Renata Zocatelli
email: renata.zocatelli@cnrs-orleans.fr

Jérémy Jacob

Sébastien Gogo

Claude Le Milbeau

Julien Rousseau

Fatima Laggoun-Défarge

Spatial variability of soil lipids reflects vegetation cover in a French peatland

Keywords: spatial variability, surface peat, molecular biomarkers, vascular plant invasion

Introduction

In recent history, many peatlands in Europe have experienced vascular plant invasion [START_REF] Hansen | Ecological studies in Danish heath vegetation[END_REF][START_REF] Berendse | Experimental manipulation of succession in heathland ecosystems[END_REF][START_REF] Chambers | Recent rise to dominance of Molinia caerulea in environmentally sensitive areas: new perspectives from palaeoecological data[END_REF][START_REF] Jacquemyn | Fire increases invasive spread of Molinia caerulea mainly through changes in demographic parameters[END_REF]. The increased abundance of trees such as Betula spp and Pinus spp, and gramineae such Molinia caerulea could be driven by natural dynamics and/or induced by direct and indirect human activities (fire, drainage, fertilization and climate change; [START_REF] Gimingham | Ecology of heathlands[END_REF][START_REF] Hunt | Response to CO 2 enrichment in 27 herbaceous species[END_REF][START_REF] Hunt | Further responses to CO 2 enrichment in British herbaceous species[END_REF][START_REF] Boeye | Phosphorus fertilization in a phosphorus-limited fen: effects of timing[END_REF][START_REF] Tomassen | Stimulated growth of Betula pubescens and Molinia caerulea on ombrotrophic bogs: role of high levels of atmospheric nitrogen deposition[END_REF][START_REF] Tomassen | Expansion of invasive species on ombrotrophic bogs: desiccation or high N deposition?[END_REF][START_REF] Aerts | Growth-limiting nutrients Sphagnum-dominated bogs subject to low and high atmospheric N supply[END_REF][START_REF] Woodin | Impacts of sulphur and nitrogen deposition on sites and species of nature conservation importance in Great Britain[END_REF][START_REF] Hogg | Acidification, nitrogen deposition and rapid vegetational change in a small valley mire in Yorkshire[END_REF]. Increased abundance of vascular plants has the potential to profoundly influence peatland biodiversity, hydrology, biogeochemistry, and landscape evolution [START_REF] Bobbink | Effect of increased nitrogen deposition. Air Pollution and Plant Life[END_REF][START_REF] Binet | A water-table dependent reservoir model to investigate the effect of drought and vascular plant invasion on peatland hydrology[END_REF][START_REF] Gogo | Invasion of a Sphagnumpeatland by Betula spp and Molinia caerulea impacts on organic matter biochemistry. Implications for carbon and nutrient cycling[END_REF]. However, although vegetation change in peatlands is widely recognized, there is a promising, if little explored, avenue for understanding their impacts on ecosystem processes [START_REF] Limpens | Peatlands and the carbon cycle: from local processes to global implications -a synthesis[END_REF].

Several studies showed a link between biomarkers imprints in soil and plant diversity in the surrounding [START_REF] Marseille | n-Alkanes and free fatty acids in humus and A1 horizons of soils under beech, spruce and grass in the Massif Central (Mont-Lozère), France[END_REF][START_REF] Trendel | Molecular characterisation of soil surface horizons with different vegetation in the Vosges Massif (France)[END_REF][START_REF] González-Pérez | Geochemical and ecological significance of soil lipids under Rhododendron ponticum stands[END_REF][START_REF] Lavrieux | Preservation of an ancient grassland biomarker signature in a forest soil from the French Massif Central[END_REF][START_REF] Lavrieux | Preservation of an ancient grassland biomarker signature in a forest soil from the French Massif Central[END_REF][START_REF] Ronkainen | The n-alkane and sterol composition of living fen plants as a potential tool for palaeoecological studies[END_REF]. In addition, the analysis of soil lipids revealed that biomarker imprints of soils can distinguish between pasture/grassland and forest [START_REF] Lavrieux | Preservation of an ancient grassland biomarker signature in a forest soil from the French Massif Central[END_REF] and that the soil chemical legacy of former land uses can be preserved for decades [START_REF] Lavrieux | Preservation of an ancient grassland biomarker signature in a forest soil from the French Massif Central[END_REF]. As remarkerd by [START_REF] Eglinton | Molecular proxies for paleoclimatology[END_REF], little attention has been paid to the parameters that control the spatial variability of biomarkers, i.e. how do they reflect spatial heterogeneities and what is the current knowledge on their production, transport and sedimentation? In this vast new field of investigation, our study aimed at testing whether relationships between plant diversity and soil chemodiversity are still valid, and are quantitative, at a small scale (1/10 th of a m 2 ) by attempting to connect (i) the spatial variability of soil lipid imprints in peat with the surrounding vegetation, and (ii) the concentration of tree biomarkers with tree biomass. La Guette peatland, located in the southeast part of the French Region Centre is particularly well adapted to the study due to the low plant diversity, which is expected to provide precise relationships between soil lipids and their plant source. Our working hypothesis was that the lipid composition of a surface peat would be directly affected by the lipid composition of plants or groups of plants that developed in the immediate vicinity. To test this hypothesis, we analysed the biomarker imprint of soils sampled at several stations under distinct vegetation cover and tree density and compared it with the lipid imprints in possible plant sources.

Material and methods

Setting

La Guette is a peatland situated in Neuvy-sur-Barangeon (Sologne, Cher) in the French Centre Region [154 m above sea level (a.s.l.); N 47°19′, E 2°16; Fig. 1)]. La Guette peatland covers 25 ha and is a transitional fen (pH ca. 4-4.5). The original peatland vegetation is dominated by Sphagnum spp. (majoritarian S. rubellum) and ericaceous shrubs -Erica tetralix and Calluna vulgaris. Aerial photographs (1944, 1952, 1966, 1973, 1976, 1983, 1999 and 2005) reveal (i) progressive afforestation of the site by Pinus sylvestris and Betula pendula up to a fire event in the mid-70s. This event re-opened the site and reset the vegetation. Since that time, a progressive afforestation is noted with, again, the development of P. sylvestris and B. pendula. Since the fire event, Molinia caerulea (Poaceae) spread in almost all parts of the site and developed tussocks. Tree species such as P. sylvestris and B. pendula are more sporadically distributed, but are closing the ecosystem (Fig. 1).

Sampling

Nine stations were selected on a NE-SW transect in order to cover the entire length of the peatland, to be representative of the main vegetation types, and to reflect gradients of afforestation (Fig. 1b). At each station, one soil sample (1.5 cm width x 10 cm length) was taken in horizontal direction at the base of 15cm high M. caerulea tussocks, above the peatland topsoil (Fig. 1c). Considering that tussocks only developed after the fire event, our samples are supposed to record vegetation changes that occurred during the last 40 yrs that is essentially the result of recent afforestation by P. sylvestris and B. pendula.

Because M. caerulea tussocks are essentially constituted by M. caerulea material, the background lipid signal is expected to be similar in all samples whereas variations in biomarker imprints are expected to be only related to the specific vegetation surrounding sampling points. Aerial parts of the six most abundant plants, S. rubellum, E. tetralix, C. vulgaris, M. caerulea, P. sylvestris (needles and bark) and B. pendula (leaves and bark), were collected as reference samples.

Tree density and dominance of P. sylvestris vs. B. pendula

Around each station, trees were counted in concentric circles of increasing radius (1, 2, 3, 4 and 5 m, giving areas of 3.14; 12.6; 28.3; 50.3 and 78.5 m 2 , respectively). A biomass index (BI) is calculated for each area as the sum of trees height divided by the area. This was individually done for P. sylvestris and B. pendula, and for both tree species (formulae a, b and c, respectively). To give account of the relative abundance of these species, the biomass proportion of each to the total tree biomass was also calculated (formulae d and e, respectively).

Lipid analysis

About 5 g of fresh soil or lyophilized plant samples were ultrasonically extracted (3x, 10 min) with CH 2 Cl 2 (15 ml). After combination of the extracts, the total extract was dried under N 2 and fractionated into neutral and acidic compounds using solid phase extraction with aminopropyl-bonded silica according to [START_REF] Jacob | Pentacyclic triterpene methyl ethers in recent lacustrine sediments (Lagoa do Caçó, Brazil)[END_REF]. Neutral and acidic fractions were dried under N 2. The neutral fraction was separated into: aliphatics and aromatics; ethers, esters, ketones and acetates; and alcohols using flash chromatography with a Pasteur pipette filled with silica (activated 24 h at 120 °C, then deactivated with H 2 O, 5% wt.), using a sequence of solvents of increasing polarity. The acid fraction was methylated by adding a mixture of anhydrous MeOH and acetyl chloride kept at room temperature for 1 h. Alcohol and acid fractions were silylated by reacting with N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) in pyridine at 60 °C for 1 h. An internal standard, 5α-cholestane, was added prior to analysis.

Lipids were assigned and quantified using gas chromatography-mass spectrometry (GC-MS with a Trace GC Ultra gas chromatograph coupled to a TSQ Quantum XLS mass spectrometer equipped with an AS 3000 autosampler; both from Thermo-Scientific, Bremen, Germany). The GC instrument was fitted with a TG-5 MS column (60 m, 0.25 mm i.d., 0.25 µm film thickness; Thermo, Bellefonte, PA, USA). The temperature program was: 40 °C (1 min) to 120 °C at 30 °C/min, then to 300 °C (held 70 min) at 3 °C/min. The sample was dissolved in toluene and 2 µl were injected in splitless mode at 280 °C. The carrier gas was He at 1.0 ml/min. The mass spectrometer was operated in the electron ionization (EI) mode at 70 eV and scanned from m/z 50 to 600. Compound assignment was based on comparison of mass spectra and retention times of authentic standards and on comparison with published data. Lipid concentration was estimated by measuring peak areas on ion specific chromatograms, converting to areas on total ion current (TIC) by applying a correction factor, then expressing values as a ratio of the area of the standard peak on the TIC, and finally normalized to the weight of sample extracted.

