Frédéric Angelier
email: frédéricangelier@cebc.cnrs.fr

Louise Prouteau

François Brischoux

Olivier Chastel

Marie-Hélène Devier

Karyn Le Menach

Stéphan Martin

Bertille Mohring

Patrick Pardon

Hélène Budzinski

High contamination of a sentinel vertebrate species by azoles in vineyards: a study of common blackbirds (Turdus merula) in multiple habitats in western France

Keywords: tebuconazole, difenoconazole, fenbuconazole, tetraconazole, agriculture, wildlife

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Introduction

Over the last century, agricultural practices have intensified to an unprecedented extent in order to increase the yields and the economic value of agriculture [START_REF] Tilman | Agricultural sustainability and intensive production practices[END_REF][START_REF] Lanz | The Expansion of Modern Agriculture and Global Biodiversity Decline: An Integrated Assessment[END_REF]. This intensification has been associated with drastic changes of agricultural landscapes [START_REF] Gardner | European Agriculture: Policies, Production and Trade[END_REF], and more specifically with the destruction of many ecological infrastructures (hedgerows, small woods), and with a significant increase in the size of land plots to allow the mechanization of agriculture (e.g., plough, seeder, combine harvester, etc.). As a result of this landscape simplification, monocultures have expanded and agricultural areas are now often characterized by a few very large plots dedicated to the production of a limited numbers of crops [START_REF] Benton | Farmland biodiversity: Is habitat heterogeneity the key?[END_REF]. Such monoculture has led to the surge of pest density and crop diseases [START_REF] Wilby | Natural enemy diversity and pest control: Patterns of pest emergence with agricultural intensification[END_REF], and thus, to the development of the pesticide industry [START_REF] Atwood | Pesticides industry sales and usage. 2008-2012 Market estimates[END_REF].

For example, fungicides are now widely used to circumvent the detrimental impact of fungi on yields in a wide variety of harvests, such as cereals, orchards, vineyards, and even ornamental flowers [START_REF] Gianessi | The Importance of Fungicides in U.S. Crop Production[END_REF][START_REF] Moore | 21st Century Guidebook to Fungi[END_REF]). Among fungicides, azoles represent the most widely used family of organic fungicides and they have been used for decades in agricultural landscapes [START_REF] Büchel | The History of Azole Chemistry[END_REF]. Their use was also democratized at the end of the 20 th century in private gardens and urban parks to deal with fungi attacks on ornamental flowers, shrubs and trees, and elevated levels of azole compounds can be found in cities [START_REF] Désert | Spatial and temporal distribution of current-use pesticides in ambient air of Provence-Alpes-Côte-d'Azur Region and Corsica, France[END_REF][START_REF] Schoustra | Environmental Hotspots for Azole Resistance Selection of Aspergillus fumigatus, the Netherlands[END_REF][START_REF] Zubrod | Fungicides: An Overlooked Pesticide Class?[END_REF]. Azoles are antifungal agents that act by inhibiting cell membrane ergosterol synthesis, thereby leading to increased cell permeability and destruction [START_REF] Vanden Bossche | Biochemical Targets for Antifungal Azole Derivatives: Hypothesis on the Mode of Action[END_REF]. This mode of action is currently one of the most effective controls of phytopathogenic fungi and azoles now represent the most used family of organic fungicides in crops, such as vineyards, orchards and cereals [START_REF] Atwood | Pesticides industry sales and usage. 2008-2012 Market estimates[END_REF]. The azole family includes numerous molecules (e.g., dozens of triazoles and imidazoles). The use of triazoles in US agriculture has increased more than 4-fold during the last decade [START_REF] Toda | Trends in Agricultural Triazole Fungicide Use in the United States, 1992-2016 and Possible Implications for Antifungal-Resistant Fungi in Human Disease[END_REF]. Although the use of some azoles has decreased since the 1980's (ex: triadimefon), the use of others has dramatically increased (ex: tebuconazole, propiconazole) and new triazoles have even started to be used during the last decade (ex: difenoconazole, metconazole, [START_REF] Toda | Trends in Agricultural Triazole Fungicide Use in the United States, 1992-2016 and Possible Implications for Antifungal-Resistant Fungi in Human Disease[END_REF].

Azoles are mostly used to circumvent fungi attacks on crops but there is increasing evidence that they can be transferred to the environment. Indeed, recent studies have shown that they can be found in the tissues of humans (e.g, hair, [START_REF] Schummer | Determination of farm workers' exposure to pesticides by hair analysis[END_REF][START_REF] Fustinoni | Biological monitoring of exposure to tebuconazole in winegrowers[END_REF][START_REF] Polledri | Determination of tebuconazole and penconazole fungicides in rat and human hair by liquid chromatography/tandem mass spectrometry[END_REF]). Triazoles can be transported into the environment through multiple mechanisms of transfer such as volatilization, infiltration and absorption [START_REF] Mottes | Pesticide transfer models in crop and watershed systems: A review[END_REF][START_REF] Zhang | Modeling spray drift and runoff-related inputs of pesticides to receiving water[END_REF]). As a consequence, the occurrence of azoles and their residues has been reported in several environmental matrices such as soil [START_REF] Pelosi | Residues of currently used pesticides in soils and earthworms: A silent threat? Agriculture[END_REF], water [START_REF] Ccanccapa | Spatio-temporal patterns of pesticide residues in the Turia and Júcar Rivers (Spain)[END_REF][START_REF] Guarda | Assessment of Ecological Risk and Environmental Behavior of Pesticides in Environmental Compartments of the Formoso River in Tocantins, Brazil[END_REF] or air [START_REF] Coscollà | Occurrence of currently used pesticides in ambient air of Centre Region (France)[END_REF][START_REF] Désert | Spatial and temporal distribution of current-use pesticides in ambient air of Provence-Alpes-Côte-d'Azur Region and Corsica, France[END_REF]). In addition, other studies have reported that triazoles can be found in invertebrate species (e.g., [START_REF] Daniele | Exposure assessment of honeybees through study of hive matrices: Analysis of selected pesticide residues in honeybees, beebread, and beeswax from French beehives by LC-MS/MS[END_REF][START_REF] Pelosi | Residues of currently used pesticides in soils and earthworms: A silent threat? Agriculture[END_REF]) and in seeds (e.g., [START_REF] Lopez-Antia | Risk assessment of pesticide seed treatment for farmland birds using refined field data[END_REF]Fernandez-Vizcaino et al. 2022), suggesting that these molecules might also be transferred to wildlife through trophic webs (Tassin de [START_REF] Montaigu | Identifying agricultural pesticides that may pose a risk for birds[END_REF]Bellot et al. 2022a). Indeed, recent studies have shown that human food could be contaminated by triazoles [START_REF] Zambonin | Solid-phase microextraction and gas chromatography-mass spectrometry for the rapid screening of triazole residues in wine and 30 strawberries[END_REF][START_REF] Cui | Cumulative risk assessment of dietary exposure to triazole fungicides from 13 daily-consumed foods in China[END_REF]). However, field data are currently lacking to assess if, and to what extent, the use of triazoles in agroecosystems and urban areas could contaminate non-target wild vertebrate species (e.g. [START_REF] Bro | Quantification of potential exposure of gray partridge (Perdix perdix) to pesticide active substances in farmlands[END_REF][START_REF] Mateo | Risk assessment of lead poisoning and pesticide exposure in the declining population of red-breasted goose (Branta ruficollis) wintering in Eastern Europe[END_REF]Esther et al. 2021), and these crucial data are lacking to better understand the threat that these contaminants may represent for wildlife. Some experimental studies have shown that azoles can affect physiological functions and have detrimental effects on fitness in animal models [START_REF] Cao | Developmental toxicity of the triazole fungicide cyproconazole in embryo-larval stages of zebrafish (Danio rerio)[END_REF][START_REF] Souders | Sublethal effects of the triazole fungicide propiconazole on zebrafish (Danio rerio) development, oxidative respiration, and larval locomotor activity[END_REF][START_REF] Sun | Exposure to prothioconazole induces developmental toxicity and cardiovascular effects on zebrafish embryo[END_REF];[START_REF] Bellot | Experimental exposure to tebuconazole affects metabolism and body condition in a passerine bird, the house sparrow (Passer domesticus)[END_REF]. A few studies have also demonstrated that the consumption of triazole-coated seeds may impair reproduction and physiological functions of granivorous birds living in agroecosystems (Fernandez-Vizcaino et al. 2020;[START_REF] Lopez-Antia | Birds feeding on tebuconazole treated seeds have reduced breeding output[END_REF]. For example, tebuconazole is known to affect metabolism in passerine birds with a potential impact on body condition and energy regulation (Bellot et al. 2022b). However, we currently lack field data to assess the contamination of wild vertebrates by multiple triazoles in several habitats. Such data are crucial not only to better understand which triazoles are the most likely to contaminate wild vertebrates, but also to determine the degree of contamination of wild vertebrates by these triazoles. These basic data are required to assess the potential threat that different triazoles may entail for wildlife, and by extension for humans [START_REF] Richard | Warning on nine pollutants and their effects on avian communities[END_REF].

