Claire Gay
email: claire.gay@cebc.cnrs.fr

Sabrina Gaba

Vincent Bretagnolle

The structure of plant-pollinator networks is affected by crop type in a highly intensive agricultural landscape

Keywords: Bipartite networks, Mass-flowering crops, Weeds, Honeybees, Spatio-temporal heterogeneity Highlights

In agricultural landscapes, bipartite networks formed by pollinators and the flowers they forage on, are characterised by the presence of species managed by humans, whether honeybees (Apis mellifera) or crop plants. These managed species can affect the structural properties of these networks because of spatial and temporal variation in the availability of resources, and competition for these resources; for example, crop plants such as oilseed rape and sunflower produce a large number of flowers during a short blooming period. Here, we examined the structure of plant-pollinator networks in an intensive agricultural landscape, the LTSER Zone Atelier Plaine & Val de Sèvre (West of France). We compiled a six-year monitoring dataset of plant-pollinator interactions, sampling by sweep-nets along transects in the main six crop types (811 fields in total). To describe networks, we computed six metrics: connectance, nodes number, modularity, specialisation and two nestedness measures. Strong differences between crops were observed in nestedness and specialisation: in oilseed rape,

Introduction

Insect biodiversity is declining worldwide, and numerous local insect extinctions have already been documented [START_REF] Habel | Agricultural intensification drives butterfly decline[END_REF]. This decline has high consequences for the ecosystem services that rely upon insects, not least pollination [START_REF] Frankie | Native bees are a rich natural resource in urban California gardens[END_REF]. In temperate regions, 88% of plants depend on zoogamous pollination for their reproduction [START_REF] Ollerton | How many flowering plants are pollinated by animals?[END_REF] and 70% of crops depend to some extent on insect pollination [START_REF] Klein | Importance of pollinators in changing landscapes for world crops[END_REF], which improves yields of insect-pollinated crops and ensure agricultural production, as in oilseed rape (OSR; Brassica sp.) and sunflower (SF; Helianthus sp.) [START_REF] Perrot | Experimental quantification of insect pollination on sunflower yield, reconciling plant and field scale estimates[END_REF][START_REF] Perrot | Bees increase oilseed rape yield under real field conditions[END_REF]. In arable farming systems, insect pollinators forage partly -and sometimes mainlyon mass-flowering crops, but availability of these crops does not cover the entire season of the pollinators' activity [START_REF] Odoux | ECOBEE: a tool for long-term honey bee colony monitoring at the landscape scale in West European intensive agroecosystems[END_REF]. Between OSR and SF blooms (the so-called 'dearth period'), the resources provided by weeds are essential for maintaining pollinators, including the honeybee Apis mellifera [START_REF] Carvalheiro | Natural and within-farmland biodiversity enhances crop productivity: Weeds maximize nature benefits to crops[END_REF][START_REF] Requier | Honey bee diet in intensive farmland habitats reveals an unexpectedly high flower richness and a major role of weeds[END_REF]. Weeds and other wild plants are therefore critical for pollinators [START_REF] Bretagnolle | Weeds for bees? A review[END_REF], and contribute to their diversity and abundance at both field and landscape scales [START_REF] Carvalheiro | Natural and within-farmland biodiversity enhances crop productivity: Weeds maximize nature benefits to crops[END_REF].

Among insect pollinators, bees and hoverflies contain generalist species that can interact with many wild flowers [START_REF] Dunn | Dual ecosystem services of syrphid flies (Diptera: Syrphidae): pollinators and biological control agents[END_REF] or crop plants, and both improve pollination service [START_REF] Garibaldi | Stability of pollination services decreases with isolation from natural areas despite honey bee visits: Habitat isolation and pollination stability[END_REF]. Hoverflies are more resilient to agricultural modifications than bees in agricultural landscapes [START_REF] Jauker | Pollinator dispersal in an agricultural matrix: opposing responses of wild bees and hoverflies to landscape structure and distance from main habitat[END_REF]; conversely, butterflies which also pollinate many wild plants, have been dramatically affected by agricultural intensification [START_REF] Johst | Influence of mowing on the persistence of two endangered large blue butterfly species: Influence of mowing on butterfly persistence[END_REF].

Overall, weeds and pollinators are closely intertwined in agricultural farmland, and form ecological interaction networks, defined by a set of nodes (species) interconnected by one or more links (interactions). There is evidence that such networks have changed in structure over the last century, due to pollinator and plant community changes, with many interactions involving specialist species being lost [START_REF] Mathiasson | Wild bee declines linked to plant-pollinator network changes and plant species introductions[END_REF], mostly as a result of This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d land use intensification [START_REF] Ferreira | What do we know about the effects of landscape changes on plant-pollinator interaction networks?[END_REF]. Indeed, the persistence of pollinators -together with the services they provide -depends on stability, hence the structure of plant-pollinator ecological networks.

