Revealing the biodiversity of wild bees (Hymenoptera: Apoidea: Anthophila) in flower strips in Mediterranean floodplains. Which monitoring method fits best?

Revealing the biodiversity of wild bees (Hymenoptera: Apoidea: Anthophila) in flower strips in Mediterranean floodplains. Which monitoring method fits best? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Revealing the biodiversity of wild bees (Hymenoptera: Apoidea: Anthophila) in flower strips in Mediterranean floodplains. Which monitoring method fits best? Oana Catalina Moldoveanu, Martino Maggioni, Daniele Vergari, Francesca Romana Dani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4846902/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Context The ongoing pollinator decline may threaten and compromise the resilience of terrestrial ecosystems. Implementing conservation action requires monitoring pollinator populations' actual status, but this is particularly difficult for pronubes insects such as wild bees. Their monitoring is difficult and time-consuming but crucial for assessing their health status. Objectives Here we compared and evaluated the efficiency of three different monitoring methods to evaluate wild bee biodiversity in lowland areas sown with entomophilous flowers to support pollinating insects in a Mediterranean environment. Methods We sampled wild bees for two years by using hand netting, pan traps and artificial nests. We compared species richness and abundance among these methods with a particular focus on how flowering coverage affects the efficiency of walking transects and pan traps and discussed the attractiveness of the different colours of pan traps. Results Hand netting captured a higher abundance of wild bees than the other two methods but a similar number of species to pan traps. Artificial nests captured fewer specimens and species. Bee assemblages were significantly different between pan traps and hand netting, and pan traps had greater potential in capturing the whole bee biodiversity, but their attractiveness is negatively influenced by the flowering coverage contrary to hand netting sampling. Conclusions Like other studies, the three sampling methods are complementary regarding species assemblages. The juxtaposition of several monitoring methods is essential to assess the biodiversity status of species with such particularly different ecological traits. biodiversity Anthophila pan traps bee nests monitoring schemes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Pollinator biodiversity and decline The complexity and resilience of terrestrial ecosystems are highly related to pollinators (Bascompte et al. 2006 ). The dependence of angiosperm species on zoogamous pollination is estimated to vary from 78% in temperate systems to about 94% in tropical areas (Ollerton et al. 2011 ). At the same time, many crops important for healthy human nutrition largely depend on animal pollination (Klein et al. 2007 ; Potts et al. 2010 ). The steady decline of pollinators globally is well documented and the need for actions to counter it is clear and accepted (Dunn et al. 2009 ; Klien et al. 2007; Potts et al. 2010 ). This problem could be particularly relevant in regions with a Mediterranean climate, known to be important hotspots for pollinator biodiversity (Orr et al. 2021 ). The European Union is working on multiple measures to reverse the trend of pollinator decline both in natural and anthropized ecosystems (Moldoveanu et al. 2024 ). These are condensed in the recently published document “A new deal for pollinators” (EU Communication, 2023), highlighting that the conservation of pollinators also requires i. improving knowledge on the causes and consequences of their decline and; ii. implementing and coordinating policy decisions on their conservation (EU Communication, 2023). Within the Biodiversity Strategy 2030, the Nature Restoration Law focuses on the restoration of 20% of the degraded terrestrial and marine ecosystems by 2030 and of all damaged environments until 2050 ( https://environment.ec.europa.eu/topics/nature-and-biodiversity/nature-restoration-law_en ) and since habitat loss and fragmentation are among the major drivers of decline of pollinator decline (IPBES 2016 ; Nieto et al., 2014 ; Potts et al. 2010 ), these actions will also favour this functional group of insects. Among pollinator species, wild bees are considered to be the most efficient group due to their great species richness and functional diversity (Michener 2007 ). The European continent counts 2.138 species of wild bees (Ghisbain et al. 2023 ) and the Mediterranean region has the greatest species biodiversity (Quaranta et al. 2018 ). Yet, the IUCN Red List of bees classified more than 56% of the species as “Data Deficient” (Nieto et al. 2014 ). The lack of data makes it impossible any classification into risk categories and decisions about actions for their protection (Nieto et al. 2014 , Potts et al. 2016 ). Despite this knowledge gap, it is estimated that about one in ten European wild bee species are threatened with extinction (Nieto et al. 2014 ; Status and Trends of European Pollinators, www.STEP-project.net ). To overcome the lack of data on the trends of wild bee populations, it is necessary to improve the monitoring efforts. Still, unlike for the butterfly species (e.g. BMS, Sevilleja et al. 2019 ), there is no consensus among states on the best standard monitoring method. Yet, monitoring schemes for wild bees are important to understand how populations respond to stressors and how they may develop in time on local and global scales (Breeze et al. 2021 ). Monitoring methods for wild bees According to many assessments (IPBES 2016 ; IUCN 2014; Millenium Ecosystem Assessment 2005) and researchers (Bell et al. 2022; Hutchinson et al. 2021; Klaus et al. 2024 ; O’Connor et al. 2018), there is a clear need for standardized methods of monitoring pollinators through a wide range of habitats and many geographical areas. The monitoring methods that are usually adopted for pollinators, particularly suitable for assessing wild bees, are divided into passive and active methods (Westphal et al. 2008 ). Active methods include primarily walking transects and observation plots (Cane et al. 2006 ; Dafni et al. 2005 ) whereas passive methods are numerous: pan traps (Rhoades et al. 2017 ; Westphal et al. 2008 ), malaise traps (Geroff et al. 2014 ), vane traps (Hall et al. 2018), and trap nests (Staab et al. 2018 ). Walking transects are permanent corridors often 250m long and 4m wide, usually divided into 10 sections of 25m each (Westphal et al. 2008 ). They differ from those used for butterfly monitoring which are longer, usually up to 500m (Sevilleja et al. 2019 ). Observers are required to walk the transect for a maximum of 50 minutes, about 5 minutes for each section, and shall annotate or capture each specimen that cannot be determined in the field (Westphal et al. 2008 ). Observation plots consist of patches of various standard sizes in which the researcher actively observes generally for 30 minutes the organisms flying in (Banaszak 1980 ). These active methods are often time-consuming and heavily dependent on observers' expertise in species determination, but they allow the collection of much more information such as that about plant-pollinator interactions (Bell et al. 2023 , Westphal et al. 2006 ). Pan traps are plastic bowls of 400–500 mL volume that are coloured with UV-reflecting varnishes, usually in blue, white and yellow, and contain water with a few drops of liquid soap to reduce the surface tension (Westphal et al. 2008 ). Vane traps are similar to pan traps, but they are made of a transparent glass jar topped with a plastic-coloured (UV-reflecting) funnel that traps insects in the jar (Stephen and Rao 2005 ). Usually, both types of traps are left in the field for up to 24–36 or 48 hours (Stephen and Rao 2005 ; Westphal et al. 2008 ). Malaise traps consist of a large tent-like structure that traps and funnels insects towards a container with a preserving liquid; the choice of positioning site is crucial to the proper functioning of the trap (Townes 1962 ). Passive methods are less time-consuming, and they are independent of the sampler knowledge in the first instance, but they may be affected by vegetation height and distribution (Cane et al. 2001; Westphal et al. 2008 ). Despite their similarity, pan traps sample mainly small-sized bees while vane traps were first designed for the sampling of large-bodied bee species (Stephen and Rao 2005 ; Westphal et al. 2006 ); moreover, malaise traps are used mainly for non-Lepidoptera species but have been rarely used for wild bees (Townes 1962 , Klaus et al. 2024 ). Trap nests are highly biased to the sampling of only cavity-nesting bee species (Dafni et al. 2005 ) and their design may vary among researchers (Klaus et al. 2024 ). Plastic tubes of 10 cm diameter and 20 cm length, filled with common reed ( Phragmites australis ) with diameters up to 10mm are the most employed trap nests (Gathmann et al. 1994 ) but book-like structures may be also employed (MacIvor 2017 ). Trap nests are placed in the field usually in the late winter season and then are analysed after the autumnal months; this allows researchers to collect data over a whole flight period with relatively low sampling effort and to gather additional information such as that from pollen analysis and trophic interactions with associated parasitic fauna (Prendergast et al. 2020 ; Staab et al. 2018 ). Since bee species richness can be relatively high and they also can exhibit great ecological diversity, many studies (Chamorro et al. 2022; Hutchinson et al. 2021; Prado et al. 2017) recommend a combination of different sampling methods to achieve efficient monitoring and an exhaustive characterisation of the wild bee community. The most used combination of sampling methods is vane traps, pan traps and standard transects (Bell et al. 2023 , Willson et al. 2008). Furthermore, several studies have compared monitoring methods to understand their pros and cons, and the most suitable to standardise results (Bell et al. 2023 ; Hutchinson et al. 2021; Nielsen et al. 2011 ; Thompson et al. 2021 ), but those methods are often difficult to confront above all when it comes to considering passive and active methods together. Many studies have compared primarily pan traps and vane traps (Bell et al. 2023 ; Rhoades et al. 2017 ). Even if many researchers demonstrated that pan traps capture the greatest abundance of bees, mostly small-sized and ground-nesting species (Thompson et al. 2021 ; Westphal et al. 2008 ), others found that, when comparing pan traps and vane traps, the second ones capture the highest number of specimens (Bell et al. 2023 ; Rhoades et al. 2017 ; Prendergast et al. 2020 ). In general, it was demonstrated that vane traps, especially the blue-coloured ones, were more efficient than pan traps by capturing a higher abundance of species and exhibiting major reliability across different types of habitats (Bell et al. 2023 ; Hall 2018 ). The reasons for the diverse attractiveness of colours are still debated (Gibbs et al. 2017 ; Hall 2018 ). According to several studies (Chamorro et al. 2022; O’Connor et al. 2018; Krahner et al. 2021 ; Thompson et al. 2021 ), there are other concerns about the efficiency of pan traps especially because they seem to be appropriate only for open sampling areas and for habitats with low abundance of flowering. Some authors demonstrated that pan traps compete with flowers for pollinators' attractiveness showing that the number of captured bees decreased when the flowering surface increased but this result was not reflected in species richness results (Kuhlman