Statistical analysis

Model II regression [START_REF] Legendre | Numerical ecology. Developments in environmental modelling 20[END_REF] was used to examine the relationships between lipid concentration and tree density at each station. Pearson regression was used to examine the relationships between the regression coefficient of the previous analysis and the counting area. Statistical analysis was performed with Statistica (Statsoft, 2008) to test the significance of the regressions (level of significance set at p < 0.05).

Molecular contents were compared through standardized principal component analysis (PCA).

Results

Cover vegetation parameters

Stations 1).

Molecular imprints of soil samples

The distribution of main lipids identified and quantified in soil samples between stations (in terms of presence/absence), the ratios between homologous of n-alkyl lipids and lipid contents are shown in Tables 2, 3 and4 3).

Selected compounds are depicted on the chromatograms of the ketone/acetate and alcohol fractions for several soil samples characterized by distinct vegetation cover (Fig. 2).

Five steroids occurred in soil samples (st1 to st5). In all samples, β-sitosterol (st4) was the major compound, followed by campesterol (st2). Twelve pentacyclic triterpenones (k1 to k12), eight pentacyclic triterpenyl acetates (ta1 to ta7), eight pentacyclic triterpenols (tp1-8)

and two pentacyclic triterpene acids (oleanoic and ursolic acids; Oa and Ua, respectively)

were detected in soils samples (Table 2; see Supplementary Data for concentrations).

Biomarkers [lupa-2,20(29)-dien-28-ol (db2), betulinic acid (db3), betulin (db4), tricyclic diterpenes (td1-6) and methoxy-serratenes (ms1 to ms5)] also detected in samples were distinguished on the basis of criteria that will be defined later (cf. 4.1.).

Plant biomarkers

Compounds supposedly likely to be recorded and to be discriminant found in soil samples and detected in plants are then described plant by plant. These compounds are listed in Table 2, together with other compounds that are a priori considered less specific. βand α-amyrins and lupeol (tp2, tp5 and tp6), as well as their ketone counterparts (k2, k5 and k8, respectively) were the major pentacyclic triterpenes in the neutral fraction of S.

Sphagnum rubellum n-Alkanes

rubellum. Some pentacyclic triterpenes were detected in trace amounts such as ketones (k3 and k11), triterpenol (tp3) and triterpenyl acetates (ta1, ta4, ta2 and ta7). n-C 10:0 to n-C 34:0 , respectively. Both exhibited a strong even predominance and maximum at n-C 28 OH, and at n-C 16:0 and n-C 28:0 , respectively. α-Amyrenyl acetate (ta4), several triterpene ketones (k2, k3, k5, k6, k8 and k12) and triterpenols (tp2, tp3, tp5 and tp6) were detected. at n-C 16:0 . Both exhibited a strong even predominance. Some pentacyclic triterpenols (tp1, tp2, tp3, tp4, tp6 and tp8) and pentacyclic triterpene ketones (k1, k2, k4, k5, k7, k8 and k11)

Calluna vulgaris n-Alkanes

were detected, together with a wide diversity of pentacyclic triterpenyl acetates (ta1, ta4, ta2, ta3, ta5 and ta7). and betulinic acid (db2, Table 2). Methyl-3α-acetoxy-lup-22(29)-en-28-oate (db1) was also detected in B. pendula bark.

3.3.6. Pinus sylvestris n-Alkanes were distributed from n-C 15 to n-C 33 with maxima at n-C 25 and a strong odd predominance. n-Alkanols and n-alkanoic acids ranged from n-C 12 OH to n-C 32 OH and from n-C 10:0 to n-C 30:0 , with maxima at n-C 28 OH and at n-C 16:0 , respectively, and a strong even predominance. The needles and bark of P. sylvestris contained sterols and tricyclic diterpenes as major compounds. Tricyclic diterpenes were exclusively detected in P. sylvestris. Among several tricyclic diterpenes in P. sylvestris, only six were assigned (td1-6, Table 2). Five methoxy-serratenes (3β,21α-dimethoxy-serrat-14-ene, ms1; 3α-methoxy-serrat-14-en-21-one, ms2; 3β-methoxy-serrat-14-en-21-one, ms3; 3-Methoxy-serrat-14-en-21-yl acetate, ms4 and 3-methoxy-serrat-14-en-21α-ol, ms5) were found in notable concentrations in bark. In needles, only 3β-methoxy-serrat-14-en-21-one (ms3) was detected and constituted the only pentacyclic triterpenes detected in P. sylvestris.

Discussion

Specificity of compounds in soil samples

With the exception of simiarenol, simiarenone and taraxasteryl acetate (tp7, k9 and ta6, respectively), all lipids detected in soil samples were also detected in plants (Table 2). These compounds could thus be synthetized by others plants present in low abundance on La Guette peatland.

The low variations in concentration, distribution and ratios of n-alkyl lipids (n-alkanes, n-alkanols and n-alkanoic acids, Table 3) in soil samples, make these compounds inefficient for discriminating the surrounding vegetation. Several studies propose that n-alkyl lipidsbased ratios can be used to reconstruct past vegetation dynamics in peatlands [START_REF] Xie | Molecular and isotopic stratigraphy in an ombrotrophic mire for paleoclimate reconstruction[END_REF][START_REF] Zhou | Reconstruction of late glacial and Holocene climate evolution in southern China from geolipids and pollen in the Dingnan peat sequence[END_REF][START_REF] Nichols | Paleohydrologic reconstruction based on n-alkane distributions in ombrotrophic peat[END_REF][START_REF] Zheng | Lipid biomarkers in the Zoigê-Hongyuan peat deposit: Indicators of Holocene climate changes in West China[END_REF][START_REF] Zhou | Postglacial climate-change record in biomarker lipid compositions of the Hani peat sequence, northeastern China[END_REF]. For These parameters, although largely used in paleoenvironmental studies based on peat deposits, have been recently debated. Several Sphagnum species were shown to produce mainly n-C 27 or n-C 29 and not n-C 23 [START_REF] Baas | A comparative study of lipids in Sphagnum species[END_REF][START_REF] Xie | Molecular and isotopic stratigraphy in an ombrotrophic mire for paleoclimate reconstruction[END_REF] n-C 27 is the dominant n-alkane in S. rubellum collected from La Guette peatland, in agreement with [START_REF] Baas | A comparative study of lipids in Sphagnum species[END_REF]. This can explain the inefficiency of P aq and n-C 23 /n-C 29 in discriminating samples, considering also that there is no major difference in n-alkane distribution for vascular plants. In addition, it is worthwhile noting that ours amples were collected on M. caerulea tussocks on which Sphagnum can barely develop. More generally, and according to [START_REF] Otto | Degradation and preservation of vascular plant derived biomarkers in grassland and forest soils from Western Canada[END_REF], it is difficult to classify vegetation types on the basis of nalkyl lipid distributions considering the significant variability observed within the same plant family, genera or even species (e.g. [START_REF] Herbin | Patterns of variation and development in leaf wax alkanes[END_REF][START_REF] Gülz | Organ-specific composition of epicuticular waxes of beech (Fagus silvatica L.) leaves and seeds[END_REF].

Likewise, as steroids are present in all the analyzed plants, their distribution in soil samples was not discriminant. Derivatives of cholesterol are present in all eukaryote cells. To a large extent, C 29 steroids derive mainly from the degradation of β-sitosterol (st4, highest content) and stigmasterol (st3) that originate from various plant sources [START_REF] Harwood | Lipids in Plants and Microbes[END_REF][START_REF] Bianchi | Plant waxes[END_REF][START_REF] Huang | Molecular and isotopic biogeochemistry of the Miocene Clarkia Formation: hydrocarbons and alcohols[END_REF]. The non-discriminative character of steroids in our samples is also in agreement with observations by [START_REF] Ronkainen | The n-alkane and sterol composition of living fen plants as a potential tool for palaeoecological studies[END_REF] who indicated that sterols in soils are of limited interest for describing the surrounding vegetation.

Similarly, compounds with an oleanane or ursane skeleton and a ∆ 12 double bond such as pentacyclic triterpene acids Oa and Ua, as βand α-amyrins, or as their ketone counterparts

were not efficient for discriminating the surrounding vegetation. 2002, 2011and 2012(Sologne Nature Environnement, 2012), only one Apocynanceae species was identified in high frequency at the site: Gentiana pneumonanthe. Further analysis should be done to ascertain whether this species contains pentacyclic triterpenyl acetates, and more specifically taraxasteryl acetate (ta6), as well as for compounds for which the source could not be identified (simiarenol and simiarenone). For Ericaceae, the distinction between C.

vulgaris and E. tetralix could also be achieved by examining taraxerone, taraxerol, multiflorenone, glutinone, glutinol, friedelin and friedelanol (k1, tp1, k4, k7, tp4, k11 and tp8), which were only present in C. vulgaris, whereas isobauerenone (k3), hop-22(29)-en-3one (k6) and lupenone (k12) were present in E. tetralix. [START_REF] Pancost | Biomarkers as proxies for plant inputs to peats: an example from a sub-boreal ombrotrophic bog[END_REF] also observed taraxerol (tp1) and taraxerone (k1) in C. vulgaris, whereas they were absent from E. tetralix.

Detection of friedelin, friedelanol and friedelanyl acetate in our samples is in accordance with [START_REF] Van Smeerdijk | Characterisation of subfossil sphagnum leaves, rootlets of ericaceae and their peat by pyrolysis high-resolution gas chromatography-mass spectrometry[END_REF] who detected friedelane derivatives in Ericaceae species.

Four methoxy-serratenes (ms1-3 and ms5) already described in the needles and/or bark of P.

sylvestris by [START_REF] Norin | Extractives from the bark of scots pine, Pinus sylvestris L[END_REF] were found in our soil samples and in P. sylvestris. The present study is the first report of the occurrence of such compounds in peatland soils, although their definition as specific biomarkers of Pinaceae has been demonstrated for mineral soils (Le Milbeau et al., 2013a). In addition, we detected 3,21-dimethoxy-serrat-14en-29-yl acetate (ms4), also assigned to Pinaceae (Le Milbeau et al., 2013b). Six tricyclic diterpenes found in soil samples were also present in needles and bark of P. sylvestris.