However, plant-pollinator ecological networks in agricultural landscapes present unique properties: first, they harbour managed and over-abundant species; second, they are highly dynamic in space and time due to crop rotations. Such characteristics are expected to impact the nature and structure of plant-pollinator networks. First, farmland ecological networks are structured around managed honeybees in addition to wild pollinators, and managed crops in addition to weeds [START_REF] Geslin | Massively Introduced Managed Species and Their Consequences for Plant-Pollinator Interactions[END_REF]. The effect of mass-flowering crops on one hand, and the presence of the honeybee on the other hand, can be related to 'invasive species' in arable systems [START_REF] Geslin | Massively Introduced Managed Species and Their Consequences for Plant-Pollinator Interactions[END_REF][START_REF] Stanley | Pollinator sharing between mass-flowering oilseed rape and coflowering wild plants: implications for wild plant pollination[END_REF], i.e. super-generalist species that interact with multiple groups of species [START_REF] Giannini | Native and Non-Native Supergeneralist Bee Species Have Different Effects on Plant-Bee Networks[END_REF], leading to asymmetry and nestedness of the network [START_REF] Ferreira | What do we know about the effects of landscape changes on plant-pollinator interaction networks?[END_REF]. Perhaps counterintuitively, this asymmetry and nestedness contributes to the stability and the resistance of networks to land use change [START_REF] Memmott | Tolerance of pollination networks to species extinctions[END_REF]. Links that emerge from these super-abundant species may, however, come at the expense of others, leading to species role shifts [START_REF] Albrecht | Consequences of plant invasions on compartmentalization and species' roles in plant-pollinator networks[END_REF], or even loss of species in both plants or pollinators, which should affect the structure and functioning of networks [START_REF] Valido | Honeybees disrupt the structure and functionality of plant-pollinator networks[END_REF]. For instance, OSR flowers number c. 500,000/ha [START_REF] Hoyle | The effect of wind direction on cross-pollination in wind-pollinated gm crops[END_REF], are brightly coloured and attractive, and produce both pollen and nectar. OSR can thus create a distortion in mutualistic networks and has already been shown to be related to the decline of specialist long-tongued bumblebees in favour of short-tongued species better adapted to its flower traits [START_REF] Diekötter | Oilseed rape crops distort plantpollinator interactions[END_REF]. Honeybees in return may decrease wild bee richness, leading to networks with fewer links [START_REF] Lázaro | Impacts of beekeeping on wild bee diversity and pollination networks in the Aegean Archipelago[END_REF].

However, a decrease or even a removal of supergeneralist (key) species may destabilise the network [START_REF] Kovács-Hostyánszki | The vulnerability of plant-pollinator communities to honeybee decline: A comparative network analysis in different habitat types[END_REF]. Secondly, agricultural landscapes display very This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d high spatio-temporal unpredictability (due to crop rotations), and seasonal heterogeneity [START_REF] Rollin | Weed-insect pollinator networks as bio-indicators of ecological sustainability in agriculture. A review[END_REF]: honeybees are present from April to September [START_REF] Odoux | ECOBEE: a tool for long-term honey bee colony monitoring at the landscape scale in West European intensive agroecosystems[END_REF], while OSR and SF, the main super-generalist plants, have very short flowering peaks [START_REF] Bretagnolle | Weeds for bees? A review[END_REF], and wild bees show generally have a short presence within the season. Crop rotation and spatial heterogeneity is therefore expected to affect the temporal and spatial dynamics of plant-pollinator networks [START_REF] Chakraborty | Seasonal dynamics of plant pollinator networks in agricultural landscapes: how important is connector species identity in the network?[END_REF].

There is currently a lack of descriptive information on plant-pollinator networks in farmland habitats, especially at large spatial scale or over longer time-scales [START_REF] Olesen | Strong, Long-Term Temporal Dynamics of an Ecological Network[END_REF].

Indeed, most studies investigated the crop compartment in relation to the semi-natural elements or its plants (e.g. [START_REF] Magrach | Plant-pollinator networks in semi-natural grasslands are resistant to the loss of pollinators during blooming of mass-flowering crops[END_REF][START_REF] Stanley | Pollinator sharing between mass-flowering oilseed rape and coflowering wild plants: implications for wild plant pollination[END_REF], while few studies aimed at investigating differences among crops (but see [START_REF] Kovács-Hostyánszki | The vulnerability of plant-pollinator communities to honeybee decline: A comparative network analysis in different habitat types[END_REF] and even fewer the effect of crop diversity. This is unfortunate, since crop effect was found to be the most statistically important effect in a vast study of predation-prey networks in agricultural habitats [START_REF] Ma | Ecological networks reveal resilience of agro-ecosystems to changes in farming management[END_REF]. Indeed, descriptive information is critical to understanding community stability and robustness, which are linked to the number of species and the number of links between them [START_REF] Carpentier | Reinterpreting the relationship between number of species and number of links connects community structure and stability[END_REF], and therefore network resilience, which relies on redundancy in the number of pollinator species per plant species [START_REF] Memmott | Tolerance of pollination networks to species extinctions[END_REF] and network nested structure [START_REF] Bascompte | The Nested Assembly of Plant-Animal Mutualistic Networks[END_REF]. Network stability is improved by pollinator diversity [START_REF] Bendel | Plant-pollinator networks in grassland working landscapes reveal seasonal shifts in network structure and composition[END_REF], nestedness and low modularity, though possibly at the expense of persistence [START_REF] Thébault | Stability of Ecological Communities and the Architecture of Mutualistic and Trophic Networks[END_REF].

