# Seasonal and habitat variations of floral visitor networks in a Mediterranean maquis

Pierre-Yves Maestracci, Laurent Plume, Caroline de Zutter, Marc Gibernau

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## To cite this version:

Pierre-Yves Maestracci, Laurent Plume, Caroline de Zutter, Marc Gibernau. Seasonal and habitat variations of floral visitor networks in a Mediterranean maquis. Arthropod-Plant Interactions, 2025, 19 (5), pp.74. 10.1007/s11829-025-10179-5. hal-05391791

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# Seasonal and habitat variations of floral visitor networks in a Mediterranean maquis

Maestracci Pierre-Yves¹,²,³, Plume Laurent¹, Caroline de Zutter² and Gibernau Marc¹

¹ CNRS – University of Corsica - Laboratory Sciences for the Environment (UMR 6134 SPE), Natural Resources Project - Vignola - Route des Sanguinaires, 20000 Ajaccio, France

² ENGIE Lab CRIGEN, 93240 Stains, France

³ University Paris-Panthéon-Assas, Laboratory Management Research (Largepa) - Maison des Sciences de gestion - 1 rue Guy de la Brosse, 75005 Paris, France

Corresponding author: Marc Gibernau (gibernau_m@univ-corse.fr)

Received:

Citation:

## ABSTRACT

In natural ecosystems, species are interacting with many others constituting complex networks. Analysis of these networks enables us to describe and visualise ecological communities, which can vary greatly depending on the habitat and the season. Here, we have studied insect floral visitors explicitly considering the four main orders Hymenoptera, Coleoptera, Diptera, and Lepidoptera over nine months in 2022 across three sites in South-West Corsica. We recorded 2,848 insect-flower interactions involving 241 insect morpho-species and 54 plant species. Seven plant species accounted 61% of all insect observations and 81% of the recorded insect species diversity. Our results revealed pronounced spatial and temporal dynamics. Distinct seasonal floral visitor communities emerged at each site, with the highest diversity observed between spring and summer. The composition of key species varied markedly across seasons, *Bombus xanthopus*, *Oedemera* spp., and *Tropinota squalida* in spring; *Hylaeus* spp and *Mordellistena* spp. in summer; *Apis mellifera* and *Hylaeus* spp in autumn. Local plant diversity and landscape heterogeneity at both short (200 m) and long (1 km) scales likely contributed to spatial differences among sites.
This study highlights the necessity of integrating understudied groups such as Coleoptera and
extending sampling across seasons to fully capture the dynamics of plant-floral visitor
networks. However, given the high diversity and taxonomic complexity of flower-visiting
insects, direct management is challenging. Instead, prioritizing plant community management
offers a practical means to support these interactions, particularly since visitor assemblages vary
according to microhabitat and landscape structure. These insights are essential for
understanding ecological processes and informing robust conservation strategies that
encompass the variability of these interactions in Mediterranean ecosystems.

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43 KEYWORDS

44 Corsica; Insects; Mediterranean ecosystems; Interaction networks; Spatio-temporal variation.

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46 INTRODUCTION

In natural ecosystems, species are interacting with many others constituting complex networks (Bascompte & Jordano 2006; Olesen et al. 2007). Analysing those networks allow us to describe and visualise ecological communities (CaraDonna & Waser 2020). In a plant-floral visitor network, an animal species is linked to a plant species if the animal visits the flowers of the plant (Olesen et al. 2008). These supposed pollination links are ecologically important since ~87% of the actual angiosperms are pollinated by animals and evolutionary primordial for the radiations of both groups (Wardhaugh 2015). With more than 400,000 flowering plants (Govaerts et al. 2021) and 350,000 pollinating species (Ollerton 2017) described, plant-pollinator networks are the most species-rich of all mutualistic networks (Hagen et al. 2012). Hence these networks can be quite simple in low diverse ecosystems or very complex when the diversity of one or both partners is particularly high. These ecological communities are moreover highly dynamic, since each species and also their interaction vary both in space (e.g. habitats composition) and time (e.g. seasonality) (Olesen & Jordano 2002; Olesen et al. 2008; Dupont & Olesen 2009; Dupont et al. 2009; Poisot et al. 2015; CaraDonna et al. 2017; Chacoff et al. 2018; CaraDonna & Waser 2020). Linking network structure to dynamics helps us to understand the ecological processes behind network dynamical processes (Olesen et al. 2008). Important metrics exist to characterize a network, such as the links per species, the nestedness or H2 index (Dormann et al. 2009) which are efficient to characterize the network temporal dynamics and stability (Pimm 1984; Olesen et al. 2008; Okuyama & Holland 2008). The Mediterranean basin is an important biodiversity hotspot (Mittermeier et al. 2004) stretching from Portugal to Jordan from West to East, and from Morocco to northern Italy from South to North. This climatic region, also present in other regions of the world (California, Chile, south-Africa, South and West-Australia), is characterized by a diversity of habitats and high plant and insect species' richness (Proctor et al. 1996; Petanidou & Lamborn 2005). The Corsica Island, located in western Mediterranean Sea, presents a significant biological diversity and a high rate of endemism, and is considered as one of the ten regional hotspots (Médail & Quezel 1999). In the recent years, several projects have studied Corsican entomofauna increasing significantly the knowledge on the insular diversity of insects and other invertebrates (Cornuel-Willermoz & Andrei-Ruiz 2021; Mengual et al. 2023; Le Divelec et al. 2024; Cornuel-Willermoz & Lebard 2024), notably through the Corsican Invertebrate Conservation Observatory (OCIC) from the Corsican Environmental Agency (OEC) and the Corsican expeditions from "Our Planet Reviewed" leadered by the National Museum of Natural History in Paris (Ichter et al. 2021; Ichter et al. 2022; Touroult et al. 2023).
Our recent works have contributed to document the diversity of floral insect visitors of low
altitude Corsican Mediterranean shrubland maquis (Plume *et al.* 2024; Maestracci *et al.* 2024,
2025).

In this study, we characterized and analysed the networks of these plant-floral visitor
interactions in this meso-Mediterranean vegetation on three different sites around the city of
Ajaccio over 9 months. This paper aims first to contribute to the knowledge of the Corsican
entomological and floristic diversity, and to better understand the functioning of Mediterranean
anthophilous insects' communities by documenting their interactions with plants (Wardhaugh
2015; Ollerton 2017; Plume et al. 2024; Maestracci et al. 2024, 2025). Secondly, we studied
the variations of these insect-plant pollinating networks between seasons and habitats.

The specific questions were: i) What is the relative contribution of the different orders of anthophilous insects to the structuring of plant-floral visitor interaction networks in this Mediterranean ecosystem? ii) How much these networks change in space and time? iii) Which species (floral visitors or plants) are key within the studied networks?

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## MATERIAL & METHODS

Study sites

The study was conducted within the broader framework of research on the mitigation hierarchy
(ERC: Avoid-Reduce-Offset), focusing on the regulatory process associated with ENGIE's
Loregaz industrial project in Ajaccio, Corsica, France. This project, like all development
projects with an impact on the environment, had to be offset. In the case of Loregaz, this means
20 hectares over the three sites to be managed for 20 years for protected local species (for
further details see Maestracci *et al.* 2025). Our study was conducted on the three site locations
all near Ajaccio namely Loretto, Suartello and Vignola (Fig. 1 and Table 1) representing the
ecological offset sites for the Loregaz project, and managed on its behalf by an association, the
Conservatoire d'Espaces Naturels de Corse (CEN-Corse). On each site, the main vegetation is
the meso-Mediterranean maquis (Fig. 2) and the sampling design took into account the
environmental differences within and among sites in order to have a good vegetation
representativeness.

The Loretto site, located a few hundred meters from the city centre of Ajaccio adjoining
the industrial Loregaz site, is made up of a plant mosaic, alternating open areas and groves (Fig.
2, Table 1, Maestracci *et al.* 2025). The site, located at the end of a largely undeveloped valley
and overlooking a stream, is predominantly covered with olive and mastic trees. The Suartello
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site, located on the edge of a wooded area, is made up of an open environment (e.g. grassland) and a plant mosaic environment (Fig. 2, Table 1). The site, located in an area that is both agricultural and natural, is mainly made-up mixed wild oak forest (cork and holm oaks) with olive trees and meadows (formerly agricultural land). Agricultural use of the adjacent areas consists mainly of market gardening and grazing. The Vignola site facing the sea (ca. 200 m inland) was partly (~11%) degraded by heavy rotary grinding in 2018, four years before the study. The site covered by a rather homogeneous maquis composed of fruity calico, olive and mastic trees, but also of Montpellier rockrose largely favoured by the vegetation destruction of 2018.

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The proximity of the sites to each other makes it possible to consider their average temperatures and precipitation as being similar. Thus, they have a warm temperate climate with an average annual temperature of 16.3°C. However, some differences exist, Vignola been more exposed to sea spray and Suartello slightly shadier due to the presence of trees on one side (Fig. 2, Table 1). The sizes of these compensation sites are not homogeneous, with differences of several hectares. However, the areas effectively studied only vary by less than 1 ha between Vignola and Loretto (Table 1). Therefore, we did not account for this factor in the analyses.

