direction of NGC 4889, which are respectively the brightest and second brightest member galaxies. Fig. 3 shows the radial shear profile γ t (R) out to the edge of the Megacam field of view (∼1 Mpc). The bottom panel of this figure shows the same profile once galaxies are rotated by 45 • , which is the curl or B-mode component of the ellipticity field. In the absence of systematic PSF correction residuals, this rotated shear profile should be consistent with zero at all scales. This is what we observe. We attempted to fit a radial shear profile as predicted by the NFW mass density profile of the form

ρ(r) = ρ s (r/r s ) -1 (1 + r/r s ) -2 (3)
coming from cosmological simulations (Navarro et al. 1997). The corresponding lensing quantities were derived by Bartelmann (1996). Table 1 These results are in good agreement with mass estimates in the literature. Kubo et al. (2007) performed a weak lensing mass estimate of Coma based on the much shallower SDSS data -resulting in n bg ≃ 1arcmin -2 -but extending out to ∼ 14 h -1 70 Mpc.

Table 1. Summary of mass estimates with and without priors on the mass-concentration relation (Macciò et al. 2008).

no prior prior p(c|M) M 200c /10 14 h -1 70 M ⊙ 5.1 They report a mass M 200c = 2.7 +3.6 -1.9 × 10 15 h -1 70 M ⊙ and concentration index c 200c = 3.8 +13.1 -1.8 that are statistically consistent with our estimates. In addition, an earlier X-ray study by Hughes (1989) gave M vir = 13 ± 2 × 10 14 h -1 70 M ⊙ , and is consistent with our estimate. Mass measurements coming from galaxy kinematics are also in good agreement with our mass constraints (e.g. The & White 1986;Geller et al. 1999;Łokas & Mamon 2003). Note however that these latter authors report a concentration parameter c vir ≃ 9, slightly larger that our best-fit value though statistically consistent.

Discussion and conclusion

We have performed a weak lensing analysis of the Coma cluster, based on a deep u * exposure and on images in other bands which allowed to derive photometric redshifts in order to remove as many foreground galaxies as possible from the background population. We thus obtain a map of the mass distribution in Coma, the radial shear profile, as well as the mass and concentration parameter.

The peak of the convergence map is nearly coincident with the X-ray center (Neumann et al. 2003) or the brightest galaxy NGC 4874. The difference is ∼ 27 h -1 70 kpc, below the spatial resolution of the convergence map. We can also notice the correspondence between the (α∼194.4, δ∼27.82) 2σ detection and the G12/G14 groups of Adami et al. (2005) and between the (α∼194.5, δ∼28.03) 2σ detection and the northern part of the west X-ray substructure (Neumann et al. 2003). Finally, the convergence map shows a 4σ (α∼195, δ∼28) extension overlapping the position of NGC 4889, confirming the existence of a mass concentration around this galaxy. Other substructures visible in X-rays and in the optical (e.g. the NGC 4911 group) do not show up in the convergence map, indicating that we are dealing with low mass systems.

The south-west extension of the convergence map is more puzzling. It does not match the light distribution. The groups G8 and G9 from Adami et al. (2005) explain part of this extension, but are not luminous enough to fully account for this mass concentration. Another explanation would be massive low redshift structures (in order to still efficiently induce a weak lensing signal and to be spread largely enough over the sky) but that would not belong to Coma. The SDSS data used by Gott et al. (2005) exhibit such a z ∼ 0.08 "Great Wall", probably not luminous enough though to explain the south west mass concentration. Moreover, the magnitude limit of the SDSS spectroscopy is not very deep, leaving room for other possible large scale structures not appearing in the SDSS sample. This will have to be confirmed by a more detailed knowledge of the immediate background of the Coma cluster (Adami et al. in preparation).

Fig. 1 .

 1 Fig. 1. Corrected ellipticities of galaxies measured in Megacam-u * and CFH12k-I bands in the inner parts of the field. A close consistancy is found with e I = (1.009 ± 0.026)e u * with a ∼ 0.77 correlation coefficient.

Fig. 2 .

 2 Fig. 2. Top panel: Convergence map for the Coma cluster. Green contours represent signal-to-noise ratios of 1, 2, . . . 5, corresponding to κ = 0.01, 0.02, . . . 0.05. Red crosses represent the bright cluster members lying on the red sequence. Bottom panel: Gray-scale view of the luminosity distribution of cluster red sequence members with the overlaid contours in green.The blue dotted contours show the excess X-ray emission over a smooth β-model X-ray emissivity map(Neumann et al. 2003).

Fig. 3 .

 3 Fig. 3. Top panel: Radial shear profile measured in Coma as a function of projected distance. The best-fit NFW profile is overlaid. Bottom panel: B-mode shear profile showing the negligible residuals in the rotated shear component.

  The two relevant quantities that we fit are the mass M 200c enclosed in the radius r 200c in which the mean density is 200 times the critical density ρ crit and the concentration parameter c 200c = r 200c /r s . This implies that ρ s = ρ crit 200 3 c 3 / [ln(1 + c) -c/(1 + c)]. Fig. 4 shows the constraints we obtain on these two parameters. Marginal distributions yield the following constraints 2 : M 200c = 5.1 +4.3 -2.1 × 10 14 h -1 70 M ⊙ and c 200c = 5.0 +3.2 -2.5 , which corresponds to r 200c = 1.8 +0.6 -0.3 Mpc. Fig. 4 also shows the massconcentration relation and its 1σ dispersion that were recently reported by Macciò et al. (2008) assuming WMAP5 cosmological parameters (Komatsu et al. 2008). The two-dimensional contours are in good agreement with these predictions. Taking the conditional p(c 200c |M 200c ) from Macciò et al. (2008) as a prior on c 200c , we marginalize again over the poorly constrained concentration parameter and obtain constraints on the mass M ′ 200c = 9.7 +6.1 -3.5 × 10 14 h -1 70 M ⊙ or again r 200c = 2.2 +0.3 -0.2 Mpc . In order to allow a comparison with other mass estimates in the literature, we calculate the virial mass M vir ≡ M 100c and the corresponding concentration c vir ≡ c 100c , since a density contrast ∆ vir ≃ 100 is better suited for the assumed cosmology. All our results regarding both (M 200c , c 200c ) and (M vir , c vir ) as well as corresponding r 200c and r vir values are given in

Fig. 4 .

