Aurélie Metay

Elena Kazakou

Camille Mayeux

Jérémie Fandos

Matthias Hans Anthony

PRA: small agricultural regions (Petites Regions Agricoles in French

Keywords: Non-cultivated plants which grow in a cultivated plot (Fried, 2019) AIC, Akaike Information Criterion ANOVA, Analysis of variance Community Weighted Variance

A special gratitude I give to my supervisor, Guillaume Fried, who was always present, kind, patient with me, good mentor given amazing advice on

Figures and tables' lists

 [START_REF] Cazenave | Flore adventice des vignes en Gironde[END_REF]Figure 2: Scheme describing the method to provide the floristic sampling of each plot (took back from [START_REF] Varela | Conception de méthodes simplifiées de relevés floristiques en viticulture à destination des réseaux DEPHY/FERME et EXPE[END_REF])

Figure 3: Hill & Smith analysis displayed with the quantitative variables in black marked out by an arrow, the qualitative variables in blue and the supplementary variable: The Fidelity Index in the interrows in red marked out by an arrow. c,d), the functional richness (e,f) and the Rao's quadratic entropy (g,h), according to the area (inter-rows in blue, rows in red), the mode of production called here certification (conventional in orange and organic in green). The horizontal bar of the boxplot represents the median, 50% of the values were located from the 1 st quartile to the 3 rd quartile (interquartile range, IQR). Blacks dots are values higher or lower than 1.5 the IQR.

Figure 6: Boxplots of the repartition of the four groups depending on the diversity's indices: Species richness (a), Abundance (b), Functional richness (c) and Rao's quadratic entropy (d). The inter-rows were represented in dark grey and the rows in pale grey, the group 1 is in yellow, the group 2 in red, the group 3 in green and the group 4 in blue.

Figure 7: Results of the global RLQ (101 fields) for the traits (on the left) and the species (on the right) Figure 8: Results of the global RLQ (101 fields) for the environment variables (on the left) and the field (on the right) Figure 9: Fourth corner analysis of the global dataset (101 fields) on the left and adjusted fourth corner analyses of the three datasets (global, rows, inter-rows) on the right. The red cells represented the positive associations and the blue cells represented the negative associations. The non-significant cells were represented in grey. The upper left triangles represent the inter-rows significant interaction, the bottom right triangles the rows and the empty squares the global interaction.

Figure 10: Results of the combination of the RLQ and the fourth-corner analyses for the global (a), the rows (b) and the inter-rows (c) analyses. On the left of each table, the fourth-corner tests between the two axes of the RLQ for the environmental gradients and the practices (AxcR1/AxcR2) and traits. On the right of each table, fourth-corner tests between the two axes of RLQ for traits (AxcQ1/AxcQ2) and the environmental variables and the practices. Table 1: Evaluation table of frequency inspired by [START_REF] Theau | Une méthode simplifiée de relevé botanique pour une caractérisation agronomique des prairies permanentes[END_REF] Table 2: Evaluation table of abundance according to Braun-Blanquet (1934) 9: Species with high fidelity index to the inter-rows (green on the left), fidelity index close to zero (orange in the middle) and high fidelity index to the rows (red on the right) Table 10: The F_value, the estimate and the significance concerning the habitat, the production mode and the interaction between both, for the species richness, the abundance, the functional richness (FRic) and the Rao's quadratic richness. The points (.) indicated a p_value above 0.05, the stars indicated a p_value between 0.05 and 0.01 with a star (*), a p_value between 0.01 and 0.001 with two stars (**) and a p_value under 0.001 with three stars (***). The adjusted R² indicated the contribution of the model to the diversity's indicators.

Tables

Table 11: F_value of linear models with interaction of the diversity's indices. The point (.) indicated a p_value above 0.05, the stars indicated a p_value between 0.05 and 0.01 with one star (*), a p_value between 0.01 and 0.001 with two stars (**) and a p_value under 0.001 with three stars (***).

Table 12: Linear models given the F_value and the estimate for the four indices of diversity: species richness (S), abundance (Abun), functional richness (FRic) and the Rao's quadratic entropy (Rao). A compartment in color is significant, green for the positive interactions and yellow for the negative ones. The point (.) indicated a p_value above 0.05, the stars indicated a p_value between 0.05 and 0.01 with one star (*), a p_value between 0.01 and 0.001 with two stars (**) and a p_value under 0.001 with three stars (***).

Introduction

History of viticultural practices

The French vineyards represent a part of the French heritage since the Antiquity [START_REF] Dion | Histoire de la vigne et du vin en France[END_REF].

Until the beginning of the twenty-one century, the French production and quality were considered as the best of the world even if all French wines do not present high quality [START_REF] Phillips | French Wine: A history[END_REF]. Despite the improvement of wine quality in new countries such as USA, Chile, South Africa or Australia, France still has a top position in the wine world and on the market. In 2018, France is the second wine maker in Europe and in the world with 46.4 million hectoliters of wine for 755 00 hectares of production [START_REF] Alim'agri | Site du ministère de l'agriculture et de l'alimentation[END_REF].

The vine (Vitis vinifera) is a perennial plant grown as monoculture and need several operations during the season to manage the field, to obtain a reasonable yield and grape quality. The fungi management (mildew, powdery mildew) represents the main part of the pesticide treatments (around 16 treatments of 20.1 in [START_REF] Agreste | [END_REF]. Although fewer in number, herbicide treatments (around two for herbicides and epamprage in [START_REF] Agreste | [END_REF] are pointed out in wine-growing regions because herbicides or their metabolites (e.g. AMPA) were the most commonly molecules found in water. Herbicides are used to avoid competition between weeds and vines for light and water, in order to save yield [START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF][START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF] and to limit the availability of shelter for pests [START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF].

The definition of weeds varies among the authors but a lot of them agree to say that a weed is an unwanted plant growing in a place managed and disturbed by humans [START_REF] Baker | The evolution of weeds[END_REF][START_REF] Randall | Defining weeds of natural areas[END_REF]Radosevich at al., 2008). Eleven thousand years ago, the first farmers selected plants for their needs (the crops) keeping the others aside and considering them as weeds [START_REF] Gasquez | Chapitre 1 : Historique de la gestion de la flore adventice[END_REF]. Nowadays and in this study, we consider that weeds can be both a major problem for crop production (Radosevich et al., 2008;[START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF] but also, they are tools able to provide ecosystems services as reducing soil erosion for example [START_REF] Ruiz-Colmenero | Vegetation cover reduces erosion and enhances soil organic carbon in a vineyard in the central Spain[END_REF]. For this study, the definition of weeds will follow the one of [START_REF] Fried | Apports des approches fonctionnelles pour l'évaluation des risques associés aux changements de végétation induits par les activités humaines, Mémoire pour l'obtention de l'habilitation à diriger des recherches[END_REF] that defines a weed as all non-cultivated plants which grow in a cultivated plot, whatever their status. Usually, several weed species grow together in the same cultivated plot and form weed communities whose composition and diversity are determined by abiotic and biotic filters. Filters are processes or constraints hindering the establishment or eliminating a species in a community [START_REF] Fried | Chapitre 2 : Diversité et réponses de la flore des champs cultivés à l'évolution des pratiques agricoles en France[END_REF]. The weeds can be associated to ecological strategies based on the competition (C), the stress (S) and disturbance (D) according to Grime [START_REF] Grime | Vegetation classification by reference to strategies[END_REF]). Grime's CSR strategies are usually used to compare flora in cultivated areas and to describe weeds communities [START_REF] Gaba | Agroecological weed control using a functional approach: a review of cropping systems diversity[END_REF][START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF][START_REF] Fried | Relative importance of environmental factors and farming practices in shaping weed communities structure and composition in French vineyards[END_REF].

To control the potential adverse effects of weeds, tillage was usually applied on the vineyards before the 1970's to weed the rows and the inter-rows [START_REF] Barralis | Evolution de la flore adventice du vignoble de Côte-d'Or sous la pression des techniques d'entretien des cultures[END_REF][START_REF] Maillet | Constitution et dynamique des communautés de mauvaises herbes des vignes de France et des rizières de Camargue[END_REF]. From the 1970s the first authorized vine herbicides were put on the market.

Chemical weeding was gradually adopted, first located on the rows, maintaining superficial tillage on the inter-rows, then carried out "in full" (rows plus inter-rows) with complete abandonment of all soil tillage operations [START_REF] Maillet | Constitution et dynamique des communautés de mauvaises herbes des vignes de France et des rizières de Camargue[END_REF]. These changes of management practices lead to major evolutions on weed communities. Tillage or herbicides have selected certain types of weeds called ruderal according to the Grime's theory [START_REF] Grime | The CSR model of primary plant strategies-origins, implications and tests[END_REF]. These ruderal species are prone to acquire available resources quickly (between two disturbances events), to grow very fast and to produce numerous small seeds [START_REF] Fried | Relative importance of environmental factors and farming practices in shaping weed communities structure and composition in French vineyards[END_REF]. The combination of no-tillage and herbicides in vineyards allows the colonization of vines by perennial plants, even woody species, whose deeps roots are difficult to reach even with herbicides [START_REF] Montégut | Pérennes et vivaces nuisibles en Agriculture[END_REF].

The massive use of herbicides has also provoked a huge decline of species diversity as well as a decrease of the abundance per species [START_REF] Barralis | Evolution de la flore adventice du vignoble de Côte-d'Or sous la pression des techniques d'entretien des cultures[END_REF] and can have unintended effect on other species than the target ones [START_REF] Fried | Chapitre 2 : Diversité et réponses de la flore des champs cultivés à l'évolution des pratiques agricoles en France[END_REF]. Moreover, the herbicides are responsible for water pollution [START_REF] Martı | Evaluation of surface-and ground-water pollution due to herbicides in agricultural areas of Zamora and Salamanca (Spain)[END_REF][START_REF] Louchart | Herbicide transport to surface waters at field and watershed scales in a Mediterranean vineyard area[END_REF] and it becomes a priority to reduce them.

Nowadays, this challenge is taken into account by the government with the decrease of the number of actives molecules retired from the market (from 21 in 2001 to 11 in 2012, [START_REF] Gaviglio | La gestion des sols viticoles[END_REF]. Moreover, more and more vineyards adopted an organic management (in 10 years, the number of organic vineyards has tripled and in 2016 it represents 9% of the total number) increasing the use of tillage in the interrows (Agence Bio/OC, 2017).

A new challenge: reducing inputs to take advantage of the ecosystem services

Many ecosystem services can be provided by the weed communities if they are well managed.

First of all, the competition between the weeds and the wine grape decreases the strength of the vines improving the grape quality (increasing the alcohol content, diminution of acidity…) [START_REF] Van Leeuwen | Incidences de l'alimentation en eau de la vigne, appréciée par l'état hydrique du feuillage, sur le développement de l'appareil végétatif et la maturation du raisin (Vitis vinifera variété Cabernet franc, Saint-Emilion[END_REF][START_REF] Pellegrino | Towards a simple indicator of water stress in grapevine (Vitis vinifera L.) based on the differential sensitivities of vegetative growth components[END_REF] and decreases the vineyards works as epamprage, thinning out of leaves or clipping and improves the microclimate, which decreases the fungal diseases. Secondly, the weeds can provide trophic resources, shelters, wintering and reproduction areas for pollinators, granivore arthropods, herbivorous and omnivorous, birds and mammals [START_REF] Bàrberi | Functional biodiversity in the agricultural landscape: relationships between weeds and arthropod fauna[END_REF], better pest regulation by favoring the crop auxiliaries as earthworms or Carabidae (balance between pests and crop auxiliary), pollination services (sexed reproduction, biofuel production or medicines with vegetal origins) and grains [START_REF] Kazakou | Chapitre 5 : Fonctions et services des plantes adventices dans les agroécosystèmes in Chauvel B[END_REF]. The earthworms and the dynamics of microorganisms present in the soil can increase the quality of the soil allowing the minerals' retention (less nitrogen lixiviation and more carbon sequestration). A better soil increases the water service during the winter period and improves the water's quality. For all these reasons, there is an important tendency in vineyards to leave a certain amount of vegetation in the vines. This vegetation includes weed species that are spontaneous [START_REF] Boisson | Influence du paysage sur la flore adventice en viticulture, Stage M2 sciences du territoire : université Grenoble Alpes[END_REF] and sometimes also a cover crop which consist of one or several chosen species deliberately sown by winegrowers [START_REF] Metay | Chapitre 15 : Gestion durable de la flore adventice en culture pérenne : enjeux et stratégies en viticulture[END_REF]).

These cover crop constitute a service crop [START_REF] Garcia | Management of service crops for the provision of ecosystem services in vineyards: A review[END_REF]. They can be temporary sown, as barley (left in winter and terminated in spring), or permanent, such as tall fescue [START_REF] Celette | WaLIS_ A simple model to simulate water partitioning in a crop association: The example of an intercropped vineyard[END_REF].

They are mainly sown in the inter-rows (less in the rows to avoid competition) but with different pattern, i.e. sometimes every inter-row, one inter-row over two, one inter-row over three, etc [START_REF] Metay | Chapitre 15 : Gestion durable de la flore adventice en culture pérenne : enjeux et stratégies en viticulture[END_REF].

The management of the rows and of the inter-rows is often different because of the proximity of vines in the rows and the width of the inter-rows which allow the use of mechanical tools [START_REF] Metay | Chapitre 15 : Gestion durable de la flore adventice en culture pérenne : enjeux et stratégies en viticulture[END_REF].

Alternative physical tools exist to regulate the weeds such as mowing, mechanical and thermic weeding, mulch and more traditional practices such as the crushing of wine branches, the rolling or the grazing [START_REF] Metay | Chapitre 15 : Gestion durable de la flore adventice en culture pérenne : enjeux et stratégies en viticulture[END_REF]. However, studies about the effects of these alternative practices remain scarce and do not allow to advise winegrowers properly on how to manage weeds in both the spontaneous cover or cover crop situations.

Using a trait-based approach to explain and predict relationships between management practices and weed communities

Classical studies on the ecology of weed communities have tried to link management practices to a given species composition [START_REF] Barralis | Evolution de la flore adventice du vignoble de Côte-d'Or sous la pression des techniques d'entretien des cultures[END_REF][START_REF] Gago | Preliminary study of the effect of soil management systems on the adventitious flora of a vineyard in northwestern Spain[END_REF]. This taxonomic approach shows some limits which can be partially overtaken by the functional approach. This latter approach is inspired by the functions performed by the plants and the role of species in the ecosystems. The concept was born in the 19 th century with Von Humbolt (1806) in [START_REF] Grime | The evolutionary strategies that shape ecosystems[END_REF] but was only developed during the 20 th century and has been widely used by the ecologists for 20 years. As the functions of species are hard to reach and measure, proxies, called traits, are used to obtain information on these functions. The term "traits" has been widely used with various meanings. [START_REF] Violle | Let the concept of trait be functional![END_REF] proposed a definition that is widely agreed upon: "a trait is any morphological physical or phenological feature measurable at the individual level from the cell to the whole-organism level, without reference to environment or any level of organization". That means nothing around the species traits, environmental factors nor population is necessary to describe the traits (of course, when measuring the trait values, it remains important to characterize the environment). The main interest of using the traits is to generalize the links between the practices and the flora.