In this study, we aimed to document for the first time the degree of contamination of a generalist wild bird species by multiple azoles that are used for plant protection and fungi pest control in multiple habitats. We captured 118 Common blackbirds (Turdus merula) in an agroecosystem (vineyard), a protected forest, and a middle-sized city in western France. We collected blood and analyzed the plasma levels of 13 triazoles and 2 imidazoles by using an on-line SPE coupled to LC-MS methodology. Plasma can be collected ethically and it is a relevant matrix to analyse the levels of azoles by LC-MS/MS because these fungicides are distributed to tissues through the circulatory system [START_REF] Gross | Uptake, Metabolism, and Elimination of Fungicides from Coated Wheat Seeds in Japanese Quail (Coturnix japonica)[END_REF]. In addition, we used passive air samplers and available environmental data to evaluate the use of azoles in each habitat (i.e. from a national database on the sales of pesticides (BNVD), City Council, and National Forestry Agency). The Common blackbird is a ubiquitous species, being found in a wide variety of habitats. It is omnivorous, feeds on invertebrates, seeds and berries, and can be found in all three targeted habitats, which allows some comparison [START_REF] Mohring | Vineyards, but not cities, are associated with lower presence of a generalist bird, the Common Blackbird (Turdus merula), in Western France[END_REF]. Overall, this species is less present in vineyards relative to urban and forest areas, suggesting that it may be impacted by agricultural practices [START_REF] Mohring | Vineyards, but not cities, are associated with lower presence of a generalist bird, the Common Blackbird (Turdus merula), in Western France[END_REF].

Accordingly, the blackbird has previously been considered as a relevant sentinel species of its environment and used to monitor the degree of contamination of its habitat [START_REF] Fritsch | Influence of landscape composition and diversity on contaminant flux in terrestrial food webs: A case study of trace metal transfer to European blackbirds Turdus merula[END_REF][START_REF] Meillère | Corticosterone levels in relation to trace element contamination along an urbanization gradient in the common blackbird (Turdus merula)[END_REF]Diez-Fernandez et al. 2022). To further examine the potential impact of triazoles on this sentinel species, we also focused on two integrative biomarkers of health status (body condition and plumage quality). These biomarkers do not systematically respond to exposure to pesticides (e.g., Lopez-Antia et al. 2018) and additional physiological measurements are necessary to fully evaluate the impact of pesticides on non-target vertebrate species. However, they are useful to consider because they can provide initial warning signs on the potential disrupting effect of pesticides on physiology (metabolism, endocrine axes). We predicted that (1) the contamination of blackbirds by triazoles wil be especially important in the vineyards, where triazoles are massively used; (2) the contamination will be moderate in the urban habitat where triazoles could be used by people in private gardens; (3) the contamination will be minimal in the protected forest where triazoles are not used. Importantly, we predicted that (4) different compounds may contaminate blackbirds in each habitat because some triazoles are used for specific crops or purposes only (e.g. difenoconazole in vineyards or tebuconazole in urban areas, [START_REF] Désert | Spatial and temporal distribution of current-use pesticides in ambient air of Provence-Alpes-Côte-d'Azur Region and Corsica, France[END_REF]. Finally, we also predicted that (5) body condition and feather quality may differ between the three habitats because of contrasted environmental condition and/or agricultural practices; (6) body condition and feather may be correlated with the degree of contamination by specific triazoles if these fungicides have important toxic and sublethal effects on non-target bird species.

Materials and methods

Study sites

All birds were captured in 2016 and 2019 during the breeding season (April-July) in three geographical areas, which are located in the 'Nouvelle Aquitaine' region in western France. These three areas consist of forest, urban and vineyard areas and they are therefore characterized by different anthropogenic activities and by contrasted use of azoles. Firstly, birds were captured in a protected fenced forest where human activity is extremely limited. This forest covers 2500 ha and is surrounded by arable land. There has been absolutely no use of any azole in this protected forest since 2001 (National Forestry Agency (ONF), personal communication) but specific azoles are used in the arable land surrounding this forest and some birds were captured within 200 m of the edge of the forest (see Annex 2). Secondly, birds were captured in the city of Niort, which is a medium-sized town with multiple parks and gardens. Birds were captured in recreational areas, such as parks or private properties. These parks and gardens were characterized by ornamental trees, shrubs and flowers. Although azoles have not been used by the city since 2014 (Niort City Council, personal communication), inhabitants could use specific azoles in their private backyards. The urban capture sites were located at least 1 km from any agricultural area. Thirdly, birds were captured in the Cognac vineyard where azoles are massively used to protect the vines and the grapes against fungi such as mildew or black rot. To evaluate the use of pesticides in this area through the study period (from 2016 to 2019), we relied on the data from the national database on the sales of pesticides (BNVD, https://geo.data.gouv.fr/), which provides the quantity of azoles that are sold each year in each municipality (see Annex 2). Common blackbirds were captured in all three habitats in 2016 while they were only captured in the vineyards and the forest in 2019. We did not conduct any capture sessions in 2018 and the field work had to be cancelled/stopped in 2017 because the capture rate was unexpectedly very low. A total of 118 blackbirds were captured in 2016 (urban: 22, forest: 28, vineyard: 28) and 2019 (forest:6, vineyard: 34).

Study species, capture, measurements and blood sampling

In this study, we focused on the Common Blackbird, a common passerine that is often used as an avian sentinel species in ecotoxicological studies [START_REF] Fritsch | Influence of landscape composition and diversity on contaminant flux in terrestrial food webs: A case study of trace metal transfer to European blackbirds Turdus merula[END_REF][START_REF] Meillère | Corticosterone levels in relation to trace element contamination along an urbanization gradient in the common blackbird (Turdus merula)[END_REF]Diez-Fernandez et al. 2022). This species is relatively abundant and can be easily found and captured in forests, cities and vineyards and it is therefore a relevant bird species to study our question of interest [START_REF] Mohring | Vineyards, but not cities, are associated with lower presence of a generalist bird, the Common Blackbird (Turdus merula), in Western France[END_REF]. Birds were captured by using mist-nets that were located between bushes, trees, or along hedgerows in the forest, urban parks or vineyards. Upon capture, the age of all individuals and the sex of adults were determined by using plumage characteristics. Immediately after capture, a blood sample was collected by puncture of the brachial vein with a 25G needle, and the two inner tail feathers were plucked for feather quality analyses [START_REF] Meillère | Growing in a city: Consequences on body size and plumage quality in an urban dweller, the house sparrow (Passer domesticus)[END_REF]. The blood (~100µL) was then centrifuged and the plasma was separated from the red blood cells. Plasma was stored at -20 °C until analyses at the LPTC-EPOC (see below). All individuals were measured (wing size) and weighed (body mass) by using a ruler (+/-0.5 mm) and an electronic scale (+/-0.1 g), respectively. Body condition was then expressed as the residuals of the regression between body mass and wing size (linear regression: F1,116 = 4.05, p = 0.046, r = 0.184). All individuals were released within 15 minutes of capture. Feathers were dried in an oven for 48 hours and were then measured with a caliper (total length in mm, ± 0.01 mm) and weighed with a precision scale (± 0.1 mg). A proxy of feather density was calculated by dividing feather mass by feather total length [START_REF] Meillère | Growing in a city: Consequences on body size and plumage quality in an urban dweller, the house sparrow (Passer domesticus)[END_REF].

Passive air samplers

In addition to the information on the use of pesticides (ONF, Niort City Council, BNVD database), passive air samplers were deployed in 2017 from the beginning of January to the end of July to with the aim of gaining a better understanding of how azoles were used in the study sites. They house a polyurethane foam (PUF) disk (Tish Environmental, Cleaves, United States), which is uniformly porous to allow chemicals to penetrate. Before application in the field, they were pre-cleaned using an Accelerated Solvent Extractor (ASE Dionex 350, Thermo Fisher Scientific, Asnières sur Seine, France) with dichloromethane solvent (pressure 100 bars; temperature 90 °C; heat 5 min; static 8 min; flush volume 50 %; purge 300 s; 2 cycles), dried and stored in sealed bags.