Along environmental gradients from less-to more-impacted by human footprint, e.g. from natural sites to monoculture farming, an increase in connectance and nestedness but a decrease in the number of nodes as well as modularity were shown [START_REF] Morrison | Agricultural intensification drives changes in hybrid network robustness by modifying network structure[END_REF].

Increased connectance is expected to decrease secondary extinctions [START_REF] Blüthgen | Specialization, Constraints, and Conflicting Interests in Mutualistic Networks[END_REF] This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d and improves robustness at the community level as well as stability [START_REF] Thébault | Stability of Ecological Communities and the Architecture of Mutualistic and Trophic Networks[END_REF].

In this study, we used a descriptive approach to qualify plant-pollinator networks in a highly intensive farming system, at a large spatial scale (study site of 450km²) with data collected on six different crop types (811 fields in total). Because our sampling effort was high, we first paid attention to two generally overlooked patterns, the effect of sample size and the degree distribution. Sampling effort has been shown to affect many network metrics, through sampling intensity and sampling bias, but also underlying species abundances [START_REF] Blüthgen | Specialization, Constraints, and Conflicting Interests in Mutualistic Networks[END_REF]. Degree distribution is highly skewed, resulting in part from many 'forbidden links' [START_REF] Jordano | Sampling networks of ecological interactions[END_REF]. The frequency distribution of links is claimed to be best described by powerlaw distributions [START_REF] Blüthgen | Why network analysis is often disconnected from community ecology: A critique and an ecologist's guide[END_REF], which have been shown to fit most natural networks. We also describe the overall visual structure of networks by seasons and crop types. Then, we investigated major features of the network, from simple network descriptors (number of nodes, connectance) to more complex ones (nestedness, modularity, specialisation), quantifying their level of variation among crop types and seasons, while accounting for sample size. We expected plant-pollinator network structure to vary seasonally because of the presence of mass-flowering crops, and the spillover or dilution effects that result from such presence. Indeed, we expected strong variations in network structure among crop types, in particular between mass-flowering and other crops, but also between arable crops and grasslands. We further predicted a clear dominance of the managed honeybee, especially during the flower blooms of OSR and SF, which dominate landscapes when flowering. In particular, we predicted that networks would be more specialised during OSR and SF blooms than in other periods and other crops. We also predicted that networks would be more nested during the blooming periods of mass-flowering crops than in the dearth periods, and thus in these crops -as a result of super-generalist and abundant species presence -more able to include the same partners as the specialists in their spectrum of interactions.

Materials and methods

Study site

Our study site is the Long Term Socio-Ecological Research (LTSER) site 'Zone Atelier Plaine & Val de Sèvre', located in the Nouvelle-Aquitaine region of southwest France, south of the city of Niort [START_REF] Bretagnolle | Towards sustainable and multifunctional agriculture in farmland landscapes: Lessons from the integrative approach of a French LTSER platform[END_REF]. It covers 450 km², harbouring c. 13,000 fields.

We focused on six years of survey, 2015-2020, which is one of the largest time intervals of plant-pollinator network studies (but see [START_REF] Chacoff | Interaction frequency, network position, and the temporal persistence of interactions in a plant-pollinator network[END_REF][START_REF] Petanidou | Long-term observation of a pollination network: fluctuation in species and interactions, relative invariance of network structure and implications for estimates of specialization: High plasticity in plant-pollinator networks[END_REF][START_REF] Ponisio | Opportunistic attachment assembles plant-pollinator networks[END_REF]. Each year, 100 to 220 different fields were surveyed (some fields were studied for more than one year) with a standardised protocol. Fields were selected in a stratified random process (see [START_REF] Bretagnolle | Towards sustainable and multifunctional agriculture in farmland landscapes: Lessons from the integrative approach of a French LTSER platform[END_REF] for details). From 2015-2020, the number of fields sampled per respective year were 174,217,195,160,117 and 115 (see ESM1a and 1b in Appendix A). Crop surveyed included 7% corn (Zea mays) and sorghum (Sorghum bicolor), 8% alfalfa (Medicago sativa), 12% meadows (temporary or permanent), 18% oilseed rape (Brassica napus), 22% sunflower (Helianthus annuus), and 33% cereal crops. Cereals included barley (Hordeum vulgare), durum wheat (Triticum turgidum), einkorn (Triticum monococcum), oat (Avena sativa), rye (Secale cereale), triticale (×Triticosecale) and winter wheat (Triticum aestivum). Sampling was performed from April to August, i.e. all crops were surveyed at least once during their respective flowering periods: OSR from April-May; sunflower from July-August; alfalfa, meadows and cereals from May-June to the end of August; and corn and sorghum from July-August.