In comparison to cultivated fields with annual crops where the concept was largely applied [START_REF] Fried | A functional analysis of large-scale temporal shifts from 1970 to 2000 in weed assemblages of sunflower crops in France[END_REF][START_REF] Gunton | Functional traits relating arable weed communities to crop characteristics[END_REF][START_REF] Fried | Trajectories of weed communities explained by traits associated with species' response to management practices[END_REF], the vineyard appear as an ideal model to improve our understanding of weed responses to various types of disturbances thanks to a higher diversity of management practices applied in vineyards over the last decades [START_REF] Gago | Preliminary study of the effect of soil management systems on the adventitious flora of a vineyard in northwestern Spain[END_REF]. The present study takes place in the Bordeaux Wine Region and is part of the VERTIGO project (supported by the CIVB) following the thesis of Lorelei [START_REF] Cazenave | Flore adventice des vignes en Gironde[END_REF]. The aim here is to characterize the response traits of weeds to viticultural practices. Thus, questions occur in accordance to this aim: ▪ (1) What are the different weed strategies (combination of traits) beyond the diversity of weeds in vineyards?

▪ (2)
To what extent can these strategies be related to the contrasted management practices in the rows and in the inter-rows and to the environmental gradient (soil, weather, landscape)? i.e. are rows and inter-rows harbouring different weed communities? ▪ (3) Do the management practices in vineyards (tillage, mowing, herbicides, fertilization) act as filters on plant traits? What are the main traits that respond to different gradients of management practices? ▪ (4) Finally, from a methodological point of view, we asked what is the best strategy for analyzing the influence of management practices on weeds between: i) the use of broad but integrative classification as organic versus conventional mode of production, ii) the use of 'cropping systems' identified as homogeneous groups of vineyards plots using similar practices, iii) the use of the raw management practices variables?

To answer these questions, the weed communities will be analyzed to understand their composition and their functioning thanks to the Grime's strategies [START_REF] Grime | The CSR model of primary plant strategies-origins, implications and tests[END_REF]. Then, the impacts of management practices and other environmental variables will be studied through the three levels of analyses (see the question #4) on the weeds' diversity and on the trait's values.

Hypotheses

(1) It exits different ecological strategies which can be based on the reproduction, the adaptation, the resources or the functional traits. The Grime's CSR strategies will be used to answer the question of weed strategies. The first hypothesis is that as in all cultivated area, the flora in vineyards is essentially composed by ruderal species because of the high level of disturbances. One the other hand, the vineyard can be managed with a diversity of practices. For example, herbicides can be applied only in the rows and mowing only in the inter-rows [START_REF] Metay | Chapitre 15 : Gestion durable de la flore adventice en culture pérenne : enjeux et stratégies en viticulture[END_REF]. Different management practices can also be applied on the same area (rows or inter-rows) at different period, such as mowing followed by tillage [START_REF] Maclaren | Management practices influence the competitive potential of weed communities and their value to biodiversity in South African vineyards[END_REF]. Therefore, the second hypothesis is that with this diversity of practices through time and space the weeds communities will adopt different strategies.

(2) Studies showed that season and latitude (location of the plots) can be the main filters to weeds communities [START_REF] Fried | Relative importance of environmental factors and farming practices in shaping weed communities structure and composition in French vineyards[END_REF], whereas others studies showed that the main impact on weed communities are management practices (Lososovà et al., 2002;[START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF]. The first hypothesis is that the abiotic filters as weather, soil properties and landscape will shape the weed communities and as they are similar on the same plot, the rows and the inter-rows weed communities will be identical whatever the practices.

The second hypothesis is that different management practices in the rows and in the inter-rows will translate on the fields by different communities in the both areas for diversity and traits.

(3) The practices applied on vineyards act as filters on plant traits [START_REF] Lososovà | Seasonal dynamics and diversity of weed vegetation in tilled and mulched vineyards[END_REF][START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF][START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF][START_REF] Maclaren | Management practices influence the competitive potential of weed communities and their value to biodiversity in South African vineyards[END_REF]. Even [START_REF] Fried | Relative importance of environmental factors and farming practices in shaping weed communities structure and composition in French vineyards[END_REF] showed a tiny impact of management practices after the season and the latitude influence on weed communities.

The first hypothesis concerns the influence of management practices on weed communities' diversity.

The hypothesis 1.A. is that the practices will impact the taxonomic and the functional diversity by direct (herbicides) and indirect effects (soil compaction through the number of mechanical passages) [START_REF] Hole | Does organic farming benefit biodiversity[END_REF][START_REF] Bruggisser | Effects of vineyard management on biodiversity at three trophic levels[END_REF]. The hypothesis 1.B. is that the practices are not responsible for the difference of diversity between production mode [START_REF] Nascimbene | Organic farming benefits local plant diversity in vineyard farms located in intensive agricultural landscape[END_REF].

The second hypothesis concerns the combination of traits found under different gradients of disturbance. The hypothesis 2.A. is that a high gradient of disturbance will favor traits values associated to ruderal strategy with high SLA, small height and low seed mass [START_REF] Grime | The CSR model of primary plant strategies-origins, implications and tests[END_REF], with early flowering onset [START_REF] Garnier | Plant functional diversity organism traits, community structure and ecosystem properties[END_REF], as under mechanical passages. At the opposite, the trait values in low disturbed areas will follow competitive strategy (gradient of resources) with high maximum height, high SLA and small seeds [START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF]. High disturbance can be associated to the use of pesticides which can be associated to a low level of pollinators. So, the self-pollination and dispersal can be expected in this context [START_REF] Fried | Apports des approches fonctionnelles pour l'évaluation des risques associés aux changements de végétation induits par les activités humaines, Mémoire pour l'obtention de l'habilitation à diriger des recherches[END_REF]. Besides, the higher is the disturbance, the more we expect a convergence of the traits (especially for tillage) whereas the lower is the disturbance, as for mowing, the more we expect a divergence of traits [START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF].The hypothesis 2.B. is that weeds will have other traits as the traits of ruderal strategy to overpass the management practices [START_REF] Garnier | Plant functional diversity organism traits, community structure and ecosystem properties[END_REF].

The traits will be a low SLA (to invest in the plant structure, less permeability to herbicides) and a high seed mass under tillage management to be able to emerge from deeper in soil and to be able to grow [START_REF] Armengot | Tillage as a driver of change in weed communities: a functional perspective[END_REF]. The maximum height will increase to acquire light faster under disturbed areas [START_REF] Maclaren | Management practices influence the competitive potential of weed communities and their value to biodiversity in South African vineyards[END_REF].

(4) Three approaches concerning the description of management practices will be applied. The first hypothesis is that the three approaches will be complementary and at each level the information will be more accurate. The second hypothesis is that the organic/conventional approach will not be enough accurate [START_REF] Maclaren | Management practices influence the competitive potential of weed communities and their value to biodiversity in South African vineyards[END_REF] used three levels of production mode), the raw variables at the opposite will give many information but hard to interpret, whereas the groups will be a good trade-off between the two other approaches. All the floristic surveys were done in 2015 in the Bordeaux vineyards [START_REF] Cazenave | Flore adventice des vignes en Gironde[END_REF]. This wine production region gets an oceanic climate and has vineyards growing on the sand, graves and siltcalcareous soil [START_REF] Vin & Vigne | Guide des vins et des vignes de France[END_REF]. Nowadays in Aquitaine, 50 % of the total cultivated area (TCA) produces wine grapes [START_REF] Agreste | Mémento de la statistique agricole[END_REF]. With the ECOPHYTO plan, which aims to reduce by 50 % the herbicides use until 2025 [START_REF] Plan | Ministère de l'agriculture[END_REF], and the cover crop success in the Bordeaux, the chemical weeding is limited under the rows only [START_REF] Agreste | [END_REF]. Moreover, the treatment frequency index (TFI) of herbicides reaches 0.7 as the national mean [START_REF] Agreste | [END_REF]. One hundred and one101 plots belonging to 51 vineyards distributed in the Gironde (98 plots), the Dordogne (2 plots) and the Lot-et-Garonne (1 plot) departments were studied. The plots belong to five networks: GIAF (2x6 plots), ECOVITI (2x7 plots), Engrais Verts (2x12 plots), DEPHY/SVBA (52 plots) (defined in the glossary).

One of the plots has been tilled before the floristic sampling (101 plots instead of 102). Figure 1 showed the distribution of the vineyards and their belonging to networks and to the small agricultural regions (Petites Regions Agricoles (PRA) in French).

A vineyard plot can be divided into three types of area: the rows, the tilled inter-rows and the covered inter-rows (spontaneous or sowed vegetation (i.e. cover crop)). The floristic sampling protocol developed by the Chamber of Agriculture of Gironde (CA33) [START_REF] Varela | Conception de méthodes simplifiées de relevés floristiques en viticulture à destination des réseaux DEPHY/FERME et EXPE[END_REF] considers this heterogeneity. For each area (rows, tilled and covered inter-rows), the sampling method was to record all species over 50 m length (2 x 25 m, around 10 vineyard stakes) with a width of 1 m, that corresponded to a total of 150 m²/plot (Figure 2). The rows (R) and the inter-rows (IR) were randomly chosen as the beginning zone for the sampling. The only rule was to avoid the edge effect, so it had to begin after 2 stakes from the edge. For the plots where the inter-rows were only tilled, the sampling was performed on two inter-rows to obtain the same surface per plot.

For each species, the frequency and the abundance were noted. Frequency was based on the presence/absence of the species on 10 sections of 5 m with a resulting frequency ranging between 1 (one individual) and 5 (species present in all the sections) (Cazenave, 2017) (Table 1). Abundance was obtained thanks to the cover percentage of each species per plot. The method comes from Braun Blanquet et al. (1934) which describes the abundance with 5 categories going from 1 for the isolated species (cover <5%) to 5 for species forming a continuous carpet (cover > 75%) (Table 2). To end, frequency and abundance were multiplied to obtain the percentage of cover of each species per plot. The species were identified in the database with their scientific, common and EPPO names (http://eppt.eppo.org/). Observed species every stake

The floristic sampling was made twice: the first one at the end of Marsh/ beginning of April before the first treatment (herbicides, tillage, mowing) were applied in the plot; and the second one at the end of June/beginning of July after that all management practices have been applied (tillage, herbicides treatment, mowing). In total, 541 floristic samples were made in 2015, containing 200 species of weeds.

Because of the yearly alternation between tilled and covered inter-rows, the distinction between those two kinds of inter-rows was not relevant and when a plot contained tilled and covered inter-rows, the maximum of the density (D2) was taken. At the end, 400 floristic samples were describing the rows, the inter-rows and the sampling period (winter and summer). The table containing the maximum of density (D2) for each plot was named the matrix L.

I.2. Environmental variables

I.2.1 Climate

A partnership with the French Institute of Wine and Vineyards (IVF) allowed obtaining the climatic data for each plot, thanks to the TRYDEA tool. It gave the mean temperature, the mean rainfall and the mean potential evapotranspiration (Table 3) for each plot every day of the year 2015.

I.2.2 Soil properties

Soil was sampled at two depths: 0-30 cm and 30-60 cm, using an auger. On each plot: 6 auger samples were taken on 2 inter-rows of the vineyards and 6 others on 2 rows of the vineyards. The choice of the inter-rows and the rows were made randomly avoiding the edges of the plot. The analyses were performed by the AUREA laboratory based in Blanquefort (Gironde) (Table 4).

I.2.3 Landscape and topography

Landscape data can help understanding how the land cover around the studied plots influences weed community composition or diversity in the vineyards. The landscape database has been obtained in a previous study [START_REF] Boisson | Influence du paysage sur la flore adventice en viticulture, Stage M2 sciences du territoire : université Grenoble Alpes[END_REF] thanks to the ArcGis® software 10.1 and its extension 'spatial analyst', produced by the ESRI firm. To characterize the landscape, the proportion of each habitat types (urban zones, annual crops, perennial crops, water areas, grasslands, vineyards, forests and woodlands, as well as a miscellaneous category) was determined. Shannon's Diversity Index (SHDI) was then calculated based on the number and relative proportion of the different habitat types, to represent landscape heterogeneity. Land cover and SHDI were computed for several buffer sizes: 50 m, 100 m, 250 m and 500 m radius around the plot. After running GLMs to explain weed species richness according to landscape diversity with different radius sizes, [START_REF] Boisson | Influence du paysage sur la flore adventice en viticulture, Stage M2 sciences du territoire : université Grenoble Alpes[END_REF] suggested to use the landscape metrics based on the 100 m radius according to the best model selected by the Akaike Information Criterion (AIC). The 100 m buffer size was considered as a good spatial scale to capture the percentage of vineyards and landscape diversity around the vineyard plots [START_REF] Boisson | Influence du paysage sur la flore adventice en viticulture, Stage M2 sciences du territoire : université Grenoble Alpes[END_REF]. Moreover, the 20 m radius landscape was also kept because it represented the nature of the habitat at the edge of the plot, i.e. the most likely habitat to send plant propagules in the vineyard. [START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF].

Gravity and self

Felt on the floor or dispersed by the plant itself 76

Wind Brought by the wind 52

Angaud (2018) performed a PCA on the landscape variables composed by SHDI and the different habitat types at 20 m and 100 m radii. She showed that the vineyard 100 m radius contributed the most to the construction of the axes and was positively correlated with the vineyard 20 m radius and the surface of the vineyard plot (m²) and was negatively correlated to SHDI. Therefore, we decided to summarize landscape variables by the percentage of vineyard that informed if the landscape around the studied plot was homogeneous or not, so if there was a source of diversity around.

The surface of the vineyard plot was also recorded. Previous studies have shown that there is a negative correlation between the size of the agricultural plots and the level of biodiversity (Fried et al., 2018). This correlation can be related to increase management intensity with higher field size or to a landscape configurational effect due to less field margin vegetation in landscape with large field size.

We also considered a topographic variable with the slope. The slope can have an influence on the species composition [START_REF] Perring | Topography gradients of chalk grassland[END_REF] in [START_REF] Hill | Influence of slope and aspect on long-term vegetation change in British chalk grasslands[END_REF] and can influence the magnitude of vegetation change [START_REF] Hill | Influence of slope and aspect on long-term vegetation change in British chalk grasslands[END_REF]. Moreover, the slope influences also the practices: for example, the mechanical engines were difficult to handle in steep vineyard plots, so there are fewer tillage in this kind of plots while chemical control or spontaneous or cover crop are more often used to avoid the soil erosion [START_REF] Vrsic | Effect of soil management systems on erosion and nutrition loss in vineyard on steep slopes[END_REF]. The surface, the slope, the proportion of vineyard in the 20 m and the 100 m radii were described in Table 5.

I.3. Agricultural practices

The management practices applied in the vineyard plots were gathered thanks to an electronic interview (google forms) of the concerned winegrowers. Based on a literature review [START_REF] Gago | Preliminary study of the effect of soil management systems on the adventitious flora of a vineyard in northwestern Spain[END_REF], the main practices to consider for their effect on weed flora were: soil tillage (with the difference between rows and inter-rows), mowing (with differentiation between the rows and the inter-rows), and herbicides treatments, fertilizations (chemical or organic) and the total number of mechanized treatments (including diseases treatments, harvest…) (Table 6). This latter variable is the sum of the number of tractors passes which could be an indirect measure of soil compaction. The table containing all the environmental variables as climate, soil properties, landscape and agricultural practices was named the matrix R.