et al. 2021 ). Ortiz-Sanchez and Aguirre-Segura ( 1992 ) also demonstrated that pan traps are biased by the vertical height of sampling, unlike vane traps. Moreover, trapped specimens are usually immersed in water or other preservative liquids, thus they must be washed and dried and this may be very time-consuming and damaging for the exemplars, hindering the identification by morphological characters (Prendergast et al. 2020 ). Additionally, if comparing passive and active methods, there is a need to consider multiple elements, including differences in sampling effort (Bell et al. 2023 ; Krahner et al. 2021 ). Hand netting is highly dependent on flowering richness and on samplers' expertise, but it may also allow to capture parasitic bees that are just sporadically patrolling flowers but usually are not detected by passive sampling methods (Popic et al. 2013 ; Thompson et al. 2021 ). These are also useful when assessing wild bee species richness or investigating the structure of wild bee communities (Nielsen et al. 2011 ; O’Connor et al. 2018; Thompson et al. 2021 ). Finally, many authors show agreement in suggesting that the choice of sampling methods must consider primarily the objectives of the experiment (Bell et al. 2023 ; Klaus et al. 2024 ) and the life-history traits of species (Hall 2018 ) but also how habitat structure may influence monitoring methods (de Assis et al. 2021 ; O’Connor et al. 2018). Here we compared bee monitoring methods, among pan traps, walking transects and trap nests, to study species richness and abundance in floodplain areas recently subjected to massive earth-moving works and then to sowing of commercial mixtures of nectariferous and polliniferous plants to support wild pollinator insects. Alongside we tried to identify the most suitable monitoring method for these specific habitats by combining the use of pant traps, walking transects and trap nests. 2. Material and Methods 2.1 Study sites and area description The data was collected during two consecutive years, 2022 and 2023, in two areas in Tuscany (Central Italy). Both areas are located in floodplain zones of two major tributaries of the Arno River (Fig. 1 ). The first area is named “Castelletti”, and it is enclosed by an elbow of the Ombrone River in the municipality of Carmignano (PO) while the second area, named “Bramasole”, is found in the municipality of Montelupo Fiorentino (FI) along the Pesa River (Fig. 2 ). Both areas are within recently realised expansion basins, they are less than 9 km apart in a beeline and are ecologically similar. The first area covers a surface of about 6.5 ha, the second about 5 ha and both are interested in a major experimental three-year project of environmental amelioration for the conservation of pollinators. Five plots of 0.3 ha were sowed within both areas with three different mixtures of entomophilous plants, and then pollinators, specifically wild bees (Hymenoptera: Apoidea: Anthophila) were monitored. The sites are embedded in peri-urban and agricultural landscapes and may experience flooding during wet seasons but are also subjected to drought during summer. These types of landscapes may be very suitable for solitary bees due to a wide range of nesting sites and because the slopes of embankments are suitable for underground nesting bees. Despite these elements, these areas, due to recent large-scale hydraulic works to reshape and excavate land to construct detention basins, are poor in trophic resources since entomophilous wild flowering plants are limited and the ground is covered mainly by gramineous weeds. 2.2 Bee sampling methods and data collection Sampling was carried out from May to September in 2022 and from March to July in the year 2023. The two periods of sampling are mismatched due to the seeding works and maintenance requirements of the areas by the competent authority that manages them (Consorzio di Bonifica Medio Valdarno 3). We used three different methods of sampling: i. hand netting (or standardised walking transects); ii . pan traps; iii. artificial bee nests. Only bee nests were used as a sampling method during the first year in the “Bramasole” area. Transects were walked from once to twice a month during sunny and no-wind days. Each transect was 250 m long, divided into 10 sections of 25 m each, and was walked for a maximum of 50 minutes. For each area, we walked five transects per sampling session. During each transect, field forms were filled with information such as atmospheric temperature, cloud cover and wind force indexes, and flowering coverage. Specimens were determined up to genera in the field and along it, the visited plant species were recorded for each observation. Specimens were netted and placed into falcon tubes of 15 mL, anaesthetised with acetic ether then placed at -20° C in the laboratory. Subsequently, they were processed and conserved in entomological boxes. Pan traps were positioned on the same days of transects, in pairs of triplets placed 15 m away along the transect path. Each triplet was composed of blue, white, and yellow bowls and was left in the field for 24 hours. Each trap of the triplet was arranged as the vertices of an equilateral 5 m-side triangle (Westphal et al. 2008 ). Traps were made of reusable plastic bowls 15 cm in diameter, had a capacity of 500 mL and were coloured with UV-reflecting paints (Spray-Color GmbpH, Westphal et al. 2008 ) Sampled specimens were put in 15 mL falcon tubes and conserved at -20° C in the laboratory. Later, they were quickly rinsed in water, dried in the air by gently shaking them in a small bag containing small pieces of blotting paper and then processed the same way as described for the netted specimens. Trap nests were placed in the late spring and were removed in December for the year 2022 while for 2023, they were placed in the early spring and removed again in December. A total of six nests were placed, three for each study area, and they were constructed in such a way that they are also pleasing to sight since citizens can exploit the experimental areas for pleasure. Trap nests were made of a wood frame and filled with drilled wooden logs and river reeds ( Arundo donax ). Both wooden logs and river reeds were 20 cm long, the diameters of the reeds ranged from 0.5 cm to 2.5 cm, and the holes in the logs were 10 cm deep and 0.4 cm in diameter. During the second year, part of the truncheons was replaced by florist's sponges with transparent straws of compostable plastic inserted to facilitate pollen observation and extraction. After the dislocation, trap nests were taken to the laboratory waiting for cavity-nesting specimens to emerge, then cavities were opened to extract pollen and to identify the remaining wild bees. Pollen was used for studies on ecological networks and trophic preferences of bees, while specimens were processed the same way as described for the transect specimens. 2.3 Species identification Only specimens of the superfamily Apoidea, clade Anthophila (Hymenoptera:Apoidea:Anthophila) (Michener 2000 ), were considered during sampling. Apis mellifera was recorded but was not considered for the analysis. In the present study, we identify at the species level all specimens belonging to the family Megachilidae; this taxon has been among the most represented groups in the two years of sampling, possibly because of the availability of several nesting microhabitats. All other specimens were identified up to the species level, or morphospecies for the Andrenidae and Halictidae families, unless necessary identification characters missing or unrecognizable. We used dichotomous keys (see References) both for genus and species identification. 2.4 Data analysis We performed statistical analysis using R 4.4.0 (R Core Team 2024). Rarefaction curves were conducted with iNext package (Hsieh et al. 2016 ) to understand how hand netting and pan trapping captured the α-diversity of the sites as the number of individuals sampled increased. The number of sampled specimens and species were used as proxies to compare active and passive methods in terms of effectiveness and sampling effort. To compare the efficiency of these two methods for capturing species richness we performed a non-parametric Wilcoxon signed rank test with continuity correction, whereas a Kruskall- Wallis test adjusted with the Bonferroni method was used to compare the attractiveness of different colours of pan traps. A GLM model was computed to evidence the influence of flowering coverage on the two sampling methods. We used the vegan package to address the composition of wild bee communities between hand netting and pan traps and we used “adonis2” function with 999 permutations to perform a distance-based multivariate analysis of variance (PERMANOVA). We then evaluated multivariate dispersion, that is how different samples are from one another, for both sampling methods using the function “betadisper”. For all these analyses we used Bray–Curtis dissimilarities. VennDiagram package was used to show both the difference of captures among the three colours of pan traps and to display the distribution of Megachilidae species among the three sampling methods. ggplot2 package was used to construct all boxplots and barplots; finally, ggtern package was used to design the ternary plot for the Megachilidae species. 3. Results We collected wild bees from 30 genera (about 51% of all genera on the Italian territory) from all six families present in Italy (Table 1), for a total of 2,703 specimens. Hand netting captured 23 genera, approximately 76.7% of all genera collected, among which 8 genera were not present in the pan traps. Pan traps sampled 21 genera, about 70% of all collected genera and 6 that were not observed with hand netting. Artificial nests trapped 5 genera, about 16.7 % of all collected genera, with one genus that was not captured by the other two methods. Among all, we sampled 6 genera of parasitic bees (cuckoo bees) with the three methods: one genus ( Coelioxys ) with artificial nests, two genera ( Nomada and Sphecodes ) with pan traps and 5 genera ( Epeolus, Melecta, Nomada, Pasites , and Sphecodes ) with hand netting. Artificial nests and hand netting caught respectively 1 and 4 unique genera of parasitic bees. The most abundant genera, in terms of sampled specimens, were Bombus (23.2%) followed by Eucera (17.9 %), Lasioglossum (13.7%), Halictus (11.5%) and Andrena (8.8%). Parasitic genera were the less abundant. Hand netting detected 2121 specimens from both observations and captures which is about 78.5% of all specimens observed. Pan traps captured 395 specimens (about 14.6% of all specimens), more than five times fewer specimens than hand netting. Finally, artificial nests trapped 187 specimens (about 6.9% of all specimens), more than ten times fewer specimens than hand netting. We collected 155 species (and morphospecies) over the two years, 96 species (61.9%) from hand netting, 95 species (61.3%) from pan traps and 14 species (7.1%) from artificial nets. We focused only on data from hand netting and pan traps to compare the monitoring methods' efficiency because, as expected, the data from artificial nests showed that they only captured a small fraction of the species. The number of species (and morphospecies) collected over the two years (Figure 3) by hand netting was significantly higher (Wilcoxon rank test, p < 0.05). Species (and morphospecies) rarefaction curve (Figure 4) cumulative on both sampling years showed that pan traps and hand netting collected almost the same number of species but pan traps have a greater potential to detect species as the number of individuals increases (see extrapolation curve of Figure 4 and Figure S1, Supplementary