Sampling of plant-pollinator interactions

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048

P r e p r i n t n o t p e e r r e v i e w e d

Insects surveyed included bees (both honeybees and wild bees), butterflies and hoverflies (Syrphidae), i.e. the three main pollinator groups in the LTSER. This study site hosts a large number of pollinator species [START_REF] Rollin | Weed-insect pollinator networks as bio-indicators of ecological sustainability in agriculture. A review[END_REF]. Each year, two (2015) or three (2016)(2017)(2018)(2019)(2020) 50 m long transects were performed per field: one in the field centre, one in the field margin (section between the field edge and the first seeding line) and the last in between, at c. 25 m from the margin (except in 2015). Field margins generally harbour a more diverse fauna compared to field cores. Transects were parallel to tram lines. For each transect, the surveyed area was a theoretical volume, 2.5 m each side, 2.5 m in front of the observer and c. 1 m above the observer's head. Surveys were conducted under calm weather conditions, i.e. sunny days with air temperatures in full sun mostly above 15°C (95% T >16°C; 12° to 45°C, in sunlight), and between 7:30 a.m. and 6:30 p.m. In 27.9% of the fields (226 of 811), two sampling sessions were conducted (early-and late-season). Each plant-pollinator interaction (i.e. an insect landing on a flower) was tallied, with both plant and pollinator being identified (pollinators being caught with a sweep-net for later identification by a professional entomologist if necessary). Transects were walked along very slowly (c. 12 s/m), for a total duration of 10 min each. The observer stopped the timer each time an insect had to be identified or captured. Butterflies were always identified by sight; however, given the time spent in flight in butterflies, all individuals crossing the sampled theoretical volume were tallied, even if not on a flower. Almost all pollinators and plants (94% and 96% respectively)

were identified to species level.

Statistical analyses

Sampling completeness and frequency distribution of degrees

We estimated sampling completeness of plants and pollinators using the Chao estimator of asymptotic species richness [START_REF] Chacoff | Evaluating sampling completeness in a desert plant-pollinator network: Sampling a plantpollinator network[END_REF], which is based on rare species (those that This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d occur only once or twice). We calculated the Chao 2 estimator using the vegan R package (v2.5.7; [START_REF] Oksanen | vegan: Community Ecology Package. R package version 2[END_REF] and evaluated the percentage of completion (ratio between the observed value and estimated value) of our sampling of plants and pollinators.

We analysed the frequency distribution of degrees in total as well as in subnetworks (per crop and per period), testing for best-fit with a priori distributions, i.e. Exponential, Poisson, Weibull, Lognormal, Negative binomial, Power law and Fisher's logseries. We used the fitdistrplus package (Delignette-Muller and Dutang, 2015), except for the Fisher's logseries (which is not implemented in this package), which we computed. Power law was tested with the poweRlaw package [START_REF] Gillespie | Fitting Heavy Tailed Distributions: The poweRlaw Package[END_REF]. Best fit was checked using the Akaike Information Criterion (AIC; [START_REF] Burnham | AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons[END_REF]. We fixed a threshold of 2 units' difference in AIC to select the best model among the candidate models.

Number of nodes and number of interactions: crop and temporal effects

From floral visitation events, we constructed bipartite networks with plant and bee nodes, using a visitation event as a plant-pollinator interaction. To analyse how the network structure varied among crops and seasons, we built interaction subnetworks. Six crop categories were retained: OSR, cereals, alfalfa, meadows, SF and corn-sorghum. We also split the data into three equal-length seasons, each 45 days long: April 1 st -May 20 th (OSR flowering peak, characterised by high resource availability for insects), May 21 st -July 9 th , characterised by a general lack of flowering crop resources available (i.e. the dearth period) -when honeybee colony sizes are, perhaps surprisingly, at their highest [START_REF] Odoux | ECOBEE: a tool for long-term honey bee colony monitoring at the landscape scale in West European intensive agroecosystems[END_REF] -and finally, July 10 th -August 22 nd , corresponding to the SF flowering peak. We also divided our dataset into six main taxonomic groups: honeybees, bumblebees, other social bees (including some Halictidae, e.g. Halictus rubicundus, Lasioglossum pauxillum), solitary bees, hoverflies and butterflies. To describe seasonal patterns in the number of interactions, we standardised our This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d data, since the number of transects per field and the number of fields sampled per crop and per year varied. We thus converted the number of interactions per transect per field into a number per hectare (ha), since each transect surveyed an area of 250 m². Next, we grouped samples per fortnight by averaging values (with standard-error, se) of interactions per ha per fortnight for each crop. Obtained values were compared with ANOVA and post-hoc Tukey tests. Data were log-transformed.

