L Bonifacio

M Sbordone

M Haywood

P Di Spite

F Matteo

Alessio Spite

P Mucciarelli

François

E Caffau

L Monaco

P Bonifacio

L Sbordone

M Spite

P Di

F Spite

A Mucciarelli

P François

A M Matas Pinto

High-speed stars: Galactic hitchhikers ⋆

Keywords: abundances -Galaxy, abundances -Galaxy, evolution -Galaxy, formation -Galaxy, kinematics and dynamics -Galaxy, halo

HAL is

Introduction

The results of the Gaia satellite (Gaia Collaboration et al. 2016) and especially those of its second data release (DR2; Gaia Collaboration et al. 2018;Arenou et al. 2018) have revolutionised our understanding of the build-up of the Galaxy. In particular it now appears that the halo stars 1 in the Gaia catalogue are dominated by stars on disc orbits and stars with low rotational angular momentum that are interpreted as the result of a major merger with a massive satellite (Belokurov et al. 2018;Haywood et al. 2018;Helmi et al. 2018;Di Matteo et al. 2019). We refer to this satellite as Gaia-Sausage-Enceladus as done by Di Matteo et al. (2020), and its mass may have been in excess of 10 10 solar masses (Belokurov et al. 2018;Mackereth & Bovy 2020). The stars on disc orbits may be prograde or retrograde, and their excursion about the Galactic plane may be as large as a few kpc (thick disc) or as small as a few hundred pc (thin disc). Quite unexpectedly, the most metal-poor star known, SDSS J1029+1729, is on a thick-disc orbit [START_REF] Caffau | Turn-Off Primordial Stars" invited talk at "The Metal-poor Galaxy[END_REF]Sestito et al. 2019;Di Matteo et al. 2020). Another extremely metal-poor star 2MASS J18082002-5104378 (Meléndez et al. 2016) with an iron abundance [Fe/H]=-3.84 (Spite et al. 2019) is on a thin-disc orbit (Schlaufman et al. 2018;Sestito et al. 2019;Di Matteo et al. 2020). More generally it now appears that when a sample of stars is chemically selected as metal poor there is always a significant fraction that is on disc orbits (see e.g. Venn et al. 2020). At the same time there is no evidence for the presence of a classical halo formed in situ as envisaged in the seminal Eggen et al. (1962) paper (Haywood et al. 2018;Di Matteo et al. 2019). The scenario in which the halo is formed from a combination of in situ early star formation and the accretion of stars and gas from tidally destroyed substructures, the latter dominating at large galactocentric radii (Tissera et al. 2013), needs to be deeply revised (see also Gallart et al. 2019). The number of significant fusions experienced by the Milky Way in the few Gyr of its existence is still debated (Myeong et al. 2019;Kruijssen et al. 2019;Reina-Campos et al. 2020).

A&A proofs: manuscript no. aa2020_38057 velocity catalogues (Roman 1955;Giclas et al. 1971;Lee 1984) in order to find metal-poor halo stars. One of the first two metalpoor stars detected (HD 19445;Chamberlain & Aller 1951) was in fact in the Roman (1955) catalogue of high-velocity stars.

In recent years a number of surveys explicitly targeted at the discovery of low-metallicity stars have been employed to search in large spectroscopic or photometric surveys for these very rare primordial stars (usually, albeit misleadingly perhaps, identified with extremely iron-poor stars). Among other, our group developed (Ludwig et al. 2008) and successfully employed a selection tool able to detect candidate extremely metal-poor and extremely iron-poor dwarf stars in the low-resolution Sloan Digital Sky Survey (SDSS; York et al. 2000) spectra for subsequent follow-up (see e.g. Caffau et al. 2013). Among other results, this allowed the discovery of one of the only two non-CN enhanced ultra-metal-poor stars, SDSS J102915+172927 ([Fe/H]=-4.99; Caffau et al. 2011), which still holds the record for the object with the lowest total metallicity (given that most extremely Fepoor stars also show extreme C, N, O enhancements).

The quality and richness of the Gaia DR2 catalogue allows us to revive the classical strategy of kinematical selection, although our objectives are more ambitious than those of observers in the past. We do not simply want to find metal-poor stars, but we want to understand how the population(s) with extreme kinetic energy fit in the formation of the Milky Way. For this reason we want to assemble a chemical and kinematical census of these extreme objects that is as complete as possible. In contrast to most deep, targeted surveys, Gaia covers the full sky. The spectrographic capabilities in DR2 are not able to identify very or extremely metal-poor objects, but thanks to the accurate distances and proper motions of nearby stars delivered by Gaia it allows the transverse velocity of a large sample of relatively bright targets to be derived.

In the Gaia DR2 catalogue, we selected stars with large transverse velocity, expecting them to be old stars on extreme, highly elliptic bound orbits with apogalacticon well into the sparsely studied outer halo. Such objects might either form in situ or be accreted from tidally dissolved substructures (dwarf galaxies, stellar clusters). Outer-halo accreted stars, in particular, have a high likelihood of coming from low-mass or ultra-faint dwarf galaxies (Tissera et al. 2013), so should generally be metal poor, and have a relatively high fraction of extremely metal-poor objects, crucial to understanding the early phases of the chemical evolution of the Universe. While this method is less likely to discover ultra-metal-poor objects with respect to targeted surveys, it has a few major advantages:

-It selects very bright objects, whose observations is much less time-consuming, while yielding spectra of much higher quality. This allows us to design observations for these objects as a bad weather or bright time filler programme.

-Instead of selecting a priori on low metallicity (that can be, but is not necessarily, a marker of a very early origin), it targets stars that are likely kinematically associated with the ancient, poorly studied outer-halo population, thus biasing the sample in a very different way.

-It efficiently exploits the less favourable observing conditions at the Very Large Telescope (VLT, i.e. thick clouds, bright time, bad seeing) to mine the incredibly large reservoir of unstudied bright cool stars; the whole sample, except for three stars, consists of stars with no reference whatsoever associated with them in SIMBAD (Wenger et al. 2000). The blue solid line is a PARSEC isochrone (Bressan et al. 2012;Marigo et al. 2017) of metallicity -2.0 and age of 12Gyr, to guide the eye.

Selection

From the Gaia DR2 catalogue, we selected stars with transverse velocity higher than 500 km/s, in the G magnitude range 14-14.5. Further constraints were put on right ascension to ensure observability in European Southern Observatory (ESO) period 104 (0h ≤ α ≤ 16h or α ≥ 20) and on declination (δ ≤ -25) to privilege a south pointing. The latter constraint was set in order to ensure that the VLT could observe our targets even in the event of fairly strong northern wind. In this way we were able to ensure observations of stars that were not too far away, and as a consequence with relatively small uncertainties on parallaxes and proper motions, and of bright objects for an 8 m class telescope, allowing good quality observations even in bad weather conditions. This selection provided us with 1 824 possible targets. Finally we selected for observation a set of 'blue' stars in the hope that we would be selecting the most metal-poor population. The Gaia colour-magnitude of all the possible targets is shown in Fig. 1. In the figure the observed stars are highlighted and a metal-poor old isochrone is shown for reference. All 72 stars shown in Fig. 1 were observed during ESO period 104.

From Fig. 1 it is clear that with our selection we favoured (i) asymptotic giant branch (AGB) stars over red giant branch (RGB) stars, (ii) metal-poor stars over solar metallicity stars, and (iii) hotter stars belonging to younger populations. There is also another bias in favour of young stars. Because of our limit on the apparent magnitude of the observed stars, the more luminous the observed stars are, the larger the explored volume is (the greater the distance from the Sun, the higher the luminosity of a star of given apparent magnitude). This is known as the Malmquist bias (Malmquist 1925): the absolute magnitude of a population is biased towards higher brightness objects, when the population is drawn from a sample, by applying an apparent magnitude limited selection.

In the kinematical and chemical investigations, we assumed that all stars are single. Spectroscopically and photometrically all stars in the sample, except perhaps one (GHS36; see Sect. 4.3), are compatible with the contribution of a single component. However, a binarity of some of the stars analysed here could affect the astrometric parameters provided by Gaia as an overestimation on the uncertainties (see e.g. Kervella et al. 2019;Penoyre et al. 2020) and as a consequence could affect the kinematic results.

The stars we observed are not known in the SIMBAD astronomical database, except for three objects, which are discussed in the Appendix.

-GHS29: Gaia DR2 5188812082642658944, also known as CSTAR 9171, a variable star of RR Lyr type. -GHS46: Gaia DR2 4629181692264635520, also known as V* Z Men, a variable star of RR Lyr type. -GHS63: Gaia DR2 2321153334969276160, also known as CTLGM 5192.

Observations

The stars were observed with FORS2 ( ). In the data reduction, done with the ESO pipeline, the sky was subtracted with the option 'optimal subtraction' to remove the sky contribution from the spectrum. The stars are bright enough to allow a good signal-tonoise ratio (S/N at 520 nm of the order of 90 for all stars, with the lower value of about 70 for GHS59 and few of the subgiant stars with about 100). The spectra of two of the most metal-poor stars in the sample are shown in Fig. 2.

Analysis

Radial velocity and kinematics

For each star we computed an ATLAS 9 (Kurucz 2005) model with the stellar parameters we derived. We computed a synthetic spectrum with SYNTHE (see Kurucz 2005;Sbordone et al. 2004) and the radial velocities were derived by templatematching (see e.g. [START_REF] Caffau | [END_REF]. Using the star's coordinates, radial velocities, Gaia Dr2 distances, and proper motions we characterised the stellar orbital parameters. For this purpose, we used the GalPot code 2 (Dehnen & Binney 1998) together with the best fit model from McMillan (2017). We derived thus the star's coordinates and velocity components in the galactocentric cylindrical (R, z, φ, v R , v Z , v φ ) and Cartesian systems (X, Y, Z, v X , v Y , v Z ). We further characterised the orbit deriving the minimum and maximum cylindrical (R min , R max ) and spherical (r min , r max ) radii, the maximum height above the galactic plane (Z max ), the total energy (E), the z-component of the angular momentum (L Z ), and the eccentricity (calculated as e=(r maxr min )/(r max +r min )). We used the default settings of GalPot, where the solar peculiar motion is the value measured by The major contribution on the speed of these stars is due to the transversal velocity; the radial velocity plays a minor role in the total speed of the stars. In this way, also with the large uncertainties on the radial velocity determinations due to the flections of FORS2 combined with the lack of telluric lines in the spectral range, we are confident with regard to the nature of the stellar kinematic. To provide more solid results, we computed the orbital parameters again by using V rad ± σ and the general behaviour does not change.