 4 Fig. 4. M 200c and c 200c parameters constraints of the NFW density profile. The Mc relation predicted by simulation and WMAP5 cosmology (with its 1σ scatter) are shown in red.

http://cas.sdss.org/astro/en/tools/

Assuming a flat uniform prior in log M 200c betwen 13 and 16 and on c 200c between 0.01 and 20 to infer respectively the marginal PDFs p(c) and p(M 200c )

Acknowledgements. RG acknowledges Barnaby Rowe for providing mock realistic Megacam images for shear calibration assessments and T. Futamase and N. Okabe for interesting discussion about weak lensing in Coma. We are grateful to the CFHT and Terapix teams, and to the French CNRS/PNG for financial support. MPU also acknowledges support from NASA Illinois space grant NGT5-40073 and from Northwestern University.


Laurent Dormont
email: laurent.dormont@cefe.cnrs.fr

Thomas Fort

Jean-Marie Bessière

Magali Proffit

Esther Garcia Hidalgo

Bruno Buatois

Bertrand Schatz

Sources of floral scent variation in the food-deceptive orchid Orchis mascula

Keywords: Orchis mascula, floral scent, intraspecific variation, plant-pollinator interactions

Intraspecific variation of floral signals, and particularly floral scent, is widespread in orchid species. Pollinator-mediated selection is often hypothesized to govern such polymorphism.

For example, circadian rhythms of floral emissions have been associated to the daily activity patterns of pollinators in several plant species. However, most of the studies on floral scent variation usually consider only one factor of variation (diel variation, pollination, flower age…). In this study, we investigated simultaneously seven sources of variation susceptible to induce changes in floral scent emissions in the food-deceptive orchid Orchis mascula: circadian rhythm, flower age, pollination, inflorescence morphology, herbivory, leaf spots and habitat. We found that all the factors studied, except plant morphology (spots on leaves, inflorescence morphology), were associated with significant changes in the volatile profile of O. mascula inflorescences. Electroantennography experiments with Bombus terrestris, the main pollinator of O. mascula, revealed that bumblebees were able to detect almost all the compounds involved in floral scent variation. Whether pollinator behaviour is influenced by changes in floral odours induced by these different factors, and whether it can affect plant reproductive success will have to be tested in further studies.

Introduction

Odour emitted by flowers represents a key floral signal used by insects to detect and select rewarding flower species [START_REF] Raguso | Wake up and smell the roses: the ecology and evolution of floral scent[END_REF][START_REF] Schiestl | Ecology and evolution of floral volatile-mediated information transfer in plants[END_REF]. Most flower-visitor species orient their flight behaviour by exploiting single volatile compounds or complex volatile blends provided by flowers [START_REF] Bruce | Insect host location: a volatile situation[END_REF]. In many plant species, chemical profiles of floral scents have been reported to vary both within and among individuals as well as among populations [START_REF] Knudsen | Diversity and distribution of floral scent[END_REF][START_REF] Delle-Vedove | Understanding intraspecific variation of floral scents in the light of evolutionary ecology[END_REF]. Why does floral scent vary within a plant species has long intrigued ecologists and has generated many research studies? Several of them have highlighted the key role of pollinators in driving chemical variation between conspecific populations, or even closely related species, through pollinator-mediated selection [START_REF] Hoballah | Single gene-mediated shift in pollinator attraction in Petunia[END_REF][START_REF] Raguso | Wake up and smell the roses: the ecology and evolution of floral scent[END_REF][START_REF] Suchet | Floral scent variation in two Antirrhinum majus subspecies influences the choice of naïve bumblebees[END_REF]. Several factors other than pollinators have also been proposed to explain intraspecific variation of floral scent, such as antagonistic interactions with herbivores and pathogens, genetic drift or phenotypic plasticity [START_REF] Majetic | The impact of biochemistry vs. population membership on floral scent profiles in colour polymorphic Hesperis matronalis[END_REF][START_REF] Majetic | Sources of floral scent variation: can environment define floral scent phenotype?[END_REF][START_REF] Schlumpberger | Geographic variation in floral scent of Echinopsis ancistrophora (Cactaceae); evidence for constraints on hawkmoth attraction[END_REF]. Other authors have investigated floral odour variation in relation to floral colour variation, and found that distinct colour morphs in a given plant species differ in scent chemistry [START_REF] Odell | Bumblebee foraging responses to variation in floral scent and color in snapdragons (Anthirrhinum: Scrophulariaceae)[END_REF][START_REF] Flamini | Analysis of the essential oil of the aerial parts of Viola etrusca from Monte Labbro (South Tuscany, Italy) and in vivo analysis of flower volatiles using SPME[END_REF][START_REF] Zucker | Modification of flower colour and fragrance by antisense suppression of the flavonone 3-hydroxylase gene[END_REF][START_REF] Li | Analysis of volatile compounds emitted from fresh Syringa oblate flowers in different florescence by headspace solid-phase microextraction-gas chromatography-mass spectrometry[END_REF]Salzmann and Schiestl, 2007). Floral colourodour associations have been proposed to result from biochemical processes that affect both traits, because pigments and volatile compounds often share common biosynthetic pathways [START_REF] Zucker | Modification of flower colour and fragrance by antisense suppression of the flavonone 3-hydroxylase gene[END_REF][START_REF] Majetic | Flower color-flower scent associations in polymorphic Hesperis matronalis (Brassicaceae)[END_REF][START_REF] Delle-Vedove | Colour-scent associations in a tropical orchid: three colours but two odours[END_REF].