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## Sampling method

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On each of the three sites, every two weeks from mid-February to mid-November 2022, all Coleoptera, Diptera, Hymenoptera or Lepidoptera visiting flowers were collected on sight during the various time slots of the day (9am-5pm). Two insect sampling methods were carried out consecutively at the three study sites (Loretto, Suartello and Vignola). The first method was dynamic during which the insects, visiting flowers, were collected along 2 transects (30 m long and 2 m wide) for 30 min/transect. The transects crossed the different types of vegetation in the studied areas. The second method was static and consisted in capturing the floral insect visitors for a period of 5 minutes on two different plants of the same species. For each field session, six characteristic flowering species were selected, resulting in a total of 12 flowering plants observed during a total period of 1 hour. By characteristic species, we refer to the most abundant plant species present at the study sites. The six species selected for each session varied throughout the year, reflecting their respective flowering periods. The two sampling methods were used in addition to better account for abundant plants occurring outside the transects. A separate methodological article will address the differences observed between these sampling approaches. In total, over the three sites, 172 half-hour transects (circa 86 hours) were sampled with the dynamic method and 144 half-hour static observations (circa 77 hours).
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For insects, two collectors were involved in the study, the fieldwork at Loretto and Suartello was achieved by the first author (P-Y.M.) and at Vignola by the second author (L.P.). The plants did not need to be collected and were identified directly in the field, except in rare cases.

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## Species identification

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The identifications of the specimens were done to the species or the morpho-species level in some cases (Hymenoptera and Lepidoptera: P-Y. Maestracci, Diptera and Coleoptera: L. Plume) using several identification keys and checklists (list in Supplementary 1). Several identifications were validated/confirmed by taxonomic experts (Hymenoptera: A. Cornuel-Willermoz; Syrphidae: V. Sarthou and T. Lebard) or CO1 barcoding (unpub. data). Plant identification has also been done thanks to reference flora publications (Jeanmonod & Gamisans 2013; Gamisans 2014).

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## Data analysis

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Diversity indices (number of species and specimens, Shannon index and the estimate number of species with the Abundance-base Coverage Estimator, Jaccard index) were calculated with Past statistical software version 4.16c (Hammer et al. 2001). The Abundance-based Coverage Estimator (ACE) considers as rare all the species with 10 or less individuals (Chao & Lee 1992). All the other analyses were performed in RStudio 12.1 statistical software (Posit team 2024). The species diversity completeness of our sampling was estimated by the function *Richness* from the R package *entropart* as the proportion of the number of observed species compared to an estimated total number of species calculated by Jacknife based on the Abundance-based Coverage Estimator (Chao & Lee 1992). The observed species accumulation curve was calculated with the function *cumsum* from the package *vegan* and plotted with the function *ggplot* from the package *ggplot2* (Marcon 2018). In addition, the corresponding rarefaction curves were produced with the function *specaccum* from the package *EntropyEstimation* (Marcon 2018). To study the temporal variation of insect assemblages (abundances), nonmetric multidimensional scaling analyses were performed using the function *metaMDS* from the package *vegan* (Oksanen et al. 2020).

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For the landscape (habitat) analyse, we applied on the Ajaccio region map the Corine Land Cover (CLC) layer using QGis 3.36 software (Qgis Development Team 2024). Considering that most wild bees forage a few hundred meters around their nest (Gathmann & Tscharntke 2002; Zurbuchen et al. 2010), and syrphids (Doyle et al. 2020; Rader et al. 2016) or large bees like bumblebees can fly more than one kilometre (Hagen et al. 2011), two

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environmental zones were considered: the close environment by measuring the surface of the
various habitats (according to CLC nomenclature) within a radius of 200m around each study
zone (e.g. where the plant-insect interactions were observed) and the long-range environment
within 1km radius around each study zone.

The network visualization was performed using the function *plotweb* and their analysis
with the functions *networklevel* (L, Nw, H2) and *DIRT_LPA_wb_PLUS* (Qw) all from the
package *bipartite* (Dormann et al., 2008, 2009, 2017). The parameters selected to evaluate
differences in network structures were: the average links per species (L), the weighted
nestedness (Nw), the network-level specialization (H2) and the weighted modularity (Qw).

The average links per species represents the average number of interactions for one
species in the network. The weighted nestedness the degree to which specialist species are
associated with generalist interactions considering the observed interaction frequencies. The
network specialization reflects differences in species specialization between networks while the
weighted modularity measures the existence of highly connected (non-overlapping) groups of
species. Moreover, the network dissimilarities (β-diversity) and their components among the 3
studied sites were performed with the function *betalinkr_multi* from the package *bipartite*. The
network dissimilarity is partitioned into the dissimilarity due to species composition differences
and due to rewiring (Poisot *et al.* 2012; Fründ 2021) as shown in the following formula β(WN)
= β(ST) + β(OS). WN represents the entire network dissimilarity, ST the proportion of
dissimilarity due to species turnover, and OS the proportion of dissimilarity due to behaviour
change of shared species (rewiring). We also used specific composition dissimilarity (S).

Another way of looking at these insect communities is to look at the contribution of
insect species to the organization of the interaction network and the cohesion of the modules
by the C-Z graphs (Fig. 3). The observed species CZ parameters were calculated with the
functions *czvalues* after getting the network modules with the function *MetaComputeModules*
(method=Beckett). High values of the parameter C (in abscissa) indicate (insect) species
contributing to the connectivity between modules (e.g. interactions with plants of different
modules), high values of the parameter Z (in ordinate) indicate species contributing to the
cohesion within the modules (e.g. interactions with different plants of the same module).
Species plotted on the bottom left part of the C-Z graph contribute little to the network (low C
and Z parameters = peripheral species), those on the bottom right are connecting different
modules, those on the top left are contributing to a module coherence (intra-module
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connections), and finally those on top right are network species structuring both the intra-module and the inter-modules connections.

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Finally, we used null models with the same dimensions and connectivity than the observed matrices (function *nullmodel* with method=vaznull and n=250), to calculate the values of the CZs and to statistically compare the selected network parameters from random distributions but also among the three studied sites (Dormann 2025). To test the significance of the network connectivity, the function *nullmodel* was implemented with method=r2dtable, since the method=vaznull constraints the connectivity of the null models to be equal to the observed matrices. These null models control for sampling bias, abundance and diversity, and are therefore important for the unbiased interpretation of changes in network metrics, and to detect network pattern that are not just random (Vázquez & Aizen 2003; Blüthgen *et al.* 2008; Blüthgen & Staab 2024; Dormann 2025).

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RESULTS

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1. Micro-habitats and specific environmental differences

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1.1 *Floral visitors' abundance and diversity per study site*

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Over the 9 months of the study, a total of 2,848 insect specimens were sampled, representing a total of 241 insect morpho-species (Table S1). On a global scale, the richest insect order was the Hymenoptera representing 38.8% of the morpho-species, followed by the Coleoptera (30.2%), the Diptera (23.5%) and finally the Lepidoptera (7.4%). On the other hand, the Coleoptera was the most abundant insect order representing 53.1% of the specimens, notably with two species *Psilothrix viridicoerulea* (212 insects) and *Tropinota squalida* (117 insects). The Hymenoptera was the second most abundant insect order representing 35.3% of the specimens, and the two most abundant species were *Apis mellifera* (204 insects) and *Bombus xanthopus* (144 insects). Lastly, the Diptera (6.8%) and the Lepidoptera (4.8%) were less abundant in our sampling. The Syrphidae family (Diptera) was the most diverse with 29 species representing 12% of the total diversity (Fig. 4). Among the Hymenoptera, three families were particularly diverse: the Apidae (9.1%), the Megachilidae (8.3%), and the Halictidae (5.4%). Other notable diverse insect families were the Bombyliidae (Diptera) (7.9%) and the Chrysomelidae (Coleoptera) (6.6%).

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Interestingly, the abundances of each order varied according to the studied site. Coleoptera were still dominant at all three sites (56.5% at Loretto, 52.6% at Suartello and 48.7% at Vignola), but the number of Hymenoptera was higher at Vignola (42.2% compared with 33%
at both the other two sites) and also the number of Diptera (8.3% compared with 7.1% and 5.8%). Curiously, the number of specimens from Lepidoptera order varied greatly representing less than 1% of the Vignola floral fauna composition, 3.5% at Loretto and 8.5% at Suartello. Another interesting fact is the greater diversity of beetles at the Vignola site (39.4%), compared to Loretto and Suartello sites (30.9% and 30.2% respectively), but a relatively lower abundance (as detailed above). This suggests a greater capture of generalist species in Loretto and Suartello, and a 'better' sampling in Vignola, perhaps due to a combined effect of the 'observer' effect and site specificities. On the other hand, Hymenoptera diversity was lower in Vignola (38.6% compared to 42.3% at Loretto and 43.4% at Suartello) as for their relative abundance pattern. Finally, Diptera diversity didn't vary greatly according to the study sites.

When comparing the three study sites (Table S2), more insects were collected in Suartello (1,110 specimens, 39% of the total sampling effort) and Loretto (1,048 specimens, 37%) than in Vignola (690 specimens, 24%). This pattern was mirrored in the observed insect diversity: 152 morpho-species were recorded in Suartello and 149 in Loretto, while only 126 were identified in Vignola. Sampling completeness estimates confirmed that Suartello and Loretto were better sampled (both with completeness values of 0.76) than Vignola (0.53). Interestingly, despite lower observed richness, the estimated total species richness was higher in Vignola (211.9 by ACE) than in Loretto (200.3 by ACE) or Suartello (191.8 by ACE). This paradox is largely explained by the higher proportion of singletons (species captured only once) in Vignola (45.7% of species) compared to Loretto (34.2%) and Suartello (30.3%). Furthermore, Jaccard similarity indices indicated that Loretto and Suartello shared slightly more species with each other than either did with Vignola (Table S3).

The sampling (e.g. temporal) accumulation curves of insect species (Fig. 5) further emphasize the uniqueness and richness of each site's insects' community. None of the curves clearly reached an asymptote by the end of the sampling period, particularly in Loretto and Suartello, indicating, consistent with richness estimators, that additional samplings would likely reveal additional species. This suggests that each site harbours diverse anthophilous insect communities and partially distinct, possibly shaped by differences in habitat heterogeneity, plant successional stages, or local microclimatic conditions (see section 1.3).