Five stars in the sample (GHS22, GHS33, GHS37, GHS58, and GHS64) return positive total energies E, and are thus unbound in the McMillan (2017) potential (total virial mass 1.3×10 12 M ⊙ ), while all the other stars belong to the Galactic halo, with the majority on retrograde orbits (44/72) and only 32% (23/72) on prograde orbits. Integration backwards of the orbits over 13 Gyr using the GalPy3 code (Bovy 2015) with the MWPotential2014 potential multiplied by 1.63 in order to match the same mass of the McMillan (2017) best fit model, supports the notion that these stars are in fact unbound to the Galaxy. Masses between 1.5×10 12 M ⊙ (for star GHS33) and 2.0×10 12 M ⊙ (for star GHS58) would be required to bound the stars although with apocentres in excess of 500 kpc. Of these five stars, only GHS64 has a direction of motion which is broadly compatible with coming from the Andromeda region. On the other hand, none is compatible with an origin in the Magellanic Clouds.

In Fig. 3 and Fig. 4 the orbital characteristics are presented. In the two figures the stars shown are (i) prograde stars as red triangles, (ii) retrograde stars as open black symbols, and (iii) the five unbound stars, when present, as filled black squares. The majority of the stars have very eccentric orbits, with 57 stars having an eccentricity higher than 0.9. Of the five unbound stars, three are prograde and two retrograde. The maximum (in plane) distance from the Galactic centre, Rmax is very large for some stars, larger than the Galactic radius. The Toomre diagram (upper right panel in Fig. 4) is comparable to the same diagram in Di Matteo et al. (2020, see their Figure 3, upper left panel). The fraction of prograde and retrograde stars was different in Di Matteo et al. (2020), being 14/42 retrograde and 28/42 prograde. The stars in Di Matteo et al. (2020), selected only for their metallicity, were observed during the ESO large programme 165.N-0276 (Cayrel et al. 2004) and analysed in several papers (see e.g. Spite et al. 2005;François et al. 2007;Bonifacio et al. 2009).

Discussion on the parallax zero point

The Gaia DR2 astrometric solution is described at length in Lindegren et al. ( 2018) and the issue of a zero-point in the Gaia parallaxes is discussed in detail. An instrument like Gaia is designed to give absolute parallaxes; however, it is still prone to systematic errors, in particular to the precise value of the basic angle, which is the angle between the two primary mirrors. For Gaia this angle has been discovered to vary in time, thanks to an on-board metrology interferometer (the Basic Angle Monitor, BAM). The variations of basic angle as measured by the BAM are taken into account in the Gaia global calibration. From the parallax distribution of 556 869 sources classified as active galactic nuclei, therefore at distances where their true parallax should be zero (2018) warn that 'the actual offset applicable for a given combination of magnitude, colour, and position may be different by several tens of µas'. In our case the magnitudes of our objects are several magnitudes brighter than the bulk of the sources used to determine the global zero point and the colours of our stars are redder. To look for a more relevant comparison sample, we consider the results of Arenou et al. (2018), who, compared the Gaia parallaxes for star members of 200 open clusters of known distance and derived the offsets, ∆̟ = -0.067 ± 0.12 mas and ∆̟ = -0.064 ± 0.17 mas, for two different catalogues of open clusters. It should be kept in mind that the error associated with these two estimates of the zero point is not statistical in nature, but systematic, deriving from two very different systematics that affect the Gaia parallaxes and the determiniation of the open cluster distances. Therefore, the error on the zero point cannot be divided by the square root of the number of clusters.

Considering the uncertainty on the Gaia parallax zero point and that the stars lie in different directions in the sky, we considered that it is a more conservative choice not to apply any zero point. Nevertheless, we investigated the effect of applying a zero point correction to our sample of stars. For this test we choose -0.065 mas, which is an average of the two values found by Arenou et al. (2018) for open clusters. When this zero-point is applied to the Gaia DR2 data, all the distances of our stars become smaller with respect to the value shown in the figures. The Article number, page 4 of 16 E. Caffau et al.: High-speed stars: Galactic hitchhikers effect is first noticeable in the colour-magnitude diagram. The luminosity of all the giant stars becomes smaller by up to two magnitudes. For the majority of the stars their position in the colourmagnitude diagram is no longer consistent with any isochrone of a metallicity consistent with the spectroscopic metallicity.

The second noticeable effect is on the kinematics. Because the stars are placed at smaller distances their transverse velocities become smaller thus all 72 stars would now be bound. The conclusions derived would be approximately the same even with this assumption. The stars remain on high-eccentricity halo orbits with a significant fraction of retrograde stars.

Stellar parameters

To derive the stellar parameters, we used the Gaia DR2 catalogue. With the parallax provided by Gaia DR2, we first derived the absolute G magnitude, and then by comparing the BP-RP colour and the G absolute magnitude to PARSEC 4 isochrones (Bressan et al. 2012;Marigo et al. 2017) of a certain metallicity, we derived the stellar parameters. The Gaia photometry was dereddened by using the maps by Schlegel et al. (1998). With the derived stellar parameters, we ran our pipeline MyG-IsFOS (Sbordone et al. 2014) to derive the chemical composition of each star. When the derived Fe abundance was not within about 0.25 dex of the metallicity of the isochrone used to derive the stellar parameters, new parameters were obtained by using isochrones of closer metallicity, and provided as input to MyG-IsFOS.

Two of the three stars already investigated in the literature, GHS29 and GHS46, are classified as RR Lyr in the Gaia DR 2 catalogue. We compared Hβ, which is available in the wavelength range observed, to synthetic spectra computed with the stellar parameters derived from the colours. The agreement is good. We made some further checks on these two stars:

-GHS29 The effective temperature (T eff =6100 K) we derived is more than 100 K hotter than 5885.0 K provided in the Gaia catalogue. The metallicity of -1.22 ± 0.22 provided in the Gaia DR2 RR Lyr catalogue is in good agreement with the value [Fe/H]=-1.38 ± 0.30 we derived. To verify the reliability of our analysis, we derived the stellar parameters by using the Gaia epoch photometry in correspondence with the phase during the FORS observation, and derived T eff =6113 K and log g=2.74, very close to the value derived from the average photometry provided by Gaia DR2 (6100 K/2.75) and used in this analysis. -GHS46 The effective temperature we derived (T eff =5976 K) is in perfect agreement with 5950.997 K provided in the Gaia catalogue. The metallicities we derived for this star ([Fe/H]=-2.54 ± 0.27) is in good agreement with the metallicity of -2.22 ± 0.22 provided by the Gaia DR2 RR Lyr catalogue. When using the Gaia epoch photometry corresponding to the phase of the observation to derive the stellar parameters, we find T eff =5931 K and log g=2.47, very close to the value adopted here and derived from the Gaia DR2 photometry (5976 K/2.50).

On the other hand, we suspected that the star GHS36 is also a RR Lyr: the observed Gaia photometry put it far from the isochrone of the metallicity derived from the analysis. This star from the photometry should be young and more metal poor. When comparing the Hβ wings to a synthetic spectrum, the star appears 4 http://stev.oapd.inaf.it/cgi-bin/cmd hotter than derived. We do not provide detailed abundances for this star, but only a probable metallicity.

In Fig. 5 the parameters we derived for all the stars in the sample are compared to isochrones of [M/H]=-1.5 for three ages, and in Fig. 6 the Gaia DR2 photometry of our sample of stars is compared to the isochrones. In Table 1 the stellar parameters are presented; as solar Fe abundance we applied A(Fe)=7.52 by Caffau et al. (2011, see also Table 2).

From Fig. 6 we can deduce that the stars appear younger that expected from their relatively low metallicity. This effect could be the result of an overcorrection of the reddening. We then compared the photometry not corrected for reddening to the isochrones. Of course the stars move towards older ages, but the problem of stars that are too young for the metallicity still persists. The two known RR Lyr in the sample, GHS29 and GHS46, have to be old stars to be in this phase of their evolution, but to derive their stellar parameters we had to accept a young age. RR Lyr are variable stars, so these stars cannot be used to discuss the reliability of the ages. On the other hand, the other stars in the sample can hardly be RR Lyr type because their parameters (except for few stars) are not compatible with this stage of stellar evolution.

A&A proofs: manuscript no. aa2020_38057 cases also from Fe ii features), Mg, Ca, Ti (both from Ti i and Ti ii features), and Ba for all or most of the stars. We also derived Ni and Sr abundances for a subsample of stars (6 and 22, respectively). The Fe abundance we derive for our sample is in the range -2.61 ≤ [Fe/H] ≤ -0.95. In Fig. 7 the comparison of the abundances derived by My-GIsFOS from neutral and single ionised lines of Ti and Fe is shown. The agreement is generally very good. The higher A(Ti) value derived from Ti ii lines with respect to that from Ti i lines is explained by NLTE effects (see Mashonkina et al. 2016).

In Fig. 8, the abundances of the two analysed α-elements, Mg and Ca, are shown. The reference solar abundances for this figure and the others are listed in Table 2. The sample shows a very homogeneous α-enhancement, even though that the analysis is based on low-resolution spectra. As the average [Mg/Fe] for the complete sample (71 stars) we derived [Mg/Fe] = +0.42±0.11 and for the 70 stars for which we have A(Ca) determination from Ca i lines [Ca/Fe] = +0.31 ± 0.10. To make this statement stronger, we compared our sample to the FORS-Pristine sample [START_REF] Caffau | [END_REF], which is a sample of similar metallicity stars, observed with the same spectrograph. These stars (open blue squares in the figure) generally show a larger scatter in [Mg/Fe] and [Ca/Fe], overall showing several stars with no evidence for the typical α-enhancement expected for the Galactic metal-poor stars. This situation is not seen in the sample of stars presented here.

For six stars we were able to derive the abundance of Ni from one single feature. In Fig. 9 these results are compared to literature values, and we see that this sample is completely compatible with stars of similar metallicity.

In Fig. 10 the abundances of the two heavy elements are shown. For the stars that also have a detection in Sr and Ba, they behave as normal Galactic metal-poor stars. In Fig. 11, [Sr/Fe] and [Ba/Fe] are plotted versus [Fe/H], and are compared to the sample by [START_REF] Caffau | [END_REF] and to literature values collected by Frebel (2010). In Fig. 11, the most metal-poor stars in our sample ([Fe/H] < -2) are compared to the stars with [Fe/H] < -2 in [START_REF] Caffau | [END_REF] and to literature values for extremely metal-poor stars.

One star, GHS48, shows a high [Ba/Fe] value and no Sr determination due to the poor reproduction of the fit on the Sr ii line, and the abundance determination was rejected by MyGIs-FOS because the probability was below threshold (for details, see Sbordone et al. 2014). The other star rich in Ba (GHS65, [Ba/Fe]=1.03) is a carbon enhanced metal-poor (CEMP) star ([C/Fe]=1.21), but its Sr is just slightly enhanced ([Sr/Fe]=0.43).

