H Li

L Martinelli

F Cadiz

A Bendounan

S Arscott

F Sirotti

A C H Rowe

Mechanical stress dependence of the Fermi level pinning on an oxidized silicon surface

A combination of micro-Raman spectroscopy and micro-XPS (X-ray photo-electron spectroscopy) mapping on statically deflected p-type silicon cantilevers is used to study the mechanical stress dependence of the Fermi level pinning at an oxidized silicon (001) surface. With uniaxial compressive and tensile stress applied parallel to the 110 crystal direction, the observations are relevant to the electronic properties of strain-silicon nano-devices with large surface-to-volume ratios such as nanowires and nanomembranes. The surface Fermi level pinning is found to be even in applied stress, a fact that may be related to the symmetry of the Pb0 silicon/oxide interface defects. For stresses up to 160 MPa, an increase in the pinning energy of 0.16 meV/MPa is observed for compressive stress, while for tensile stress it increases by 0.11 meV/MPa. Using the bulk, valence band deformation potentials the reduction in surface band bending in compression (0.09 meV/MPa) and in tension (0.13 meV/MPa) can be estimated.

As semiconductor electronic devices continue to shrink in size, surface-to-volume ratios increase and the electronic properties of the surface become more important to the overall device characteristics. In particular, the energy at which the surface Fermi level is pinned due to interface defects can significantly modify both the electronic [1] and optical [2] properties of semiconductor nano-objects. In some cases this has been identified as a means to produce new device functionalities [3], while in other cases it demands changes to device design rules [4]. In parallel with reductions in device size, mechanical stress is also a key ingredient of the so-called semiconductor road map, designed to help evaluate technical innovations in the micro-electronics industry [5].

In strained silicon technologies, stress is used to improve the bulk electronic properties of CMOS devices [START_REF] Hoyt | Electron Devices Meeting[END_REF] but there is some evidence from electrical transport measurements, including studies of gate leakage currents in MOS devices [START_REF] Choi | [END_REF] and data retention times in flash memory devices [START_REF] Toda | Reliability Physics Symposium[END_REF], that it also modifies the electronic properties of deep defect states at the silicon/oxide interface. Recently, the stress-dependence of surface Fermi level pinned by these states has been implicitly invoked to explain unusual piezo-resistive behavior in silicon nanoobjects [START_REF] Rowe | [END_REF][10][11] and giant piezo-impedance observed during space-charge-limited-current transport in silicon-oninsulator (SOI) [12]. Interpretation of these transport phenomena is often difficult because they yield only indirect access to a physically important quantity, the stressinduced change in the surface Fermi level pinning due to changes in the activation energy, E a , of interface defects [13].

Here, the mechanical stress-induced change in the surface Fermi level pinning at a natively-oxidized silicon * alistair.rowe@polytechnique.edu (001) surface is studied spectroscopically using surface science tools, in particular X-ray photo-electron spectroscopy (XPS) of the Si 2p core levels [14,15]. Using the known values of the bulk valence band deformation potentials [16,17] it is then possible to obtain a spectroscopic estimation of the stress-dependence of the surface band bending.

Experiments are performed on macroscopic silicon cantilevers fabricated using standard lithographic methods. The boron doped (ρ < 0.01 Ωcm) device layer is 5 µm thick, the buried oxide layer is 1 µm thick and the handle is d = 400 µm thick. Once processed (see Supplementary Material), the wafer is diced with a diamond saw into cantilevers l = 11 mm long and b = 3 mm with the long axis parallel to the 110 crystal direction (see Fig. 1(a)). The cantilevers are protected during this procedure with a 1 µm thick photo-resist layer and were subsequently stored with the photo-resist in place for two months. Two weeks prior to the experiments reported here, the photoresist was removed and the surfaces rinsed in acetone, iso-propyl alcohol and de-ionized water. No further surface treatment was made prior to XPS experiments, a conscience choice made in order to study surfaces more relevant to nano-scale electronic devices than the usual reconstructed surfaces used in XPS studies.

The cantilevers are loaded into sample mounts (seen in Fig. 1(a)) with their fixed ends firmly clamped to the mount while the free ends can be deflected upwards or downwards using an actuator screw as shown in Figs. 1(b) and(c). This classic cantilevered beam with a freeend point load [START_REF] Hicks | Civil engineering formulas[END_REF] results in a uni-axial compressive (tensile) stress parallel to the 110 crystal direction at the top surface for an upwards (downwards) deflection of the free end. Its magnitude is maximum at the fixed end and falls linearly to zero at the free end [START_REF] Hicks | Civil engineering formulas[END_REF]. The neutral bending axis is approximately at the vertical center of the handle so that the uni-axial stress obtained in this way is approximately constant across the device layer thickness. The (maximum) stress at the fixed end is obtained using the formula X max = 3Ed∆z/2l 2 where E = 170 GPa is Young's modulus of silicon along the 110 crystal direction [START_REF] Wortman | [END_REF]. Using the cantilever dimensions given above, for a typical deflection of ∆z = 0.2 mm, a maximum stress magnitude of approximately 170 MPa at the fixed end is expected. This is experimentally verified on statically deflected cantilevers by measuring the sign and magnitude of the stress via the shift in the LO phonon Raman peak [20] as a function of position along the length and width of the cantilever.

Using the usual convention for mechanical stress (compressive stress is negative, tensile stress is positive) maps of the statically applied stress on two cantilevers, one in compression, one in tension, are obtained (see Fig. 2). Not only do the maps indicate a quasi-linear increase in the stress from the free to the fixed end, but the order of magnitude is in agreement with that estimated from textbook formulae [START_REF] Hicks | Civil engineering formulas[END_REF]. Without any further adjustment of the actuator screw, a spatial map of the kinetic energy, E k , of electrons photo-emitted from the Si 2p core levels is made. Since the stress is a function of position, a combination of kinetic energy and Raman maps can be used to determine the shift in the Si 2p core level position with mechanical stress for a single value of the cantilever deflection, ∆z.

XPS experiments were performed on the ultra-high vacuum (UHV) experimental station of the TEMPO beamline at the Soleil synchrotron using a Scienta SES 2002 electron energy analyzer operated in the swept and fixed modes with a E ph = 200 eV photo-excitation and a 100 eV pass energy. Under these conditions intense Si 2p core level peaks from silicon atoms within a 0.12 nm mean free path [21,22] of the silicon/silicon oxide interface can be obtained. This extreme interface sensitivity permits a measurement of the stress-dependence of the surface Fermi level pinning energy according to the graphical arguments given in Fig. 3 stress whereas stress modifies the core level binding energy, E b , the valence (E V B ) and conduction band (E CB ) edges [16], and the activation energy of interface defects, E a [13] at which the surface Fermi level is pinned. The stress-dependence of the photo-emitted electron kinetic energy, E k is then equal (but opposite) to E a . As indicated by the black (X = 0) and red (X > 0) in Fig. 3(a), knowledge of the stress-dependence of the E V B can then be used to translate this into the stress-dependence of the band bending itself.

Fig. 3(b) shows example Si 2p core level spectra plotted as a function of E k for three different tensile stresses, 0 MPa (red), 80 MPa (blue) and 160 MPa (black). The shift due to the applied stress is clear from the raw data shown (inset). With an SES analyzer energy resolution of about 50 meV, and using the usual Voigt function fitting procedure [23,24], the relative positions of intense Si 2p core level peaks can be determined to within approximately 1 meV (see the Supplementary Material). In the fitting procedure each state is represented by a doublet with a spin-orbit splitting of 0.6 eV, and an intensity ratio 1:2 between 1/2 and 3/2 spin-orbit split states. The intensity, area, kinetic energy shift, and Gaussian broadening of the Voigt functions for the spectrum shown in Fig. 3(b) are summarized in Table I.