Beyond the direct key influence of pollinators on floral scent variation, several other sources of spatial and temporal variation in floral scent emissions have been reported [START_REF] Raguso | Concerted changes in floral colour and scent, and the importance of spatio-temporal variation in floral volatiles[END_REF][START_REF] Delle-Vedove | Understanding intraspecific variation of floral scents in the light of evolutionary ecology[END_REF]. For examples, intraspecific changes in floral volatiles have been observed in relation with pollination [START_REF] Tollsten | Variation and post-pollination changes in floral odors released by Platanthera bifolia (Orchidaceae)[END_REF][START_REF] Negre | Regulation of methylbenzoate emission after pollination in snapdragon and petunia flowers[END_REF][START_REF] Theis | The effect of pollination on floral fragrance in thistles[END_REF][START_REF] Muhlemann | Postpollination changes in floral odor in Silene latifolia: Adaptive mechanisms for seed-predator avoidance?[END_REF][START_REF] Proffit | Signalling receptivity: comparison of the emission of volatile compounds by figs of Ficus hispida before, during and after the phase of receptivity to pollinators[END_REF], circadian rhythms [START_REF] Kolosova | Regulation of circadian methyl benzoate emission in diurnally and nocturnally emitting plants[END_REF][START_REF] Raguso | Fragrance chemistry, nocturnal rhythms and pollination "syndromes" in Nicotiana[END_REF][START_REF] Theis | The challenge of attracting pollinators while evading floral herbivores: patterns of fragrance emission in Cirsium arvense and Cirsium repandum (Asteraceae)[END_REF][START_REF] Okamoto | Floral adaptations to nocturnal moth pollination in Diplomorpha (Thymelaeaceae)[END_REF][START_REF] Fenske | Circadian rhythms in floral scent emission[END_REF][START_REF] Chapurlat | Diel pattern of floral scent emission matches the relative importance of diurnal and nocturnal pollinators in populations of Gymnadenia conopsea[END_REF][START_REF] Chapurlat | Conflicting selection on floral scent emission in the orchid Gymnadenia conopsea[END_REF], herbivory [START_REF] Kessler | Testing the potential for conflicting selection on floral chemical traits by pollinators and herbivores: predictions and case study[END_REF][START_REF] Kessler | Changing pollinators as a means of escaping herbivores[END_REF][START_REF] Kessler | Petunia flowers solve the defence/apparency dilemma of pollinator attraction by deploying complex floral blends[END_REF][START_REF] Gish | Herbivore-induced plant volatiles in natural and agricultural ecosystems: open questions and future prospects[END_REF], flower age [START_REF] Schade | Fragrance volatiles of developing and senescing carnation flowers[END_REF][START_REF] Steenhuisen | Variation in scent emission among floral parts and inflorescence developmental stages in beetle-pollinated Protea species (Proteaceae)[END_REF][START_REF] Filella | Floral advertisement scent in a changing plantpollinators market[END_REF][START_REF] Wang | Sexual and temporal variations in floral scent in the subdioecious shrub Eurya japonica Thunb[END_REF], or habitat origin [START_REF] Majetic | Sources of floral scent variation: can environment define floral scent phenotype?[END_REF][START_REF] D'auria | Fragrance components of Platanthera bifolia subsp. osca and Platanthera chlorantha collected in several sites in Italy[END_REF]. However, very few studies have examined the combined effects of these distinct sources of variation. How these factors may successively or simultaneously act on floral volatiles within a single plant species (and even within one population), and what are the relative influences of these different factors remains largely unknown.

The orchid family represents a very interesting case of floral polymorphism, which is generally associated with animal pollination [START_REF] Van Der Cingel | An atlas of orchid pollination-European orchids[END_REF][START_REF] Claessens | The flower of the European orchid: Form and function[END_REF]. Approximately one-third of all orchid species achieve pollination through food deception, i.e., flowers contain no nectar or other rewards but resemble or mimic floral signals of rewarding plants to attract pollinators (Jersakova et al., 2006). Intraspecific variation in floral traits, and particularly odours, is expected to be high in food-deceptive orchids, because flowers must delay the avoidance learning of pollinators (Jersakova et al., 2006;[START_REF] Schiestl | On the success of a swindle: pollination by deception in orchids[END_REF]. Many orchids exhibit such odor polymorphism, both rewarding and deceptive species [START_REF] Tollsten | Variation and post-pollination changes in floral odors released by Platanthera bifolia (Orchidaceae)[END_REF][START_REF] Moya | Variation in the floral fragrance of Epidendrum ciliare (Orchidaceae)[END_REF][START_REF] Olesen | Scent profiles of colour morphs of Corydalis cava (Fumariaceae) in relation to foraging behaviour of bumblebee queens (Bombus terrestris)[END_REF][START_REF] Azuma | Intraspecific variation of floral scent chemistry in Magnolia kobus DC. (Magnoliaceae)[END_REF][START_REF] Dötterl | Qualitative and quantitative analyses of flower scent in Silene latifolia[END_REF]Salzmann et al., 2007;[START_REF] Dormont | Floral scent emitted by white and colored morphs in orchids[END_REF][START_REF] Dormont | Intraspecific variation in floral color and odor in orchids[END_REF]. However, most of these studies have analyzed floral scent by performing a single sampling session, and did not considered other possible sources of odor variation. In fact, as mentioned above, intraspecific variation of floral scent may occur under the influence of various biotic or abiotic factors.

In this study, we investigated simultaneously the effects of different factors on the floral scent of the food-deceptive orchid Orchis mascula L. This species is distributed throughout Europe, and typically exhibits red-purple flowers, except some populations where rare white-flowered individuals can be observed within purple-flowered patches. Floral volatiles of these two-colour morphs have been investigated in previous studies [START_REF] Dormont | Rare white-flowered morphs increase the reproductive success of common purple morphs in a food-deceptive orchid[END_REF][START_REF] Dormont | Floral scent emitted by white and colored morphs in orchids[END_REF], and revealed no differences of volatile profile between the two-colour morphs. The objective of this study was to examine whether the floral scent emissions of O. mascula are affected by different biotic or abiotic conditions: flower age, circadian rhythm, plant morphology (number of flowers per inflorescence, spots on leaves), plant habitat, pollination, or herbivore attacks. All these factors were investigated for the same populations and at the same period, allowing to estimate different causes of variations and their compared amplitude for same individuals in a same context. We also investigated the electrophysiological responses of naïve bumblebees, the most frequent pollinators of O. mascula flowers, to relevant identified volatile organic compounds which were found to vary under the influence of these factors.

Material and methods

Study sites and organism

The experiments were carried out in two different sites, separated by 3.5 km, in a mountainous area of south-central France. Both sites were located about 70 km north of Montpellier, on the Causse du Blandas, an extensive limestone plateau (43°55' N, 3°29' E, 710 m altitude). In each site, we surveyed several patches of O. mascula individuals (8 patches in site 1, and 8 patches in site 2), each of which consisted of about 20 to 100 individuals and was separated by at least 50 m (usually 300 m or more) from the nearest neighbouring patch. In this study, and because most patches were very distant from each other, each patch of 20-100 individuals was considered as a "population". The first site consisted of an open dry grassland, while populations of the second site were situated in the undergrowth of pine wood.