Tricyclic diterpenes are reputed specific biomarkers for conifers (i.e. [START_REF] Stefanova | Composition of the extract from a Carboniferous bituminous coal-I. Bulk and molecular constitution[END_REF].

Therefore, and considering their absence in other analyzed plants, both these compounds can be used as specific biomarkers of P. sylvestris. Tricyclic diterpenes concentrations were lower than those of methoxy-serratenes in all soil samples (Supplementary Data). The high correlation between the sum of tricyclic diterpenes concentrations and that of methoxyserratenes (r = 0.997; p ≤ 0.0001, n=9) confirms that they derive from the same source and did not suffer differential degradation.

Betulin, betulinic acid and their derivatives found in soil samples were also abundant in the extracts of leaves and bark from B. pendula. This is consistent with the report of betulinic acid, betulin and methyl-3β-acetoxy-lup-22(29)-en-28-oate (db1, db3 and db4, respectively)

in Betula spp bark by [START_REF] Schnell | Contrasting diagenetic pathways of higher plant triterpenoids in buried wood as a function of tree species[END_REF]. In B. pendula samples (leaves and bark), betulin was overexpressed compared to lupeol (tp6). According to [START_REF] Ekman | The suberin monomers and triterpenoids from the outer bark of Betula verrucosa Ehrh[END_REF] betulin extracted from the bark of Betulaceae represents up to 30% of dry wt. [START_REF] Ukkonen | Birch bark extractives[END_REF] showed that lupeol concentration decreased whereas betulin levels increased with tree age. In La Guette peatland, lupeol cannot be solely associated to B. pendula because, although in low amounts, M. caerulea and S. rubellum also lupeol.

Lipids detected in S. rubellum corroborate the results of [START_REF] Baas | A comparative study of lipids in Sphagnum species[END_REF] and [START_REF] Pancost | Biomarkers as proxies for plant inputs to peats: an example from a sub-boreal ombrotrophic bog[END_REF]. We identified steroids (st1-5), n-alkanes, n-alkanols, n-alkanoic acids, β-amyrin, α-amyrin, oleanoic acid, ursolic acid and lupeol (tp2, tp5, tp6, Oa and Ua, respectively).

These compounds, as discussed below, were not specific. Additional terpenes (ta1, ta2, ta4, ta7, k3, k11, tp3, Table 2) detected in trace amounts have never been reported so far in Sphagnum species. Because these compounds were also detected in C. vulgaris and E.

tetralix, a contamination of S. rubellum by these overlying plants cannot be excluded.

The analysis of soil samples combined with that of the main plants growing in La Guette peatland thus allowed us discriminating between non-specific and specific compounds, i.e. those that potentially allow depicting the surrounding vegetation from the chemical imprints of soils. The specific character of biomarkers can rely on several criteria. In our case, the specificity of tricyclic diterpenes was linked to their skeleton, as for betulin derivatives, except for lupeol and lupenone. The specificity of methoxy-serratenes relies both on their original skeleton and on the presence of a methoxyl group. For triterpenyl acetates, their specificity is due to the acetate functional group, independently of the pentacyclic triterpene skeleton. Early diagenetic processes occurring in soils could alter the specificity of compounds to its source plant. For example, [START_REF] Rullkötter | Early diagenesis of terrigenous triterpenoids and its implications for petroleum geochemistry[END_REF] and ten Haven et al.

(1992) showed that pentacyclic triterpenes can suffer structural rearrangements and functional group transformation. If this can account for biomarkers that were shown not to be specific (amyrins and amyrenones for example), this had no or little influence on the biomarkers listed above as specific of La Guette vegetation. Although early diagenesis can affect tricyclic diterpenes, it will produce other tricyclic diterpenes that will remain specific for P. sylvestris.

The same applies for methoxy-serratenes and, to a lesser extent, to betulin derivatives.

Therefore, we considered that early diagenesis [that is mild considering high CPI values of nalkyl lipids (Table 3), and the absence of degraded triterpenes] did not alter the plantbiomarker relationships established here above.

On this basis, we defined (i) k1, k4, k7, k11, tp1, tp4, tp8, ta2, ta3, ta5 and ta7 as tracers of C. vulgaris, (ii) k3 and k12 as tracers of E. tetralix, (iii) methoxy-serratenes (ms1-5) and tricyclic diterpenes (td1-6) as tracers of P. sylvestris, and (iv) db1, db2, db3 and db4 as tracers of B. pendula.

Distribution of individual compounds in open vs. closed stations

To discriminate the sampling stations based on the presence/absence and on the abundance of the biomarkers in soil samples, a PCA was undertaken. Ericaceous biomarkers have high loadings on Factor 1 (45.95% of the variance, compounds k1, k3, k4, k7, k12, tp1, tp4, tp8, ta2, ta3, ta5 and ta7 all positive loadings; Fig. 3a) and to a less extent on Factor 3 (9% of the variance, k11 negative loading; Fig. 3a). These two axes are defined by Ericaceae biomarkers and thus may discriminate sites based on C. vulgaris and E. tetralix abundances:

StII and StIII on one side and the other sites on the other side (Fig. 3b). Because we could not estimate the biomass of these two species we cannot further interpret the distribution of samples and variables in the F1/F3. Nevertheless, these results suggest that there might be a control of Ericaceae biomarkers for which much attention should be paid in future research.

We then focus on Factor 2 and Factor 4 that allow discriminating between biomarkers of P. sylvestris and B. pendula, for which we have quantitative biomass data. P. sylvestris biomarkers have high loadings on Factor 2 (35.68% of the variance, compounds ms1, ms3, ms4, ms5, td1, td2, td3, td4, td5 and td6; Fig. 3c). Stations StV and StIII, the two stations with the highest P. sylvestris biomass index (BI P ; Table 1), had the highest loading on the Factor 2 axis (Fig. 3d). This shows that larger amounts of P. sylvestris specific biomarkers were found in the soil when this plant is abundant in the surroundings.

B. pendula biomarkers db1 and db3 have high loadings on Factor 4 (5.23% of the variance; Fig. 3c). StVIII, which displays the highest biomass index of B. pendula (BI B ; Table 1), had the highest loading on the Factor 4 axis (Fig. 3b) that corresponds to B. pendula 1) lower than 0.50.

specific
The best relationships were obtained for abundant compounds such as db3, ms1, ms3, ms4, ms5 and tricyclic diterpenes, whereas some discrepancies are noted for the less abundant compounds (db1, db2, db4 and ms2). Thus, and except for these minor compounds, PCA showed that overlying tree vegetation clearly imprints its chemical signature on underlying

Influence of tree biomass on soil biomarker concentration

We then further explore whether biomarker concentrations can be used to quantitatively reflect tree biomass in the neighborhood of sampling sites. For this purpose, the individual and combined concentrations of specific biomarkers of P. sylvestris (only methoxy-serratenes since methoxy-serratenes and tricyclic diterpenes concentrations are correlated, cf. 4.1.) and of B. pendula (betulin derivatives: db1-4; Supplementary data) were compared to the respective biomass index (BI P and BI B ), estimated at each station (n=9; Fig. 4a). This allowed determining a correlation (R) between biomarker concentration in soils and BI for each area. Figure 4a gives an example for total methoxy-serratenes where, for a surface of 12.8 m 2 , R was 0.94.

Then, R values obtained are displayed against increasing area for total and individual P.

sylvestris biomarkers (Fig. 4b) and for total and individual B. pendula biomarkers (Fig. 4c). In the smallest area (3.16 m 2 , Fig. 4bc), the correlations equaled zero as there was no trees just at the point of sampling. For P. sylvestris, there was a good correlation between methoxyserratenes other than ms2 and the BI P (Y axis on Fig. 4a, R = 0.45). Inversely, for B. pendula,

there was an only one good correlation between db3 and the BI B (Fig. 4b, R = 0.91; P<0.001).

The lowest correlations were observed for db1, db2, db4 and ms2, i.e. biomarkers detected in low amounts. The best correlations were obtained from 28.3 m 2 for the total concentration of methoxy-serratenes (R > 0.96, P<0.001) and for ms1, ms3 and ms4, after an intermediate state where lower correlations were found (Fig. 4b). The best correlations were obtained from 12.6 m 2 for the total concentration of betulin derivatives (R = 0.91, P<0.001) as well as for db3 (Fig. 4c). In both cases, the quantitative relationships between soil biomarker concentrations and tree biomass are valid up to, at least, 80 m 2 . These results show that biomarker concentrations at a specific site are not solely representative of vegetation biomass present directly above the sampling point, but integrate a larger surface. The use of selected biomarkers for these relationships could limit any bias related to individual biomarker behavior (variations in production, transport, or early diagenesis).

Potential in palaeoenvironmental studies

Peatlands are prone to successional change since plant growth causes the filling in of lakes and the accumulation of peat on waterlogged sites [START_REF] Hogg | Acidification, nitrogen deposition and rapid vegetational change in a small valley mire in Yorkshire[END_REF] and the OM accumulated over time registers the compositional changes in vegetation [START_REF] Comont | Evolution of organic matter indicators in response to major environmental changes: The case of a formerly cut-over peat bog (Le Russey, Jura Mountains, France)[END_REF][START_REF] Gogo | Invasion of a Sphagnumpeatland by Betula spp and Molinia caerulea impacts on organic matter biochemistry. Implications for carbon and nutrient cycling[END_REF]. These ecosystems can develop from aquatic systems to forested areas (terrestrialization). At any developmental stage, events such as fire or hydrological modifications can (i) reset the system to previous stage, or (ii) accelerate the natural dynamics. As such, reopening and reclosing of the system can occur. Indeed, in La Guette peatland, tree cover increased from the 1940's to the mid 1970's. In 1976, a fire eliminated all the trees and opened the site.

