Community-level trait matching between flowers and bees on a continental scale

Community-level trait matching between flowers and bees on a continental scale | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 November 2025 V1 Latest version Share on Community-level trait matching between flowers and bees on a continental scale Author : Tamar Keasar 0000-0002-4925-0823 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176221997.79447755/v1 188 views 159 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Predicting pairwise species interactions in bipartite networks is a longstanding challenge in community ecology. In pollination networks, the shape of flowers often matches the mouthparts of their animal pollinators. This trait-matching facilitates the task of forecasting which flower-insect links exist within a given network. This study asks how trait-matching varies across entire flower-bee communities. I combined a database of European pollination networks across ~150 sites with information on bee and flower traits. I tested for correlations between the average depth and symmetry of visited flowers and the average proboscis length, inter-tegular distance, and sociality of their wild bee visitors. Communities of mostly shallow and radial flowers interacted with more bee species, and had fewer interactions with eusocial bees, than communities dominated by deep and bilateral flowers. Furthermore, communities of shallow radial flowers were visited by smaller and shorter-tongued wild bees. Including flower interactions with honey bees in the analyses weakened the trait matching. Latitude significantly predicted the wild bees’ proboscis length, the proportions of eusocial species in the networks, and the match between proboscis lengths and floral depths, but not floral depth and symmetry. This work promotes integration of species-level traits into analyses of plant-pollinator networks at large geographical scales. Community-level trait matching between flowers and bees on a continental scale ABSTRACT Predicting pairwise species interactions in bipartite networks is a longstanding challenge in community ecology. In pollination networks, the shape of flowers often matches the mouthparts of their animal pollinators. This trait-matching facilitates the task of forecasting which flower-insect links exist within a given network. This study asks how trait-matching varies across entire flower-bee communities. I \RL combined a database of European pollination networks across tested for correlations between the average depth and symmetry of visited flowers and the average proboscis length, inter-tegular distance, and sociality of their wild bee visitors. Communities of mostly shallow and radial flowers interacted with more bee species, and had fewer interactions with eusocial bees, than communities dominated by deep and bilateral flowers. Furthermore, communities of shallow radial flowers were visited by smaller and shorter-tongued wild bees. Including flower interactions with honey bees in the analyses weakened the trait matching. Latitude significantly predicted the wild bees’ proboscis length, the proportions of eusocial species in the networks, and the match between proboscis lengths and floral depths, but not floral depth and symmetry. This work promotes integration of species-level traits into analyses of plant-pollinator networks at large geographical scales. INTRODUCTION Interactions between species drive the structure and function of ecological communities, yet documenting and interpreting all interactions in communities with multiple trophic levels is often challenging (Dalla Riva et al., 2019). Hence, much ecological research restricts itself to bipartite interactions between two groups of organisms. For example, pollination networks provide convenient descriptions of the interactions between flowering plants and their animal pollinators (Vasquez et al., 2009, Schwartz et al. 2020). Many quantitative indices, based on graph theory, have been developed to describe the structure of pollination networks, e.g. connectance, nestedness, and interaction strength (Vizentin-Bugoni et al., 2018). Analyses of pollination networks from different ecosystems reveal common structural features, such as modularity (Olesen et al., 2007) and asymmetric specialization (Vasquez and Aizen, 2004) in large networks. Yet, predicting which plant-pollinator pairs interact in a given network remains a challenge (Olito and Fox, 2015, Peralta et al., 2024). It is difficult to predict pairwise links because pollination interactions tend to be generalized (many species in the network interact with several partners, Bascompte et al., 2007) and change over time (Petanidou et al., 2008; Bramon Mora et al., 2020). Identifying matches between traits of flowers (e.g., color, scent, morphology, flowering phenology) and pollinators (e.g., color vision, chemoreception, body size) improves forecasts of plant-pollinator links. Such matches, often referred to as ‘pollination syndromes’, help predict the broad group of pollinators (for example, birds, bats, beetles) that is most likely to visit a flower. Among bee-pollinated plants, trait-matching occurs at a finer scale: the length of floral tubes often matches the proboscis length of their bee visitors. Other morphological features of flowers, such as shape class and symmetry, also predict bee