The species O. mascula is a perennial non-rewarding orchid species, widely distributed in Europe, western Asia, and northern Africa, where it is present from 0 to 2500m. This species is usually present in open habitats, but it is also shade tolerant (Jacquemyn et al., 2009b). Inflorescences consist generally of 5 to 20 typically purple flowers. In some populations, a few white-flowered individuals [START_REF] Dormont | Rare white-flowered morphs increase the reproductive success of common purple morphs in a food-deceptive orchid[END_REF] and pink-flowered individuals [START_REF] Schatz | Presence, distribution and pollination effects of white, pink and purple morphs in the orchid Orchis mascula[END_REF] occur mixed with the purple-flowered individuals. O. mascula flowers are pollinator-dependent for setting seeds [START_REF] Nilsson | Anthecology of Orchis mascula (Orchidaceae)[END_REF]B. Schatz, unpublished), and are visited and pollinated by bumblebees (Bombus spp.) or honeybees, cuckoo bumblebees (Psithyrus spp.) and Nomada sp., solitary bee species of several genera (Eucera, Nomada, Andrena, Halictus and Osmia), a chafer beetle (Cetonia aurata) and more anecdotally by one diptera (Bombylius major) and one lepidoptera (Zygaena transalpina) [START_REF] Nilsson | Anthecology of Orchis mascula (Orchidaceae)[END_REF][START_REF] Bournérias | Les orchidées de France, Belgique et Luxembourg : deuxième édition[END_REF][START_REF] Cozzolino | Evidence for pollinator sharing in Mediterranean nectar-mimic orchids: Absence of premating barriers?[END_REF]Jacquemyn et al., 2009b;[START_REF] Joffard | Effect of pollination strategy, phylogeny and distribution on pollination niches of Euro-Mediterranean orchids[END_REF]. Flowering occurs early in spring, and O. mascula is known to exploit newly emerged insect pollinators, suggesting that pollination in this species is performed mainly by visits of naïve, inexperienced insects [START_REF] Nilsson | Anthecology of Orchis mascula (Orchidaceae)[END_REF][START_REF] Van Der Cingel | An atlas of orchid pollination-European orchids[END_REF]. Adult O. mascula plants are known to show a pronounced small-scale aggregation, because seedlings are clustered around adult plants, with approximately 90% of all recruits located within 16 and 24 cm of the adults in populations (Jacquemyn et al., 2009a(Jacquemyn et al., , 2009b)).

Floral volatiles

Sampling of floral volatiles

Floral volatiles were monitored using solid-phase microextraction (SPME), a nondestructive, solvent-free sampling technique permitting the sampling of volatiles in situ on living plant individuals. Sampling by SPME was performed using 65 µm polydimethylsiloxane / divinyl benzene (PDMS-DVB) fibres (Supelco®). The whole inflorescence was enclosed in a bag made from polyethylene terephtalate (Nalophan®; Kalle Nalo GmbH, Wursthüllen, Germany), a nonreactive plastic. After the equilibration time (15 min), the fibre was introduced with a manual holder into the Nalophan® bag containing the inflorescence. The fibre was exposed for 45 min in close proximity (1 cm) to flowers. For each sampled population of O. mascula individuals, a control bag was also sampled: an SPME fibre was inserted into an empty Nalophan bag, in order to monitor volatiles from the air surrounding the plant.

Most of the time, the period of floral volatile sampling in situ was defined with respect to both the flowering period (April 2014) and the period of maximum activity of insects during the day, i.e., between 11:00 and 15:00. We explored different possible sources of floral scent variations by carrying volatile sampling under different situations. For each situation, a particular attention was given to sample individuals under the influence of only one factor of variation. For example, when studying the possible effects of habitat, inflorescences were sampled among plants that were non-pollinated (assessed by a rigorous visual check of each flower before sampling), showing no herbivore attack, and sampling was performed at 13.00 on individuals at mid-flowering period.

-Habitat: 20 O. mascula individuals were randomly selected and sampled for scent collection in the first site (open dry grassland), and 20 other individuals were sampled in the second site (pine wood).

-Circadian rhythm: odour emissions from 20 O. mascula individuals, with a similar number of flowers per inflorescence, of the first site were sampled at different times of the day, at 9.00-10.30 a.m., at 13.30-15.00 p.m, and 18.00-19.30 p.m.

-Flower age: the same 20 individuals were sampled at different periods of flowering, at 1, 6 and 13 days after blooming. Individuals were surveyed every day before blooming to determine the first day of sampling.

-Inflorescence morphology: Four categories of individuals were defined, with respect to the number of flowers per inflorescence (less than 5 flowers, 6-11 flowers, 12-20 flowers, more than 21 flowers). 15 individuals of each category, i.e. of different inflorescence size, were sampled as described above.

-Spots on leaves: some O. mascula individuals exhibit dark spots on the basal leaves, while other individuals have no spots. We sampled both types of individuals, 20 individuals of each type.

-Pollination: odours from 20 O. mascula individuals were sampled 2 days after blooming, were then left during 8 days until manual pollination of the flowers was performed, and odours were then sampled again two days after pollination. 10 other "control" individuals were sampled similarly as above, but were not manually pollinated and inflorescences were protected with bags until second odour collection.

-Herbivory: 20 other O. mascula individuals of the first site were randomly selected and volatile emissions were sampled on day 3 after blooming. Insect flower damage was simulated by cutting both a piece of leaf (total length of 10 cm cutting) and labellum extremity (half of the flowers from each inflorescence was treated). Odour sampling was performed two days after artificial damage, to avoid isolation of possible damage-related volatile compounds immediately after damage.

Overall, a total of 162 O. mascula inflorescences (162 different individuals) were sampled for floral scent during this study.

Gas chromatography-mass spectrometry of floral volatiles

GC-MS analyses of the SPME extracts were performed using electronic impact ionization mode on a Varian Saturn 2000 ion trap spectrometer, interfaced with a Varian GC CP-3800 apparatus. The Varian CP-3800 was equipped with a 1079 split-splitless injector (260 °C) and a 30 m x 0.25 mm x 0.25 µm film thickness ID WCOT CPSil 8CB fused silica capillary column (Chrompack®, Bergen op Zoom, The Netherlands), with helium as carrier gas (1 ml min-1), and programmed 2 min isothermal at 50 °C, then 50 °C to 220 °C at 4 °C min-1. Mass spectra were recorded in electronic impact (EI) at 70eV, and identified by comparison with data of the NIST 98 software library. Floral volatile compounds were identified based on retention time of external synthetic standards, and with mass spectra analyses of GC-MS data. Peaks were quantified using Star Chromatography Software®. The relative importance of each compound was expressed with respect to total volatiles in order to compare the volatile profile of the samples.