Network structure descriptors

A large number of indices is available to describe network structure (see ESM2, which presents their respective properties in Appendix A). We selected the following ones: number of nodes (i.e. the number of species in interaction), connectance (the ratio between the number of links observed and the total number of possible links), modularity -which gives insights into the presence of subnetworks among the network (called modules, i.e. weakly interlinked subsets of species that are constituted of strongly interacting species) and which was calculated using the greedy Louvain algorithm method [START_REF] Blondel | Fast unfolding of communities in large networks[END_REF]) -and nestedness, described by NODF (Nestedness metrics based on Overlap and Decreasing Fill; [START_REF] Almeida-Neto | A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement[END_REF]. The latter is a property describing to what extent the partners of specialists are a subset of the partners of generalists. We also used WNODF (Weighted Nestedness metrics based on Overlap and Decreasing Fill; Almeida-Neto & Ulrich, 2011), the quantitative alternative to NODF that accounts for the number of similar interactions. Finally, we focused on the specialisation H 2 ' (standardised two-dimensional Shannon entropy) -the degree of specialisation between the two levels of the entire network [START_REF] Blüthgen | Measuring specialization in species interaction networks[END_REF] and the d' species-level specialisation measure (standardised Kullback-Leibler distance, [START_REF] Blüthgen | Measuring specialization in species interaction networks[END_REF], corrected by de [START_REF] De Manincor | How biased is our perception of plant-pollinator networks? A comparison of visit-and pollen-based representations of the same networks[END_REF], representing the degree of interaction specialisation at the species level. Network descriptors were obtained using the R packages bipartite [START_REF] Dormann | Indices, Graphs and Null Models: Analyzing Bipartite Ecological Networks[END_REF], igraph [START_REF] Csardi | The Igraph Software Package for Complex Network Research[END_REF], NetIndices [START_REF] Kones | Are network indices robust indicators of food web functioning? a Monte Carlo approach[END_REF] and vegan [START_REF] Oksanen | vegan: Community Ecology Package. R package version 2[END_REF]. Some of these metrics (i.e. connectance, nestedness) are sensitive to sample or network size [START_REF] Dormann | Indices, Graphs and Null Models: Analyzing Bipartite Ecological Networks[END_REF]. We thus checked how indices varied with increasing numbers of samples (according to periods and crops) by calculating all metrics (excluding d') with increasing numbers of sampled fields. Five to 195 fields (every 10 field steps) were randomly sampled (with replacement) from the overall sample. At each step (i.e. field number), 100 groups of fields were constituted, the six network metrics were computed and the mean and the 95% confidence interval (CI) were calculated. The maximum bootstrap threshold to compare the three seasons was 175 field groups, when OSR period samples stopped, but we also went up to 195 fields to compare only dearth period and sunflower period. When comparing the six different crop types, we had to stop at a threshold of 35 fields per group (100 groups) in the bootstrap because we had very few fields of corn-sorghum and alfalfa. To increase the sampling effort, we had to lose crops, and were left with only four crops with 100 groups of 85 fields. However, when focusing on the two mass-flowering crops, OSR and SF, we were able to analyse 100 groups of 165 fields. Therefore, the number of fields bootstrapped varied according to the crops to be compared.

Field selection

To calculate Chao 2 estimates and the number of interactions per hectare, we included the fields in which no insects had been observed (138 fields, 17.0%). However, to represent interaction networks and calculate metrics on aggregated fields, we only retained the fields on which at least one interaction was observed in the dataset.

All analyses were run with R software v.4. 1.0 (R Core Team, 2021).

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d

Results

Sampling completeness and frequency distribution of degrees

A total of 37,133 plant-pollinator interactions were tallied over 811 fields (127 of which were studied in at least two years between 2015 and 2020), occurring between 200 species of pollinators foraging on 165 species of plants (including individuals that were only identified to genus level). We found 96 species of solitary bees, 35 species of social bees (including the honeybee A. mellifera and 14 species of bumblebees), 38 species of butterflies and 31 species of hoverflies. A total of 758 interactions were obtained between one plant or one pollinator occurring only once. No interaction was found between one plant and one pollinator occurring both only once. In regard to pollinators, over the full network, 80 species (40.0%) interacted with only one plant (crop or weed), 29 species (14.5%) interacted with two, while 91 species (45.5%) were more generalist (>2 plants, up to 92 plants). Similar values were obtained for plants, with 38 weed species interacting with only one insect species (23%), and 16 with two insect species (9.7%). There were 138 fields without any observed interaction (17.0%). We caught 64.5% of pollinators, and recorded 78.6% of the plant community, based on Chao 2 estimators.

Overall, the frequency distribution of degrees (i.e. interacting plants/pollinators) fitted better to the Fisher's log-series (see ESM3 in Appendix A) than to Lognormal (Δ AIC = 73) or Power laws (Δ AIC = 77). When splitting by crop or per season, similar results were found.

Differences in the goodness-of-fit of Fisher's log-series and Lognormal were, however, much lower in alfalfa (Δ AIC = 12) and in corn and sorghum crops (Δ AIC = 3) than in other crops.

Number of nodes and number of interactions: crop and temporal effects

The total number of nodes varied highly among crops (165,114,186,178,161 and 75 in meadows,alfalfa,cereals,SF,respectively). Accumulation curves This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d per crop indicated that these differences among crops (Fig. 1) could be detected even from low sample sizes (c. n = 50). Thus, independently of the number of sampled fields, meadows showed the highest values of node numbers, followed by alfalfa, cereals, OSR and SF (Fig. 1). Year did not affect the pattern of the accumulation curves (see ESM4 in Appendix A), although 2017 was a species-rich year regardless of the sampling effort.

