Joan Casanelles-Abella

Stefanie Müller

Alexander Keller

Cristiana Aleixo

Marta Alós Orti

François Chiron

Nicolas Deguines

Tiit Hallikma

Lauri Laanisto

Pedro Pinho

Marco Dr

Moretti

Roeland Samson

Piotr Tryjanowski

Anskje Van Mensel

Loïc Pellissier

Marco Moretti

How wild bees find a way in European cities: Pollen metabarcoding unravels multiple feeding strategies and their effects on distribution patterns in four wild bee species

Keywords: cavity-nesting bees, feeding behaviour, remote sensing, trap-nests, urbanization, urban biodiversity, species distribution models

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Wild bees are responsible for major ecosystem functions and make many contributions relevant to people, including pollination and maintenance of ecosystem stability, and they represent social and cultural values

Accepted Article

This article is protected by copyright. All rights reserved (e.g. [START_REF] Potts | Safeguarding pollinators and their values to human well-being[END_REF]. Over the last decades, bee populations have dramatically decreased [START_REF] Zattara | Worldwide occurrence records suggest a global decline in bee species richness[END_REF]. Multiple causes have been identified [START_REF] Goulson | Bee declines driven by combined stress from parasites, pesticides, and lack of flowers[END_REF], and the loss of floral resources is among the most important [START_REF] Goulson | Bee declines driven by combined stress from parasites, pesticides, and lack of flowers[END_REF]. Wild bees critically depend on large amounts of nectar and pollen to survive and reproduce during their life cycle, and most species display some degree of fidelity to specific plant taxa [START_REF] Goulson | Foraging strategies of insects for gathering nectar and pollen, and implications for plant ecology and evolution[END_REF][START_REF] Vanderplanck | How does pollen chemistry impact development and feeding behaviour of polylectic bees?[END_REF]. Consequently, diet specialization and diet preference are two key traits determining the sensitivity to land-use changes (e.g. urbanization; [START_REF] Dharmarajan | Anthropogenic disturbance favours generalist over specialist parasites in bird communities: Implications for risk of disease emergence[END_REF] and the distribution patterns of wild bees (e.g. [START_REF] Fournier | The origin of urban communities: From the regional species pool to community assemblages in city[END_REF].

Urban ecosystems can harbour large and diverse bee communities, helping to preserve and promote wild bee diversity. Although urbanization has major negative impacts on biodiversity [START_REF] Theodorou | Urban areas as hotspots for bees and pollination but not a panacea for all insects[END_REF], a significant number of wild bee species can thrive in cities. Documentation of wild bees in urban ecosystems has frequently indicated diverse wild bee communities [START_REF] Baldock | Where is the UK's pollinator biodiversity ? The importance of urban areas for flower-visiting insects[END_REF][START_REF] Casanelles-Abella | Applying predictive models to study the ecological properties of urban ecosystems: A case study in Zürich, Switzerland[END_REF], although this ultimately depends on each species' traits and its response to urbanization.

Urban ecosystems are warmer [START_REF] Roth | Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology[END_REF], have higher landscape heterogeneity (Turrine & Knopp, 2015) and are generally less polluted by pesticides [START_REF] Scheyer | Pesticides analysed in rainwater in Alsace region (Eastern France): Comparison between urban and rural sites[END_REF]) than intensive agricultural areas.

Moreover, while intensified agricultural systems have impoverished floral resources, in cities these resources might be maintained, thanks to social investment, high availability of woody species (e.g. street trees) in highly urbanized areas, and the presence of flower-rich habitats [START_REF] Somme | Food in a row: urban trees offer valuable floral resources to pollinating insects[END_REF][START_REF] Tew | Quantifying nectar production by flowering plants in urban and rural landscapes[END_REF]. In both public and private urban greenspaces, there are important efforts to establish and maintain flowering plant assemblages, with each phase reflecting the preferences and needs of the specific owners and managers [START_REF] Harrison | Urban drivers of plant-pollinator interactions[END_REF]. Therefore, there is a major opportunity to promote wild bee fitness and reproduction by increasing and improving wild bee habitats.

Urban ecosystems can induce dietary changes in species, due to their distinct availability of various food resources. In urban ecosystems, natural food resources are complemented with anthropogenic food resources [START_REF] Faeth | Trophic dynamics in urban communities[END_REF], whose accessibility is modulated by each species' diet specialization. Urban floral resources are especially diverse in cities, due to gardening and horticultural activities, with many native and exotic species planted for different purposes. Some of these species provide additional sources of food for pollinators within their range of foraging preference, phenology and trait matching [START_REF] Garbuzov | Quantifying variation among garden plants in attractiveness to bees and other flower-visiting insects[END_REF][START_REF] Harrison | Urban drivers of plant-pollinator interactions[END_REF]. Therefore, generalist wild bee species with a broad dietary range might be better able to exploit the existing urban resources, access and forage on a greater variety of patches, and consequently be more widely distributed.

Knowledge on bee diet preferences could reveal which plant species are important for their survival and reproduction and could be translated into important decisions concerning the planning and management of floral resources, e.g. what species to plant. Plant identification with DNA metabarcoding techniques has increased in diet studies and provides new knowledge about the feeding preferences of animals, which can

Accepted Article

This article is protected by copyright. All rights reserved help us to understand their distribution along environmental gradients [START_REF] Pitteloud | The structure of plant-herbivore interaction networks varies along elevational gradients in the European Alps[END_REF]. So far, diet preferences have mostly been assessed indirectly through observations of adult bee plant visitation (e.g. [START_REF] Marquardt | Evaluation of the importance of ornamental plants for pollinators in urban and suburban areas in Stuttgart, Germany[END_REF] or through the morphological identification of pollen grains [START_REF] Haider | Intra-and interpopulational variation in the ability of a solitary bee species to develop on non-host pollen: Implications for host range expansion[END_REF][START_REF] Sedivy | Closely related pollen generalist bees differ in their ability to develop on the same pollen diet: Evidence for physiological adaptations to digest pollen[END_REF]. Nonetheless, specific sampling methodologies, such as trap-nests that allow standardized sampling [START_REF] Staab | Trap nests for bees and wasps to analyse trophic interactions in changing environments-A systematic overview and user guide[END_REF], combined with metabarcoding techniques promise to be a powerful tool to characterize and study larval bee diets [START_REF] Bell | Pollen DNA barcoding: Current applications and future prospects[END_REF][START_REF] Keller | Evaluating multiplexed next-generation sequencing as a method in palynology for mixed pollen samples[END_REF]. Trap-nests target larval pollen and thus can better describe bee diet preferences than measurements of adult visitation, while pollen metabarcoding techniques can identify a larger number of taxa with a higher taxonomic resolution than pollen morphological identification; this is particularly useful in urban ecosystems with unique and rich plant pools. Metabarcoding techniques reduce the need for taxonomic expertise associated with pollen morphological identification, thus broadening its application across multiple sites.

Here, we investigated the larval diet and distribution of four widespread wild bee species of urban ecosystems, representing a gradient of decreasing diet specialization (Chelostoma florisomne, Osmia bicornis, Osmia cornuta and Hylaeus communis), along urban intensity gradients in five European cities (Antwerp, Paris, Poznan, Tartu and Zurich). In particular, we asked the following questions: (1) What is the taxonomic and trait-based composition of the bee diets in different urban areas? (2) How consistent are the bee diets across urban areas? (3) How does diet specialization influence the bee distribution in urban ecosystems? We expected that specialized bee species (i.e. C. florisomne) would have strong preferences for specific plant taxa and thus a highly consistent diet across urban areas and within urban gradients.

Conversely, we predicted that more generalist bees (i.e. O. cornuta, O. bicornis and H. communis) would have a more flexible diet and be capable of switching to alternative floral resources, including exotic taxa, and thus have a higher turnover in the diet composition (at the plant family, genus and species level) and a less consistent diet. Finally, we hypothesized that bee species with greater diet specialization would have low flexibility in terms of switching their diet to other plant taxa and thus would be more sensitive to urban intensity.

MATERIALS AND METHODS

Cities and study sites

We investigated wild bee diets in five cities in Europe: Antwerp (Belgium), Greater Paris (France, hereinafter referred as Paris), Poznan (Poland), Tartu (Estonia), and Zurich (Switzerland), covering a large part of the climatic variability in mainland Europe. Site selection followed Casanelles- Abella, Frey et al. (2021). Overall, we selected sites from the urban green areas mapped and defined in the pan-European Urban Atlas (EEA, 2012). We used an orthogonal gradient of patch area and connectivity. In particular, we calculated connectivity using the proximity index (PI), which considers the area and the distance to all nearby patches with a favourable habitat, within a given search radius. We considered as favourable habitat the land cover classes urban green areas, forest and low urban density with < 30% impervious

Accepted Article

This article is protected by copyright. All rights reserved surface. We set the search radius to 5 km from each focal patch, the maximum value possible with the available cartography. This resulted in the final selection of 80 sites: 32 in Zurich and 12 in each of the remaining four cities (Fig. S1, Table S1). We maintained a minimum distance of 500 m between selected sites (except for two sites in Zurich selected based on their position in the patch and connectivity gradient, which were separated by 260 m).

Bee sampling

At each site we installed trap-nests in trees, and in three cases (one in Paris, one in Tartu and one in Zurich) in other vertical structures (e.g. lamp post). We constructed trap-nests with reeds and cardboard tubes (Fig. S1). Our sampling trap-nests consisted of a standardized wood box with three plastic pipes 15 cm in diameter and 20 cm long. We assembled the first two pipes using 200-300 reeds from Phragmites australis (Cav.) Trin. and 5-10 bamboo reeds with diameters of 1-10 mm and a length of 20 cm to cover all requirements of the cavity-nesting bee community. We assembled the third pipe only using cardboard tubes of 7.5 mm diameter specific for Osmia spp. (WAB Mauerbienenzucht, Konstanz, Germany). We installed trap-nests at 2.5-3.5 m height with direct sunlight and SE or SW exposition, and kept them in the field from January until October 2018. In October, we collected the trap-nests and stored them at ca. 5°C until February 2019, and then transferred them to a new room at ambient temperature to recreate springlike conditions. Bees hatched and were identified to the species level from February to June 2019.

Study organisms

We collected pollen from the nests of four solitary bee species, Chelostoma florisomne (Linnaeus, 1758), Hylaeus communis (Nylander, 1852), Osmia bicornis (Linnaeus, 1758), and Osmia cornuta (Latreille, 1805). These species encompass a gradient of diet specialization (i.e. the number of different plant families exploited as resources, from the oligolectic C. florisomne to the highly polylectic H. communis), differ in phenology, and are common species in urban areas in Europe. In our study, each species was present in at least three of the studied cities. For more details about the ecology of these four wild bee species, see Text S1.