To avoid any potential effect of rain, sunlight, wind or particle deposition and to standardize the sampling process, each PUF disk (14 cm diameter; 1.35 cm thickness; 365 cm 2 surface area) was deployed in a conventional 'flying saucer' housing that was fixed on a pole at a height of 1.30 m [START_REF] Tuduri | Polyurethane foam (PUF) disks passive air samplers: Wind effect on sampling rates[END_REF][START_REF] Estellano | Using PUF disk passive samplers to simultaneously measure air concentrations of persistent organic pollutants (POPs) across the Tuscany Region, Italy[END_REF]. A passive air sampler was deployed in the vineyards while another one was deployed in the protected forest. No passive air sampler was deployed in the urban area because we did not have access to any private properties and such systems had previously been stolen in public urban areas. Passive air samplers were not deployed in 2019 because we did not have the authorization to deploy such systems in any private properties in the urban or vineyard areas during this specific year. Each PUF was left in the field for approximately a month. After each month, the PUF was removed from the device, stored in aluminum foil and in a Ziploc freezer bag and kept at -20 °C until further analysis at the LPTC-EPOC.

Plasma levels of azoles

Plasma levels of azoles were determined using LC-MS/MS methodology. Briefly, samples were first extracted using the following procedure: a 1.5 mL microtube, 25 µL of plasma samples, and 30 µL of internal standards solution prepared at 1 ng.g -1 in acetonitrile, were weighed. After a centrifugation step to precipitate proteins, ultra-pure water was added to the supernatant. Then, 600 µL of each extract was injected in the analytical system composed of a Poroshell 120 SB C18 guard column (Agilent) used as on-line SPE cartridge. They were loaded at 2 min at a 1000 µL.min -1 rate with Milli-Q water as eluent. Target compound separation was achieved on a Poroshell Phenyl-Hexyl column (Agilent) using a temperature of 35 °C and a flow rate of 0.3 mL.min -1 . Mobile phase A consisted of water with 5 ammonium acetate plus 0.1 % formic acid and mobile phase B of methanol plus 0.1 % formic acid. The following gradient program was applied: 0 to 100 % B over 18 mins, holding at 100 % B for 0.5 mins and returning to initial conditions at 19 mins for 3 mins to even up. The quantification of azoles was performed by using the 6490 Triple Quad mass spectrometer (Agilent) in positive electrospray mode (ESI+) in dynamic multiple reactions monitoring (dMRM). Finally, we checked the analytical sequence with quality controls and we used internal standards to calculate the concentration. The limit of quantification (LOQ) of azoles differed between compounds and varied between 10 and 400 pg.g -1 of plasma except for triadimenol (1500 pg.g -1 of plasma).

PUF levels of azoles

A disk quarter was weighed and coiled in stainless-steel 34 mL pre-conditioned cells (Thermo Fisher Scientific, Asnières sur Seine, France). Into the cells, 20 µL of internal standards solution were also added at 0.5 µg.g -1 in methanol. The conditioning and extraction programs were the same as previously described using acetonitrile instead of dichloromethane solvent. 45 mL of extracts were recovered and evaporated to 3 mL with RapidVap (Labconco, Illkirch, France). The 3 mL were then evaporated to 300 µL under a gentle nitrogen stream. The extracts were then refrigerated until quantification. Quantification was performed by injecting 5 µL of the extracts into a 6495 or 6495B LC-MS/MS (Agilent) with the same separation conditions previously described for the plasma.

Concentrations were calculated in ng.PUF -1 . LOQ were established taking the maximum extrapolation between LOQ calculated for sample concentration or spiked PUF at 50 ng.PUF -1 (see Appendix 1).

Extraction and quantification recoveries were also obtained for the spiked PUF (see Appendix 1).

Concentrations in ng.PUF -1 were then converted in order to estimate the air concentrations (ng.m -3 ) by using estimated air sampling rate for the PUFs (m 3 .day -1 ) and the duration of PUF deployment in the field. Classically, these rates range from 1 to 8 m 3 .day -1 [START_REF] Chaemfa | Field calibration of polyurethane foam (PUF) disk passive air samplers for PCBs and OC pesticides[END_REF][START_REF] Harner | Global pilot study for persistent organic pollutants (POPs) using PUF disk passive air samplers[END_REF] and the mean of these two values (4.5 m 3 .day -1 ) was therefore used. The average duration of PUF deployment (i.e., 35 days) was chosen to calculate the estimated LOQ in ng.m -3 .

Statistical analyses

All statistical analyses were run using SAS University edition. Firstly, descriptive and basic statistics were provided for the concentrations of azoles that were quantified in the air and the plasma of blackbirds in the vineyard and the forest habitats. Secondly, Chi-squared tests were used to compare the probability of quantifying specific azoles in the plasma of blackbirds between habitats (forest, urban, and vineyard). General linear models (GLMs) were also run to test the influence of the habitat (urban, forest, and vineyard), the year of sampling (2016,2019), the date of sampling, and the status (juvenile, adult male, adult female) on the probability of detecting specific azoles in the plasma of blackbirds (the results are detailed in the Appendix 2). These additional GLMs were only run for the four azoles that were detected in a sufficient number of blackbirds (> 15 % of all sampled blackbirds, see Appendix 2). Thirdly, GLMs were used to test the influence of the date of sampling, the year of sampling (2016 and 2019), and the status of the bird (juvenile, adult male, adult female) on the plasma levels of azoles. These analyses were run in the vineyard habitat only and for the four azoles that were quantified in at least 15 % of the sampled blackbirds. They were not run for the urban and the forest habitats because azoles were not quantified in a sufficient number of blackbirds to allow such analyses. Fourthly, GLMs were used to test the influence of the habitat (urban, forest, vineyard), the date of sampling, the year of sampling, and the status of the birds on body condition, and feather density. Finally, GLMs were used to separately test the influence of four azoles (difenoconazole, fenbuconazole, tebuconazole, tetraconazole) on body condition, and feather density (independent variables: year of sampling, date of sampling, habitat, specific plasma azole levels). These analyses were run in the vineyard habitat only and for the four azoles that were quantified in at least 15 % of the sampled blackbirds living in vineyards. They were not run for the urban and the forest habitats because azoles were not quantified in a sufficient number of blackbirds to allow such analyses. All plasma levels of azoles were log transformed to improve the distribution and the variance of the residuals.

Results

Description of the levels of azoles: Passive air samplers

Several triazoles were quantified in the passive air samplers that were deployed in the forest and the vineyard areas (cyproconazole, epoxiconazole, propiconazole, metconazole, tebuconazole, tetraconazole, difenoconazole, penconazole, fenbuconazole), but interestingly, three of them could only be quantified in the vineyard habitat (difenoconazole, penconazole, fenbuconazole) and were totally absent from the forest habitat (Table 1). These quantified azoles correspond overall to the azoles that were sold in 2017 in the vineyard area and in the areas surrounding the forest site (BNVD database, see Annex 2). Importantly, the same azoles were sold from 2016 to 2019 in these areas despite some slight inter-annual variations.

In both habitats, azoles were not quantified until April (except tebuconazole, which was quantified at very low levels in March/April in the forest habitat). From April to June, most azoles were similarly quantified in the forest and the vineyard habitat at relatively low (cyproconazole, metconazole, propiconazole: < 0.03 ng.m -3 ) or intermediate (epoxiconazole: < 0.06 ng.m -3 ) concentrations (Table 1). However, in May and to a greater extent in June, five azoles were quantified at high concentrations (difenoconazole, penconazole: > 0.20 ng.m -3 in June) or very high concentrations (fenbuconazole, tebuconazole, tetraconazole: > 0.60 ng.m -3 in June) in the vineyard while they were not quantified (difenoconazole, fenbuconazole, and tetraconazole) or only at low concentrations (tebuconazole: < 0.08 ng.m -3 in June) in the forest habitat (Table 1; Fig. 1).

Description of the levels of azoles: Blackbird plasma samples

Several azoles were found in blackbird plasma samples (cyproconazole, difenoconazole, fenbuconazole, flusilazole, metconazole, tebuconazole, tetraconazole) although others were not quantified, notably epoxiconazole, flutriafol, imazalil, penconazole, prochloraz, propiconazole, prothioconazole, tradimefon, and triadimenol (Table 2). No azoles were quantified in all sampled urban blackbirds (Table 2), while several azoles were quantified in vineyard birds, sometimes at very high concentrations, and in most, if not all, captured blackbirds (e.g., tetraconazole, Table 2). In forest birds, a few azoles were quantified in a small percentage of the captured blackbirds, but often at low concentrations (e.g., cyproconazole, tebuconazole, Table 2).