The number of plant-pollinator interactions, here standardised per hectare of sampling effort (which can be considered as an instantaneous visitation rate per hectare), was on average 338.43±23.19 interactions/ha (mean±se). This value, however, showed strong seasonal variations, with most interactions occurring in early July when considering all crops, or mid-June when excluding SF (Fig. 2). There were also strong variations among crops, with interactions in SF being overwhelmingly dominant and driving seasonal variation. In early July, we found significant differences in the average number of interactions among crops (F 5,62 = 20.16, p-value < 0.0001), with significantly higher values for SF than in alfalfa (T HSD p-adj < 0.001), meadows (T HSD p-adj < 0.001), cereals (T HSD p-adj < 0.001) and OSR (T HSD padj = 0.0015).

Network structure differed strongly among crops in relation to variation of the total number of nodes, either in the repartition of interactions between the main groups of plants or between the main groups of pollinators (Fig. 3). In flowering crops, the crop flower was generally the most visited flower in the crop considered. For instance, 95.27% of pollinators visited SF plant in SF fields, and 86.07% OSR plants in OSR fields. However, this was true only for mass-flowering crops: alfalfa represented approximately a third of all visited flowers in alfalfa fields (36.12%) and corn was poorly visited in corn-sorghum fields (7.55%). In those crops, pollinators preferentially foraged on wild flowers (89.62%). Moreover, honeybees' dominance was not of the same magnitude in all crops: they were overrepresented in SF (91.95%) and OSR (76.60%), while hoverflies rather than honeybees were the most-This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d interacting group in other crops (32.55% in corn and sorghum crops, 56.42% in cereal crops, 36.02% in alfalfa crops and 53.38% in meadows). Interestingly, when found in other crops, OSR and SF flowers showed the same interaction pattern, i.e. interacting mainly with honeybees. Moreover, hoverflies foraged more on wild flowers than did any other insect group, potentially explaining their high presence in weed-rich crops such as meadows or alfalfa. For instance, whereas the total percentage of weed visits by all pollinator groups was 63.78% in alfalfa crops, it reached 77.94% when considering only hoverfly visits.

The overall pattern of the network is depicted on Fig. 4, which represents standardised interactions between pollinators and plants according to their degree (i.e. number of interaction partners). Species with the highest degrees were not necessarily the most frequent, although species' frequency of occurrence and degree were strongly related (i.e. decreasing pattern of species degrees were concomitant with decreasing species frequencies) (Fig. 4). In terms of the number of interactions, honeybees and bumblebees were most prevalent (83.72% of the total interactions number, n = 31,089), followed by hoverflies (8.21%, n = 3,050), solitary bees (2.69%, n = 998) and butterflies (1.36%, n = 506). Honeybees were by far the dominant insect species (80.80% of all interactions, n = 30,007), the next two being Sphaerophoria scripta (3.80%, n = 1,412) and Episyrphus balteatus (2.19%, n = 812), two hoverflies. However, although S. scripta was found 20 fewer times than honeybees in total, its number of plant partners (92) was higher than that of honeybees (75). Butterflies were far less often observed, with only 81 interactions for the most common butterfly, Pieris rapae.

Concerning plants, SF represented 72.36% (n = 26,871) of the whole sample, seven times more than OSR (10.69%, n = 3,971), and 60 times more than the most-interacting weed, Torilis arvensis (1.20%, n = 446). Therefore, the complete network was dominated by honeybees and mass-flowering crops: honeybees interacting with OSR or SF represented 77.44% of all interactions.

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Network structure

Seasonal effect on network structure

The average number of nodes during the dearth period (mid-May to early July) was similar to the average number of nodes for all periods, regardless of the sampling effort (Fig. 5a), reaching 214 (CI 95% : 195-230) species when considering a sampling effort of 175 fields. The average number of species was nearly equal during SF and OSR flowering blooms (154 (142-165) and 148 (146-151), respectively, in 175 fields); that is to say, 1.4-times lower than during the dearth period, but with a weaker variability among fields during OSR flowering.

Mean connectance did not vary a lot with period, being 3% (2.6-3.4) during bloom periods and 2.5% (2.2-2.8) during the dearth period in 175 fields. Mean modularity was slightly higher for the OSR blooming network (0.42 (0.41-0.43)) than for other periods (0.39 (0.36-0.41) in SF blooming and 0.37 (0.35-0.38) in dearth period)). Specialisation of the network remained constant during dearth and SF periods when considering network sizes above 100 fields (see ESM2 for details in Appendix A), but decreased much more quickly in the OSR blooming period, with no overlapping 95% in CIs. This highlights that OSR blooming period was represented by more generalist species than the other periods. Specialisation was higher during the SF bloom and dearth periods, with average values between 0.49 (0.43-0.55) and 0.50 (0.46-0.52), respectively, compared to the OSR flowering bloom (0.30 (0.29-0.33)). The OSR blooming period was characterised by a higher nestedness (37.2 (35.6-38.6) in 175 fields) than the SF blooming period (24.0 (21.3-26.9)) and dearth period (27.0 (24.4-29.9)). Similar patterns were observed when using weighted NODF: 14.8 (12.7-16.8) during the dearth period, 13.3 (11.8-15.0) during the SF blooming period, and 21.9 (20.6-23.1) during the OSR blooming period. Thus, it seems that the flowering period of the OSR was the most prone to host specialist species that had partners similar to generalist species.