This procedure is carried out for each pixel in the E k map, obtained by scanning the cantilever in 100 µm steps under the soft X-ray beam which is focused to a Gaussian spot of half width ≈ 100 µm. In order to correlate the relative core level peak positions to the applied stress an oxide-thickness related correction must first be applied to the data. As seen in the Supplementary Material the oxide thickness is found to be systematically thicker near the edges of the cantilevers, a fact attributed to the 2-month storage time mentioned above. Unfortunately, oxide thickness influences the Si 2p core level binding energies [25] so that in order to extract a stress-dependence of E a , the spatial variation of the oxide thickness must I. be accounted for. This is achieved by extracting (for each value of the coordinate x) only pixels whose oxide thickness is the same along the y-direction, at least to within an arbitrarily imposed 2 % variation around some mean value. The pixel color in Fig. 4(a) then represents the binding energy relative to the average value obtained in the rectangle at the free (i.e. zero stress) end of the cantilever. The remaining pixels, shown in gray, are no longer used in the following analysis.

In Figs. 4(a) and (b), the stress-induced E a shifts, calculated from the kinetic energy of photo-electrons emitted from the Si 2p 3/2 core level according to the schematic in Fig. 3(a), are seen to be large and positive around the fixed end (y = 0 mm) of the cantilever for both compressive and tensile stresses, the first suggestion that E a exhibits an even response in applied stress. These two maps are separated into areas containing approximately the same number of pixels (15), denoted by the gray rectangles. The pixels in the red, green and yellow rectangles on the compressively stressed surface are selected to demonstrate how the data is subsequently plotted in Fig. 5(a). The data histograms from these three rectangles are shown, color coded, in Fig. 4(c).

The stress-induced shift in the center-of-mass of these histograms is clearly visible, moving from lower E a shifts in the yellow rectangle (free end) to higher E a shifts in the red rectangle (fixed end). In each rectangle the mean and standard error of E a shifts are calculated and marked in Fig. 4(c) with a black line and gray box respectively. From the fixed to the free end they are 14.6 ± 1.1 meV, 6.5 ± 0.7 meV and 1.5 ± 0.4 meV respectively. These values, which are relatively robust to changes in the rectangle sizes (i.e. the number of points chosen in each rectangle) establish the mean E a shift as a function of the mean y-position of the pixels in each rectangle. By performing a similar procedure using the same pixels in the Raman maps of Fig. 2, it is then possible to obtain a plot, shown in Fig. 5(a), of the stress-induced E a shift versus the applied stress. The result of this procedure, shown in Fig. 5(a), makes the even response in stress explicitly clear. In compression E a increases by 0.16 meV/MPa while for tensile stresses it increases by 0.11 meV/MPa. These stressinduced shifts can be converted into a stress-dependence of the surface band bending i.e. the difference between the valence band edge in the bulk, and the valence band edge at the silicon/oxide interface shown in Fig. 3(a). In order to estimate this it is necessary to account for the stress-induced shifts in the bulk valence bands [16] which are known from piezoresistance measurements on p-type silicon [17]. Both heavy (HH) and light hole (LH) bands shift to higher energies under compressive stress, with deformation potentials of approximately -3.75 eV and -12.5 eV respectively. Note that the negative sign accounts for the fact that by convention compressive stresses are negative. Using these values, the Young's modulus of silicon, and the known applied stress levels, the surface band bending reduces by 0.09 meV/MPa in compression, and by 0.13meV/MPa in tension.

The even symmetry of the E a shift is unusual compared to stress-induced variations in bulk electronic levels which are odd in stress, but it is consistent with interface leakage currents measurements [START_REF] Choi | [END_REF][START_REF] Toda | Reliability Physics Symposium[END_REF]. The exact origin of this even symmetry is not clear, but the symmetry of the intrinsic Pb 0 Si/SiO 2 was previously speculated to be responsible [START_REF] Choi | [END_REF][START_REF] Toda | Reliability Physics Symposium[END_REF]. This interpretation is comforted by the fact that stress-induced shifts in E a of Si/SiO 2 interface traps obtained by indirect transport measurements on MOS capacitors are of similar magnitude [13] to the shifts obtained here.

To explore this idea further, Fig. 5(b) shows the two possible Pb 0 interface defect structures on a (001) silicon surface. In the left structure, referred to as the parallel geometry, the red dangling bond points in the 111 crystal direction and therefore has a spatial component parallel to the 110 crystal direction along which stress is applied. In the right panel of Fig. 5(b), referred to as the perpendicular geometry, the red, dangling bond points in the 111 crystal direction and therefore has a spatial component perpendicular to the 110 crystal direction. In both cases the stress can be visualized as a force applied uniformly along the thick, black edges of the unit cell parallel to the light blue plane. The arrows in the left panel of Fig. 5(b) represent the applied force that yields in a uni-axial tensile stress. In the following, a simple relative estimation of the Coulomb potential at the end of the dangling bond due to the electronic charge present in the back bonds (shown in blue and green) will be made as a function of the applied stress. The motivation for this [START_REF] Toda | Reliability Physics Symposium[END_REF] is the notion that this energy will determine E a for Pb 0 interface traps.

Using the compliance tensor for silicon [START_REF] Wortman | [END_REF], the effect of applied stress on the bond angles, and hence on the relative changes in distance between the ends of the bonds can be estimated. This can then be used to estimate the stress-induced changes to the total Coulomb potential at the dangling bond. The calculated changes in the back bond angles are shown in Fig. 5(c). For a tensile stress the bond angle between the red dangling bond and green back bond in the parallel geometry, θ rg , increases (see filled, green circles in Fig. 5(c)), while for the perpendicular geometry, θ ⊥ rg slightly decreases due to Poisson's effect (see filled, green triangles in Fig. 5(c)). A compressive stress results in the opposite behavior. The bond angle between the red dangling bond and the blue back bonds in the parallel geometry, θ rb , decreases under tensile stress due to Poisson's effect (see filled, blue circles in Fig. 5(c)), while in the perpendicular geometry, θ ⊥ rb , decreases (see filled, blue triangles in Fig. 5(c)). Again, the opposite is true for a compressive stress. A simple geometric calculation, assuming constant bond lengths, then yields a relative estimate of the Coulomb potential as a function of applied stress, and this is found to be even in stress, despite the fact that the bond angles themselves are odd in stress. This is possible because the stress-induced changes to θ rg are partially compensated by opposite changes in θ rb and θ ⊥ rb . While this is not proof that the symmetry of the Pb 0 centers is the sole origin of the even response in the stress-dependence of E a , it is a proof of principle that two odd angular contributions can result in an overall even response.

Although the XPS measurements of the stressdependence of E a presented here provide important design parameters for nano-scale, strained-silicon devices, further progress towards a better physical understanding of the even stress-dependence of E a will require a more detailed calculation of the stress-dependence of the electronic trap eigenvalues, for example using ab initio methods [26].

I. SUPPLEMENTARY MATERIALS

The Supplementary Material contains full details of the sample fabrication procedure, along with details of the oxide thickness variation across the cantilever surface.

FIG. 1 .