I.4. Plant traits

Different traits and ecological performance indicators were used in this study. These two types of indicators came from the data base BIOFLOR [START_REF] Klotz | BIOLFLOR -Eine Datenbank zu biologisch-ökologischen Merkmalen der Gefäβpflanzen in Deutschland[END_REF] and baseflor [START_REF] Julve | Baseflor: Index botanique, écologique et chronologique de la flore de France[END_REF]. There were qualitative traits (Table 7) including the biological types, the pollination and the dispersal modes.

There also were quantitative traits (Table 8) including the functional traits and the Ellenberg's indicators, which characterize the response of species to environmental factors [START_REF] Ellenberg | Zeigerwerte von Pflanzen in MittelEuropa[END_REF]. Ellenberg

(1992) has listed these 6 indicators which were adapted to the central Europe conditions and [START_REF] Julve | Baseflor: Index botanique, écologique et chronologique de la flore de France[END_REF] adapted it for the French vegetation. The matrix containing the traits was named the matrix Q (traits in columns and species in rows). Responds to disturbance events and change (harvest and agricultural practices) of diversity [START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF]) by being able to flower several times.

Light (L)

Response to light -7.22 ± 0.96 3 -9 Tolerance to shade [START_REF] Hill | s indicator values for British plants[END_REF] Ellenberg L can discriminate weed response to competition [START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF].

Temperature (T)

Response

to temperature -5.71 ± 0.96 4 -8 Tolerance to coldness.

Continentality (C)

Response to temperature in summer and in winter -4.41 ± 0.86 2 -8 Tolerance to continental climates (dry in summer, cold in winter).

Soil_humidity (F)

Response to moisture of the soil -4.98 ± 1.34 2 -9 Tolerance to dryness [START_REF] Hill | s indicator values for British plants[END_REF].

pH (R)

Response to soil richness (acid or basic soil) -7.55 ± 1.17 2 -8 Tolerance to acidity [START_REF] Hill | s indicator values for British plants[END_REF].

Trophic level (N)

Response to nutrients contents in the soil -5.78 ± 1.59 1 -9 Tolerance to soil infertility [START_REF] Hill | s indicator values for British plants[END_REF]. Ellenberg N can reflect the plant growth to soil nitrogen availability [START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF].

In this study, the analyses are performed on a complete dataset. As the missing data represented less than 5% of the total dataset (2.6% (73/2800) for the matrix Q and 0.5% (16/3232) for the matric R), they were filled with predictive mean matching using the mice package. In order to avoid outliers, the seed mass and the plant height were log-transformed.

All the data were coming from the thesis or Lorelei Cazenave and some precisions were added thanks to Tristan Boisson for the landscape metrics and Maude Angaud for the number of variables. The addition and treatment of the plant trait data and the choice of the relevant variables for each analysis were the topic of this internship.

II. Data analyses

II.1. Characterization of weed communities II.1.1 Weed community diversity

Each plot was first characterized by species richness (S), given the number of different species in a plot and the total abundance, the sum of abundance of all species in the plot. Then a number of indices were computed in order to describe the functional diversity and structure of the communities.

Many studies showed that the species diversity was not enough to describe the ecosystem and that the functional diversity was needed to better understand their functioning [START_REF] Tilman | The influence of functional diversity and composition on ecosystem processes[END_REF][START_REF] Hulot | Functional diversity governs ecosystem response to nutrient enrichment[END_REF][START_REF] Díaz | Vive la difference: Plant functional diversity matters to ecosystem processes[END_REF][START_REF] Heemsbergen | Biodiversity effects on soil processes explained by interspecific functional dissimilarity[END_REF][START_REF] Schleuter | A user's guide to functional diversity indices[END_REF]. The functional diversity can be defined by 3 types of indicators which are functional richness (FR), functional evenness (FE) and functional divergence (FD) [START_REF] Schleuter | A user's guide to functional diversity indices[END_REF], which are considered as independent in a random community. The first indicator taken is the functional richness (Fric), which measured how the niche was occupied by the species and if it was optimally used or not [START_REF] Schleuter | A user's guide to functional diversity indices[END_REF]. The index used for this functional richness was coming from the hull volume of [START_REF] Cornwell | A trait-based test for habitat filtering: convex hull volume[END_REF] [START_REF] Mason | A guide for using functional diversity indices to reveal changes in assembly processes along ecological gradients[END_REF]. The second indicator is the functional divergence represented by the Rao's quadratic entropy.

Rao's quadratic entropy gave the distance of traits between a pair of species and was a very used index for its flexibility [START_REF] Schleuter | A user's guide to functional diversity indices[END_REF]. It represented a generalization of the Simpson index and can be calculated with the following formula [START_REF] Leps | Quantifying and interpreting functional diversity of natural communities: practical considerations matter[END_REF]:

𝐹𝐷(𝑅𝑎𝑜) = ∑ ∑ 𝑑𝑖𝑗. 𝑝𝑖. 𝑝𝑗 𝑆 𝑗=1 𝑆 𝑖=1
with S, the total number of species in the community, pi and pj, respectively the proportion of the species i and j, dij the dissimilarity between the species i and j. dii = 0 because there was no dissimilarity in the same species. If dij = 1, the pair of species was totally different, and another formula was used (Botta-Dukàt 2005):

𝐹𝐷(𝑅𝑎𝑜) = 1 -∑ 𝑝𝑖² 𝑆 𝑖=1
The species richness and the abundance give information on the number of plants in the niche and the quantity of each of them. The functional indicators which are the functional richness (FRic) and the Rao's quadratic entropic (Rao) bring more information on the functional side of the study with the comparison with an optimum thanks to FRic and a distance between the traits thanks to the Rao's index.

The Gower's distance calculated for these two indicators allowed to compute distances for qualitative variables as the biological type and non-continuous variables as the Ellenberg indicators (c.f. I.4 Plant traits) [START_REF] Karadimou | Functional diversity exhibits a diverse relationship with area, even a decreasing one[END_REF]. So, the four indicators were supplementary and described from different angle the diversity of the plots. The dbFD function returns FRic and Rao indicators per plot [START_REF] Laliberté | A distance-based framework for measuring functional diversity from multiple traits[END_REF].

II.1.2 Weed community functional structure and composition

The community weighted mean (CWM) and the community weighted variance (CWV) were calculated for each trait. CWM could be computed thanks to the dbFD() function, which took into account the relative abundance of species per each plot. The CWM values "represent the average of traits values for a unit of biomass inside a community" [START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF]. For the qualitative traits, it gave the percentage of presence of each trait in each plot. The CWV was calculated for each plot only for the quantitative traits by this formula [START_REF] Hulshof | Intra-specific and inter-specific variation in specific leaf area reveal the importance of abiotic and biotic drivers of species diversity across elevation and latitude[END_REF][START_REF] Bestovà | Ecological and biogeographical drivers of freshwater green algae biodiversity: from local communities to large-scale species pools of desmids[END_REF]:

𝐶𝑊𝑉 = ∑ 𝐴𝑖 × (𝑡𝑖 -𝐶𝑊𝑀)² 𝑆 𝑖=1
where Ai was the abundance of species i, and ti is the trait value of species i.

II.2. Characterization of management practices through environmental conditions

We chose to describe management practices by using an increasing level of complexity:

i) First of all, we used a very raw but consistent and integrative classification of plots based on the production mode followed by the winegrowers: organic versus conventional farming.

ii) The second strategy for analysing the data was to identify homogeneous groups of plots based on similar management practices. This is based on the idea that beyond organic and conventional production mode, there can be differences (e.g., one can assume that there are different types of organic farming and different levels of intensity within conventional winegrowers). First, the table with the 101 plots described by the eight management variables were subjected to a Principal Component Analysis (PCA) followed by a Hierarchical Cluster Analysis (HCA). Four groups were identified using the optimal number of clusters given by Mantel statistic, which compared the original distance matrix and the binary matrices computed from the dendrogram cut at various levels [START_REF] Borcard | Numerical ecology with R[END_REF]. The management practices that best discriminates the four groups were identified thanks to the test value (v_test), which calculated the gap between the global mean and the mean of a modality and allowed the groups' description (catdes (FactoMineR) [START_REF] Husson | Analyses de données avec R[END_REF].

iii) In the last strategy, we tested directly the influence of all the 14 raw management practices variables including the soil, the landscape and the climate on weed species (functional) diversity and composition.

II.3 Statistical analysis

II.3.1 Analysis of weed functional strategies

A Hill & Smith analysis was performed on the matrix Q (200 species x 14 traits). A Hill & Smith analysis is equivalent to a Principal Component Analysis (PCA) adapted to matrixes mixing qualitative and quantitative variables [START_REF] Hill | Principal component analysis of taxonomic data with multi-state discrete characters[END_REF]. The Grime categories competitors, stresstolerant and ruderal [START_REF] Grime | Vegetation classification by reference to strategies[END_REF] were added as supplementary variables. They allowed to understand the repartition of the species categories depending on the traits and see if it corresponded to the strategies described by Grime. Then a Kruskal Wallis test was applied followed by a Dunn test to understand the distribution of Grime strategies along the first axes.

On the same matrix Q, the fidelity index to the inter-rows was calculated to determinate the species that are specific to each area (rows and inter-rows) and then linked to the traits. This index was calculated from the [START_REF] Chytrý | Determination of diagnostic species with statistical fidelity measures[END_REF] formula:

𝜑 = 𝑁. 𝑛p -𝑛. 𝑁𝑝 √𝑛. 𝑁𝑝. (𝑁 -𝑛). (𝑁 -𝑁𝑝)
with N the number of samples used (400 floristic samples), Np, the number of samples in the inter-rows (201 floristic samples), n, the number of occurrences of the species in the whole dataset and np, the number of occurrences of the species in the inter-rows. This index ranged from -1 (species associated to the rows) and 1 (species associated to the inter-rows) [START_REF] Fried | Relative importance of environmental factors and farming practices in shaping weed communities structure and composition in French vineyards[END_REF].

II.3.2. Influence of environmental variables on weed community diversity and on weed community composition i) Linear models with interaction applied to the three methods

The statistics were performed on the three methods level: the production mode (conventional versus organic), the groups practices and the raw variables and the aim was to compare the influence of each method and of the area (rows/inter-rows) on the diversity (taxonomic: S, Abundance; functional: FRic, Rao) and on the traits (CWM and CWV). To obtain such analyses, linear models with interaction were performed on the 400 floristic samples. An ANOVA (type III) was applied to emphasize the significance of the diversity's indicators and the traits' values (CWM and CWV). For the production mode, the model significance was tested thanks to the glht function from the multcomp package (Tukey test). For the groups, the model significance was evaluated thanks to the testInteraction function from the phia package. And for the raw variables, the model was selected thanks to the Akaike Information Criterion (AIC). The AIC is an estimator of an expected distance developed by [START_REF] Kullback | On information and sufficiency[END_REF] which represents the loss of information between the reality and the model.

The aim was to maximize resemblance to the reality with the less variables as possible. The model chosen will be the one with the smallest AIC [START_REF] Posada | Model selection and model averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian approaches over likelihood ratio tests[END_REF]. Then, the Shapiro test was applied on all linear models to verify the normality of the residuals of the ANOVA. The test was passed if the null hypothesis is accepted (p_value above 0.05). However, the dataset contained 400 floristic samples and Shapiro did not always work, so the normality was also evaluated on traits' histograms (Appendix III). The variation inflation factor (VIF) was also applied to evaluate the collinearity into the model, if VIF value exceeded 10, the variables were removed from the models. A predictability of the model (repartition of the fitted results along the first bisector) was operated to assure the veracity of the model. The CWM of all biological types except therophytes, the flowering duration, the auto-apogamous and the facultative entomogamous and all CWV were sqrt-transformed to follow a normal distribution.

ii)

Complementary approach and relationship between the environmental variables and the traits: RLQfourth corner analyses [START_REF] Dray | Combining the fourth-corner and the RLQ methods for assessing trait responses to environmental variation[END_REF] developed an approach combining two analyses to show significant relationship between the traits and the environmental variables. For that purpose, a RLQ analysis was performed on the three matrices, the floristic matrix L, the traits matrix Q and the environmental matrix R. It is similar to a multivariate analysis which identify the best viewing of the "cloud's points". The RLQ displayed axes that best discriminates species (based on their traits) and sites (based on their environmental characteristics) and maximizes the covariance between environmental variables and the traits. It was the first step to have an idea of the correlation between the traits and the practices. Before the RLQ analysis, preliminary statistics were applied on each matrix. First, a Correspondence Analysis (CA) was performed on the floristic matrix L after a square-root-transformation on the matrix. This analysis was used as a canonical factor for the following analyses. Secondly, a Hill & Smith was performed on the traits' matrix Q, which was scaled with the columns scores of the CA of the matrix L.

Thirdly, a PCA was performed on the environmental matrix R, which was scaled with the rows scores of CA of the matrix L.

One of the drawbacks of the RLQ analysis is that it does not allow to make a direct correlation between the environment variables and a single trait. So, a second analysis was performed which is called the fourth-corner analysis. It realized simultaneously statistical tests on bivariate associations (one trait and one environment variable at a time). 9 999 permutations were made to adjust the p-value and the threshold of the significance was set at α = 0.05. However, this analysis does not consider the combinations of practices on one hand and the combinations of the traits on the other hand. So, the two analyses (RLQ and fourth corner) were combined to avoid their inconvenient when they are used separately. This combination of analyses allowed highlighting the significant correlation between the combinations of environmental practices and each trait, and the significant correlation between the combinations of traits and each management practice.

This approach was also implemented separately on the rows and on the inter-rows, and on the winter and the summer sampling periods, using the 400 floristic samples to test the influence of the areas (rows/inter-rows) and the sampling period (summer/winter), which were not considered in the global analysis performed on the 101 plots gathering the two sampling periods.

All the statistics ran thanks to the ade4, car, FactoMineR, FD, MASS, multcomp, phia and vegan packages and ggplot2 package displayed the figures.

Results

I.

Analysis of weed functional strategies (Grime's strategies)

I.1. Axes of specialization of vineyards weeds

The aim of this Hill & Smith analysis was to highlight suites of traits associated to the fidelity of weeds to the IR and to interpret them in the light of the Grime strategies. We kept the first four axes of the Hill & Smith analysis which explained 35.86 % of the traits' distribution (Figure 3, see the inertia table in Appendix II and contribution of the variable to the axes). The first axis was positively correlated with Ellenberg's indicators for continentality (39.7 % of relative contribution to the construction of the axis 1) and for trophic level (34.6 %), flowering duration (39.2 %) and SLA (26.6 %). The first axis was negatively correlated with Ellenberg's indicators for temperature (-37.1 %) and for light (-21.8 %). The second axis was positively correlated with therophytes (32.2 %) and negatively with maximum of plant height (-40.4 %) and flowering onset (-40.3 %). This second axis also opposed annual species (therophytes) to perennial species (various types of geophytes and hemicryptophytes).

I.2. Vineyards weed functional traits related to Grime's strategies

The Grime strategies describe three types of species, the ruderals, the stress-tolerant and the competitors, and the combinations between the three types of species. The Figure 4.a) showed the belonging of each species to a Grime's strategy according to species position in the trait multivariate space (Figure 3). It occurred a similarity between this graph and the Grime's triangular model (1974) explaining the strategies with the combination CSR at the center, the C, S and R shaping a triangle around the center and between two strategies, the combination of both was found. According to the . Linking the strategies to the traits (Figure 3), the ruderal strategy was correlated with low seed mass, low maximum height and early flowering onset.