A similar pattern was observed for flowering plant diversity. Of the 54 plant species recorded across all sites, 36 flowered in Suartello, 35 in Loretto, and only 20 in Vignola (Table S4). Among these, 26 species were unique to a single site, 19 occurred at two sites, and just 9 were shared across all three. The Asteraceae family was by far the most diverse, comprising 19
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species and accounting for 35% of the total flowering plant diversity (Fig. 4). Other notable families included the Fabaceae (7.4%), the Apiaceae, and the Brassicaceae (each 5.6%).

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Overall, a total of 2,848 flower-visiting insects were documented on these 54 plant species.

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Seven plant species were particularly frequently visited, *Daucus carota*, *Foeniculum vulgare*, *Asphodelus ramosus*, *Urospermum dalechampii*, *Cistus monspeliensis*, *Dittrichia viscosa*, and *Carlina corymbosa*. Altogether they accounted for 61% of all insect observations and 81% of the recorded insect species diversity (Fig. 6).

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## 1.2 Plant-floral visitor interaction networks

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The three sites presented different insect and plant communities (see previous section) resulting in different plant-floral visitor networks. Due to their higher plant and insect diversities, the two networks from Loretto and Suartello appeared to be more complex with a greater number of boxes on both levels (insects and plants) and a stronger link interweaving, than that of Vignola (Fig. 7). This complexity is measured by the average number of interactions for each species (e.g. mean number of links), statistically higher in Suartello and Loretto than in Vignola (Table 2). Note that the network complexity is significantly lower than that of random networks due to probably a combination of seasonality (e.g. temporal impossibility encounter) and interaction specialization (see below). The three studied networks appeared to be significantly more specialised than corresponding random networks (Table 2). Moreover, the network in Vignola was significantly more specialized (higher H2) than both in Loretto and Suartello. There was no difference among sites of the network weighted nestedness, but they all were significantly lower than the corresponding random networks (Table 2). This result indicates that specialized species were less involved in interactions with generalist species explaining the network specialization observed previously. Finally, the network modularity (Qw) was higher in all the observed networks than that of the random networks indicating that these networks were constituted of distinct modules (e.g. groups of highly interacting species) sharing no species (Table 2). Moreover, the modularity of the network at Vignola was the higher, intermediate in Loretto and lower in Suartello. Such results ‘parallel’ the network specialization (H2) suggesting that specialized species tended to constitute module(s) different from generalist ones resulting also in a global low network nestedness (Table 2).

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When comparing the network dissimilarities (β-among diversity; Fig. 7, Vignola's network appeared to be more different in terms of species composition from the two other networks of

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Loretto and Suartello (S = 0.57 and 0.49 respectively) than Loretto from Suartello (S = 0.44; Fig. 7). In terms of network interactions, the three networks were highly dissimilar (WN > 0.71), the largest dissimilarity was found between Vignola and Suartello (WN = 0.82) whereas Loretto was similarly different from Vignola (WN = 0.73) or Suartello (WN = 0.71). Interestingly these community interactions differences were due to different dissimilarity component according to the pairs of sites. Between Vignola and Suartello it was mainly due to differences in species (insects and plants) community composition ('species turnover', ST = 61%) whereas it mainly resulted from partners differences for the shared species (same species with different behaviours) between Vignola and Loretto ('rewiring', OS = 56%). Between Loretto and Suartello, the species communities' differences were equally due to 'species turnover' and to 'rewiring'.

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329 **1.3 Landscape and habitats analysis**

The three study sites, Vignola, Suartello, and Loretto, are geographically close to one another in the Ajaccio region, yet they displayed marked differences in their geomorphological settings and habitat types (Fig. 8). Loretto was characterized by maquis vegetation interspersed with an old terraced olive grove overlooking the Saint-Antoine valley at an elevation of 85 meters, situated only a few hundred meters from the city centre within the industrial area of Loregaz. In contrast, Suartello lies on a plateau at 90 meters above sea level, featuring grasslands adjacent to tall maquis woodlands dominated by holm oak (*Quercus ilex*) and cork oak (*Q. suber*), within a largely agricultural and peri-urban zone close to both industrial and residential areas. On the other hand, Vignola, is a coastal area of low maquis bordering the Terre Sacrée beach, flanked by the reliefs of the “Crêtes” massif, ranging from Mont Cinaraggio (528 m) in the east to Punta Alta (283 m) in the west. These sites also differed in their landscape composition, forming mosaics of habitats of varying complexity and structure (Fig. 8).

When examining the immediate surroundings (within a 200 m radius) of the three study sites, habitat structures appear relatively homogeneous in terms of the number of habitat types (typically two per site), yet they differed markedly in their relative proportions (Fig. 8). At Suartello, the landscape was equally divided between agricultural habitats and sclerophyllous vegetation (Fig. 8, top right inset). At Loretto, agricultural habitats were largely dominant, covering 89% of the nearby area (Fig. 8, lower center inset). In contrast, Vignola's immediate environment was overwhelmingly composed of sclerophyllous vegetation (94%) (Fig. 8, left inset). Expanding to a broader scale (1 km radius), the landscape becomes more complex, forming habitat mosaics, except at Vignola where sclerophyllous vegetation still dominated

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(62%), with the sea occupying a substantial portion (26%) of the area, unsuitable for insects (Fig. 8). At Loretto, the landscape was roughly equally distributed among cultivated fields (25%), agricultural lands interspersed with vegetation (30%), and sclerophyllous vegetation (36%). Suartello presented the most heterogeneous environment, comprising sclerophyllous vegetation (30%), agricultural lands with substantial vegetation (27%), industrial areas (17%), cultivated fields (14%), and an urbanized zone (9%) (Fig. 8).

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These ecological contrasts likely contributed to the observed differences in the diversity and abundance of flower-visiting insects detailed before and their associated plant communities across the three sites, despite their geographic proximity.

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## 2. Seasonal variations pattern

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### 2.1 Anthophilous insect phenology and communities associated over a year

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During the 9 months survey on the three sites, insect species floral activity changed significantly over time. Six insects temporal ‘groups’ (e.g. communities) were identified for each study site (Loretto and Suartello and Vignola) with MANOVA tests showing that their species composition were significantly different (Table S5, Fig. 9). The temporal succession of insects materialized by these communities slightly varied from one site to another with different starting and ending weeks for some of these communities (Table 3). The insect communities on the sites of Loretto and Suartello were synchronized for almost all the sampling year except for the last two communities (L5-L6 and S5-S6) that presented a two-weeks ‘difference’ (Table 3). The site of Vignola showed a temporal shift more marked with ‘shorter’ communities in spring (V2-V3) resulting in a time lag of one week earlier for the summer communities (V4-V5, Table 3).

Taking a closer look at the diversity of these communities (Table 4 and Fig. 9), the richest and most diverse insect communities occurred in June represented by the community n°4 at all sites. The following summer communities (n°5, July to September) were almost as rich and diverse (Table 4). The best sampled insect communities, with the highest completeness, were different among sites (L5 = 0.75; S4 = 0.73; V3 = 0.70, excluding the poor V1 community). The communities representing the annual beginning (L1, S1 and V1) of the sampling were the poorest and the less diverse in each site. Interestingly, only the community representing the ending of the sampling in Suartello (S6) showed a significant reduction of species richness and abundance. This was not the case in Loretto and Vignola where these autumn communities (L6 and V6) were similarly diverse and abundant than the previous summer ones (Table 4).
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## 2.2 Species contributions to anthophilous insect and plant communities

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### 2.2.1 Insects visitors

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When looking at the species contribution to the network structuration of all sites (Fig. 10 and S1), numerous Coleoptera species appeared to be important, in particular in spring and early summer (communities 3 and 4). However, although several of these insects contributed to several sites, or even all three, many of the structuring species were restricted to one site and one community.

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At Loretto (Fig. 10 – left column), the early-season community L1 was composed almost exclusively of a few peripheral insect species, indicating a poorly structured network at this stage. In community L2 (end of March to early April), the beetle *Tropinota squalida* emerged as an important connector between modules. During community L3 (mid-April to end of May), the network became more intricate: seven beetle species contributed significantly to network structure. *Psilothrix viridicoerulea* acted as a key network species (with high C and Z values), while *Oxythyrea funesta*, *Mordellistena pumila* (Coleoptera), and the bumblebee *Bombus xanthopus* (Hymenoptera) behave as module connectors. Meanwhile, *Tropinota squalida*, *Bruchidius rubiginosus*, and *Oedemera crassipes* were module hubs, reinforcing intra-module cohesion. In community L4 (June to mid-July), *Mordellistena minima* was identified as a network species, while the wild bee *Lithurgus chrysurus* and the beetle *Mylabris variabilis* contributed to intra-module cohesion. Six other species, predominantly Coleoptera, acted as connectors across modules. In L5 (mid-July to end of August), the community composition shifted, with two Hymenoptera becoming prominent: *Heriades crenulata* maintained intra-module cohesion, and *Polistes dominula* acted as a module connector. Finally, in community L6 (September to end of November), *Apis mellifera* and *Hylaeus* sp1 were central network species, while three others served as module connectors, two Diptera (*Eumerus barbarus* and *Syritta pipiens*) and one Lepidoptera (*Lycaena phlaeas*).