Difenoconazole, fenbuconazole, tebuconazole, and tetraconazole were more frequently quantified in blackbirds from vineyards relative to those from the protected forest (difenoconazole: p = 0.001, fenbuconazole: p = 0.013, tebuconazole: p = 0.027, tetraconazole: p < 0.001) while this was not the case for cyproconazole, flusilazole, and metconazole (cyproconazole: p = 0.939, flusilazole: p = 0.456, metconazole: p = 0.826). Similarly, difenoconazole, fenbuconazole, tebuconazole, and tetraconazole were more frequently quantified in blackbirds from vineyards relative to those from the urban area (difenoconazole: p = 0.008, fenbuconazole: p = 0.045, tebuconazole: p = 0.002, tetraconazole: p < 0.001) while this was not the case for cyproconazole, flusilazole, and metconazole (cyproconazole: p = 0.394, flusilazole: p = 0.549, metconazole: p = 0.293). Finally, there was no significant difference in the quantification of any azole between the blackbirds from the protected forest and those from the urban area (all p > 0.05).

Plasma levels of azoles in vineyard blackbirds

In vineyards, 4 azoles were found in a large proportion of birds and were further analyzed (difenoconazole, fenbuconazole, tebuconazole and tetraconazole). All the others were not quantified in a sufficient percentage of birds to allow further statistical analyses (< 15 % of sampled birds, Table 2).

Difenoconazole, fenbuconazole, and tetraconazole levels were significantly affected by the date of sampling with increasing difenoconazole levels throughout the season (difenoconazole: F1,57 = 5.30, p = 0.025, fenbuconazole: F1,59 = 9.71, p = 0.003, tetraconazole: F1,60 = 28.26, p < 0.001; Fig. 2) but this relationship was not significant for tebuconazole levels (F1,59 = 2.10, p = 0.153, Fig. 2). In addition, difenoconazole, fenbuconazole, tebuconazole levels were significantly higher in 2016 relative to 2019 (difenoconazole: F1,57 = 5.13, p = 0.027, fenbuconazole: F1,59 = 7.68, p = 0.007, tebuconazole: F1,60 = 17.05, p = 0.001, Fig. 2) but this difference was not significant for tetraconazole levels (F1,59 = 0.45, p = 0.507, Fig. 2). Finally, difenoconazole, fenbuconazole, tebuconazole, and tetraconazole levels were not significantly affected by the status of the birds and they did not differ between juveniles, adult males, and adult females (difenoconazole: F2,57 = 2.00, p = 0.144, fenbuconazole: F2,57 = 0.08, p = 0.927, tebuconazole: F2,57 = 0.79, p = 0.457, tetraconazole: F2,57 = 0.48, p = 0.624).

Body condition

Body condition was significantly affected by the status of the bird (F2,112 = 15.57, p < 0.001, Fig. 3A) and the habitat of sampling (F2,112 = 3.79, p = 0.026, Fig. 3A), but body condition did not differ between 2016 and 2019 (F1,111 = 2.08, p = 0.152). Body condition also decreased through the breeding season (F1,112 = 4.14, p = 0.044). Specifically, females were in better condition than males (χ² = 21.44, p < 0.001, Fig. 4A) and juveniles (χ² = 24.92, p < 0.001, Fig. 3A), but males and juveniles did not significantly differ in body condition (χ² = 0.77, p =0.381, Fig. 3A). In addition, urban blackbirds were in better condition than vineyard (χ² = 5.40, p = 0.020, Fig. 3A) and forest blackbirds (χ² = 7.06, p = 0.008, Fig. 3A), but vineyard and forest blackbirds did not significantly differ in body condition (χ² = 0.67, p =0.412, Fig. 3A).

In vineyards where four azoles were detected in a sufficient number of blackbirds, we then tested whether body condition was related to the plasma levels of these azoles. In these birds from vineyards, body condition was not correlated with the plasma levels of difenoconazole (F1,56 = 2.70, p = 0.106), fenbuconazole (F1,56 = 0.20, p = 0.660), tebuconazole (F1,56 = 0.16, p = 0.687), and tetraconazole (F1,56 = 1.11, p = 0.296). However, body condition was significantly affected by the status of the bird (F2,56 = 10.86, p < 0.001, Fig. 3A) with a higher body condition for females relative to males (χ² = 16.94, p < 0.001) and juveniles (χ² = 14.25, p < 0.001). Body condition also decreased with the date of sampling (F1,56 = 7.24, p = 0.009, Fig. 3A), but body condition did not differ between 2016 and 2019 (F1,56 = 0.64, p = 0.426).

Feather quality

Feather density was significantly affected by the status of the bird (F2,100 = 7.41, p = 0.001, Fig. 3B) but not by the habitat of sampling (F2,100 = 0.85, p = 0.429, Fig. 3B). Feather density did not differ between 2016 and 2019 (F1,100 < 0.01, p = 0.948) but feather density decreased through the breeding season (F1,100 = 5.30, p = 0.023). Specifically, males had denser feathers than females (χ² = 14.82, p < 0.001) but males and juveniles did not significantly differ in feather density (χ² = 2.79, p =0.095, Fig. 3B). Females and juveniles did not significantly differ in feather density (χ² = 3.20, p =0.073, Fig. 3B).

In vineyards where four azoles were detected in a sufficient number of blackbirds, we then tested whether body condition was related to the plasma levels of these azoles. Feather density was not correlated with the plasma levels of difenoconazole (F1,51 = 0.93, p = 0.338), fenbuconazole (F1,51 = 0.51, p = 0.480), tebuconazole (F1,51 = 0.19, p = 0.662), and tetraconazole (F1,51 < 0.01, p = 0.961).

However, feather density was to all intents and purposes significantly affected by the status of the bird (F2,52 = 2.99, p = 0.059, Fig. 4A) with a higher feather density for males relative to females (χ² = 5.63, p = 0.017) but no difference between females and juveniles (χ² = 0.34, p = 0.558) and males and juveniles (χ² = 2.53, p = 0.110). Feather density was not correlated with the date of sampling (F1,52 = 2.35, p = 0.131), and feather density did not differ between 2016 and 2019 (F1,52 < 0.01, p = 0.974).

Discussion

In this study, we examined the plasma levels of multiple azoles of a wild bird species in several habitats. Importantly, we found that blackbirds living in vineyards have high plasma levels of multiple azoles. This high level of contamination was not found in the blackbirds from the protected forest and from the urban habitat. Interestingly, we also report that the contamination of blackbirds living in vineyard was especially great at the end of Spring and the beginning of Summer. This level of contamination by azoles matches perfectly with the results from the passive air samplers that were deployed in the forest and the vineyard habitats in 2017 (i.e., high levels of azoles in the air of vineyards during June and July). However, we did not find any correlation between the levels of plasma contamination by azoles and two simple integrative biomarkers of health (feather density and body condition) in this sentinel species. To better evaluate the impact of such contamination on the health of non-target vertebrate species, experimental studies focusing on key physiological and biochemical mechanisms are now required.

Contamination of blackbirds by azole fungicides

We analyzed the plasma of a wild vertebrate sentinel species to detect specific azole fungicides that are used for pest control in multiple habitats. Importantly, we showed that the degree of contamination of wild birds by azole fungicides varies significantly between habitats. Indeed, multiple azoles were found in very high concentration in the plasma of blackbirds living in vineyards.

In contrast, and contrary to our prediction, azoles could not be quantified in the plasma of urban blackbirds. Finally, some azoles were found in the plasma of some forest blackbirds but at relatively low concentrations.

These results demonstrate that azole fungicides can contaminate wild birds living in agroecosystems, and especially in the vineyards that are characterized by a massive use of fungicides in general, and of azoles in particular (Komarek et al. 2010). These findings corroborate recent studies which have reported the presence of azole fungicides in the urine or the hair of humans working in vineyards [START_REF] Fustinoni | Biological monitoring of exposure to tebuconazole in winegrowers[END_REF][START_REF] Mercadante | Assessment of penconazole exposure in winegrowers using urinary biomarkers[END_REF][START_REF] Polledri | Determination of tebuconazole and penconazole fungicides in rat and human hair by liquid chromatography/tandem mass spectrometry[END_REF]. Regarding wild vertebrates, data are however very limited. A few studies have found that triazoles could be found in wild vertebrates living in agroecosystems but these studies have only found residues of azole fungicides in a limited number of very specific matrices (e.g., eggs of birds, [START_REF] Bro | Quantification of potential exposure of gray partridge (Perdix perdix) to pesticide active substances in farmlands[END_REF][START_REF] Millot | Little field evidence of direct acute and short-term effects of current pesticides on the grey partridge[END_REF]; feces of birds, Esther et al. 2021) or in the food that could be consumed by wild vertebrates (e.g., seeds, Fernandez-Vizcaino et al. 2020, 2022;[START_REF] Lopez-Antia | Risk assessment of pesticide seed treatment for farmland birds using refined field data[END_REF]invertebrates, Pelosi et al. 2021). To our knowledge, the level of contamination of wild vertebrates by azole fungicides has never been determined in vineyard ecosystems. Here, we report very high levels of contamination for four specific compounds that could be of serious concern for non-target species (tetraconazole, tebuconazole, difenoconazole, fenbuconazole), and that could contribute to the current declines of farmland birds [START_REF] Cabodevilla | Farmland composition and farming practices explain spatio-temporal variations in red-legged partridge density in central Spain[END_REF].