Materials). Also, the number of singletons (species for which a single specimen was collected) greatly differs between pan traps (49) and hand netting (20). The attractiveness of pan traps and the efficiency of walking transects are significantly dependent on the flower coverage of the monitored site (Table 2 a and b). While the effectiveness of pan traps is high at low flowering coverage, the opposite is true for walking transects. Wild bee assemblage differs between the two sampling methods (Figure 5, R2 = 0.093, F = 2.88, p 0.05), although higher for pan traps. Pan traps of different colours attracted a similar number of individuals (124, 144 and 127 respectively for blue, white and yellow pan traps). All three colours attracted the same number of genera (14 genera for each colour), but the blue ones attracted a unique genus more than the other two colours. Yellow pan traps attracted more species (and morphospecies) among the three colours but there is no significant difference (Figure 6, Kruskall-Wallis, p > 0.05). Blue pans attracted Amegilla, Dasypoda and Tetralonia as unique genera; the yellow ones trapped Hoplitis and Panurgus as unique genera; the white ones collected Chelostoma and Hylaeus as exclusive genera (Figure 7a.). The relative proportion of the abundance of each genus among the three pans colours (Figure 7b.) showed some unique patterns: bees of the genus Systropha were mostly attracted by the white colour as most of the Andrena specimens ; Eucera individuals were most abundant in the blue pans, Lasioglossum preferred the yellow traps and finally, Halictus bees were equally distributed among the three colours. The three colours attracted a similar number of species and morphospecies (respectively 54, 48 and 38 of all collected species for the white, yellow and blue pan traps). Focusing on the species of the Megachilidae family, we collected 9 genera (30% of all sampled genera) and 38 species (24.5% of all species collected) with one genus, represented by one single species, the cuckoo bee Coelioxys mandibularis . Hand netting (20) and artificial nests (14) collected a greater number of species than pan traps (12). Only one species, Megachile analis, was captured by all three sampling methods, while hand netting and artificial nests shared 6 species, hand netting and pan traps 2 and artificial nests and pan traps shared only one species. Hand netting captured (Figure 8a) the highest number of unique species (n=13, 34.2%), followed by pan traps (n=10, 26.3%) and artificial nests (n=8, 21.1%). Visualization by the ternary plot of the Megachilidae species assemblage collected with each method (Figure 8b) showed that species tend to cluster for each monitoring method, as many species tend to be sampled exclusively or mostly by hand netting and artificial nests. Table 1 . List of all collected genera by each of the three sampling methods: Hand netting, Pan traps and Artificial nests. Numbers indicate the number of specimens for each genus and the total individuals collected by each method. * indicates the parasitic genera. Table 2. Parameter estimates of the Generalized Linear Models (GLMs) with Poisson distribution and log-link function assessed as most parsimonious according to the Akaike information criterion (AIC). The model explains respectively the significative negative and positive influence of high flowering coverage on the number of captures by pan traps ( a ) and hand netting ( b ). 4. Discussion This study demonstrates how different monitoring methods, regarding wild bee communities, are complementary, with each method capable of collecting mutually exclusive species. Active and passive methods are often difficult to compare, and, to the best of our knowledge, only a few studies report comparison based on extensive sampling and a high effort in species resolution (Nielsen et al. 2011 , Rhoades et al. 2017 , O'Connor et al. 2018, Krahner et al. 2021 , Kuhlman et al. 2021 , Thompson et al. 2021 ). Furthermore, the strong dependence of these methods on habitat types, vegetation composition and bee fauna can weaken the comparison. Many studies point out the use of trapping, especially pan traps, as the most efficient monitoring method, as these can attract both more specimens and more species than other methods (Stephen and Rao 2005 , Westphal et al. 2008 , Nielsen et al. 2011 , Krahner et al. 2021 ). However, the use of pan traps raises doubts regarding the effect of their repetitive and extensive use on the bee communities and bycatches of other insect groups (Gezon et al. 2015 ); as well as on the relative lack of standardisation due to the influence of vegetation (LeBuhn et al. 2012, Kuhlman et al. 2021 ). The efficiency of pan traps is highly dependent on the height of the vegetation and thus on the different heights at which the traps are placed, so that they are competitive with the surrounding vegetation (Westphal et al. 2008 ). Furthermore, as we also found, pan traps can lose efficiency when flowering coverage is abundant since, in addition to colour, many pollinators are attracted by other factors that characterise flowers but not pans (O’Connor et al. 2018). Finally, pan traps are subject to weather phenomena for at least 24 hours, thus resulting in high variability of the results, which may be affected by sudden violent weather phenomenons or to large animals tipping over, with consequent loss of sampling data (LeBuhn et al. 2012, O’Connor et al. 2018, Kuhlman et al. 2021 ). Our results are consistent with those of Cane et al. ( 2000 ) and Thompson et al. ( 2021 ) who reported that generally, hand netting captured more specimens and species than pan traps. In our case, however, the lower efficiency of pan traps could be due to other factors, and this is visible in the rarefaction and extrapolation curves divided by year. This study took place in an experimental field where areas were sown with strips of entomophilous plants specifically designed to attract pollinating insects. The strips were sown in the early spring of 2022 and monitoring took place immediately at their flowering, a couple of months after sowing (see Supplementary Materials). The flowering and vegetation coverage in that year was rather low due to the work carried out to sow the seeds and the fact that most plants generally have greater blooming in their second year (Kowalska et al. 2022 ). In addition, the areas are subject to high hydric stress during the dry summer season, so in the first year, vegetation cover and flowering did not reach high values. The traps were always placed on the ground and captured more species than transects, which in turn depended heavily on the presence of blooms on which to observe and net specimens. The flower coverage dependence of pan traps and transects is inversely proportional (O’Connor et al. 2018) and this is validated also in our study. Whereas as flower cover increases, walking transects are more efficient in attracting specimens, pan traps lose efficiency. Pan traps were less attractive to bees and transects captured more species in 2023. However, the total rarefaction and extrapolation curves are in line with the results of other studies in indicating the great potential of pan traps in attracting a high diversity of bee species at the same sample size (Nielsen et al. 2011 , Hall 2018 , O’Connor et al. 2018, Krahner et al. 2021 ). Pan traps catch many more singletons than transects, offering the possibility of attracting more species based on fewer captured specimens. However, an ideally exhaustive sampling of all species at a given site, based solely on the use of pan traps, would require a greater sampling effort than hand netting in terms of several sampling sessions and higher hours of placement of traps. Finally, pan traps tend to catch a more diverse and variable community of bees than hand netting which are inclined to sample much more similar communities each time. Our results are consistent with the study by Kuhlman et al. ( 2021 ) who also investigated seasonal and flower abundance patterns on the efficiency of pan traps and hand netting. In our case, hand netting was also crucial for the collection of parasitic genera that can sometimes be found feeding on nectar on some flowering species but are rarely attracted to pans. The use of artificial nests as an exclusive monitoring method entails, in the case of wild bees, the monitoring of only a fraction of diversity, that is confined to cavity-nesting species. As in Nielsen and colleagues ( 2011 ), nests captured the fewest specimens and species, with Osmia (5 species and 146 specimens) and Megachile (5 species and 31 specimens) being the most abundant, but they were crucial for capturing 8 species that had not been observed otherwise (5.2% of total species and 21.1% of Megachilidae species). Among Osmia , most individuals belonged to the species O. caerulescens , in contrast to the other species of Osmia that were mostly observed through walking transects and pan traps. The wild bee communities sampled by hand netting and by pan traps were distinctive between the two monitoring methods, confirming, as already seen in other studies (Nielsen et al. 2011 , O’Connor et al. 2018), that these two methods are highly complementary to each other. Some studies (Stephen and Rao, 2005 ; Hall 2018 ) support the hypothesis of a strong preference for the blue colour of traps for most of the wild bee fauna. In our study, there is no significant overall preference for one of the three colours, although, the greatest abundance of individuals and the greatest diversity of species was captured by the white and yellow traps. The blue traps, on the other hand, attracted both lower abundance and lower species diversity. According to Hall ( 2018 ) in the northern hemisphere the blue-coloured traps have greater attractiveness because of a greater abundance of bumblebees that prefer this colour. This study took place in a Mediterranean and lowland habitat and although bumblebees are, from an abundance point of view, the dominant group in the walking transects, very few specimens were caught with both blue and white traps. A possible explanation may also be that we used pan traps and not vane traps, unlike Hall ( 2018 ). The lower abundance of bumblebees sampled with pan traps is consistent with the results of Bell and colleagues ( 2023 ) who demonstrated that vane traps are more efficient at catching bumblebees than pan traps. The blue traps, however, attracted more exclusive genera than the others, in particular the genera Amegilla, Dasypoda and Tetralonia . These three are genera of long ligula bees, that have a strong preference for Boraginaceae and Malvaceae, all of which are flowers in the blue-purple range (Michener 2007 ). The blue traps also had greater attractiveness towards bees of the genus Eucera , which also have strong preferences towards Boraginaceae and blue purple Cichorioideae (Michener 2007 ). The white traps, on the other hand, attracted high abundances of Hylaeus (exclusive genus), and Systropha . These short ligula genera show preferences for white-coloured flowers such as Umbelliferae and Convolvulaceae (Michener 2007 , https://www.beewatching.it/ ). Finally, yellow traps attracted abundant specimens of the genus Lasioglossum . Although the species of this genus are largely polylectic, they are often found foraging on mostly yellow-coloured Asteraceae (Michener 2007 ). These patterns indicate how indeed, according to the literature (Nielsen et al. 2011 , Rhoades et al. 2017 , Kuhlman et al. 2021 ), the most suitable sampling method is extremely influenced by the studied habitat and by the local composition of the bee fauna. Therefore, if interested in monitoring