Table 9: Species with high fidelity index to the inter-rows (green on the left), fidelity index close to zero (orange in the middle) and high fidelity index to the rows (red on the right)

Table 10: The F_value, the estimate and the significance concerning the habitat, the production mode and the interaction between both, for the species richness, the abundance, the functional richness (FRic) and the Rao's quadratic richness. The points (.) indicated a p_value above 0.05, the stars indicated a p_value between 0.05 and 0.01 with a star (*), a p_value between 0.01 and 0.001 with two stars (**) and a p_value under 0.001 with three stars (***). The adjusted R² indicated the contribution of the model to the diversity's indicators. The competitors were positively correlated with maximum height, hemicryptophytes (tussock and erected), soil properties and climatic conditions (continentality, trophic level, pH and soil moisture).

The stress tolerant species were positively correlated with Ellenberg's indicators for temperature and for light and negatively to Ellenberg's indicators for trophic level and for soil moisture.

I.3. Describing the fidelity index to the inter-row by the traits' variables

The index of fidelity to the inter-rows (IR) ranged from -0.098 for Hedera helix (the species with the highest fidelity to the rows (R)) to 0.44 for the Rumex crispus (the species with the highest fidelity to the IR). Table 9 list the fidelity scores for the more frequent plants based on the presence/absence data. It seemed that there were more species with high fidelity to the IR than species with high fidelity to the R. The species with a high fidelity to the IR were all hemicryptophytes, mainly anemogamous and entomogamous, heliophilous and nitrophilous. The species with high fidelity to the R, which are treated chemically by herbicides, were mainly split in two categories. The first one counted small annuals with short life cycle until March/April and the second one, the woody species like Hedera helix possessing deep roots and waxy leaves giving natural tolerance to herbicides sprays.

The fidelity index to the IR was significantly linked to Ellenberg's indicators for temperature (cor = -0.16, P = 0.03) and for soil moisture (cor = 0.27, P = 0.0001), axis 2 (cor = -0.15, P = 0.03) and axis 3 (cor = -0.24, P = 0.0008). Based on the trait that contributed the most to axes 2 and 3 (Figure 2, Appendix II), this analysis highlight that the fidelity index to the IR was positively correlated with traits' combination: high maximum height, high seed mass, late flowering onset and various perennials life forms (geophytes with rhizomes, erected and tussock hemicryptophytes, opposed to therophytes), short flowering duration and low SLA. Based on Grime's strategies, the fidelity index to the IR was positively linked to competitors and negatively to ruderals.

II.

Influence of environment on weed community (functional) diversity

II.1. Effect of conventional and organic production mode

This part aimed to understand if there was an influence of production mode (organic vs conventional), of the areas (R/IR) and/or their interaction on the weed community diversity. The abundance was the only diversity index showing a significant difference between the production modes (Table 10) with a higher score for organic one (Figure 5). The four diversity indices showed a significant difference between the areas (Table 10) with a higher score for the IR (Figure 5).

II.2. Effect of management practices

In this part, the aim was to test if a classification of vineyard plots into groups with similar management practices can more accurately explain weed community diversity. Based on the three first axes of the PCA (representing 67.8% of the total variability of the practices' matrix), the Hierarchical Cluster Analysis and the Mantel statistic, we were able to distinguish four groups of plots (Appendix III). The first group (in yellow) represented plots where mowing was applied in both the R and the IR, where organic manure was used and where there was a high number of mechanical passages. The group 2 (in red) represented plots where winegrowers implemented tillage in the R, mowing in the IR and who used organic manure. The group 3 (in green) represented plots where winegrowers implemented herbicides in the R, mowing in the IR and who used chemical fertilization. The group 4 (in blue)

represented plots where winegrowers implemented tillage in the R and in the IR with high number of mechanical passages.

The four diversity indices showed a significant difference between the areas, the groups and their interaction (Table 11). The species richness and the abundance showed the same results (Figures 6a and6b), with a higher the species richness and the abundance in the IR in comparison with the R except for the group 4 (tillage in both R and IR) which presented similar results between both areas.

For the IR, species richness and abundance were significantly lower in the group 4 compared to groups 1, 2 and 3 while there were no significant differences between these three latter groups. For R, species richness and abundance were higher for groups 1 and 2 compared to groups 3 and 4. Functional richness (FRic) was higher for the IR than for the R in groups 2 and 3 while there was no significant difference between the R and the IR for groups 1 and 4 (Figure 6c). Groups 1, 2 and 3 had a higher FRic than group 4 for the R but no difference was found between the groups for the IR. The Rao's quadratic entropy was higher in the IR than in the R except for the group 4, which presented no significant difference (Figure 6d). In the IR, groups 1, 2 and 3 had no significant but groups 1 and 3 showed a higher Rao's quadratic entropy than the group 4. In the R, there was no significant difference between the groups.

II.3. Relationship between the traits and the environmental variables

The aim of this part was to test the effect of each management practices and their relative influence compared to climatic, soil and landscape variables. After performing a PCA (Appendix IV) with the environmental variables, 14 variables were kept: the vineyard percentage at 100 m radius ( V_100 m), the sand percentage (Sand), the organic matter (MO), the C/N ratio (CN), the pH, the rainfall and the eight the management practices: mowing in the R (ntont_sr), mowing in the IR (ntont_ir), tillage in the R (wsr), tillage in the IR (wir), organic manure (famo), chemical fertilization (feng) and number of mechanical passages (npm). Table 12 displayed the results of linear models using the 14 variables to explain variations in species richness, abundance, functional richness and Rao's quadratic entropy. The four indices were significantly higher during the winter period than during the summer period. The indices were also positively correlated with the use of organic manure (except the species richness) and the number of mechanical passages (except for the Rao's index). The four indices were significantly lower in the R compared to the IR, and they were negatively correlated with the number of tillage in the IR, the number of mowings in the R (except for the Rao's index) and sand percentage (except for FRic).

Besides, the number of herbicides treatments was only negatively correlated with species richness and abundance but not to functional diversity's indices. The proportion of vineyard in the landscape was negatively correlated with abundance and Rao's index. The Rao's index was negatively correlated with pH and the number of tillage in the R and positively correlated with the C/N ratio.

III. Influence of environmental variables on weed community functional composition

In this part, some traits were discarded from the modelling process; the Shapiro tests and the visual inspections of histograms of traits indicated that the residuals of the models for the CWM of biannual, geophytes with bulbs and rhizomes, erected hemicryptophytes and woody species did not follow a normal distribution. Moreover, concerning the Ellenberg's indicators, only the one for trophic level was kept because the other were hardly interpretable.

III.1. Effects of conventional and organic production mode

The CWM of traits showed a significant difference between the areas (R/IR) with a higher score in the IR for the CWM of hemicryptophytes (tussock, rosettes, stolons), flowering onset, anemogamous and animal dispersal; and a higher score in the R for the CWM of therophytes, SLA, flowering duration, auto-apogamous, facultative entomogamous, wind dispersal and Ellenberg's indicator for trophic level.

The CWM of traits also showed a difference between the production mode (organic/conventional) with a higher score in organic farming for the CWM of hemicryptophytes (tussock, stolons) and anemogamous; and a higher score in conventional farming for the CWM of therophytes, facultative entomogamous, wind dispersal and Ellenberg's indicator for trophic level. The CWV (variance) of all traits were higher in the IR and in organic production mode (Appendix V).

III.2. Effects of management practices

The CWM of traits showed a significant difference between the areas for the group 3 only (except for the tussock hemicryptophytes which showed a significant difference in the 4 groups and the hemicryptophytes with rosettes in the groups 1 and 3), with a higher CWM in the IR of hemicryptophytes with stolons, flowering onset, facultative entomogamous and animal dispersal; and a higher CWM in the R of therophytes, SLA, flowering duration and Ellenberg's indicator for trophic level. The CWM of traits showed a significant difference between the groups in the IR, especially for the group 4, with a higher CWM of therophytes and facultative entomogamous and a lower score for the CWM of hemicryptophytes (tussock, rosettes, stolons) and animal dispersal. There was also a significant difference in the R between the groups 1 and 2 and the groups 3 and 4, with a higher score for CWM of hemicryptophytes with stolons and maximum height in the groups 1 and 2; and with a higher score for the CWM of the therophytes and flowering duration in the groups 3 and 4. The CWV of all traits were higher in the IR for the groups 1, 2 and 3. In the IR, the group 4 had lower CWV than the other groups and in the R, the group 3 has lower CWV than the groups 1 and 2 and sometimes 4 (Appendix VI). The first two axes of the RLQ accounted for 81.31% of the total inertia (65.26% and 10.75% for the RLQ axes 1 and 2, respectively). The first axis of the RLQ discriminated the species (Figure 7) according to the biological type. The hemicryptophytes were positively correlated to the axis 1 and opposed to the therophytes. The SLA was linked to the therophytes as well as to the geophytes and to an early flowering onset. The second axis of the RLQ discriminated the species according to traits related to competitive ability (seed mass, plant height, trophic level) and to the mode of dispersal and pollination. The axis 2 was positively correlated with the wind dispersal, the wind-pollinated species, auto-apogamous species and flowering duration. At the opposite, the axis 2 was negatively correlated with the seed mass and the maximum height, entomogamous (obligate outcrossing plant species that depend on insect pollinators), the trophic level and animal-dispersed species. The woody biologic type contained few species and could be considered as an outlier (with the Hedera helix). Its position on the positive loadings of Axis 1 together with other perennials (hemicryptophytes) is however consistent.

The axes of the RLQ separated the practices (Figure 8) on the first axis and the environmental variables on the second axis. The first axis of the RLQ was positively correlated with the number of mowing in the R and in the IR and the evapotranspiration. On the other side, the first axis of the RLQ was negatively correlated with the number of soil tillage in the R and in the IR, the total number of mechanical passages, the vineyard percentage and the organic manure. The second axis of the RLQ was positively correlated with the chemical fertilization, the number of herbicide treatments and sand percentage. The second axis was negatively correlated with the pH, the organic matter, the temperature and the rainfall. Appendix VII displayed the RLQ performed separately on the R and on the IR. The season was the principal contributor to the axes' construction for both areas. The summer sampling period was positively correlated with the first axis as same as the flowering onset, the anemogamous, the woody plants and the maximum height, the rainfall, the mowing in the R and in the IR for the IR only and the organic matter for the IR only. At the opposite along the first axis, the winter sampling period was positively correlated with the flowering duration, the SLA and the auto-apogamous for the traits and the sand for the R and the IR. The combination between the traits and the environmental variables were still almost the same as the global RLQ.

The fourth corner analysis showed 14 significant correlations (over 270 possible ones) between environmental variables and traits (Figure 9). The hemicryptophytes were positively correlated with the mowing in the IR (r = 0.083, Padj = 0.0054) and negatively with the sand percentage (r = -0.092, Padj = 0.006), the tillage in the R (r = -0.12, Padj = 0.0054) and in the IR (r = -0.14, Padj = 0.0054) and the number of mechanical passages (r = -0.078, Padj = 0.006).

10: Results of the combination of the RLQ and the fourth-corner analyses for the global (a), the rows (b) and the inter-rows (c) analyses. On the left of each table, the fourth-corner tests between the two axes of the RLQ for the environmental gradients and the practices (AxcR1/AxcR2) and traits. On the right of each table, fourth-corner tests between the two axes of RLQ for traits (AxcQ1/AxcQ2) and the environmental variables and the practices.

The therophytes showed the opposite combination with a positive correlation with the percentage of sand (r = 0.079, Padj = 0.012), the number of tillage in the R (r = 0.10, Padj = 0.006) and in the IR (r = 0.14, Padj = 0.0054) and the number of mechanical passages (r = 0.073, Padj = 0.006) and a negative correlation with the number of mowing in the IR (r = -0.075, Padj = 0.0054). The level of soil organic matter was positively correlated with the animal dispersal (r = 0.042, Padj = 0.037) and negatively correlated with the wind dispersal (r = -0.043, Padj = 0.048). The seed mass was positively correlated with the pH (r = 0.063, Padj = 0.020), which represented rich soil mainly calcareous. To end, the number of mechanical passages was positively correlated with the trophic level (r = 0.065, Padj = 0.0081). The fourth corner analyses in the R and in the IR showed a positive correlation between the summer sampling period, the maximum height, the flowering onset and anemogamous pollination and a negative correlation between the winter and these traits. The results of the IR and the R fourth corner analyses for the other traits were very similar to the global one. Additional results were interesting as the positive correlation between the flowering duration and the number of herbicides treatments (cor = 1.0, Padj = 0.004) for the R.

For the RLQ and 4 th corner combination, the first axis was positively associated with the hemicryptophytes and the number of mowing in the R and in the IR (Figure 10a.). On the other hand, the axis 1 was negatively associated with the therophytes, the sand percentage, the organic manure, the number of tillage in the R and in the IR, the number of mechanical passages and the temperature average.

The second axis of the RLQ was negatively associated with the seed mass, the maximum plant height and the soil pH. That meant the richer was the soil, the bigger was the seed and the higher was the stem.

For R/IR combinations (Figures 10 b andc), more traits were associated but the correlation between the height, the seed mass and the pH did not occur. Both of these analyses combined the hemicryptophytes, the maximum height, the flowering onset, the anemogamous, the summer sampling period, the number of mowing in the R (for the R) and the number of mowing in the IR (for the IR). These traits were opposed to the therophytes, the SLA, the flowering duration and the auto-apogamous, the winter sampling period and the number of herbicides for the R and opposed to the therophytes, the autoapogamous, the winter sampling period, the sand percentage, the organic manure, the tillage in the R and in the IR, the number of mechanical passages and the temperature for the IR.

Unlike the global combination, the second combination of R and IR combinations took back the opposition between the hemicryptophytes and the therophytes. For the R, the combination opposed the hemicryptophytes, the number of mowing in the R and in the IR on one side and the therophytes, the sand percentage, the pH, the number of tillage in the R and the IR, the number of mechanical passages and the temperature on the other side. For the IR, the combination opposed the hemicryptophytes, the winter sampling period, the number of herbicides, the number of mowing in the R and in the IR on one side and the therophytes, the summer period, the number of tillage in the R and in the IR, the number of mechanical passages and the temperature on the other side.

To summarize, the first axis described the practices along a soil disturbance gradient which split the species according to their biological type with therophytes being associated to soil tillage and the hemicryptophytes being associated to mowing.

Whereas the second axis reflected a soil resource gradient related to soil pH, discriminating species according to their seed mass, with species with a low seed mass found on low soil pH and species with a high seed mass found on high soil pH.

III.3.2. Linear models' results

The linear models' results showed similar results as the RLQfourth corner analyses (III.3.1), with a detailed description in Appendix VIII, with correlation between the management practices, the sand percentage and the temperature; and the biological type (therophytes and hemicryptophytes)

showing a opposite combination. The positive correlation between the pH and the seed mass also occurs in these results, as well as the positive correlation between the flowering duration and the number of herbicide treatments. Moreover, some information can be added to the RLQfourth corner analyses.

The organic manure has a positive correlation with CWM of traits (hemicryptophytes, therophytes, seed mass, facultative entomogamous, baro-autochory and animal dispersal), whereas the chemical fertilization has a negative correlation with CWM of traits (compulsory entomogamous and animal dispersal). There was no correlation with the mowing in the inter-rows.