In cities, azole fungicides are mainly used against fungi attacks of ornamental flowers, shrubs and fruit trees [START_REF] Politi Bertoncini | Local gardening practices shape urban lawn floristic communities[END_REF][START_REF] Muratet | Contrasting impacts of pesticides on butterflies and bumblebees in private gardens in France[END_REF] but the absence of azole contamination in urban blackbirds suggests that azole fungicides are used to a very limited extent in cities, or at least in the city where we sampled blackbirds. Our study therefore suggests that urban blackbirds probably do not suffer from any potential detrimental effects of triazoles on physiology or performance (survival or reproduction, see [START_REF] Lopez-Antia | Birds feeding on tebuconazole treated seeds have reduced breeding output[END_REF]or Bellot et al. 2022b for examples of such effects in birds) although they may be affected by other urban contaminants (e.g., heavy metals, [START_REF] Meillère | Corticosterone levels in relation to trace element contamination along an urbanization gradient in the common blackbird (Turdus merula)[END_REF][START_REF] Fritsch | Exposure to Pb impairs breeding success and is associated with longer lifespan in urban European blackbirds[END_REF]Bauerova et al. 2020).

The contamination of a few blackbirds by azole fungicides in the protected forest was unexpected because of the absence of use of fungicides in that habitat. This astonishing result could be explained by the past use of azole fungicides to protect the harvested wood when the forest was not protected [START_REF] Reinprecht | Fungicides for wood protection-World viewpoint and evaluation/testing in Slovakia[END_REF]) but this is highly unlikely because this forest has been protected for almost 20 years and the half-life of most azole fungicides is limited to a maximum of a few months. Instead, this result could be explained by the surroundings of the protected forest, which is characterized by cereal crops, in which azole fungicides are used and can contaminate soil and invertebrates [START_REF] Pelosi | Residues of currently used pesticides in soils and earthworms: A silent threat? Agriculture[END_REF]. Indeed, the National database on the sale of pesticides (BNVD) confirms that several azoles are used in the crops located around the forest (especially cyproconazole, epoxiconazole, metconazole, propiconazole, tebuconazole, and tetraconazole, see Annex 1). The use of azole fungicides in these crops could potentially contaminate forest blackbirds if the azoles are transferred to the forest through air circulation, as suggested by the results from our passive air samplers that were deployed in the middle of this protected forest. In addition, blackbirds could be contaminated by azoles while foraging on the edges of the protected forest (i.e., in cereal crops), especially if they feed on treated seeds (Fernandez-Vizcaino et al. 2020;Tassin de Montaigu & Goulson 2020).

However, it is important to note that azole fungicides were found in only a few birds and at very moderate levels (see Table 2), demonstrating that blackbirds living in this protected forest are not highly contaminated by azole fungicides.

Passive air samplers and the contamination of blackbirds by azole fungicides

Our study reports that the degree of contamination of blackbird plasma by azole fungicides mirrors what is released in the environment. Firstly, we found that 4 main azoles were quantified at high concentrations in the PUFs that were deployed in the vineyards, and these 4 same azoles were quantified in a large percentage of vineyard blackbirds. Importantly, these azoles were quantified mostly in June/July, when they were also detected in the plasma of blackbirds. In addition, they were not quantified before June either in the PUFs or in the blackbird plasmas. Finally, three of these four azoles were not detected in the PUFs that were deployed in the protected forest (difenoconazole, fenbuconazole, tetraconazole) and they were also not quantified in the blackbirds living in this forest (or in only a very small percentage of plasma samples). Interestingly, a few other triazoles were quantified in the PUFs and concomitantly in blackbird plasma samples, either in the vineyard or in the protected forest (e.g., metconazole, cyproconazole). For example, tebuconazole was quantified in the PUF that was deployed in the protected forest and this specific triazole was also quantified in some blackbirds living in this forest. All these results demonstrate that the Common Blackbird is an appropriate sentinel species to investigate the release of azole fungicides in the environment. Unfortunately, we were not able to deploy a PUF in the urban habitat, precluding us from linking the levels of azoles in the passive air samplers with those that were quantified in urban blackbirds. It is also important to note that the passive air samplers were deployed in 2017 while the sampling of blackbirds was conducted in 2016 and 2019 (unfortunately, our capture rate was very low in 2017 in most habitats). This could explain some of the discrepancies that we reported between passive air samplers and blackbird plasmas (e.g., epoxiconazole that was detected in the PUFs but not in blackbirds). However, it is very unlikely that this affected our interpretation since the same azole fungicides were sold in the vineyard sites where we sampled blackbirds over that period (i.e., from 2016 to 2019, BNVD database, see Annex 1). Moreover, the same main azoles were quantified in blackbird plasma in 2016 and 2019 in the vineyards (tetraconazole, tebuconazole, fenbuconazole, difenoconazole) and in the protected forest (tebuconazole), futher supporting the data from the BNVD database (see Annex 1), and the idea that the same azoles were used over that period in vineyards.

Among the 4 main azoles that were quantified in vineyard blackbirds, we found that the levels of azoles in blackbird plasma was much more important for some of them (e.g., tetraconazole) than others (e.g., difenoconazole). This may be related to a higher use or release of specific azoles in the environment but also suggests that some triazoles are more likely to contaminate wild vertebrates than others, or at least more likely to be quantified in wild bird plasma. This could be related to specific chemical properties of specific triazoles (half-life, solubility) and to the ability of the organisms to metabolize these azole fungicides (see [START_REF] Gross | Uptake, Metabolism, and Elimination of Fungicides from Coated Wheat Seeds in Japanese Quail (Coturnix japonica)[END_REF]). Thus, some azole fungicides may be more prone to contaminate the food web than others (soils, plants, invertebrates, etc.), and as a result, blackbirds may ingest higher quantities of some triazoles relative to others. It is also possible that blackbirds absorb some triazoles to a greater extent than others. Similarly, blackbirds, and wild vertebrates in general, may metabolize some azoles quicker than others [START_REF] Gross | Uptake, Metabolism, and Elimination of Fungicides from Coated Wheat Seeds in Japanese Quail (Coturnix japonica)[END_REF].

Complementary studies are definitely needed to better understand the metabolism and the elimination of azoles fungicides in wild vertebrates (see [START_REF] Hao | Enantioselective accumulation, elimination and metabolism of fenbuconazole in lizards (Eremias argus)[END_REF] for an example).

Impact of the plasma levels of azoles on body condition and feather density

Despite important differences in plasma levels of azole fungicides between the three habitats, we did not report any strong difference in two proxies of health status between these habitats. Feather density was similar between the vineyard, the urban and the forest habitats. We found that urban birds were in better condition than vineyard blackbirds, but it is very unlikely to be related to the contamination by azoles. Indeed, vineyard blackbirds were of similar body condition as forest blackbirds despite much higher levels of azoles in their plasma. In addition, we did not find any significant relationship between either body condition or feather density and any plasma levels of azoles for the birds living in vineyards, suggesting that the most contaminated birds were not dramatically impacted by this contamination. However, previous studies have found that this specific triazole can impair reproduction in vertebrates (e.g., [START_REF] Lopez-Antia | Experimental exposure of red-legged partridges (Alectoris rufa) to seeds coated with imidacloprid, thiram and difenoconazole[END_REF], 2021) and we must remain very cautious because azole fungicides can disappear quite quickly from the plasma after ingestion or inhalation [START_REF] Gross | Uptake, Metabolism, and Elimination of Fungicides from Coated Wheat Seeds in Japanese Quail (Coturnix japonica)[END_REF][START_REF] Hao | Enantioselective accumulation, elimination and metabolism of fenbuconazole in lizards (Eremias argus)[END_REF]. This fast elimination from the plasma could also explain why we did not find strong correlations between plasma levels of azoles and our proxies of health status. The study of complementary and integrative matrices appears relevant to further test the link between the degree of contamination and the health of non-target species (e.g., feces, Esther et al. 2021 andfeather: Rutkowska et al. 2018). Importantly, our study also demonstrates that the plasma levels of azole fungicides are especially high in June/July, which corresponds to the period of reproduction, and therefore, to the period of development for many vertebrate species. This means that chicks and juveniles can endure very high levels of plasma azoles during the ontogenetic phase of their organismal systems, and this is of particular concern because previous studies have found negative impacts of azole fungicides on reproduction and development in vertebrates (e.g., [START_REF] Fernández-Vizcaíno | Multi-level analysis of exposure to triazole fungicides through treated seed ingestion in the red-legged partridge[END_REF][START_REF] Lopez-Antia | Birds feeding on tebuconazole treated seeds have reduced breeding output[END_REF]. In this study, we only looked at two simple and integrative biomarkers of health status that are also known to be affected by multiple environmental factors (e.g., food abundance, weather). These simple biomarkers did not necessarily allowe us to detect sublethal effects and further studies are needed to test the impact of azole fungicides on several physiological mechanisms such as glucose and protein regulation, mitochondrial function, metabolism, and endocrine mechanisms (see [START_REF] Li | Parental exposure to tebuconazole causes thyroid endocrine disruption in zebrafish and developmental toxicity in offspring[END_REF][START_REF] Cao | Developmental toxicity of the triazole fungicide cyproconazole in embryo-larval stages of zebrafish (Danio rerio)[END_REF][START_REF] Souders | Investigation into the sub-lethal effects of the triazole fungicide triticonazole in zebrafish (Danio rerio) embryos/larvae[END_REF]Fernandez-Vizcaino et al. 2020;Bellot et al. 2022b for some examples). Finally, our study reports the plasma levels of azoles for birds living in multiple habitats, including highly contaminated ones. These data will be of primary importance for further experimental captive studies because they will allow ecotoxicologists to know which doses of azoles need to be used to test the impact of environmentally realistic doses of azole fungicides on physiological systems and performance of vertebrates.