The G-band is generally strong for cool stars, so for the majority of the stars (55 stars) we were able to derive the C abundance. For the others, we provide an upper limit. Two stars (GHS65 and GHS69) are CEMP ([C/Fe] > 1). GHS65 is a CEMP star also rich in Ba ([Ba/Fe] = 1.03). It is worth repeating that several evolved stars in this sample are probably AGBs, so that part of their C has been converted into N.

To derive the C abundances or upper limits, by using SYN-THE and the model atmosphere with the parameters of the star, we computed a grid of synthetic spectra varying in A(C). With a χ 2 minimisation, we derive A(C). When A(C) is known, from the CN band at 380 nm, it is possible to derive the N abundance with the same technique. This CN band is much weaker than the G-band, and at this resolution is hardly detectable; however, for a subsample of stars (35 stars, generally the cooler and the most metal-rich in the sample) we were able to derive A(N). We have so many N detections because these stars are mostly cold, but also because some of our stars are AGBs and their N content is enhanced. In Fig. 12, the C abundance derived from the G-band is plotted as a function of the stellar [Fe/H] (upper panel of the figure). We compare our values to the results of the high-quality analysis by Spite et al. (2005). The comparison sample is definitely more metal poor, but we can see the very similar pattern. In the central panel of Fig. 12, [N/Fe] is shown, while in the lower panel [C+N/Fe]. The stars in our sample showing an enhanced A(N) are on average hotter than those in Spite et al. (2005), but this could be explained by looking at the Hertzsprung-Russell (HR) diagram in Fig. 5, several (probably the majority) of the evolved stars in our sample are AGBs; it is likely that on average they are more evolved than the stars in the sample of Spite et al. (2005), whose average temperature is lower, so that a fraction of them probably still belong to the red giant branch (RGB).

Discussion

Chemical composition

Due to the extreme kinematics requested in the selection, the stars presented here almost surely belong to the halo, or are unbound stars passing through the Galaxy. We would have expected a large distribution in metallicity (passing-by stars could be of any metallicity), with a preference towards low metallicity, due to the preference we gave to blue objects in the colour selection. Instead, we have a small range in metallicity. Taking into account the complete sample [Fe/H] = -1.51 ± 0.42; but if we restrict to [Fe/H] > -2, we obtain [Fe/H] = -1.37 ± 0.24, a scatter star-to-star that is comparable to the average line-to-line scatter in the [Fe/H] determination of each star. We have a clear peak at [Fe/H] ≈ -1.4, which is higher than the average value derived by Allende Prieto et al. (2014) in their investigation on metal-poor stars (see Fig. 13).

We expected that some of these high-speed stars were objects captured from dwarf galaxies or even stars belonging to disrupted dwarf galaxies, but here we expected, at least for a few cases, stars with low α-enhancement because stars in this metallicity range belonging to some dwarf galaxies have this characteristic. One of the explanations for the low α-content in the dSph galaxies is that they are characterised by slow, bursting, or gasping star formation (e.g. Marconi et al. 1994;Font et al. 2006). On the contrary, most (if not all) of the stars in this sample appear α-enhanced by the amount expected for metal-poor Galactic stars. For the complete sample we find [Mg/Fe] = 0.42 ± 0.11 and [Ca/Fe] = 0.31 ± 0.10. The average line-toline scatter in A(Mg) is 0.14 dex and in A(Ca) 0.21 dex. Only one star in the sample, GHS66, has the ratios of both α-indicators over Fe below 0.2 ([Mg/Fe]=0.18 and [Ca/Fe]=0.17); GHS71 is low in Ca ([Ca/Fe]=0.07), but its Mg is normally enhanced ([Mg/Fe]=0.31); GHS65 has [Mg/Fe]=0.28 [Ca/Fe]=0.17.

Already Haywood et al. (2013) had, in the sample they discussed, a small number of slightly metal-poor α-enhanced stars that are young. It is true that the metallicity in their sample is higher ([Fe/H]∼ -0.5) than the metallicity of our young stars, but they could still belong to the same population. Chiappini et al. (2015) discussed a group of α-enhanced stars analysed by CoRoT. These stars with precise age determinations happen to be young and with metallicity from solar to slightly metal poor. According to the authors, these stars could originate near the Galactic bar. We are not sure if our sample of metal-poor young stars is connected to these stars, with good age determinations, studied in the literature.

In Fig. 14 the two α-elements analysed here, Mg and Ca, are compared to the analysis by Nissen & Schuster (2010). For Ca we can see no difference in the three populations analysed by Nissen & Schuster (2010, the low-α, accreted population; the high-α, halo population; the thick disc population), but for Mg there is a clear distinction, and from the upper panel of Fig. 14 it is clear that our stars are more consistent with the in situ population by Nissen & Schuster (2010).

In Fig. 15, (as a proxy for [α/Fe]) as a function of [Fe/H] our stellar sample is compared to literature values from a few local group galaxies. From the figure it is clear that the stars analysed here are compatible with Galactic stars (light grey in the figure), as it was clear from Fig. 8, and with stars belonging to Sgr (black open circles in the figure). If these highspeed stars were formed in dwarf galaxies, then these galaxies should have a star formation history similar to that of Sgr, starting the descent of the α-to-iron ratio at a metallicity higher than -1.3. This suggests fairly massive galaxies, at least like Sgr. Alternatively, we know that globular clusters have very homogeneous Ca-to-Fe ratios (Mg may vary because of the presence of a Mg-Al anti-correlation, initially found by Gratton et al. 2001 and confirmed by many subsequent studies; see Gratton et al. 2004 andBastian &Lardo 2018 for reviews on multiple populations in globular clusters), with Ca being enhanced, as in field Milky Way stars. Therefore, our high-speed stars could also have come from globular clusters. High-resolution spectroscopy could shed light on the origin of these stars. If, for instance, they were formed in globular clusters, we would expect to find a Na-O anti-correlation, and perhaps also a Mg-Al anti-correlation. At the very least we should be able to find a sizeable dispersion in Na abundances in any given metallicity bin.

It may be more difficult to find chemical signatures that point to an origin in a dwarf galaxy. For example, in Sgr a top-light initial mass function has been invoked to explain the observed abundances of neutron capture elements (especially for Eu/O) and of Cu (McWilliam et al. 2013). However, most of the signal comes from stars with [Fe/H] > -1.0; for the metal-poor population the abundance ratios are the same as those of Milky Way stars (Hansen et al. 2018). Roederer et al. (2018) have claimed that stars with enhanced neutron capture formed in the r-process (r-enhanced for short) have been formed in dwarf galaxies and accreted to the Milky Way; however, Di Matteo et al. ( 2020) refuted this claim on the grounds that r-enhanced stars are kinematically indistinguishable from r-normal stars. Skúladóttir et al. (2019) have shown that the growth of [Y/Mg] and [Ba/Mg] as a function of age are clearly different among different galaxies and among samples of Milky Way stars in different metallicity bins. In Fig. 16 our [Ba/Mg] results for bins in ages are compared to the curves for different galaxies by Skúladóttir et al. (2019). Clearly our values are in better agreement with the Milky Way results than with any of the other dwarf galaxies shown in the plot.

For [Ba/Fe] the comparison of our sample with the sample analysed by Nissen & Schuster (2011), shown in Fig. 17, also suggests that our sample is compatible with the Milky Way (Venn et al. 2004;Reddy et al. 2006); black open circles, Sgr dSph main body stars (Monaco et al. 2005;[START_REF] Sbordone | Galaxy Evolution Across the Hubble Time[END_REF]Sbordone et al. , 2015Sbordone et al. , 2020;;Hansen et al. 2018); open blue squares, dSph (Venn et al. 2004;Letarte 2007); green 'x' signs, Car dSph (Venn et al. 2004;Koch et al. 2008); orange open triangles, Scl dSph (Venn et al. 2004;Geisler et al. 2005) and thus not accreted. The comparison in Fig. 18 also shows an agreement with the Galactic sample more than a sample from dwarf spheroidal galaxies.

Overall, we expected to find a large chemical variation from star to star in the sample; instead, we find a homogeneous sample from this low-resolution analysis, with no chemical-peculiar star except two CEMP stars (GHS65 and GHS69). Could these stars, at least the one with [Fe/H]> -2, belong to a disrupted globular cluster? Or could they all come from a disrupted small galaxy, similar to Sgr?

Variability

The two known RR Lyr stars (GHS29, GHS46) lie in the instability strip of the fundamental mode according to Bono & Stellingwerf (1994). In Fig. 19 they are compared to the other star in the sample. Four more stars lie in the instability strip: GHS24, GHS36, GHS42, GHS56. GHS36 has a strange spectrum, with broad H-lines. For the other three we have no special remarks.

Ages

The subgiant stars (log g> 3) lie in an HR part of the diagram where, once the metallicity is known, the age of the star can be inferred by using an isochrone (see Fig. 6). We are absolutely aware of the uncertainty of this method, but it can give us a rough estimation of the age or at least tell us if the ages of these metalpoor stars are compatible with ages of the order of or larger than about 10 Gyr, as expected for metal-poor stars. On the contrary, all these stars are compatible with ages of 8 Gyr or younger. One problem could be the reddening that shift the stars to younger positions in the HR diagram. If no reddening is applied, some stars will surely take places in the colour-colour diagram close to an isochrone of a more appropriate older age, but not all. We conclude that the reddening can play a role in making some stars to appear younger, but not all. We could assume that these metalpoor apparently young stars are blue stragglers. Assuming that this is the case, by selecting high-speed stars that are bluer than Bono & Stellingwerf (1994).

the -2.0 metallicity isochrone, we end up with all the subgiant stars being blue stragglers. Is high speed a result of the formation of a blue straggler?

For the more evolved (log g< 3) stars things are more complicated. There are 19 stars for which we use isochrones of 10 or 12 Gyr to derive the parameters, but for the other 44 stars younger ages are needed to fit well the photometry of the stars on the isochrones. What is evident is that the isochrones of different ages are close enough to make it difficult to rule out that most of the stars are in fact about 10-12 Gyr old; even so, Fig. 6 convinces us that several of these stars are in fact younger than 12 Gyr. Without a doubt, some of the stars are in the HR diagram in places where no 10 or 12 Gyr old isochrone can be found. In addition, if our selection results in so many young subgiant stars that we could easily classify as blue stragglers, why is it not possible that some of these more evolved stars are the evolution of these blue stragglers? Or, even if the subgiant populations is in fact composed of young stars, why should it not also be the case for the giant stars?

The point is then to find an explanation that justifies the high speed and young ages in this sample of stars.