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At Suartello (Fig. 10 – center column), compared to the previous site none of the communities consisted solely of peripheral species. In S1 (February to March) and S2 (end of March to mid-April), only one key species per period shaped the network: *Apis mellifera* and *Mordellistena* sp2 respectively, both acting as module hubs. In S3 (mid-April to end of May), several beetles structured the network, with three species (*Tropinota squalida*, *Oedemera podagrariae*, and *Bruchus* sp1) serving as module connectors and *Psilothrix viridicoerulea* reinforcing intra-module links. In S4 (June to mid-July), beetles became even more prominent: four species acted as connectors (*Bruchidius rubiginosus*, *Bruchidius* sp2, *Odynerus* sp1, and *Seladonia* sp1),

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while six species, including *Mylabris variabilis*, *Mordellistena pseudorhenana*, *Mordellistena pumila*, *Oedemera flavipes*, *Ceratina cucurbitina*, and *Lithurgus chrysurus*, functioned as module hubs. Some of these species maintained their network roles in S5 (mid-July to mid-October), with *Mylabris variabilis* and *Ceratina cucurbitina* continuing to ensure module cohesion. Meanwhile, *Seladonia* sp1 shifted from connector to module hub, and *Bruchidius* sp1 maintained inter-module connections. In S6 (mid-October to November), only *Apis mellifera* (as module hub) and the butterfly *Lycaena phlaeas* (as connector) stood out in structuring the network.

At Vignola (Fig. 10 – right column), *Bombus xanthopus* was pivotal early in the season, acting as a network species in V1 and transitioning to a module hub in V2 (mid-February to mid-April). In V3 (mid-April to mid-May), only *Mordellistena* sp6 contributed significantly to intra-module cohesion. By V4 (mid-May to end of June), two species, a Coleoptera and a Hymenoptera (*Oedemera flavipes* and *Hylaeus* sp1), functioned as module connectors, while small beetles (*Mordellistena minima* and *Mordellistena pseudorhenana*) and the bee fly *Chalcochiton holosericeus* enhanced module cohesion. In V5 (July to August), two wild bees (*Hylaeus* sp1 and *Megachile argentata*) maintained cohesion within their modules, while *Megachile* sp2 served as a connector. Finally, in V6 (September to mid-November), five species structured the community: the honeybee (*Apis mellifera*) and two Diptera (*Chrysotoxum intermedium* and *Bombylius minor*) reinforced intra-module connections, whereas two other small wild bees *Heriades crenulata* and *Colletes similis* acted as connectors between modules.

## 2.2.2 Plants

This site-specificity was also reflected on the plant side, with most network-structuring species being unique to a particular site and sampling period (e.g. temporal insect community). However, some species, such as *Chondrilla juncea* and *Heliotropium europaeum*, were observed across multiple sites. *Chondrilla juncea* consistently acted as a module hub regardless of location, while *Heliotropium europaeum* shifted from a hub role at Suartello to a connector role at Vignola, illustrating how species' structural roles may vary depending on local ecological context and suggest functional plasticity depending on the site.

Additionally, some species remained important across different sampling periods within the same site, highlighting their temporal importance. This was the case for *Knautia integrifolia* at Loretto (L3–L4) and *Bunias erucago* at Suartello (S2–S3), which both maintained a structuring role over two successive sampling periods (Table 3).
457 Hymenoptera was the most diverse order of flower-visiting insects across our study sites, 458 accounting for 39% of the morpho-species. This group, especially the Anthophila (bees), is a 459 major component of pollinator communities, with around 20,000 wild bee species known for 460 their high pollination efficiency due to their hairy bodies and behaviour (Michener 2007; 461 Wardhaugh 2015; Ollerton 2017; Ollerton 2021). Their richness peaks in Mediterranean 462 regions, which host about 720 species (Ropars et al. 2020), making them key pollinators in 463 these ecosystems (Michener 2007; Ollerton 2017; Ghisbain et al. 2023). This diversity is 464 reflected in their varied functional traits, such as pollen-collecting structures, tongue length, 465 foraging behaviours, and plant preferences. 466

466 Over the 9 months of our study, three families of wild bees out of the six collected appeared to 467 be particularly well represented in terms of abundance and diversity: the Megachilidae, the 468 Apidae and the Halictidae. This is consistent with one of the most insightful studies on floral 469 visitors in Mediterranean ecosystems, conducted by Herrera (1988). In our case, the recent 470 taxonomic revision of Corsican Megachilidae, comprising 91 species, of which we sampled 471 about 20%, helped overcome identification challenges by focusing specifically on this regional 472 fauna (Le Divelec et al. 2024).

473 The other two families haven't yet been specifically studied in Corsica, except for a few genera 474 such as *Bombus* (Rasmont & Adamski 1995). Hence, we expect to discover new species or new 475 records for Corsica (Flaminio *et al.* 2024), especially considering that in Europe, 10.4% of the 476 described species of Apidae belong to the cuckoo-bees genus *Nomada*, and 8.4% of the 477 Halictidae are *Lasioglossum* species (Ghisbain *et al.* 2023). In our data, these genera were 478 poorly diverse with 2 species of *Nomada* and 5 species of *Lasioglossum* (at the date of analysis 479 of this paper), and this may come from diverse factors. Our sampling method may be biased 480 and underestimate their diversity, these genera being relatively discreet and easier to detect by 481 looking for their hosts' nests rather than on flowers (Oertli *et al.* 2005). Another factor could be 482 linked to their ecology as the studied low altitude sclerophyllous maquis (e.g. meso- 483 Mediterranean vegetations < 90m elevation), exposed to sea spray for one site, may represent 484 less favourable habitats for these bee genera.
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From the Apidae family, two species were very abundant on the flowers: *Apis mellifera* and *Bombus xanthopus*. Honey bees is the most cosmopolitan bee species in the world (Geslin *et al.* 2017), hyper generalist and foraging almost all year round, and thus with a high competition potential for floral rewards (Ropars *et al.* 2022). In the studied anthophilous insect communities, the Corsican ecotype of *Apis mellifera* represented 7.2% of the observed specimens and was active during the 9 months of surveys. This species didn't appear to have a significant ecological role in the plant-floral visitor insect network until the autumn communities (n°6) probably because its activity was overshadowed by diverse very abundant anthophilous beetles (see next section networks).

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*Coleoptera*

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Pollination by beetles or cantharophily is an important pollination syndrome. Actually, there are an estimate of more than 77,000 pollinating species of Coleoptera (Ollerton 2017; Peña-Kairath *et al.* 2023). Beetle pollination is particularly diverse in the tropics but not negligible in Mediterranean ecosystems (Proctor *et al.* 1996; Bernhardt 2000; Moore *et al.* 2018; Li *et al.* 2021). For example, in Catalunya, north-east of Spain, beetles (mainly Mordellidae, Dasytidae, Scarabaeidae and Chrysomelidae) represented 12% of the flower visits for the 17 most abundant plants of the studied Mediterranean grassland community (Bosch *et al.* 1997). In an agroecosystem from Catalunya, beetles (mainly Dermestidae and Mordellidae) were major floral visitors (23.3% in 2015; 50.6% in 2016) of the five dominant weed species (Morrison *et al.* 2021). In a Greek phryganic ecosystem, 70 beetle species from 19 families were recorded as floral visitors, and represented 11% of the anthophilous diversity (Petanidou & Ellis 1993). Also, in southern Spain shrublands, beetles (mainly Dermestidae, Nitidulidae and Melyridae) represented 16% of the diversity and 33% of the abundance of the anthophilous insects (Herrera 1988). It's interesting to note the significant differences between our data and this latter study, both in terms of abundance and diversity. In our study, Coleoptera emerged as the most dominant floral visitor order, representing 53.1% of the total specimens collected. This high abundance was notably driven by few dominant species, *Psilothrix viridicoerulea* (Melyridae), *Tropinota squalida* (Scarabaeidae), *Bruchidius rubiginosus* (Chrysomelidae) and *Mordellistena* spp (Mordellidae) which together accounted for a significant portion of the Coleoptera individuals. Herrera's study also highlighted a different families composition within Coleoptera, with Dermestidae (17.7%), Nitidulidae (4.8%), and Dasytidae (4%) as the main families, collectively making up the majority of the Coleoptera community. On the other hand, in our study sites, Coleoptera represented 30.2% of the diversity. The most recorded families
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were the Mordellidae, Chrysomelidae, Melyridae, Scarabaeidae and Oedemeridae. The
difference in dominant families and species contrasting with other studies could reflect
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vegetation, ecological or geographical variability, as well as differences in sampling methods
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or temporal factors. While both studies recognize Coleoptera as a key component of insect
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assemblages visiting flowers, our findings suggest a more pronounced dominance of certain
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species, which may influence pollination dynamics and community interactions in the studied
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ecosystem. Interestingly, the records of their floral visits were strongly seasonal, mostly active
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in spring-early summer (March-July) which is consistent with published results by Herrera
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(1988) showing their large numbers on flowers from April to June and nearly absent during the
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other seasons.
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Hence, beetles are commonly reported as floral visitors or pollinators. For example, the pintail
beetles (Mordellidae; Ivie 2002), also known as tumbling flower beetles, are common flower
visitors. The adults feed on the pollen of many different plants, especially from the families
Apiaceae and Asteraceae (Morrison et al. 2021). They have been reported to be (potential)
pollinators in different Mediterranean ecosystems (Herrera 1988; Petanidou & Ellis 1993;
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Bosch et al. 1997; Liu et al. 2018). In south Spain shrubland, *Mordellistena* individuals were
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reported to visit flowers of five species: *Armeria velutina*, *Cistus lebanotis*, *C. salvifolius*,
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*Helianthemum croceum*, and *Osyris alba* (Herrera 1988). They appear to be more specific in a
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North-East Spain grassland since mordellids appeared to be the dominant visitors (67.7%
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visitors) of *Daucus carota* (Bosch et al. 1997). Similarly, in a North-East Spain agroecosystem,
Mordellidae were major floral visitors of weed species (34% in 2015 and 22% in 2016), mainly
found also on *D. carota*, but not on *Sonchus oleraceus* (Morrison *et al.* 2021). In our study
Mordellidae represented 13.3% of the recorded floral visits and were identified as
*Mordellistena minima*, *M. pseudorhenana* and *M. pumila* plus several non-identified species.
Although the existence of the first two taxa as species is debated, we have chosen to separate
*M. minima* and *M. pseudorhenana* according to Selnekovič *et al.* (2021).
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Flower beetles usually feeding on pollen spend long periods of time on each visited flower
(Allen-Perkins et al. 2024). This is particularly the case for Chrysomelidae, especially from the
genera *Bruchus* and *Bruchidius*, important visitors in our data on *Urospermum dalechampii* and
*Daucus carota*, but only a few studies have focused on their role as visiting insects (Petanidou
& Ellis, 1993; Bernhart 2000).
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*Psilothrix viridicoerulea* (Melyridae) and *Tropinota squalida* (Scarabaeidae) were
among the most frequent floral visitors, equalizing in abundance the two supergeneralists
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552 Hymenoptera (Apis mellifera and Bombus xanthopus). Psilothrix viridicoerulea represented up to 7.4% of total specimens and 14% of the Coleoptera almost twice the abundance of Tropinota squalida (representing 4.1% of the specimens and 7.7% of the Coleoptera). The first species is a common anthophilous and considered as a pest in some agroecosystems because of his foraging behaviour damaging the flowers (Khater et al. 2003). The latter species is also a flower-dwelling beetle on many species in spring, reported to structure some of the interaction networks studied in the Mediterranean region (Magrach et al. 2021; Allen-Perkins et al. 2024) (see next section network), which was also highlighted in our results for two consecutive seasonal networks. Tropinota squalida and another scarab beetle, Oxythyrea funesta, are mentioned to visit up to 28 plant species, particularly thistles, in a Spanish maquis and cited as probable pollinators for 20 of them (Ruiz 2019). On our sites, they visited flower from 13 species, only 7 were in common with Ruiz's study, six (mainly thistles) from the pollination list (Carduus pycnocephalus, Carlina corymbosa, Cirsium arvense, Eryngium campestre, Daucus carota, Foeniculum vulgare) and one from the visitor list (Chondrilla juncea) (Ruiz 2019). Interestingly, the three most visited species by the two scarab beetles on our sites were not present on the Spanish studied site namely Asphodelus ramosus, Cistus monspeliensis, and Urospermum dalechampii.