Pollen identification

We extracted a total of 464 pollen samples (Table S1) from undeveloped cells (i.e. where the larva had died) in nests where at least one adult had emerged and thus taxonomic identification of the bees was possible. Specifically, for C. florisomne we used 121 samples distributed in 3 cities and 18 sites (2 in Antwerp, 1 in Tartu and 15 in Zurich), for O. cornuta we used 66 samples distributed in 3 cities and 20 sites (6 in Antwerp, 5 in Paris and 9 in Zurich), for O. bicornis we used 176 samples distributed in 5 cities and 37 sites (3 in Antwerp, 10 in Paris, 8 in Poznan, 1 in Tartu and 15 in Zurich), and for H. communis we used 101 samples distributed in 5 cities and 33 sites (4 in Antwerp, 6 in Paris, 6 in Poznan, 9 in Tartu and 8 in Zurich). DNA metabarcoding (isolation, amplification and sequencing) was performed by AllGenetics laboratories (AllGenetics & Biology SL, A Coruña, Spain). We followed the method described by Sickel et

Accepted Article

This article is protected by copyright. All rights reserved al. ( 2015) and [START_REF] Vierna | PCR cycles above routine numbers do not compromise high-throughput DNA barcoding results[END_REF] to produce a pooled amplicon library on the ITS2 genomic region for the Illumina platform (Illumina, 2017). See Text S3 for details on the laboratory procedure of pollen metabarcoding. Bioinformatics followed mainly the procedure described in [START_REF] Campos | Standard methods for pollen research[END_REF] with minor modifications: We used VSEARCH v2.14.2 [START_REF] Rognes | VSEARCH: a versatile open source tool for metagenomics[END_REF] to join paired ends of forward and reverse reads. We also used VSEARCH to remove reads shorter than 200 bp, complete quality filtering (EE < 1 Edgar & Flyvbjerg, 2015) and de-novo chimera filtering, and define amplicon sequence variants (ASVs), as previously done successfully for pollens (e.g. [START_REF] Wilson | Many small rather than few large sources identified in long-term bee pollen diets in agroecosystems[END_REF].

The ITS2 rDNA reads were first directly mapped with VSEARCH global alignments and an identity cut-off threshold of 97% against a floral ITS2 reference database generated with the BCdatabaser [START_REF] Keller | BCdatabaser: On-the-fly reference database creation for (meta-)barcoding[END_REF], which consisted of plants recorded within the study regions. For the remaining unclassified reads, we first used global alignments against a global reference database (Ankenbrand 2015;[START_REF] Keller | Evaluating multiplexed next-generation sequencing as a method in palynology for mixed pollen samples[END_REF].

For reads that were still unclassified, we used SINTAX [START_REF] Edgar | SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences[END_REF] to assign taxonomic levels as deep as possible but a maximum of genus level with the same global reference database. In total, 82% of species recorded at the sites were present in the local database, 83% of species in the global database (direct classification). Further, 92% of genera were covered by the global database for hierarchical classification. Please note that the global database contains 112.115 unique species, 11.321 genera and 710 families in total, with a very high likelihood of coverage for species and genera of any interest for anthropogenic use, including exotic garden species.

Environmental variables

We assembled variables that were potential drivers of bee diets and distributions and that represented different aspects of the urban environmental gradients. Specifically, we focused on proxies of stress (particularly thermal stress), amount of habitat and resource availability at different spatial scales. We inferred resource availability at the local scale by performing floristic inventories on standardized plots, as explained in Casanelles- Abella, Frey et al. (2021) and in Supplementary Text S4. Furthermore, we collected information on two functional plant traits sensitive to bee-plant interactions, i.e. growth form (Tables S2-S3) and blossom type (Tables S2-S3) using information available in Casanelles- Abella, Frey et al. (2021). See Text S4 and Tables S2-S3 for additional information on the definition of the traits.

We used local and landscape connectivity metrics, local land cover metrics and landscape remote sensing based indices to infer thermal stress and the amount of available habitat, particularly regarding resource availability. As connectivity metrics we used patch size and the proximity index. We obtained the local land cover metrics by mapping grasslands, artificial surfaces, bare land, coniferous trees and deciduous broadleaved trees and then calculating their proportions at different spatial scales (i.e. 8, 16 and 32 m) from the focal trap-nest (see Text S5 for additional details). Finally, we used remote sensing based indices on land surface temperature, impervious surfaces, soil, water and vegetation at different spatial scales (50, 100, 200, 400, 800, 1600 m). Specifically, we used land surface temperature (LST), the urban index (UI), the colour index (CI), the normalized difference water index (NDWI) and the normalized difference vegetation

Accepted Article

This article is protected by copyright. All rights reserved index (NDVI), which can be used to characterize existing vegetation and urban infrastructure. In addition, we performed a principal component analysis (PCA) on the explanatory variables to define new meaningful underlying variables while reducing the dimensionality of the data set (see section 2.6). See Text S6 for details on the calculation of the remote sensing based indices and Fig. S2 for the distribution of values of each predictor in each city.

Statistical Analysis

We conducted all analyses in R version 4.0.2 (R Core Team, 2019) with RStudio version 1.4.1106(RStudio Team, 2020).

Species diet analysis

We performed taxonomic and trait-based metrics on the bee diets at the city and site level. Specifically, we computed the proportion of different plant taxa at the family, genus and species level (Table S4).

Furthermore, we calculated the species, genus and family richness and the Shannon diversity index.

Concerning trait-based responses, we calculated the proportion of the different categories of the three studied traits (Table S5). For each of the four studied bees, we performed Pearson correlations to investigate the relationships between the taxonomic and the trait-based diet metrics with the proxies of urban intensity, habitat amount and resource availability. We assessed these relationships: [START_REF] Requier | Beyond flowers: including non-floral resources in bee conservation schemes[END_REF] for each single city and (2) for all the cities combined.

We calculated the pairwise correlations between cities for each bee species to study diet consistency. Specifically, we first assembled binary trophic interaction matrices between the four bee species and the plant taxa at the family, genus and species level and then calculated the Pearson correlations of the binary trophic interaction matrices between pairs of cities for each bee and plant level. However, the trophic interaction matrix for a given city, and thus the pairwise correlations between cities, is influenced by the available plant pool. To avoid effects of plant composition, we first created a list with the plant pool occurring in each city at the family, genus and species level. We used the plant species sampled within a 100-m buffer by Casanelles- Abella, Frey et al. (2021) and complemented with the plant species recorded in GBIF at each city for the period 2000-2018 (Fig. S3). If a plant family, genus or species was missing in one of the plant pools of a pair of cities, we removed the interaction when performing the correlations.

Moreover, we computed a Chi-squared ( 2 ) test on the family and trait composition between cities' plant species pools (Text S7, Table S6).

Species distribution of urban gradients

We studied bee distribution patterns with species distribution models (SDMs). We assembled occurrence matrices indicating the occurrence of each bee species in the different sites. From the candidate environmental predictors, we evaluated the statistical relevance of each predictor using the predictive power (D 2 ; Table S7) and then manually picked three predictors that had correlations <0.7 to avoid

Accepted Article

This article is protected by copyright. All rights reserved collinearity (Fig. S4) for each bee separately. We used an ensemble of two common modelling techniques to account for model uncertainty and specificity [START_REF] Buisson | Uncertainty in ensemble forecasting of species distribution[END_REF]. Specifically, we used two regressionbased models, i.e. generalized linear models (GLMs) and generalized additive models (GAMs), and two tree-based models, i.e. gradient boosting machines (GBMs) and random forests (RFs), that show a higher complexity in their fitting procedures than GLMs and GAMs. We used city as a fixed factor to account for the nested structure of the data with a binomial probability distribution. We parameterized each modelling technique in the following way: we calibrated GLMs with first-order polynomials, GAMs with a spline smoothing term of intermediate complexity (k=4), RFs with a node size of 5 (nodesize = 5) and 1000 trees, and GBMs with an interaction depth of 1, a shrinkage of 0.001 and 1000 trees. We ran the models using the R packages mgcv version 1.8-30, RandomForest version 4.6-14 and gbm version 2.1.5. We randomly split the species records of the four bees into two sets containing 80% of the data for model calibration and 20% of the data for model evaluation. We repeated the procedure five times. We assessed model performance with the True Skill Statistic (TSS; [START_REF] Allouche | Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS)[END_REF]. TSS evaluates model skill in distinguishing absences from presences. The predictive performance of the different models was deemed acceptable when TSS > 0.4, following a commonly used minimum threshold (Thuiller et al., 2019).

Thus, we discarded models with TSS values lower than 0.4. We used the selected models of each studied bee species to predict the probability of occurrence over the environmental space of the studied cities.

RESULTS

Species diet analysis

A total of 41 plant families, 93 genera and 135 species were identified from the nests of the four bee species (Fig. 1, Tables S8-S9). Over half of the species were native (55%), there were more herbs (42%) than trees (34%), and dish-bowl blossoms were more common (56%) (Figs S6-S8, Table S9). The number of plant species per bee nest was similar among bee species (Table S9). The total number of collected plant taxa varied greatly among bee species, reflecting their differences in diet specialization: 1 family and 4 species in C. florisomne, 12 families and 33 species in O. cornuta, 18 families and 51 species in O. bicornis, and 32 families and 81 species in H. communis. (Fig. 1, Table S9). At the city level, we found dominance patterns in pollen abundance for some bees (Fig. 1). In O. bicornis, the most abundant species in pollen were Quercus robur (Antwerp, 70%), Acer pseudoplatanus (Paris, 64 %; Poznan 44 %; and Zurich 33 %). In H. communis, Styphnolobium japonicum was the most abundant species in pollen but only in Paris (52 %) and Poznan (32 %), with the vast majority of species representing 1-14 % of the pollen abundance. Interestingly, in C. florisomne, the most abundant Ranunculus spp. in pollen changed between cities (R. acris in Antwerp, R. repens in Tartu and R. bulbosus in Zurich). Finally, in O. cornuta, no species made up more than 37 %, being the most abundant ones A. pseudoplatanus (Antwerp, 37%; Paris, 21 %; Zurich, 24 %) and Prunus lusitanica (Paris, 33 %). In addition, very few nests were dominated by a single plant species and mostly in C. florisomne (Fig. S6).

Accepted Article

This article is protected by copyright. All rights reserved

We found different levels of diet conservatism across cities at the plant family and plant genus level, according to the bee specialization degree and taxonomic resolution of the plant taxa (Fig. 2). At the family and genus level, diet consistency was high for C. florisomne and declined with broader feeding niches, particularly at the genus level (Fig. 2a, Table S10). Conversely, we found major variation at the plant species level, which was particularly prominent for the broad generalist H. communis, which switched from herbaceous pollen to tree pollen with increasing urbanization (Fig. 3b).