Kinematics

Some stars have a maximum distance from the centre of the Galaxy that is much larger than the estimated radius. Twentythree stars reach distances larger than 100 kpc from the centre of the Galaxy. In particular, three stars (GHS32, GHS56, and GHS57) reach more than 300 kpc from the centre of the Milky Way. Clearly, for these stars in particular, to be sure that they are really bound and in order to derive their orbits and orbital parameters, we should take into account all the dwarf local galaxies in the potential, but probably also the Magellanic clouds and perhaps even Andromeda.

Clearly the sample is small, but we still tried to see if we could identify streams. We first checked whether our stars are consistent with the streams in Malhan et al. (2018), but we could find none. In a few cases the coordinates were consistent with known streams, but our stars are much further away. We looked for a subsample of stars close in proper motion, and we found Article number, page 11 of 16 two of them. These stars are probably close in proper motion just by chance; in fact, we did not find in the Gaia DR2 catalogue other stars that could form a stream consistent with these stars.

Five stars in the sample have unbound orbits (GHS22, GHS33, GHS37, GHS58, GHS64) at 1σ significance, due mainly to the uncertainty on the parallax. When considering the formal error on the total speed, we find that for all five stars it still exceeds the Galactic escape speed (taken to be 528 +24 -25 kms -1 ; Deason et al. 2019) even after subtracting a 1σ error from it, but not if we subtract 2σ. The same holds true by taking into account the variation of the escape velocities with the distance from the Galactic centre (see Monari et al. 2018).

Possible origins of our sample high-speed stars

The selection of our sample, as described in Sect. 2, is heavily kinematically biased in colour and luminosity. In this respect we have to keep in mind that we are selecting a very rare population. Our colour-magnitude selection of the already rare high-speed sample selected only 4% of the stars. It is therefore legitimate to also consider the possibility that our stars trace several very rare events. If this were the case we would expect our stars to be very diverse in terms of chemical composition.

Cross-matching Gaia DR2 with LAMOST allowed us to identify 68 high-speed late-type stars for which the LAMOST pipeline provides atmospheric parameters and chemical abundances (Du et al. 2018a(Du et al. ,b, 2019)). None of these stars is also in our sample. Although [START_REF] Du | [END_REF] comment that 'Most of the high velocity star sare metal-poor and α-enhanced', inspection of figure 4 of Du et al. (2018a) and Fig. 2 of Du et al. (2018b) shows that there is a sizeable spread in α-to iron ratios, with several stars with low ratios and also a considerable spread in metallicity. This is not the case in our sample. The precision in abundances is probably the same for the LAMOST data and ours; we use a slightly higher resolving power, but the LAMOST spectra have a much larger spectral coverage. It should be noted that the selection of the two samples of stars is different: Du and collaborators were interested in detecting unbound stars; we were aiming at the metal-poor population of high-speed stars. Another difference is that we performed spectroscopic observations following our hybrid kinematical and colour-magnitude selection, while Du and collaborators just picked the stars that were independently targeted by LAMOST without any kinematical selection.

The availability of Gaia DR2 triggered many works searching for unbound stars, and many of these studies can be found in the works of Du and collaborators; we mention here only the work of Bromley et al. (2018). Star Gaia DR2 5846998984508676352 is in common between Bromley et al. (2018) and our sample of high-velocity stars, but we did not observe it because it was too cool and faint for our selection. Although they did not select preferentially brighter and bluer stars, as we did, it is nicely illustrated in their figure 7 that the unbound stars are the most luminous for any given colour. We interpret this as an effect of the Malmquist bias, as for our sample. This implies that the speed selections will always be biased towards brighter younger and metal-poor stars. However there is no obvious reason why the metallicity range should be limited.

The results of our analysis show that, on the one hand the metallicity range of our sample is very limited and that the abundance ratios are also very tightly clustered. While our preference for blue stars does explain the fact that all of the sample is metal poor, we expected a larger metallicity spread; in particular, we expected to observe at least one extremely metal-poor star with [Fe/H]< -3.0, as found by Du et al. (2018a). In the same spirit, we expected a large spread in α-to-iron ratios and barium-to-iron ratios, which is hardly the case.

Unless we admit that these stars were born with such extreme velocities we must assume that they were accelerated. Let us begin by examining the possible causes for acceleration:

1. Merging of an external galaxy with the Milky Way; 2. Interaction with black hole in the Galactic centre (Hills 1988);

3. Origin in an external galaxy (e.g. Andromeda, see Sherwin et al. 2008) and ejection through interaction with its central black hole;

4. Star in a binary system with a massive primary that ended its life as a SN and gave a 'kick' to the secondary;

5. Star in a hierarchical triple system, with a massive primary that ended its life as a supernova (SN) and gave a kick to the outer couple that remained bound;

6. Origin in a globular cluster and ejection through gravitational interaction in an intermediate mass black hole at the centre of the cluster (see e.g. Fragione & Gualandris 2019, and references therein).

Scenario 1 requires that the merging galaxy be rather massive (but still less than the MW) and with a star formation history that implies a constant α-to-iron ratio in the metallicity range probed by the present sample. However, in such a case, according to the simulations of Jean-Baptiste et al. ( 2017), in the phase-space domain explored we should find both MW stars and stars formed in the accreted galaxy, although the latter are favoured among high kinetic energy stars. Even if in the presently explored metallicity regime the two populations were indistinguishable in terms of αto-iron ratios, as we move to a more metal-rich regime we expect that the two populations should split, the accreted galaxy showing lower α-to-iron ratios. Figure 1 shows clearly that among these high-speed stars selected (black dots) the majority lie to the red of the metal-poor isochrone ([M/H]=-2.0), so they are indeed more metal-rich than the observed stars. It would therefore be extremely interesting to measure α-to-iron ratios in the more metal-rich high-speed stars to test this possible interpretation.

Scenarios 2, 3, 4, and 5 are all plausible, and not mutually exclusive. In particular, there are four targets whose minimum distance from the Galactic Centre is less than 0.5 kpc, making an origin in this environment (scenario 2) plausible. The star GHS64 is compatible with scenario 3 (see Sec. 5.4), yet there is no reason why a multiplicity of these rare events should end up with such a high chemical homogeneity, as observed. Scenario 5 in particular would also be able to explain the presence of evolved blue stragglers: after the kick due to the SN explosion, the remaining binary undergoes merging and becomes a blue straggler.

Scenario 6 is the most appealing. The weak point is the fact that no conclusive evidence yet exists for the presence of intermediate black holes at the centres of globular clusters, although there are strong theoretical reasons to suggest that this is the case [START_REF] Greene | ARA&A[END_REF]. There are two reasons that make this scenario appealing: in the first place, the metallicity distribution and α-to-iron ratios of our sample are consistent with their origin in Galactic globular clusters (GGC); in the second place, this would explain the presence of blue stragglers, which Article number, page 12 of 16 we know populate GGC. This idea can be put directly to test in two ways. We know that GGC are characterised by multiple stellar populations that give rise to several correlations, and anti-correlations among abundance ratios, the most prominent being the Na-O anti-correlation, as mentioned in Sect. 5.1. Unfortunately our data resolution is too low to provide reliable Na abundances; the only Na i lines detected are the D-doublet, and at our resolution it is difficult to disentangle the stellar absorption from the interstellar absorption and the atmospheric emission, even for the stars with the highest radial speeds. Thus, the first test would be to observe a sizeable fraction of these stars at high resolution and see if a Na-O anti-correlation becomes apparent, or at least a large spread in Na abundances. In absence of this we could easily rule out the origin in GGC.

The second test concerns the subgiant and turn-off stars that appear to be younger than 10 Gyr. If they are blue stragglers then they should show no measurable Li if they were formed from the coalescence of the components of a binary system, or Li should be depleted (A(Li)∼ 1) if the blue straggler was formed by collision (Glebbeek et al. 2010). If the stars are not blue stragglers, then their lithium abundance should lie on the Spite plateau at A(Li)∼ 2.2 (Spite & Spite 1982;[START_REF] Sbordone | [END_REF].

We can see that the metallicity of this sample of stars peaks at about -1.4 (see Fig. 13), in surprising agreement with the peak of the Gaia-Sausage-Enceladus sample (see Figure 19 in Di Matteo et al. 2019). This intriguing fact deserves further investigation.

Conclusions

We presented here the chemodynamical investigation of a sample of 72 stars, selected from the Gaia DR2 catalogue for their high transversal velocity and observed with FORS at the VLT. Our main conclusions, discussed in detail in the previous section, are summarised below:

-The chemical pattern of these stars is extremely homogeneous. Although the stars in the sample analysed here suffer from a strong selection bias, we believe that the relatively narrow range in metallicity, from metal poor to very metal poor, is too extreme to be explained by our selection bias.

Even if the analysis is based on low-resolution spectra, implying a larger uncertainty, the homogeneity in their abundances is comparable to the high-quality sample analysed by Spite et al. (2005).

-The subgiant stars in the sample all appear to be younger than expected at this metallicity range, being of 8 Gyr of age or younger. With this selection we could have picked up mainly (or only) blue stragglers.

-For the giant stars, a minority (30%, 19/63) are as old as expected, while others appear generally much younger than expected (70%, 44/63 with an age of 8 Gyr or younger, up to 1 Gyr). They could be evolved blue stragglers.

-Most or all of these stars show a high [α/Fe], compatible with the MW population or (in this metallicity regime) the population of massive satellites like Sgr.

-Some stars have extreme orbits. As expected, all the other stars show halo orbits. Few stars reach such large distances from the centre of the Galaxy to cast doubts on their boundness.

-We identified several acceleration mechanism (see Sec. 5.5) of which the one able to explain the chemical homogeneity is an origin of these stars in GGCs. We suggested observations to probe this scenario.

-We were able to identify two small subsamples of stars close in proper-motion, but we could not find other stars in the Gaia DR2 catalogue belonging to these hypothetical streams.

Fig. 1 .

 1 Fig. 1. Gaia colour-magnitude diagram of all stars selected for observation in ESO period 104. The filled red dots are the 72 stars observed.The blue solid line is a PARSEC isochrone(Bressan et al. 2012;Marigo et al. 2017) of metallicity -2.0 and age of 12Gyr, to guide the eye.

Fig. 2 .

 2 Fig.2. Spectra of the two most metal-poor stars. Both spectra have been normalised, and the spectrum of GHS05 has been shifted up by 1, for easy visualisation.

Fig. 3 .

 3 Fig. 3. Portion of the stars in the XY and RZ planes. The stars are classified as prograde stars (red triangles), retrograde stars (open black symbols), and unbound stars (filled black squares).

Fig. 4 .

 4 Fig. 4. Orbital parameters for the stars. Symbols are as in Fig. 3.

Fig. 5 .

 5 Fig. 5. Hertzsprung-Russell diagram comparing the observed stars with isochrones of metallicity [M/H]=-1.5, at three ages (1, 6, and 12 Gyr), the value closest to the metallicity derived for the largest fraction of the stars.