 1 FIG. 1. (a) p-type silicon cantilevers with ohmic contacts visible at each end that make contact with the earthed sample holder shown right. Using a mechanical screw to apply a force as shown in (b) and (c), the free end of the cantilever is deflected by ∆z. (b) An upwards deflection yields a uniaxial, compressive stress parallel to the 110 crystal direction on the top surface, with a maximum at the fixed end. (c) A downwards deflection yields an equivalent tensile stress.

  FIG.2. Maps of the uniaxial mechanical stress applied parallel to the y-direction obtained using micro-Raman spectroscopy for a cantilever whose top surface is under compression (a) and tension (b). Color bars correspond to the measured stress in MPa.

FIG. 3 .

 3 FIG. 3. (a)Energy level diagram for the surface of p-type silicon showing the electronic structure in the bulk and in the space charge region (SCR) with (red) and without (black) stress. (b) An example of a normalized Si 2p core level XPS spectrum from atoms within 0.12 nm of the silicon/oxide interface for three value of stress, 0 MPa (red), 80 MPa (black) and 160 MPa (blue). Each core level is fitted using Voigt doublets with the parameters shown in TableI.

FIG. 4 .

 4 FIG. 4. Maps of the surface Fermi level pinning energy, Ea, relative to that obtained at the free end of the cantilever as a function of beam position under (a) compressive stress and (b) tensile stress. The active (colored) pixels shown in maps are selected because they have the same oxide thickness. (c) The histogram plots of the relative Ea of the pixels shown in the colored rectangles of (a). The mean and standard error values of these data, represented as a black line and a gray box respectively, are combined with the micro-Raman spectroscopy data to obtain Ea as a function of applied stress.

FIG. 5 .

 5 FIG. 5. (a) The Ea shift exhibits an even response in stress. The inset shows the two possible orientations of the Pb0 defect at the (001) surface. Stress is applied in the 110 direction, and the case of compression is indicated by the blue arrows. The blue 1 10 plane serves as a guide to the eye. (b) The stress induced changes in bond angles versus strain, the blue curves correspond to the changes in bond angle between the red dangling bond and the blue back bonds; the green curves correspond to that between the red dangling bond and the green bond. (c) The estimated Coulomb potential energy of an electron in the red dangling bond with respect to stress.

TABLE I .

 I The fitting parameters used in Fig.3(b).

	Core level	Label Intensity Rel. E k shift Width
			×10 5	(eV)	(eV)
	Si 2p	B	22.1	0	0.4
	Si 2p 2 nd plane	I	1.2	-0.25	0.35
	Si+	Si+	2.08	-0.99	0.38

ACKNOWLEDGMENTS

This work was partially financed by the French Agence Nationale de la Recherche, contract ANR-17-CE24-0005. The authors thank F. Rochet for useful discussions.


Coline Monchanin
email: coline.monchanin@univ-tlse3.fr

Amaury Blanc-Brude

Erwann Drujont
email: e.drujont@gmail.com

Mohammed Mustafa Negahi

Cristian Pasquaretta
email: cristian.pasquaretta@univ-tlse3.fr

Jérôme Silvestre
email: silvestre@ensat.fr

David Baqué
email: david.baque@univ-tlse3.fr

Arnaud Elger
email: arnaud.elger@univ-tlse3.fr

Andrew B Barron
email: andrew.barron@mq.edu.au

Jean-Marc Devaud
email: jean-marc.devaud@univ-tlse3.fr

Mathieu Lihoreau
email: mathieu.lihoreau@univ-tlse3.fr

Chronic exposure to lead impairs honey bee learning

Keywords: Apis mellifera, heavy metal pollution, PER conditioning, reversal learning, morphometry

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche

Introduction

Metallic trace element (MTE) are naturally present in the environment [START_REF] Bradl | Sources and origins of heavy metals[END_REF].

However, their widespread uses in industrial and domestic applications has elevated their levels in soils [START_REF] Wuana | Heavy metals in contaminated soils: a review of sources, chemistry, risks and best available strategies for remediation[END_REF], water reservoirs, and plant tissues [START_REF] Hladun | Cadmium, copper, and lead accumulation and bioconcentration in the vegetative and reproductive organs of Raphanus sativus: implications for plant performance and pollination[END_REF] far above natural baselines around industrialized and urbanized areas, mining sites and agricultural regions [START_REF] Zhou | Identifying sources of environmental contamination in European honey bees (Apis mellifera) using trace elements and lead isotopic compositions[END_REF]. Some MTE are wellknown neurotoxins [START_REF] Mason | Pb neurotoxicity: neuropsychological effects of lead toxicity[END_REF]) and constitute a direct threat for animals inhabiting contaminated environments, including humans [START_REF] Chen | Metals and neurodegeneration[END_REF], whose neural development and cognitive functions may be impaired.

Pollinators, such as bees, are in front line. These insects have a severely constrained brain size optimised to perform cognitive operations for exploiting scattered plant resources [START_REF] Giurfa | Cognition with few neurons: higher-order learning in insects[END_REF]. Bees can encounter airborne MTE particles while flying [START_REF] Negri | Honey bees (Apis mellifera L.) as active samplers of airborne particulate matter[END_REF], and may bring back contaminated water, nectar and pollen to their colony nest [START_REF] Roman | Content of some trace elements in fresh honeybee pollen[END_REF][START_REF] Formicki | Metal content in honey, propolis, wax, and bee pollen and implications for metal pollution monitoring[END_REF]. MTE bio-accumulate in the bodies of insects [START_REF] Perugini | Heavy metal (Hg, Cr, Cd, and Pb) contamination in urban areas and wildlife reserves: honeybees as bioindicators[END_REF][START_REF] Lambert | Bees, honey and pollen as sentinels for lead environmental contamination[END_REF], because of biochemical and structural similarities to non-toxic molecules [START_REF] Clarkson | Metal toxicity in the central nervous system[END_REF] and their incorporation in metabolic pathways [START_REF] Mertz | The essential trace elements[END_REF]), but they can also contaminate the nest. In honey bee hives, MTE are found in pollen, honey, wax and propolis [START_REF] Satta | Combination of beehive matrices analysis and ant biodiversity to study heavy metal pollution impact in a post-mining area (Sardinia, Italy)[END_REF][START_REF] Zhou | Identifying sources of environmental contamination in European honey bees (Apis mellifera) using trace elements and lead isotopic compositions[END_REF], and can be transferred to the larvae [START_REF] Balestra | Bees, honey, larvae and pollen in biomonitoring of atmospheric pollution[END_REF].