a particular group of bees, it is necessary to assess beforehand what might be the best method or combination of methods to capture the diversity of the entire group. In the Megachilidae family, we find genera and species with very diverse trophic and nesting habits (Michener 2007 , Danforth et al. 2019 ). For example, within the genus Osmia or Megachile , we find species that nest in hollow stems and holes in wood, but also many species that prefer to nest on the ground or in snails (Michener 2007 , Danforth et al. 2019 ). The trophic habits are also different, while the genus Megachile is known to be related to Fabaceae, the genus Chelostoma prefers Ranunculaceae and Asteraceae species ( https://www.beewatching.it/ ). This makes it difficult to identify a single sampling method. Our results showed that all three monitoring methods are strongly complementary. We did not find any Megachilidae species of conservation concern in our monitoring, but they are very important species concerning the pollination of fodder crops and fruit trees, and therefore of some importance to human activities (Ollerton et al. 2021). Furthermore, the use of artificial nests also makes it possible to investigate parasites, parasitoids and hyperparasites of these species, which make up a not-so-insignificant fraction of the biodiversity of an ecosystem (Klaus et al. 2024 ). Indeed, the only parasitic species among the Megachilidae was captured thanks to the use of artificial nests. In addition to this, it is also possible, through the use of nests, to investigate the species and quantity of pollen preferred by wild bees as well as the presence of pesticides and other pollutants in the stored pollen (Staab et al. 2018 ). These kinds of studies show that we should not underestimate the aspects of “standardised” monitoring, especially in the case of organisms as diverse as pollinators, and that we should always include more than just one method in our monitoring to best capture the biodiversity of a site, habitat type or a certain kind community. 5. Conclusions Mediterranean areas are a hot spot for the biodiversity of wild bees. We found that bee communities have distinct compositions depending on the monitoring method. In our experimental areas, hand netting caught a higher abundance and species richness of wild bees but pan traps had greater attractiveness when flowering coverage was low. Artificial nests only sampled a smaller fraction of species but were fundamental to capturing unique Megachilidae species, including parasitic ones. In conclusion, we recommend the use of multiple highly complementary methods when we want a comprehensive assessment of a wild bee community's biodiversity. Declarations Acknowledgements Not applicable. Funding Open access funding was provided by Università degli Studi di Firenze within the CRUI-CARE Agreement. O.C.M. acknowledges the support of Programma Operativo Nazionale Ricerca e Competitività. F.R.D. and M.M. acknowledge the support of the NBFC to the University of Florence, funded by the Italian Ministry of University and Research, PNRR, Missione 4 Componente 2, “Dalla ricerca all’impresa”, Investimento 1.4, Project CN00000033. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author contributions O.C.M., M.M. and F.R.D. authors contributed to the monitoring work; D.V. supported the agronomic fieldwork; O.C.M. conceived the study; O.C.M. and M.M. identified the specimens, performed data analysis and wrote the paper; all authors read, commented and approved the manuscript. Data availability All data analysed for this paper are available on request. References Banaszak J (1980) Studies on methods of censusing the numbers of bees (Hymenoptera, Apoidea). Polish ecological studies, 6: 355-365. 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Ecological Entomology, 31(4): 389-394. https://doi.org/10.1111/j.1365-2311.2006.00801.x Wilson JS, Griswold T, Messinger OJ (2008) Sampling Bee Communities (Hymenoptera: Apiformes) in a Desert Landscape: Are Pan Traps Sufficient? Journal of the Kansas Entomological Society 81(3): 288-300. https://doi.org/10.2317/JKES-802.06.1 www.STEP-project.net Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialsMoldoveanuetal.2024.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4846902","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346095297,"identity":"8ededb80-e5ca-401c-ac0a-7810e500f43b","order_by":0,"name":"Oana Catalina Moldoveanu","email":"","orcid":"","institution":"University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Oana","middleName":"Catalina","lastName":"Moldoveanu","suffix":""},{"id":346095298,"identity":"e418c890-5361-4a79-87b8-28f86e59f57e","order_by":1,"name":"Martino Maggioni","email":"","orcid":"","institution":"University of Palermo","correspondingAuthor":false,"prefix":"","firstName":"Martino","middleName":"","lastName":"Maggioni","suffix":""},{"id":346095299,"identity":"58b6519d-2316-4066-9cc7-3ad89b5901be","order_by":2,"name":"Daniele Vergari","email":"","orcid":"","institution":"Consorzio di Bonifica Medio Valdarno 3","correspondingAuthor":false,"prefix":"","firstName":"Daniele","middleName":"","lastName":"Vergari","suffix":""},{"id":346095300,"identity":"ef9b8dee-4f0e-490d-9f62-38c47f295a60","order_by":3,"name":"Francesca Romana Dani","email":"data:image/png;base64,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","orcid":"","institution":"University of Florence","correspondingAuthor":true,"prefix":"","firstName":"Francesca","middleName":"Romana","lastName":"Dani","suffix":""}],"badges":[],"createdAt":"2024-08-02 08:32:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4846902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4846902/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63569354,"identity":"5af4c639-e61f-4273-98eb-1530738b5eb2","added_by":"auto","created_at":"2024-08-29 16:59:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":586705,"visible":true,"origin":"","legend":"\u003cp\u003eThe study area of “Bramasole” in Montelupo Fiorentino (FI) with details of flowering species of one of the seeded mixtures in early spring.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4846902/v1/a30a8befcafb1bff1af3b27b.png"},{"id":63569711,"identity":"cd9c7def-521f-4c12-82bf-70ebac9410db","added_by":"auto","created_at":"2024-08-29 17:07:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1211352,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the sampling sites (Tuscany, Italy).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4846902/v1/f34d5049c5569de6e5e3257e.png"},{"id":63569712,"identity":"28475205-67d9-4add-a2f4-48249d2d9d83","added_by":"auto","created_at":"2024-08-29 17:07:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25778,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies and morphospecies sampled by hand netting and pan traps over 2022 and 2023 (Wilcoxon rank test, V=144.5, p**= 0.01074).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4846902/v1/37f31216766c2ce9594fc9ae.png"},{"id":63569353,"identity":"7bf826c4-a129-489d-b6a4-8f852097e391","added_by":"auto","created_at":"2024-08-29 16:59:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84102,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies (and morphospecies) rarefaction and extrapolation curves built on the samplings of both 2022 and 2023.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4846902/v1/377a1ca02bd6f0f1400cf71f.png"},{"id":63569713,"identity":"90ffcc88-187a-4c8a-91f8-c997d7076839","added_by":"auto","created_at":"2024-08-29 17:07:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":118188,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric multidimensional scaling (NMDS, stress = 0.191) plot of wild bee community according to sampling dates and methods. Green dots represent communities sampled by hand netting and red dots represent those sampled by pan traps. The wild bee communities described by the two methods were found to be significantly different (PERMANOVA, p \u0026lt;0.001). The distance within samples for each group is similar but larger for pan traps.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4846902/v1/15ae2db92a1ed9736c139536.png"},{"id":63569356,"identity":"18169a64-184a-492a-a9c6-fa6f680e3b74","added_by":"auto","created_at":"2024-08-29 16:59:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":25881,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies and morphospecies attracted by the pan traps of the three different colours in the 13 sampling sections (Kruskall- Wallis test, p \u0026gt; 0.05). In total, the blu pantraps collected 38 species or morphospecies, the white 48 and the yellow 54.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4846902/v1/55045a1625a2becc2c690a0c.png"},{"id":63569715,"identity":"4af2ca65-5099-4ac0-8e62-c1a9b121dbfb","added_by":"auto","created_at":"2024-08-29 17:07:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":101994,"visible":true,"origin":"","legend":"\u003cp\u003eVenn Diagram showing genera attracted by the pan traps of each colour (\u003cstrong\u003ea\u003c/strong\u003e). The assemblage of all sampled genera, in terms of abundance, among the three colours of the pan traps (\u003cstrong\u003eb\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4846902/v1/84f3d85a517d69a04b4236a0.png"},{"id":63569351,"identity":"eceb94e3-9618-4cd9-ae02-b6011c74f669","added_by":"auto","created_at":"2024-08-29 16:59:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":193618,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram showing the unique Megachilidae species sampled by the three sampling methods (\u003cstrong\u003ea\u003c/strong\u003e). The distribution of each megachilid species among the three monitoring methods (\u003cstrong\u003eb\u003c/strong\u003e); the position of each circle indicates, for each species, the percentage of specimens captured with each sampling method; the size of each circle indicates the abundance of specimens per species; the colour of each circle indicates the associated species. Arrows are linked to the species unique to each sampling method.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4846902/v1/8bd494e5afdb1f311ef2bde9.png"},{"id":66433191,"identity":"4c2b57cc-ac23-4a4d-aedb-5cb0c0c1025a","added_by":"auto","created_at":"2024-10-11 21:31:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2837547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4846902/v1/cd95d606-36a7-4eb2-8d37-677e1233a6a2.pdf"},{"id":63569359,"identity":"b2ce46cd-1774-4718-9c38-1e032d4fb653","added_by":"auto","created_at":"2024-08-29 16:59:07","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":238478,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsMoldoveanuetal.2024.docx","url":"https://assets-eu.researchsquare.com/files/rs-4846902/v1/bd7bd83a1b00f1919f269e2d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revealing the biodiversity of wild bees (Hymenoptera: Apoidea: Anthophila) in flower strips in Mediterranean floodplains. Which monitoring method fits best?