Fig. 6 .

 6 Fig. 6. Hertzsprung-Russell diagram of G Gaia vs. BP -RP, comparing the observed stars with isochrones of metallicity [M/H]=-1.5, for three ages (1, 6, and 12 Gyr). The colour-coding for the stars defines the metallicity of the isochrone used to derive the stellar parameters. The blue symbols correspond to the stars that used isochrones of [M/H]=-1.5, as in the plot.

Fig. 7 .

 7 Fig. 7. Comparison of the abundances derived from neutral and ionised elements. To provide an idea of the uncertainties, an average error bar is shown in the plot.

Fig. 8 .

 8 Fig. 8. Two α-elements Mg and Ca vs. [Fe/H] (filled red circles) in comparison with the Caffau et al. (2020) sample (blue squares) and the literature compilation from Frebel (2010, cyan symbols).

Fig. 9 .

 9 Fig. 9. [Ni/Fe] vs. [Fe/H] (red filled circles) in comparison with the literature compilation from Frebel (2010) (cyan dots).

Fig. 10 .

 10 Fig. 10. elements [Sr/Fe] and [Ba/Fe] vs. [Fe/H] (filled red circles) compared to the Caffau et al. (2020) sample (blue open squares) and the literature compilation from Frebel (2010) (cyan symbols).

Fig. 11 .

 11 Fig. 11. [Sr/Ba] vs. [Ba/Fe] for the stars in our sample with [Fe/H]< -2 (red filled circles) compared to the sample by Caffau et al. (2020, black open symbols) and to metal-poor stars from the literature (blue open symbols).

  Fig. 12. On a [Fe/H] scale, we show for our programme stars (red symbols), [C/Fe] (upper panel), [N/Fe] (central panel), and [C+N/Fe] (lower panel). The red star symbols surrounded by a circle highlight stars with log g> 3.0. The comparison sample shows the very high-quality analysis by Spite et al. (2005).

Fig. 13 .

 13 Fig. 13. Normalised [Fe/H] distribution (solid red) compared to the result from Allende Prieto et al. (2014) (solid blue).

Fig. 14 .

 14 Fig. 14. Abundances of α-elements vs. [Fe/H]: solid red symbols are the stars here analysed, compared with the data by Nissen & Schuster (2010) as open squares, green the low-α, black the high-α and blue thick-disc stars.

Fig. 15 .

 15 Fig. 15. [Ca/Fe] as a proxy for the α-enhancement vs. [Fe/H] for our sample of stars (red filled circles) compared to data of the Milky Way and the dwarf local group galaxies from the literature. Shown are open grey triangles, MW stars(Venn et al. 2004;Reddy et al. 2006); black open circles, Sgr dSph main body stars(Monaco et al. 2005;[START_REF] Sbordone | Galaxy Evolution Across the Hubble Time[END_REF]Sbordone et al. , 2015Sbordone et al. , 2020;;Hansen et al. 2018); open blue squares, dSph(Venn et al. 2004;Letarte 2007); green 'x' signs, Car dSph(Venn et al. 2004;Koch et al. 2008); orange open triangles, Scl dSph(Venn et al. 2004;Geisler et al. 2005). All remaining symbols are from Venn et al. (2004): open magenta diamonds, Sex dSph; brown triangles Leo dSph; yellow plus signs, Umi dSph; open cyan stars, Dra dSph.

  Fig. 17. [Ba/Fe] vs. [Fe/H]: solid red symbols are the stars analysed here, compared with the data by Nissen & Schuster (2011) as open squares, low-α in green, high-α in black, and thick-disc stars in blue.

Fig. 18 .

 18 Fig. 18. [Ba/Fe] vs. [Fe/H], comparison with the literature values. Symbols are as in Fig. 15.

Fig. 19 .

 19 Fig. 19. Hertzsprung-Russell diagram. The two known RR Lyr stars are highlighted by blue circles. The suspected RR Lyr star or binary, GHS36, by a green square. The two black curves are the blue edge and the red edge of the fundamental mode of pulsation of RR Lyr according toBono & Stellingwerf (1994).

  Appenzeller et al. 1998) at VLT-ESO, during the programme ID 104.D-0259, 1 h OB per star, translated in 2762 s of exposure time. The observations were done with GRISM 600B+22, 0 ′′ . 28 wide slit providing a resolving power of about 2800 in the spectral range 330-621 nm, with 1 × 1 binning of the CCD (0.075 nm/pixel). Of the 72 OBs, 66 are classified as A and 6 as B. The programme was executed in service mode, as a filler, so all the observations were done in extreme condition of Moon illumination and/or air mass. Of the sample of 72 stars, 23 stars were observed in dark time, 10 in grey time, and 39 in bright time. The distance from the Moon was sometimes small (up to 33 •

  =0.014 kpc, and v φ (R 0 )=233.1 km/s for the Sun. It should be noted that in the GalPot conventions the Sun rotates in the negative φ direction, and thus prograde motion corresponds to negative values of v φ and L Z .

	al. (2010, U ⊙ =11.1,V ⊙ =12.24,W ⊙ =7.25 km/s) and R 0 =8.21 kpc, Z 0
	Schönrich et
	2 https://github.com/PaulMcMillan-Astro/GalPot

Table 1 .

 1 Coordinates, atmospheric parameters, and iron abundance of the programme stars. The solar reference Fe value is fromCaffau et al. (2011).

	Star	RA	Dec	G T eff log g	ξ [Fe/H]
		J2000	J2000 mag	K [cgs] kms -1	dex
	GHS01	22:29:17.30 -59:38:10.7 14.05 5269 2.17	1.8 -2.05
	GHS02	22:32:24.51 -60:53:42.2 14.13 5080 1.87	2.0 -2.57
	GHS03	22:11:09.40 -43:41:49.3 14.32 5280 2.41	1.8 -1.11
	GHS04	21:23:03.71 -49:12:05.7 14.17 5278 2.26	1.8 -1.30
	GHS05	21:41:52.75 -63:48:58.4 14.08 5068 2.03	1.8 -2.61
	GHS06	21:36:17.54 -57:36:41.7 14.34 5270 2.41	1.8 -1.18
	GHS07	21:28:45.20 -43:17:19.4 14.12 5233 2.38	1.8 -1.10
	GHS08	21:53:38.77 -68:57:10.8 14.25 5093 2.00	2.0 -1.30
	GHS09	20:41:39.32 -32:06:56.9 14.24 5289 2.42	1.8 -1.15
	GHS10	20:52:51.11 -71:14:05.4 14.13 5533 2.39	1.8 -2.15
	GHS11	21:12:03.94 -47:45:56.3 14.17 5352 2.41	1.8 -1.11
	GHS12	20:43:20.18 -50:11:51.1 14.31 5392 2.30	1.8 -1.33
	GHS13	20:43:20.13 -54:43:22.3 14.33 5296 2.53	1.8 -1.86
	GHS14	20:51:30.43 -34:01:20.3 14.09 5440 2.45	1.8 -1.11
	GHS15	20:52:26.36 -62:49:28.6 14.04 5319 2.45	1.8 -1.03
	GHS16	20:45:41.05 -62:21:42.6 14.32 5412 2.30	1.8 -1.25
	GHS17	20:05:15.34 -36:12:55.2 14.19 5354 2.26	1.8 -1.37
	GHS18	20:24:05.00 -55:03:59.7 14.08 5313 2.44	1.8 -1.16
	GHS19	20:01:17.72 -60:23:17.9 14.13 5323 2.20	1.8 -1.17
	GHS20	20:13:02.23 -42:10:58.1 14.07 5280 2.16	1.8 -1.14
	GHS21	20:33:09.23 -59:21:36.1 14.01 5376 2.25	1.8 -1.32
	GHS22	20:34:31.49 -66:45:37.0 14.16 5480 2.34	1.8 -1.37
	GHS23	20:26:26.29 -44:15:47.1 14.21 5381 2.29	1.8 -1.36
	GHS24	20:38:54.10 -61:01:58.2 14.17 5841 2.56	1.8 -1.43
	GHS25	20:17:44.81 -67:27:20.8 14.26 5275 2.23	1.8 -1.66
	GHS26	20:11:51.62 -44:45:42.0 14.12 5493 2.42	1.8 -1.53
	GHS27	05:50:13.77 -66:39:45.6 14.11 5720 2.78	1.8 -1.47
	GHS28	06:18:12.42 -33:45:49.3 14.28 5332 2.30	1.8 -1.34
	GHS29 a 10:12:54.98 -87:38:22.9 14.22 6100 2.75	1.8 -1.38
	GHS30	10:58:56.08 -34:18:14.9 14.02 5421 2.29	1.8 -1.42
	GHS31	11:10:27.47 -43:18:52.7 14.29 5554 2.46	1.8 -1.37
	GHS32	11:45:46.72 -28:00:07.5 14.03 5469 2.39	1.8 -1.53
	GHS33	11:58:28.87 -49:03:19.4 14.26 5347 2.31	1.8 -1.57
	GHS34	13:12:41.92 -27:33:23.5 14.10 5378 2.34	1.8 -1.53
	GHS35	13:14:57.18 -27:00:42.5 14.03 5160 2.30	1.8 -1.12
	GHS36 a? 13:43:34.21 -49:53:43.9 14.17 5800 2.50	1.8 ∼ -1.5
	GHS37	15:21:41.46 -75:28:48.0 14.19 5346 2.25	1.8 -1.90
	GHS38	15:37:33.91 -70:03:36.2 14.26 5555 2.42	1.8 -1.65
	GHS39	23:53:02.76 -59:02:41.7 14.34 5297 2.17	1.8 -1.12
	GHS40	23:16:36.55 -74:40:33.6 14.18 5465 2.37	1.8 -1.49
	GHS41	23:04:39.33 -54:03:33.9 14.26 5338 2.30	1.8 -1.30
	GHS42	22:42:44.70 -83:50:33.7 14.20 5734 2.50	1.8 -2.59
	GHS43	23:06:03.81 -67:47:45.3 14.04 5175 2.31	1.8 -1.09
	GHS44	23:09:00.49 -61:49:08.8 14.06 5007 1.90	2.0 -2.55
	GHS45	23:04:20.66 -61:08:43.4 14.04 5110 1.95	2.0 -1.38
	GHS46 a 04:15:51.59 -74:43:37.3 14.31 5976 2.50	1.8 -2.54
	GHS47	04:50:42.29 -38:33:22.2 14.07 5205 2.08	1.8 -1.56
	GHS48	03:39:56.29 -40:59:12.5 14.34 5198 2.17	1.8 -1.67
	GHS49	02:46:11.28 -44:32:50.2 14.19 4960 1.74	2.0 -1.72
	GHS50	02:05:35.63 -67:07:43.7 14.07 5486 2.38	1.8 -1.45
	GHS51	01:36:26.10 -38:31:00.1 14.26 5580 2.50	1.8 -1.36
	GHS52	01:31:20.23 -39:31:43.1 14.16 5580 2.42	1.8 -1.26
	GHS53	01:50:10.23 -56:53:17.3 14.26 4908 1.72	2.0 -1.82
	GHS54	02:01:17.74 -25:51:54.9 14.08 5222 2.36	1.8 -1.04
	GHS55	01:16:36.75 -63:30:13.5 14.26 5233 2.36	1.8 -1.16
	GHS56	01:22:25.54 -72:18:17.4 14.12 5650 2.47	1.8 -1.53
	GHS57	00:48:49.83 -74:47:14.3 14.25 5551 2.34	1.8 -1.67
	GHS58	00:27:59.60 -52:19:49.6 14.31 5168 2.04	1.8 -2.12
	GHS59	00:11:03.59 -63:26:02.2 14.19 5025 2.15	1.8 -1.21
	GHS60	00:26:15.43 -62:37:45.6 14.16 5499 2.35	1.8 -1.11
	GHS61	00:19:19.52 -68:32:51.5 14.24 5260 2.24	1.8 -1.23
	GHS62	00:14:15.55 -48:42:56.4 14.15 5191 2.06	1.8 -0.95
	GHS63	00:11:12.94 -29:08:25.8 14.04 5507 2.42	1.8 -1.23
	GHS64	22:19:10.14 -45:09:24.6 14.49 5664 3.17	1.5 -1.34
	GHS65	21:37:15.25 -41:26:44.4 14.31 6452 3.72	1.5 -2.35
	GHS66	20:50:27.35 -70:04:30.9 14.18 6800 3.94	1.5 -1.02
	GHS67	20:24:19.57 -60:59:37.0 14.16 5992 3.65	1.5 -1.35
	GHS68	20:20:11.70 -76:00:57.5 14.09 6197 3.35	1.5 -1.88
	GHS69	06:50:08.83 -87:34:10.5 14.44 6665 3.81	1.5 -1.94
	GHS70	15:01:14.26 -77:33:17.5 14.29 6478 3.74	1.5 -1.59
	GHS71	00:55:34.23 -58:02:58.2 14.21 6178 3.42	1.5 -2.06
	GHS72	01:05:49.02 -42:14:22.2 14.03 5671 3.17	1.5 -1.49
	a RR Lyr stars