The extent to which bee diet taxonomic and trait-based composition were conserved also varies according to the degree of specialization. Chelostoma florisomne had the most conserved diet, composed exclusively of native Ranunculus spp. (Figs 1, 2a, Tables S8-S9). Osmia cornuta primarily collected the pollen of native tree and shrub species, mainly with dish-bowl or brush type blossoms, from the families Sapindaceae, Salicaceae and Rosaceae (Figs 1, S7-S10, Tables S4,S5, S8-S11). Nevertheless, in Paris and Zurich we also found a considerable proportion of Ranunculaceae (Fig. 1b). Both C. florisomne and O. cornuta taxonomic and trait-based metrics showed no or little variation along urban intensity gradients (Fig. S11, Table S12). In O. bicornis, native tree species with dish-bowl or brush blossoms from the families Sapindaceae and Fagaceae represented a large part of the diet (Figs 1, S7-S10, Tables S4,S5, S8-S11), but we found some variation across cities concerning the remaining species in the diet (Figs. 1b,S10).

Hylaeus communis had the most diverse and variable diet. The Fabaceae family represented 34% in Paris and 42% in Poznan of the species found in the larval diet (Fig. 1b) and a minor part in the remaining cities (Fig. 1b). Furthermore, exotic species were more frequent for H. communis than for the other three bee species (Figs 3a, S7, Tables S5,S9). In addition, we found family richness, species richness and pollen diversity to be positively correlated with NDVI for H. communis for all cities except Poznan (Fig. S11, Table S12). Finally, in Paris, an important part of the diet was trees with flag type blossoms (Fig. 3b), in part due to the contribution of Styphnolobium japonicum (Fig 1a, Table S8), which became more dominant in the diet with increasing urban intensity (e.g. decreasing NDVI and increasing UI and CI at different scales; Fig. S12).

3, Table

Species distributions along urban gradients

The PCA conducted on the explanatory variables returned two main axes that explained 38% and 12% of the variation, respectively. The first axis was composed of remotely sensed variables, with larger values of the PC axis indicating less vegetation (i.e. higher UI, CI and LST, and lower NDVI; Fig. S12) independently of the landscape scale considered. The second axis was mostly composed of local land cover variables and metrics representing the available floral resources. Specifically, larger values on the PC axis indicated larger proportions of grasslands and lower proportions of deciduous trees and artificial surfaces, independently of the local scale considered (Fig. S12).

Accepted Article

This article is protected by copyright. All rights reserved

We found two distinct distribution patterns of the four bee species along urban intensity gradients. The first type of response was composed of C. florisomne, O. cornuta and O. bicornis. The probability of occurrence of these three bee species decreased rapidly with increasing urban intensity at the landscape scale (Figs 4a-b, S13) and increased with higher proportions of grasslands at the local scale (Fig. 4c-d). Strikingly, the probability of occurrence peaked at low proportions of broad-leaved trees ( Figs 4c-d, S13), even though the diets of O. cornuta and O. bicornis were composed largely of tree pollen. By contrast, the probability of occurrence of H. communis remained constant for larger values of urban intensity (Figs 4a-b, S13).

Moreover, the probability of occurrence was positively affected by the amount of deciduous broad-leaved trees at local scales ( Figs 4c-d, S13).

DISCUSSION

With biodiversity loss increasing at an unprecedented speed [START_REF] Leclère | Bending the curve of terrestrial biodiversity needs an integrated strategy[END_REF] and urbanization expanding worldwide, there is a pressing need to better understand the niche requirements of different taxa in order to satisfy them in urban ecosystems through targeted planning and management [START_REF] Aronson | Biodiversity in the city: key challenges for urban green space management[END_REF]. Here, using trap-nests and pollen metabarcoding techniques, we studied the larval diet and distribution patterns of four wild bee species in five European cities.

The larval diet of the studied oligolectic and intermediate polylectic bees was conserved in terms of plant family composition, representing successful strategies in urban ecosystems as an alternative to generalism. As hypothesized, diet consistency decreased with decreasing wild bee degree of specialization and was relatively high at the plant family level and to lower extend at the plant genus level. Moreover, for C. florisomne, O. cornuta and O. bicornis, bee diet at the plant family level in the studied cities was consistent with prior studies in non-urban ecosystems (e.g. [START_REF] Haider | Phylogeny and floral hosts of a predominantly pollen generalist group of mason bees (Megachilidae: Osmiini)[END_REF]. Diet conservatism regarding plant family composition has also been observed in other polylectic bee species such as Bombus spp. [START_REF] Wood | Global patterns in bumble bee pollen collection show phylogenetic conservation of diet[END_REF] and Osmia spp. [START_REF] Haider | Phylogeny and floral hosts of a predominantly pollen generalist group of mason bees (Megachilidae: Osmiini)[END_REF][START_REF] Vaudo | Pollen Protein: Lipid Macronutrient Ratios May Guide Broad Patterns of Bee Species Floral Preferences[END_REF], indicating that diet conservatism might be widespread despite a lack of data. Finally, diet consistency at the plant species level, with the exception of highly specialized C. florisomne, was very low, possibly because the specific trait values (e.g. pollen properties imposing cognitive or physiological restrictions; [START_REF] Vaudo | Pollen Protein: Lipid Macronutrient Ratios May Guide Broad Patterns of Bee Species Floral Preferences[END_REF] driving plant-pollinator interactions in less specialized wild bees might be relatively consistent within plant families or genera.

Across different animal taxa, broad feeding niches (i.e. low diet conservatism) have been identified as a key characteristics to pass the urban ecological filtering, and hence to thrive in cities [START_REF] Fournier | The origin of urban communities: From the regional species pool to community assemblages in city[END_REF], with some species even undergoing rapid phenotypic changes to broaden their diet (e.g. [START_REF] Eggenberger | Urban bumblebees are smaller and more phenotypically diverse than their rural counterparts[END_REF]. While widely distributed species inside cities are typically generalists, especially when highly mobile and with specific nesting and sociality modes (i.e. cavity nesting or social), intermediate specialization might be an alternative advantageous strategy in urban ecosystems. Focusing on wild bees,

Accepted Article

This article is protected by copyright. All rights reserved species whose preferred plant families are selected and facilitated by stakeholders (in Central European cities, e.g. Rosaceae, Fagaceae, Salicaceae; [START_REF] Ossola | The Global Urban Tree Inventory: A database of the diverse tree flora that inhabits the world's cities[END_REF] could have very diversified resources, due to the co-occurrence of spontaneous and cultivated native and exotic plant species. Here, we show that this is the case for O. cornuta and O. bicornis. Consequently, wild bees could still switch pollen hosts at the species level and better exploit existing resources while avoiding the costs associated with broad niches, as some pollen types are indigestible or toxic [START_REF] Eckhardt | Pollen mixing in pollen generalist solitary bees: A possible strategy to complement or mitigate unfavourable pollen properties[END_REF][START_REF] Praz | Specialized bees fail to develop on non-host pollen: Do plants chemically protect their pollen[END_REF]. On the other hand, strict specialization, as in C. florisomne, can still be a successful strategy in urban ecosystems when pollen hosts are highly facilitated and widespread in urban ecosystems. Nonetheless, strict specialists are vulnerable to the partial or total loss of pollen hosts, due to e.g. urban sprawl across the habitat of the bee or pollen host species, changes in social investment when gardening or managing greenspaces, or the arrival of pests.

The degree of specialization in bees is associated with distribution patterns along urban gradients. Our results suggest that increasing specialization leads to a higher sensitivity towards increased urban intensity. Diet specialization determines the possibility of occupying new patches, and greater specialization represents a strong limitation when the nutritional requirements are not met (e.g. with agricultural intensification; [START_REF] Peters | Diets maintained in a changing world: Does land-use intensification alter wild bee communities by selecting for flexible generalists? Ecology and Evolution[END_REF]. Rarity in bumblebees has been associated with narrower feeding niches [START_REF] Goulson | Foraging strategies of insects for gathering nectar and pollen, and implications for plant ecology and evolution[END_REF], and this is also the case for other wild bees (e.g. [START_REF] Deguines | Functional homogenization of flower visitor communities with urbanization[END_REF]. Some types of urban greenspaces consistently contain high bee diversity, including several specialists [START_REF] Baldock | Opportunities and threats for pollinator conservation in global towns and cities[END_REF][START_REF] Salisbury | Enhancing gardens as habitats for flower-visiting aerial insects (pollinators): Should we plant native or exotic species?[END_REF]. Still, only a handful of usually broad generalists can colonize various types of urban greenspaces. Hence, these species are widespread within urban ecosystems (Casanelles- [START_REF] Casanelles-Abella | Applying predictive models to study the ecological properties of urban ecosystems: A case study in Zürich, Switzerland[END_REF][START_REF] Fournier | The origin of urban communities: From the regional species pool to community assemblages in city[END_REF], even though not only the degree of specialization but also other correlated functional traits are involved in the response to urbanization (e.g. stress tolerance, dispersal; [START_REF] Harrison | Urban drivers of plant-pollinator interactions[END_REF]. To promote a larger number of bees in previously unoccupied areas of the city, wild bee habitat must be strategically increased, including enhancing the availability of high floral resource diversity. A first step to achieve this is to map the existing floral resources within the different urban land covers, making use of ongoing inventories [START_REF] Ossola | The Global Urban Tree Inventory: A database of the diverse tree flora that inhabits the world's cities[END_REF] or sampling schemes from research projects [START_REF] Baldock | A systems approach reveals urban pollinator hotspots and conservation opportunities[END_REF]Casanelles-Abella, Frey et al., 2021). These products could be combined and compared with wild bee diet preferences to detect where and what kind of planning actions can be taken. For instance, strategically increasing plant diversity rather than overall quantity with targeted taxonomic and trait groups is a measure that has been successful in other ecosystem types (e.g. agroecosystems; [START_REF] Sutter | Enhancing plant diversity in agricultural landscapes promotes both rare bees and dominant crop-pollinating bees through complementary increase in key floral resources[END_REF], although other wild bee requirements (e.g. nesting mode) must also be satisfied to successfully promote them [START_REF] Requier | Beyond flowers: including non-floral resources in bee conservation schemes[END_REF].