Many MTE (e.g. lead, cadmium, selenium, aluminium, manganese) can have detrimental effects on the physiology [START_REF] Gauthier | Low dietary levels of Al, Pb and Cd may affect the non-enzymatic antioxidant capacity in caged honey bees (Apis mellifera)[END_REF][START_REF] Nikolić | Laboratory bioassays on the response of honey bee (Apis mellifera L.) glutathione S-transferase and acetylcholinesterase to the oral exposure to copper, cadmium, and lead[END_REF], foraging behaviour [START_REF] Søvik | Negative impact of manganese on honeybee foraging[END_REF][START_REF] Chicas-Mosier | The effects of ingested aqueous aluminum on floral fidelity and foraging strategy in honey bees (Apis mellifera)[END_REF][START_REF] Sivakoff | Soil lead contamination decreases bee visit duration at sunflowers[END_REF] and survival [START_REF] Di | Laboratory bioassays on the impact of cadmium, copper and lead on the development and survival of honeybee (Apis mellifera L.) larvae and foragers[END_REF][START_REF] Hladun | Metal contaminant accumulation in the hive: Consequences for whole-colony health and brood production in the honey bee (Apis mellifera L.): Impact of metal contaminants on honey bee health[END_REF] of bees. However, potential effects on cognition have been largely unexplored. Recent work indicates that acute exposure to some MTE can reduce the sensitivity of bees to sucrose rewards [START_REF] Hladun | Selenium toxicity to honey bee (Apis mellifera L.) pollinators: effects on behaviors and survival[END_REF][START_REF] Burden | Acute sublethal exposure to toxic heavy metals alters honey bee (Apis mellifera) feeding behavior[END_REF] as well as elemental forms of learning and memory [START_REF] Burden | Acute exposure to selenium disrupts associative conditioning and long-term memory recall in honey bees (Apis mellifera)[END_REF]; while not necessarily being detected by the sensory organs [START_REF] Burden | Acute sublethal exposure to toxic heavy metals alters honey bee (Apis mellifera) feeding behavior[END_REF]. Just as for agrochemical pollution [START_REF] Gill | Combined pesticide exposure severely affects individual-and colony-level traits in bees[END_REF][START_REF] Henry | A common pesticide decreases foraging success and survival in honey bees[END_REF], the potential sublethal effects of MTE on bee cognition due to a chronic exposure may have long-term dramatic consequences on populations. In these central-place foraging insects, any alteration in the learning or memory performances of the foragers may reduce food collection and ultimately impair colony development [START_REF] Gill | Combined pesticide exposure severely affects individual-and colony-level traits in bees[END_REF][START_REF] Klein | Why bees are so vulnerable to environmental stressors[END_REF].

Here, we experimentally investigated the effects of chronic lead exposure on honey bee cognition. Lead is of public health significance because of its high degree of toxicity and worldwide distribution [START_REF] Cameron | A guide for site and soil description in hazardous waste site characterization[END_REF]. Although worldwide lead emissions have been significantly reduced since the phase-out of leaded gasoline [START_REF] Kierdorf | The use of antlers to monitor temporal variation in environmental lead levels: a case study from an industrialized area in Germany[END_REF][START_REF] Chadwick | Lead levels in Eurasian otters decline with time and reveal interactions between sources, prevailing weather, and stream chemistry[END_REF], they are still important in some countries [START_REF] Li | The estimated atmospheric lead emissions in China, 1990-2009[END_REF]) and the concentrations of legacy lead remain high in soils [START_REF] Han | Industrial age anthropogenic inputs of heavy metals into the pedosphere[END_REF][START_REF] Zhou | Identifying sources of environmental contamination in European honey bees (Apis mellifera) using trace elements and lead isotopic compositions[END_REF]) and plant tissues [START_REF] Rashed | Bee honey as environmental indicator for pollution with heavy metals[END_REF]. We exposed caged honey bee colonies to field-realistic concentrations of lead for 10 weeks and monitored impacts on the morphology and cognition of individual bees, as well as on colony dynamics. We also evaluated the basal relationship between morphological development and cognitive performances in non-contaminated bees from uncaged colonies foraging on natural plant resources.

Materials and Methods

Bee colonies

We ran the experiments with caged hives from 14/06/2019 (day 1) to 23/08/2019 (day 70) using nine colonies of Apis mellifera (Buckfast) maintained in 5 frame hives (Dadant). Each colony was placed in an outside tent (3m x 3m) at our experimental apiary (University Paul Sabatier, France) to control the food intake and the foraging experience of bees. Each tent contained a feeder with sucrose solution (with or without lead, see below) and a water supply. Both were located 1m apart, 2m in front of the hive entrance. Caged colonies were given pollen patties (Icko, Bollène, France) once a week directly into the hives. We also ran experiments with bees from uncaged hives, by randomly collecting bees from colonies in the same apiary. These non-contaminated bees had free access to natural plant resources.

Lead exposure

We assigned the caged colonies to one of three lead treatments (three colonies per treatment): unexposed (hereafter 'control bees'), exposed to a low (0.075 mg.L -1 ) concentration of lead ('L bees'), exposed to a high (0.75 mg.L -1 ) concentration of lead ('H bees'). Bees were exposed to lead by ingesting 50% (w/v) sucrose solution from the feeder, to which we added lead (II) chloride (PbCl2) (Sigma-Aldrich, Lyon, France).

The low and high lead concentrations fell within the range of concentrations measured in floral nectar [START_REF] Eskov | Content of heavy metals in melliferous vegetation, bee bodies, and beekeeping production[END_REF] and honey [START_REF] Lambert | Bees, honey and pollen as sentinels for lead environmental contamination[END_REF]) in contaminated environments. Both concentrations are sublethal to adult honey bees [START_REF] Di | Laboratory bioassays on the impact of cadmium, copper and lead on the development and survival of honeybee (Apis mellifera L.) larvae and foragers[END_REF].

Control hives were fed 50% (w/v) sucrose solution. We maintained the hives in these conditions for 70 days, during which colonies consumed on average 8.5±0.6 (SE) kg of sucrose solution and 616±25 (SE) g of pollen (N=9). We kept track of the foraging experience of the nectar foragers (number of days since the onset of foraging) from each colony by paint-marking bees with a colour code while feeding on the sucrose solution feeder (Posca pen, Tokyo, Japan).

Learning assays

We assessed the cognitive performances of bees from caged and uncaged colonies using olfactory conditioning of the proboscis extension reflex (PER) (Giurfa & Sandoz 2012).

For caged colonies, we tested bees exposed during their whole life (foragers collected between days 46 and 70 after the start of the exposure) from 8 of the 9 colonies (one control hive showed low foraging activity).

All bees were submitted to a reversal learning task, a two-task assessing the cognitive flexibly of bees in response to changes in flower rewards [START_REF] Raine | Nectar production rates of 75 bumblebee-visited flower species in a German flora (Hymenoptera: Apidae: Bombus terrestris)[END_REF]. Phase 1 is a differential learning phase, in which the bees must learn to differentiate an odour A reinforced with sucrose (50% w/v in water) and an odour B not reinforced (A+ vs. B-). Phase 2 is a non-elemental learning phase, in which the bees must learn the opposite contingency (A-vs. B+). We used pure limonene and eugenol (Sigma-Aldrich, Lyon, France) as odours A or B alternately on successive days, so that each combination was used for about half of the bees for each treatment.

We tested new foragers (between 24 and 48 hours after the onset of foraging) to avoid variations in cognitive performances caused by inter-individual differences in foraging experiences [START_REF] Cabirol | Relationship between brain plasticity, learning and foraging performance in honey bees[END_REF]. On the morning of each test, we collected foragers from each hive on the feeders (except from one control hive due to a low foraging activity), cooled them on ice and harnessed them in restraining holders, which allowed free movements of their antennae and mouthparts [START_REF] Matsumoto | Revisiting olfactory classical conditioning of the proboscis extension response in honey bees: A step toward standardized procedures[END_REF]) (Fig. 1A). Rotational movements of the head were prevented by fixing the back of the head with melted bee wax. We then tested all bees for PER by stimulating their antennae with 50% sucrose solution, and kept only those that responded for the conditioning (77% of the 543 bees tested in total). These bees were fed 5µL of sucrose solution and left to rest in a dark incubator for 3h (temperature: 25±2°C, humidity: 60%).