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Crop effect on network structure

All crops' subnetworks were similarly poorly connected (c. 3% at a threshold of 85 fields) (Fig. 5b). Although OSR blooming period was the most modular period (see above), mean modularity was almost identical whatever the crop considered, at about 0.5. At an equal sampling survey effort of 85 fields, modularity was 0.43 (CI 95% : 0.39-0.47) for meadows, 0.45 (0.40-0.49) for SF and 0.48 (0.45-0.52) for OSR. With a higher sampling rate (n = 165 fields), but at the cost of losing crop types in the comparisons, OSR showed significant higher modularity values than SF. The two mass-flowering crops' networks also had very different specialisation (H2'), showing the two most extreme values among our six crops. OSR was far less specialised (0.31 (0.29-0.33) at 165 fields) than all other crops, while SF (0.58 (0.55-0.61)) showed the most specialised network; the other crops showed intermediate values, e.g. 0.45 (0.43-0.47) for meadows and 0.40 (0.34-0.46) for cereals at a survey effort of 85 fields.

Low specialisation in OSR could be partially explained by the low specialisation of the most abundant insect, the honeybee (d' = 0.27) and the low specialisation of the crop flower, OSR (d' = 0.21). Indeed, in OSR, the average d' of all species was 0.26 for insects (n = 97, median = 0.20) and 0.23 for plants (n = 68, median = 0.44), meaning that the network was lowly specialised at both trophic levels and highly driven by the honeybee and OSR flower dominance. In SF, specialisation was 0.67 for SF (more than three times higher than in OSR) and 0.42 for the honeybee (1.5-times higher than in OSR). SF was thus highly connected to honeybees despite honeybees being slightly less specialised than SF, indicating a larger partners' spectrum. The average d' of all insect species in SF was 0.41 (n = 109, median = 0.50) and 0.67 for plants (n = 65, median = 0.51), again reflecting the preponderant roles of honeybees and SF. Nestedness (NODF) increased with sampling effort, being higher in OSR than in any other crops (mean value of 29.8 (22.5-36.8) when sampling effort was low, i.e. 35

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d fields), especially in meadows (16.1 (11.0-21.3)), cereals (21.3 (15.1-28.4)) and )), rather than in alfalfa (25.5 (21.0-30.8)). When increasing sampling effort (85field threshold), nestedness reached 33.0 (27.7-38.2) in OSR networks, c. 25% more than in cereals (23.9 (19.9-28.5)) and in ). Weighted NODF average values showed similar patterns: SF reached a WNODF mean value of 14.5 (13.1-15.7) when considering a threshold of 165 fields, whereas OSR networks peaked on average at 21.6 (20.3-22.5).

Discussion

The detection of several hundreds of species in the plant-pollinator network indicates that the LTSER ZAPVS is characterised by a high diversity of species despite its intensive farming system. While the honeybee, SF and OSR are by far the species that establish most interactions, they are not necessarily those with the greatest number of interacting partners.

Although OSR flower is the species with the highest degree (number of interaction partners), confirming its structuring role in the network [START_REF] Diekötter | Oilseed rape crops distort plantpollinator interactions[END_REF][START_REF] Stanley | Pollinator sharing between mass-flowering oilseed rape and coflowering wild plants: implications for wild plant pollination[END_REF], it is closely followed by wild plant species, generally hosted in less-intensively managed but more diverse crops such as alfalfa or meadows. Moreover, the hoverfly Sphaerophoria scripta showed far more partners than honeybee, including OSR and SF flowers, suggesting that its role in the network has to be explored in more detail. Bombus species are also frequent visitors of SF, as Syrphidae of OSR [START_REF] Jauker | Hover flies are efficient pollinators of oilseed rape[END_REF]. Although these are rarely studied as key species in networks, they might be strongly involved in OSR and SF pollination [START_REF] Breeze | Pollination services in the UK: How important are honeybees? Agriculture[END_REF], and could act as pollination insurance if honeybees were to decline dramatically (Sánchez-Bayo and Wyckhuys, 2019).

Completeness of survey effort was, however, moderate: 65% for insects but 80% for plants according to the Chao 2 estimator, despite our dataset covering six months (from April to This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d September) and six years. The phenology of some plants and insects being very short may explain why not all species were detected, in addition to the classic and inherently imperfect detection method. In addition, the low connectance within crops (around 2-3%) suggests that few interactions are carried out among all possible ones. These forbidden links [START_REF] Jordano | Sampling networks of ecological interactions[END_REF] could result from spatial or temporal constraints [START_REF] Valdovinos | Mutualistic networks: moving closer to a predictive theory[END_REF]. Indeed, one of our possible explanations is that each crop network corresponds to a set of different fields that belongs to different landscape entities and different sampling weeks, which generates a high community variability in terms of phenology and location. But a low connectance, i.e. high proportion of forbidden links, is in certain cases one of the indicators of destabilised networks [START_REF] Landi | Complexity and stability of ecological networks: a review of the theory[END_REF].