The CWV of traits showed a positive correlation with the winter sampling period, the interrows, the organic matter and manure and the number of mechanical passages and a negative correlation with summer sampling period, the rows, the vineyard and sand percentages, the number of herbicide treatment, tillage in the inter-rows and mowing in the rows.

Discussion

I. CSR strategies of Grime suited to weed communities in Bordeaux vineyards

The question was to describe the weeds communities present in the Bordeaux vineyards and to know if weeds strategies were identifiable. Plotting Grime's strategies showed that a first axis of differentiation of weeds of vineyards can be interpreted as a resource gradient, opposing stress-tolerant species to ruderal and competitive species while a second axis of differentiation can be interpreted as a disturbance gradient opposing ruderal strategies to competitive strategies. That confirms the second hypothesis assuming the occurrence of different Grime's strategies among vineyard weeds instead of ruderal species only.

It is striking that the ordination of weed flora of vineyard based on a selection of few traits suit so well with Grime's triangular model. Especially since the traits used here do not fully cover those used by [START_REF] Hodgson | Allocating CSR plant functional types: a soft approach to a hard problem[END_REF] (canopy height, dry matter content, flowering period, flowering start, lateral spread, leaf dry weight, SLA) or [START_REF] Pierce | A global method for calculating plant CSR ecological strategies applied across biomes world-wide[END_REF] (SLA, Leaf Dry Matter Content, Leaf Area).

Based on the selected traits, ruderal weeds can be defined as annual plants with a short stature, a high SLA, an early flowering onset and a long flowering duration. Weeds with a competitor strategy are hemicryptophytes with a high stature and high seed mass. The stress tolerant weeds are heliophilous and thermophilous species, resistant to drought and temperature variations (continentality). Compared to weeds of annual crops (wheat, maize) where weed communities are more skewed toward ruderal weeds [START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF], it is also interesting to note that weeds of vineyards cover more evenly all the different CSR strategies. This can be explained by a higher diversity of management practices (tillage, herbicides, mowing) in the different vineyards and within a same vineyard plot (row versus inter-rows).

Consistently, the projection of the fidelity index to the inter-rows on the multivariate space of weed species trait was correlated to a disturbance gradient opposing ruderal species on the rows and competitive species on the inter-rows. Interestingly, no single traits explained well the fidelity index to the inter-rows indicating that the species belonging to the inter-rows are better characterized by a combination of traits (high height, important seed mass, late flowering onset, small SLA, short flowering duration and various life forms as geophytes with rhizomes, erected and tussock hemicryptophytes, but not therophytes). As the rows and the inter-rows have different types of disturbances and that some microclimate can exist [START_REF] Metay | Chapitre 15 : Gestion durable de la flore adventice en culture pérenne : enjeux et stratégies en viticulture[END_REF], the difference of weeds communities can be understood.

However, the fidelity index goes only from -0.098 to 0.44 while the theoretical limits are -1 to 1 [START_REF] Fried | Relative importance of environmental factors and farming practices in shaping weed communities structure and composition in French vineyards[END_REF]. In fact, the rows and the inter-rows are very close areas and can exchange species through dispersal process as wind, animals or agricultural engines. Moreover, the yearly alternation of tilled and covered inter-rows can bring the management of the inter-rows closer to the management of the rows [START_REF] Angaud | Caractérisation de la flore adventice en viticulture : Importance du paysage, du pédo-climat et des pratiques agricoles dans la structuration des communautés[END_REF]. So, the difference between the rows and the inter-rows can be explained by the disturbance gradient between both areas but the proximity of the areas allowing plant dispersal avoids a complete separation of the two areas.

II. Differences of weed communities in the rows and in the inter-rows depending on the management practices and the environmental variables (weather, landscape and soil properties).

The fidelity index in the inter-rows (IR) showed a difference of weed communities' composition due to the various strategies adopted by the plants. First, our results showed a higher diversity (taxonomic: species richness and abundance; functional: functional richness and Rao's quadratic entropy) in the inter-rows than in the rows for the production mode, all the groups combining practices except the one with tillage management only and the linear model. These results emphasize that the higher diversity in the inter-rows comes from a difference of management between the areas. The weed communities managed by tillage in the rows and the inter-rows do not show a difference of diversity between both areas. That confirms the hypothesis 2, which assumed that the different managements in the rows and in the inter-rows through time and space create difference weed communities in both areas.

In fact, the pressure of management practices is lower in the inter-rows then in the inter-rows due to the proximity of the vines (competition) [START_REF] Metay | Chapitre 15 : Gestion durable de la flore adventice en culture pérenne : enjeux et stratégies en viticulture[END_REF]. The traits were also different between the rows and the inter-rows, with in the inter-rows hemicryptophytes, late flowering onset, anemogamous and animal dispersal; and in the rows favor therophytes, high SLA, long flowering duration, auto-apogamous, facultative entomogamous and Ellenberg's indicator for trophic level. This difference occurs between organic and conventional production mode, the group 3 representing mowing in the inter-rows and herbicides in the rows and in the linear model. The mowing in the inter-rows has less impact on diversity whereas herbicides or tillage management had an important impact on diversity and the same conclusion hold true for the traits. Same results were found in [START_REF] Saatkamp | Decrease of plant alpha and beta-diversity with management intensity in vineyards and the influence of landscape context[END_REF]; [START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF] and [START_REF] Fried | Relative importance of environmental factors and farming practices in shaping weed communities structure and composition in French vineyards[END_REF], which consider mowing as low filter and tillage/herbicides as strong filter on weed communities. Former studies as [START_REF] Maillet | Constitution et dynamique des communautés de mauvaises herbes des vignes de France et des rizières de Camargue[END_REF] showed opposite results with higher diversity under high intensity management but it was before the occurrence of systemic herbicides (glyphosate) which selected few species tolerant to them. However, the first hypothesis is also partially confirmed due to the influence of external factors impacting the weed communities. The chemical products (insecticides and fungicides) containing copper can decrease the weed communities' diversity in the rows [START_REF] Brun | Effects of elevated soil copper on phenology, growth and reproduction of five ruderal plant species[END_REF]. The microclimate of the rows can be different to the inter-rows due to the shape of the vines on the rows [START_REF] Shrestha | Grapevine row orientation affects light environment, growth, and development of black nightshade (Solanum nigrum)[END_REF], the rain intensity [START_REF] Dabney | Using winter cover crops to improve soil and water quality[END_REF] and the size between the rows [START_REF] Agreste | [END_REF].

As [START_REF] Fried | Relative importance of environmental factors and farming practices in shaping weed communities structure and composition in French vineyards[END_REF], our results revealed that it is possible to consider the rows and the inter-rows as different habitat or ecosystems (Millennium ecosystem, 2005) as "a dynamic complex of plant, animal and microorganism communities and their non-living environment interacting as a functional unit" at the field level because of the different management practices applied on them, allowing different response traits and diversity (taxonomic and functional) at the vineyard scale.

III. Management practices and environmental variables acting as filters on biological type, dispersal and pollination modes and trophic level

The third question was to know if the management practices act as filters on weed communities and which are the main traits responding to different gradients. As seen in the previous paragraph, the difference of practices between the rows and the inter-rows provokes differences between weed communities. First, the management practices act as filters on diversity whether taxonomic and functional. In fact, the decrease of taxonomic diversity is often followed by a decrease of functional diversity, i.e. less species equal less different traits [START_REF] Fried | Local and regional changes in taxonomic and functional diversity of arable weed communities in Burgundy (France) between 1970s and the 2000s[END_REF]. The first part of the first hypothesis is confirmed. In fact, the organic manure and the number of mechanical passages which can be associated to organic production mode, enhance the diversity, whereas the weeding practices (tillage in IR, mowing in R, number of herbicide treatments), the proportion of sand and vineyard percentages at the landscape level (100m radius) decrease it.

The management practices as herbicides and tillage represent high disturbance and according to [START_REF] Saatkamp | Decrease of plant alpha and beta-diversity with management intensity in vineyards and the influence of landscape context[END_REF], high disturbed areas decrease the number of species because only the specialized ones can maintain their population. Sandy soil is considered as a poor soil and increases diversity in some studies [START_REF] Fried | Chapitre 2 : Diversité et réponses de la flore des champs cultivés à l'évolution des pratiques agricoles en France[END_REF] but not here. The negative correlation between diversity and sandy soil can come from the correlation between tillage and sand percentage and the bigger influence of management practices than soil properties on diversity. The landscape also decreases the diversity with the homogeneity of landscape around the plot. The same result has been found in [START_REF] Roschewitz | The effect of landscape complexity on arable weed species diversity in organic and conventional farming[END_REF] where homogeneous landscapes provide less species to fit with areas than heterogenous ones.

In the IR, the tillage management has a lower diversity and, in the rows, it is the herbicides management. Moreover, the diversity is higher in the winter sampling period (March/April), which correspond to the period before the vineyards' works and high rate of rainfall and favorable conditions of soil moisture and temperature, so the spring period has the highest diversity [START_REF] Baumgartner | Cover-crop systems affect weed communities in a California vineyard[END_REF].

Secondly, RLQfourth corner, linear models, conventional versus organic production mode and groups analyses showed a positive correlation between the hemicryptophytes and the mowing management in the R and in the IR on one hand and a positive correlation between the tillage and the therophytes species. Those results are associated to the tillage management, which is absent when herbicide treatments are applied. [START_REF] Froud-Williams | Influence of cultivation regime on weed floras of arable cropping systems[END_REF][START_REF] Trichard | Identification of weed community traits response to conservation agriculture[END_REF] and [START_REF] Fried | Apports des approches fonctionnelles pour l'évaluation des risques associés aux changements de végétation induits par les activités humaines, Mémoire pour l'obtention de l'habilitation à diriger des recherches[END_REF] observe the same correlations. Moreover, as [START_REF] Lososovà | Seasonal dynamics and diversity of weed vegetation in tilled and mulched vineyards[END_REF], [START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF][START_REF] Maclaren | Management practices influence the competitive potential of weed communities and their value to biodiversity in South African vineyards[END_REF], the gradient of disturbance also shapes the weeds communities. The herbicides and tillage management can be considered as strong filters, selecting ruderal species (high SLA, low height and low seed lass) [START_REF] Garnier | Plant functional diversity organism traits, community structure and ecosystem properties[END_REF], whereas the low disturbed management (mowing) acts as weak filter favoring hemicryptophytes, low SLA, high maximum height and high seed mass. The correlation between biological type and gradient of disturbance is also described in [START_REF] Porcova | The effect of the different vineyards management to composition of weed species[END_REF]. [START_REF] Lososovà | Seasonal dynamics and diversity of weed vegetation in tilled and mulched vineyards[END_REF] study explained that by an effect of a transition from intensive agricultural management with repeated tillage and massive use of herbicides to soft management which takes care of environment with mowing and mulching. In vineyards with high level of herbicides treatment and high number of mechanical passages, weed communities contain more species with an early flowering onset and a long flowering duration. According to [START_REF] Gaba | Agroecological weed control using a functional approach: a review of cropping systems diversity[END_REF] and [START_REF] Garnier | Plant functional diversity organism traits, community structure and ecosystem properties[END_REF], these trait values allows a bypass by the species to the treatments, with a growing before the herbicides' application [START_REF] Meiss | Perennial Lucerne affects weed community trajectories in grain crop rotations[END_REF].

The high disturbed areas also favor auto-apogamous, baro-autochory dispersal or facultative entomogamous, at the opposite, the low disturbed areas favor anemogamous, compulsory entomogamous and animal dispersal. The high disturbance involves other treatments as pesticides which can decrease the pollinators population and impact the weed communities and their reproduction [START_REF] Fried | Relative importance of environmental factors and farming practices in shaping weed communities structure and composition in French vineyards[END_REF]. The negative correlation between chemical fertilization and compulsory entomogamous/animal dispersal confirms this hypothesis.

There is no impact of the fertilization on the trophic level (Ellenberg N) whether by the organic or the chemical one. This Ellenberg index can be interpreted as the growth of the plant thanks to the soil nitrogen availability [START_REF] Moreau | A plant nitrophily index based on plant leaf area response to soil nitrogen availability[END_REF]. At the opposite of what we expected, a positive correlation between Ellenberg_N and fertilization which represent species response to soil enrichment [START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF], this correlation does not appear here.

Another combination has been highlighted by the RLQ -4 th corner analyses, with the positive correlation between the pH and the seed mass. The pH represents the rich soil with CaCO3 content and high pH. In Germany [START_REF] Tautehahn | On the biogeography of seed mass in Germanydistribution patterns and environmental correlates[END_REF] and in Fried et al. (2018) the same results have been found. The hypothesis proposed by [START_REF] Tautehahn | On the biogeography of seed mass in Germanydistribution patterns and environmental correlates[END_REF] is that plant respond to competition for resources by bigger seeds to ensure the survival of population on alkaline soils. In fact, the competition on alkaline soil is high because the fertility is higher thus the density of vegetation is higher. Besides, [START_REF] Ellenberg | Vegetation Mitteleuropas mit den Alpen in ökologischer, dynamischer und historischer Sicht[END_REF] showed that the calcareous species are mainly competitors and this hypothesis was confirmed after a comparison between the seed mass of ruderal and competitors [START_REF] Tautehahn | On the biogeography of seed mass in Germanydistribution patterns and environmental correlates[END_REF].

Some results in the study can be difficult to explain because of indirect effects. For example, the number of mechanical passages reflect the compaction of the soil, but also be synonym of the higher traffic in organic farming [START_REF] Mezière | Ecophyto R&D, vers des systèmes de cultures économes en produits sanitaires. Tome III, analyses comparatives de différents systèmes en viticulture[END_REF], so, it can be understood that the number of mechanical passages has a positive impact on diversity.

Thirdly, the CWV of traits for the three approaches concerning the influence of management practices are lower under management practices (herbicides, tillage in the inter-rows, mowing in the rows), soil properties (sand percentage), vineyard percentage and under the rows. The scaled estimate of linear model showed also a lowest impact for the mowing that for the tillage of herbicide management.

That means the high disturbed areas present a convergence of traits whereas the low disturbed areas present a divergence of traits. The same results have been found in [START_REF] Grime | Trait convergence and trait divergence in herbaceous plant communities: mechanisms and consequences[END_REF] and [START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF]. That confirms that practices act on communities as filters reducing the range of possible trait values.

IV. What level of details in the description of farming practices is needed to explore the weeds' response?

The last question was about the best methodological point of view to adopt for analyzing the influence of management practices. Our results showed that the three approaches give more and more accurate information from production mode to raw variables of environment and management practices.

So, the first hypothesis where the three approaches are complementary is partially confirmed, because different significant result appear in the three levels of method. However, combining all those results information is not easy. First, the even if the production mode results are significant, the causes of the results are confused. [START_REF] Bruggisser | Effects of vineyard management on biodiversity at three trophic levels[END_REF] and [START_REF] Nascimbene | Organic farming benefits local plant diversity in vineyard farms located in intensive agricultural landscape[END_REF] showed that the impact of the production mode does not come from the management practices, so maybe it is the same here.

Moreover, it exists different degrees of organic management with either tillage or mowing as the dominant vegetation management practices and different intensity of conventional management depending on the types of chemical products and the quantity applied on the field [START_REF] Gaba | Agroecological weed control using a functional approach: a review of cropping systems diversity[END_REF].