Limitations and prospects

Accepted Article

This article is protected by copyright. All rights reserved Pollen metabarcoding only yields relative abundance data and can be subject to PCR or taxon dependent biases as discussed in several studies. (e.g. [START_REF] Bell | Pollen DNA barcoding: Current applications and future prospects[END_REF][START_REF] Keller | Evaluating multiplexed next-generation sequencing as a method in palynology for mixed pollen samples[END_REF] The semi-quantitative abundances obtained in metabarcoding analyses are however still useful to identify the relative proportions of taxa and differentiate dominant, common and rare contributions to a mixed pollen sample by showing correlations (even though not perfect) between read and grain numbers [START_REF] Keller | Evaluating multiplexed next-generation sequencing as a method in palynology for mixed pollen samples[END_REF] . It is further agreed that pollen metabarcoding can identify to deep taxonomic levels, is able to detect rare taxa and is well comparable between studies [START_REF] Bell | Pollen DNA barcoding: Current applications and future prospects[END_REF]. In addition, the prevalence of certain plant families and species across cities (Fig. 1), and the existing information on C. florisomne, O. cornuta and O. bicornis [START_REF] Haider | Intra-and interpopulational variation in the ability of a solitary bee species to develop on non-host pollen: Implications for host range expansion[END_REF][START_REF] Haider | Phylogeny and floral hosts of a predominantly pollen generalist group of mason bees (Megachilidae: Osmiini)[END_REF][START_REF] Sedivy | Patterns of host-plant choice in bees of the genus Chelostoma: The constraint hypothesis of host-range evolution in bees[END_REF] showing patterns similar to those observed here support our findings. Combining molecular and morphological approaches would provide more robust relative abundance estimates as suggested by [START_REF] Sickel | Increased efficiency in identifying mixed pollen samples by meta-barcoding with a dual-indexing approach[END_REF]. Further, if taking dry weight of the collected pollen samples is an option (e.g. [START_REF] Lihoreau | Radar Tracking and Motion-Sensitive Cameras on Flowers Reveal the Development of Pollinator Multi-Destination Routes over Large Spatial Scales[END_REF], these relative data could be converted to absolute abundances per sample. Furthermore, we could only use morphological plant traits to investigate diet composition and shifts along urban gradients. However, bee choices when selecting larval food are likely to be influenced by the nutritional properties of the pollen, such as the content of protein, sugar or other essential nutrients [START_REF] Vanderplanck | How does pollen chemistry impact development and feeding behaviour of polylectic bees?[END_REF][START_REF] Vaudo | Pollen Protein: Lipid Macronutrient Ratios May Guide Broad Patterns of Bee Species Floral Preferences[END_REF]. Evidence seems to indicate important differences in the nutritional value of the available floral resources between urban land cover types [START_REF] Tew | Quantifying nectar production by flowering plants in urban and rural landscapes[END_REF] and possibly between ornamental plant species [START_REF] Garbuzov | Quantifying variation among garden plants in attractiveness to bees and other flower-visiting insects[END_REF]. Incorporating nutritional traits in future dietary studies will better elucidate the mechanisms behind diet composition and trophic niche shifts.

Finally, our results open future research directions. First, further studies could consider whether there are signals of feeding behavioural changes due to anthropogenic factors (e.g. plant or management preferences by stakeholders), as seen in other taxa (e.g. [START_REF] Cucherousset | Freshwater killer whales": beaching behavior of an alien fish to hunt land birds[END_REF], and if these are reflected in the variation of other functional traits (e.g. tongue length; [START_REF] Eggenberger | Urban bumblebees are smaller and more phenotypically diverse than their rural counterparts[END_REF]. To achieve this, it would be critical to study and compare bee diets in both urban and non-urban ecosystems. Second, assessing the effects of intraspecific diet variability on fitness (e.g. number and sex-ratio of offspring, adult and larval survival) would provide important mechanistic insights for anticipating how plant management choices might affect urban bee communities.

Policy implications

The uncovered taxonomic and functional diet preferences can support the planning and management of urban greenspaces to promote wild bees, particularly in wild bee species where the diet patterns are consistent across cities. Typical common urban weeds (e.g. Taraxacum officinale, Bellis perennis, Trifolium pratensis, T. repens) that are important floral resources in urban areas [START_REF] Baldock | A systems approach reveals urban pollinator hotspots and conservation opportunities[END_REF][START_REF] Kanduth | Red and white clover provide food resources for honeybees and wild bees in urban environments[END_REF][START_REF] Larson | Pollinator assemblages on dandelions and white clover in urban and suburban lawns[END_REF] contributed little or nothing to the larval diet of the studied bees. Conversely, both native and exotic woody species proved to be a widely used floral resource. For example, O. cornuta and O. bicornis collected large amounts of tree pollen from different plant families, which might enable them to exploit secondary pollen hosts by mixing high quality pollens with less digestible ones (e.g.

Accepted Article

This article is protected by copyright. All rights reserved Ranunculus spp., Asteraceae; [START_REF] Eckhardt | Pollen mixing in pollen generalist solitary bees: A possible strategy to complement or mitigate unfavourable pollen properties[END_REF][START_REF] Praz | Specialized bees fail to develop on non-host pollen: Do plants chemically protect their pollen[END_REF]. Thus, the maintenance of different vegetation and urban habitat types (e.g. meadows, street trees, shrublands) is of major importance for preserving bees. In addition, our results indicate that the occurrence of specific plant taxa (e.g. at the family or genus level) or trait values is more important than the origin of the plants [START_REF] Harrison | Urban drivers of plant-pollinator interactions[END_REF]. For example, non-native plants have been observed to retain the blooming time of their original region [START_REF] Godoy | Flowering phenology of invasive alien plant species compared with native species in three Mediterranean-type ecosystems[END_REF], and thus plant species from the Northern Hemisphere (e.g. planted urban trees or shrubs such Acer spp., Salix spp., Crataegus spp and, Quercus spp.) may bloom in synchrony with natives species, providing additional resources for wild bees. Furthermore, urban trees have been shown to be an important resource for several wild bee species [START_REF] Somme | Food in a row: urban trees offer valuable floral resources to pollinating insects[END_REF]. For instance, in our study, H. communis increasingly collected more pollen from ornamental trees (mainly Styphnolobium japonicum) with decreasing amounts of greenspace. Because the tree distribution in cities is mostly driven by anthropogenic factors, it represents an important point of action for greening strategies, specifically in densely urbanized city areas with limited herbaceous vegetation. Overall, floral preferences obtained from pollen metabarcoding, particularly when combined with existing information on available floral resources, could help to improve current strategies for developing bee-friendly cities (e.g. in the EU, see [START_REF] Wilk | A Guide for Pollinator-friendly Cities: Environment A guide for pollinator-friendly cities: How can spatial planners and land-use managers create favourable urban environments for pollinators? Guidance prepared by ICLEI Europe for the European Commission[END_REF]. In particular, characterizing bee diets could inform planning, management and decision making (e.g. what species, genera or families to plant), involving stakeholders, policymakers, nurseries and plant centres, for urban greenspaces to preserve and further promote wild bees in urban ecosystems.

FIGURE 1.

Bee larval diet composition in the studied cities. (a) For each bee species, the collected plant species in each city where the bee species was recorded (three cities for Chelostoma florisomne and Osmia cornuta, five cities for Osmia bicornis and Hylaeus communis) are shown. The size of the circle represents the mean relative abundance of plant species contributing to pollen samples per city and bee species. (b) For each bee species, the proportion in the pollen of the different collected plant families in the studied cities is shown (mean relative abundance of plant species contributing to pollen samples per city and bee species). Only families with a proportion in pollen ≥0.01 are plotted, whereas the remaining ones are represented in the category "Other families". Note that the proportion in pollen for O. bicornis in Antwerp and Tartu was obtained using only four and one samples, respectively. For each bee and city, we provide the number of sites where pollen samples were taken, and the total number of samples.

Information on the computation of the phylogenetic tree can be found in Text S2 and Fig. S5. Data supporting (a) can be found in Table S8. Ast, Asteraceae; Api, Apiaceae; Fab, Fabaceae; Fag, Fagaceae; Sal, Salicaceae; Br, Brassicaceae; Ma, Malvaceae; Sap, Sapindaceae; Ra, Ranunculaceae; Pa, Papaveraceae. Note also that the pollen for O. bicornis in Antwerp and Tartu was obtained using only four and one samples, respectively. Data supporting Fig. 2 can be found in Table S10.

Accepted Article

Accepted Article

Accepted Article

Accepted Article

FIGURE 2 .

 2 FIGURE 2. Pairwise correlations of the larval diet composition among cities. For each of the four studied bee species, the city pairwise correlations of the collected plant taxa are shown at the family (a), genus (b), and species (c) level. The colour of the dots indicates the value of the correlation, with lower and higher values in orange and blue, respectively. Note that the correlation values are expressed as absolute values.

FIGURE 3 .

 3 FIGURE 3. Trait-based larval diet composition in Hylaeus communis. (a-c) Composition of the diet according to the origin status (a), growth form (b) and blossom class (c) of the plant species in the larval pollen. (d-g) first-order GLMs of the proportion of the different plant trait levels in relation to the first (d-f) and second (g-i) PC axes for the origin status (d and f), growth form (e and g), and (f and i) blossom class. Grey shaded bands indicate 95% confidence intervals. Higher PC1 values indicate less vegetation (lower normalized difference vegetation index) and more artificial surfaces (urban index, land surface temperature, colour index). Higher PC2 values indicate a larger proportion of grasslands and lower proportion of deciduous trees at local scales. PC1 explained 38% of the variation and PC2 explained 12% of the variation. See Figs S7-S9 for the trait-based composition and change along urban gradients of the other three bee species.

FIGURE 4 .

 4 FIGURE 4. Bee distribution along urban gradients. (a and c) Loess smoothing of the mean predicted probability of occurrence of the four bee species in relation to the (a) first PCA axis (PC1) and (c) second PCA axis, performed on the explanatory variables, representing 38% and 12% of the variation, respectively. The mean predicted probability of occurrence results from the predicted probabilities of occurrence of the models with TSS > 0.4. Bands represent 95% confidence intervals. (b and d) Variation in the explanatory variables contributing the most to PC1 (b) and PC2 (d). (b) Larger values of PC1 correspond to higher values of impervious surfaces (urban index, UI), bare land (colour index, CI) and land surface temperature (LST) and lower vegetation cover (normalized difference vegetation index, NDVI) at different landscape scales (i.e. 100 and 400 m). (d) Lower values of PC2 correspond to higher proportions of deciduous trees and lower proportions of grasslands and floral resources (plant species richness) at local scales. Other scales and variables have been omitted here for simplicity (see Fig. S13). See also Fig. S12 for more details on the PCA.

ACKNOWLEDGEMENTS

This study was supported by the Swiss National Science Foundation (project 31BD30_172467) within the ERA-Net BiodivERsA project "BioVEINS: Connectivity of green and blue infrastructures: living veins for biodiverse and healthy cities" (H2020 BiodivERsA32015104). We thank AllGenetics and especially A.