Table 2 .

 2 Solar abundances.

	Element A(X) Reference
	C N Mg Ca Ti Fe Ni Sr Ba	8.50 7.86 7.54 6.33 4.90 7.52 6.23 2.92 2.17	Caffau et al. (2011) Caffau et al. (2011) Lodders et al. (2009) Lodders et al. (2009) Lodders et al. (2009) Caffau et al. (2011) Lodders et al. (2009) Lodders et al. (2009) Lodders et al. (2009)
	4.4. Abundances
	With the code MyGIsFOS (Sbordone et al. 2014), we derived the chemical composition of the stars of our sample. MyGIsFOS compares the selected features to a pre-computed grid of syn-

thetic spectra, which we computed with the spectral-synthesis code SYNTHE (see

Kurucz 2005;Sbordone et al. 2004

) based on 1D model atmospheres we computed with ATLAS 12 (Kurucz 2005), and which makes a χ 2 minimisation. We could derive the abundances of Fe (by using Fe i features, and in few Article number, page 6 of 16 E.

Caffau et al.: High-speed stars: Galactic hitchhikers

  . All remaining symbols are from Venn et al. (2004): open magenta diamonds, Sex dSph; brown triangles Leo dSph; yellow plus signs, Umi dSph; open cyan stars, Dra dSph.Fig. 16. [Ba/Mg] vs. age for Sculptor, Fornax, and Milky Way stars with [Fe/H]<-0.3, from Skúladóttir et al. (2019), compared with our high-speed stars. The latter seem very consistent with MW stars, but not with Sculptor or Fornax.

Article number, page 2 of 16 E.Caffau et al.: High-speed stars: Galactic hitchhikers

https://github.com/jobovy/galpy Article number, page 3 of 16

Article number, page 9 of 16

Acknowledgements. We wish to thank the anonymous referee for the help provided in improving the paper. Based on observations collected at the European Southern Observatory under ESO programme 0104.D-0259. We gratefully acknowledge support from the French National Research Agency (ANR) funded project "Pristine" (ANR-18-CE31-0017). EC and PB are thankful to ESO -Santiago for hosting them during the analysis of this sample of stars. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/ gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France.

Appendix A: Gaia identifier

The stars we observed are not known in the SIMBAD astronomical database, except for three objects.

-GHS29 Gaia DR2 5188812082642658944, also known as CSTAR 9171, RRab type according to Gaia DR 2 (Gaia Collaboration et al. 2018), that provides a period of 0.591620 days and an epoch of 2456935.5943. Gaia, in 2014), without classification. Demers & Irwin (1991) provide the B and V magnitude (V=13.85 and B-V=-0.09) in their study of stars located in the southern sky and between the Magellanic Clouds. Lopez & Girard (1990) put this star in a sample of suspected variable stars and provide an improved position. Kukarkin et al. (1971) provide for this star a period of 0.6878 days and an RR type. This star is an object by Shapley (1953) in the article on the distribution of RR Lyr, and he provides a period of 0.688 days. [START_REF] Boyce | [END_REF] studied this star in his sample of variable stars; he derived a period of 0.68781 days and put the star in a cluster.

-GHS63 Gaia DR2 2321153334969276160 is also known as CTLGM 5192. For this star, which is in the area of the open cluster Blanco 1, Platais et al. (2011) provide position and proper motion, which are consistent within the uncertainties with the values provided by Gaia DR2. They give probability zero that the star belongs to the cluster.

A&A proofs: manuscript no. aa2020_38057


Anthony ; Olivier

Cédric ; Roy

Véronique Vassal
email: veronique.vassal@developpement-durable.gouv.fr

Luc Ector

Bart Van De Vijver

Vincent Roubeix

Samuel Pauvert

Stéphanie Fayolle

Pond turtle carapaces, an alternative natural substrate for the use of a diatom-based water quality index

Keywords: Epizoic diatoms, Emys orbicularis, epilithic diatoms, epiphytic diatoms, wetland, biomonitoring, BDI, water quality assessment Epizoic diatoms, Emys orbicularis, epilithic diatoms, epiphytic diatoms, wetland, biomonitoring, BDI, water quality assessment

Benthic diatoms are widely used as bio-indicators in water quality monitoring of rivers and streams, but less commonly of stagnant waterbodies such as pools and lakes. Currently, a new method for assessing the ecological status of lakes is being tested in France to meet the requirements of the European Water Framework Directive (WFD) 2000/60/EC. This new method uses epilithic and epiphytic diatoms growing on stones and aquatic plants. Alternatively, freshwater turtles and the epibiotic algae found on their carapaces may provide an excellent substrate for estimating water quality. Our study proposes an original diatombased method for assessing the quality of water bodies in which turtles live. The main objective was to compare the Biological Diatom Index (BDI) values measured from epizoic diatom samples taken on the carapace of turtles with classical epilithic or epiphytic samples. Epizoic samples were taken from the carapaces of the turtle Emys orbicularis in two shallow French Mediterranean wetlands. Our results indicate that diatom assemblages differ slightly within the three substrates studied with limited variations in index value. However, some variability among results from the same site suggests that several turtle individuals should be sampled.

Introduction

Primary producers (phytoplankton, phytobenthos and macrophytes) are widely used as ecological indicators in the European Water Framework Directive (WFD) 2000/60/EC (European Union 2000, [START_REF] Kelly | Data rich, information poor? Phytobenthos assessment and the Water Framework Directive[END_REF]). Diatoms (Bacillariophyceae), small unicellular algae characterized by the presence of an outer silica carapace (Round et al. 1990), are considered one of the most powerful bio-indicators due to their short generation times, their high diversity and their fast reaction to even very slight variations in the physicochemical characteristics of their aquatic environment (pH, salinity, organic matter, nutrients, metal pollution). Diatoms are commonly found on all kinds of submerged vegetal and inert mineral substrates such as the submerged parts of helophytes, aquatic plants, stones and sand grains [START_REF] Kelly | Recommendations for the routine sampling of diatoms for water quality assessments in Europe[END_REF]. The presence of a broad array of taxa indicating low water quality provides an excellent early warning signal of degraded water conditions for managers [START_REF] Kelly | Redundancy in the ecological assessment of lakes: Are phytoplankton, macrophytes and phytobenthos all necessary?[END_REF].

In France, the Biological Diatom Index (BDI) has been commonly used in the past 15 years for monitoring running waters (AFNOR 2007(AFNOR , 2016;;Coste et al. 2009). The index reflects the level of organic (saprobity) and trophic (nitrogen) pollution, as well as contamination by toxic substances such as mineral or synthetic micropollutants.

Currently, a diatom-based method for assessing the ecological status of European lakes is being tested in France to meet the European Water Framework Directive requirements (WFD, 2000/60/EC) for stagnant waterbodies. The sampling method uses both epilithic and epiphytic diatoms. Apart from living on these inert substrates, diatoms found epizoically on many aquatic animals (e.g. Wu Freshwater turtles such as the European pond turtle (Emys orbicularis Linnaeus, 1758) are an important component of freshwater ecosystems worldwide. This species has a vast longitudinal and latitudinal range of distribution, from Portugal to the Aral Sea and from North Africa to Russia, respectively [START_REF] Fritz | Die Europäische Sumpfschildkröte[END_REF]. Their large carapaces provide a perfect inert natural and challenging habitat for the growth of epizoic algae. In the past, a fairly large diversity of different algal groups has been described on the carapaces and skin of freshwater turtles (Edgren et al. 1953;[START_REF] Soylu | Epizoic freshwater algae on Emys orbicularis (Testudinata: Emydidae) from the Central Anatolia region of Turkey[END_REF][START_REF] Garbary | Epizoic algae from freshwater turtles in Nova Scotia[END_REF][START_REF] Wetzel | Luticola deniseae sp. nov. a new epizoic diatom from the Rio Negro (Amazon hydrographic basin)[END_REF][START_REF] Wetzel | Tursiocola podocnemicola sp. nov., a new epizoic freshwater diatom species from the Rio Negro in the Brazilian Amazon Basin[END_REF][START_REF] Wu | Diatoms on the carapace of common snapping turtles: Luticola spp. dominate despite spatial variation in assemblages[END_REF]. A study on the European pond turtle in the Camargue region (South France) identified 77 epizoic algal taxa on the carapaces, amongst which diatoms formed the most diverse group with 51 recorded taxa (Fayolle et al. 2016).