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003ePollinator biodiversity and decline\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe complexity and resilience of terrestrial ecosystems are highly related to pollinators (Bascompte et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The dependence of angiosperm species on zoogamous pollination is estimated to vary from 78% in temperate systems to about 94% in tropical areas (Ollerton et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). At the same time, many crops important for healthy human nutrition largely depend on animal pollination (Klein et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Potts et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The steady decline of pollinators globally is well documented and the need for actions to counter it is clear and accepted (Dunn et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Klien et al. 2007; Potts et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This problem could be particularly relevant in regions with a Mediterranean climate, known to be important hotspots for pollinator biodiversity (Orr et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The European Union is working on multiple measures to reverse the trend of pollinator decline both in natural and anthropized ecosystems (Moldoveanu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These are condensed in the recently published document \u0026ldquo;A new deal for pollinators\u0026rdquo; (EU Communication, 2023), highlighting that the conservation of pollinators also requires i. improving knowledge on the causes and consequences of their decline and; ii. implementing and coordinating policy decisions on their conservation (EU Communication, 2023). Within the Biodiversity Strategy 2030, the Nature Restoration Law focuses on the restoration of 20% of the degraded terrestrial and marine ecosystems by 2030 and of all damaged environments until 2050 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://environment.ec.europa.eu/topics/nature-and-biodiversity/nature-restoration-law_en\u003c/span\u003e\u003cspan address=\"https://environment.ec.europa.eu/topics/nature-and-biodiversity/nature-restoration-law_en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and since habitat loss and fragmentation are among the major drivers of decline of pollinator decline (IPBES \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nieto et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Potts et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), these actions will also favour this functional group of insects. Among pollinator species, wild bees are considered to be the most efficient group due to their great species richness and functional diversity (Michener \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The European continent counts 2.138 species of wild bees (Ghisbain et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the Mediterranean region has the greatest species biodiversity (Quaranta et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Yet, the IUCN Red List of bees classified more than 56% of the species as \u0026ldquo;Data Deficient\u0026rdquo; (Nieto et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The lack of data makes it impossible any classification into risk categories and decisions about actions for their protection (Nieto et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Potts et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite this knowledge gap, it is estimated that about one in ten European wild bee species are threatened with extinction (Nieto et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Status and Trends of European Pollinators, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://environment.ec.europa.eu/topics/nature-and-biodiversity/nature-restoration-law_en\" target=\"_blank\"\u003ewww.STEP-project.net\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.STEP-project.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To overcome the lack of data on the trends of wild bee populations, it is necessary to improve the monitoring efforts. Still, unlike for the butterfly species (e.g. BMS, Sevilleja et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), there is no consensus among states on the best standard monitoring method. Yet, monitoring schemes for wild bees are important to understand how populations respond to stressors and how they may develop in time on local and global scales (Breeze et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eMonitoring methods for wild bees\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAccording to many assessments (IPBES \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; IUCN 2014; Millenium Ecosystem Assessment 2005) and researchers (Bell et al. 2022; Hutchinson et al. 2021; Klaus et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; O\u0026rsquo;Connor et al. 2018), there is a clear need for standardized methods of monitoring pollinators through a wide range of habitats and many geographical areas. The monitoring methods that are usually adopted for pollinators, particularly suitable for assessing wild bees, are divided into passive and active methods (Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Active methods include primarily walking transects and observation plots (Cane et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Dafni et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) whereas passive methods are numerous: pan traps (Rhoades et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), malaise traps (Geroff et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), vane traps (Hall et al. 2018), and trap nests (Staab et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Walking transects are permanent corridors often 250m long and 4m wide, usually divided into 10 sections of 25m each (Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). They differ from those used for butterfly monitoring which are longer, usually up to 500m (Sevilleja et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Observers are required to walk the transect for a maximum of 50 minutes, about 5 minutes for each section, and shall annotate or capture each specimen that cannot be determined in the field (Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Observation plots consist of patches of various standard sizes in which the researcher actively observes generally for 30 minutes the organisms flying in (Banaszak \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). These active methods are often time-consuming and heavily dependent on observers' expertise in species determination, but they allow the collection of much more information such as that about plant-pollinator interactions (Bell et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Westphal et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePan traps are plastic bowls of 400\u0026ndash;500 mL volume that are coloured with UV-reflecting varnishes, usually in blue, white and yellow, and contain water with a few drops of liquid soap to reduce the surface tension (Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Vane traps are similar to pan traps, but they are made of a transparent glass jar topped with a plastic-coloured (UV-reflecting) funnel that traps insects in the jar (Stephen and Rao \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Usually, both types of traps are left in the field for up to 24\u0026ndash;36 or 48 hours (Stephen and Rao \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Malaise traps consist of a large tent-like structure that traps and funnels insects towards a container with a preserving liquid; the choice of positioning site is crucial to the proper functioning of the trap (Townes \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1962\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePassive methods are less time-consuming, and they are independent of the sampler knowledge in the first instance, but they may be affected by vegetation height and distribution (Cane et al. 2001; Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Despite their similarity, pan traps sample mainly small-sized bees while vane traps were first designed for the sampling of large-bodied bee species (Stephen and Rao \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Westphal et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); moreover, malaise traps are used mainly for non-Lepidoptera species but have been rarely used for wild bees (Townes \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1962\u003c/span\u003e, Klaus et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Trap nests are highly biased to the sampling of only cavity-nesting bee species (Dafni et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and their design may vary among researchers (Klaus et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Plastic tubes of 10 cm diameter and 20 cm length, filled with common reed (\u003cem\u003ePhragmites australis\u003c/em\u003e) with diameters up to 10mm are the most employed trap nests (Gathmann et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) but book-like structures may be also employed (MacIvor \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Trap nests are placed in the field usually in the late winter season and then are analysed after the autumnal months; this allows researchers to collect data over a whole flight period with relatively low sampling effort and to gather additional information such as that from pollen analysis and trophic interactions with associated parasitic fauna (Prendergast et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Staab et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince bee species richness can be relatively high and they also can exhibit great ecological diversity, many studies (Chamorro et al. 2022; Hutchinson et al. 2021; Prado et al. 2017) recommend a combination of different sampling methods to achieve efficient monitoring and an exhaustive characterisation of the wild bee community. The most used combination of sampling methods is vane traps, pan traps and standard transects (Bell et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Willson et al. 2008). Furthermore, several studies have compared monitoring methods to understand their pros and cons, and the most suitable to standardise results (Bell et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hutchinson et al. 2021; Nielsen et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Thompson et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but those methods are often difficult to confront above all when it comes to considering passive and active methods together.