Electroantennography assays

Electroantennography assays were performed using a commercial colony of naïve Bombus terrestris. This species is known to be among the main pollinators of O. mascula in southern France [START_REF] Nilsson | Anthecology of Orchis mascula (Orchidaceae)[END_REF][START_REF] Joffard | Effect of pollination strategy, phylogeny and distribution on pollination niches of Euro-Mediterranean orchids[END_REF]Schatz B., unpublished data). The colony was purchased from BioBest Group NV® (Westerlo, Belgium). Only female workers were used in our tests. Each female bumblebee was immobilized 5 minutes at 3°C. The head was cut and separated from the thorax, and put into a glass capillary (L: 76 mm, Ø: 1.12 mm) (World Precision Instrument, Sarasota, USA) pulled and cut using a vertical micropipette-puller (P-30 model, World Precision Instruments, Hertfordshire, United Kingdom). The capillary was previously filled with an electrolytic solution of ringer (8.0 gl-1 NaCl, 0.4 gl-1 KCl, 0.4 gl-1 CaCl2) and connected to a reference electrode. The tip of one antenna was connected into the recording electrode, also filled with the electrolytic solution, at the opposite side of the reference electrode. A continuous flow of purified and humidified air, passing through a glass tube to the antenna at a flow rate of 330 ml min -1 , was provided by a stimulus controller (CS-55 Syntech, Hilversum, The Netherlands). The antenna was then stimulated with a single compound, and antennal electrical responses to volatile compounds were measured for 31 female bumblebees and digitized using an IDAC board (acquisition controller IDAC-2; Syntech, Hilversum, the Netherlands) and data was processed with a PCbased interface and software package (GcEad 1.2.5, Syntech). Sixteen synthetic volatile compounds, which were identified in the floral scents of O. mascula, were tested. These compounds were the main components of O. mascula floral scent, and included the compounds that were found to vary during this study: linalool, limonene, α-pinene, 6-methyl-5-hepten-2-one, linalool-oxide, eucalyptol, myrcene, methyl-decanoate, β-farnesene, 4phenyl-2-butanone, methyl-octanoate, terpinyl-acetate, β-pinene, (Z)-3-hexenyl-acetate, ocimene, methyl-cinnamate (Sigma-Aldrich®, US, Bellefonte; TCI Chemical ® , Stockholm, Sweden; and Extrasynthese ® , Genay, France).

Each synthetic compound was individually tested at different dilutions, solutions being made at 0.1%, 1%, and 10% v/v in dichloromethane. For each test, 1 µl of pure solution was deposited on a strip of filter paper and left for evaporation during 5 min in ambient conditions in a separate laboratory room. The antenna was stimulated with pulses of 0.5 s each, using a purified and moistened airflow of 11.3 mL s-1 across a Pasteur pipette containing the filter paper. Each of the sixteen compounds was delivered once for each dilution; compounds were presented in the same order so that all the antennae were submitted to a similar stimulus sequence, thus allowing comparisons between antennal signals. Stimuli were separated by a 40 s interval, to avoid saturation of the olfactory receptors. A control was performed at the beginning of each experimental series by measuring the antenna response to clean air and to 1 µl of dichloromethane deposited on a filter paper. For each bumblebee antenna, the response amplitude was normalized to the maximum response recorded.

Data analysis

The chemical composition of floral volatiles was analysed in R 2.14.1 (R Development Core Team, 2011) using multivariate analysis incorporated in the Vegan package (Oksanen et al., 2012). For each comparison, the relative proportion of each compound that accounted for more than 1% of total volatiles was included in the analyses.

Following [START_REF] Soler | Evidence for intersexual chemical mimicry in a dioecious plant[END_REF], the data were first transformed using squared root and Wisconsin double standardization, and then a data matrix of pairwise Bray-Curtis dissimilarity indices between samples was build. Secondly, Permutational Multivariate Analysis of Variance (PERMANOVA) on the distance matrices based on 999 permutations [START_REF] Anderson | Permutation tests for univariate or multivariate analysis of variance and regression[END_REF] were performed to test for significant differences in the floral scent composition among different situations. In addition, a multivariate Levene's test was performed to test the homogeneity of group variances for each factor. When factors were observed to affect significantly orchid volatiles, a similarity percentage (SIMPER) was used to identify the compounds responsible for more than 30 % of the variation.

To determinate the antennal response to each compound in electroantennography assays, we considered the amplitude of depolarization induced by the stimulus compared to the corresponding baseline, and significance of the responses were tested by using Paired ttests as described by [START_REF] Vittecoq | The evolution of communication in two ant-plant mutualisms[END_REF].

Results

General composition of floral volatiles

A total of 92 volatile compounds were isolated from the inflorescences of O. mascula when considering the different samplings (Table 1). The volatile profile was largely dominated by terpene components: 63 of the 92 identified compounds (68%) originated from the terpenoid biosynthetic pathway. Among the twelve most abundant compounds, eleven are terpenes; the cumulated value of relative proportion of all compounds is 19.56%. The chemical composition of the floral scent was often dominated by one terpene product, limonene, which accounted for a mean of 25.5 % of floral volatiles. Six other compounds were predominant (>5% of the profile) in the floral scent of O. mascula: (E)-ocimene (8.8%), linalool (7.7%), α-pinene (6.6%), myrcene (6.3%), 6-methyl-5-hepten-2-one (5.6%), and linalool-oxide (5.3%).

Variation of floral volatiles

Among the possible sources of scent variation explored in this study, five environmental and biological factors (habitat, flower age, circadian rhythm, pollination, herbivory) were associated with a significant modification of volatile profiles (Table 2, Figure 1). Variation always consisted of changes in the proportion of some volatile compounds, no qualitative changes were observed for these factors. The two last factors related to morphology (number of flowers, spots on leaves) had no significant influence on floral scent composition.

Floral scent variation and habitat

The dissimilarity analysis showed that habitat had a substantial effect on the variation in floral volatiles (PERMANOVA F 1,38 = 3.386; p<0.001). Comparison of the floral volatile profiles between open and closed habitats was characterized by an increase proportion of some terpenes (myrcene, β-pinene, eucalyptol) in the woody pine site, while other compounds were observed to decrease in this closed habitat (limonene, sabinene hydrate, linalool oxide) (Table 1).

Floral scent variation and flower age

Modification of floral volatile profiles was particularly great in relation with flower age (PERMANOVA F 1,38 = 3.386; p=0.001). Difference in the variance among groups (F 1,38 = 5.472; p = 0.007) may partly explain this variation. Several compounds were found to vary in their emission over the period of flowering, such as for limonene or 3-hexenyl acetate, which decreased with flower age, or for 6-methyl-5-hepten-2-one and eucalyptol, which both increased at the end of the flowering period.

Floral scent variation and circadian rhythm

Floral volatiles were observed to vary also with circadian rhythm (PERMANOVA F 2,52 = 2.393; p<0.001). Modification of floral emissions was particularly significant for βpinene, and p-cymene, which increased over the day.

Floral scent variation and pollination

A significant relationship was also observed between floral emissions and pollination: floral samplings before and after pollination revealed a modification of volatile profiles (PERMANOVA F 1,28 = 3.806; p<0.001). Several volatile compounds showed a drastic change in their emission after pollination. In particular, the floral emissions were characterized by a significant increase of the proportion of α-pinene, myrcene, 6-methyl-5-hepten-2-one and eucalyptol, while the proportions of other compounds were lower after pollination, such as for Z-ocimene and linalool.