We hypothesize in the present study that epizoic diatoms living on turtle carapaces can be used as water quality indicators. In the present study we investigated in two different Mediterranean localities, (1) whether diatom assemblages on Emys orbicularis were habitat-specific or similar to those living on the surrounding mineral or vegetal substrates, and (2) the consequences on the BDI of the choice of an epizoic instead of an epilithic or epiphytic diatom sample. The final goal is to extend the possibilities of diatom sampling for the application of a diatom index, especially in sites where mineral or vegetal substrates are not available.

Materials and methods

The two sampling localities investigated in this study, are located in the south of France.

The first site (Charpines, 43°71'48'' N, 5°31'42'' E, altitude 147 m, date of sampling: 06/29/2016) is a wetland with a small surface area (0.22 ha), shallow and very silted (maximum water depth of 1 m and 0.10 m on accessible shores), located near the lower reaches of the Durance River. Following the collapse of the riverbed [START_REF] Renet | Distribution de la Cistude d'Europe Emys orbicularis (Linnaeus, 1758) en Basse-Durance : bilan après quatre années de prospection (2013-2016)[END_REF], this wetland is only connected to the Durance during major floods. The inflow comes from a water treatment plant resulting in an overall low water quality. The second site (Esquineau, 43°51'20'' N, 4°66'76'' E, altitude 1 m, sampling date: 06/08/2017) is a vast (16 ha) rather shallow (average water depth of 50 cm) wetland area located in the natural reserve of the Tour du Valat in the Camargue region and composed of semipermanent marshes and man-made managed irrigation canals [START_REF] Olivier | Assessing spatial and temporal population dynamics of cryptic species: an example with the European pond turtle[END_REF]). The wetland area has a moderate water quality.

Epilithic and epiphytic diatoms were sampled as recommended by the IRSTEA method (DCE Plan d'eau Phytobenthos Irstea REBX Version 1.2 February 2013). The epilithic and epiphytic samples were collected separately as pooled biofilm extracts from at least five pebbles which had been previously placed in both sites for 4 weeks, and five submerged helophyte stems (Phragmites australis (Cav.) Trin. ex Steud.), respectively.

The two sites host two distinct turtle populations that are yearly monitored using capture-recapture methods [START_REF] Ficheux | Rapid response of a long-lived species to improved water and grazing management: The case of the European pond turtle (Emys orbicularis) in the Camargue, France[END_REF][START_REF] Renet | Distribution de la Cistude d'Europe Emys orbicularis (Linnaeus, 1758) en Basse-Durance : bilan après quatre années de prospection (2013-2016)[END_REF]. Epizoic diatoms were collected at each site by brushing off an area of 20 cm² on the back of ten adult turtle carapaces using single-use toothbrushes. Half of the samples were pooled to form a single mixed epizoic sample. The resulting number of samples from each site was 13 (11 epizoic, one epilithic and one epiphytic). The samples were preserved in 90 % ethanol.

Diatom identifications were performed after having prepared permanent slides following BDI standard NF T90-354. The organic matter in the small fraction of raw samples was digested with hot (90°C., ±10h) concentrated solution of hydrogen peroxide (30% v/v) using a digestion block system (DigiPREP systems, SCP Science) and calcium salts were dissolved with concentrated hydrochloric acid (30% v/v).

Following digestion and decantation, cleaned material was rinsed and diluted with deionized water and mounted permanently on glass slides using Naphrax® resin (refractive index = 1.74). Light microscopy (LM) analysis was performed using a Nikon (Eclipse Ni) light microscope equipped with phase contrast plan apochromat ×100 oil optic. In each sample, at least 400 valves were identified to the lowest taxonomic level possible and counted along random transects. Most taxa were determined according to [START_REF] Lange-Bertalot | Navicula sensu stricto. 10 genera separated from Navicula sensu lato[END_REF], 2017), Krammer (2002[START_REF] Krammer | Cymbopleura, Delicata, Navicymbula, Gomphocymbellopsis, Afrocymbella. Diatoms of Europe[END_REF], [START_REF] Levkov | The diatom genus Gomphonema from the Republic of Macedonia[END_REF] and [START_REF] Lange-Bertalot | Freshwater benthic diatoms of Central Europe: over 800 common species used in ecological assessment. English edition with updated taxonomy and added species[END_REF]. The BDI was calculated from diatom counts using Omnidia software, version 6.0.8 (2018) [START_REF] Lecointe | Omnidia": software for taxonomy, calculation of diatom indices and inventories management[END_REF]. Data analysis was performed using R (R Core Team 2018). The comparison of epizoic, epiphytic and epilithic diatom communities was carried out on the basis of their specific composition using Ward's hierarchical classification method with Canberra distance. Only the relative abundances of species with at least three valves counted were considered. The IndVal analysis of indicator species in clusters was done using the labdsv R package [START_REF] Roberts | labdsv: Ordination and Multivariate Analysis for Ecology. R package version 2.0-1[END_REF]. BDI values between clusters were compared using the non-parametric Kruskal-Wallis test.

Results

A total of 81 diatoms taxa (including species, varieties and forms, with abundance ≥ 2 valves) were identified in 26 samples: 28 were recorded in Charpines and 53 in Esquineau (Supplementary material 1). At both sites, the cluster analysis did not show a strong differentiation between communities taken from conventional substrates and from turtle carapaces (Fig. 1). Indeed, the first node of each tree separated epizoic samples, which means that the type of substrate was not the most important factor determining diatom assemblages. The analysis also revealed considerable heterogeneity among epizoic communities. However, the pattern was not the same at the two sites. In Charpines, three main groups could be identified: the first one included only the epiphytic and epilithic samples (n=2). This first group was closer to most epizoic samples, forming Group 2 (n=7), than to a third marginal group of 4 epizoic samples (Fig 1a). In Esquineau, the communities were more homogeneous than in Charpines, but similarly, three groups could likewise be distinguished (Fig. 1b). In a first large cluster, the epilithic and epiphytic as well as most epizoic samples were grouped (n=9).

Two pairs of samples, forming groups 2 and 3, were separated from the main group by an increasing distance, corresponding to an increasingly different composition of communities.

The consequences of variations in communities between groups of substrates on the diatomic index BDI was greater in Charpines than in Esquineau. Indeed, group 3 of epizoic samples from Charpines had significantly higher BDI (p<0.05, Kruskall-Wallis test), between 3 and 4 points above the values of the other groups (Fig. 1a). These differences in BDI could lead to different assessments of water quality (poor or bad) depending on the samples considered for this site. The communities in Group 3 were characterized by Sellaphora pupula and Nitzschia linearis, both species showing relatively high specific BDI values (Table 1). In Esquineau, the differences in BDI between the three groups were smaller (between 1 and 2 points, p=0.05, Fig 1b). Groups 2 and 3 had lower values and were characterized by indicator species such as Tryblionella littoralis or Craticula cuspidata (group 2) and Fragilaria gracilis or Mastogloia smithii (group 3, Table 1). Nevertheless, considering only the mixed epizoic samples being representative of the carapace substrate, the absolute differences in BDI between epizoic and standard samples were similar in Charpines and Esquineau (≤1.5, Figure 1).

Discussion

The main result of this study was a higher degree of similarity in diatom communities and BDI values between samples from Emys orbicularis carapaces and from stones or macrophytes than we expected, given the specific characteristics of the animal substrate.

The chitin structure of the carapaces and the production of secretions and excreta by turtles (Edgren et al. 1953) could be selection factors for epizoic species. The movements of the turtles may also increase the risk of species abrasion, especially in case of friction between the carapace and the macrophyte vegetation in which the turtles live. It also generates variable abiotic conditions that can be stressful for microalgae.

Light intensity on the carapace may change considerably according to the position of the turtle in the water column. Moreover, European pond turtles often exit their aquatic environment for basking at sunlight. This out-of-water thermoregulation activity can last several hours in spring when the water and air temperatures are low. The resulting desiccation of the biofilm on the carapace during sun exposure may probably affect the composition of attached diatom communities. Recurrent emersion may be a strong pressure that epilithic or epiphytic biofilm may not commonly experience, unless there are large water level fluctuations. Also, the overall similarity of the communities on stones, macrophytes and turtles carapace suggests that the type of substrate is not a determinant of periphyton specific composition as strong as the water quality to operate. This 'mass effect' hypothesis [START_REF] Shmida | Biological determinants of species diversity[END_REF] could explain why there is no clear differentiation between epizoic and epiphytic or epilithic communities. However, some differences were observed between turtles especially in the Charpines site. They may be due to differences between individuals in size, age, sex or behaviour.

If the turtles can be considered, because of their movement, more representative of a water body than stones or macrophytes, they may use only a part of a lake and even expand their living area to another neighboring freshwater system [START_REF] Olivier | Ecologie, traits d'histoire de vie et conservation d'une population de cistude d'Europe Emys orbicularis en Camargue[END_REF] . The case of the Charpines site showed that there is a risk of considerable deviation of the BDI and misclassification if only one individual is considered. Thus, it should be recommended to sample biofilm on several individuals to reduce variance, analogously as for stones in the BDI protocol. In addition, it may be preferable to select individuals of the same species to avoid more variability due to differences in the ecology of chelonian species [START_REF] Soylu | Epizoic freshwater algae on Emys orbicularis (Testudinata: Emydidae) from the Central Anatolia region of Turkey[END_REF], such as Emys orbicularis and Mauremys leprosa in Europe. The BDI, initially developed for running waters, may be replaced in the next years by a new index more appropriate for lakes or wetlands. However, the conclusion of this study concerning the suitability of the turtle carapace substrate for diatom bioindication should be still valid when another specific index will be used.

Conclusion

The method applied in this study represents a potential tool for the assessment of wetlands where Emys orbicularis is monitored using CMR methods. It implies the application of a diatom index such as the BDI, on epizoic diatoms. The European pond turtle carapace offers an alternative substrate in wetlands without stone or macrophytes available for sampling, allowing managers to monitor in the same time a protected species and the ecological status of wetlands which are generally not included in the Table 1. Indicator species of the three clusters considered in each site with indication of their IndVal value and their BDI score (0 to 20).

Site

  2018), could likewise provide an excellent population for the application of water quality indices.