569 The genus Oedemera was also one of the important anthophilous genera (5.4% of total abundance), mainly composed by four species: O. crassipes, O. flavipes, O. nobilis and O. podagrariae. This genus appeared to be very important in our study (see next section network).

572 *Mylabris variabilis* (Meloidae) is commonly observed on flowers in South Europe but no detailed data are available, it's reported to visit flowers from *Halimium halmifolium* (Cistaceae) in Spain (Herrera 1988). In our study sites, it interacted with flowers from 6 angiosperm families: Asteraceae (6 spp); Apiaceae, Boraginaceae, Brassicaceae, Campanulaceae and Scrophulariaceae (1 sp each).

577 Anthophilous Coleoptera are frequently reported feeding on flowers from diverse plant species during their activity period, but their role as pollen vector is probably underestimate (Wardhaugh 2015; Sayers *et al.* 2019) and often neglected (Rader *et al.* 2016; Requier *et al.* 2024) despite they are regularly mentioned as frequent and abundant floral visitors. Such statement is confirmed by the results from our network analyses and suggest that these taxa should be specifically studied to measure their pollinating efficiency and specificity.

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584 Diptera
Pollination by Diptera is not negligible with an estimate 55,000 pollinating fly species but fly pollination is less studied than other syndromes (Orford et al. 2015; Rader et al. 2016; Ollerton 2017). At high latitude and altitudes, this order even replaces other insects' visitors (Elberling & Olesen 1999; Lefebvre et al. 2018). The first important Diptera pollinator family is the Syrphidae (6,000 species) (Larson et al. 2001; Woodcock et al. 2014; Doyle et al. 2020). In France, this family represents approximately 340 species of which 168 are present in Corsica (Reverté et al. 2023; Cornuel-Willermoz & Lebard 2024). A second important pollinator family is the Bombyliidae which is also a diverse group (4,500 species) with no fewer than 335 species of Bombyliid flies in Europe (Pape et al. 2015). Their larvae are mostly insect parasites but adults are reported to visit flowers (Herrera 1988; Petanidou 1991, Petanidou & Ellis 1993) and even being pollinators but their efficiency in pollen transport is debated, particularly in the Mediterranean zone (Yeates & Greathead 1997; Kastinger & Weber 2001; Boesi et al. 2009; Ollerton, 2017). In our data, anthophilous Diptera were highly diverse representing 23.5% of the morpho-species, particularly the families Syrphidae and Bombyliidae (29 and 19 species respectively) representing 12% and 7.9% of the total species respectively, which is consistent with previous work on floral visitor assemblages (Herrera 1988).

Despite the high species diversity of these two insect families observed in our study, only few of these species appeared to play a structuring role within the interaction networks analysed. This suggests that, although these groups contribute substantially to overall biodiversity, their individual interactions are more diffuse and do not dominate the architecture of the networks in the ecosystems studied (see next section network).

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### Lepidoptera

Interestingly, Lepidoptera, were the less diverse group of anthophilous insects in our data (18 species). Our result contrasts with the large diversity of this order with 140,000 estimated described species in the world (Wardhaugh 2015; Ollerton 2017), even though most of these species are nocturnal (Wong & Didham 2024). Insularity, particularly the size of the island and the distance from the continent is known to affect butterfly populations (Özden et al. 2008). In Corsica 57 species are listed (Berquier & Andrei-Ruiz 2017) out the 463 species reported in the Mediterranean basin (Numa et al. 2016), and we registered a notable proportion of the insular lepidopteran diversity (31.6%) considering the total low surface effectively studied of approximately 2,5 ha. Despite our methodology bias, the relative high proportion of butterfly species observed may be due to a combination of ecological factors such as the complexity of the mosaic landscape for two of the studied sites and/or the diversity of habitats and plant
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species of the thermo-/ meso-Mediterranean sclerophyllous vegetation. The most abundant
butterfly was the small copper, *Lycaena phlaeas*, representing 29.4% of the Lepidoptera
abundance on our sites, which is not surprising considering its widespread distribution and
generalist behaviour (Streitberger *et al.* 2014).

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Plant-floral visitor interactions networks

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*Insect key species*

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In networks, generalists and abundant species have an essential role for maintaining
communities (Guimerà & Amaral 2005; Olesen et al. 2007; Ollerton 2017). They play key
functional roles (e.g. hubs, connectors and network species) in the interaction network
contributing both to the ecosystem robustness and the coexistence of specialized and rare
species which interact with them (Johnson & Steiner 2000; Olesen et al. 2008). These key
species (e.g. keystone species, core-generalists) in pollination interaction networks can be plant
or insect species (González et al. 2010; Ollerton 2021). They contribute to the network stability
and resilience against perturbations (Pocock et al. 2011; Bartomeus 2013; Ropars et al. 2020),
but they can also impact other anthophilous species being highly efficient floral reward
competitors (Herrera 2020; Ropars et al. 2022).

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In our study system, *Bombus xanthopus* appeared to be a major network species at Vignola at the end of the winter-beginning of spring due to its highly specialized and numerous interactions with *Asphodelus ramosus*. This species has been considered for decades as a subspecies of *Bombus terrestris* (Rasmont *et al.* 2008), a worldwide distributed species particularly because of its use in agricultural pollination (Dafni *et al.* 2010; Boni *et al.* 2023). Today the Corsican endemic *B. xanthopus* is considered to be a valid species (Lecocq *et al.* 2015; Rasmont *et al.* 2021). Bumblebees are mentioned to be uncommon and not abundant in Mediterranean habitats (Ropars *et al.* 2022; Herrera 1988), but in our surveys they were very common, notably the endemic Corsican species, being the third most abundant species and representing 5.1% of the total number of the observed interactions, and 14.3% of the Hymenoptera floral visits. Its abundance differed among sites the species been more abundant at Vignola (58% of the species specimens. This site presented the simplest land cover largely dominated by the sclerophyllous vegetation compared to two other study sites that were somewhat more anthropized (Fig. 8). *Apis mellifera* only appeared to be ecologically relevant in the autumn community, likely due to earlier dominance of abundant flower-visiting beetles. The high abundance of honey bees is often viewed as a sign of anthropogenic pressure, since excessive hive densities in the landscape
can negatively impact wild pollinators and disrupt plant-pollinator interaction networks (Valido et al, 2019; Geslin et al, 2023). Although this species is frequently demonized or marginalized in analyses of plant-floral visitor networks, its role deserves a more nuanced interpretation that considers the specific environmental context. In some degraded or species-poor habitats, honey bees may actually help sustain pollination networks by maintaining visitation flows, despite evidence that they are generally fewer effective pollinators than many wild species (Garibaldi et al, 2011). This highlights the importance of assessing their impact on a case-by-case basis, rather than assuming uniformly detrimental effects.

Another bee genus ecologically important across the three studied networks is the genus *Hylaeus*. In the studied anthophilous communities, it encompassed 5 morpho-species (mostly *H. sp1*, *H. sp2* and *H. deceptorius*) and represented 4.9% of the total interactions and 13.8% of the Hymenoptera recorded. One morpho-species, *Hylaeus sp1* appeared to be particularly important and structured part of the plant-floral visitor interaction networks from end of spring to autumn. These small wild bees seem to have an important role in maintaining temporal dynamics over time (Martinez-Nunez & Sakai 2024). However, their taxonomical identification is challenging and they represent a relatively high number of species in Corsica (A. Cornel-Willermoz pers. comm.). There is a real challenge for apprehending their specific ecological role in interaction networks, since the risk of not recognizing a species among this complex genus is particularly high as they seem to occupy similar ecological niches.