visitors at the genus level (Ornai and Keasar, 2020). Two complementary adaptive hypotheses aim to explain the trait matching between flowers and bees. The first hypothesis takes a foraging perspective and suggests that feeding on matching flowers optimizes the nectar intake rates of bees by decreasing the time required to handle the flowers and increasing the amount of nectar collected. Foragers reduce their flower handling time by avoiding flowers that are too deep to reach with their mouthparts. They increase their caloric intake by avoiding shallow flowers, which typically produce less nectar than deep ones (Klumpers et al., 2019, Keasar and Bodner, 2025). The second hypothesis focuses on learning as a barrier to bee foraging on some flower morphologies. According to this interpretation, flower shapes can be classified along a continuum of accessibility, from generalized to specialized. Generalized flowers are often radial and dish-shaped and are visited by a wide range of pollinator taxa. Specialized flowers have bilateral symmetry, long nectar tubes and other morphological structures that make them less accessible to pollinators (Keasar, 2018). Such flowers require a learning period to be handled efficiently (Muth et al., 2015, Krishna & Keasar, 2019), and consequently have a narrower range of visitors (Yoder et al., 2020) despite their higher nectar production. This raises the question how pollinators are able to cross the learning barrier and provide pollination services to specialized flowers. A spatially explicit model predicts that long-distance fliers evolve higher learning abilities than short-distance fliers (Keasar & Wajnberg, 2025). Pollinator learning abilities, in turn, are predicted to be positively correlated with the frequency of complex flowers. The mechanism behind this prediction is that highly mobile pollinators encounter many flowers during their lifetime, providing them with ample opportunities to learn how the handle specialized flowers. Consequently, bees’ flight ranges (or predictors thereof) are expected to correlate with the morphological specialization (corolla depth and symmetry) of the flowers they visit. The present study extends the concept of trait matching to the level of entire flower-bee communities. It tests whether the mean flower traits in a plant community predict the mean trait values of their bee visitors. It further asks whether entire flower-bee networks can be characterized according to their mean trait values, and whether these community-level trait values vary geographically. Using a recently published large database of pollination networks, I first tested the predictions that emerge from the two adaptive hypotheses of trait matching. The hypothesis that stresses foraging efficiency predicts that plant communities with more specialized flowers are visited by longer-tongued bees. The interpretation that focuses on bee learning predicts that communities of more specialized flowers are visited by bees that are better fliers. Flight distances of bees increase with sociality and with intra-tegular distance (a common measure of body size) in interspecific comparisons (Grüter and Hayes, 2022, Kendall et al., 2022). I therefore correlated the mean depth and symmetry of the visited flowers with the mean proboscis length, ITD, and sociality of their bee visitors, treating each locality of plant-pollinator observations as a data point. To explore the geographical variation in community-level trait matching, I tested the effect of latitude on the flowers’ and on the bees’ community-level traits. I also related latitude to the mean mismatch between the tongue lengths of the bees and the corolla depths that they visited, as a direct measure of flower-bee size matching. MATERIAL AND METHODS Pollination networks : I analyzed a recently compiled database of pollination networks, collected from 255 localities in 23 European countries (Lanuza et al., 2025). The database contains more than 600,000 plant-pollinator interactions from 52 published and unpublished studies. I removed records of interactions with non-hymenopterans and with honey bees from the database and retained 134,263 interactions between flowers and wild bees. Honeybees were omitted from the initial analyses because they often originate from commercial colonies and thus do not represent the bee fauna that has naturally coevolved with the flowers. However, the data were also re-analyzed with the flower-honeybee interactions included. I further excluded a study (Study-ID 49 in Lanuza et al., 2025) that included a single flower species and a second study (Study-ID 24) where trait information on most bee species (see below) was lacking. Finally, I omitted 108 localities where fewer than 30 flower-bee interactions were observed. This reduced dataset, which was used for analyses, included 82,977 bee-flower interactions from 147 localities across Europe (Appendix S1). Flower traits : I compiled records on the corolla tube length (n=2,448 species) and symmetry pattern (radial/bilateral, scored as 0/1, n=4,053 species) of flowers, based on published studies and on my own field-collected data. When multiple records of corolla tube length for a plant species were available, they were averaged (Appendix S2). Bee traits : I collected information on bee inter-tegular distances (ITD, n=1,575 species) and proboscis lengths (n=414 species), based on published studies. The ITD is commonly used to estimate bee body sizes and flight ranges in interspecific comparisons (Kendall et al., 2019, 2022). The ITDs of 594 additional bee species were imputed by averaging all listed ITD values of other species in their genus. Family-specific allometric equations reliably predict bees’ proboscis lengths from their ITDs (Cariveau et al., 2016). By applying these allometric models to bees in the dataset, proboscis length estimates for 1,761 additional species were obtained. Altogether, the dataset includes information on proboscis length for 2,175 species of bees. I also tabulated information on the bees’ social organization level (eusocial – scored as 1, or not – scored as 0, n=1930 species), as an additional proxy of flight range (Grüter and Hayes, 2022, Kendall et al., 2022, Appendix S3). Data analysis : I calculated the flower-bee size mismatch for each interaction by subtracting the visitor’s proboscis length from the corolla tube length of flower that it visited. Next, I computed the mean values of corolla tube length, corolla symmetry score, bee ITD, bee proboscis length, bee sociality score, and flower-bee mismatch for each of the 147 flower-bee communities. I then calculated Pearson’s correlation coefficients between the per-community mean bee traits, flower traits, flower-bee mismatch, and site latitude. Linear models were used to calculate the residuals from the regression of the bees’ proboscis length on latitude and from the regression of the sociality score on latitude. GLMs with family Gamma and log-link functions were applied to test the effects of the two residuals and of latitude on flower depth and symmetry. ITDs were not entered into the GLMs because of multicollinearity with proboscis lengths. The flower-bee mismatch variable was normally distributed. I therefore used a linear model to evaluate the effects of the residuals and of latitude on the flower-bee mismatch value. The Eta 2 metric was calculated for all models to estimate the relative importance of the predictor variables. R version 4.2.2 was used for all statistical analyses (R Core Team, 2022). The packages BeeIT (Carivau et al., 2016), lme4 (Bates et al., 2015), effectsize (Ben-Shachar et al., 2020) and corrtable (van der Laken, 2023) were applied for data manipulation and analysis. RESULTS Bee species richness and trait correlations : As the fraction of flower-bee interactions that involved bilateral flowers (the flower community’s symmetry score) increased, bee species richness decreased (correlation coefficient: -0.257, P<0.01). The correlation between mean tube length of the interacting flowers and bee richness was not significant (correlation coefficient: -0.124). Plant communities with more specialized (deep and/or bilateral) flowers were visited by larger, longer-tongued bees, and social bee species contributed a higher proportion of the flower visits in these communities (Table 1). These correlations were weaker when flower interactions with honey bees were included in the analyses (Table 1). Latitudinal trends : The bees’ community-level ITDs, proboscis lengths and sociality scores increased with latitude (correlation coefficients: 0.405***, 0.372***, 0.510***, respectively, Fig. 1). The flowers’ community-level traits, on the other hand, did not show statistically significant latitudinal trends (Fig. 2), while the mean flower-bee size mismatch declined (correlation coefficient: -0.273***). ITD and proboscis length were highly correlated (correlation coefficient: 0.941***). Although latitude did not directly influence flower tube length and symmetry (Fig. 2), an indirect effect may nonetheless exist, if latitude influences the bee community that in turn selects for flower traits. To explore this possibility, the bees’ community-level proboscis lengths and sociality scores were regressed on latitude. The residuals from both regressions represent the community-level bee trait values after accounting for the effect of latitude. These residuals, together with latitude, were used as independent variables in models to predict the community-level plant tube lengths, plant symmetry scores and flower-bee size mismatches. The proboscis length residuals significantly predicted the flowers’ tube lengths and symmetry scores, and the size mismatches. The sociality residuals significantly predicted only the flowers’ symmetry scores. Latitude did not impact floral tube length and symmetry, but had a negative effect on the flower-bee mismatches (Table 2). DISCUSSION This study takes advantage of a large, continental-scale database of pollination networks to analyze flower-bee trait matching at the scale of entire ecological communities. It allows, for the first time, comparisons of trait matching across communities and environmental gradients. We found that plant communities dominated by shallow and radial flowers interacted more with small solitary bees, whereas communities dominated by deep and bilateral flowers received more visits from large and eusocial species. These patterns are compatible with the two adaptive hypotheses proposed for trait matching. The first hypothesis suggests that matching between proboscis length and flower depth, as found in this study, optimizes the foragers’ nectar intake rates (Klumpers et al., 2019). In addition, longer-tubed flowers tended