Bees were trained using an automatic stimulus delivery system (Fig. 1A; [START_REF] Aguiar | Can honey bees discriminate between floral-fragrance isomers?[END_REF]). Each training phase included five trials with the reinforced odorant and five trials with the non-reinforced odorant in a pseudo-random order with an eight-minute inter-trial interval. Each conditioning trial (37 s in total) started when a bee was placed in front of the stimulus delivery system, which released a continuous flow of clean air (3,300 mL.min -1 ) to the antennae. After 15 s, the odour was introduced to the airflow for 4 s, the last second of which overlapped with sucrose presentation to the antennae using a toothpick (Fig. 1A) and subsequent feeding for 4 seconds for the rewarded trials. For the unrewarded trials, no sucrose stimulation was applied. The bee remained another 15 s under the clean airflow. Bees were kept in the incubator for 1h between the two learning phases (A+ vs. B-and A-vs. B+).

During conditioning, we recorded the presence or absence of a conditioned PER to each odorant at each trial (1 or 0). Each bee was given a learning score for phase 1

(1 if the bee responded to A+ and not to B-in the last trial of phase 1, 0 otherwise) and for phase 2 (1 if the bee responded to B+ and not to A-on the last trial, 0 otherwise) [START_REF] Cabirol | Relationship between brain plasticity, learning and foraging performance in honey bees[END_REF]). Short-term memory (1 h) was assessed by comparing the responses at the last trial of phase 1 and the first trial of phase 2. Each bee was given a memory score for the two odorants (1 if the bee still responded appropriately to the A+ and B-on the first trial of the phase 2, 0 otherwise).

Morphometry

We evaluated developmental differences among bees by conducting morphometric measures on frozen individuals (-18°C) from caged and uncaged hives. To test the effect of lead exposure on morphology, we collected foragers from the caged hives the day before exposure (day 0), during exposure (day 53) and at the end of the experiment (day 70), and measured their head length and head width (Fig. 2A). We also sampled emerging adult bees every week from each of the caged hives (before exposure, during exposure, and at the end of the exposure period), making sure that no Varroa sp. mites were present in the cell. We measured their fresh body weight (±0.001g) (precision balance ME103T, Mettler-Toledo Gmbh, Greifensee, Switzerland) and eight morphometric parameters: head length, head width, forewing length, forewing width, femur length, tibia length, basitarsus length, basitarsus width (Fig. 2A) [START_REF] Mazeed | Morphometry and number of spermatozoa in drone honeybees (Hymenoptera: Apidae) reared under different conditions[END_REF][START_REF] De Souza | Morphometric identification of queens, workers and intermediates in in vitro reared honey bees (Apis mellifera)[END_REF]. To test the basal relationship between cognitive performances and morphology, we measured the head length and head width of the non-contaminated bees randomly collected from uncaged hives after the conditioning experiments. We took all measurements (±0.01 mm) using a Nikon SMZ 745T dissecting scope (objective x0.67) with a Toupcam camera model U3CMOS coupled to the ToupView software.

Colony dynamics

We assessed the effect of lead exposure on colony dynamics through continuous measurement of hive parameters in the caged colonies. Colony weight (±0.01 kg) was recorded every hour with an electronic scale (BeeGuard, Labège, France) below each hive. Every two weeks we opened the hives and took pictures of both sides of each frame with a Panasonic Lumix DMC-FZ200 equipped with a F2.8 25-600 mm camera lens. From the pictures, we estimated the areas of capped brood and food stores using CombCount [START_REF] Colin | The development of honey bee colonies assessed using a new semi-automated brood counting method: CombCount[END_REF]. We weighted each frame, after gently removing the adult bees, and determined the total weight of adult bees (bee mass) by subtracting the tare of the hive and the weight of the frames from the weight of the hive. Five times during the experiment (day 0, day 24, day 38, day 53, day 70), we estimated Varroa loads by collecting ca. 300 bees from uncapped brood frames in a jar and adding 15g of icing sugar. We shook the jar for two minutes, released the bees and counted the number of fallen mites. The number of mites was standardised and expressed as number of Varroa sp. phoretic mites per 100 bees [START_REF] Dietemann | Standard methods for varroa research[END_REF].

Lead quantification

We analysed lead in sucrose solution and bees from caged hives using Inductively Coupled Plasma Emission Spectroscopy (ICP-OES, quantification limit: 5-20 µg.kg -1 , precision measure: 1-5%; AMETEK Spectro ARCOS FHX22, Kleve, Germany). We assessed lead concentration in sucrose using the high concentration sucrose solution (the low concentration solution fell under the ICP-OES detection thresholds). The solution was acidified at 3% of HNO3 with ultra-pure 69% HNO3 to avoid precipitation or adsorption in containers. It was then diluted with a HNO3 3% solution to reduce the spectral interference and viscosity effects. We assessed lead content in bees collected during the 4 th week of exposure. For each sample, we pooled bees in batches of 5. Each batch was rinsed with 5 mL HNO3 at 3% for 30 s. Bees were wet mineralized in 50mL polypropylene tubes using a Digiprep system (SCP Science, Quebec, Canada) with 5 mL of 69% nitric acid. A digestion phase was carried out at room temperature overnight, followed by a second phase of heating at 80°C for 60 min. The nitric acid was evaporated, and the samples were diluted with 9 mL of 3% HNO3. Final solutions were at 3% HNO3 and total dissolved solids below 5%.

Statistics

We ran all analyses with R Studio v.1.2.5033 [START_REF] Team | RStudio: integrated development for R[END_REF]. We compared lead content of bees using a Kruskal-Wallis test (package FSA; [START_REF] Ogle | FSA: Fisheries Stock Analysis[END_REF]). We evaluated the effects of lead exposure on colony parameters with a multi-model approach (MMI), with treatment, exposure duration (standardised using rescale function, package arm; [START_REF] Gelman | arm: Data analysis using regression and multilevel/hierarchical models[END_REF]) and their interaction as fixed effects, and hive identity as random factor. We ran a model selection (package MuMIn; [START_REF] Barton | MuMIn: Multi-Model Inference[END_REF]) and applied a conditional model average to evaluate the effects of the different factors on the response variables. To evaluate the effect of Varroa sp. load, we used a subset of the colony data for the days when mites were counted. We ran a MMI followed by a conditional model average to assess the effects of treatment, time, Varroa counts and their interactions on brood area (square-root transformed), food stores area and bee mass.

For learning assays, we tested colony effects by comparing the proportions of bees with learning or reversal scores of 1 between hives in each treatment group. We used proportion tests, followed by pairwise comparisons with a Bonferroni correction (package RVAideMemoire; [START_REF] Hervé | RVAideMemoire: Testing and plotting procedures for biostatistics[END_REF])), to evaluate whether lead exposure changed sucrose responsiveness (i.e. proportions of unresponsive bees across treatments). We extracted individual learning slopes from the raw data (using a linear mixed effect model (LMM, package nlme; [START_REF] Pinheiro | nlme: Linear and nonlinear mixed effects models[END_REF])) with individual nested in test day as a random effect and trial as random slope). We then compared learning slopes between treatments, during each learning phase, by performing generalized linear mixed-effects models (GLMM) (package lme4; [START_REF] Bates | Fitting linear mixed-effects models using lme4[END_REF]), with hive identity as random factor and treatment and standardized duration of exposure as fixed effects.

We compared proportions of successful responses during the 5 th trial of each learning phase using a binomial GLMM, with odorants, treatments, standardized duration of exposure and their interactions as fixed effects, and bee identity nested in the hive identity as random factors. We ran a similar GLMM to compare the learning, reversal and memory scores. We used a MMI and model average to evaluate the effect of treatment and standardised duration of exposure, and their interactions, on the behavioural variables (PER responses, learning, reversal and memory scores).