. Table 1: Concentrations of azoles in the passive air samplers that were deployed in the vineyard and the forest habitats. PUFs were deployed for approximately a month before being replaced by a new one. The concentrations of azoles were calculated in ng.m -3 after taking into account the duration of deployment of each PUF and an estimated air sampling rate for the PUFs (4.5 m -3 .day -1, , see [START_REF] Chaemfa | Field calibration of polyurethane foam (PUF) disk passive air samplers for PCBs and OC pesticides[END_REF]. Urban birds were in better condition than vineyard and forest birds. Adult females (white) were also in better condition than adult males (black) and juveniles (grey). Data are expressed as means ± standard errors. Different letters represent significant differences between habitats (p < 0.05).

Figure legends

Figure 1 :

 1 Figure 1: Azole air concentrations (ng.m -3 ) derived from deployments of PUF disk passive air samplers

Figure 2 :

 2 Figure 2: Plasma levels of the azoles (pg.g -1 of plasma) that were quantified in at least 15% of the

Figure 3 :

 3 Figure 3: Influence of age, sex and habitat on body condition (A) and feather density (B) in blackbirds.

  Figure2:

Acknowledgements

This research has been funded by the Agence Nationale de la Recherche (ANR project URBASTRESS, ANR-16-CE02-0004-01 to F.A., and ANR project VITIBIRD to F.A.), by the Région Nouvelle-Aquitaine (MULTISTRESS project), by the Agence Nationale de Sécurité Sanitaire de l'Alimentation, de l'Environnement et du Travail (ANSES project BiodiTox # 2019/1/031), by the Contrat Plan-Etat Région (CPER) ECONAT, and by the Centre National de la Recherche Scientifique. We thank H. Bidault, C. Eraud, M. Lahouarnat, Y. Lechauve, H. Lormée, and A. Villers (Office Français de la Biodiversité, OFB) for their great and invaluable help during field work. We also thank the ONF (Office national des Forêts), the 'Mairie de Niort', C. and J-C. Barbraud, C. and B. Bégaud, A. Fuseau, and R. Seguin for giving us access to capture sites. We thank L. Runacre for correcting and improving the English.


Manon Lizzana

Fabien Malbet

Fabrice Pancher

Thierry Lepine

Alain Leger

Theia

Theia : High precision differential astrometry for exoplanets and dark matter

Theia : High precision differential astrometry for exoplanets and dark matter

Manon Lizzana (1,2,3), Fabien Malbet (1), Fabrice Pancher (1), Thierry Lepine (4), Alain Leger (5)

Scientific objectives :

• The goal of Theia is to explore planetary systems in the Sun's vicinity and investigate the nature of the dark matter in the Milky way

• We focus here on rocky exoplanets in the habitable zone detection because it's the most demanding aspect of the Theia mission

Theia mission :

•

-Calibration of optical distortion with fictive stars (red beam)

Main idea of the simulation :

• Simulate a star field

• Calculate the position of the stars on the detector (thanks to optical computing)

• Fit a polynomial function (T or T1oT2) between the star position on the sky and on the detector, see fig 7,8

Procedure during the space mission :

• Fit the polynomial on reference stars using the Gaia catalog

• Apply the polynomial to the target star position on the detector to estimate the position on sky

• Measure the movement of the target star relative to reference stars

T or T1oT2

Goal of this study :

We want to show that a polynomial model is able to calibrate the telescope distortion with a precision smaller than 5e-6 px that allows us to detect an Earth like planet Recent work on telescope stability (Malbet et al. 2022, SPIE 12180, 1F) has shown that the reference stars in the field of the telescope can be used as metrology sources in order to compute the distortion function

We want to show that this procedure works in laboratory with simulated stars (red beam)

First results :

A precision of 5e-6 px can be reached with about 100 stars and an 8th order polynomial, if the M2 tilt is below 7e-5'' for that exposure , see fig 9

Ongoing work :

•

  The reflex motion of the star produced by an Earth like planet orbiting a sun like star 10pc from us is 0.3 μas corresponding to 5e-6 px in the case of Theia mission (see Theia's characteristics in the right panel)• This precision can only be reached by long exposure on pointed targets using relative astrometry, contrary to Gaia which performs absolute astrometry over the entire sky• To achieve this goal, the TRL (technology readiness level) of 2 specific aspects must be improved :○ calibration of new detectors with very large number of pixels → see 1, 2○ telescope stability or its monitoring by calibrating the field distortion thanks to the stars in the field of view → see 31 -Characterize an adequate CMOS detector with an integrating sphereNew technologies supply large detectors made of small pixels (4.4 μm), that allow to observe small movements in a large field of viewTo cover the whole field of view of Theia 30 000 x 30 000 px are needed, but for now experiments are carried out with a 8320 x 5436 px = 46 Mpx, see fig 4. Pyxalis company plans to make 220 Mpx detectors, meaning that 4 of them will cover the whole field of view.2 -Interferometric calibration of the pixels centroid position (blue beam)On fig 5, the blue beams originate from fibers which illuminate the detector with Young's fringe. Each pixel sees a modulation of the light when the phase modulators are in action. This allows to calculate the exact positions of pixels centroids.Previous work has shown that this is possible with small detector matrices(Crouzier et al. 2016, A&A 595, A108), see fig 6. The goal is now to check the performances and validate this method with the new very large detectors.

  Fig 2 : example of field of view (target star at the center and reference stars all around)


Baptiste Chopin

Mohamed Daoudi

Angela Bartolo

Avatar Reaction to Multimodal Human Behavior ⋆

Keywords: avatar reaction, human motion prediction, facial expression

In this paper, we propose a virtual agent application. We develop a virtual agent that reacts to gestures and a virtual environment in which it can interact with the user. We capture motion with a Kinect V2 camera, predict the end of the motion and then classify it. The application also features a facial expression recognition module. In addition, to all these modules, we include also OpenAI conversation module. The application can also be used with a virtual reality headset.

Introduction

The development of a virtual agent that can interact with users in a virtual environment has potential applications in fields such as gaming, education, and healthcare. Interaction with these virtual agents is provided and has usually been studied, through several modalities: expression recognition and generation [START_REF] Dipaola | A multi-layer artificial intelligence and sensing based affective conversational embodied agent p[END_REF], action recognition and interaction with objects [START_REF] Daoudi | A new computational approach to identify human social intention in action[END_REF], multi-modal input and output [START_REF] Dipaola | A multi-layer artificial intelligence and sensing based affective conversational embodied agent p[END_REF]. Some methods can even be used for both virtual agents and robots [START_REF] Hua | Towards more realistic human-robot conversation: A seq2seq-based body gesture interaction system[END_REF]. However, the challenge of building an interactive virtual agent differs from that of building an interactive robot. Heting et al. [START_REF] Wang | Building an emotionally responsive avatar with dynamic facial expressions in human-computer interactions[END_REF] discuss the use of virtual agents, specifically avatars, in human-computer interactions. The paper presents an emotionally responsive avatar named Diana, which recognizes human affect and responds with natural facial expressions to improve the user experience in the interaction. The more recent work focus on facial expression generation in dyadic interactions [START_REF] Luo | Reactface: Multiple appropriate facial reaction generation in dyadic interactions[END_REF], while our work is interesting by the reaction of an avatar to action. Daoudi et al. [START_REF] Daoudi | A new computational approach to identify human social intention in action[END_REF] propose a new computational approach to identify human social intention in action by analyzing the trajectories of the human arm. The proposed method for predicting social intention is based on the trajectories of the human arm, which has been used to improve social communication between humans and virtual agents. To our knowledge there exist no virtual agent that is able to react to communicative gesture. Our virtual agent is able to react to communicative gestures using non-intrusive data acquisition devices while having realistic behavior.