Vizcaíno for conducting the pollen metabarcoding in the middle of a pandemic. We thank the local

Accepted Article

This article is protected by copyright. All rights reserved Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N., & Zimmermann, N. E. (2019). Uncertainty in

DATA AVAILABILITY STATEMENT

Data on diet composition supporting this paper available via EnviDat v.0.1 repository https://doi.org/10.16904/envidat.249 (Casanelles Abella, J., et al., 2021)

authorities of Zurich -particularly Grün Stadt Zurich (GSZ), Antwerp, Paris, Poznan and Tartu for supporting this study. Moreover, we acknowledge L. Roquer, J. Bosch and A. Rodrigo for their input on the trap-nest and pollen collection. We thank E. Eggenberg, K. Kilchhofer, P. Bischof, R. Veenstra, Ł. Myczko, A. Zanetta, D. Frey and L. Villarroya-Villalba for helping collect and process the bee data. We deeply thank B. Peters, C. Bello and C. Waldock for their input on data analysis, A. Müller for his guidance on bee biology and nutrition, C. Pitteloud for her help preparing the metabarcoding analyses, M. T. J. Johnson for his input in urban eco-evolutionary dynamics, and J. Bascompte for providing valuable comments on the manuscript.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

AUTHORS' CONTRIBUTIONS

Accepted Article

This article is protected by copyright. All rights reserved JCA, SM, MM, and LP conceived the ideas. MM, FC, LL, RS, PP and PT developed the site selection methodology. JCA collected the bee data. AK performed the bioinformatic analyses on the pollen. JCA, SM, ND, TH and MAO collected the plant data. MAO, TH and LL provided the local land cover metrics. CA and PP provided the remote sensing data. JCA and SM analysed the data. JCA led the manuscript writing, with editing and supervision by MM and LP. All the authors reviewed and revised the manuscript and gave final approval for publication.


Mariana Burca
email: mariana.burca2@univ-rouen.frmaria.augustinova@univ-rouen.fr

Virginie Beaucousin

Pierre Chausse

Ludovic Ferrand

Benjamin A Parris

Maria Augustinova

Keywords: Stroop interference, Single-letter coloring and cueing, Semantic conflict, Response conflict

Further evidence for semantic conflict in the Stroop task 1 Is there semantic conflict in the Stroop task? Further evidence from a modified two-to-one Stroop paradigm combined with single-

Introduction

The typical result in the well-known Stroop task [START_REF] Stroop | Studies of interference in serial verbal reactions[END_REF] is that individuals' color-identification times are longer for color-incongruent (e.g., "BLUE" displayed in yellow; hereafter BLUE yellow ), than for color-neutral items (e.g., the word "DEAL" displayed in yellow, hereafter DEAL yellow ). This difference -called the Stroop interference effect -reveals that, despite being explicitly instructed not to, individuals still attend to the irrelevant worddimension of compound Stroop items (i.e. process its meaning), which in turn slows their performance. Therefore, numerous studies have tried to identify experimental manipulations that improve attentional selectivity in the Stroop task.

Among such manipulations, the effects of single-letter coloring and cueing (SLCC; [START_REF] Besner | The stroop effect and the myth of automaticity[END_REF] have perhaps received the most attention (e.g., [START_REF] Küper | Attentional Focus Manipulations Affect Naming Latencies of Neutral But Not of Incongruent Stroop Trials[END_REF] for a review). Despite differences in the methodologies employed 1 , all of these studies have reported a substantial reduction or even elimination of Stroop interference when only a single letter (as opposed to all letters) of a target display was colored in an incongruent color from the response set and spatially pre-cued (hereafter SLCC vs. ALCC [all-letters colored and cued]). Because SLCC (unlike ALCC) is thought to keep two informational sources (i.e., color and word) separate (i.e., odd-one-out effect of SLCC, [START_REF] Besner | The stroop effect and the myth of automaticity[END_REF], a response induced by the irrelevant word-dimension of compound Stroop items (e.g., say "blue"/press blue for BLUE yellow ) interferes less with that induced by the relevant color-dimension (e.g., say "yellow"/press yellow for BLUE yellow ) in SLCC than in ALCC. Indeed, according to dominant single-stage response competition (e.g., [START_REF] Roelofs | Goal-referenced selection of verbal action: Modeling attentional control in the Stroop task[END_REF] models, a single conflict between responses (hence response conflict) is the driving force behind the Stroop interference effect.

In contrast to this unitary view of Stroop interference, other (i.e., multi-stage) models 1 In some studies, the coloring manipulation was coupled with variations in spatial pre-cueing such that, for instance, (a) small arrow(s) appeared on the screen to indicate the position(s) subsequently occupied by the colorcarrier(s) (i.e., target letter(s)) whose color was to be named. Some studies (e.g., [START_REF] Manwell | Single letter coloring and spatial cuing eliminates a semantic contribution to the Stroop effect[END_REF] also added empty spaces between letters that were filled -in other studies (e.g., [START_REF] Labuschagne | Automaticity revisited: When print doesn't activate semantics[END_REF] -with nonletter characters.

anticipate that incidental processing of the irrelevant word-dimension of Stroop items actually generates an additional (i.e., semantic or stimulus) conflict (e.g., Zhang et al., 1999;[START_REF] Zhang | The effects of stimulus-response mapping and irrelevant stimulus-response and stimulus-stimulus overlap in four-choice Stroop tasks with[END_REF]. On the basis of this idea, a handful of studies set out to investigate whether the SLCC-manipulation affects semantic and/or response conflict. Manwell and colleagues (2004) consequently reported that SLCC reduced Stroop interference depicted above (e.g., BLUE yellow -DEAL yellow ) but eliminated semantic-associative Stroop interference (e.g., SKY yellow -DEAL yellow , see also [START_REF] Labuschagne | Automaticity revisited: When print doesn't activate semantics[END_REF].

Because these studies viewed the former (hereafter standard Stroop interference) as a product of both semantic and response conflicts, and viewed semantic-associative Stroop interference as being free of response conflict (e.g., Neely & Kahan, 2001 2 ; but see e.g., [START_REF] Klein | Semantic power measured through the interference of words with colornaming[END_REF], they consequently concluded that SLCC affects the Stroop interference effect early (e.g., it curtails semantic conflict altogether, [START_REF] Manwell | Single letter coloring and spatial cuing eliminates a semantic contribution to the Stroop effect[END_REF], Account 2; see also Besner et al., 2016 for discussion). But this conclusion contrasts with other studies reporting that SLCC reduces standard but leaves semantic-associative Stroop interference [START_REF] Augustinova | Influence de la présentation bicolore des mots sur l'effet Stroop[END_REF][START_REF] Augustinova | Single-letter coloring and spatial cuing do not eliminate or reduce a semantic contribution to the Stroop effect[END_REF][START_REF] Augustinova | Some further clarifications on age-related differences in Stroop interference[END_REF], and the associated N400-like ERP-activity 3 [START_REF] Augustinova | Behavioral and electrophysiological investigation of semantic and response conflict in the Stroop task[END_REF] unaffected. These studies subsequently claimed that SLCC affects the Stroop interference effect late (i.e., it reduces or even eliminates response conflict, see [START_REF] Augustinova | Automaticity of Word Reading: Evidence from the Semantic Stroop Paradigm[END_REF] for discussions) but leaves semantic conflict unaffected. In sum, the processing stage at which SLCC-manipulation actually takes its effect (semantic vs. response) remains unclear.

However, this lack of clarity is not entirely due to the contrasting findings across the aforementioned studies. Indeed, as noted above, all of these studies have employed the semantic-associative Stroop trials (e.g., SKY yellow ) to investigate the extent to which SLCC 2 Since SKY is not part of the response set, it does not activate (pre-)motor responses linked to the associated color (e.g. press a blue button on seeing SKY; see [START_REF] Schmidt | Dissociating stimulus-stimulus and response-response effects in the Stroop task[END_REF] for a direct demonstration). 3 N400-like corresponded to fronto-central negativity occurring from 380 to 480msec after the presentation of a Stroop item taken as evidence of semantic processing in the Stroop task. affects semantic conflict. Because the irrelevant word-dimension "sky" is strongly associated with "blue", attending to its meaning is thought to interfere with the meaning of the relevant color-dimension (yellow for SKY yellow ). Indeed, "(…) delays of processing occur whenever distinct semantic codes are simultaneously activated, and that these delays become acute when the conflicting codes are values on a single dimension or closely related dimensions" (Seymour, 1977, p. 263). Yet, this assumption is at odds with the aforementioned unitary models of Stroop interference such as that proposed by [START_REF] Roelofs | Goal-referenced selection of verbal action: Modeling attentional control in the Stroop task[END_REF]. This model predicts that both standard and semantic-associative Stroop interference are not qualitatively distinct but both result from different quantities of a single (i.e., response) conflict taking place in the language production unit. Specifically, for semantic-associative items, the incorrect response (e.g., say "blue"/press blue for SKY yellow ) is activated through the association of the distractor SKY with the response set colors (i.e., blue is a part of the response set) in such a way that it subsequently interferes with the production of the correct response (e.g., "yellow"/press yellow button for SKY yellow ).

As a result, the influence of SLCC on semantic conflict remains an open issue because the extent to which semantic conflict is the actual driving force behind semantic-associative interference (e.g., SKY yellow -DEAL yellow ) is itself unclear (see Parris et al., 2021 for discussion). To address this more fundamental issue, the present study substituted semanticassociative items with items producing semantic interference in such a way that cannot be accounted for by single-stage response competition models (i.e., cannot be the result of response conflict).

The present study

To this end, the study only used color-words as distractors (i.e., BLUE, RED, GREEN, YELLOW). Importantly, following De Houwer (2003), the same response-key (e.g., actioned with left hand) was used for making responses to blue and red (i.e., target) items, and the other response-key (e.g., actioned with right hand) was used for making responses to green and yellow items. This so-called two-to-one response-mapping therefore generated two kinds of color-incongruent trials. For different-response trials like BLUE yellow the correct response ("BLUE") is indicated using the pointing finger on the right hand, and the incorrect response is indicated by using the pointing finger of the left hand. There is no such (response) interference on same-response trials like BLUE red , because the responses primed by both the distractor "BLUE" and the target "RED" are indicated using the pointing finger on the left hand4 .

Consequently, any significant interference caused by same-response trials cannot be attributed to response conflict. Indeed, in line with multi-stage models of Stroop interference (e.g., [START_REF] Zhang | The effects of stimulus-response mapping and irrelevant stimulus-response and stimulus-stimulus overlap in four-choice Stroop tasks with[END_REF], it is commonly attributed to semantic conflict [START_REF] De Houwer | On the role of stimulus-response and stimulus-stimulus compatibility in the Stroop effect[END_REF], see e.g., Hershman & Henik, 2020 for the most recent example).