Even [START_REF] Maclaren | Management practices influence the competitive potential of weed communities and their value to biodiversity in South African vineyards[END_REF] took three level of production mode with organic, low input and high input, which are more representative. It is still interesting to see each step, one after another and to discover which a trait is linked to a practice, to a group and to a production mode. At the opposite, the raw variables are very accurate and explain each variable and their impact and allow comparing the level of disturbance between the practices. However, the linear models emphasize too many results and they are not always easy to describe and to interpret. The RLQ and fourth corner analyses are a good alternative to the linear model and return also combination which are significant and helpful to describe the practices impacts and the combinations of traits. We are never sure that the chosen practices are all relevant. The analysis by groups of similar management practices seem a good trade-off between both because the quantity of results is informative but not too detailed, it regroups the global strategy (IR/R) and it is more meaningful for the winegrowers.

V. Perspectives

The objectives of the thesis of Lorelei Cazeneuve (2017) were to know accurately the composition and the ecological characteristics of the viticultural flora, to determine the main filters allowing the present or not of the flora and to highlight the "sustainable" practices of the vineyards soil applied on the flora.

The data of traits comes from BIOFLOR [START_REF] Klotz | BIOLFLOR -Eine Datenbank zu biologisch-ökologischen Merkmalen der Gefäβpflanzen in Deutschland[END_REF], baseflor [START_REF] Julve | Baseflor: Index botanique, écologique et chronologique de la flore de France[END_REF], LEDA [START_REF] Kleyer | The LEDA Traitbase: A database of life-history traits of North West European flora[END_REF], SID (Royal botanic garden, 2019) and Flora Gallica [START_REF] Tison | Flora Gallica, flore de France[END_REF]. The next step will be to measure the height, the seed mass, the SLA and other relevant traits on the field, instead to take the average trait values from traits databases. A strict protocol can be applied with a samples' collect describing in the handbook of [START_REF] Pérez-Harguindeguy | New handbook for standardised measurement of plant functional traits worldwide[END_REF], which indicate the number of replicated needed and describe the sampling protocol and the way to measure the traits.

Moreover, some the biological traits can be added to the study to obtain information about the practices into the soil. For that, the volume and size of roots can be relevant to understand the effects of the intensity and the depth of soil tillage [START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF], or to understand how some plants escape to herbicides use. To end, the number of seeds can also inform on the tillage timing [START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF] and being compared to the Grime's strategies [START_REF] Grime | Plant strategies and vegetation processes[END_REF]. Another parameter which can be added to the study could have been the yield, which is an important element for the winegrower. However, two years are needed to get this data and the correlation between the weeds communities and the yield are not always clear because of some indirect effect. [START_REF] Kazakou | A plant trait-based response-and-effect framework to assess vineyard inter-row soil management[END_REF] tried to demonstrate the difference of yield between mowing and tillage but the results were the same for both and a better characterization of the resources is preconized.

Conclusion

The weeds present in the Bordeaux vineyards respond to both management practices and the environment along a gradient of disturbance, competition and stress following Grime's strategies and combination of traits. The species in the inter-rows have a high maximum height at maturity, high seed mass, late flowering onset, short life cycle, low SLA and thus are often classified as competitor species.

We emphasize that the practices (herbicides, mowing, tillage and fertilization) are important filter for selecting these strategies. The disturbance gradient exists and is important. The biological type is the first impacted trait, with therophytes in highly disturbed fields (herbicides, tillage) and hemicryptophytes in less disturbed fields (mowing). In highly disturbed fields, the traits converge with low height, high SLA, high flowering duration, early flowering onset and auto-apogamous pollination.

In low disturbed fields, the traits diverge with short life cycle, low SLA, high maximum height, late flowering onset and anemogamous pollination. The environmental conditions are also responsible of traits selection such as sand percentage or the temperature, but always in combination with management practices. It is interesting to notice that there is a stronger relationship between combination of environmental variables and combination of traits, rather than between individual traits and individual environmental variable. When different practices are applied in the rows and in the inter-rows, the traits of the community differ, and as a consequence, the two areas (row/inter-rows) can be considered as two distinct habitats. The three approaches applied in this study put lights on different complementary results. However, the groups method (clustering fields according to their practices) and the RLQfourth corner analyses seem to be the easiest way to answer the wine growers' questions with relevant results.

The 2 nd group (in red) contained 28 fields, 14 organic and 14 conventional fields. The group 2 was positively correlated with the number of tillage in the rows (v_test = 3.97, p_value = 7.28e-05) and negatively with the number of tillage in the inter-rows (v_test = -2.26, p_value = 2.34e-02), with both organic (v_test = -2.66, p_value = 7.91e-03) and chemical fertilization (v_test = -5.74, p_value = 9.61e-09) and with the number of herbicides treatments (v_test = -4.37, p_value = 1.24e-05) (Figure 6). Table 15 also indicated that this group had the highest mean for the organic fertilization and a high mean for the mowing in the inter-rows.

The 3 rd group (in green) contained 44 fields with 6 organic fields and 38 conventional fields, it's the biggest group. This group was positively correlated with the number of herbicides treatments(v_test = 8.63, p_value = 6.07e-18), the number of chemical fertilization (v-test = 4.57, p_value = 4.79e-06) and the number of mowings in the inter-rows (v_test = 2.66, p_value = 7.75e-03). The group 3 was negatively correlated with the number of mechanical passages (v_test = -2.14, p_value = 3.20e-02), the number of tillage in the rows (v_test = -6.72, p_value = 1.82e-11) and in the inter-rows (v-test = -2.20, p_value = 2.78e-02) and the number of mowings in the rows (v-test = -3.65, p_value = 2.62e-04) (Figure 6). Table 15 confirmed the high means of group 3 for the number of herbicides treatments, the number of mowings in the inter-rows and the number of chemical fertilizations.

The 4 th group (in blue) contained 12 fields with 8 organic fields and 4 conventional fields. This group was positively correlated with tillage in both the inter-rows (v_test = 7.62, p_value = 2.56e-14) and the rows (v-test = 5.40, p_value = 6.66e-08). The group 4 was negatively correlated with the number of herbicides treatments (v_test = -3.09, p_value = 1.99e-03) and mowing in the inter-rows (v_test = -6.19e-10) (Figure 6). Table 15 confirmed the high means of group 4 for the number of tillage in the rows and in the inter-rows and additionally highlighted the high number of mechanical passages. The F_value, the estimate and the significance concerning the habitat, the production mode and the interaction between both for the CWV values. The points (.) indicated a p_value above 0.05, the stars indicated a p_value between 0.05 and 0.01 with a star (*), a p_value between 0.01 and 0.001 with two stars (**) and a p_value under 0.001 with three stars (***). The adjusted square-root (R²) indicates the contribution of area, production mode and their interaction of each trait. Figure 14: Boxplots of CWV of traits with the areas in blue for the inter-rows, in red for the rows, in yellow for conventional and in green for organic production mode.

Appendix VI: Influence of groups of practices on the CWM and CWV of traits

The CWM of hemicryptophytes (tussock and rosettes) and anemogamous were higher in the interrows than in the rows (Table 18). The CWM of hemicryptophytes with stolons, flowering onset, facultative entomogamous and animal dispersal were higher in the inter-rows than in the rows for the group 3 whereas the CWM of therophytes, SLA, flowering duration and Ellenberg's indicator for trophic level were higher in the rows than in the inter-rows for the group 3 (Appendix VII). The CWM of tussock hemicryptophytes was higher in the inter-rows than in the rows for the four groups. The CWM of hemicryptophytes were higher in the inter-rows than in the rows for the group 1 and 3. The CWM of anemogamous were higher in the inter-rows than in the rows for the groups 2 and 3.

For the inter-rows, the CWM of hemicryptophytes (tussock, rosettes, stolons) and animal dispersal were higher in the groups 1,2 and 3 than in the group 4, whereas the CWM of therophytes and facultative entomogamous were higher in the group 4 than in the other groups. The CWM of seed mass was higher in the groups 2 and 3 more than in the group 4. The CWM of compulsory entomogamous was higher in the group 2 than in the group 4. The CWM of wind dispersal was higher in the group 1 than in the group 3 and was higher in the group 4 than in the groups 2 and 3.

For the rows, the CWM of hemicryptophytes with stolons was higher in the groups 1 and 2 than in the groups 3 and 4, whereas the CWM of therophytes was higher in the groups 3 and 4 than in the groups 1 and 2. The CWM of maximum height was higher in the group 2 than in the group 3, whereas the CWM of baro-autochory was higher in the group 3 than in the group 2. The CWM of flowering duration was higher in the group 3 than in the other groups. The CWM of tussock hemicryptophytes was higher in the groups 2 and 3 than in the group 4. The CWM of compulsory entomogamous was higher in the group 2 than in the groups 3 and 4.

The CWV of all traits were higher in the inter-rows than in the rows for the groups 1, 2 and 3, except for the CWV of seed mass, which was higher in the inter-rows in the four groups (Table 19). In the inter-rows, the CWV of SLA, flowering onset, flowering duration and Ellenberg's indicator for trophic level were higher in the groups 1, 2 and 3 than in the group 4. In the rows, the CWV of SLA, seed mass, flowering onset, Ellenberg's indicator for trophic level were higher in the groups 1 and 2 than in the group 3. The CWM of tussock hemicryptophytes, therophytes, SLA, flowering duration, auto-apogamous, anima dispersal, wind dispersal and Ellenberg's indicator for trophic level were higher during the winter period, whereas the CWM of hemicryptophytes with stolons, maximum height, flowering onset, anemogamous and baro-autochory were higher during the summer period. The CWM of hemicryptophytes (tussock, rosettes, stolons), flowering onset, anemogamous and animal dispersal were higher in the IR, whereas the CWM of therophytes, SLA, flowering duration, auto-apogamous, facultative entomogamous, baro-autochory and Ellenberg's indicator for trophic level were higher in the R (Table 13).

The sand percentage and the organic matter were positively correlated with the CWM of therophytes, facultative entomogamous and Ellenberg's indicator for trophic level and negatively with hemicryptophytes, flowering onset, auto-apogamous and wind dispersal. The pH was positively correlated with the CWM of maximum height, seed mass, facultative entomogamous, baro-autochory and Ellenberg's indicator for trophic level and negatively with the CWM of hemicryptophytes, autoapogamous and wind dispersal. The C/N ratio was positively correlated with hemicryptophytes (all) and negatively with therophytes, height and Ellenberg's indicator for trophic level (Table 13).

The organic manure was positively correlated with CWM of hemicryptophytes, therophytes, seed 13).

The rainfall was positively correlated with CWM of maximum height and wind dispersal and negatively with baro-autochory.

The CWV of all traits showed similar results with higher CWV during winter period (except for the CWV of flowering onset which was higher during the summer period) and higher CWV in the IR than in the R. The CWV of the traits were positively correlated with organic matter content (except for seed mass), organic manure (except for seed mass and flowering onset) and number of mechanical passages (except for flowering duration).

Moreover, the CWV of seed mass was positively correlated with C/N ratio and mowing in the IR.

The CWV of traits were negatively correlated with vineyard and sand percentages (except for the CWV of SLA and maximum height), number of herbicide treatments, tillage in the IR (except for the CWV of maximum height and seed mass) and mowing in the R (except for the CWV of seed mass) (Table 14). Abstract: The aim of this study was to understand the impact of viticultural practices on weed flora using a trait-based approach, to help winegrowers managing the weeds in an environmentally friendly way without economic losses. The study takes place in 101 Bordeaux vineyards plots, where floristic samples have been made twice (April, June 2015) in two distinct areas (rows, inter-rows). Statistical analyses have been performed to test the relationships between species trait values, environmental variables (weather, soil properties, landscape) and management practices, using linear models, and multivariate analyses (RLQfourth corner analyses). Three approaches have been developed at different levels of management practices (using either organic versus conventional, homogeneous groups of plots with similar management systems based on a multivariate classification of raw variables, or directly the raw variables). First, the results showed a difference in the Grime CSR strategies along a gradient of disturbance. In disturbed areas (on the rows and where herbicides and/or tillage was applied), a convergence of trait values of weeds was observed with a ruderal strategy, characterized by an annual life cycle, an auto-apogamous reproduction and a baro-autochory dispersal. In less disturbed areas (on the inter-rows and were mowing was applied), the range of trait values of weeds was higher and they harbor a competitive strategy characterized by a pluri-annual life cycle (hemicryptophytes), an entomogamous pollination and an animal dispersal. To a lesser extent, an influence of the resource gradient was also highlighted with more competitive species (higher seed mass and stature) found on richer soils (higher soil pH). The clustering of groups with similar management practices appeared as a good compromise to understand patterns and to communicate towards winegrowers.

Key Words: Bordeaux vineyards, Vitis vinifera, management practices, tillage, mowing, herbicide treatment, taxonomic and functional diversity, trait-based approach.

Résumé : Le but de cette étude était de comprendre l'impact des pratiques viticoles sur la flore adventice en utilisant l'approche fonctionnelle des traits, afin d'aider les viticulteurs à gérer les adventices tout en respectant l'environnement et sans pertes économiques. L'étude se déroule à Bordeaux sur 101 parcelles où des relevés floristiques ont été effectués deux fois (Avril, Juin 2015) dans deux zones (rangs, inter-rangs). Les analyses statistiques effectuées testent les relations entre les traits des espèces, les variables environnementales (météo, sol, paysage) et les pratiques agricoles, utilisant des modèles linéaires et des analyses multivariées (RLQ -4 th corner). Trois approches ont été développées à différents niveaux de pratiques (une gestion soit biologique, soit conventionnelle, des groupes homogènes de parcelles basés sur la classification des pratiques, ou à partir des pratiques brutes). Premièrement, les résultats ont montré une différence de stratégies de Grime CSR suivant un gradient de perturbation. Dans les zones très perturbées (le rang et les zones d'application du labour ou des herbicides), une convergence des traits a été observée avec une stratégie rudérale caractérisée par un cycle de vie annuel, une reproduction auto-apogame et une dispersion baro-autochore. Dans les zones peu perturbées (inter-rangs, zones d'application de la tonte), la gamme de traits était plus importante et adoptait une stratégie compétitrice caractérisée par un cycle de vie pluriannuel (hémicryptophytes), une pollinisation entomogame et une dispersion zoochore. De manière moins marquée, une influence du gradient de ressources a été mis en avant avec des espèces très compétitrices (importante masse de graines et hauteur) trouvées sur des sols riches (pH plus élevé). La mise en place de groupes ayant les mêmes pratiques apparaitrait être un bon compromis pour comprendre les modèles et communiquer avec les viticulteurs.

Mots clés : vignobles bordelais, Vitis vinifera, pratiques viticoles, labour, tonte, herbicides, diversités taxonomique et fonctionnelle, approche fonctionnelle des traits.

FiguresFigure 1 :

 1 FiguresFigure1:Location of the 101 plots of vineyards[START_REF] Cazenave | Flore adventice des vignes en Gironde[END_REF]

Figure 4 :

 4 Figure 4: Grime strategies as supplementary variables on the Hill & Smith (a), C: Competitor, S: Stresstolerant and R: Ruderal, and the significance of the distribution along the 1 st axis (b) and the 2 nd axis (c).

Figure 5 :

 5 Figure5: Diversity indices: i.e. the species richness (a,b), the abundance (c,d), the functional richness (e,f) and the Rao's quadratic entropy (g,h), according to the area (inter-rows in blue, rows in red), the mode of production called here certification (conventional in orange and organic in green). The horizontal bar of the boxplot represents the median, 50% of the values were located from the 1 st quartile to the 3 rd quartile (interquartile range, IQR). Blacks dots are values higher or lower than 1.5 the IQR.