Overview

We set several constraints to the application we build: the avatar appearance must be realistic, the environment must be believable, the user's motion data must be collected in an non-intrusive manner, the reaction from the agent must be related to the action of the user and realistic, finally the reaction motion must start quickly after the user perform a gesture. To respect the constraints we set for our virtual agent application, we must first be able to react quickly to the user gesture. This implies that, in a short time, we must understand the motion the user is performing and produce a reaction to this motion. In these conditions, waiting for the user to complete the motion is not ideal. Therefore, we choose to start by predicting the end of the user motion based on its start. Then we classify the predicted motion and send the information to the avatar that will react accordingly by selecting the corresponding prerecorded reaction motion. The result of the classification will let the system decide which reaction to perform. However, as we will see in Section.3.1, all the gestures we choose indicate discomfort for the user. Depending on the degree of said discomfort, a reaction might not be needed. To take this possibility into account, we add a facial expression recognition module to decide if the virtual agent has to react. Fig. 1 shows the overall architecture of the application. A final issue to address is the need for data. While facial expression databases are plentiful and we do not need data to generate the virtual agent's response, we do need data to train networks to predict and recognize the user's gesture. In summary, the main contributions of our work are:

1. Development of a virtual agent that reacts to gestures and interacts with the user in a virtual environment. 2. A new dataset of 3D gestures of 11 subjects to train our models that are used with the virtual agent, is proposed. Each 3D gesture is recorded by motion capture. The motions were performed by 11 healthy volunteer participants who were asked to perform gestures indicating their mental state.

3 Proposed Method

Motion Database

There is currently no database containing the kind of communicative gestures that we are looking for when interacting with the avatar (e.g "I'm cold", "My ears hurt"...). Most motion databases contain motions that represent action performed alone (walking, kicking...) or in interaction with other persons (exchanging an item, fighting...). In the latter, the interactions require physical contact between the two parties, either directly or through an object. Gesture databases are also available, however, the gestures included are gestures that do not require a reaction from another person. Furthermore, the existing gesture databases that could be used to make another person react are culturally coded, the motion performed might not have the same meaning or no meaning at all for a French person. To get data that we could use in our scenarios we build a database of communicative motions. We show in Table 1 the different scenarios and motions used in our database as well as a description and the number of samples recorded. Since different motions can be used for each of the scenarios given to the participants, they were also instructed to avoid certain types of motion (e.g they were not allowed to pull the collar of their shirt during the "I am hot" scenario). We added an additional class of motions containing random idle motions to train our networks. It is used to recognize when no motion of particular significance is being performed. By doing that, we can avoid the misclassification of random motions as meaningful ones leading to an unwanted reaction from the avatar. The scenarios were selected from situations where people might perform a gesture to indicate their mental state but that can also be performed to elicit a response from another person (e.g. the gesture of "coming here" oblige observers to modify their behavior. In this case, people move toward the executor). Mental state gestures are gestures that communicate a feeling (e.g. I am cold). In all our scenarios the motion indicates a discomfort and the avatar must react by helping the user. The motion capture was performed at the Equipex Continuum using the facilities of the Fédération de Recherche "Sciences et Cultures du visuel" at Tourcoing, France. We recruited 11 healthy volunteer participants among the students and staff of the lab team (8 males, 3 females; age between 20-60 yrs). Participants did not receive financial compensation for their participation in the study. Participants were asked to stand in front of the Kinect at a place marked by a cross on the ground. Fig. 4 shows the view participants had. Their initial position was then adjusted depending on their height (to allow the capture of all markers by the motion capture system). They were asked to perform the gestures from Table .1. The motions from class "Idle" were only performed by 2 participants as they are random, meaningless, motions. The participants performed each motion at least 10 times. Each participant spent between 30 and 45 minutes to complete the entire recording session. The motions are captured on two different systems. The first is a high-precision motion capture system with 6 Qualisys motion capture cameras and nine markers on the participant's body: one on the forehead between the eyebrows, one at the base of the neck, and one above the navel, for each arm we have markers on the top of the shoulder, on the elbow and on the outer side of the wrist. Fig. 3 shows the position of the markers on a participant. The motion capture system is calibrated to have an average positional error of less than 2 millimeters. Each sample is recorded for exactly 5 seconds regardless of the actual duration of the motion at 100 fps. The second capture system we use consists of a single Kinect V2 camera. We decided to also capture the motion with a Kinect camera since the motion capture system would give us extremely precise data that we will not be able to obtain with the system we have put in place for our application. The Kinect allows us to capture real-world data of the motion present in the motion capture database which will help our models become more resilient to the noise there is in real-world data. With the Kinect, we capture joint positions of the entire body but then only keep the nine joints corresponding to those from the Motion capture. The capture setup is illustrated in Fig. 4. Due to the difficulty of setting up synchronous capture between the Kinect and the motion capture system, the capture from the Kinect is started and stopped by an experimenter following the instruction from the experimenter working with the motion capture system. Due to this, the duration of the Kinect capture varies between 73 and 530 frames with a median of 155 frames and a mean of 157 frames with a framerate of 30 fps which means that on average the motion duration average is close to 5s. Samples that were too noisy or where joint positions were lost for too long were removed. When a joint position was lost for a short amount of time we estimate its position to be the last known position. All recorded samples were checked by a human curator before being used in the database. This amount to 728 motion samples for the MoCap data and 728 samples for the Kinect data.

Data Capture

To avoid having to wear captors when using the application, we can capture the motion data using depth cameras or RGB cameras. Both allow for the capture of skeletons through different toolkits. For example, OpenPose [START_REF] Cao | Openpose: Realtime multi-person 2d pose estimation using part affinity fields[END_REF] for RGB camera or the Kinect SDK for the depth cameras. We tried a RGB camera with BlazePose [START_REF] Bazarevsky | Blazepose: On-device real-time body pose tracking[END_REF], Kinect depth camera, and Intel real sense depth camera with Nu-iTrack SDK 6 . The result led us to choose the Kinect camera. Blazepose, while good for 2D joint detection, fails to correctly estimate the depth coordinates and causes the recorded data to be noisy. The intel real sense with NuiTrack often fails completely to detect the joint position of the arm and the recorded motion looks nothing like the actual motion. On the other hand, Kinect gives an accurate recording of the motion even if it is quite sensitive to occlusion.

For the face data, we use a RGB camera with facial landmark detection software. Out of the many libraries that exist (OpenFace7 , dlib8 ...) we choose to use Mediapipe FaceMesh9 which uses a facial detector [START_REF] Bazarevsky | Blazeface: Sub-millisecond neural face detection on mobile gpus[END_REF] coupled with a face mesh predictor [START_REF] Kartynnik | Real-time facial surface geometry from monocular video on mobile gpus[END_REF]. We choose to use this method due to the speed and light weight of the model but also because of its accuracy and higher resistance to occlusion. Even if it uses a face mesh instead of proper landmarks, the results on expression recognition are similar to other landmarks detectors.

Action Prediction and recognition

Action Prediction. Prediction Module is based on [START_REF] Chopin | 3-d skeleton-based human motion prediction with manifold-aware GAN[END_REF]. We have shown that the prediction speed is fast enough to be used in real time which is mandatory to be used in our application. In [START_REF] Chopin | 3-d skeleton-based human motion prediction with manifold-aware GAN[END_REF] we predicted the same number of frames as what we were given as input. Here, however, we take an input of 25 frames (1s) to predict 50 frames (2s). The Predictive Network is trained on our dataset, where we mix the Kinect and Mocap data. The motion sequences were cut and resampled to have a duration of 3s. Action Recognition. To classify the predicted motions we use a Transformer encoder that uses spatial attention and the skeleton adjacency module presented in [START_REF] Chopin | 3-d skeleton-based human motion prediction with manifold-aware GAN[END_REF]. The Transfomer encoder is followed by a simple multi-layer perceptron to classify the motions. Fig. 5 shows the architecture of the action recognition network. . We then train the network on 3s of motion composed of 1s of real data and the 2s of motion predicted by the prediction module. We train with predicted data instead of real data because the recognition module will always receive predicted data. This module estimates the class of the concatenation of the recorded motion and the predicted motion. Table . 2 shows the accuracy of the network on our dataset. For this experiment, we split the dataset between train and test. Out of the 11 subjects we randomly choose subject 2 and subject 7 to be the test subjects. For class "Idle", since the samples are not connected to a particular subject we randomly take 11 samples from the Kinect data and 11 samples from the Mocap data to create the test set. With this, we have 1192 training samples and 264 test samples. We compare the result of 3s of captured data, 1s of captured and 1s of captured data completed by 2s of predicted data.