However, despite this consensus, the extent to which same-response trials actually induce semantic conflict is still an open empirical issue. Indeed, with only a few exceptions (see below), all studies employing same-response trials to induce semantic conflict -including De Houwer (2003) -have used color-congruent trials as the baseline. This means that in line with what unitary models of Stroop interference would predict [START_REF] Roelofs | Goal-referenced selection of verbal action: Modeling attentional control in the Stroop task[END_REF], the difference between same-response and color-congruent trials might not involve any semantic conflict but be entirely driven by facilitation on color-congruent trials. Consistent with this idea, Hasshim andParris (2014, 2015) reported significantly longer RTs for same-response trials than for color-congruent trials, but no difference between same-response and color-neutral word trials that are free from facilitation [START_REF] Brown | The relationship between Stroop interference and facilitation effects: Statistical artifacts, baselines, and a reassessment[END_REF][START_REF] Macleod | Half a century of research on the Stroop effect: an integrative review[END_REF] for discussion)5 . Somewhat in contrast, [START_REF] Hershman | Pupillometric contributions to deciphering Stroop conflicts[END_REF] reported evidence for a difference between same-response and non-word color-neutral trials but in pupillometric measures only6 , whereas Burca found preliminary -as yet unpublished -evidence suggesting the presence of semantic conflict in RTs even when color-neutral words are used as baseline. In sum, because of the theoretical implications for composite (e.g., [START_REF] Zhang | The effects of stimulus-response mapping and irrelevant stimulus-response and stimulus-stimulus overlap in four-choice Stroop tasks with[END_REF] as opposed to unitary (e.g., [START_REF] Roelofs | Goal-referenced selection of verbal action: Modeling attentional control in the Stroop task[END_REF] Stroop interference, the unambiguous demonstration of same-response trials inducing (or not) semantic conflict still remains an empirical void to fill.

Therefore, the present study used color-neutral words to measure the magnitude of interference induced respectively by same-(e.g., BLUE red ) and different-response (BLUE yellow ) trials. Specifically, color-neutral trials were matched with color-incongruent ones in such a way that they appeared in the same colors an equal number of times (e.g., BLUE only appeared in red, green, yellow, and so did its counterpart DEAL). This allowed for comparisons of same-(e.g., BLUE red ) and different-response (BLUE yellow ) trials with their corresponding (in terms of response contingency) color-neutral baselines (e.g., DEAL red , and DEAL yellow respectively). In sum, as in past studies of SLCC-effect outlined above, the magnitudes of both semantic (e.g., BLUE red -DEAL red ) and standard Stroop interference (BLUE yellow -DEAL yellow ) were derived.

However, this was done without using the same color-neutral baseline twice (see Shichel & Tzelgov, 2018 for discussion).

If, in agreement with single-stage response competition models (e.g., [START_REF] Roelofs | Goal-referenced selection of verbal action: Modeling attentional control in the Stroop task[END_REF], there is no contribution of semantic conflict to the overall Stroop interference effect, sameresponse trials will not induce any Stroop interference (i.e., their RTs will be comparable to those observed for color-neutral trials -as previously reported by Hasshim andParris (2014, 2015)). Different-response trials will be responded to slower than same-response trials [START_REF] De Houwer | On the role of stimulus-response and stimulus-stimulus compatibility in the Stroop effect[END_REF], and this in the absence of a significant difference between the same-response and their neutral counterparts. In this case -confirming that the response conflict is the unique driving force of the Stroop interference effect -, only the magnitude of standard Stroop interference (the significant difference between different-response and color-neutral trials) will be reduced in SLCC compared to ALCC.

In contrast, if the multi-stage models of Stroop interference (e.g., [START_REF] Zhang | The effects of stimulus-response mapping and irrelevant stimulus-response and stimulus-stimulus overlap in four-choice Stroop tasks with[END_REF] are correct and semantic conflict genuinely contributes to overall Stroop interference, then same-response trials will produce significant semantic Stroop interference such that the difference between same-response and color-neutral trials will be significant (Burca, unpublished; see also Hershman and Henik's pupillometry data). The semantic conflict could then be affected by SLCC in several ways. In line with past studies reviewed above, SLCC might eliminate semantic interference altogether (Manwell et al.'s Account 2;[START_REF] Labuschagne | Automaticity revisited: When print doesn't activate semantics[END_REF], or leave its magnitude unaffected [START_REF] Augustinova | Single-letter coloring and spatial cuing do not eliminate or reduce a semantic contribution to the Stroop effect[END_REF][START_REF] Augustinova | Behavioral and electrophysiological investigation of semantic and response conflict in the Stroop task[END_REF][START_REF] Augustinova | Some further clarifications on age-related differences in Stroop interference[END_REF].

Finally, SLCC might simultaneously reduce both semantic and standard interference (Manwell et al.'s Account 1)7 -a finding that would suggest that SLCC affects both semantic and response conflicts. The aim of the experiment reported below was to directly test these different hypotheses regarding the contribution of semantic conflict to Stroop interference and regarding its reduction by SLCC.

Method

Participants

Ninety-two native French-speakers (64 females and 28 males; M age =20.95; SD=1.612) with normal or corrected-to-normal color-vision volunteered to take part in this experiment, which was approved by the local ethics committee (2018-02-A).

Design

The study used a 4 (Stimulus-Type: different response vs. same response vs. neutral different response vs. neutral same response) × 2 (Coloring: ALCC vs. SLCC) withinparticipant design for data collection.

Stimuli

The (French) stimuli were presented in lowercase Courier font, size 18, on a black [START_REF] New | Lexique 2: A new French lexical database[END_REF].

Apparatus and Procedure

The participants were seated 70cm in front of a 17'' computer screen. Eprime 2.0 software was used for data presentation and recording. Because the experimental trials only used words as distractors, the participants were instructed to identify the color of the letter indicated by the arrow as quickly and accurately as possible by pushing the appropriate colorbutton and to ignore everything else in the display. To this end, and similarly to Augustinova et al. (2010, Experiment 1), the participants were instructed to concentrate on the fixation cross ("+") that appeared for 2000msec in the center of the screen at the beginning of each trial. A small white arrow (height of 1.2° of visual angle, displayed 0.6° below) then appeared for 150msec. As it served as a spatial pre-cue, it was located at the position that was subsequently occupied by the target letter. This position varied randomly from trial-to-trial, being located at the initial, optimal viewing position (OVP), middle, or final letter of the distractor-word [START_REF] Parris | An Optimal Viewing Position Effect in the Stroop Task When Only One Letter Is the Color Carrier[END_REF]. In order to avoid an additional color-color interference in the SLCC-condition 8 , the spatially pre-cued letter was the only one that appeared in an incongruent color from the response set and the rest of the letters appeared in white (i.e., a color that was not part of the response set; see e.g., [START_REF] Augustinova | Influence de la présentation bicolore des mots sur l'effet Stroop[END_REF][START_REF] Besner | The stroop effect and the myth of automaticity[END_REF][START_REF] Brown | Automaticity in Reading and the Stroop Task : Testing the Limits of Involuntary Word Processing[END_REF] for the use of gray or black). In the ALCC-condition, all letters (including the one that was spatially pre-cued) appeared in incongruent colors from the response set. The entire display remained on screen until the participant responded or until 3500msec had elapsed.

The participants responded manually using a modified SRBox® consisting of two handles, each of which had a single response button at the top (placement of the handles in the right or left hand, respectively, was counterbalanced across participants). The participants pushed these response buttons with their thumbs. This allowed them to hold each handle comfortably in their palm with the remaining four fingers (see Supplementary Materials, left panel) while resting their arms on an armrest.

One response button was flanked by blue and red color-stickers and the other by yellow and green color-stickers (see Supplementary Materials, middle and right panels). Because of this color assignment, together with the fact that color-stimuli only appeared in incongruent colors, a single presentation of the full set of color-stimuli in all possible colors resulted in eight same-response items (i.e., 4 in ALCC and 4 in SLCC; e.g. "blue" presented entirely in red, "red" presented entirely in blue, "green" presented entirely in yellow and "yellow" presented entirely in green in ALCC) and sixteen different-response items. To control for contingency, the same presentation was used for the items' color-neutral counterparts (e.g., PAGE only appeared in red, yellow and green, exactly like its color-incongruent counterpart BLUE). All stimulus types were therefore seen an equal number of times, meaning that 5 repetitions (making it possible to fully control for the letter-position effects, see above) of the full set of 48 different stimuli resulted in a total of 240 experimental stimuli. In each coloringcondition, we therefore collected RTs on 20 same-response (SR) incongruent trials, 20 colorneutral trials paired with SR-incongruent trials, 40 different-response (DR) incongruent trials, and 40 color-neutral trials paired with DR-incongruent trials all included in a single block.

Before completing this experimental block, the participants first completed 128 practice trials [START_REF] Macleod | The Stroop Task in Cognitive Research[END_REF] consisting of asterisks in order to learn the color-button correspondence.

They proceeded to the experimental block once their accuracy rate was above 95% (2 participants had to redo the training, one of them was later excluded from further analyses).

Results and Discussion

Four participants were excluded from the analysis due to faulty data recordings. The data of the remaining eighty-eight participants were first analyzed in an omnibus 4 (Stimulus-Type: DR-incongruent trials vs. SR-incongruent trials vs. DR-neutral trials vs. SR-neutral trials) × 2 (Coloring: ALCC vs. SLCC) ANOVA. This analysis on RTs was conducted on mean correct latencies using both 2.5 (e.g., [START_REF] Labuschagne | Automaticity revisited: When print doesn't activate semantics[END_REF] and 3SD cut-offs (Augustinova and colleagues' studies). Given their convergence and to permit comparisons with our past studies, only analyses using the 3SD cut-off (leading to the exclusion of 1.12% of the total data) are reported below. In omnibus ANOVA, this latter analysis revealed main effects This extreme Bayesian evidence, is in favor the model where the both main effects and their interaction are significant. Nevertheless, an effect of the interaction alone was not supported by Bayesian evidence (BF 10 =0.606/BF 01 =1.648 10 , see Table S1 in Supplementary Materials for a comparison of models in a Bayesian repeated-measures ANOVA].

These same analyses of error percentages (see Table 1) revealed only significant main 9 BF 10 corresponds to the Bayesian probability of the occurrence of a hypothesis (H1) and the likelihood of another null hypothesis (H0). It was calculated with JASP (JASP Team, 2017) and interpreted according to [START_REF] Lee | Bayesian Cognitive Modeling: A Practical Course[END_REF]Wagenmakers (2013 adjusted from Jeffreys, 1961). All priors were equal. 10 BF value reported for interaction only is the one associated with the model including the interaction and the two main effects divided by BF value observed for the model including the two main effects only. [START_REF] Augustinova | Single-letter coloring and spatial cuing do not eliminate or reduce a semantic contribution to the Stroop effect[END_REF][START_REF] Augustinova | Behavioral and electrophysiological investigation of semantic and response conflict in the Stroop task[END_REF][START_REF] Augustinova | Some further clarifications on age-related differences in Stroop interference[END_REF]. Indeed, the present results suggest that SLCC reduces both semantic and standard Stroop interference in tandem.