For the morphometric analyses on caged bees, we used LMMs for each parameter, considering treatment, duration of exposure and their interaction as fixed effects, and hive identity as random factor. To assess the global effect of lead, we collapsed the nine parameters into the first component of a principal component analysis (PCA) (package FactoMineR, [START_REF] Lê | FactoMineR: An R Package for Multivariate Analysis[END_REF]). Bees were clustered into subgroups based on PCA scores, and clusters were compared with a permutational multivariate analysis of variance (PERMANOVA) (package vegan; [START_REF] Oksanen | vegan: Community Ecology Package. R package version 2[END_REF]). We ran a LMM on individual coordinates from the PCA, with treatment, exposure duration and their interaction as fixed effects, and hive identity as random factor. To assess the effect of head size on cognitive performance of uncaged bees, we collapsed the head width and length measures into the first component of a PCA and ran a binomial GLMM on learning, memory and reversal scores, with individual coordinates from the PCA as fixed effect, and test day as random factor.

Results

Lead bio-accumulated in bees but did not affect colony dynamics.

We quantified lead concentration in sucrose solutions and in the caged bees using ICP-OES. The amount of lead in the high concentration sucrose solution was recovered at 96% (nominal concentration: 0.75 mg.L -1 , actual concentration: 0.71 mg.L -1 ). After four weeks of exposure, we found no difference in the lead content of control bees and L bees (control: 0.126±0.031 mg.kg -1 d.m., N=3; L bees: 0.130±0.002 mg.kg -1 d.m., N=3; Kruskal-Wallis: H=7.636, df=1, p=0.712). However, H bees accumulated significant amounts of lead (H bees: 0.809±0.044 mg.kg -1 d.m., N=5; Kruskal-Wallis: H=7.636, df=1, p=0.039), which validated our method of lead exposure.

We tested the effect of lead exposure on colony dynamics using continuous monitoring of hive parameters in caged colonies. Syrup and pollen consumption, dynamics of brood production (Fig. S1A), food stores (Fig. S1B), adult bee mass (Fig. S1C), colony mass (Fig. S1D) and Varroa sp. loads (Fig. S1E) were similar in all colonies across treatments (LMM: Treatment effect: p>0.05 for all parameters). We found a strong effect of time since the beginning of the experiment on all these parameters (LMM: brood: F1,51=75.27, p<0.001; food stores: F1,51=4.738, p=0.035; bee mass: F1,51=28.38, p<0.001; colony mass: F1,636=760.44, p<0.001; Varroa sp.: F1,41=14.19, p<0.001). In all colonies, brood production, adult bee mass and colonies mass decreased over time (LMM: p<0.001) while food stores (LMM: p=0.035) and Varroa sp. load increased (LMM: p=0.046), irrespective of treatment. Therefore, lead exposure had no influence on colony dynamics and parasite loads. Interestingly, however, colony infestation by Varroa interacted with several parameters (Table S1), so that infested colonies exhibited a weaker reduction of brood area (LMM: Time:Varroa effect: p=0.028) and of adult bee population (LMM: Time:Varroa effect: p=0.046), and a weaker increase in food stores (LMM: Time:Varroa: p=0.037), suggesting that infected colonies allocated part of their foraging workforce to a collective response to stress induced by Varroa.

Exposure to high lead concentration reduced learning performance.

We assessed the effect of lead exposure on cognition using an olfactory reversal learning task bees from caged hives. The proportion of bees that responded to the antennal stimulation of sucrose was similar across treatments (control bees: 74% N=113, L bees: 69% N=122, H bees: 76% N= 132; χ²=1.423, df=2, p=0.491), indicating that lead exposure did not affect appetitive motivation or sucrose perception.

Treatment had no significant effect on phase 1, although H bees tended to perform less well (Fig. 1B). Learning slopes were similar across treatments, as well as the proportions of bees that learned to discriminate the two stimuli (learning score equals to 1) (Tables 1 andS2). These results were independent of the odours used as stimuli A+ and B-(Binomial GLMM: F1,266=0.905, p=0.526). The proportion of learners at the end of the first phase was similar across hives (Control bees (47%; 48%): χ²<0.001, df=2, p=1; L bees (33%; 38%; 55%): χ²=2.993, df=2, p=0.224; H bees (41%; 18%; 41%): χ²=3.291, df=2, p=0.193). Therefore, exposure to lead did not affect the performance of bees in the differential conditioning task.

By contrast, treatment had a significant effect on phase 2 (Fig. 1C). Bees from all treatments had similar learning slopes for odour A-(LMM: p=0.590 for L bees; p=0.063 for H bees). However, H bees started to respond to B+ later than control bees (LMM: p=0.027). L bees behaved similarly to controls (LMM: p=0.830). By the end of phase 2, L bees and H bees responded less to B+ than controls (Conditional model average: L bees, p=0.007; H bees, p<0.001) (Tables 1 andS2, Fig. 1C). Treatment also influenced responses to A-, but here L bees responded more (p=0.033) than H bees and controls (6% of control bees responded to A+, 10% of H bees, 13% of L bees). Overall, H bees exhibited lower reversal scores (13% of learners) than L bees (21%) and controls (33%) (Conditional average model: L bees, p=0.088; H bees, p=0.001) (Tables 1 andS2, Fig. 1C). There was no effect of the odours used as stimuli A-and B+ (Binomial GLMM: F1,266=1.300, p=0.636), nor of the hive on the proportion of learners (control bees: χ²=0.505, df=2, p=0.477; L bees: χ²=0.211, df=2, p=0.9; H bees: χ²=0.973, df=2, p=0.615). Therefore, exposure to a high concentration of lead reduced the performance of bees in the reversal learning task.

Comparing the responses of bees between the last trial of phase 1 and the first trial of phase 2, one hour later, gives insight about short-term memory. We only found a tendency for H bees to show a reduced percentage of correct responses between the two phases (Conditional average model: H bees, p=0.078) (Tables 1 andS2). The duration of exposure to lead did not affect the response levels of bees, nor their learning scores, at the end of either learning phases (Tables 1 andS2).

Bees exposed to the high lead concentration were shorter with smaller heads.

We assessed the effect of lead exposure on bee development by measuring morphological parameters on foragers and emerging adults from the caged hives.

Foragers collected on the day before the beginning of lead exposure (day 0), had similar head measurements irrespective of treatment (LMM: Treatment effect: p=0.529 for head width, p=0.509 for head length). However, foragers collected on the 8 th or 10 th week of lead exposure had significantly smaller heads than controls (L bees: head length p=0.017; H bees: head width p=0.040, head length p<0.001; Table S3). This effect was independent of the duration of exposure (LMM: Treatment:Time effect: p=0.711 for head width, p=0.663 for head length).

We further explored the effects of lead treatment on the morphology of adults (Fig. 2A) using a PCA on the nine morphometric parameters measured on emerging bees (Fig. 2B, Table S4). Two PCs explaining 57% of the variance were sufficient to separate control bees and H bees into two distinct clusters, while L bees remained in between (PERMANOVA: Pseudo-F=3.313, p=0.004; control bees vs. L bees: p=1; C bees vs. H bees: p=0.021; L bees vs. H bees, p=0.177). PC1 explained variations in general body size while PC2 explained variations in wing and basitarsus width, and femur length. PC1 was negatively correlated with the interactive effect between concentration and duration of exposure (LMM: Treatment:Time effect: p<0.001).