(

  Cellamare et al. 2012) or the date of sampling(Cetin 2008). It can also be assumed a high turnover of periphyton on turtle carapaces due to frequent attachment/detachment in relation to host motion and therefore little time for selection of most adapted species

  ecology and related subjects. Over the last 20 years, he has been organizing and teaching numerous training courses on diatom ecology and taxonomy designed for biologists, technicians and ecologists, contributing to the continuous improvement in the Water Framework Directive implementation in Europe. Contribution: Provision of literature, bibliographic research, help with identification of diatom species, discussion of results, revision and editing of the manuscript. Bart Van de Vijver is a full-time senior researcher at Meise Botanic Garden, Belgium, and a part-time professor in botany at the University of Antwerp, Belgium. His research focuses mainly on the taxonomy, morphology and biogeography of Antarctic freshwater and terrestrial diatoms. He has been studying non-marine diatoms in various parts of the Antarctic region for more than 20 years. He has described almost 400 new taxa and revised an additional 250 taxa. Currently his research interest also includes the diversity and ecology of epizoic diatoms living mainly on marine vertebrates. Contribution: Discussion of results, revision and editing of the manuscript. Vincent Roubeix is a R&D consultant in hydrobiology and ecology, specialist of diatoms. Contribution: Data analysis, discussion of results and manuscript co-writing and edition. Anthony Olivier is a nature reserve ranger and herpetologist at the Tour du Valat. He is interested in the ecology and conservation of amphibians and reptiles in the Camargue and the Mediterranean basin. In particular, he has been leading a long-term study on the Emys orbicularis population of the Tour du Valat since 1997. Contribution: Sample collection and help with the redaction of the manuscript.

  Plaine Saint-Denis Cedex: Association Française de Normalisation (AFNOR); p. 1-119. Cellamare, M, S Morin, M Coste, J Haury. 2012. Ecological assessment of French Atlantic lakes based on phytoplankton, phytobenthos and macrophytes. Environ Monit Assess. 184(8):4685-4708. doi:10.1007/s10661-011-2295-0 Cetin, AK. 2008. Epilithic, epipelic, and epiphytic diatoms in the Göksu Stream: Community relationships and habitat preferences. J Freshw Ecol. 23(1):143-149. doi:10.1080/02705060.2008.9664565 Coste, M, S Boutry, J Tison-Rosebery, F Delmas. 2009. Improvements of the Biological Diatom Index (BDI): Description and efficiency of the new version (BDI-2006). Ecol Indic. 9(4):621-650. doi:10.1016/j.ecolind.2008.06.003 Edgren, RA, MK Edgren, LH Tiffany. 1953. Some North American turtles and their epizoophytic algae. Ecology. 34(4):733-740. doi:10.2307/1931336 European Union (2000) Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for community action in the field of water policy. Off J Eur Commun. L327:1-72. Fayolle, S, C Moriconi, B Oursel, C Koenig, M Suet, S Ficheux, M Logez, A Olivier. 2016. Epizoic algae distribution on the carapace and plastron of the European pond turtle (Emys orbicularis, Linnaeus, 1758): A study from the Camargue, France. Cryptogam Algol. 37(4):221-232. doi:10.7872/crya/v37.iss4.2016.221 Ferrario, ME, AO Cefarelli, A Fazio, P Bordino, OE Romero. 2018. Bennettella ceticola (Nelson ex Bennett) Holmes on the skin of Franciscana dolphin (Pontoporia blainvillei) of the Argentinean Sea: an emendation of the generic description. Diatom Res. 33(4):485-497. doi:10.1080/0269249X.2019.1572651

  Nitzschia linearis var. tenuis (W.Smith) Grunow in Cleve & Nitzschia palea var. debilis (Kützing) Grunow in Cleve & ://mc.manuscriptcentral.com/tabg Email: abg-editorinchief@societebotaniquedefrance.://mc.manuscriptcentral.com/tabg Email: abg-editorinchief@societebotaniquedefrance.fr Botany Letters

Figure 1 .

 1 Cluster analysis and BDI values of the diatom samples collected at Charpines (a) and Esquineau (b) sites. Three clusters (numbered 1 to 3) have been considered in each site. BDI values were represented for each sample separately (middle barplots) and in each cluster (bottom boxplots). Eph= epiphytic, Eli=epilithic, Ez1-10= epizoic, EzM= mixed epizoic sample. Percentage abundance of the diatom species identified in the Charpines and Esquineau sites. EzM= mixed epizoic samples, Eph= epiphytic sample, Eli= epilithic sample.

	Botany Letters							Page 20 of 22
	Navicula simulata Manguin					0.5
	Diatom species Eolimna subminuscula (Manguin) Gerd Moser, Lange-Bertalot & Metzeltin Navicula trivialis Lange-Bertalot Navicula veneta Kützing Supplementary material 1. Diatom species Charpines cluster Ind-Val p-val 0.5 7.8 3.7 3.8 12.3 22.2 Naviculadicta cosmopolitana Lange-Bertalot 0.5 Esquineau Charpines 1 0.83 0.003 % (occurrence ≥2 valves) EzM Eph Eli EzM Eph Nitzschia Hassall 1.2 0.5 Nitzschia sp Hassall 1 0.80 0.025 Amphora copulata (Kützing) Schoeman & R.E.M.Archibald 2 Nitzschia amphibia Grunow	BDI score 8.7 Eli 4.3 nd 1
	Planothidium frequentissimum (Lange-Bertalot) Lange-Bertalot Achnanthidium minutissimum (Kützing) Czarnecki	2.7	1	1.2	0.92	0.8	0.025 1.2	11.7
	Cocconeis placentula Ehrenberg Diadesmis confervacea Kützing Amphora pediculus (Kützing) Grunow Luticola goeppertiana (Bleisch) D.G.Mann in Round et al. Anomoeoneis sphaerophora (Ehrenberg) Pfitzer	0.5	2 2 2		0.84 0.68 0.73		0.017 0.006 0.5 0.001	15.4 1 5 0.5
	Nitzschia amphibia Grunow Bacillaria paxillifera (O.F.Müller) T.Marsson		2		0,75	1.3	0.001	7.7
	Nitzschia palea (Kützing) W.Smith Pseudostaurosira trainori E.Morales Sellaphora seminulum (Grunow) D.G.Mann Lemnicola hungarica (Grunow) Round & Basson Nitzschia linearis (C.Agardh) W.Smith Sellaphora pupula (Kützing) Mereschkowski Tryblionella littoralis (Grunow) D.G.Mann in Round et al. Eunotia bilunaris (Ehrenberg) Schaarschmidt Navicula cryptotenella Lange-Bertalot Navicula lundii E.Reichardt Sellaphora seminulum (Grunow) D.G.Mann Amphora copulata (Kützing) Schoeman & R.E.M.Archibald Craticula cuspidata (Kützing) D.G.Mann in Round et al. Pinnularia sp. Ehrenberg Sellaphora pupula (Kützing) Mereschkowski Tryblionella littoralis (Grunow) D.G.Mann in Round et al. Diploneis Ehrenberg ex Cleve Fragilaria gracilis Østrup Mastogloia smithii Thwaites Navicula simulata Manguin Navicula veneta Kützing Tabularia fasciculata (C.Agardh) D.M.Williams & Round nd = no data F o r P e e r R e v i e w O n l y 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 Cocconeis placentula Ehrenberg Esquineau 0.5 F o r F o r Craticula buderi (Hustedt) Lange-Bertalot 0.7 Diadesmis confervacea Kützing F 0.5 Encyonema ventricosum (Kützing) Grunow in Schmidt et al. Eolimna minima (Grunow) Lange-Bertalot 2.7 o r P e e r R P e e r Eolimna subminuscula (Manguin) Gerd Moser, Lange-Bertalot & Metzeltin 1.7 Eunotia bilunaris (Ehrenberg) Schaarschmidt P Eunotia soleirolii (Kützing) Rabenhorst Eunotia Ehrenberg Fallacia pygmaea ssp. subpygmaea Lange-Bertalot, Cavacini, Tagliaventi & Alfinito e e r 0.7 e v i e w O n l 2.5 24 0.65 0.83 0.67 0.94 0.90 0.85 0.75 0.63 0.73 0.84 0.81 0.82 1.00 0.88 0.68 0.93 0.96 0.90 1.00 0.83 0.59 0.60 0.5 4.3 y 228x271mm (150 x 150 DPI) R e v i e w O n l y Fistulifera saprophila (Lange-Bertalot & Bonik) Lange-Bertalot 1.3 Fragilaria famelica (Kützing) Lange-Bertalot Fragilaria gracilis Østrup Geissleria acceptata (Hustedt) Lange-Bertalot & Metzeltin R e v Gomphonema acidoclinatum Lange-Bertalot & E.Reichardt Gomphonema affine var. rhombicum E.Reichardt Gomphonema capitatum Ehrenberg i e w Gomphonema clavatum Ehrenberg Gomphonema gracile Ehrenberg Gomphonema parvulum Kützing 1.7 74. 3.8 O n 1 l Gomphonema saprophilum (Lange-Bertalot & E.Reichardt) N.Abarca, R.Jahn, J.Zimmermann & Enke 11.9 5.7 2.3 y	0.5 1 5.8 1.8 1.3 2.3 0.5 1.8 1 0.5 0.8	0.002 0.002 0.001 0.001 0.003 0.005 0.049 0.029 0.003 0.003 0.002 0.007 0.017 0.011 0.017 0.045 0.028 0.035 0.028 0.022 0.005 0.041 1 1.7 7.5 3 1 1.2 1 6 6.2 1.2 2.2 1.5	5 nd 2.9 3 12.1 9.4 2.9 20 16 12.7 2.9 14.2 2.6 nd 9.4 2.9 nd 17 nd 9.2 2.3 3.5 0.5 2 1 0.7 1 1
	Luticola goeppertiana (Bleisch) D.G.Mann in Round et al.	9		1.2
	Mayamaea Lange-Bertalot				0.8	1
	Mayamaea permitis (Hustedt) Bruder & Medlin				1	0.5		1
	Navicula antonii Lange-Bertalot							4.5
	Navicula cincta (Ehrenberg) Ralfs in Pritchard						0.7
	Navicula cryptotenella Lange-Bertalot					2	2	1
	Navicula eidrigiana J.R.Carter					1.3	1.5
	Navicula erifuga Lange-Bertalot in Krammer & Lange-Bertalot					2	4.7	0.5
	Navicula gregaria Donkin					1.3
	Navicula lundii E.Reichardt					3.3	6	1.2

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Acknowledgements

We are indebted to the many people who participated in the turtle catches, in particular Maëlys Marage (Tour du Valat) and Rozen Rocher (CEN PACA) as well as to Sophie Guingand for her assistance in data processing. We thank the French Ministry of Environment for giving us the permission to capture European pond turtle in Esquineau and Charpines. We are grateful to the Association des Diatomistes de Langue Française (ADLaF) for allowing us to present this work at the conference held in Dijon (September 2017), and to the anonymous reviewers for their useful comments on the manuscript. This study was made possible thanks to the support of Séverine Lopez and the management of the Service Biodiversité Eau et Paysage of the DREAL PACA. We would like to express our sincere thanks to them.

is herpetologist and coordinator of the regional actions plan for Emys

Notes on contributors

Véronique Vassal is hydrobiologist at the Direction Régionale de l'Environnement, de