The relationships between biomarkers retrieved from sedimentary archives and their source organisms has been used for decades to reconstruct paleovegetations (i.e. [START_REF] Cranwell | Organic geochemistry of lacustrine sediments: triterpenoids of higher plant origin reflecting post-glacial vegetational succession[END_REF][START_REF] Volkman | A review of sterol markers for marine and terrigenous organic matter[END_REF][START_REF] Rieley | The biogeochemistry of Ellesmere Lake, U.K. I: source correlation of leaf wax inputs to the sedimentary record[END_REF][START_REF] Meyers | Applications of organic geochemistry to paleolimnological reconstructions: a summary of examples from the Laurentian Great Lakes[END_REF][START_REF] Xie | Molecular and isotopic stratigraphy in an ombrotrophic mire for paleoclimate reconstruction[END_REF][START_REF] Jacob | Pentacyclic triterpene methyl ethers in recent lacustrine sediments (Lagoa do Caçó, Brazil)[END_REF][START_REF] Jia | Neutral monosaccharides as biomarker proxies for bog-forming plants for application to palaeovegetation reconstruction in ombrotrophic peat deposits[END_REF][START_REF] Zocatelli | Molecular evidence for recent turf cultivation in Northeast Brazil (Lagoa do Boqueirão, RN State)[END_REF]Regenery et al., 2013). However, and as noted by [START_REF] Eglinton | Molecular proxies for paleoclimatology[END_REF], their are remaining questions on (i) the spatial relationships embedded in sedimentary biomarkers and on (ii) the processes that control their input, notably through production, dispersal, and sedimentation. For instance, [START_REF] Stefanova | Geochemical appraisal of palaeovegetation and climate oscillation in the Late Miocene of western Bulgaria[END_REF] and [START_REF] Regnery | Comparison of the paleoclimatic significance of higher land plant biomarker concentrations and pollen data: A case study of lake sediments from the Holsteinian interglacial[END_REF] have shown that some biomarkers archived in sediments were in lower abundance than expected on the basis of pollen data, highlighting the difficulties in interpreting the meaning and the representativeness of biomarker concentrations compared to pollen data.

Our innovative approach allows us determining the spatial and quantitative imprint of present-day vegetation on underlying soils. Such an approach should be expanded in order to better constrain the quantitative representativeness of biomarkers in sedimentary archives such as peats and the spatial heterogeneities of ancient peatlands. Tiny discrepancies in our data could result from differential diagenesis (although we expect similar behavior for compounds of similar structures) or could be the expression of the contribution of past vegetation biomarkers to the soil that would bias/blur the direct relationship between vegetation distribution and soil biomarkers.

Conclusions

This study is one of the first attempts to explore spatial chemodiversity in soils related to biodiversity based on an exhaustive biomarker inventory in peatland soils compared to molecular imprints of surrounding vegetation and then to the evaluation of tree biomass.

Among the relevant specific biomarkers tricyclic diterpenes and methoxy-serratenes stand out as specific biomarkers of P. sylvestris; and betulin derivatives as specific biomarkers of B. This study contributes to our understanding of how biomarker distribution and concentration in spatially constrained archives, such as peat cores, can render the spatial heterogeneity of vegetation distribution that formerly covered the catchment. Such an approach could allow paleo-reconstructions to better account for the diversity of contexts in palaeolandscapes.

Zocatelli, R., Lavrieux, M., Disnar, J.-R., [START_REF] Lavrieux | Preservation of an ancient grassland biomarker signature in a forest soil from the French Massif Central[END_REF] Free fatty acids in Lake Aydat catchment soils (French Massif Central): sources, distributions and potential use as sediment biomarkers. Journal of Soils and Sediments 12, 734-748.

FIGURE CAPTIONS

  BI P = Σ Pinus height in surface S / surface S (a) BI B = Σ Betula height in surface S / surface S (b) BI T = Σ all trees height in surface S / surface S (c) P % = percentage of P. sylvestris biomass (BI P / BI T x 100) (d) B % = percentage of B. pendula biomass (BI B / BI T x 100) (e)

  were distributed from n-C 17 to n-C 33 , with maxima at n-C 25 , n-C 27 and n-C 29 , and a strong odd predominance. n-Alkanols and n-alkanoic acids ranged from n-C 16 OH to n-C 32 OH and from n-C 12:0 to n-C 34:0 , respectively, exhibiting a strong even predominance. n-Alkanols and n-alkanoic acids had maximum values at n-C 28 OH and at n-C 16:0 , respectively.

  ranged from n-C 21 to n-C 33 with maxima at n-C 27 and n-C 29 and a strong odd predominance. n-Alkanols and n-alkanoic acids ranged from n-C 11 OH to n-C 32 OH and from

  were distributed from n-C 17 to n-C 35 with maxima at n-C 31 and n-C 33 and a strong odd predominance. n-Alkanols ranged from n-C 11 OH to n-C 32 OH with maximum at n-C 24 OH and n-C 28 OH, whereas n-alkanoic acids ranged from n-C 10:0 to n-C 34:0 with maximum

  were distributed from n-C 21 to n-C 33 with maxima at n-C 27 and n-C 29 and a strong odd predominance. n-Alkanols and n-alkanoic acids ranged from n-C 18 OH to n-C 32 OH and from n-C 12:0 to n-C 32:0 , respectively, exhibiting a strong even predominance. n-Alkanols and n-alkanoic acids had maximum at n-C 28 OH and at n-C 16:0 , respectively. Except for nalkanes, n-alkanoic acids and steroids, the major lipids in M. caerulea were essentially limited to βand α-amyrenones (k2 and k5, respectively), lup-20(29)-en-3-one (lupenone; k12), fernenone (k10), βand α-amyrins and lupeol (tp2, tp5 and tp6). 3.3.5. Betula pendula n-Alkanes were distributed from n-C 15 to n-C 33 with maxima at n-C 25 and n-C 27 and a strong odd predominance. n-Alkanols and n-alkanoic acids ranged from n-C 12 OH to n-C 32 OH and from n-C 10:0 to n-C 34:0 , respectively, exhibiting a strong even predominance. n-Alkanols and n-alkanoic acids had maximum at n-C 28 OH and at n-C 16:0 , respectively. Pentacyclic triterpenes essentially consisted of lupane-derived compounds in both leaves and bark of B.pendula: lup-22(29)-en-3-one (k8), lupeol (tp6), lupa-2,20(29)-dien-28-ol (db2), betulin (db4)

  example, the n-C 23 /n-C 29 (or n-C 23 /n-C 31 ) n-alkanes ratio was used to estimate the relative input of Sphagnum species compared to vascular plants, based on the high proportions of n-C 23 in Sphagnum species. Similarly, and based on preliminary studies that estimated the relative proportions of submerged vs emersed and terrestrial plant in lacustrine systems[START_REF] Ficken | An n-alkane proxy for the sedimentary input of submerged/floating freshwater aquatic macrophytes[END_REF], the P aq ratio [Paq = (n-C 23 + n-C 25 )/(n-C 23 + n-C 25 + n-C 29 + n-C 31 )] was proposed to estimate past hydrological conditions, based on the same observation that Sphagnum species mainly produce n-C 23 whereas vascular plants produce longer chain nalkanes.

  soils. Three different vegetation cover could be distinguished: (i) closed vegetation dominated by P. sylvestris; (ii) closed vegetation dominated B. pendula and (iii) open vegetation (StI and StII) or semi-open vegetation (early colonization by trees, StIV, StVI, StVII, StIX).

  pendula, as already defined in previous studies. Here, Taraxerone, Multiflorenone, Glutinone, Friedelin, Taraxerol, Glutinol, Friedelanol, Multiflorenyl acetate, Glutinyl acetate, Isobauerenyl and Friedelanyl acetate were defined as tracers of C. vulgaris and k3 and k12 as tracers of E. tetralix. Multivariate analysis of soil biomarker concentrations reveals that they describe the overlying vegetation, allowing distinguishing three different vegetation cover: (i) closed vegetation dominated by P. sylvestris; (ii) closed vegetation dominated B. pendula and (iii) open vegetation or semi-open vegetation (early colonization by trees). Quantitative information on tree biomass could be estimated through specific biomarker concentration.

Fig. 1 .

 1 Fig. 1. Location of La Guette peatland and sampling stations. Photos of four stations illustrate

Fig. 2 .

 2 Fig. 2. Ketone/acetate and alcohol fractions extracted from soil samples collected at different

Fig. 3 .

 3 Fig. 3. Variables (a) and individuals (b) PCA plots. In the variable plot, Factor 1 axis

Fig. 4 .

 4 Fig. 4.Correlation between total methoxy-serratenes concentrations and biomass index (BI P , calculated at 28.3 m 2 ) of P. sylvestris (a), and the evolution of between these correlations with increasing area (from 3.1 m 2 to 78.5 m 2 ) used to calculate the BI for individual

  . Considering that Betula sp. produce also n-C 27 as the major n-alkane homolgue,[START_REF] Andersson | Impacts of paleohydrological changes on n-alkane biomarker compositions of a Holocene peat sequence in the eastern European Russian Arctic[END_REF] concluded that the respective contribution of Betula sp. and Sphagnum sp. cannot be discriminated in peat deposits on the basis of P aq and n-C 23 /n-C 29 . Additionally,[START_REF] Ronkainen | The n-alkane and sterol composition of living fen plants as a potential tool for palaeoecological studies[END_REF] found similarities in n-C 23 /n-C 29 n-alkane between Sphagnum spp. and vascular plants collected in bog peatlands. The efficiency of n-alkane ratios to differentiate species or plant group contributions in peatland is thus jeopardized.

  These compounds are synthetized by almost all plants sampled in the peatland, except B. pendula and P. sylvestris. Pentacyclic triterpenyl acetates were detected only in Ericaceae species (C. vulgaris and E. tetralix), while pentacyclic triterpene ketones were overrepresented (Table 2) in these species, which are part of open vegetation. β-Amyrenyl, multiflorenyl, glutinyl, α-amyrenyl,

isobauerenyl and friedelanyl acetates were found in C. vulgaris, whereas α-amyrenyl acetate (ta4) was the only pentacyclic triterpenyl acetate detected in E. tetralix (Table

2

). Triterpenyl acetates have been proposed as specific markers for Asteraceae, but can also occur in other taxa

[START_REF] Lavrieux | Occurrence of triterpenyl acetates in soil and their potential as chemotaxonomical markers of Asteraceae[END_REF]

. Triterpenyl acetates found in high amount in the soil samples (ta2 and ta7) are those produced by typical peatland vegetation (C. vulgaris). Taraxasteryl acetate (ta6) was detected in low amounts except in StI and StIX. It was not found in any of the plants analyzed, but it has been described in several taxa such as Apocynaceae

[START_REF] Lavrieux | Occurrence of triterpenyl acetates in soil and their potential as chemotaxonomical markers of Asteraceae[END_REF]

and references therein). According to the plant inventory of La Guette carried out in 1999,

  biomarkers. The stations others than StV and StIII (P. sylvestris) and StVIII (B.

pendula) are the most open stations, with total biomass index (BI T ; Table

Table 2 :

 2 Presence of molecular biomarkers in surface soil and plant samples from La Guette peatland (Numbers refer to peaks in the chromatograms on Fig.2:x, present; tr trace; -not detected).