Among Coleoptera order, several taxa appeared to be important floral visitors structuring at some point the recorded interaction networks, namely: *Psilothrix viridicoerulea* (Melyridae), *Tropinota squalida* and *Oxythyrea funesta* (Scarabaeidae), *Bruchidius* and *Bruchus* spp (*Bruchidius rubiginosus*, *Bruchidius sp2*, *Bruchus sp1*; Bruchus; Chrysomelidae), *Mordellistena* spp (*M. pumila*, *M. pseudorhenana*, *M. minima*, *M. sp6*; Mordellidae), *Oedemera* spp (*O. flavipes*, *O. podagrariae*, *O. crassipes*; Oedemeridae) and *Mylabris variabilis* (Meloidae).

Among these beetles, several small taxa have emerged as key floral visitors, playing a significant role in shaping interaction networks during early summer and summer. This is especially true for members of the Mordellidae family, commonly known as tumbling flower beetles. Due to taxonomic complexities, most were identified at the morphospecies level (Ivie 2002). These beetles are frequent visitors to flowers and have been documented as potential pollinators across various Mediterranean ecosystems. Adults primarily feed on pollen from a broad spectrum of plant species, with a notable preference for Asteraceae and Apiaceae, which is also confirmed in our study (Herrera 1988; Petanidou & Ellis 1993; Bosch et al. 1997; Liu et
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al. 2018; Morrison et al. 2021; Maestracci et al. 2025). Interestingly, a diversity of Mordellidae species played distinct roles within the interaction networks across the three sites. *Mordellistena minima* was particularly prominent in the summer floral visitor communities of Loretto and Vignola, where it primarily visited *Daucus carota* and functioned respectively as a network species and a module hub. Similarly, *M. pumila* was significant in Suartello and Loretto during this period of low floral diversity, acting as a module hub in the former and a module connector in the latter. *M. sp6* also stood out, serving as a connector in Loretto and a hub in Vignola. Finally, *M. pseudorhenana* played an important role as a module hub in both Suartello and Vignola. This variability underscores how even closely related species can fulfil complementary structural roles in structuring these plant-floral visitor interaction networks.

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*Psilothrix viridicoerulea* is a spring flower-dwelling beetle that helps shape interaction networks in the Mediterranean (Magrach *et al.* 2021; Allen-Perkins *et al.* 2024). Although the networks specifically involving this species have not been well documented to date in the Mediterranean, its role as the dominant visitor of certain floral interactions in our data suggests that it has a significant influence on the connectivity and modularity of networks, giving rise to the idea that this species should be taken into account in the conservation of plant-insect visitor interactions.

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On the other hand, the genus *Oedemera* with four major species in our study, covers a wider range of roles in these networks. Indeed, depending on the study site, these species appeared to have different functions within their respective networks: *O. crassipes* was almost a module hub in Loretto, whereas *O. podagrariae* was a connector in Suartello and *O. flavipes* in Vignola. Frequently observed on flowers in the Mediterranean region (Montalto & Bologna 2011), their connector role can be explained by their generalist ecology, and their hub module role by their significant local abundance (Fadda 2016). The case of the *Oedemera* beetles deserves particular attention. Although this group in our samples comprised only a single genus and seven species, it displayed a remarkable diversity of behaviours despite their relatively similar body sizes (8–12 mm). In the Mediterranean basin, *Oedemera* exemplifies a genus of beetles with a broadly generalist ecology, yet showing distinct affinities for certain flowering plant species (Proctor *et al.* 1996; Carloni & Bologna 2005; Montalto & Bologna 2011; Willmer 2011; Plume 2025).

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For the Diptera order, in Loretto two Syrphidae, *Eumerus barbarus* and *Syritta pipiens*, were module connectors, while in Vignola one Bombyliidae (*Bombylius minor*) and one syrphid (*Chrystoxum intermedium*) were module hubs. These species from the two most diverse Diptera
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families made a significant contribution in structuring autumn networks in these two sites despite their low abundance. In fact, the species with the highest abundances in our samples, *Eumerus barbarus* (Syrphidae) and *Bombylius minor* (Bombyliidae) represented respectively 0.8% and 0.4% of the total anthophilous insects. Due to networks’ small sizes, these species were detected as important species.

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In summary, the insect's key species were relatively numerous in our data, varying according to the sites studied and the time of year, mainly Coleoptera in spring and summer, Hymenoptera in winter and autumn, and, more marginally, Diptera and Lepidoptera in autumn. This variability introduces an additional layer of complexity to study these taxa, as their fluctuating roles and community memberships make it challenging to characterise them precisely, and to include them in conservation management strategy. In comparison, focusing on plants may offer a relatively more stable target for conservation efforts, given their lower diversity and greater temporal persistence across sites.

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### Plants related to insect key species

About 60% of the floral visits were recorded on species from two angiosperm families: the Asteraceae (32.7%) and the Apiaceae (27.9%). These families, which are reported, on a global scale, to be the richest families behind Orchidaceae (Jeffrey 2007), represented altogether 40.7 % of the studied flora diversity. Interestingly, two other species from less diverse families, appeared to represent an important local floral resource for anthophilous insects, namely *Asphodelus ramosus* (Asphodelaeaceae) and *Cistus monspeliensis* (Cistaceae) since they were together associated with ~18% of the total floral visits. These two species were locally abundant and markers of the local Mediterranean vegetation. Note also the importance of ruderal species of Brassicaceae, representing almost 6% of all the visit records, that were abundant on the two sites (Suartello and Loretto) presenting more complex (i.e. mosaic) and anthropized landscapes. Most of the registered plant species produced open flowers with easy access to the stamens and nectaries representing a resource to visitors with short mouthparts, in particular beetles, hoverflies and short-tongued bees (Proctor *et al.* 1996); but many insects with long mouthparts, as Bombyliidae or long-tongued bees, were also observed visiting these flowers. Two Apiaceae species, fennel and wild carrot, are good examples of such important flowering plant, attracting a taxonomically diverse insect fauna including bees, wasps, beetles, hoverflies and bee flies, which is also reported in other studies (Skaldina 2020, Morrison *et al.* 2021). These two plants are core generalist plants, their numerous small flowers provide each few resources, but
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collectively have a high nutritional importance (Ollerton 2021). Adding this floral trait to their
high nectar replenishment rate and abundant presence in their habitats, these species are
essential in maintaining populations of visiting insects when flowering (Ollerton 2021).

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In our data, only *Daucus carota* appeared to be important in the networks, and only in Vignola, while *Foeniculum vulgare* does not appear in any of the networks. Conversely, some species that have attracted few visitors were also structuring their network. This is the case for *Knautia integrifolia*, *Chondrilla juncea* or *Bunias erucago* (< 2% of total visit). These species are potentially interesting and should be specifically further studied.

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Well-established in Mediterranean ecosystems, these plants display diverse phenologies spanning from spring to autumn and offer essential nectar and pollen resources that support a wide range of insect visitors, thereby reinforcing their pivotal role in shaping plant-floral visitor interaction networks.

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*Spatial and temporal variations of community's assemblages*

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Beyond the dynamic presence of pivotal insects and plants taxa, the composition and structure of plant-insect interactions are also profoundly shaped by spatial and temporal dynamics. Exploring how these communities vary across sites and seasons provides further insight into the complexity of Mediterranean plant-floral visitor interaction networks.

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Among the three studied sites, Vignola exhibited less diverse plant-insect communities, which could partly be explained by the lower floral diversity observed at this site, only 38 plant species were recorded in the plant-floral visitor networks, compared to 52 at Loretto and 57 at Suartello. This reduced diversity may also result from local environmental conditions. The vegetation at Vignola primarily consists of a low, dense maquis, with few open grassland areas and very few deciduous trees, aside from some scattered wild olive trees. Additionally, the nearest wooded areas are situated further away than in the other two sites, occurring higher on surrounding hills at over 700 meters. This landscape likely accounts for the absence of several species typically found in open or cultivated habitats, such as *Cirsium arvense*, *Leontodon tuberosus*, *Hypochaeris achyrophorus*, *Plantago lanceolata*, *Calendula arvensis*, *Silene gallica*, *Campanula rapunculus*, and *Lupinus angustifolius*. While a few temporary ponds are present, the closest true wetlands are the Capo di Feno ponds, located several kilometres north of the study site 'behind' a ridge line. Altogether, these features suggest that both the limited floral composition and the specific habitat structure at Vignola contribute to its lower diversity of plant-floral visitor interaction networks compared to Loretto and Suartello. Regarding these other sites, Loretto comprises fallow areas established on former olive groves, situated near

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wooded environments dominated by tall maquis, including oak woodlands with *Quercus ilex*, *Q. suber*, and *Q. pubescens*. This habitat configuration provides favourable conditions for numerous species whose larvae develop within tree cavities or depend on forested habitats. The study area itself lies on a hillside overlooking the Arbitrone stream, just a few meters below, which forms an ecologically valuable wetland. This slow-flowing stream, bordered by reed beds, offers an organic-rich environment well-suited to many species with aquatic or moisture-dependent larval development.

Similarly, the Suartello site consists of a well-exposed grassland environment adjacent to tall maquis and oak woodlands. This mosaic of habitats supports species associated both with herbaceous plants typical of open areas and with trees, whether dead or alive, found in forested settings. Like Loretto, Suartello also includes a wetland feature to the north, where the Verdana stream crosses private pasturelands. This additional humid environment likely favours species that rely on moist habitats and are otherwise relatively uncommon in predominantly maquis landscapes.