Overall, H bees displayed a decrease in the same order of magnitude for most of the parameters, except for weight (-12.5%) and head width (-2.99%) (Table S5). This indicates that bees exposed to the high concentration of lead during larval development had a lighter body with shorter legs and wings, as well as smaller heads, compared to unexposed bees (Table S6).

Bees with bigger heads showed better learning performance.

We explored the basal relationship between head size and learning performance by testing bees unexposed to lead from uncaged hives to a reversal learning task (N=149).

The first component (PC1) of the PCA collapsing head width and length explained 73.6% of the variance and represented the global head size. In phase 1, the proportion of learners (79% N=118) increased with head size (Fig. 3A), while short-term memory recall was unaffected (Fig. 3B). In phase 2, the proportion of learners (16% N=23) was also affected by head size (Fig. 3C). Therefore, bees with bigger heads showed better learning performances in absence of any cage confinement or lead treatment.

Discussion

Recent studies suggest that MTE can have sublethal effects on individual pollinators, with potential detrimental consequences for populations [START_REF] Søvik | Negative impact of manganese on honeybee foraging[END_REF][START_REF] Burden | Acute exposure to selenium disrupts associative conditioning and long-term memory recall in honey bees (Apis mellifera)[END_REF][START_REF] Burden | Acute sublethal exposure to toxic heavy metals alters honey bee (Apis mellifera) feeding behavior[END_REF][START_REF] Skaldina | Ecotoxicological effects of heavy metal pollution on economically important terrestrial insects[END_REF]. Here, we demonstrate that honey bees chronically exposed to field-realistic concentrations of lead in food have reduced body sizes and learning abilities. The positive correlation between head size and learning performances in unexposed bees suggests that consumption of this single trace element affects bee development, constraining brain size and cognitive function, thus constituting a major source of environmental stress for bees.

The impact of lead was independent of the duration of exposure. While our experimental setup allowed contamination through foraging over several weeks, similar effects were observed in adults exposed for varying durations. Yet, all bees had undergone their larval stage during the exposure period, suggesting that most of the detrimental effects of lead is caused by larval ingestion of contaminated food brought by foragers. Lead alters larval development in flies and bees [START_REF] Cohn | Lead retards development of Drosophila melanogaster[END_REF][START_REF] Safaee | Effects of lead on the development of Drosophila melanogaster[END_REF][START_REF] Di | Laboratory bioassays on the impact of cadmium, copper and lead on the development and survival of honeybee (Apis mellifera L.) larvae and foragers[END_REF]) and, in mammals, alterations in neural development are correlated with cognitive impairments, including learning and memory [START_REF] Grandjean | Developmental neurotoxicity of industrial chemicals[END_REF][START_REF] Giordano | Developmental neurotoxicity: some old and new issues[END_REF][START_REF] Mason | Pb neurotoxicity: neuropsychological effects of lead toxicity[END_REF].

In our experiments, lead did not generally affect olfactory or gustatory perception or appetitive motivation, as previously observed in more systematic sucrose sensitivity tests [START_REF] Burden | Acute sublethal exposure to toxic heavy metals alters honey bee (Apis mellifera) feeding behavior[END_REF], nor differential learning (first learning phase) or short term memory. Rather, we found a specifically decreased ability in reversal learning (second learning phase), a task involving cognitive plasticity that allows bees to update their memories about relevant associations between flower cues and food [START_REF] Ferguson | Sensitivity to a change in reward is heritable in the honeybee, Apis mellifera[END_REF]. A higher sensitivity to lead in reversal learning has been documented in rats [START_REF] Hilson | Analyses of response patterns clarify lead effects in olfactory reversal and extradimensional shift tasks: assessment of inhibitory control, associative ability, and memory[END_REF][START_REF] Garavan | Enduring effects of early lead exposure: evidence for a specific deficit in associative ability[END_REF], monkeys [START_REF] Bushnell | Persistence of impaired reversal learning in young monkeys exposed to low levels of dietary lead[END_REF] and humans [START_REF] Evans | Reversal learning tasks may provide rapid determination of cognitive deficits in lead-exposed children[END_REF][START_REF] Finkelstein | Low-level lead-induced neurotoxicity in children: an update on central nervous system effects[END_REF]). Most probably, this reflects a stronger impact of lead intoxication on the development and/or function of brain circuits that are more specifically involved in a non-elemental task, such as reversal (e.g. orbitofrontal cortex in mammals [START_REF] Schoenbaum | Changes in functional connectivity in orbitofrontal cortex and basolateral amygdala during learning and reversal training[END_REF]), than in simpler forms of learning like differential learning that do not involve the resolution of ambiguities [START_REF] Giurfa | Cognition with few neurons: higher-order learning in insects[END_REF].

In honey bees, effective reversal requires the normal function of the mushroom bodies (MBs), that are brain centers otherwise dispensable for differential conditioning [START_REF] Boitard | GABAergic feedback signaling into the calyces of the mushroom bodies enables olfactory reversal learning in honey bees[END_REF]. The specific reversal impairment of lead-exposed bees might thus be understood if neural circuits would be more affected in the MBs than in other brain regions. Since reversal learning requires GABAergic transmission in the MBs [START_REF] Boitard | GABAergic feedback signaling into the calyces of the mushroom bodies enables olfactory reversal learning in honey bees[END_REF], alterations in GABA release observed in mammals exposed to lead [START_REF] Lasley | Influence of exposure period on in vivo hippocampal glutamate and GABA release in rats chronically exposed to lead[END_REF]) might contribute to the specific impairment of reversal learning in exposed bees. More generally, lead exposure is known to alter the maturation of the brain excitation/inhibition balance during development, through multiple effects such as loss of GABAergic interneurons [START_REF] Stansfield | Early-life lead exposure recapitulates the selective loss of parvalbumin-positive GABAergic interneurons and subcortical dopamine system hyperactivity present in schizophrenia[END_REF], altered maturation of GABAergic neurons [START_REF] Wirbisky | Novel dose-dependent alterations in excitatory GABA during embryonic development associated with lead (Pb) neurotoxicity[END_REF][START_REF] Neuwirth | Perinatal Pb2+ exposure alters the expression of genes related to the neurodevelopmental GABA-shift in postnatal rats[END_REF], decrease in GABA and glutamate release [START_REF] Lasley | Influence of exposure period on in vivo hippocampal glutamate and GABA release in rats chronically exposed to lead[END_REF][START_REF] Xiao | Pb2+ impairs GABAergic synaptic transmission in rat hippocampal slices: A possible involvement of presynaptic calcium channels[END_REF] or transport [START_REF] Strużyńska | Relationships between glutamine, glutamate, and GABA in nerve endings under Pb-toxicity conditions[END_REF], inhibition of post-synaptic glutamatergic action [START_REF] Neal | Molecular neurobiology of lead (Pb2+): effects on synaptic function[END_REF]. In insects, although no specific effect on GABAergic signalling has been demonstrated yet, the known effects of lead exposure on synaptic development [START_REF] Morley | Effects of chronic lead exposure on the neuromuscular junction in Drosophila larvae[END_REF], presynaptic calcium regulation [START_REF] He | Chronic lead exposure alters presynaptic calcium regulation and synaptic facilitation in Drosophila larvae[END_REF]) and acetylcholinesterase activity [START_REF] Nikolić | Laboratory bioassays on the response of honey bee (Apis mellifera L.) glutathione S-transferase and acetylcholinesterase to the oral exposure to copper, cadmium, and lead[END_REF]) are compatible with a disruption of the excitation/inhibition balance. We proposed earlier that reaching an optimal value for such balance in MB circuits is what determines efficient reversal learning in mature adults, and particularly in the so-called calyces of the MBs, where a sparse coding of odorants is believed crucial for efficient reversal [START_REF] Cabirol | Experience during early adulthood shapes the learning capacities and the number of synaptic boutons in the mushroom bodies of honey bees (Apis mellifera)[END_REF][START_REF] Cabirol | Relationship between brain plasticity, learning and foraging performance in honey bees[END_REF]. Developmental lead exposure could jeopardize this balance in many ways, like in mammals, and result in impairment of the reversal learning phase only. Indeed, early environmental conditions shape MB circuits and reversal learning performances of adult bees [START_REF] Cabirol | Experience during early adulthood shapes the learning capacities and the number of synaptic boutons in the mushroom bodies of honey bees (Apis mellifera)[END_REF].