	P. sylvestris	needles
	P. sylvestris	bark
	B. pendula	leaves
	B. pendula	bark
	M. caerulea
	C. vulgaris
	E. teralix
	S. rubellum
	Peat samples
	Trivial name
	Compound
	#	n-Alkyl lipids

Table 3 :

 3 Abundances and distribution parameters of n-alkyl lipids in soils samples of La Guette peatland. Alkane carbon preference index; CPI = 2(odd n-C 23 to n-C 31 )/(even n-C 22 to n-C 30 + even n-C 24 to n-C 32 ). c Average chain length, ACL = (Σ[Ci] x i)/Σ[Ci] where i = carbon number ranges (n-alkanes from n-C 23 to n-C 33 ; n-alkanols and n-acids, from C 22 to C 32 ), Ci = contents of the homologues containing i carbon atoms. d Terrestrial/freshwater plant ratio, Paq = (n-C 23 + n-C 25 ) / (n-C 27 + n-C 29 + n-C 31 ). e n-Alkanols and n-alkanoic acids, CPI = 2(even C 22 to C 30 )/(odd C 21 to C 29 + odd C 23 to C 31 ).

				n-Alkanes				n-Alkanols				n-Alkanoic acids
	#	Total (µg/g)	major a CPI b	ACL c	Paq d	n-C23/ n-C29	(n-C31+n-C33) / (n-C27+n-C29)	Total (µg/g)	major a	CPI e	ACL c	Total (µg/g)	major a	CPI e	ACL c
	StI	9.6	n-C29 2.0 28.8 0.53	0.5	0.7	5.6	n-C28OH	23.1 25.6	20.3	n-C14:0	5.7 24.7
	StII	16.2 n-C33 6.5 30.5 0.38	2.3	3.3	11.4	n-C28OH	34.3 26.4	91.9	n-C26:0	6.2 25.3
	StIII	13.9 n-C33 2.8 29.6 0.50	0.8	1.4	9.0	n-C28OH	37.8 26.3	62.8	n-C26:0	7.8 25.6
	StIV	6.0	2.5 27.6 0.61	0.2	0.4	38.0	n-C28OH	38.4 26.8	11.9	n-C16:0	7.0 24.5
	StV	5.4	n-C33 3.5 30.2 0.25	0.2	2.1	5.2	n-C28OH	39.1 26.7	47.3	n-C14:0	7.2 24.0
	StVI	11.1 n-C33 3.8 30.1 0.28	0.2	1.6	19.8	n-C28OH	49.4 27.8	39.3	n-C14:0	7.7 24.2
	StVII 8.3	n-C33 3.5 29.7 0.30	0.3	1.3	12.0	n-C28OH	28.2 26.9	12.2	n-C22:0	5.8 24.0
	StVIII 5.0	n-C27 1.7 28.1 0.53	0.4	0.5	7.1	n-C28OH	23.5 25.8	24.2	n-C14:0	2.0 24.1
	StIX	5.2	1.8 28.5 0.57	0.5	0.7	0.2	n-C26OH	4.7	23.4	11.6	n-C22:0	5.0 23.7
	Total = total abundance (µg/g of soils).

a Chain length of the most abundant homologue; b

Acknowledgements

This work was part of the PRINCIPASOL project (http://www-projets.isto.cnrsorleans.fr/principasol/) supported by the Conseil Général du Loiret and FEDER funding. S.G. was funded by the Spatioflux project and the Labex VOLTAIRE (ANR-10-LABX-100-01).

We are grateful to J. Sauze for the GIS map layers used for Fig. 1. We acknowledge those who helped during sampling, in particular M.-P. Gauthier (Aromatechnologies) and B.

Soengas (LVMH-Recherche). The study site, La Guette peatland, is part of the CNRS Service National d'Observation Tourbières. The authors also wish to thank the anonymous reviewers their constructive comments.


Sylvain Fleury

Éric Jamet

Achraf Ghorbel

Aurélie Lemaitre

Eric Anquetil

Application of the Resources Model to the Supervision of an Automated Process

The present study was designed to ascertain how far flagging up potential errors can improve the automatic interpretation of technical documents. We used the resources model to analyze the supervised retro-conversion of architectural floor plans, from the perspective of distributed cognition. Results showed that automated assistance helps users to correct errors spotted by the system and also saves time. Surprisingly, they also showed that flagging up possible errors may make users less effective in identifying and correcting errors that go unnoticed by the system. Responses to a questionnaire probing the participants' confidence in the system suggested that they were so trusting that they lowered their vigilance in those areas that had not been signaled by the system, leading to the identification of fewer errors there. Thus, while the participants' confidence in the automated assistance system led to improved performances in those areas it highlighted, it also meant that areas to which the system did not draw attention were less thoroughly checked.

Errors not spotted by the system 3.4. Degree of trust in the automated system 4. DISCUSSION AND CONCLUSION

INTRODUCTION

Symbol recognition is currently a rapidly developing field with many applications. One promising area is the recognition of the symbols contained in technical documents, with a view to obtaining interpretations compatible with computer-aided design (CAD) software-a process known as retro-conversion. In this context, several recent research projects have focused on the development or improvement of tools for retro-converting architectural plans [START_REF] Ahmed | Improved automatic analysis of architectural floor plans[END_REF][START_REF] Lu | A new recognition model for electronic architectural drawings[END_REF], electrical diagrams [START_REF] Ouyang | A visual approach to sketched symbol recognition[END_REF] and road maps [START_REF] Chiang | A general approach for extracting road vector data from raster maps[END_REF]. The study described in this article was conducted under the aegis of the MobiSketch (http://mobisketch.irisa.fr/) project to design software capable of interpreting plans such that they are compatible with CAD software1 . MobiSketch aims to develop generic, penbased software for structured document analysis, design and editing, focusing on technical documents such as architectural floor plans. The idea is to offer a comprehensive, one-stop-shop solution, from paper document recognition to pen-based document sketching on a table PC. Our software is designed to interpret the symbols on a scanned plan in order to make it directly compatible with the main types of architecture software. The problem is that this kind of system is liable to make mistakes. Our own software has been found to have a 9% error rate [START_REF] Ghorbel | Competitive hybrid exploration for off-line sketches structure recognition[END_REF]. Users therefore have to be able to identify and correct these errors. Several studies have been conducted to determine how best to improve the interface in order to make this task easier (Fleury et al., 2013a;Fleury et al., 2013b;Fleury & Jamet, 2013). The specific functionality we tested here is the software's ability to highlight areas carrying a risk of error. Our software is able to assign a likelihood score to each of its symbol interpretations, that is, to assess its own risk of error for each interpretation [START_REF] Ghorbel | Incremental learning for interactive sketch recognition[END_REF][START_REF] Ghorbel | Interactive competitive breadth-first exploration for sketch interpretation[END_REF]. Users have to verify the symbol interpretations in order to detect the errors -a task that bears some similarities with the detection of weapons in X-ray images of luggage, which has been the subject of several studies. In one such study, conducted by [START_REF] Wiegmann | Age and automation interact to influence performance of a simulated luggage screening task[END_REF], participants were shown a series of images and asked to indicate whether they could see a knife. In this experiment, 20% of the images contained a knife. Some participants had to perform this task without any assistance, while others benefited from automated assistance. This assistance consisted of a simulated automatic pattern recognition system, capable of detecting the presence of a knife in an image with an accuracy of 0.90 and a false alarm rate of 0.25. Depending on the experimental condition, the automated assistance could take one of three forms: a message displayed before the image appeared, a message displayed at the same time as the image, or a circle around the relevant area in the image. Results showed that, compared with the textual cues, the spatial cue (circling the target) significantly improved detection performances. The experiment conducted by [START_REF] Goh | Effects of Automation Failure in a Luggage Screening Task: A Comparison Between Direct and Indirect Cueing[END_REF] confirmed this result by demonstrating the superiority of a direct cue surrounding the potential target over an indirect cue indicating its supposed presence somewhere in the image. In a study featuring a monitoring task, [START_REF] Skitka | Does automation biais decision-making?[END_REF] showed that automated Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-5 -assistance could improve participant accuracy. However, when the assistance made mistakes, participants tended to err, too. The authors called this an error of complacency [START_REF] Skitka | Does automation biais decision-making?[END_REF], in that participants trusted the automated assistance more than they should have done, given its actual reliability. The same pattern of results was obtained by [START_REF] Alberdi | CAD in mammography: lesion-level versus case-level analysis of the effects of prompts on human descisions[END_REF] in a study involving automated assistance for lesion detection in mammography. Again, the participants' detection performances increased when the assistance provided relevant information, but declined when it provided irrelevant information. However, neither [START_REF] Skitka | Does automation biais decision-making?[END_REF] nor [START_REF] Alberdi | CAD in mammography: lesion-level versus case-level analysis of the effects of prompts on human descisions[END_REF] actually measured participant confidence in order to confirm their interpretations of the results.

Resources model

A general model, dealing with cognition as a distributed phenomenon, namely the resources model [START_REF] Fields | Objectives, strategies and resources as design drivers[END_REF][START_REF] Wright | Distributed information resources: A new approach to interaction modelling[END_REF]Wright, Fields, & Harrison, 2000), could provide a relevant way of interpreting it. The task performed by the participants in the experiment was therefore analyzed from the perspective of distributed cognition [START_REF] Hutchins | Cognition in the wild[END_REF], via the resources model. This model focuses on the system composed of human-machine interactions, rather than solely on the user's cognitive functioning. This system contains a number of informational resources that can be stored either in the user's memory (user-embedded) or else in the machine or even a paper-based manual (device-embedded). According to [START_REF] Wright | Distributed information resources: A new approach to interaction modelling[END_REF], a variety of resources can be contained in the system, including plans, interaction states, goals, affordances, histories and action-effect mappings.