These ecological differences could explain the observed differences in diversity and abundance of visiting insects and associated plants between sites despite the fact that they are geographically close (<6 km). The plant-insect visitors communities display marked spatial heterogeneity, reflecting fine-scale ecological dynamics (Galiana *et al.* 2022). Indeed, the three networks were highly dissimilar (WN ≥ 0.71), with a substantial proportion of this dissimilarity driven by species turnover (ST = 44–61%). Such differences likely stem from the distinct assemblages of micro-habitats presents at each site, which, both locally and across the landscape, shape the occurrence of species dependent on particular ecological conditions (Trøjelsgaard & Olesen 2016). While micro-temperature variations could also play a role (Von Schmalensee 2023), it appears that this mosaic of specific habitat features ultimately governs the composition of plant and anthophilous insect communities, leading to site-specific interactions and unique network structures.

Indeed, despite comparable methodological approaches and the use of indices specifically designed to minimize the influence of network size (number of species of the two trophic levels), such as H2' for specialization, weighted modularity (Qw), and weighted nestedness, Vignola's network still exhibited higher values of specialization and modularity, but not coupled with lower nestedness. Highly modular networks often have lower nestedness, as the presence of modules decouples the network into loosely connected sub-units. Highly specialised networks also tend to have lower nestedness, as specialisation implies less sharing
of partners, which breaks down the nested structure. This can be explained by the modules themselves being nested: in other words, there is a strong nested structure within the modules. Some work suggests that a network can combine modularity and nestedness locally (Olesen et al. 2007, Fortuna et al. 2010). Or nestedness can be dominated by a set of very general interactions, in addition to a few specialised modules. Such structure results in a globally nested network but with strong modular substructures. It might be worth digging deeper to understand how nestedness and modularity coexist in these networks.

On the other hand, Loretto's and Suartello's networks, have an inverse pattern with lower value metrics except the higher mean links per species (Table 2). A network with low nestedness, modularity and specialization, yet with a relatively high mean number of links per species, depicts a broadly connected and generalized system. Within these networks, interactions are evenly spread without clear compartments or hierarchy, suggesting an opportunistic community structure with extensive partner sharing across species.

In terms of temporal variations in plant-floral visitor interaction networks, in our study, there was also a large intra-annual variation in anthophilous insects' and plants' species composition and the resulting plant-floral visitor networks. A seasonal structured partitioning into several insect communities could be highlighted in all studied sites suggesting marked seasonal insect phenologies (Parrish & Bazzaz 1979; Rotenberry 1990; Olesen et al. 2011; Encinas-Viso et al. 2012). These seasonal insect communities (e.g. at a global scale winter, spring, summer and autumn), have been identified in other studies integrating temporal surveys (Bosch et al. 1997; Herrera 1988; Balfour et al. 2018; Ollerton 2021; Morente-López et al. 2018; Aitakka et al. 2024). Logically, plants also showed a strong seasonal phenology with key flowering dominating various seasons that were mainly visited such as asphodel and rockrose in spring, fennel and the wild carrot in summer, or the European turn-sole in autumn. The extended yearly insect activity may be due to both the mild climate until November and the extended flowering period in the meso-Mediterranean environments (Morente-López et al. 2018).

The temporal succession of insects materialized by species partitioning forming these different communities are characterized not only by the species presence, but also by a different arrangement of interactions resulting in seasonal specific networks (Burkle & Alarcon, 2011; CaraDonna et al., 2017, 2021; CaraDonna & Waser, 2020; Chacoff et al., 2018; Magrach et al. 2023).

By combining temporal and spatial analysis, we can see differences on Suartello site where only the community representing the ending of the sampling in Suartello (S6) showed a
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significant reduction of species richness and of their abundances compared to the two other sites. This is explained by the extended period of summer (community n°5), suggesting a specific microclimate that delays the phenology of species, probably due to the woody landscape.

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In our study system, insects and plants species differed across seasons and sites, underscoring the inherent complexity and temporal dynamics of these networks, patterns that would have been overlooked by short-term or purely aggregated approaches. This variability further complicates the conservation of these taxa, as their shifting roles and fluctuating community membership hinder the development of consistent management strategies. In contrast, focusing on plants offers a relatively more stable conservation target due to their lower diversity and greater temporal persistence across sites.

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These findings highlight how certain plant species are fundamental to sustaining diverse and temporally stable communities of anthophilous insects in Mediterranean habitats. Given the high diversity of floral visitors documented in our study, a pattern consistent with global observations, it becomes evident that conservation efforts should also account for this level of ecological complexity (Tylianakis et al. 2010). The varying status and distribution of these insects across species and sites present significant challenges for local management and conservation planning. Nevertheless, their crucial role in maintaining the stability and resilience of networks make them priority targets in strategies aimed at preserving and enhancing pollination functions in these ecosystems. Importantly, since even plant communities can undergo substantial turnover, there is a clear need for integrated approaches that simultaneously consider the dynamics of plant populations and the variability of their associated insect assemblages.

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## CONCLUSION

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Anthophilous insects represent an important proportion of the insect diversity and studying these communities is primordial for understanding insect-plant interactions at the ecosystem level. In this study, we considered the four main orders of anthophilous insects, rather than focusing solely on well-studied groups, as is often the case. This approach is essential for accurately representing the functioning of interaction networks between plants and their visitors. For example, although beetles are often reported as abundant floral visitors, they remain largely overlooked in functional analyses, and their potential role as pollen vectors may be significant. Therefore, including beetles into these type of studies is especially important,
and they deserve greater attention in future research on interaction networks, particularly in Mediterranean ecosystems. This inclusive approach across the four main orders is essential to accurately represent the functioning of plant–plant–floral visitor interaction networks.

Our study also highlights the importance of conducting multiple sampling sessions throughout the year to effectively capture the seasonal dynamics of pollinating insects, flowering plants, and their interactions. This approach offers a more faithful representation of an ecosystem's ecological reality by accounting for the phenological variability of both floral species and their visitors. Indeed, plant–insect community composition can shift markedly between seasons, driven by changes in floral resource availability, climatic conditions, and the life cycles of individual species. Implementing a long-term protocol not only allows the detection of species and interactions specific to certain periods, but also helps identify the most stable and structuring components of the network, valuable insights for ecosystem management. This is especially true for autumn in Mediterranean environments, a season that remains largely understudied despite its crucial role in supporting many flower-visiting insects. This underrepresentation can be partly explained by the fact that most field studies do not extend into this period of the year for various reasons, even though increased efforts would be essential to address this gap.

However, due to their high diversity and the taxonomic challenges they present, directly managing flower-visiting insects is unfortunately not the most practical approach for preserving their interactions. Focusing instead on managing the plant communities that sustain these insects could offer a more effective way to incorporate this ‘dimension’ of ecosystem functioning into conservation strategies. Especially since the variability of insect visitor communities can be intensified according to the type of (micro)habitat studied, but also according to the surrounding habitats.

Looking ahead, it would be valuable to explore how floral traits, such as colour, shape, scent, and rewards, drive the attraction of insect visitors and shape the architecture of these interaction networks. Linking these structural networks to the actual function of pollination would deepen our understanding of their ecological significance and resilience. Moreover, while this study captured important intra-annual (seasonal) dynamics, investigating inter-annual variations would provide critical insights into how these networks fluctuate over longer temporal scales, particularly in response to climate variability, species phenology or habitat changes. This research approach will be explored in future work on our dataset. Such integrative approaches
921 could help better anticipate the stability of plant-floral visitor interaction systems and inform

922 more robust conservation strategies for insect visitors, plant associated and their interactions.

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924
925 Fundings

926 UMR SPE 6134, CPER project N°40137 "BiodivCorse – Explorer la biodiversité de la Corse"
927 (Collectivité de Corse – Ministère de la Cohésion du territoire et des Relations avec les
928 Collectivités territoriales), ENGIE Solutions/Storengy and CIFRE N°2022-0393 doctoral
929 programme (ENGIE/Lab. CRIGEN-Univ. Corsica-Univ. Panthéon-Assas).

930

931 Credit authorship contribution statement

932 **Pierre-Yves Maestracci**: Conceptualization, Formal analysis, writing – original draft, Data
933 sampling, Identification, Data Curation, Data management. **Marc Gibernau**:
934 Conceptualization, Formal analysis, Writing – original draft, Supervision, Funding acquisition.

935 **Laurent Plume**: Writing – original draft, Data sampling, Identification, Data Curation.

936 **Caroline de Zutter**: Writing – original draft, Supervision, Funding acquisition.

937

938 Declaration of competing interest

939 This study was partly funded by the energy company ENGIE with a PhD position on the Loregaz
940 compensation project directed by partner universities (Università di Corsica Pasquale Paoli and
941 Université-Paris-Panthéon-Assas) guaranteeing academic freedom in publishing results. We
942 certify that the results have not been influenced by any economic interests.

943

944 Acknowledgements

945 We want to thank Alexandre Cornuel-Willermoz and Veronique Sarthou for their help in
946 identifying certain specimens, and all the students who helped us since 2021 to study these
947 pollinating insects and their interactions with flowering plants. We also thank two anonymous
948 reviewers for improving this manuscript with their pertinent remarks and corrections.