The hypothesis of the dominance of honeybee interactions with crop flowers during OSR flowering and SF flowering, and not in other periods, was largely verified. Overdominance of managed species may destabilise networks [START_REF] Valido | Honeybees disrupt the structure and functionality of plant-pollinator networks[END_REF]. The stability of a network can be evaluated through the values of some network metrics [START_REF] Thébault | Stability of Ecological Communities and the Architecture of Mutualistic and Trophic Networks[END_REF].

A stable bipartite mutualistic network is often a network with a high connectance and many species, as well as a nested -but not very modular -network [START_REF] Thébault | Stability of Ecological Communities and the Architecture of Mutualistic and Trophic Networks[END_REF].

In our case, connectance was always very low (<5%), whether we considered the total network or the subnetworks per season or per crop. When the connectance is so low, modularity and nestedness are usually positively correlated [START_REF] Fortuna | Nestedness versus modularity in ecological networks: two sides of the same coin[END_REF], hence a situation with a nested but not modular network is unlikely, therefore possibly suggesting instability in our networks. In particular, the corn-sorghum network, which is highly modular and comprises few species, may show particularly low stability, though we had relatively small sample sizes. Meadows were a special case, endowed with a large number of species but not very nested and not very connected compared to other crops. Thus, although meadow networks appear to be resilient networks thanks to the number of species they host (Okuyama This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d and Holland, 2008), they display a certain vulnerability because the sharing of partners is weak, like in the studied SF crops, but contrary to the OSR crops. Indeed, the most striking differences in networks were found between OSR and SF, both being mass-flowering crops. As expected, these two crops drove the overall behaviour of networks during their blooming season (approximately the same metrics of behaviour between the OSR crop and OSR period, and between the SF crop and SF period), although the meadows, weeds in cereals, and alfalfa were flowering at the same time. However, the OSR and SF crops' networks were somewhat opposed in some of the key metrics, hence possibly showing different network stability [START_REF] Duchenne | Seasonal structure of interactions enhances multidimensional stability of mutualistic networks[END_REF]. The OSR network did not harbour many species, but was highly nested compared to the other crop networks, which therefore suggests it was a resilient network. Its nestedness may result from the fact that the OSR network was mostly composed of species with a high degree of generalisation (i.e. the average species specialisation was quite low in OSR crops), leading to a relatively nested structure [START_REF] Fontaine | Are insect pollinators more generalist than insect herbivores?[END_REF]. Thus, as the OSR network was the most nested network in our study, it was expected that the general core of species it included were generalists rather than specialists, which was indeed the case. The interaction between OSR and honeybees has a structuring effect for the network, corroborating studies that highlight OSR flower and its partners as a hub that positively influences the visitation and pollination of neighbouring wild plants, especially those with similar morphology [START_REF] Thompson | Oilseed Rape Shares Abundant and Generalized Pollinators with Its Co-Flowering Plant Species[END_REF]. OSR is also known to host diverse insect communities [START_REF] Stanley | Pollinators and pollination of oilseed rape crops (Brassica napus L.) in Ireland: ecological and economic incentives for pollinator conservation[END_REF], while honeybees remain one of the most efficient pollinators and, as demonstrated here, the most important partner of OSR.

Conversely, SF appears less stable than OSR because it is much less nested for an equivalent diversity of species, and is composed of more specialised species, including the honeybee-SF duo, which both qualify as specialist species, interacting strongly and almost entirely with each other -a pattern that was not necessarily found in other studies (e.g. [START_REF] Valido | Honeybees disrupt the structure and functionality of plant-pollinator networks[END_REF].

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4378048 P r e p r i n t n o t p e e r r e v i e w e d Such a quasi-exclusive relationship between honeybees and SF as we found here may reflect either competition within pollinators or plant communities in SF crops, or a strong niche differentiation between wild and managed species. Conversely, OSR may host more generalist species that interact with specialist species and therefore enhance the persistence of the network [START_REF] Liao | A patch-dynamic metacommunity perspective on the persistence of mutualistic and antagonistic[END_REF].

Conclusion

Our results suggest that the honeybee and crop flower pair may differ between OSR and SF, being an almost exclusive relationship in SF that prevents other partners by competition, while allowing other modules in OSR hence increasing nestedness and consequently improving network stability. This divergence may possibly result from phenological difference, or as a consequence of flower shape -a divergence that can be further explored with species trait approaches.

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Acknowledgements

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We thank in particular Jean-Luc Gautier, Alexis Saintilan and Marilyn Roncoroni for the monitoring, sampling and identification of the pollinator' data. We also thank the farmers of the LTSER 'Zone Atelier Plaine & Val de Sèvre' involved in the research program. CG received funding for her PhD grant from the project SHOWCASE (SHOWCASing synergies between agriculture, biodiversity and ecosystem services to help farmers capitalising on native biodiversity) within the European Union's Horizon 2020 Research and Innovation Programme under grant agreement N° 862480. This study was supported by the 'Programme Pollinisateurs' (2015-2020) funded by the Ministère de l'Ecologie et de l'Environnement.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at […]

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