to be bilaterally symmetric (correlation coefficient between tube length and symmetry score: 0.188, P<0.01), and eusocial foragers tended to have longer tongues than solitary bees (correlation coefficient between proboscis length and sociality score: 0.534, P<0.001). Taken together, these correlations can explain the observed associations between flower depth, flower symmetry, bee tongue length and bee sociality. The second hypothesis predicts, based on theoretical modeling, that floral specialization coevolves with long-distance flying capabilities and high learning abilities of bees in plant-pollinator communities (Keasar and Wajnberg, 2025). In line with this prediction, the bees’ body sizes and eusociality scores (both of which are proxies of flight range) increased in localities dominated by specialized flowers. Additionally, the species richness of bees declined as their proportion of interactions with bilateral flowers increased, supporting previous evidence that bilateral flowers are more specialized than radial ones (Yoder et al., 2020). The number of bee species per locality was not related to the mean depth of the interacting flowers, contrary to the expectation that deep flowers are more specialized than shallow ones (Stang et al., 2006). Similarly, corolla tube depth was not correlated to the number of visiting insect species in 35 species of Asteraceae (Torres & Galetto, 2002), and the ‘openness’ of flowers did not predict the numbers of their insect visitors in a sample of 37 pollination networks from various ecosystems (Olesen et al., 2007). The mean body size and the representation of eusocial species in the bee communities increased with latitude, while flower traits did not show latitudinal trends. Bee body sizes were previously shown to increase from southern to northern Europe, conforming to Bergmann’s rule (Gérard et al., 2018). The increased proportion of social species in high-elevation sites likely reflects the dominance of the eusocial bumble bees in cold climates. Interestingly, the match between the bees’ tongue length and flower depth also increased with latitude, suggesting more specialized pollination interactions in northern localities. Lanuza et al., (2025), who compiled the database analyzed here, similarly found a latitudinal decline in the residual connectance of pollination networks. A latitudinal increase in specialization was also found in an earlier global analysis of 58 pollination networks, which was based on a network-level specialization metric (H2 ’ ) and did not consider trait-matching (Schleuning et al., 2012). Including honey bees in the interaction dataset weakened the matching between flower and bee traits, although most of the trait correlations remained statistically significant. Interactions between flowers and honey bees were very common (>380,000 interactions, 82.2% of all interactions with bees). Honeybees adjust their nectar collection technique (lapping or sucking) to the depth of the flower and the sugar concentration of the nectar (Wei et al., 2023). 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Biology letters , 16 (9), 20200307.\RL‏ Table 1: Correlation coefficients and associated significance values (* - <0.05, *** - <0.001) between community-level floral specialization traits and traits of their wild bee visitors. Pollination networks from 147 localities in Europe were analyzed. Values in parentheses include both wild bees and honey bees. Tube length Symmetry score Bee traits ITD 0.378*** (0.242***) 0.375*** (0.165*) Proboscis length 0.377*** (0.230**) 0.442*** (0.228*) Sociality score 0.208* (-0.049) 0.299*** (0.199**) Table 2: Summary statistics for the effects of latitude and residual bee traits on flower depth, symmetry and flower-bee size mismatch. Statistically significant effects are indicated in bold. Predictors Parameter estimate and SE P-value Eta 2 Parameter estimate and SE P-value Eta 2 Parameter estimate and SE P-value Eta 2 Latitude 0.007±0.005 0.189 0.002 -0.007±0.008 0.348 0.004 -0.105±0.030 0.001 0.08 Latitude-Proboscis residuals 0.998±0.021 <0.0001 0.150 0.147±0.031 <0.001 0.26 -0.345±0.119 0.004 0.06 Latitude-Sociality residuals 0.098±0.150 0.516 0.003 0.531±0.223 0.019 0.04 0.485±0.868 0.577 0.002 FIGURE CAPTIONS Fig. 1: The relationship between latitude and mean inter-tegular distance (a), proboscis length (b) and sociality score (c) of bees in the pollination networks. Fig. 2: The relationship between latitude and mean corolla tube length (a) and symmetry score (b) of flowers in the pollination networks. FIGURES Fig. 1a Fig. 1b Fig. 1c Fig. 2a Fig. 2b Information & Authors Information Version history V1 Version 1 04 November 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords bee eusociality flower morphology inter-tegular distance latitude pollination network proboscis length Authors Affiliations Tamar Keasar 0000-0002-4925-0823 [email protected] University of Haifa Faculty of Natural Sciences View all articles by this author Metrics & Citations Metrics Article Usage 188 views 159 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Tamar Keasar. Community-level trait matching between flowers and bees on a continental scale. Authorea . 04 November 2025. DOI: https://doi.org/10.22541/au.176221997.79447755/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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