Figure 1 :

 1 Figure 1: Location of the 101 plots of vineyards[START_REF] Cazenave | Flore adventice des vignes en Gironde[END_REF]

Figure 3 :

 3 Figure 3: Hill & Smith analysis displayed with the quantitative variables in black marked out by an arrow, the qualitative variables in blue and the supplementary variable: Fidelity Index in the interrows in red marked out by an arrow.

Figure 4 :

 4 Figure 4: Grime strategies as supplementary variables on the Hill & Smith analysis (a), C: Competitor, S: Stress-tolerant and R: Ruderal, and the significance of the distribution along the 1 st axis (b) and the 2 nd axis (c) of the multivariate analysis

Figure 4

 4 Figure 4.b), the first axis opposed the ruderal and the competitor strategies (C, CR and R) to the stresstolerant strategies. The second axis clearly opposed the ruderal strategy to the competitor strategy (Figure 4.c)). Linking the strategies to the traits (Figure 3), the ruderal strategy was correlated with low

Figure 5 :

 5 Figure 5: Diversity's indices, i.e. the species richness (a,b), the abundance (c,d), the functional richness(e,f) and the Rao's quadratic entropy (g,h), according to the area (inter-rows in blue, rows in red), the mode of production called here certification (conventional in orange and organic in green). The horizontal bar of the boxplot represents the median, 50% of the values were located from the 1 st quartile to the 3 rd quartile (interquartile range, IQR). Blacks dots are values higher or lower than 1.5 the IQR.

Table 11 :Figure 6 :

 116 Figure 6: Boxplots of the repartition of the four groups depending on the diversity's indices: Species richness (a), Abundance (b), Functional richness (c) and Rao's quadratic entropy(d). The interrows were represented in dark grey and the rows in pale grey, the group 1 is in yellow, the group 2 in red, the group 3 in green and the group 4 in blue.

Figure 7 :Figure 8 :

 78 Figure 7: Results of the global RLQ (101 fields) for the traits (on the left) and the species (on the right)

Figure 9 :

 9 Figure 9: Fourth corner analysis of the global dataset (101 fields) on the left and adjusted fourth corner analyses of the three datasets (global, rows, inter-rows) on the right. The red cells represented the positive associations and the blue cells represented the negative associations. The non-significant cells were represented in grey. The upper left triangles represent the inter-rows significant interaction, the bottom right triangles the rows and the empty squares the global interaction.

Figure

  Figure 10: Results of the combination of the RLQ and the fourth-corner analyses for the global (a), the rows (b) and the inter-rows (c) analyses. On the left of each table, the fourth-corner tests between the two axes of the RLQ for the environmental gradients and the practices (AxcR1/AxcR2) and traits. On the right of each table, fourth-corner tests between the two axes of RLQ for traits (AxcQ1/AxcQ2) and the environmental variables and the practices.

Figure 13 :

 13 Figure 13: Boxplots of CWM of traits with the areas in blue for the inter-rows, in red for the rows, in yellow for conventional and in green for organic production mode.

  mass, auto-apogamous and animal dispersal and negatively with CWM of flowering onset. The chemical fertilization was negatively correlated with CWM of compulsory entomogamous and animal dispersal. The number of herbicide treatments was positively correlated with CWM of tussock hemicryptophytes, flowering duration, auto-apogamous and Ellenberg's indicator for trophic level and negatively with CWM of height, flowering onset and anemogamous. Soil tillage in the inter-rows was positively correlated with CWM of therophytes, SLA, auto-apogamous, facultative entomogamous and baroautochory and negatively with CWM of hemicryptophytes, maximum height, anemogamous, compulsory entomogamous and animal dispersal. Mowing in the rows was positively correlated with CWM of hemicryptophytes and anemogamous and negatively with CWM of therophytes, flowering duration and baro-autochory. There was no correlation between the mowing in the inter-rows and the traits. The number of mechanical passages was positively correlated with CWM of therophytes, flowering duration, auto-apogamous and Ellenberg's indicator for trophic level and negatively with CWM of hemicryptophytes, flowering onset, anemogamous and compulsory entomogamous (Table

  accueil : LSV -Laboratoire de Santé des Plantes -ANSES 755 Avenue du Campus Agropolis CS 30016 34988 Montferrier-sur-lez Cedex Maîtres de stage : Guillaume Fried, Stéphane Cordeau Titre français : Les traits de réponse de la flore adventices permettent-ils de comprendre l'impact des pratiques agricoles dans les vignobles ? Titre anglais: Can the traits help us understanding the response of weed flora to viticultural practices?

Table 3 :

 3 Description of climate variables

Table 4 :

 4 Description of the soil properties

Table 5 :

 5 Description of the selected landscape variable

Table 6 :

 6 Description of the agricultural practices

Table 7 :

 7 Description of qualitative traits

Table 8 :

 8 Description of quantitative traits and ecological performance indicatorsTable

Table 1 :

 1 Evaluation table of frequency inspired by[START_REF] Theau | Une méthode simplifiée de relevé botanique pour une caractérisation agronomique des prairies permanentes[END_REF]

	Table 2: Evaluation table of abundance according to Braun-Blanquet (1934)
	Class		Covering	Mean density (D2)
	1	Species covering 1% to 5%	0.025
	2	Species covering 5% to 25%	0.15
	3	Species covering 25% to 50%	0.375
	4	Species covering 50% to 75%	0.625
	5	Species covering 75% to 100%	0.875
			Table 3: Description of climate variables
	Climatic variables	Definition	Mean -sd	Min -Max
	Rainfall (mm/year)			674.50 ± 46.32	593.68 -848.52
	Temperature average (°C/year)	Average on the year 2015	12.45 ± 1.23	10.32 -14.10
	Evapotranspiration (mm/day)		2.67 ± 0.08	2.47 -2.89

Table 4 :

 4 Description of the soil properties

	Abbreviations Definition	Unit	Mean -sd	Min -Max
	Pl_A	Clay percentage	%	19.04 ± 10.79	2.7 -54.9
	Pl_L	Silt percentage	%	33.35 ± 17.37	5.1 -70.7
	Pl_S	Sand percentage	%	47.81 ± 23.51	3.0 -87.2
	Pl_CaCO3	Calcareous content in the soil	g/kg of soil	34.15 ± 71.60	0.0 -271.0
	Pl_MO	Organic matter content in the soil	g/kg of soil	1.82 ± 0.81	0.4 -4.58
	PD_CN	Carbon/Nitrogen ratio	No unit	10.28 ± 2.57	4.0 -17.4
	PD_PH_EAU	pH measured with water	No unit	7.49 ± 0.99	5.3 -12.7
	PD_PH_KCl	pH measured with KCl	No unit	6.33 ± 0.91	4.1 -7.9
	PD_CEC	Cation exchange capacity	mol/kg of soil	9.13 ± 4.85	2.8 -24.8
	PD_P2O5	P2O5 content	g/kg	0.095 ± 0.090	0.01 -0.451
	PD_K20	K20 content	g/kg	4.01 ± 37.76	0.043 -374
	PD_MgO	MgO content	g/kg	0.24 ± 0.16	0.042 -0.82

Table 5 :

 5 Description of the selected landscape variables

	Abbreviations Definition	Unit	Mean -Sd	Min -Max
	Surf	Surface of the plot	Square meter (m²)	7776 ± 4905	784.1 -24580
	Slope	Angle of the slope	degree (°)	2.61 ± 1.75	0.307 -9.07
	V_20 m	Vineyards 20 m radius	%	50.97 ± 17.06	5.45 -84.33
	V_100 m	Vineyards 100 m radius	%	77.26 ± 17.32	29.42 -100

Table 6 :

 6 Description of the agricultural practices

Table 7 :

 7 Description of qualitative traits

	Abbreviations		Definition	Unit			Mean -Sd Min -Max
	feng	Chemical fertilization	Number of fertilizations over the last 5 years	2.13 ± 0.16	0 -5
				(scale from 0 to 5)
		Organic soil improver, return organic	Number of fertilizations over the 5 last years	6.46 ± 2.41	0 -13
	famo	matter from the vine shoot and green	(Famo = Fam + Rmo, Fam: scale from 0 to 5
		manure.		for the organic soil improver and Rmo: scale 0
				to 10 for the vine shoot and green manure
				restitution)
		Herbicides which can be selective or not.				0.88 ± 1.05	0 -3
	nherb	They were applied on the rows only but	Number of herbicides passage per year
		can also have an impact on the inter-rows.
	wir	Tillage in the inter-rows	Number of passages per year x the number of	1.77 ± 1.75	0 -8
				inter-rows of the plot/2
	wsr	Tillage in the rows	Number of passages per year		2.21 ± 2.36	0 -8
	ntont_ir	Mowing in the inter-rows	Number of mowing per year x the number of	3.50 ± 1.89	0 -7
				inter-rows/2
	ntont_sr	Mowing in the rows	Number of mowing per year		0.62 ± 1.40	0 -5
	npm	Number of mechanical passages	Number of passages per year		26.7 ± 5.42	15 -40
	Biological type	Meaning	Number		Justification
	T		Therophytes		98	Biological types reflect the variation
	Hsto		Hemicryptophytes with stolon	21	of responses to stress and
	Heri		Erected hemicryptophytes	18	disturbances,	established	by
	Hros Hces Gbul Grhi Bis		Hemicryptophytes with rosettes Tussock hemicryptophytes Geophytes with bulbs Geophytes with rhizomes Biannual	16 9 12 11 9	Raunkiaer in 1934. The therophytes die after the seed production, the geophytes had an organ into the ground and the hemicryptophytes lose the aerial part (Navas and Garnier, 2013).
	W		Woody plants		6
	Pollination mode	Meaning	Number		Justification
	Anemogamous		Pollination by wind		42	Capacity of the plants to reproduce
	Auto-apogamous		Self and asexual pollination	23	alone (auto-apogamous), with
	Optional entomogamous	Different ways of pollination including insects	69	abiotic factors (wind) or thanks to
	Compulsory entomogamous	Pollination with insects only	66	biotic factors (insects). For this last case, the hypothesis can be that the
						entomogamous plants were more
						sensitive to the decrease of
						pollinators then to intensive
						management practices. It is also the
						only traits which give links with
						others organisms.
	Dispersal	Meaning	Number		Justification
	Animals		Eaten or attached on animals	72	Colonisation capacity and relation
						with the seed mass and the
						reproductive height

Table 8 :

 8 Description of quantitative traits and ecological performance indicators (Units given by[START_REF] Bourgeois | What makes a weed a weed? A large scale evaluation of arable weeds through a functional lens[END_REF]

	Traits	Definition	Unit	Mean ± Sd	Min -Max	Justification
	SLA_final	Ratio between the leaf		25.49 ± 7.21	5.55 -53.24	Responds to competition for light, to
		area and the leaf mass	m².kg -1			nitrogen inputs and indicates the
						investment in plant function (PS)
						(Gaba et al., 2017).
	Max_height_FG Maximum height at	m	0.91 ± 2.11	0.1 -30	Responds to dispersal mode, to
		maturity				competition for light, to nitrogen
						input and to insect pollination (Gaba
						et al., 2017).
	Seed mass final Seed mass	g	3.04 ± 10.18	0.0095 -120 Reflects the competitive ability of
						seedlings.	Responds	to	seed
						predation, dispersal distance and
						emergence depth (Gaba et al., 2017).
	Flowon		month	4.20 ± 1.57	1 -8	Responds to timing of disturbance
		Month of flowering				events (harvest and agricultural
		onset				practices) and avoid competition for
						light by early flowering onset.
	Flowdur		month	5.13 ± 2.23	2 -12
		Number of months for
		flowering duration

Table 15 :

 15 Means of each practice per group (numbers in bold are significant) The practices' names are described in the material and method part I.3 Agricultural practices

	moy	Feng	Famo	Nherb Wir	Wsr	Ntont_ir Ntont_sr Npm
	1	2.24	8.41	0.06	0.33	1.94	4.76	2.76	30
	2	0.21	5.43	0.14	1.16	3.71	3.18	0.5	25.86
	3	3.20	6.23	1.91	1.05	0.41	4.05	0.05	25.39
	4	2.50	6.92	0	5.67	5.67	0.50	0	28.83

.9/0.1 * Hemicryptophytes with stolons 0.07 29.7/-0.1 *** 6.6/-0.06 * 5.9/0.1 * Therophytes 0.11 44.2/0.2 *** 11.2/0.1 ** 6.2/-0.1 * SLA 0.03 13.6/0.7 ***

				0.4/0.1	0.6/-0.3
	Height	0.02	0.9/-0.02	2.6/0.1	1.04/0.04
	Seed mass	<0.001	0.1/-0.02	0.8/0.1	<0.001/-0.001
	Flowering onset	0.02	10.0/-0.3 **	0.1/-0.04	1.5/0.2
	Flowering duration 0.08	25.6/0.1 ***	0.5/-0.02	2.6/-0.05
	Anemogamous	0.13	58.4/-0.1 ***	5.1/-0.04 *	12.4/0.1 ***
	Auto-apogamous	0.02	7.5/0.1 *	0.04/<0.001	2.9/-0.1
	Facultative	0.03	7.9/0.03 *	6.2/0.03 *	1.0/-0.02
	entomogamous
	Compulsory	-0.01	0.2/0.01	0.5/0.01	0.7/-0.02
	entomogamous
	Animal dispersal	0.03	13.1/-0.1 ***	1.5/-0.02	1.9/0.03
	Baro-autochory	0.01	2.6/0.02	0.4/-0.01	0.3/0.01
	Wind dispersal	0.1	4.9/0.03 *	4.9/0.03 *	5.0/-0.04 *
	Trophic level	0.04	16.2/0.2 ***	3.9/0.1 *	1.5/-0.1

Table 17 :

 17

Table 18 :

 18 Linear model with interaction's results of the diversity's indices. The points (.) indicated a p_value above 0.05, The stars indicated a p_value between 0.05 and 0.01 with a star (*), a p_value between 0.01 and 0.001 with two stars (**) and a p_value under 0.001 with three stars (***).

	CWM Traits	Area (rows/inter-rows)	Groups	Interaction
	Tussock	11.6 ***	7.7 ***	0.5
	hemicryptophytes
	Hemicryptophytes	9.4 **	7.7 ***	7.0 ***
	with rosettes
	Hemicryptophytes	0.4	22.1 ***	5.3 **
	with stolons
	Therophytes	3.5	13.1 ***	5.2 **
	SLA	3.6 .	0.7	0.9
	Height	0.03	1.0	0.8
	Seed mass	0.1	5.7 ***	1.3
	Flowering onset	0.3	0.1	1.1
	Flowering duration	1.4	1.8	3.0 *
	Anemogamous	4.1 *	1.4	2.1 .
	Auto-apogamous	0.2	1.0	0.9
	Facultative	2.0	4.4 **	1.6
	entomogamous
	Compulsory	0.6	3.4 *	1.2
	entomogamous
	Animal dispersal	0.4	7.7 ***	1.2
	Baro-autochory	2.7	3.5 *	0.3
	Wind dispersal	1.6	8.6 ***	2.1
	Trophic level	1.4	0.1	1.6
	Table 19: Linear model with interaction's results of the diversity's indices. The points (.)
	indicated a p_value above 0.05, the stars indicated a p_value between 0.05 and 0.01 with a star (*), a
	p_value between 0.01 and 0.001 with two stars (**) and a p_value under 0.001 with three stars (***).
	CWV traits	Area (rows/inter-rows)	Groups	Interaction
	SLA	9.8 **	6.2 ***	4.8 **
	Height	5.4 *	2.2 .	2.3 .
	Seed mass	12.3 ***	2.4 .	3.5 *
	Flowering onset	15.4 ***	8.3 ***	6.9 ***
	Flowering duration	5.0 *	5.6 ***	2.1
	Trophic level	10.4 **	6.6 ***	3.7 *

Table 13 :

 13 Linear models given the F_value and the estimate for the CWM traits values. A compartment in color is significant, green for the positive interactions and yellow for the negative ones. The point (.) indicate a p_value above 0.05, the stars indicated a p_value between 0.05 and 0.01 with one star (*), a p_value between 0.01 and 0.001 with two stars (**) and a p_value under 0.001 with three stars (***).