The types of informational resources

A plan is a sequence of actions, events or states. It can be either user-embedded, stored in memory, or device-embedded, in the form of a written step-by-step procedure on the screen or a manual. The plan may either be available from the very outset, or else be built by the user in the course of the interaction. It may contain conditional loops. The interaction state is the set of values of the relevant variables for the interaction at a specific point in time. In the case of a website, for instance, it corresponds to being on a particular page. The site may or may not be in a loading state. The transition from one state to another may either follow an action of the user or else result from an action by the machine itself. The state may or may not be external (e.g., visible on the screen). The user can certainly see which page he is or she is browsing, but the loading of elements is not necessarily reported (internal state). The goal is a description of the state to be achieved. This description may be either external, if it is represented on the screen or in a manual, or internal, if it is constructed in the user's memory. A goal and a plan are two different things. A plan is a procedure, a sequence of events designed to reach a goal, whereas a goal is solely the targeted state. For example, in the Tower of Hanoi game, the goal is the final configuration the player must achieve, whereas the plan is the set of disk moves needed to achieve that goal. The term affordances refer to "the actionable properties between the world and an actor" (Norman, 1999, p.38). More specifically, in this model, the affordances are to the set of possible actions the user can perform in a given interaction state. These actions can be identified by the user. For example, a button Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-6 -with an arrow pointing towards the right in a website has an affordance (following page). A history is the set of actions, events or states that have taken place since the start of the interaction. A history can be stored either in the machine, thereby allowing the undo function to cancel the last action that was performed, or else in the user's memory. Histories resemble plans because they are sequences of actions, events or states. However, unlike plans, histories cannot contain conditional loops, as they concern events that took place in the past. Action-effect relations are the set of causal relations between user actions and changes in the interaction state. For example, in many programs, the action of clicking on the cross has the effect of closing the window. These action-effect relations may be either external, explained in user manuals, or else internal, stored in the user's memory.

Interaction strategies

In the resources model, an interaction sequence is defined as a succession of steps, that is, changes in resource configuration [START_REF] Wright | Distributed information resources: A new approach to interaction modelling[END_REF]. Each step is characterized by the execution of two processes: the choice of a future action that takes account of current resources, and the re-configuration (or updating) of resources to take account of previous actions. For example, the plan is updated at every step [START_REF] Wright | Distributed information resources: A new approach to interaction modelling[END_REF]. [START_REF] Fields | Objectives, strategies and resources as design drivers[END_REF] defined six main interaction strategies in view of available resources. In the plan-following strategy, the interaction state allows users to know where they are in the plan. The plan tells them which action to do next, and users go back and forth between plan and state. The planning strategy consists in producing a plan that can then be used in a plan-following strategy. The planning is done by coordinating the goals, states, action-effect relations and affordances for each stage of the plan [START_REF] Fields | Objectives, strategies and resources as design drivers[END_REF]Wright et al., 2000). The semantic matching strategy uses the same resources as the planning strategy. The users decide what they will do by matching action-effect relations with goals, and trying to see if the resulting state of the system meets those goals. Display-based interaction is identical to semantic matching, except that the actioneffect relations and affordances are externally matched. In goal-directed exploration, users do not have a plan, and move forward on the basis of trial and error. They try something from the available affordances and compare the state of the system with the goal. If the two do not match, they try something else. Finally, with learning-byexploring, users create new plans by looking at the history of a series of actions with positive outcomes. This strategy can also expand the action-effect repertoire for the purposes of future semantic matching [START_REF] Fields | Objectives, strategies and resources as design drivers[END_REF].

Many publications in the field of human-computer interaction (HCI) cite the resources model as an important reference for task-modeling tools (e.g., [START_REF] Blandford | DiCoT: A methodology for applying distributed cognition to the design of team working systems[END_REF][START_REF] Doherty | Resources for situated actions[END_REF]. For example, it can be applied to the design of virtual wayfinding environments [START_REF] Smith | Evaluating distributed cognitive resources for wayfinding in a desktop virtual environment[END_REF], the modeling of smart environments [START_REF] Harrison | Engineering crowd interaction within smart environments[END_REF], the design of graphic user interfaces [START_REF] Mehrnejad | Security analyzing and designing GUI with the resources model[END_REF], and the design of healthcare technology [START_REF] Rajkomar | Distributed cognition for evaluating healthcare technology[END_REF].

Trust in the software

Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-7 -

The resources model focuses on information in the distributed cognitive system. It does not, therefore, take subjective variables into account, even though they could have a significant impact on the interaction. In the present case, user confidence in the machine could be an important factor [START_REF] Jian | Foundations for an empirically determined scale of trust in automated systems[END_REF]. In previous applications of the resources model, the machine did not make mistakes [START_REF] Fields | Air traffic control as a distributed cognitive system: A study of external representations[END_REF][START_REF] Smith | Using the resources model in virtual environment design[END_REF][START_REF] Wright | Distributed information resources: A new approach to interaction modelling[END_REF]. In our study, however, the questioning of participants by the software was driven by uncertainty about the symbol interpretations. It was the risk of error that prompted the software to ask for the participants' input. There might or might not, therefore, be an actual mistake. In addition, some potential errors in the retro-conversion might be missed by the software. This meant that the participants' confidence in the software was crucial. If they did not trust the machine, the system's requests for input would be pointless, as the participants would still check every inch of the plan with the same intensity. This is what [START_REF] Fogg | The elements of computer credibility[END_REF] call the incredulity error. Conversely, if participants placed too much faith in the software, they might be less vigilant and miss errors that were not flagged up. In this case, they would be committing a gullibility error [START_REF] Fogg | The elements of computer credibility[END_REF]. In the field of HCI, trust can be defined as "the attitude that an agent will help achieve an individual's goals in a situation characterized by uncertainty and vulnerability" (Lee & See, 2004, p. 51). According to [START_REF] Fogg | The elements of computer credibility[END_REF], "trust indicates a positive belief about the perceived reliability of, dependability of, and confidence in a person, object, or process" (p. 81). A user's assessment of a system's credibility is not always in line with that system's actual level of accuracy. Credibility can be evaluated accurately, but it can also be overestimated or underestimated. Being a form of interpersonal trust, trust in a system is dynamic, changes across interactions, and increases or decreases over time [START_REF] Hoffman | Trust in Automation[END_REF]Lee & Moray, 1992;[START_REF] Khasawneh | A model for predicting human trust in automated systems[END_REF]. In general, a system tends to gain credibility when it provides correct, accurate, and relevant information. Conversely, it loses credibility when it supplies false information [START_REF] Khasawneh | A model for predicting human trust in automated systems[END_REF]. According to [START_REF] Lee | Trust in automation: Designing for appropriate reliance[END_REF], when the level of confidence corresponds to the true level of reliability, it tends to induce an appropriate use of the system. However, excessively high confidence in relation to actual reliability can lead to misuse, while an excessively low level of trust may result in cessation of use. The over-confidence is called complacency by [START_REF] Miller | Designing For Flexible Interaction Between Humans and Automation: Delegation Interfaces for Supervisory Control[END_REF]. In this case, the operator tends to become less vigilant. This attentional disengagement was modeled by [START_REF] Parasuraman | Complacency and biais in human use of automation: An attentional integration[END_REF] in the form of a loop involving trust, the allocation of attentional resources, and feedback from the system. According to this model, the operator allocates attentional resources to all nonautomated tasks, but the amount of resources allocated to automated tasks depends on his or her degree of trust in the system. Feedback provided by the system on the outcome of the participants' actions further affects their trust. The over-confidence effect has also been demonstrated in research on a collision alarm system for car drivers [START_REF] Abe | Dynamics of drivers' trust in warning systems[END_REF]. In this study, participants who were highly confident in the system performed more poorly on obstacle avoidance, when the automated assistance was no longer working, than participants who were less confident in it.

Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-8 -Generally speaking, designers seek to make their systems as credible as possible. However, [START_REF] Lee | Trust in automation: Designing for appropriate reliance[END_REF] recommend designing systems so that trust is simply appropriate, rather than too high.

Hypotheses

If they are to pinpoint errors of interpretation, participants must implement a semantic matching strategy, whereby they decide which actions to take (i.e., which interpretations to correct) by matching the action-effect relation with the goals and system state. In this situation, the goals are the correct interpretations of the symbols, as described in the instructions, and the system state refers to the interpretations that are performed by the software. When these two informational resources fail to match up, the user must perform a correction in order to make them correspond. As we mentioned earlier, the software is able to assess the degree of certainty for each of its interpretations. Thus, it is able to tell the user which interpretations carry a risk of error. By flagging up each risky interpretation in turn, the software provides the user with a plan (i.e., a set of areas to check in a given order). Thus, it allows the user to implement a second interaction strategy (plan-following strategy) in parallel with the semantic-matching strategy. This plan-following strategy, which focuses more on the risky areas, promotes the detection of highlighted errors. Our first hypothesis (H1) therefore predicted that more participants would correct errors flagged up by the system than the same errors which are not signalled.

The fact that the software was capable of making mistakes might have an impact on trust. For each plan, four of the six potential error flagged up by the system were indeed errors. We therefore expected participants in the without-interruption condition to find more of the errors that were not flagged up by the machine in the with-interruption condition. Machine errors tend to decrease what [START_REF] Merritt | Not all trust is created equal: Dispositional and history-based trust in human-automation interactions[END_REF] call historybased-trust. However, [START_REF] Wiegmann | Age and automation interact to influence performance of a simulated luggage screening task[END_REF] showed that in a luggage screening task, a system for automatically detecting knives had a positive impact on the users' confidence in their own judgments. They therefore attributed credibility to the system whenever it intervened. Indeed, the phenomenon of complacency induced by the automation should occur [START_REF] Skitka | Does automation biais decision-making?[END_REF]. The second hypothesis of this study (H2) was thus that users asked for their input have greater confidence in the machine. Our third hypothesis (H3) was that these users correct fewer unreported errors than those who are not asked for their input, because their increased confidence reduces the extent of checks on areas not flagged up by the machine.