Floral scent variation and herbivory

Herbivory, which was simulated by damaging both leaves and labellum, also resulted in a significant change of floral volatiles (PERMANOVA F 1,28 = 3.006; p<0.001). Artificial herbivore damage was followed by increased proportions of sabinene, myrcene, eucalyptol, and ocimene, while the levels of other compounds (limonene, 3-hexenyl acetate) were lower after damage.

Antennal detection of synthetic compounds

All synthetic compounds were detected by antennae and elicited strong EAG responses at the different dilutions tested (Fig. 2). Tests with clean air or control samples (dichloromethane) had no effect on antennal responses. The highest amplitude of depolarization was recorded for linalool. α-pinene and β-pinene induced a significant response from the antenna only for 10% dilutions, but no significant electric responses could be recorded for 0.1% or 1% solutions.

Discussion

Our study demonstrated that floral scent emissions of O. mascula can vary under the influence of several biotic or abiotic factors: while plant morphology (spots on leaves, inflorescence size) had no effect on floral volatiles, all the other factors (flower age, circadian rhythm, pollination, habitat, herbivory) were associated with significant changes in the volatile profile of O. mascula inflorescences. The most important effects were recorded for flower age and pollination, although a possible inherent confounding effect may occur between these two factors: for a few volatile compounds, variation of compound levels after pollination might also partly results from the effect of flower age. Modification of the floral scent induced by these different factors always consisted of changes in the ratio of a few number of volatile compounds, mostly monoterpenes. No qualitative changes of the floral scent composition were induced by these factors.

Many other studies have already investigated the effects of similar factors on floral scent profile, but in most case experiments were limited to the analysis of only one or two factor(s) of variation. Significant changes in the floral emissions were found for most of the factors studied. For example, studies that examined how floral scent may vary after pollination reported substantial variation of the volatile profile, consisting mainly of modification in the relative amounts of volatile compounds observed to have a key-role for pollinator attraction [START_REF] Schiestl | Post-pollination emission of a repellent compound in a sexually deceptive orchid: a new mechanism for maximising reproductive success?[END_REF][START_REF] Theis | The effect of pollination on floral fragrance in thistles[END_REF][START_REF] Muhlemann | Postpollination changes in floral odor in Silene latifolia: Adaptive mechanisms for seed-predator avoidance?[END_REF]. Diel variation in floral scent has also been reported for number of plants, including orchid species [START_REF] Nilsson | Pollination ecology and adaptation in Platanthera chlorantha (Orchidaceae)[END_REF][START_REF] Chapurlat | Diel pattern of floral scent emission matches the relative importance of diurnal and nocturnal pollinators in populations of Gymnadenia conopsea[END_REF][START_REF] Steen | Volatiles composition and timing of emissions in a moth-pollinated orchid in relation to hawkmoth (Lepidoptera: Sphingidae) activity[END_REF]. Numerous examples of temporal variation in floral scent through floral development from anthesis to senescence have also been provided from literature [START_REF] Schade | Fragrance volatiles of developing and senescing carnation flowers[END_REF][START_REF] Steenhuisen | Variation in scent emission among floral parts and inflorescence developmental stages in beetle-pollinated Protea species (Proteaceae)[END_REF][START_REF] Filella | Floral advertisement scent in a changing plantpollinators market[END_REF][START_REF] Wang | Sexual and temporal variations in floral scent in the subdioecious shrub Eurya japonica Thunb[END_REF]. Similarly, changes in floral scent patterns following herbivore attacks have been described in many plant families [START_REF] Kessler | Testing the potential for conflicting selection on floral chemical traits by pollinators and herbivores: predictions and case study[END_REF][START_REF] Kessler | Changing pollinators as a means of escaping herbivores[END_REF][START_REF] Kessler | Petunia flowers solve the defence/apparency dilemma of pollinator attraction by deploying complex floral blends[END_REF][START_REF] Gish | Herbivore-induced plant volatiles in natural and agricultural ecosystems: open questions and future prospects[END_REF]. Finally, other authors have also observed intraspecific variation in floral scent in relation to geographic origin or population membership [START_REF] Svensson | Chemistry and geographic variation of floral scent in Yucca filamentosa (Agavaceae)[END_REF][START_REF] Majetic | The impact of biochemistry vs. population membership on floral scent profiles in colour polymorphic Hesperis matronalis[END_REF][START_REF] Schlumpberger | Geographic variation in floral scent of Echinopsis ancistrophora (Cactaceae); evidence for constraints on hawkmoth attraction[END_REF][START_REF] D'auria | Fragrance components of Platanthera bifolia subsp. osca and Platanthera chlorantha collected in several sites in Italy[END_REF].

All these studies focused on one factor likely responsible for floral scent variation, whereas we tentatively investigated in this study the different sources of changes in floral emissions within a single meta-population of O. mascula. Such a sampling allowed to compare causes of variations on same individuals, and thus help understanding the relative importance of different factors. Our results confirmed that these different factors have individually a drastic effect on floral scent emissions, which likely explains the high level of intraspecific variation of O. mascula scent already reported in literature [START_REF] Nilsson | Anthecology of Orchis mascula (Orchidaceae)[END_REF]Salzmann et al., 2007;Jacquemyn et al 2009b;[START_REF] Dormont | Rare white-flowered morphs increase the reproductive success of common purple morphs in a food-deceptive orchid[END_REF][START_REF] Dormont | Floral scent emitted by white and colored morphs in orchids[END_REF]. We showed that several factors may act simultaneously on the floral scent emissions. Interestingly, different compounds were observed to vary, according to the source of variation. However, variation was mostly recorded for compounds that were both dominant in the profile (>3%) and frequent in the samples (occurrence > 100 of the 162 samples).