	Traits/Var	Date	Area R V_100	Sand MO	CN	pH	feng	famo	nherb	wir	wsr	Nton	Ntont_	npm	Rainfall
		Winter		m										t_ir	sr
	Hemi	40.0	78.3		4.5	6.2	19.0				4.1	13.3			10.1	15.8
	tussock	0.08	-0.1		-0.02	-0.02	0.03				0.01	-0.03			0.02	-0.03
		***	***		*	*	***				*	***			**	***
	Hemi		66.0		12.1	14.5	4.3	6.3		5.1	3.7	20.8
	rosettes		-0.1		-0.03	-0.03	0.02	-0.02		0.01	-0.01	-0.03
			***		***	***	*	*		*	.	***
	Hemi	6.1	38.9		52.3	6.3	9.8	7.2		2.4		40.3			9.5	6.9
	stolon	-0.03	-0.09		-0.06	-0.02	0.03	-0.02		-0.01		-0.05			0.02	-0.02
		*	***		***	*	**	**				***			**	**
	Therophyt	102.1	73.2		31.5	11.1	7.1	3.2		12.2	2.5	38.5			12.4	16.2	3.0
	es	0.1	0.1		0.05	0.03	-0.02	0.01		0.03	0.01	0.05			-0.03	0.03	-0.01
		***	***		***	***	**	.		***		***			***	***	.
	SLA	102.5	20.5						2.8			10.8	7.0
		1.4	0.6						-0.1			0.3	-0.2
		***	***						.			**	**
	Height	291.5				3.2	4.7	12.6	2.0	2.8	23.6	4.8			2.8	7.0
		-0.3				0.02	-0.02	0.03	0.01	-0.02	-0.05	-0.02			0.01	0.02
		***				.	*	***		.	***	*			.	**
	Seed mass			3.9		19.3	3.7	53.7	3.1	9.0		3.1	4.2
				0.04		0.09	-0.04	0.1	-0.04	0.06		-0.04	0.05
				*		***	.	***	.	**		.	*
	Flowering	739.7	27.2	2.8	5.7	3.4	2.0		2.7	21.3	36.3	2.6	2.8			4.1	3.2
	onset	-1.1	-0.2	-0.04	-0.06	-0.05	0.04		0.04	-0.1	-0.2	-0.05	-0.05			-0.04	0.04
		***	***	.	*	.				***	***		.			*	.
	Flowering	109.7	37.8				2.9		2.6	3.4	10.3		9.2		6.6	26.2
	duration	0.1	0.08				-0.01		-0.01	0.01	0.03		-0.03		-0.02	0.04
		***	***				.			.	**		**		*	***
	Anemogam	111.0	64.4	10.3			2.0	3.1			4.7	5.9			6.7	5.6	3.6
	ous	-0.1	-0.08	0.02			0.007	-0.009			-0.01	-0.01			0.01	-0.01	0.01
		***	***	**				.			*	*			*	*	.
	Auto_apo	195.5	7.1	6.9	7.6	12.9	3.8	5.6		11.8	36.9	5.1				4.7	2.7
	pollination	0.2	0.03	-0.02	-0.02	-0.03	0.02	-0.02		0.02	0.04	0.02				0.01	-0.01
		***	**	**	**	***	.	*		***	***	*				*
	Facultative		8.9		14.6	9.2	3.5	9.6	3.7		2.8	5.30				3.1
	entomoga		0.03		0.02	0.02	-0.01	0.02	0.01		-0.009	0.01				0.008
	mous		**		***	**	.	**	.		.	*				.
	Compulsor	3.3		3.9	5.0			2.6	4.5			7.9	3.5			4.2	2.7
	y entomo-	0.02		-0.01	-0.01			0.008	-0.01			-0.02	0.01			-0.01	-0.008
	gamous	.		*	*				*			**	.			*
	Animal	25.7	14.8			8.6			5.4	10.0		24.2
	dispersal	0.05	-0.04			0.01			-0.01	0.02		-0.03
		***	***			**			*	**		***
	Baro_auto	69.1	8.5	13.6	11.0	2.3		10.4		2.1		9.0	8.3		6.8	8.0
	chory	-0.08	0.03	0.02	0.02	0.00		0.02		-0.008		0.02	-0.02		-0.01	-0.02
		***	**	***	**	8		**				**	**		**	**
	Wind	13.0		8.2	8.4	23.9		19.9	2.8				6.4		2.7	8.0
	dispersal	0.03		-0.01	-0.01	-0.02		-0.02	0.008				0.01		0.008	0.01
		***		**	**	***		***	.				*			**
	Trophic	10.2	22.9			18.9	41.2	7.3		2.3	5.0			3.0	2.1	76.5	3.4
	level	0.1	0.2			0.08	-0.1	0.05		0.03	0.04			-0.03	-0.03	0.1	-0.03
		**	***			***	***	**			*			.		***	.

Table 14 :

 14 Linear models given the F_value and the estimate for the CWV traits values. A compartment in color is significant, green for the positive interactions and yellow for the negative ones. The point (.) indicated a p_value above 0.05, the stars indicated a p_value between 0.05 and 0.01 with one star (*), a p_value between 0.01 and 0.001 with two stars (**) and a p_value under 0.001 with three stars (***).

	Traits/Var	Date	Habita	V_100	Sand MO	CN	pH	feng	famo	nherb	wir	wsr	Ntont_	Ntont_	npm	Rainfall
		Winter	t R	m										ir	sr
	SLA	67.7	173.6	13.3	2.9	17.7				15.9	26.7	7.0	2.6		10.6	16.5
		9.2	-14.7	-2.2	-1.0	2.5				2.4	-4.7	-2.0	-1.6		-2.5	2.5
		***	***	***	.	***				***	***	**			**	***
	Height	220.1	98.0	16.9		18.8		2.0	4.1	9.6	4.3			3.8	9.0	4.6	4.2
		0.6	-0.4	-0.09		0.09		0.03	-0.05	0.08	-0.06			0.05	-0.07	0.05	-0.05
		***	***	***		***			*	**	*			.	**	*	*
	Seed mass	34.4	181.3	32.1	6.1	2.8	9.5	2.5			30.6			5.6	2.3
		0.4	-1.0	-0.2	-0.1	0.08	0.1	-0.06			-0.2			0.1	-0.06
		***	***	***	*	.	**				***			*
	Flowering	29.1	222.8	15.5	13.2	5.7	3.6			3.7	39.0	10.0	3.4		7.3	8.1
	onset	-1.0	-2.8	-0.4	-0.4	0.3	0.2			0.2	-1.0	-0.4	-0.3		-0.4	0.3
		***	***	***	***	*	.			.	***	**	.		**	**
	Flowering	142.6	102.6	10.9	4.9	11.7			1.9	22.7	7.6	4.9	4.3		13.9	30.3
	duration	3.4	-2.9	-0.5	-0.3	0.5			-0.3	0.8	-0.7	-0.4	-0.5		-0.7	-0.7
		***	***	**	*	***				***	**	*	*		***	***
	Trophic	61.0	170.8	12.0	8.1	18.8				21.0	29.1	12.9			11.4	26.9
	level	2.1	-3.5	-0.5	-0.4	0.6				0.7	-0.8	-0.6			-0.6	0.7
		***	***	***	**	***				***	***	***			***	***

Acknowledgments

I would like to express my deepest appreciation to all those who provided me the possibility to complete my internship and my report.

Appendices

Appendix I: Histograms of diversity's indices, CWM and CWV of traits Appendix II: Inertia' tables explaining the traits' distribution to the axes of the Hill & Smith analysis applied on the matrix Q Appendix III: Groups results after performing a PCA and a HCA on the practices (101 plots) Appendix IV: PCA performing to choose environmental variables including the climatic conditions, landscape, the soil properties and the management practices for linear models Appendix V: Production modes' influence (organic/conventional) on CWM and CWV of traits Appendix VI: Influence of groups of practices on the CWM and CWV of traits Appendix VII: RLQ analyses of the rows (a) and the inter-rows (b)

Appendix VIII: Linear model for CWM and CWV of traits

Appendix I: Histograms of diversity's indices, CWM and CWV of traits The diversity's indices

The CWM

The CWV Appendix II: Inertia' tables explaining the traits' distribution to the axes of the Hill & Smith analysis applied on the matrix Q Appendix III: Groups results after performing a PCA and a HCA on the practices (101 plots)

For the first part of this analysis, we worked at the level of the 101 fields (instead of the 400 floristic samples). Based on the Hierarchical Cluster Analysis and the Mantel statistic, we found four groups to best describe the practices. The three first axes of the PCA explained 67.77% of the total variability of the practices' matrix (Figure 11). The 1 st group (in yellow) contained 17 fields including 10 organic plots and 7 conventional plots. It was positively correlated with the number of mowings in the rows (v_test = 6.92, p_value = 4.54e-12) and in the inter-rows (v_test = 3.18, p_value = 1.48e-04), and also with organic fertilization (v_test = 3.68, p_value = 2.37e-04) and the number of mechanical passages (v_test = 2.75, p_value = 5.67e-03). The group 1 was negatively correlated to the number of herbicides treatments (v_test = -3.54, p-value = 4.07e-04) (Figure 6). Table 15 shows the mean values of management practices per group and confirmed the highest mean of the group 1 the mowing in the rows (2.76) and in the inter-rows (4.76), the organic fertilization (8.41) and the number of mechanical passages (30).

Appendix IV: PCA performing to choose environmental variables including the climatic conditions, landscape, the soil properties and the management practices for linear models

Figure 12: Correlation circle of environmental variables with their contribution on the left and with the habitat and the sampling period on the right

The correlation circle on the left gave the contribution of each variable for the axes. It represented 30.9 % of the total inertia. On the right, a Hill & Smith was performed to see the impact of the sampling period (winter/summer) and of the habitat (rows/inter-rows), which are qualitative variables. The habitat was correlated with the first axis, positively for the rows and negatively for the inter-rows. The sampling period was correlated with the second axis, positively with the winter period and negatively with the summer period. Besides, the clay and the silk percentages were negatively correlated with the sand percentage. The water pH was positively correlated with the cation exchange capacity (CEC), the CaCO3, the MgO and the K2O. The pH indicated a rich soil in this study. The rainfall was negatively correlated with the evapotranspiration (ETP) and the temperature. The C/N ratio was positively correlated with the P2O5. The vineyard percentage at 100 m radius was positively correlated with the surface and the vineyard percentage at 20 m radius. The slope was positively correlated with the number of herbicides, and the organic matter content.

So, the variables kept, for the linear models with interaction, were: the vineyard percentage at 100 m radius ( V_100 m), the sand percentage (Sand), the organic matter (MO), the C/N ratio (CN), the pH (pH), the rainfall and the eight the management practices: mowing in the rows (ntont_sr), mowing in the inter-rows (ntont_ir), tillage in the rows (wsr), tillage in the inter-rows (wir), organic manure (famo), chemical fertilization (feng) and number of mechanical passages (npm).

Appendix V: Production modes' influence (organic/conventional) on CWM and CWV of traits

The CWM of hemicryptophytes (tussock, rosettes, stolons), flowering onset, anemogamous and animal dispersal were higher in the inter-rows than in the rows (Table 16, Figure 13). At the opposite, the CWM of therophytes, SLA, flowering duration, auto-apogamous, facultative entomogamous, wind dispersal, Ellenberg's indicator for trophic level were higher in the rows than in the inter-rows. The CWM of hemicryptophytes (tussock, stolons) and anemogamous higher for organic production mode.

The CWM of therophytes, facultative entomogamous, wind dispersal and Ellenberg's indicator for trophic level were higher in the conventional production mode. There were some significant interactions between the area and the production mode for the CWM of hemicryptophytes (tussock, stolons), therophytes, anemogamous, auto-apogamous and wind dispersal. The CWV of all traits were higher in the inter-rows than in the rows and higher in the organic than in the conventional production mode (except maximum of height which was not significant for the production mode) (Table 17, Figure 14).

The were some significant interaction between areas and production mode for the CWV of flowering onset.

Table 16: The F_value, the estimate and the significance concerning the habitat, the production mode and the interaction between both for the CWM. The point (.) indicates a p_value above 0.05, the stars indicates a p_value between 0.05 and 0.01 with one star (*), a p_value between 0.01 and 0.001 with two stars (**) and a p_value under 0.001 with three stars (***). The adjusted square-root (R²) indicates the contribution of area, production mode and their interaction of each trait.

Appendix VII: RLQ analyses of the rows (a) and the inter-rows (b)

The first two axes of the RLQ for the rows (a) accounted for 81.24% of the total inertia (58.37 % and 22.87% for the RLQ axis 1 and 2, respectively). The first axes of the RLQ for the inter-rows (b) accounted for 86.11 % of the total inertia (65.02 % and 21.09 % for the RLQ axis 1 and 2, respectively).

First of all, for the rows, the first axis was positively correlated with the flowering onset, the anemogamous, the height, the woody and biannual plants. At the opposite, the first axis was negatively correlated with the flowering duration, the auto-apogamous and the SLA. The second axis was positively correlated with the hemicryptophytes, the wind dispersal, the animal dispersal and the compulsory entomogamous. At the opposite, the second axis was negatively correlated with the therophytes, the trophic level, the seed mass, the facultative entomogamous and the baro-autochory.

Secondly, for the inter-rows, the first axis was positively correlated with the flowering onset, the height, the anemogamous and the woody plant. At the opposite, the second axis was negatively correlated with the SLA, the flowering duration, the auto-apogamous, the wind dispersal and the trophic level. The second axis was positively correlated with the hemicryptophytes, the compulsory entomogamous and the animal dispersal. At the opposite, the second axis was negatively correlated with the therophytes, the baro-autochory, the facultative entomogamous, the geophytes, the biannual and the seed mass.

For the environmental variables including the management practices, the rows (a) and the interrows (b) had a positive correlation between the first axis and the summer sampling period and a negative correlation between the first axis and the winter sampling period. For the rows, the mowing practices in the rows and in the inter-rows were opposed to the tillage in the rows and in the inter-rows, to the sand percentage, the number of mechanical passages, the temperature, the organic matter and the pH. The number of herbicides, the chemical fertilization and the organic manure were opposed to the rainfall and the evapotranspiration. The opposition seemed to be the same for the inter-rows. In both case, the mowing was opposed to the tillage management practices and the herbicides and the chemical fertilization were orthogonal to this opposition as a third direction.