METHOD

Participants

Participants in the present study were 48 volunteers (11 men and 37 women), none of whom worked or studied in the field of architecture. The youngest was aged 18.75 years and the oldest 44.17 years. Their mean age was 26.23 years (SD = 5.98). For their participation, volunteers receive a voucher valued at 15 euros. Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

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Material

In this experiment, each of the four tasks was performed on a Tablet PC Asus Eee Slate 12.1, and a prototype software capable of automatically recognizing various symbols was used to create the plan interpretations (see Fig. 1). Each participant had to correct the errors of interpretation of four plans in succession, in one of two experimental conditions: interruption by the system (IS) and without interruption by the system (WIS). In the WIS condition, the original plan was immediately displayed on the screen, and the automatic recognition process then began to unfold. Participants were able to halt this process at any time to correct errors. The IS condition was identical to the WIS one, except that when the system encountered a possible error, it halted the interpretation and displayed the possible corrections on the screen in clickable buttons. Participants had to correct errors by selecting the right symbol (door, casement, sliding window, wall or furniture). Each of the plans we used had 60 symbols and six misinterpretations. In the IS condition, there were six interruptions by the system, four of which concerned genuine errors and two correct interpretations. Thus, two errors in each plan failed to trigger an interruption by the system.

In the WIS condition, the task consisted in watching the retro-conversion as it unfolded and monitoring the interpretations proposed for each symbol by the prototype software. When participants noticed a misinterpretation, they had two ways of correcting it. The first was to click on the pause button first, to halt the retro-conversion process, and then click on the misinterpreted symbol to view the range of possible corrections. The Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-10 -second was to click directly on the misinterpreted symbol, which had the effect of blocking the retro-conversion process and displaying the correction options. Our decision to create this shortcut met the minimal actions and the flexibility criterions [START_REF] Bastien | Ergonomics criteria for the evaluation of human-computer interfaces[END_REF], in that it eliminated one of the steps in the error correction process and allows the participants to perform the task in different ways.

This loop was repeated for each error that was identified until the retro-conversion process came to an end. The participants' goal was an error-free plan interpretation. This goal was included in the printed instructions and available throughout the task. More specifically, the meanings of all the symbols were provided in the instructions, and the goal was thus device-embedded. It should be noted that the symbols were not chosen arbitrarily. All of them had some similarities with the furniture they symbolize. For instance, a single bed was represented by an elongated rectangle and a table by a square. It was therefore possible for participants to guess the meaning of a given symbol without consulting the instructions.

During the retro-conversion there were very few affordances, as the only two possible actions were halting the retro-conversion process by clicking on the pause button, and viewing the range of possible corrections for a given symbol by clicking directly on it. These relation action-effects were explained in the instructions, but in any case the pause button was displayed on the screen throughout the task and there was no ambiguity as to its function. The aim of the participants was to match the goals (relevant interpretations described in the printed instructions) with the state of the system (interpretations displayed on the screen), that is, to detect and correct errors of interpretation. This task therefore involved implementing a semantic matching strategy. To do this, participants had to compare the constantly evolving state (i.e., the interpretations gradually performed by the software) with the goals. As they were dealing with a continuous flow of information, they had to act quickly. Whenever they detected a mismatch between goal and state, they either clicked the pause button or directly on the symbol in need of correction, and a window opened, displaying the possible interpretations of the selected symbol.

In the IS condition, whenever the prototype detected a particularly low likelihood score for an interpretation displayed on the screen (i.e., a high risk of misinterpretation), it automatically stopped and asked the user to confirm or correct it (see Fig. 2). By so doing, the software provided the user with a plan (i.e., a set of specific areas to check in a given order). Thus, a plan-following strategy could be implemented by users in parallel with the semantic matching strategy. Whenever the user signals an error, the software suggests several corrections and the user has to select one of them. -11 -

Procedure

An architectural floor plan was displayed on the screen. This plan was then interpreted by the prototype and participants were asked to correct interpretation errors. Each participant thus checked four different plans (see Fig. 3) in the same experimental condition. The order of presentation of the four plans was counterbalanced within each experimental group in order to cancel any effect of plan difficulty. Thus, each of the four plans used in this experiment was used the same number of times, in each of the 24 possible orders. Participants did not receive any feedback about their performance between plans.

Figure 3. 2D architectural floor plans used in the experiment

Before beginning the task, the participants were asked to carefully read through the instructions. They then monitored the retro-conversion process and corrected any errors they detected. At the end of this process, they had to state whether or not they had consulted the instructions during the interaction. Finally, participants were asked to answer a questionnaire measuring their trust or distrust in the system [START_REF] Jian | Foundations for an empirically determined scale of trust in automated systems[END_REF]. This questionnaire consisted of 12 items. Half of them probed trust in the system (e.g., "The system is reliable") and half probed distrust (e.g., "The system is deceptive"). For each item, the participants were asked to specify their level of agreement on a 7-point Likert-like scale ranging from 1 (Not at all) to 7 (Extremely).

The factors manipulated in this study were condition and training. Condition was a categorical between-participants variable with two modalities (WIS and IS), and training was an ordinal within-participants variable with four modalities denoting the successive trials. The two dependent variables were task duration and corrected errors. We distinguished between two types of error: the four errors that the system spotted in each plan and the two errors that it did not spot in each plan. Because of this distinction, the Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-12 -number of errors in each category was low, making it unlikely that the scores could be distributed in order to allow for the comparison of variances and means. These variables were therefore recoded as binomial variables, with 0 signifying at least one error in the plan, and 1 the absence of this error type in the plan. Depending on the variable, the zero indicated the absence of either a signaled error or an unsignaled error.

RESULTS

Task duration

As the times were continuous values bounded by a minimum but not a maximum, and as we were seeking to assess the effects of a between-participants variable (condition) and a within-participants variable (training), we modeled times with a gamma distribution in a repeated-measure framework using generalized estimating equations [START_REF] Ballinger | Using generalized estimating equations for longitudinal data analysis[END_REF][START_REF] Ghisletta | An introduction to generalized estimating equations and application to assess selectivity effects in a longitudinal study on very old individuals[END_REF].

There were significant main effects of training, Wald χ2(3, N = 191) = 77.619, p < .001, and condition on task duration, Wald χ2(1, N = 191) = 4238, p = .040. (see Fig. 4). By contrast, there was no significant interaction between training and condition, Wald χ2(3, N = 191) = 5.534, p = .137. There was no difference in the time it took the software to analyze each plan, either in the two conditions or in the four successive trials. The differences in task durations therefore stemmed from differences in correction duration.

Errors

Errors spotted by the system Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-13 -

The proportion of errors flagged up by the system that were successfully corrected by the participants was calculated in relation to training and condition, using a logistic regression model with repeated measures. There were significant main effects of training, Wald χ2(3, N = 191) = 410.325, p < .001, and condition on the proportion of errors spotted that were corrected, Wald χ2(1, N = 191) = 106.589, p < .001 (see Fig. 5). By contrast, there was no significant interaction between the order and condition variables, Wald χ2(2, N = 191) = 1.321, p = .517. This result is consistent with H1, which stated that errors highlighted by the system would be corrected by a higher proportion of participants than errors that the system missed.

Figure 5. Percentage of participants who corrected all the errors spotted by the system as a function of condition and trial number.

Errors not spotted by the system

The proportion of errors not spotted by the system that were successfully corrected was calculated in relation to training and condition using a logistic regression model with repeated measures. There were significant main effects of training, Wald χ2(3, N = 191) = 270.145, p < .001, and condition on the proportion of corrected errors that had not been flagged up by the system, Wald χ2(1, N = 191) = 101.186, p < .001. In addition, the interaction between training and condition was significant, Wald χ2(2, N = 191) = 603.430, p < .001. In Figure 6, we can see that the superiority of the WIS group only emerged from the second plan onwards. This result is consistent with our hypothesis that requests for user input generate overconfidence (H2) which, in turn, has a negative impact on the detection of unspotted errors. Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-14 -A complementary analysis comparing the proportions of participants who corrected all the errors failed to reveal any significant difference between the WIS group (65.28%) and the IS group (67.73%), Wald χ2(1, N = 191) = .064, p = .800. As the condition variable had inverse effects on error correction, depending on whether or not the error had been spotted by the system, when no distinction was made between these two types of errors, no difference emerged.

Degree of trust in the automated system

Because we had translated the items used to measure trust and distrust into French for the purposes of our experiment, we conducted a confirmatory factor analysis to ensure that we found the same factor structure as [START_REF] Jian | Foundations for an empirically determined scale of trust in automated systems[END_REF]. This analysis confirmed the one-dimensionality of trust, χ 2 = 20.15, p = 0.13, RMSEA = 0.1, and distrust, χ 2 = 9.33, p = 0.41, RMSEA = 0.03. Moreover, when we calculated Cronbach's alpha to assess the items' internal consistency, we found high values for both trust (α = .87) and distrust (α = .74).

We ran Levene's test to ensure that the variances could be considered as equal, a necessary condition to use analysis of variances (ANOVA). Levene's test on the distribution of distrust scores revealed acceptable homoscedasticity, F(1, 46) = .002, p = .966. . A one-way ANOVA showed no statistically significant difference between the WIS and IS groups, F(1, 46) = .961, MSE = .973, p = .332 (see Fig. 7). Levene's test on the distribution of trust scores also revealed acceptable homoscedasticity, F(1, 46) = .006, p = .936. A one-way ANOVA revealed the statistically significant superiority of trust in the IS group compared with the WIS group, F(1, 46) = 5.713, MSE = 5.685, p = .021. This result is consistent with H2, in that the participants in the IS group had greater confidence in the system than the participants in the WIS group. Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

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DISCUSSION AND CONCLUSION

The first objective of this study was to test the predictive power of the resources model for a task of supervising an automated process. The second objective was to assess the contribution of the subjective dimension trust to modeling this type of task. Our first hypotheses (H1) derived from the resources model were validated by our results. H1 predicted that participants who were asked to provide input would be better at correcting the errors spotted by the system than participants who were not asked for their input. Participants in the IS group were significantly more likely to correct all the errors that were flagged up than participants in the WIS group. In the WIS condition, participants used the semantic matching strategy, which consists in matching the state of the system with the goals in order to detect errors. In the IS condition, the software provided participants with a plan, by flagging up potential errors during the retro-conversion process. This plan shows the participants each symbol they must check and informs when they have to do it. This enabled them to implement a plan-following strategy for checking each successive interpretation provided by the software. However, this strategy was implemented in conjunction with the semantic matching strategy, for even in the IS condition, some unsignaled errors were corrected. This result is also coherent with the findings of [START_REF] Wiegmann | Age and automation interact to influence performance of a simulated luggage screening task[END_REF], who observed an improvement in target detection performances when the system cued the presence or absence of targets with a 90% hit rate.