General Discussion and Conclusions

With regards to the first aim of addressing controversies concerning the role of semantic conflict in contributing to Stroop interference, the results reported above provided extreme (ALCC) and strong (SLCC) Bayesian evidence for differences between same-response trials and color-neutral word baselines (e.g., DEAL red ). This means that the present study reports -for the first time -a genuine contribution of semantic conflict to overall Stroop interference.

Indeed, in all past Stroop studies this contribution was possibly confounded either with response conflict (e.g., when semantic-associative items [SKY blue ] were used to induce semantic conflict) or facilitation (when color-congruent items [BLUE blue ] were used as the baseline from which to derive the magnitude of semantic conflict generated by same-response items). Also, and importantly, magnitudes of semantic interference reported in the present paper are not inflated either by the use of color-neutral non-word baselines (see [START_REF] Brown | The relationship between Stroop interference and facilitation effects: Statistical artifacts, baselines, and a reassessment[END_REF] for discussion of this latter issue) or by the inclusion of color-congruent trials that are also known to amplify interference (e.g., [START_REF] Roelofs | Tracking eye movements to localize Stroop interference in naming: Word planning versus articulatory buffering[END_REF]. This unambiguous contribution of a genuine semantic conflict to the (overall) Stroop interference effect -at least with manual responses that the twoto-one paradigm [START_REF] De Houwer | On the role of stimulus-response and stimulus-stimulus compatibility in the Stroop effect[END_REF] necessarily involves -constitutes the most significant result reported in the present study.

Whilst it is unclear why it was observed in the present study and not in other studies comparing the interfering effect of same-response trials against a color-neutral word base-line [START_REF] Hasshim | Two-to-one color-response mapping and the presence of semantic conflict in the Stroop task[END_REF], 2015), the unambiguous presence of a genuine semantic conflict has at least two immediate implications for several still unresolved issues in the Stroop literature.

First, it provides clear evidence that so-called informational conflict (e.g., [START_REF] Macleod | Interdimensional interference in the Stroop effect: Uncovering the cognitive and neural anatomy of attention[END_REF] in the Stroop task includes semantic conflict. Indeed, because of the aforementioned confounds, the evidence in favor of this contribution from semantic conflict available was still inconclusive prior to the present paper (see [START_REF] Parris | The loci of Stroop effects: A critical review of methods and evidence for levels of processing contributing to colour-word Stroop effects and the implications for the loci of attentional selection[END_REF], for a thorough discussion of this issue). Second, the unambiguous presence of genuine semantic conflict clearly implies that selection occurs at the level of semantics -a finding that runs counter to the dominant single-stage response competition models of Stroop interference. Therefore, it is likely that these latter models need to be modified to make room for this type of conflict.

Despite the data being collected in a considerable sample [START_REF] Brysbaert | Power Analysis and Effect Size in Mixed Effects Models: A Tutorial[END_REF] and analyzed with both classical (frequentist) and Bayesian inferential approaches, the results reported above are clearly less conclusive with regards to the second aim which was to address controversies concerning the reduction of semantic conflict by SLCC-manipulation. Indeed, the lack of Interference-Type × Coloring interaction (along with a Stimulus-Type × Coloring only being present in standard frequentist ANOVA) seems most consistent with Manwell et al.'s Account 1 suggesting that SLCC reduces both semantic and standard interference in tandem 14 .

In line with [START_REF] Kinoshita | The semantic Stroop effect is controlled by endogenous attention[END_REF] see also e.g. [START_REF] Besner | Varieties of Attention: Their Roles in Visual Word Identification[END_REF], this means that SLCC reduces semantic conflict in the Stroop task. However, the results with regards to the SLCCeffect on SR-incongruent trials come with caveats. Indeed, the Bayesian analyses reported above provided anecdotal evidence for the simple main-effect of Coloring on these trials whereas this effect was strong for DR-incongruent trials. This points to the fact the while the interpretation in terms of reduced semantic conflict (i.e., the common denominator of both semantic and standard Stroop interference) is possible, it is likely to be incomplete. In line with past studies (e.g., [START_REF] Augustinova | Single-letter coloring and spatial cuing do not eliminate or reduce a semantic contribution to the Stroop effect[END_REF][START_REF] Augustinova | Behavioral and electrophysiological investigation of semantic and response conflict in the Stroop task[END_REF][START_REF] Augustinova | Some further clarifications on age-related differences in Stroop interference[END_REF], it therefore remains possible that SLCC also reduces response conflict as the aforementioned strong simple main-effect of Coloring on DR-incongruent trials suggest. Although the response conflict was not significant overall 15 , this possibility cannot be ruled out entirely, as it was significant on slow trials (see Supplementary Materials). This reduction is again probably too small to fully explain the present pattern of results. The remaining possibility is that SLCC actually reduces task conflict (i.e., a more general conflict that -for all readable Stroop items including color-neutral onesderives from the simultaneous preparation of two task sets: word-reading vs. color-naming, 14 This conclusion is reinforced by the exploratory 2 (Interference-Types: Stroop interference vs. Semantic interference) x 2 (Coloring: ALCC vs. SLCC) ANOVA conducted on slow trials only (i.e., trials revealing response conflict). Indeed, it yielded a marginally significant main effect of Interference-Type [F(1,87)=3.50; p < .065, η p 2 = 0.039], a significant main effect of Coloring [F(1,87) =5.39; p=.023, η p 2 = 0.058], but a non-significant Interference-Type × Coloring interaction [F(1,87)=.294; p< .589, η p 2 =0.003] suggesting again that Coloring manipulation affects both types of interference in tandem. 15 This unexpected absence of significant response conflict (that our group has actually replicated since) is at odds with past studies using the two-to-one Stroop paradigm -including De Houwer's initial study (2003). This could be explained by differences in methodology applied in the present study (e.g., absence of color-congruent items that are known to amplify the interfering effects of color-incongruent items (e.g., [START_REF] Roelofs | Tracking eye movements to localize Stroop interference in naming: Word planning versus articulatory buffering[END_REF], the equal percentage of color-incongruent vs color-neutral trials [START_REF] Macleod | The Stroop Task in Cognitive Research[END_REF] with no contingency issues involved [START_REF] Hasshim | Two-to-one color-response mapping and the presence of semantic conflict in the Stroop task[END_REF][START_REF] Schmidt | Questioning conflict adaptation: Proportion congruent and Gratton effects reconsidered[END_REF]. More studies are needed to assess these different possibilities directly. e.g., [START_REF] Goldfarb | Evidence for task conflict in the Stroop effect[END_REF][START_REF] Hershman | Dissociation between reaction time and pupil dilation in the Stroop task[END_REF]. The lack of SLCC-effects on colorneutral stimuli in the present study is seemingly at odds with this latter idea (but see e.g., [START_REF] Manwell | Single letter coloring and spatial cuing eliminates a semantic contribution to the Stroop effect[END_REF]. Indeed, any reduction of task conflict should in principle facilitate processing of all compound stimuli (i.e., including the color-neutral items) and even more so in the present study given that only one color from the response set was used to implement the SLCC-manipulation. It is, however, noteworthy that two colors are still present in our SLCCcondition (a color from the response set and white) -making it plausible that an additional color-color interference still occurs in SLCC as compared to ALCC. Its known lengthening effect on color-neutral items (e.g. [START_REF] Küper | Attentional Focus Manipulations Affect Naming Latencies of Neutral But Not of Incongruent Stroop Trials[END_REF][START_REF] Monahan | Coloring Single Stroop Elements: Reducing Automaticity or Slowing Color Processing[END_REF] might be cancelled in the RTs by the concomitant shift in attentional focus toward the relevant color-dimension in SLCC, which in turn reduces the task conflict. In sum, future studies -which should include a direct measure of task conflict -need to address these possibilities directly, perhaps with a more fine-grained measures than RTs (e.g., [START_REF] Hershman | Dissociation between reaction time and pupil dilation in the Stroop task[END_REF]. The decrease in the magnitude of overall Stroop interference that is specifically due to the reduction of task conflict implies -contrary to the reduction of semantic conflict -a simultaneous increase in Stroop facilitation [START_REF] Parris | Task conflict in the Stroop task: When Stroop interference decreases as Stroop facilitation increases in a low task conflict context[END_REF], which was also not measured in the present study. It is of course possible that SLCC actually affects all the components of the overall Stroop interference (response, semantic and task conflict).

In conclusion, whilst the influence of SLCC on semantic conflict (but also on other types of conflicts) still remains an open issue, the presence of semantic conflict in the Stroop task -at least when administered in the form of the two-to-one Stroop paradigm -, is no longer one. Therefore, the present study provides impetus for future empirical work on SLCC (see above). Furthermore, the present work also strongly encourages the development of new integrative models of the Stroop inference effect, as only one existing model effectively accounts for either semantic [START_REF] Zhang | The effects of stimulus-response mapping and irrelevant stimulus-response and stimulus-stimulus overlap in four-choice Stroop tasks with[END_REF]Zhang et al., 1999) or task conflict [START_REF] Kalanthroff | Task conflict and proactive control: A computational theory of the Stroop task[END_REF], respectively, but none currently accounts for the probable coexistence of task, semantic and response conflicts (see [START_REF] Parris | The loci of Stroop effects: A critical review of methods and evidence for levels of processing contributing to colour-word Stroop effects and the implications for the loci of attentional selection[END_REF] Inferential approach: ns non-significant; * significant at p < .05; ** significant at p < .01; †significant at p < .001; Bayesian approach (presented after the slash): ns no evidence of an effect, with both BF10 and BF01 values between 0-1; ns* anecdotal evidence in favor of H1, but moderate evidence in favor of H0, with BF10 value between 0-1, and BF01 value between 3-10; * moderate evidence in favor of H1, with BF10 value between 3-10; ** strong evidence in favor of H1, with BF10 value between 10-30; † very strong evidence in favor of H1, with BF10 between 30 -100; † †extreme evidence in favor of H1/of an effect, with BF10 <100. Again, the additional sequential analyses of response conflict in fast vs. slow trials suggested that the probability of finding this type conflict if more observations were added, is unlikely (see Figure 3 here below). Note. Inferential approach: ns non-significant; °marginally significant (p < .10), * significant at p < .05; ** significant at p < .01; †significant at p < .001; Bayesian approach (presented after the slash): ns no evidence of an effect, with BF 01 value between 1-3; ns* anecdotal evidence in favor of H1,with BF 10 value between 1-3; * moderate evidence in favor of H1, with BF 10 value between 3-10; ** strong evidence in favor of H1, with BF 10 value between 10-30; † very strong evidence in favor of H1, with BF 10 between 30 -100; † †extreme evidence in favor of H1/of an effect, with BF 10 <100.

  background and subtended an average visual angle of 0.9° high × 3.0° wide. They consisted of four color-words: rouge [red], jaune [yellow], bleu [blue], and vert [green]; and four non-color counterparts: plomb [lead], liste [list], page [page], cave [basement], that were paired in length and frequency via Lexique 3.38

  of261)=38.09; p<.001, η p 2 =0.304; BF 10 =4.160e+18 9 , indicating extreme evidence of an effect of Stimulus-Type] and of Coloring [F(1,87)=11.57; p=.001, η p 2 =0.117; BF 10 =29.223, indicating strong evidence of an effect of Coloring]. It also revealed a significant Stimulus-Type × Coloring interaction, [F(3,261)=3.236; p=.023, η p 2 =0.036; BF 10 =2.229e+20].