For each modality, we train the network on the corresponding data. As for the prediction network we use both the Mocap and Kinect data. We see that using 3s of data the network correctly classifies 63.6% of action. Some actions of our dataset like "Hello" and "I am hot" (see Table 1 for the detailed motions) are similar and often confused which can explain the relatively low score. If we use only 1s of data we see a decrease in performance down to 47.7% accuracy. If we use our prediction network to predict 2s of motion and combine it with the 1s of input data to get a 3s sequence we increase the results to 60.2% close to the original 63.6%. This highlights the advantage of using our prediction network.

We can are able to classify motion with only one-third of the data nearly as accurately. In practice this allows the avatar to react thrice as fast since the prediction process only takes a few milliseconds.

Facial Expression Recognition

To avoid reacting to random motion or communicative motion that does not require a reaction from the avatar, we add a facial expression recognition module that estimates the valence and arousal value of the user expression. We use a simple MLP consisting of 4 Dense layers with dropout that we train on the AFEW-VA [START_REF] Kossaifi | Afew-va database for valence and arousal estimation in-the-wild[END_REF] dataset using landmark data captured with Mediapipe. While the network might seem extremely simple, it is efficient. On the AFEW-VA dataset, with valence and arousal values comprised between -10 and 10, we obtain an error of only 1. State of the art method might perform even better but since our goal is only to set a threshold for discomfort while being fast we do not need as much accuracy as the state-of-the-art methods. We want to prevent the avatar from reacting when the user is in a neutral or positive mood since the motion performed might not indicate discomfort in that case. To do so we ignore motions performed while valence > 0 as it correlates with positive feelings. To avoid reacting when the user is in a neutral mood, we decide to only consider motion performed while valence < -3 and |arousal| > 3. We capture the face of the user with a second camera independent from the Kinect. The Kinect must be far enough to see the entire upper body and can not be simultaneously used to capture a detailed face.

Avatar Environment

Our avatar module is a Unity application where our avatar evolves in a scene consisting of a room with furniture as shown in Figure 7. The room contains a desk behind which the avatar sits, the avatar can interact with several objects in the scene: cigarette, window, curtains, HiFi system, and a keyboard. We choose a simple appearance for the avatar with a tee shirt so that users will not focus on its appearance but rather on its reaction. When reacting to the user gesture the avatar will stand up (except for the "This smells bad" scenario) and perform an action behind the desk (See Table 3 for a complete description of each reaction). This module can also work in virtual reality and the entire room is modelized in unity as seen in Figure 6. When using the virtual reality version, since the user needs to wear a virtual reality helmet we can not use facial expression recognition. In that case, the output of the module is ignored. The full application combines the aforementioned modules. The motion data is captured with a Kinect V2 camera and the face data with any RGB camera such as a webcam. From the motion data from the Kinect, we keep only the 3D skeleton data that we send to our prediction network every 25 frames. In the prediction network, the data will first be normalized and transformed into Stop idle motion and turn to face and look at the user Before turning attention to the user look at the computer screen while typing on the keyboard. Sometimes turns its head to look at the notebook.

Attention state look at the user and sometimes at objects on the desk.

Table 3. Avatar reaction to the gestures described in Table 1 The Square Root Velocity (SRVF) [START_REF] Srivastava | Shape analysis of elastic curves in euclidean spaces[END_REF], [START_REF] Drira | 3D face recognition under expressions, occlusions, and pose variations[END_REF], [START_REF] Chopin | 3-d skeleton-based human motion prediction with manifold-aware GAN[END_REF]. Then we predict the 50 future frames of the motion in SRVF format and rebuild the corresponding skeleton sequence. The predicted sequence is concatenated with the sequence recorded by the Kinect for a total length of 75 frames. This concatenated sequence is fed to the action recognition network which outputs a class label. Simultaneously 25 frames of the face data are sent to the facial expression recognition module where we extract the facial landmark from the RGB data with MediaPipe's face mesh. The landmarks are treated by the network which outputs arousal and valence values. Our scenarios are all negative ones, the user is bothered by something and wants the avatar to do something about it. We choose valence and arousal intervals that contain expressions that users may perform in these scenarios. If the values obtained by the network are outside these intervals we consider that the motion performed by the user does not show a discomfort needing a reaction or that it is unrelated to the scenarios. In these cases, we prevent the avatar from reacting. Depending on the output of the motion recognition and of the facial expression recognition network the application will decide if the avatar needs to react and if so start the appropriate motion. If no reaction motion is required because the predicted class is 'Idle' or due to the valence and arousal values, the avatar stays in its 'Attention' state. This cycle is repeated until the application is closed. When using virtual reality users can move the camera with their heads and can move around in the room. However, the position of the avatar remains unchanged and a change in user position too drastic will interfere with the body data capture from the Kinect. Figure . 1 describes the full application.

In addition to the above modules, we have also developed a module for facial expression with VR headset. Indeed, recognizing emotions while wearing a VR headset is a much more complicated task, as the headset hides information. Vive offers a facial tracker that uses sensors to capture facial movements. Facial expression is defined on the basis of 38 characteristic movements, which are assigned a weight according to their presence in the expression. To recognize certain emotions (joy, anger, surprise, sadness), we selected from the 38 movements those that best defined the desired emotion, then set a threshold on the weight of these movements. If the weight associated with an emotion exceeds this threshold, then the user is considered to be expressing that emotion. To be able to converse with the avatar, we used the OpenAI API integrated into Unity, as well as Microsoft Azure's text-to-speech and speech-to-text services. We convert the user's voice to text and send it to the OpenAI model (we use the gpt-3.5-turbo model) using speech-to-text conversion. We then use text-to-speech conversion to make the model's response audible.

Acknowledgments

The authors would also like to thank Hugo Pina Borges and Julian Hutin for performing part of the experimentation.

Conclusions and Future Work

In this paper, we propose a virtual agent application that reacts to gestures and a virtual environment in which it can interact with the user. The application captures motion with a Kinect V2 camera, predicts the end of the motion, and then classifies it. It also features a facial expression recognition module and an OpenAI conversation module. The application can be used with a virtual reality headset. In addition, we propose a new database of communicative motions that were recorded using a Kinect V2 camera. The motions were performed by 11 healthy volunteer participants who were asked to perform gestures indicating their mental state. One limitation of our work is to establish criteria to evaluate the human-avatar interaction.

Fig. 1 .

 1 Fig. 1. Overview of the virtual agent application architecture

Fig. 2 .

 2 Fig. 2. Examples of motion from the new database (motion capture). We show three different classes. Visualization obtained after motion sequences were cut and resampled to have a duration of 3s (see Sec.3.3)

Fig. 3 .

 3 Fig. 3. Motion capture markers position

Fig. 4 .

 4 Fig. 4. Motion capture and Kinect setup. Point of view of the captured subject.

Fig. 5 .

 5 Fig. 5. Architecture of our Interformer-based motion classifier

Fig. 6 .

 6 Fig.6. Aerial view of the scene in the unity builder (ceiling is removed). The entire room is modelized for virtual reality.

  If the window is open: stand up, close the window, sit down. I am hot If the window is closed: stand up, open the window, sit down. This is too loud if the volume is high: stand up, turn the volume down on the hi-fi system, sit down This smells bad if the cigarette is lit: put out cigarette It is too bright if the curtain is open: stand up, close the curtain, sit down Hello

Fig. 7 .

 7 Fig. 7. Our avatar in its resting state.

Table 1 .

 1 Content of the gesture dataset

	Scenario	Motions	number of samples MoCap number of samples Kinect
	I am cold	rub both arms with hands	115	109
	I am hot	wave one or both hands near the neck/ mouth	115	115
	This is too loud	cover both ears with hands	113	99
	This smells bad pinch nose with one hand or cover mouth and nose with both hands	115	110
	It is too bright	cover eyes with one or both hands	111	109
	Hello	wave with one or both hands	100	121
	Idle	random and meaningless motions	59	65
	Total		728	728

Table 2 .

 2 Recognition accuracy on our dataset. Comparison between 1s of motion captured by the Kinect, 3s of motion captured by the Kinect, and 1s of motion captured by the Kinect concatenated to 2s of predicted motion Capture 1s Capture 3s Capture 1s + Prediction 2s

	Accuracy 47.7%	63.6%	60.2%

https://nuitrack.com/

https://github.com/TadasBaltrusaitis/OpenFace

http://dlib.net/

https://google.github.io/mediapipe/solutions/face_mesh

⋆ This work was supported by French government funding managed by the National Research Agency under grants ANR-21-ESRE-0030 (CONTINUUM), by CNRS through the 80-Prime program, and ANR-16-IDEX-0004 ULNE.