The resources model was originally intended to allow the modeling of HCIs from a distributed cognition perspective [START_REF] Wright | Distributed information resources: A new approach to interaction modelling[END_REF]Wright et al., 2000). It has already been applied to Microsoft's Excel spreadsheet application by its authors as an example [START_REF] Wright | Distributed information resources: A new approach to interaction modelling[END_REF], as well as to an air traffic control task [START_REF] Fields | Air traffic control as a distributed cognitive system: A study of external representations[END_REF] and even to the design of a virtual environment [START_REF] Smith | Using the resources model in virtual environment design[END_REF], and many recent publications have described modeling with the resources model (e.g., [START_REF] Harrison | Engineering crowd interaction within smart environments[END_REF][START_REF] Mehrnejad | Security analyzing and designing GUI with the resources model[END_REF][START_REF] Rajkomar | Distributed cognition for evaluating healthcare technology[END_REF]. Here, the application of this model to a task of supervising an automated retro-conversion process bore fruit owing to the distribution of resources involved in supervision. The resources model thus provides a relevant analytical framework for this type of task, although it does not explain all the results, as some are linked to subjective variables. For instance, in our study, the IS group corrected fewer unspotted errors than the WIS group. This difference which is coherent with our third hypothesis only emerged from the second plan onwards. The differences in the level of trust between the IS and WIS conditions stemmed from the degree of relevance of the requests for user input. As there is no difference between both groups before the beginning of the first plan analysis, we assume that trust levels were the same in both groups at the beginning of the experiment. Therefore, this trust level gradually changed in the course of the interaction. At the start of the task, participants had no experience of the software. Their trust had therefore not been affected and should have been the same in Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-16 -both groups. In accordance with our second hypothesis, the level of trust measured at the end of the task differed between the two groups. This means that the change in the level of trust took place during the task. This idea is consistent with the description of trust as a dynamic phenomenon that is constantly changing (Lee & Moray, 1992). Nevertheless, in the absence of measures of trust levels after each plan, it is impossible to be sure that the change in trust was a gradual process in this experiment. In addition, the IS group trusted the system more than the WIS one did. It seems that the requests for input increased the participants' confidence, such that it generated a gullibility error [START_REF] Fogg | The elements of computer credibility[END_REF]. The participants' confidence in the machine undoubtedly affected their interaction strategy, as they presumably felt that the system was capable of effectively flagging up all the potential errors. Thus, the gradually appearing negative effect of sollicitations on the detection of unsignalled errors suggests that the participants using the plan-following strategy partially neglected the semantic matching strategy because of the increasing trust. They were less vigilant in searching for errors that were unreported. Because of the trust, they tend to consider that following the plan provided by the software is sufficient to detect the errors.

Moreover, 79% of the participants in the WIS group stated that they consulted the printed instructions during the task, compared with just 46% in the IS group. When users can choose between several strategies to perform a given task, the trust they feel may influence the choices they make. A high level of trust encourages users to select strategies that are based more on information from the software. Conversely, a low level of trust presumably encourages users to select strategies that make the result less dependent on the software's judgments. Globally, we assume that participants tend to memorize the instruction while reading them during the task. This is coherent with the learning effect obtained for the task duration and error correction.

The IS participants believed that the system was more reliable and that they therefore had less need to monitor its interpretations and thus check the instructions. [START_REF] Wiegmann | Age and automation interact to influence performance of a simulated luggage screening task[END_REF]'s results did not distinguish between the images in which the system cued the target and those in which it did not. In addition, the authors showed that when a judgment made by the automatic aid was consistent with the participants' response, they expressed a higher degree of confidence in their response than when the judgment was discordant. This shows that the participants took the system's advice into account and thus placed a greater degree of faith in it. If the system's judgment was trusted in [START_REF] Wiegmann | Age and automation interact to influence performance of a simulated luggage screening task[END_REF]'s experiment, as it was in ours, the participants who were provided with assistance were possibly less accurate in detecting targets when they were not cued by the system than participants who received no such assistance.

Because [START_REF] Wiegmann | Age and automation interact to influence performance of a simulated luggage screening task[END_REF] also did not distinguish between performances on the images in which the system's cuing was correct and those for which no assistance was given despite the presence of a knife, they may have missed a negative effect of the automated aid on those images that did not generate assistance. Overall, the results of our study are consistent with the resources model, showing that it can have both a predictive and a prescriptive value for tasks involving the supervision of an automated process. It is, however, important to stress that it focuses on information distributed across the system, Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-17 -and does not include subjective dimensions. Faith in the system is an important variable in HCI, and was needed to explain our results. Therefore, the predictive nature of the resources model could be improved if it incorporated subjective aspects. Some experiments and design projects using the resources model may yield erroneous conclusions or design choices owing precisely to the model's failure to take subjective factors into account.

The main limitation of the present study was that the comparison was based on the straightforward absence or presence of a system for requesting input. To confirm our interpretation of the results, we would therefore need to vary the relevance of the requests and examine the impact of these variations on participants' trust and performance. More relevant requests would improve error correction rates, but by the same token, the attendant increase in confidence in the system would reduce the detection of unreported errors. Conversely, less relevant ones would mean that fewer errors were corrected with the help of the system, and users would also pay more attention to checking the rest of the plan.

The results of the present study could lead to a number of concrete interface design recommendations for CAD, beautification, retro-conversion or even target detection software. Our study shows that the introduction of interruptions by the system to ask for user input when there is a risk of error has benefits, but also disadvantages. On the one hand, it can save time and improve the identification of some types of errors. On the other hand, it may result in other errors being missed. Concretely, software that incorporates this functionality does not necessary improve overall performance in tracking errors. The negative effect on some error detection may be problematic and difficult to anticipate. Our results suggest that the negative effect of requests for input could be compensated for by more training. Thus, performance could be maximized by implementing requests to users if the users perform the task frequently, in order to benefit from their training. In addition, as suggested by [START_REF] Lee | Trust in automation: Designing for appropriate reliance[END_REF], a more appropriate level of confidence (i.e. that matches with the level of reliability of the software) may be obtained by communicating the software's actual performance to the user. For example, the instructions could have included the information that, on average, one third of the errors made by the software are not signaled. This type of objective information would ensure that the level of trust corresponded to the software's degree of reliability.

Finally, the value of this type of assistance seems to be largely dependent upon its accuracy. The more accurate the assistance is in detecting targets, the smaller the set of targets that undergo the local negative effect. The results of our experiment do not allow us to say whether more accurate assistance is accompanied by greater trust by the user. Further research is therefore needed to investigate effects related to the accuracy of the assistance.

Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

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FIGURES Figure 1. Photograph of the interface with a fully interpreted plan

Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-26 - Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-27 - Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-28 - Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-29 - Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-30 - Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-31 - Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

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NOTES

Support. This study was conducted as part of the "MobiSketch" French National Research Agency (ANR) project .

Figure 1 .

 1 Figure 1. Photograph of the interface with a fully interpreted plan

Figure 2 .

 2 Figure 2. Example of software solicitation. The software asks if this interpretation is an error (left), and it suggests other interpretations (right).

  Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

Figure 4 .

 4 Mean task duration (and standard deviation) as a function of condition and trial number.

Figure 6 .

 6 Figure 6. Percentage of participants who corrected all the unspotted errors as a function of condition and trial number

Figure 7 .

 7 Means and standard deviations for the trust and distrust scores

FIGURE CAPTIONS Figure 1 .Figure 2 .

 CAPTIONS12 FIGURE CAPTIONS

Figure 3 .Figure 4 .

 34 Figure 3. 2D architectural floor plans used in the experiment

Figure 5 .

 5 Figure 5. Percentage of participants who corrected all the errors spotted by the system as a function of format and number of trials.

Figure 6 .

 6 Figure 6. Percentage of participants who corrected all the unspotted errors as a function of format and number of trials

Figure 2 .

 2 Figure 2. Example of software solicitation. The software asks if this interpretation is an error (left), and it suggests others interpretations (right).

Figure 3 .

 3 Figure 3. 2D architectural floor plan used in the experiment

Figure 4 .

 4 Figure 4. Mean task duration (and standard deviation) as a function of format and number of trials

Figure 5 .

 5 Figure 5. Percentage of participants who corrected all the errors spotted by the system as a function of format and number of trials

Figure

  Figure 6. Percentage of participants who corrected all the unspotted errors as a function of format and number of trials

Figure 7 .

 7 Figure 7. Means and standard deviations for the trust and distrust scores Distrust Trust M SD M SD WIS 3.94 1.02 3.76 .93 IS 4.22 1.00 4.45 1.07

This software is under development as part of the "MobiSketch" French National Research Agency (ANR) project, within the framework of the "Content and Interaction" program (ref. 09-CORD-015).

Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-21 -Wright, P. C., Fields, R. E., & Harrison, M. D. (2000). Analysing human-computer interaction as distributed cognition: The resources model. Human Computer Interaction, 15(1), 1-42.

Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.

-22 -FOOTNOTES 1 This software is under development as part of the "MobiSketch" French National Research Agency (ANR) project, within the framework of the "Content and . Fleury, S., Jamet, E., Ghorbel, A., Lemaitre, A., & Anquetil, E. (2014). Application of the Resources Model to the Supervision of an Automated Process. Human-Computer Interaction.