949

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<figure><img src="image_3.png" /></figure>

1294

1295

1296

**Figure 1.** Left: location of the studied region on Corsica. Right: Geographical localisation of the three studied sites and total specimen abundances sampled per site.
<figure><img src="image_4.png" /><figcaption>1297
1298 **Figure 2.** Types of habitats on the sites of Loretto (left), Suartello (middle) and Vignola (right)</figcaption></figure>

<caption>1299 (Maestracci *et al.* 2025).</caption>
<figure><img src="image_5.png" /></figure>

1300

1301 **Figure 3.** Schematical C-Z plot of species roles adapted from Olesen *et al.* 2007 (cut-off

1302

values: c=0.62 in abscissa and z=2.5 in ordinate)
<figure><img src="image_6.png" /></figure>

1303

1304

<caption>**Figure 4.** Number of species per family for insects (left) and plants (right)</caption>
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Accumulation and rarefaction curves per study site according to the number of sampling weeks. The cumulative total insect richness over the three studied sites is also (horizontal red line).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Percentages of visit abundance (in % of the total number of visits recorded (N=2848)) and insect species diversity (in % of the total number of species (N=241)) for the seven most visited plant species.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Comparative bipartite networks of plants and their floral visitors across the three study sites (blue boxes: plant species, red boxes: insect species). The size of the boxes is proportional to the number of links connected to the box. Inside the upper boxes: WN represents the entire network dissimilarity, composed by ST (the proportion of dissimilarity due to species turnover), and OS (the proportion of dissimilarity due to behaviour change of shared species=rewiring). S represents the specific composition dissimilarity.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Mapping of habitats (Corine Land Cover) of the region Ajaccio including the three study sites. Insert pies represent the habitat proportions (see text for details) around each study site within a radius of 200m (dashed left circles – left pie) and 1km (dotted right circles - right pie).</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Figure 9.** NMDS on the temporal anthophilous insect abundances (Euclidian distances on Log(x+1) abundances) according to sampling weeks on the three sites (L for Loretto, S for Suartello and V for Vignola, see Table 3 for the corresponding dates).</figcaption></figure>
Loretto

1

<figure><img src="image_12.png" /></figure>

2

<figure><img src="image_13.png" /></figure>

3

<figure><img src="image_14.png" /></figure>

4

<figure><img src="image_15.png" /></figure>

5

<figure><img src="image_16.png" /></figure>

6

<figure><img src="image_17.png" /></figure>

Suartello

1

<figure><img src="image_18.png" /></figure>

2

<figure><img src="image_19.png" /></figure>

3

<figure><img src="image_20.png" /></figure>

4

<figure><img src="image_21.png" /></figure>

5

<figure><img src="image_22.png" /></figure>

6

<figure><img src="image_23.png" /></figure>

Vignola

1

<figure><img src="image_24.png" /></figure>

2

<figure><img src="image_25.png" /></figure>

3

<figure><img src="image_26.png" /></figure>

4

<figure><img src="image_27.png" /></figure>

5

<figure><img src="image_28.png" /></figure>

6

<figure><img src="image_29.png" /></figure>

**Figure 10.** C-Z graphs per study site and per groups (1 to 6, see corresponding dates in Table 3 and diversity information in Table 4). Important network structuring species are namely indicated (In black: Hymenoptera; in blue: Coleoptera; in purple: Diptera, in yellow: Lepidoptera).
<table><thead><tr><th rowspan="2">Locality</th><th colspan="2">Geographical coordinates</th><th rowspan="2">Orientation</th><th rowspan="2">Main Vegetation</th><th rowspan="2">Area (ha)</th><th rowspan="2">1334 Studied area (ha)</th></tr><tr><th>Latitude, longitude</th><th>Altitude (m)</th></tr></thead><tbody><tr><td>Loretto</td><td>41°93'35''N 8°71'82'' E</td><td>85</td><td>S</td><td>Wasteland [CORINE-Biotope: 87.1]<br>Matorral with olive trees and mastic trees [CORINE-Biotope: 32.12]</td><td>1.9</td><td>0.33</td></tr><tr><td>Suartello</td><td>41°95'31''N 8°75'58'' E</td><td>90</td><td>SSE</td><td>Grassland [CORINE-Biotope: 34.4]<br>High maquis of the western Mediterranean [CORINE-Biotope: 32.311]</td><td>2.5</td><td>0.89</td></tr><tr><td>Vignola</td><td>41°91'19''N 8°65'00'' E</td><td>30</td><td>SW</td><td>Medium maquis with <i>Cytisus laniger</i> and <i>Pistacia lentiscus</i> in mosaic with <i>Olea europea</i> – Fruity calicotome [CORINE-Biotope: 32.215]<br>Maquis with <i>Cistus monspeliensis</i> [CORINE-Biotope: 32.341]</td><td>18</td><td>1.27</td></tr></tbody></table>
**Table 2.** Network metrics per study site and their corresponding null model values (± standard deviation). Each observed network metric was statistically different (p<10⁻⁵) from its null distribution (not indicated in the table). Moreover, metric differences among sites were tested, values with different letters are statistically different (Dorman, 2025).

<table><thead><tr><th></th><th>Loretto</th><th>Suartello</th><th>Vignola</th><th>Probability</th></tr></thead><tbody><tr><td>Diversity insects</td><td>149</td><td>152</td><td>126</td><td></td></tr><tr><td>Diversity plants</td><td>35</td><td>36</td><td>20</td><td></td></tr><tr><td>Mean links per species</td><td>1.799<sup>a</sup></td><td>1.957<sup>a</sup></td><td>1.534<sup>b</sup></td><td>P ≤ 0.005</td></tr><tr><td>Null model</td><td>3.159 ± 0.038</td><td>3.581 ± 0.040</td><td>2.559 ± 0.039</td><td></td></tr><tr><td>Weighted nestedness</td><td>7.321</td><td>6.524</td><td>7.026</td><td>N.S.</td></tr><tr><td>Null model</td><td>13.988 ± 1.035</td><td>12.05 ± 0.738</td><td>12.849 ± 0.969</td><td></td></tr><tr><td>H2 (specialization)</td><td>0.561<sup>a</sup></td><td>0.530<sup>a</sup></td><td>0.649<sup>b</sup></td><td>P < 0.05</td></tr><tr><td>Null model</td><td>0.309 ± 0.019</td><td>0.302 ± 0.017</td><td>0.33 ± 0.027</td><td></td></tr><tr><td>Qw (modularity)</td><td>0.591<sup>ab</sup></td><td>0.565<sup>a</sup></td><td>0.638<sup>b</sup></td><td>P < 0.05</td></tr><tr><td>Null model</td><td>0.303 +/-. 0.045</td><td>0.343 +/-. 0.031</td><td>0.341 +/-. 0.049</td><td></td></tr></tbody></table>
1339 **Table 3.** Temporal repartition of the groups, representing insect communities with different species compositions, for the three studied sites (same colour code as for Figure 9).
1340

<table><thead><tr><th>Months</th><th>Feb</th><th>March</th><th>April</th><th>May</th><th>June</th><th>July</th><th>Aug</th><th>Sep</th><th>Oct</th><th>Nov</th></tr></thead><tbody><tr><th>Day</th><td>7 21 7 21 4 18 2 16 1 13 27 11 25 8 22 19 3 17 1 14</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Loretto</th><td>L1</td><td>L2</td><td>L3</td><td></td><td>L4</td><td></td><td>L5</td><td></td><td></td><td>L6</td></tr><tr><th>Suartello</th><td>S1</td><td>S2</td><td>S3</td><td></td><td>S4</td><td></td><td>S5</td><td></td><td></td><td>S6</td></tr><tr><th>Vignola</th><td>V1</td><td>V2</td><td>V3</td><td></td><td>V4</td><td></td><td>V5</td><td></td><td></td><td>V6</td></tr></tbody></table>

1341
<table><caption>Table 4. Diversity indices (species richness, insect abundance, Shannon index), total species diversity estimator ACE (which considers as rare all the species with 10 or less individuals) and completeness (observed diversity / estimated diversity) for each insect community of the three studied sites (same colour code as for Table 3 and Figure 9).</caption><thead><tr><th rowspan="2">Group</th><th colspan="6">Loretto</th><th colspan="6">Suartello</th><th colspan="6">Vignola</th></tr><tr><th>L1</th><th>L2</th><th>L3</th><th>L4</th><th>L5</th><th>L6</th><th>S1</th><th>S2</th><th>S3</th><th>S4</th><th>S5</th><th>S6</th><th>V1</th><th>V2</th><th>V3</th><th>V4</th><th>V5</th><th>V6</th></tr></thead><tbody><tr><td>Richness</td><td>16</td><td>22</td><td>47</td><td>65</td><td>43</td><td>39</td><td>6</td><td>12</td><td>55</td><td>69</td><td>69</td><td>25</td><td>11</td><td>9</td><td>30</td><td>55</td><td>31</td><td>37</td></tr><tr><td>Abundance</td><td>42</td><td>174</td><td>300</td><td>281</td><td>112</td><td>139</td><td>22</td><td>88</td><td>392</td><td>286</td><td>222</td><td>100</td><td>84</td><td>40</td><td>152</td><td>232</td><td>78</td><td>104</td></tr><tr><td>Shannon index</td><td>2,508</td><td>2,358</td><td>3,24</td><td>3,602</td><td>3,579</td><td>3,123</td><td>1,402</td><td>1,958</td><td>3,166</td><td>3,891</td><td>3,786</td><td>2,565</td><td>1,048</td><td>1,476</td><td>2,937</td><td>3,439</td><td>3,172</td><td>3,039</td></tr><tr><td>Estimator ACE</td><td>35,97</td><td>34,84</td><td>67,49</td><td>107,3</td><td>57,23</td><td>69,38</td><td>13,77</td><td>22,39</td><td>86,38</td><td>94,03</td><td>109,5</td><td>46,8</td><td>14,85</td><td>29,48</td><td>42,94</td><td>86,58</td><td>63,84</td><td>81,2</td></tr><tr><td>Completeness (based on ACE)</td><td>0,44</td><td>0,63</td><td>0,70</td><td>0,61</td><td>0,75</td><td>0,56</td><td>0,44</td><td>0,54</td><td>0,64</td><td>0,73</td><td>0,63</td><td>0,53</td><td>0,74</td><td>0,31</td><td>0,70</td><td>0,64</td><td>0,49</td><td>0,46</td></tr></tbody></table>

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