Two main questions emerge from these results on O. mascula: (i) Are insects (pollinators, visitors, or herbivores) capable to detect such variation in floral scent, and does this variation have consequences on pollinator behaviour? And (ii) Why do the floral emissions vary? Many insects, and particularly pollinator species, are known to respond to minor changes in the odour profile of their host plant [START_REF] Wright | Intensity and the ratios of compounds in the scent of snapdragon flowers affect scent discrimination by honeybees (Apis mellifera)[END_REF][START_REF] Lawson | Bumblebees distinguish floral scent patterns, and can transfer these to corresponding visual patterns[END_REF]. Modifying the specific ratio of few compounds in a volatile blend usually results in altering the behaviour of insects [START_REF] Bruce | Insect host location: a volatile situation[END_REF][START_REF] Bruce | Perception of plant volatile blends by herbivorous insectsfinding the right mix[END_REF][START_REF] Tasin | Antennal and behavioral responses of grapevine moth Lobesia botrana females to volatiles from grapevine[END_REF][START_REF] Cha | Eavesdropping on plant volatiles by a specialist moth: significance of ratio and concentration[END_REF][START_REF] Salvagnin | Adjusting the scent ratio: using genetically modified Vitis vinifera plants to manipulate European grapevine moth behaviour[END_REF], so that small changes in the volatile profile may have drastic consequence for pollinator choice and plant pollination. In our case, since we did not examine the olfactory responses of pollinators to such changes in O. mascula volatiles, we cannot assume that variation in the scent profile have consequences on pollinator behaviour nor on plant reproductive success. However, most of the compounds that were shown to vary in our study (myrcene, 6-methyl-5-hepten-2-one, eucalyptol, ocimene, linalool, linalool oxide, limonene, 3-hexenyl acetate…) were detected by bumblebees during electroantennography assays, as well as in previous studies [START_REF] Suchet | Floral scent variation in two Antirrhinum majus subspecies influences the choice of naïve bumblebees[END_REF]. These compounds have also been reported to elicit EAG responses for other O. mascula pollinator species, such as for Cetonia aurata [START_REF] Steenhuisen | Effects of volatile compounds emitted by Protea species (Proteaceae) on antennal electrophysiological responses and attraction of Cetoniine beetles[END_REF] or Apis mellifera [START_REF] Henning | Honey bee (Hymenoptera: Apidae) behavioral response to primary alfalfa (Rosales: Fabaceae) floral volatiles[END_REF]. Further studies will thus have to test, using the main pollinator species of O. mascula, whether variation of floral scent such as those we observed during this study, actually influences pollinator behaviour.

Another question that remains unanswered is why does floral scent vary? Although several hypotheses have been proposed, current explanations for polymorphism in floral odours most frequently rely on the key role of insects through pollinator-mediated selection [START_REF] Delle-Vedove | Understanding intraspecific variation of floral scents in the light of evolutionary ecology[END_REF]. In our case, a change in the floral signal over flower phenology (flower age) and after pollination likely helps O. mascula to guide pollinators to receptive flowers and to avoid visitations to already pollinated flowers, respectively. Diel variation of floral emissions may synchronize the floral attractive scent with circadian rhythms of particular pollinators, such as it has been shown in several plant species [START_REF] Hoballah | The composition and timing of flower odour emission by wild Petunia axillaris coincide with the antennal perception and nocturnal activity of the pollinator Manduca sexta[END_REF][START_REF] Farré-Armengol | Floral volatile organic compounds: between attraction and deterrence of visitors under global change[END_REF]Prieto-Benitez et al., 2016;[START_REF] Chapurlat | Diel pattern of floral scent emission matches the relative importance of diurnal and nocturnal pollinators in populations of Gymnadenia conopsea[END_REF][START_REF] Fenske | Circadian clocks of both plants and pollinators influence flower seeking behavior of the pollinator hawkmoth Manduca sexta[END_REF][START_REF] Steen | Volatiles composition and timing of emissions in a moth-pollinated orchid in relation to hawkmoth (Lepidoptera: Sphingidae) activity[END_REF].

Regarding changes in O. mascula scent following artificial herbivory, it is well established that plants being attacked by herbivores start releasing particular volatile compounds, so-called "herbivore-induced plant volatiles" [START_REF] Dicke | The evolutionary context for herbivore-induced plant volatiles: beyond the 'cry for help[END_REF]. Floral scent has also been shown to vary under herbivore attacks [START_REF] Lucas-Barbosa | The effects of herbivore-induced plant volatiles on interactions between plants and flower-visiting insects[END_REF]. While such volatile emissions have often been considered to have a defensive function [START_REF] Turlings | Tritrophic interactions mediated by herbivore-induced plant volatiles: mechanisms, ecological relevance, and application potential[END_REF], e.g. by attracting predators and parasitoïds, the ecological significance and evolution of herbivore-induced plant volatiles are still under debate [START_REF] Dicke | The evolutionary context for herbivore-induced plant volatiles: beyond the 'cry for help[END_REF][START_REF] Turlings | Tritrophic interactions mediated by herbivore-induced plant volatiles: mechanisms, ecological relevance, and application potential[END_REF].

Floral emissions were also observed to vary among populations from different habitats (open grassland vs. pine wood site) in our study. Intraspecific variation in floral scent in relation to population membership or even geographic origin have often been associated with distinct pollinator guilds (Sun et al., 2013;[START_REF] Peter | A pollinator shift explains floral divergence in an orchid species complex in South Africa[END_REF], but the effect of other environmental factors or the occurrence of phenotypic plasticity have been also reported [START_REF] Majetic | The impact of biochemistry vs. population membership on floral scent profiles in colour polymorphic Hesperis matronalis[END_REF][START_REF] Majetic | Sources of floral scent variation: can environment define floral scent phenotype?[END_REF][START_REF] Schlumpberger | Geographic variation in floral scent of Echinopsis ancistrophora (Cactaceae); evidence for constraints on hawkmoth attraction[END_REF][START_REF] D'auria | Fragrance components of Platanthera bifolia subsp. osca and Platanthera chlorantha collected in several sites in Italy[END_REF].

Interestingly, inflorescence morphology in our study (comparison of four categories of inflorescence size, defined by the number of flowers) was not associated with a change in floral scent. However, it should be note that the total amount of scent emitted by large inflorescences (with more flowers) may be greater than smaller inflorescences: we did not examine this possible effect on pollinator attraction in our study. Variation of this floral trait, which is associated with a change in floral display, has been reported to have a drastic effect on plant fruit set in rewardless orchids [START_REF] Pellegrino | The effects of inflorescence size and flower position on female reproductive success in three deceptive orchids[END_REF][START_REF] Suetsugu | Pollination system and the effect of inflorescence size on fruit set in the deceptive orchid Cephalanthera falcata[END_REF]. It has been suggested that variation in inflorescence size may have more influence on pollinator attraction than changes in floral scent in some cases [START_REF] Kindlmann | Effect of floral display on reproductive success in terrestrial orchids[END_REF].

All these examples suggest the key role of insects in floral scent variation: pollinatormediated selection likely explain numerous situations of polymorphism in floral emissions. In O. mascula, the exact role of pollinators in floral scent variation remains unknown, because behavioural responses of insects to varying floral emissions was not examined in our study. It has also been proposed that variation in floral scent may also result from the influence of natural enemies, non-selective agents, or even environmental factors [START_REF] Majetic | Sources of floral scent variation: can environment define floral scent phenotype?[END_REF][START_REF] Raguso | Concerted changes in floral colour and scent, and the importance of spatio-temporal variation in floral volatiles[END_REF][START_REF] Delle-Vedove | Understanding intraspecific variation of floral scents in the light of evolutionary ecology[END_REF]. Such factors will have to be considered in further studies in O. mascula.