\u003c/p\u003e \u003cp\u003eMany studies have compared primarily pan traps and vane traps (Bell et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rhoades et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Even if many researchers demonstrated that pan traps capture the greatest abundance of bees, mostly small-sized and ground-nesting species (Thompson et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), others found that, when comparing pan traps and vane traps, the second ones capture the highest number of specimens (Bell et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rhoades et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Prendergast et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In general, it was demonstrated that vane traps, especially the blue-coloured ones, were more efficient than pan traps by capturing a higher abundance of species and exhibiting major reliability across different types of habitats (Bell et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hall \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The reasons for the diverse attractiveness of colours are still debated (Gibbs et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hall \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to several studies (Chamorro et al. 2022; O\u0026rsquo;Connor et al. 2018; Krahner et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thompson et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), there are other concerns about the efficiency of pan traps especially because they seem to be appropriate only for open sampling areas and for habitats with low abundance of flowering. Some authors demonstrated that pan traps compete with flowers for pollinators' attractiveness showing that the number of captured bees decreased when the flowering surface increased but this result was not reflected in species richness results (Kuhlman et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Ortiz-Sanchez and Aguirre-Segura (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) also demonstrated that pan traps are biased by the vertical height of sampling, unlike vane traps. Moreover, trapped specimens are usually immersed in water or other preservative liquids, thus they must be washed and dried and this may be very time-consuming and damaging for the exemplars, hindering the identification by morphological characters (Prendergast et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, if comparing passive and active methods, there is a need to consider multiple elements, including differences in sampling effort (Bell et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Krahner et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hand netting is highly dependent on flowering richness and on samplers' expertise, but it may also allow to capture parasitic bees that are just sporadically patrolling flowers but usually are not detected by passive sampling methods (Popic et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Thompson et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These are also useful when assessing wild bee species richness or investigating the structure of wild bee communities (Nielsen et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; O\u0026rsquo;Connor et al. 2018; Thompson et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, many authors show agreement in suggesting that the choice of sampling methods must consider primarily the objectives of the experiment (Bell et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Klaus et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and the life-history traits of species (Hall \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) but also how habitat structure may influence monitoring methods (de Assis et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; O\u0026rsquo;Connor et al. 2018).\u003c/p\u003e \u003cp\u003eHere we compared bee monitoring methods, among pan traps, walking transects and trap nests, to study species richness and abundance in floodplain areas recently subjected to massive earth-moving works and then to sowing of commercial mixtures of nectariferous and polliniferous plants to support wild pollinator insects. Alongside we tried to identify the most suitable monitoring method for these specific habitats by combining the use of pant traps, walking transects and trap nests.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study sites and area description\u003c/h2\u003e \u003cp\u003eThe data was collected during two consecutive years, 2022 and 2023, in two areas in Tuscany (Central Italy). Both areas are located in floodplain zones of two major tributaries of the Arno River (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The first area is named \u0026ldquo;Castelletti\u0026rdquo;, and it is enclosed by an elbow of the Ombrone River in the municipality of Carmignano (PO) while the second area, named \u0026ldquo;Bramasole\u0026rdquo;, is found in the municipality of Montelupo Fiorentino (FI) along the Pesa River (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both areas are within recently realised expansion basins, they are less than 9 km apart in a beeline and are ecologically similar. The first area covers a surface of about 6.5 ha, the second about 5 ha and both are interested in a major experimental three-year project of environmental amelioration for the conservation of pollinators. Five plots of 0.3 ha were sowed within both areas with three different mixtures of entomophilous plants, and then pollinators, specifically wild bees (Hymenoptera: Apoidea: Anthophila) were monitored. The sites are embedded in peri-urban and agricultural landscapes and may experience flooding during wet seasons but are also subjected to drought during summer. These types of landscapes may be very suitable for solitary bees due to a wide range of nesting sites and because the slopes of embankments are suitable for underground nesting bees. Despite these elements, these areas, due to recent large-scale hydraulic works to reshape and excavate land to construct detention basins, are poor in trophic resources since entomophilous wild flowering plants are limited and the ground is covered mainly by gramineous weeds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Bee sampling methods and data collection\u003c/h2\u003e \u003cp\u003eSampling was carried out from May to September in 2022 and from March to July in the year 2023. The two periods of sampling are mismatched due to the seeding works and maintenance requirements of the areas by the competent authority that manages them (Consorzio di Bonifica Medio Valdarno 3). We used three different methods of sampling: \u003cem\u003ei.\u003c/em\u003e hand netting (or standardised walking transects); \u003cem\u003eii\u003c/em\u003e. pan traps; \u003cem\u003eiii.\u003c/em\u003e artificial bee nests. Only bee nests were used as a sampling method during the first year in the \u0026ldquo;Bramasole\u0026rdquo; area. Transects were walked from once to twice a month during sunny and no-wind days. Each transect was 250 m long, divided into 10 sections of 25 m each, and was walked for a maximum of 50 minutes. For each area, we walked five transects per sampling session. During each transect, field forms were filled with information such as atmospheric temperature, cloud cover and wind force indexes, and flowering coverage. Specimens were determined up to genera in the field and along it, the visited plant species were recorded for each observation. Specimens were netted and placed into falcon tubes of 15 mL, anaesthetised with acetic ether then placed at -20\u0026deg; C in the laboratory. Subsequently, they were processed and conserved in entomological boxes. Pan traps were positioned on the same days of transects, in pairs of triplets placed 15 m away along the transect path. Each triplet was composed of blue, white, and yellow bowls and was left in the field for 24 hours. Each trap of the triplet was arranged as the vertices of an equilateral 5 m-side triangle (Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Traps were made of reusable plastic bowls 15 cm in diameter, had a capacity of 500 mL and were coloured with UV-reflecting paints (Spray-Color GmbpH, Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSampled specimens were put in 15 mL falcon tubes and conserved at -20\u0026deg; C in the laboratory. Later, they were quickly rinsed in water, dried in the air by gently shaking them in a small bag containing small pieces of blotting paper and then processed the same way as described for the netted specimens.\u003c/p\u003e \u003cp\u003eTrap nests were placed in the late spring and were removed in December for the year 2022 while for 2023, they were placed in the early spring and removed again in December. A total of six nests were placed, three for each study area, and they were constructed in such a way that they are also pleasing to sight since citizens can exploit the experimental areas for pleasure. Trap nests were made of a wood frame and filled with drilled wooden logs and river reeds (\u003cem\u003eArundo donax\u003c/em\u003e). Both wooden logs and river reeds were 20 cm long, the diameters of the reeds ranged from 0.5 cm to 2.5 cm, and the holes in the logs were 10 cm deep and 0.4 cm in diameter. During the second year, part of the truncheons was replaced by florist's sponges with transparent straws of compostable plastic inserted to facilitate pollen observation and extraction. After the dislocation, trap nests were taken to the laboratory waiting for cavity-nesting specimens to emerge, then cavities were opened to extract pollen and to identify the remaining wild bees. Pollen was used for studies on ecological networks and trophic preferences of bees, while specimens were processed the same way as described for the transect specimens.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Species identification\u003c/h2\u003e \u003cp\u003eOnly specimens of the superfamily Apoidea, clade Anthophila (Hymenoptera:Apoidea:Anthophila) (Michener \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), were considered during sampling. \u003cem\u003eApis mellifera\u003c/em\u003e was recorded but was not considered for the analysis. In the present study, we identify at the species level all specimens belonging to the family Megachilidae; this taxon has been among the most represented groups in the two years of sampling, possibly because of the availability of several nesting microhabitats. All other specimens were identified up to the species level, or morphospecies for the Andrenidae and Halictidae families, unless necessary identification characters missing or unrecognizable. We used dichotomous keys (see References) both for genus and species identification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cp\u003eWe performed statistical analysis using R 4.4.0 (R Core Team 2024). Rarefaction curves were conducted with iNext package (Hsieh et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to understand how hand netting and pan trapping captured the α-diversity of the sites as the number of individuals sampled increased. The number of sampled specimens and species were used as proxies to compare active and passive methods in terms of effectiveness and sampling effort. To compare the efficiency of these two methods for capturing species richness we performed a non-parametric Wilcoxon signed rank test with continuity correction, whereas a Kruskall- Wallis test adjusted with the Bonferroni method was used to compare the attractiveness of different colours of pan traps. A GLM model was computed to evidence the influence of flowering coverage on the two sampling methods. We used the vegan package to address the composition of wild bee communities between hand netting and pan traps and we used \u0026ldquo;adonis2\u0026rdquo; function with 999 permutations to perform a distance-based multivariate analysis of variance (PERMANOVA). We then evaluated multivariate dispersion, that is how different samples are from one another, for both sampling methods using the function \u0026ldquo;betadisper\u0026rdquo;. For all these analyses we used Bray\u0026ndash;Curtis dissimilarities. VennDiagram package was used to show both the difference of captures among the three colours of pan traps and to display the distribution of Megachilidae species among the three sampling methods. ggplot2 package was used to construct all boxplots and barplots; finally, ggtern package was used to design the ternary plot for the Megachilidae species.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eWe collected wild bees from 30 genera (about 51% of all genera on the Italian territory) from all six families present in Italy (Table 1), for a total of 2,703 specimens. Hand netting captured 23 genera, approximately 76.7% of all genera collected, among which 8 genera were not present in the pan traps. Pan traps sampled 21 genera, about 70% of all collected genera and 6 that were not observed with hand netting. Artificial nests trapped 5 genera, about 16.7 % of all collected genera, with one genus that was not captured by the other two methods. Among all, we sampled 6 genera of parasitic bees (cuckoo bees) with the three methods: one genus (\u003cem\u003eCoelioxys\u003c/em\u003e) with artificial nests, two genera (\u003cem\u003eNomada\u003c/em\u003e and \u003cem\u003eSphecodes\u003c/em\u003e) with pan traps and 5 genera (\u003cem\u003eEpeolus, Melecta, Nomada, Pasites\u003c/em\u003e, and \u003cem\u003eSphecodes\u003c/em\u003e) with hand netting. Artificial nests and hand netting caught respectively 1 and 4 unique genera of parasitic bees. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe most abundant genera, in terms of sampled specimens, were \u003cem\u003eBombus\u003c/em\u003e (23.2%) followed by \u003cem\u003eEucera\u003c/em\u003e (17.9 %), \u003cem\u003eLasioglossum\u003c/em\u003e (13.7%), \u003cem\u003eHalictus\u003c/em\u003e (11.5%) and \u003cem\u003eAndrena\u003c/em\u003e (8.8%). Parasitic genera were the less abundant. Hand netting detected 2121 specimens from both observations and captures which is about 78.5% of all specimens observed. Pan traps captured 395 specimens (about 14.6% of all specimens), more than five times fewer specimens than hand netting. Finally, artificial nests trapped 187 specimens (about 6.9% of all specimens), more than ten times fewer specimens than hand netting. We collected 155 species (and morphospecies) over the two years, 96 species (61.9%) from hand netting, 95 species (61.3%) from pan traps and 14 species (7.1%) from artificial nets. We focused only on data from hand netting and pan traps to compare the monitoring methods\u0026apos; efficiency because, as expected, the data from artificial nests showed that they only captured a small fraction of the species. The number of species (and morphospecies) collected over the two years (Figure 3) by hand netting was significantly higher (Wilcoxon rank test, p \u0026lt; 0.05). Species (and morphospecies) rarefaction curve (Figure 4) cumulative on both sampling years showed that pan traps and hand netting collected almost the same number of species but pan traps have a greater potential to detect species as the number of individuals increases (see extrapolation curve of Figure 4 and Figure S1, Supplementary Materials). Also, the number of singletons (species for which a single specimen was collected) greatly differs between pan traps (49) and hand netting (20).\u003c/p\u003e\n\u003cp\u003eThe attractiveness of pan traps and the efficiency of walking transects are significantly dependent on the flower coverage of the monitored site (Table 2 a and b). While the effectiveness of pan traps is high at low flowering coverage, the opposite is true for walking transects.\u003c/p\u003e\n\u003cp\u003eWild bee assemblage differs between the two sampling methods (Figure 5, R2 = 0.093, F = 2.88, p \u0026lt;0.001) but the distance between- samples for each group is not significant (Beta Dispersion Analysis, p \u0026gt; 0.05), although higher for pan traps.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePan traps of different colours attracted a similar number of individuals (124, 144 and 127 respectively for blue, white and yellow pan traps). All three colours attracted the same number of genera (14 genera for each colour), but the blue ones attracted a unique genus more than the other two colours. Yellow pan traps attracted more species (and morphospecies) among the three colours but there is no significant difference (Figure 6, Kruskall-Wallis, p \u0026gt; 0.05). Blue pans attracted \u003cem\u003eAmegilla, Dasypoda\u003c/em\u003e and \u003cem\u003eTetralonia\u003c/em\u003e as unique genera; the yellow ones trapped \u003cem\u003eHoplitis\u003c/em\u003e and \u003cem\u003ePanurgus\u003c/em\u003e as unique genera; the white ones collected \u003cem\u003eChelostoma\u003c/em\u003e and \u003cem\u003eHylaeus\u003c/em\u003e as exclusive genera (Figure 7a.). The relative proportion of the abundance of each genus among the three pans colours (Figure 7b.) showed some unique patterns: bees of the genus \u003cem\u003eSystropha\u003c/em\u003e were mostly attracted by the white colour as most of the \u003cem\u003eAndrena\u003c/em\u003e specimens\u003cem\u003e; Eucera\u003c/em\u003e individuals were most abundant in the blue pans, \u003cem\u003eLasioglossum\u0026nbsp;\u003c/em\u003epreferred the yellow traps and finally, \u003cem\u003eHalictus\u003c/em\u003e bees were equally distributed among the three colours. The three colours attracted a similar number of species and morphospecies (respectively 54, 48 and 38 of all collected species for the white, yellow and blue pan traps).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFocusing on the species of the Megachilidae family, we collected 9 genera (30% of all sampled genera) and 38 species (24.5% of all species collected) with one genus, represented by one single species, the cuckoo bee \u003cem\u003eCoelioxys mandibularis\u003c/em\u003e. Hand netting (20) and artificial nests (14) collected a greater number of species than pan traps (12). Only one species, \u003cem\u003eMegachile analis,\u003c/em\u003e was captured by all three sampling methods, while hand netting and artificial nests shared 6 species, hand netting and pan traps 2 and artificial nests and pan traps shared only one species. Hand netting captured (Figure 8a) the highest number of unique species (n=13, 34.2%), followed by pan traps (n=10, 26.3%) and artificial nests (n=8, 21.1%). Visualization by the ternary plot of the Megachilidae species assemblage collected with each method (Figure 8b) showed that species tend to cluster for each monitoring method, as many species tend to be sampled exclusively or mostly by hand netting and artificial nests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. List of all collected genera by each of the three sampling methods: Hand netting, Pan traps and Artificial nests. Numbers indicate the number of specimens for each genus and the total individuals collected by each method. * indicates the parasitic genera.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"486\" height=\"696\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Parameter estimates of the Generalized Linear Models (GLMs) with Poisson distribution and log-link function assessed as most parsimonious according to the Akaike information criterion (AIC). The model explains respectively the significative negative and positive influence of high flowering coverage on the number of captures by pan traps (\u003cstrong\u003ea\u003c/strong\u003e) and hand netting (\u003cstrong\u003eb\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"586\" height=\"202\"\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study demonstrates how different monitoring methods, regarding wild bee communities, are complementary, with each method capable of collecting mutually exclusive species. Active and passive methods are often difficult to compare, and, to the best of our knowledge, only a few studies report comparison based on extensive sampling and a high effort in species resolution (Nielsen et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Rhoades et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, O'Connor et al. 2018, Krahner et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Kuhlman et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Thompson et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the strong dependence of these methods on habitat types, vegetation composition and bee fauna can weaken the comparison. Many studies point out the use of trapping, especially pan traps, as the most efficient monitoring method, as these can attract both more specimens and more species than other methods (Stephen and Rao \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Nielsen et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Krahner et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the use of pan traps raises doubts regarding the effect of their repetitive and extensive use on the bee communities and bycatches of other insect groups (Gezon et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); as well as on the relative lack of standardisation due to the influence of vegetation (LeBuhn et al. 2012, Kuhlman et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The efficiency of pan traps is highly dependent on the height of the vegetation and thus on the different heights at which the traps are placed, so that they are competitive with the surrounding vegetation (Westphal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Furthermore, as we also found, pan traps can lose efficiency when flowering coverage is abundant since, in addition to colour, many pollinators are attracted by other factors that characterise flowers but not pans (O\u0026rsquo;Connor et al. 2018). Finally, pan traps are subject to weather phenomena for at least 24 hours, thus resulting in high variability of the results, which may be affected by sudden violent weather phenomenons or to large animals tipping over, with consequent loss of sampling data (LeBuhn et al. 2012, O\u0026rsquo;Connor et al. 2018, Kuhlman et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our results are consistent with those of Cane et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Thompson et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) who reported that generally, hand netting captured more specimens and species than pan traps. In our case, however, the lower efficiency of pan traps could be due to other factors, and this is visible in the rarefaction and extrapolation curves divided by year. This study took place in an experimental field where areas were sown with strips of entomophilous plants specifically designed to attract pollinating insects. The strips were sown in the early spring of 2022 and monitoring took place immediately at their flowering, a couple of months after sowing (see Supplementary Materials). The flowering and vegetation coverage in that year was rather low due to the work carried out to sow the seeds and the fact that most plants generally have greater blooming in their second year (Kowalska et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, the areas are subject to high hydric stress during the dry summer season, so in the first year, vegetation cover and flowering did not reach high values. The traps were always placed on the ground and captured more species than transects, which in turn depended heavily on the presence of blooms on which to observe and net specimens. The flower coverage dependence of pan traps and transects is inversely proportional (O\u0026rsquo;Connor et al. 2018) and this is validated also in our study. Whereas as flower cover increases, walking transects are more efficient in attracting specimens, pan traps lose efficiency. Pan traps were less attractive to bees and transects captured more species in 2023. However, the total rarefaction and extrapolation curves are in line with the results of other studies in indicating the great potential of pan traps in attracting a high diversity of bee species at the same sample size (Nielsen et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Hall \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, O\u0026rsquo;Connor et al. 2018, Krahner et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Pan traps catch many more singletons than transects, offering the possibility of attracting more species based on fewer captured specimens. However, an ideally exhaustive sampling of all species at a given site, based solely on the use of pan traps, would require a greater sampling effort than hand netting in terms of several sampling sessions and higher hours of placement of traps. Finally, pan traps tend to catch a more diverse and variable community of bees than hand netting which are inclined to sample much more similar communities each time. Our results are consistent with the study by Kuhlman et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) who also investigated seasonal and flower abundance patterns on the efficiency of pan traps and hand netting. In our case, hand netting was also crucial for the collection of parasitic genera that can sometimes be found feeding on nectar on some flowering species but are rarely attracted to pans. The use of artificial nests as an exclusive monitoring method entails, in the case of wild bees, the monitoring of only a fraction of diversity, that is confined to cavity-nesting species. As in Nielsen and colleagues (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), nests captured the fewest specimens and species, with \u003cem\u003eOsmia\u003c/em\u003e (5 species and 146 specimens) and \u003cem\u003eMegachile\u003c/em\u003e (5 species and 31 specimens) being the most abundant, but they were crucial for capturing 8 species that had not been observed otherwise (5.2% of total species and 21.1% of Megachilidae species). Among \u003cem\u003eOsmia\u003c/em\u003e, most individuals belonged to the species \u003cem\u003eO. caerulescens\u003c/em\u003e, in contrast to the other species of Osmia that were mostly observed through walking transects and pan traps.