Further evidence supports the hypothesis of a developmental effect of lead, since bees exposed to the highest concentrations developed lighter bodies, with shorter legs and wings and smaller heads. Interestingly, unexposed bees with smaller and shorter heads had a lower learning performance (albeit not specifically in the reversal phase). In bees, head width tend to be correlated with the volume of the brain (Honey Irrespective of the detailed mechanisms by which lead impairs bee learning, our results on an ecologically relevant cognitive task raise the concern of pollinators exposed to environmental metallic pollution. Importantly, lead contents measured in the bodies of exposed bees in our experiments ranged within the measurements from bees in natural conditions [START_REF] Goretti | Heavy metal bioaccumulation in honey bee matrix, an indicator to assess the contamination level in terrestrial environments[END_REF]. The two concentrations of lead in the sucrose solutions used for chronic exposure (0.075 and 0.75 mg.L -1 ) fell below the maximum level authorized in food (3 mg.kg -1 ; Codex Alimentarius 2015) and irrigation water (5 mg.L -1 ; [START_REF] Ayers | Water quality for agriculture. FAO irrigation and drainage paper[END_REF], and the lowest concentration was under the threshold set for honey by the European Union (0.10 mg.kg -1 ; Commission Regulation 2015). This suggests that bees foraging on flowers in contaminated environments also exhibit cognitive and developmental impairments. Ultimately, these effects can alter colony function, for instance by reducing the collective foraging efficiency and gradually causing population declines, as found with the effects of pesticides at sublethal concentrations [START_REF] Klein | Why bees are so vulnerable to environmental stressors[END_REF]. Although our experiment, and recent similar approaches [START_REF] Hladun | Metal contaminant accumulation in the hive: Consequences for whole-colony health and brood production in the honey bee (Apis mellifera L.): Impact of metal contaminants on honey bee health[END_REF], did not capture these effects at the population level, differences in colony performances are expected over the longer term. Our results thus call for future studies to better characterize the impact of lead exposure in bee populations, including in combination with other MTE as such cocktails are often found in contaminated areas [START_REF] Badiou-Bénéteau | Honeybee biomarkers as promising tools to monitor environmental quality[END_REF][START_REF] Goretti | Heavy metal bioaccumulation in honey bee matrix, an indicator to assess the contamination level in terrestrial environments[END_REF]. More generally, a better assessment of the contribution of heavy metal pollutants to the widespread declines of insects has become an urgent necessity for preserving ecosystem services. 1 andS2). Bar plots show the proportions of learners (black) and non-learners (white) in the last trial of phase 1 (B) and phase 2 (C), with sample size displayed. Statistical comparisons were obtained with p-values from the model average following MMI procedure (Table 1). (ns: non-significant, p>0.05; *p<0.05; **p<0.01; ***p<0.001)

Figures

  bees: Gronenberg & Couvillon 2010; Bumblebees : Riveros & Gronenberg 2010), the MBs and the MB calyces (Honey bees: Mares et al. 2005; Bumblebees: Smith et al. 2020). Lead exposure during larval development is thus likely to have impaired aspects of head and brain development, that affect preferentially MB-dependent cognitive tasks such as reversal learning. In our experimental conditions, continuous exposure to environmentally realistic amounts of lead resulted in bioaccumulation of the metal in the bees' bodies, which may explain the dose-dependent effects observed on cognition and morphology.

Figure 1 :

 1 Figure 1: Learning and memory performances of bees from caged hives. A) Photo

Figure 2 :

 2 Figure 2: Morphometric analysis of bees from caged hives. A) Details of the

Figure 3 :

 3 Figure 3: Relationship between head size and cognitive performance in unexposed

Figure S1 :

 S1 Figure S1: Amount of brood, food stores, bees, colony mass and Varroa sp.

Figure S2 :

 S2 Figure S2: Morphological measurements of bees from caged hives upon 815

Table S5 : Median, minimum and maximal values of each morphological parameter of emerging bees from caged hives, per treatment and percentage of variation between medians compared to unexposed bees.

 S5 Percentage of variationfor each parameter was the percentage of variation of median values between treatments. We considered variation of the same order of magnitude as isometric scaling, if not as allometric scaling.

	High concentration Null model		-1.2064	0.3780 2 340.4 1.62 0.165 0.001
	Time df=degree of freedom; AICc=Akaike's information criterion corrected for small sample -0.0292 0.3088 0.925
	Memory score Intercept size; AICc=differences in AIC between all the models and the most parsimonious one; -0.6467 0.2175 0.003
	Low concentration Wi=relative importance values of each model. 0.0083	0.3249	0.980
	High concentration		-0.5906	0.3337	0.078
	Time			0.5077	0.2754	0.066
	760
	Variable	Control	Low concentration	High concentration	% variation low concentration	% variation high concentration
	Bee weight	0.12	0.11	0.1			-5.41%	-12.5%
	Head length Head width Wing length Wing width Femur length Tibia length Basitarsus length Basitarsus width	0.10-0.14 2.99 2.67-3.13 2.41 2.11-2.71 8.78 8.42-9.08 3.12 2.71-3.35 2.30 2.15-2.53 3.04 2.90-2.24 1.05-1.4 3.18 2.07 1.94-1.16	0.06-0.12 3.04 2.57-3.10 2.42 2.30-2.58 8.89 8.64-9.11 3.15 2.72-3.34 2.28 2.08-2.48 3.04 1.04-1.25 2.81-3.15 2.02 1.95-2.15 1.16	2.97 0.07-0.13 2.34 2.25-3.14 8.73 2.10-2.51 3.23 7.57-9.17 2.27 2.56-3.4 3.02 1.90-2.41 0.96-1.27 2.03 2.6-3.2 1.63-2.18 1.1			+1.64% +0.41% +1.24% +0.95% -0.88% 0% -2.23% 0%	-0.67% -2.99% -0.57% +0.96% -1.32% 0% -1.72% -5.45%

Acknowledgements

We thank Olivier Fernandez for assistance with beekeeping, BeeGuard for lending us electronic scales, and the Obervatoire Midi-Pyrénées Ecolab platform PLANET for the ICP-OES analyses. This work was funded by the CNRS. Additionally, CM was supported by a PhD fellowship from French Ministry of Higher Education, Research and Innovation. ABB was supported by a Future Fellowship from of Australian Research Council (FT140100452) and the Eldon and Anne Foote Trust. CP and ML were supported by a grant of the Agence Nationale de la Recherche to ML (ANR-16-CE02-0002-01).

Tables Table 1: Parameter estimates from the conditional model average for response levels at the end of both learning phases, and for learning, reversal and memory score models in bees from caged hives. S6) are given for the interaction between treatment and time, significant results (p<0.05) are shown in bold.