Our study showed that floral scent may vary, successively or simultaneously, under the influence of various factors. Different compounds are involved in floral scent variation, depending on the factor considered. To what extent pollinators may drive such variation, and how these changes in floral emissions affect plant reproductive success remain to be determined. For example, further olfactory tests that examine behavioural responses of different pollinator species to varying O. mascula volatiles, are strongly needed to further evaluate the role of pollinator-mediated selection in intraspecific variation of floral scent.

Fig. 1 :

 1 Fig. 1: Non-metric multi-dimensional scaling of the relative proportions of O. mascula floral volatiles under the influence of different factors of scent variation.

Fig. 2 :

 2 Fig. 2: Average EAG responses of female Bombus terrestris antennae (n=31 individuals) to synthetic compounds identified from Orchis mascula floral volatiles. Each synthetic compound was individually tested at different dilutions, the figure shows results with dilution 1% v/v in dichloromethane. Bars followed by the same letter were not significantly different (Wilcoxon test). n.s.: EAG response was not significantly different to the depolarization recorded with a control stimulus (dichloromethane extract).

Table 1 .

 1 Floral volatiles emitted by O. mascula inflorescences: list of volatile compounds, and 687 quantitative changes under the influence of 5 sources of variation. RT: retention time; O: 688 occurrence (n=162 samples); Mean: mean composition of floral volatiles, values are 689 expressed as a percentage relative to total volatile compounds (mean composition for 20 690 individuals sampled at mid-day, before pollination, without herbivore attack, in open 691 grassland site, with mean size inflorescences, and no leaf spots). SE: standard error. For each

	692
	693	factor, increased ( ) or decreased ( ) of the relative amount of the compounds that contribute
	694	the most to the dissimilarity observed between the groups are presented (one-way SIMPER,
	695	testing factor day). For the columns "Flower age" and "Circadian rhythm", the two arrows
	696	correspond to the two sampled periods (e.g. for Circadian rhythm, first arrow refers to 9.00 vs
	697	13.30, and second arrow to 13.30 vs 18.00).
	698
	699
			RT	O	Mean	SE	Factor of variation
						Flower	Circadian	Pollination Herbivory	Habitat
						age	rhythm	open vs wood
		Fatty acid derivatives
		3-hexen-2-one	4.42	34	1.78	0.40
		(Z)-3-hexenol	5.71	3	0.10	0.07
		6-methyl-heptanol-1	9.21	8	0.41	0.17
		decane	9.40	63	0.86	0.16
		3-hexenyl-acetate	9.60	58	3.88	0.62
		6-methyl-heptanol-2	10.41	12	0.54	0.21
		5-methyl-heptanol	10.71	4	0.20	0.11
		octatrienal	12.10	30	0.11	0.02
		6-methyl-heptanol-3	12.90	23	0.39	0.12
		methyl-octanoate	13.10	4	0.08	0.05
		hexenyl-butyrate	15.09	9	0.08	0.03
		nonan-2-one	15.90	5	0.03	0.02
		undecanal	18.37	35	0.25	0.05
		methyl-caprate	18.70	4	0.85	0.43
		tetradecanal	21.07	4	0.02	0.01
		butyl-octanol	23.30	31	0.16	0.03
		methyl-laurate	23.80	4	0.15	0.08
		butyl-caprate	26.50	33	0.41	0.09
		nonadecane	30.12	4	0.01	0.01
		docosane	33.19	5	0.05	0.02
		isopropyl-palmitate	33.90	10	0.08	0.05
		Benzenoids styrene	6.50	13	0.14	0.05
		acetophenone	11.49	13	0.47	0.17
		ethylbenzaldehyde	14.31	4	0.02	0.01
		cinnamaldehyde	14.96	6	0.02	0.01
		toluene-3-5-dimethoxy	17.23	3	0.06	0.04
		ethyl-2-benzofurane	17.70	4	0.02	0.01
		Terpenoids α-thujene	7.38	23	0.11	0.04
		α-pinene	7.60	159	6.57	0.46
		camphene	8.03	8	0.18	0.07
		sabinene	8.67	147	3.42	0.22
		β-pinene	8.81	132	1.15	0.08
		6-methyl-5-hepten-2-one	9.02	120	5.66	0.56
		myrcene	9.15	106	6.26	0.70
		3-thujene	9.62	2	0.01	0.01
		α-terpinene	9.90	10	0.02	0.01
		p-cymene	10.20	58	0.31	0.05
		limonene	10.33	159	25.5	1.72
		phellandrene	10.34	4	0.19	0.10
		eucalyptol	10.40	111	4.18	0.52

Table 2 .

 2 Results of the statistical test performed on volatile compound proportions emitted byOrchis mascula. The significance of each factor was tested independently using a permutational analysis of variance (PERMANOVA), and the homogeneity of variance groups was tested using a multivariate Levene's test. Before analysis, data were transformed using squared root and Wisconsin double standardization, followed by a matrix of pairwise comparisons using Bray-Curtis dissimilarity indices.

	Factor of		PERMANOVA		homogeneity
	variation					variance
		DF	(F)	r2	p	(F)	p
	Flower age	2; 47	4.696	0.167	0.001	5.472	0.007
	Circadian	2; 52	2.393	0.084	0.001	3.118	0.053
	Herbivory	1; 28	3.006	0.097	0.001	0.698	0.411
	Pollination	1; 28	3.806	0.120	0.001	0.958	0.336
	Habitat	1; 38	3.386	0.082	0.001	0.008	0.931
	Inflorescence	3;65	1.111	0.049	0.270	1.943	0.132
	Spots leaves	1; 68	1.074	0.016	0.359	0.329	0.568

Acknowledgments

We thank Simon Chollet, Catherine Soler, and Jean-Dominique Lebreton for help in experimental design, and Camille Guillem for comments on the manuscript. We also thank the GDR MediatEC N°3658 CNRS and the GDR Pollineco n°2058 CNRS-MTES for useful discussion.

Author contribution

LD, BS, and TF designed the study. LD, BS, TF, and EGH conducted the experiments in the field. TF, BB, and EGH performed EAG experiments. BB and JMB analysed gas chromatography/mass spectrometry data. MP performed statistical analyses. LD, BS, MP drafted the manuscript. All authors contributed with revisions and approved the final manuscript.