Figure 2 .

 2 Figure 2. Sequential analysis of RC in ALCC vs. SLCC condition (probability of finding a RC if more observations are added)

Figure 3 .

 3 Figure 3. Sequential analysis of RC in fact vs. slow trials (probability of finding a RC if more observations are added)

  ). This latter conclusion is reinforced by BF 10 =130.036, providing extreme evidence of semantic interference in the ALCC-condition and BF 10 =19.354, providing strong evidence of semantic interference in the SLCC-condition 13 . The presence of significant semantic interference overall, and in both coloring conditions, is thus consistent with the idea that a semantic conflict indeed genuinely contributes to the overall Stroop interference effect even when color-neutral words are used as baseline (see also[START_REF] Hershman | Pupillometric contributions to deciphering Stroop conflicts[END_REF]' pupillometric measures, but see Hashim & Parris, 2014, 2015; Hershman & Henik, 2020' RTs). Coloring interaction, the specific BF value for the interaction (see TableS4in Supplementary Materials for a comparison of the models) was only BF 10 =0.401 and again BF 01 =2.491 actually provided anecdotal evidence for a null effect of the Interference-

	effects of Stimulus-Type [F(3, 261)=6.91; p< .001, η p 2 =0.74; BF 10 =182.463, indicating extreme was anecdotal. Finally, this simple main effect of Coloring on both DR-neutral [F(1,87) =
	evidence of an effect of Stimulus-Type]. Both the main effect of Coloring [F(1,87)=2.83; 0.117; p =.733, η p 2 =0.001; BF10 =0.125/BF 01 =8.025] and SR-neutral trials [F(1,87) = 1.519;
	p=.096, η p p=.221, η p	2 = 0.32; BF 10 =0.210/BF 01 =4.810], and Stimulus-Type × Coloring interaction [F(3, 2 =0.017; BF10 = 0.244/BF 01 =4.091] revealed that color-neutral trials remained
	261)=.12; p=.950, η p unaffected by Coloring manipulation. 2 = 0.01] remained non-significant. Indeed, BFs provided very strong
	evidence against the effect of interaction alone (BF 10 =0.013/BF 01 =68.52, see Table S2 in the To further examine the SLCC-effect in the two-to-one Stroop paradigm, we then
	Supplementary Materials for a comparison of the models). conducted a 2 (Interference-Type: Standard vs. Semantic) × 2 (Coloring: ALCC vs. SLCC)
	ANOVA to analyze the extent to which magnitudes of both standard and semantic Stroop
	Is there semantic conflict in the Stroop task? interference were affected by SLCC (see Table 1). This analysis revealed main effects of
	Interference-Type [F(1,87)=7.285; p=.008, η p	2 =0.077; BF10=2.207], and Coloring
	[F(1,87)=6.066; p=.016, η p 2 =0.065; BF10 = 5.243] but only a non-significant Interference-Type
	× Coloring interaction, [F(1,87)=1.925; p=.169, η p 2 =0.0322]. Indeed, in line with the
	aforementioned Stimulus ×
	significant in both ALCC [F(3,85)=22.762; p<.001, η p 2 =0.445; BF 10 =6.986e+11] and SLCC p<.001, η p 2 =0.133; BF 10 =50.129, which indicates very strong evidence of Coloring] and SR-
	[F=(3,85)=12.898; p<.001, η p 2 =0.313; BF 10 =368930.025 12 ]. In addition to significant standard incongruent trials [F(1,87)=5.912; p =.017, η p 2 =0.064; BF 10 =1.981/BF 01 =0.529]. It should be
	Stroop interference (DR-incongruent -DR-neutral trials) in both ALCC-and SLCC-condition noted however, that the latter Bayesian evidence for SLCC shortening SR-incongruent trials
	(all p s <.01, see Table 1 for descriptive statistics), further pairwise comparisons, again
	13 In line with a specific contribution of response conflict to standard Stroop interference -latencies for DR-
	incongruent trials tended to be longer than those for SR-incongruent trials (see Table 1). But although the observed unequivocally revealed the presence of significant semantic Stroop interference (SR-12msec difference was indeed marginally significant (p=.072) in ALCC, BF10=.569/BF 01 =1.758 tend to suggest
	otherwise. Despite this, response conflict in ALCC is still evident in percentages of errors [t(87)=3.80, p<.001; incongruent -SR-neutral trials) in both ALCC-and SLCC-conditions (see Table 1 for BF 10 =77.45]. In SLCC, the 7msec difference between mean response latencies for DR-incongruent and SR-
	incongruent trials was not significant (p=.173, BF10=.291/BF 01 =3.436), but response conflict was however present
	in percentages of errors [t(87)=2.79, p<.05; BF 10 =4.34]. The additional sequential analyses of response conflict in
	ALCC vs. SLCC condition suggested that the probability of finding this type conflict if more observations were

To answer this key question, we first decomposed the aforementioned main effect of the Stimulus-Type on mean correct latencies. It revealed the presence of both semantic and standard Stroop interference. The SR-incongruent trials were indeed responded to slower than their SR-neutral counterparts (p<.001; BF 10 =44197.61) and so were the DR-incongruent trials compared to their DR-neutral counterparts (p<.001; BF 10 =1.342e+12) 11 . To explore this issue of semantic conflict further, we then decomposed the aforementioned Stimulus-Type × Coloring interaction (i.e., interaction supported at least by frequentist statistics) by testing the simple main-effect of Stimulus-Type at each level of Coloring. This simple main-effect was

11

The difference between the RT's of DR and SR incongruent trials (10msec), which is representative of the response conflict, did not reach significance (p=.135). However, BF 10 =2.962/BF 01 =0.338 values of paired samples T-Tests provided at least anecdotal evidence in favor of such an effect. 12 A Bayesian repeated measures ANOVA compares a series of different models against a null model. It therefore cannot be decomposed by testing simple main effects per se. Therefore, BFs provided test main effect of Stimulus-Type in ALCC and SLCC respectively -which explains why a Bayesian approach that is generally more conservative, actually yields larger effects.

magnitudes<Insert Table 1 about here>

How does SLCC affect semantic conflict?

The aforementioned significant semantic interference in SLCC-conditions runs counter to Manwell et al.'s Account 2 (see also

[START_REF] Labuschagne | Automaticity revisited: When print doesn't activate semantics[END_REF]

suggesting that SLCC eliminates semantic interference altogether. However, in line with Manwell and colleagues' Account 1 (and counter to past results observed by Augustinova, Ferrand and colleagues), SLCC (as opposed to ALCC) shortened RTs in both DR-incongruent trials [F(1,87)=13.396; added, is unlikely (see Figure

2

in Supplementary Materials). Therefore, for each level of Stimulus and of Coloring, we binned slow vs. fast trials. The additional analyses (see Supplementary Materials) suggest that the lack of response conflict reported above is driven by the lack of response conflict in the fast trials (see Table

3S

in Supplementary Materials). Again, the additional sequential analyses of response conflict in fast vs. slow trials suggested that the probability of finding this type conflict if more observations were added, is unlikely (see Figure

3

in Supplementary Materials).

Type × Coloring interaction. These results are therefore clearly inconsistent with Augustinova and colleagues' past results showing that SLCC leaves the magnitude of semantic Stroop interference unaffected

Table 1 .

 1 for further discussion). single-carrier stimuli. Journal of Experimental Psychology: Human Perception and Performance, 24(1), 3-19. Zhang, H. H., Zhang, J., & Kornblum, S. (1999). A parallel distributed processing model of stimulus-stimulus and stimulus-response compatibility. Cognitive Psychology, 38(3), Color-Identification Performance (Means, Standard Deviation, 95%-Confidence Intervals and Error Percentages) observed as a Function of Stimulus-or Interference-Type and Coloring

	386-432.

Table 3S .

 3S Color-Identification Performance (Means, Standard Error, 95%-Confidence Intervals) observed as a Function of Stimulus× Bin interaction

Note that these items are likely to involve at least some response facilitation[START_REF] Hasshim | Two-to-one color-response mapping and the presence of semantic conflict in the Stroop task[END_REF]; 2015 for a discussion).

The absence of semantic conflict was supported further by Bayesian evidence for the null-hypothesis and by the unchanged magnitude of associated pre-response pupillometric measures of effort (i.e., a reliable measure of the potential differences between conditions,[START_REF] Hasshim | Assessing stimulus-stimulus (semantic) conflict in the Stroop task using saccadic two-to-one color response mapping and preresponse pupillary measures[END_REF].

In the case of RTs, this study perfectly replicatedHasshim and Parris (2014, 2015) including conclusions based on Bayes factors. Based on this dissociation, these authors concluded that RTs are not the best-suited measures to addressed a more fine-grained components of the Stroop interference effect (see also[START_REF] Hershman | Dissociation between reaction time and pupil dilation in the Stroop task[END_REF].

This causes the elimination of semantic-associative Stroop interference (SKY yellow -DEAL yellow ) as it is usually proportionally smaller than its standard counterpart (BLUE yellow -DEAL yellow ).

An additional color-color interference occurs when remaining letters are colored in other (incongruent) colors from the response set. Since this is known to increase RTs on color-neutral trials in the SLCC-condition (e.g.[START_REF] Küper | Attentional Focus Manipulations Affect Naming Latencies of Neutral But Not of Incongruent Stroop Trials[END_REF][START_REF] Monahan | Coloring Single Stroop Elements: Reducing Automaticity or Slowing Color Processing[END_REF] it might inflate the SLCC-induced reduction of both standard and semantic Stroop interference (e.g.[START_REF] Manwell | Single letter coloring and spatial cuing eliminates a semantic contribution to the Stroop effect[END_REF].

The work reported was supported by ANR Grant ANR-19-CE28-0013 and RIN Doctorant et RIN Tremplin Grant 19E00851 of Normandie Région, France. The data and materials are available at https://osf.io/u72p6/