\u003c/p\u003e \u003cp\u003eThe wild bee communities sampled by hand netting and by pan traps were distinctive between the two monitoring methods, confirming, as already seen in other studies (Nielsen et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, O\u0026rsquo;Connor et al. 2018), that these two methods are highly complementary to each other. Some studies (Stephen and Rao, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hall \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) support the hypothesis of a strong preference for the blue colour of traps for most of the wild bee fauna. In our study, there is no significant overall preference for one of the three colours, although, the greatest abundance of individuals and the greatest diversity of species was captured by the white and yellow traps. The blue traps, on the other hand, attracted both lower abundance and lower species diversity. According to Hall (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in the northern hemisphere the blue-coloured traps have greater attractiveness because of a greater abundance of bumblebees that prefer this colour. This study took place in a Mediterranean and lowland habitat and although bumblebees are, from an abundance point of view, the dominant group in the walking transects, very few specimens were caught with both blue and white traps. A possible explanation may also be that we used pan traps and not vane traps, unlike Hall (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The lower abundance of bumblebees sampled with pan traps is consistent with the results of Bell and colleagues (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) who demonstrated that vane traps are more efficient at catching bumblebees than pan traps. The blue traps, however, attracted more exclusive genera than the others, in particular the genera \u003cem\u003eAmegilla, Dasypoda\u003c/em\u003e and \u003cem\u003eTetralonia\u003c/em\u003e. These three are genera of long ligula bees, that have a strong preference for Boraginaceae and Malvaceae, all of which are flowers in the blue-purple range (Michener \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The blue traps also had greater attractiveness towards bees of the genus \u003cem\u003eEucera\u003c/em\u003e, which also have strong preferences towards Boraginaceae and blue purple Cichorioideae (Michener \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The white traps, on the other hand, attracted high abundances of \u003cem\u003eHylaeus\u003c/em\u003e (exclusive genus), and \u003cem\u003eSystropha\u003c/em\u003e. These short ligula genera show preferences for white-coloured flowers such as Umbelliferae and Convolvulaceae (Michener \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.beewatching.it/\u003c/span\u003e\u003cspan address=\"https://www.beewatching.it/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Finally, yellow traps attracted abundant specimens of the genus \u003cem\u003eLasioglossum\u003c/em\u003e. Although the species of this genus are largely polylectic, they are often found foraging on mostly yellow-coloured Asteraceae (Michener \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These patterns indicate how indeed, according to the literature (Nielsen et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Rhoades et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Kuhlman et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the most suitable sampling method is extremely influenced by the studied habitat and by the local composition of the bee fauna. Therefore, if interested in monitoring a particular group of bees, it is necessary to assess beforehand what might be the best method or combination of methods to capture the diversity of the entire group. In the Megachilidae family, we find genera and species with very diverse trophic and nesting habits (Michener \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Danforth et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, within the genus \u003cem\u003eOsmia\u003c/em\u003e or \u003cem\u003eMegachile\u003c/em\u003e, we find species that nest in hollow stems and holes in wood, but also many species that prefer to nest on the ground or in snails (Michener \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Danforth et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The trophic habits are also different, while the genus \u003cem\u003eMegachile\u003c/em\u003e is known to be related to Fabaceae, the genus \u003cem\u003eChelostoma\u003c/em\u003e prefers Ranunculaceae and Asteraceae species (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.beewatching.it/\u003c/span\u003e\u003cspan address=\"https://www.beewatching.it/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This makes it difficult to identify a single sampling method. Our results showed that all three monitoring methods are strongly complementary. We did not find any Megachilidae species of conservation concern in our monitoring, but they are very important species concerning the pollination of fodder crops and fruit trees, and therefore of some importance to human activities (Ollerton et al. 2021). Furthermore, the use of artificial nests also makes it possible to investigate parasites, parasitoids and hyperparasites of these species, which make up a not-so-insignificant fraction of the biodiversity of an ecosystem (Klaus et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Indeed, the only parasitic species among the Megachilidae was captured thanks to the use of artificial nests. In addition to this, it is also possible, through the use of nests, to investigate the species and quantity of pollen preferred by wild bees as well as the presence of pesticides and other pollutants in the stored pollen (Staab et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These kinds of studies show that we should not underestimate the aspects of \u0026ldquo;standardised\u0026rdquo; monitoring, especially in the case of organisms as diverse as pollinators, and that we should always include more than just one method in our monitoring to best capture the biodiversity of a site, habitat type or a certain kind community.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eMediterranean areas are a hot spot for the biodiversity of wild bees. We found that bee communities have distinct compositions depending on the monitoring method. In our experimental areas, hand netting caught a higher abundance and species richness of wild bees but pan traps had greater attractiveness when flowering coverage was low. Artificial nests only sampled a smaller fraction of species but were fundamental to capturing unique Megachilidae species, including parasitic ones. In conclusion, we recommend the use of multiple highly complementary methods when we want a comprehensive assessment of a wild bee community's biodiversity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen access funding was provided by Universit\u0026agrave; degli Studi di Firenze within the CRUI-CARE Agreement. O.C.M. acknowledges the support of Programma Operativo Nazionale Ricerca e Competitivit\u0026agrave;. F.R.D. and M.M. acknowledge the support of the NBFC to the University of Florence, funded by the Italian Ministry of University and Research, PNRR, Missione 4 Componente 2, \u0026ldquo;Dalla ricerca all\u0026rsquo;impresa\u0026rdquo;, Investimento 1.4, Project CN00000033.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eO.C.M., M.M. and F.R.D. authors contributed to the monitoring work; D.V. supported the agronomic fieldwork; O.C.M. conceived the study; O.C.M. and M.M. identified the specimens, performed data analysis and wrote the paper; all authors read, commented and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analysed for this paper are available on request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBanaszak J (1980) Studies on methods of censusing the numbers of bees (Hymenoptera, Apoidea). 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Journal of the Kansas Entomological Society 81(3): 288-300. https://doi.org/10.2317/JKES-802.06.1\u003c/li\u003e\n\u003cli\u003ewww.STEP-project.net\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"biodiversity, Anthophila, pan traps, bee nests, monitoring schemes","lastPublishedDoi":"10.21203/rs.3.rs-4846902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4846902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eContext\u003c/h2\u003e \u003cp\u003eThe ongoing pollinator decline may threaten and compromise the resilience of terrestrial ecosystems. Implementing conservation action requires monitoring pollinator populations' actual status, but this is particularly difficult for pronubes insects such as wild bees. Their monitoring is difficult and time-consuming but crucial for assessing their health status.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eHere we compared and evaluated the efficiency of three different monitoring methods to evaluate wild bee biodiversity in lowland areas sown with entomophilous flowers to support pollinating insects in a Mediterranean environment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe sampled wild bees for two years by using hand netting, pan traps and artificial nests. We compared species richness and abundance among these methods with a particular focus on how flowering coverage affects the efficiency of walking transects and pan traps and discussed the attractiveness of the different colours of pan traps.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHand netting captured a higher abundance of wild bees than the other two methods but a similar number of species to pan traps. Artificial nests captured fewer specimens and species. Bee assemblages were significantly different between pan traps and hand netting, and pan traps had greater potential in capturing the whole bee biodiversity, but their attractiveness is negatively influenced by the flowering coverage contrary to hand netting sampling.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eLike other studies, the three sampling methods are complementary regarding species assemblages. The juxtaposition of several monitoring methods is essential to assess the biodiversity status of species with such particularly different ecological traits.\u003c/p\u003e","manuscriptTitle":"Revealing the biodiversity of wild bees (Hymenoptera: Apoidea: Anthophila) in flower strips in Mediterranean floodplains. 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