RESEARCH ARTICLE The Allometry of Proboscis Length and Its Uses in Ecology

Daniel P. Cariveau1,2*, Geetha K. Nayak2, Ignasi Bartomeus3, Joseph Zientek2, John S. Ascher4, Jason Gibbs5, Rachael Winfree2

1 Department of Entomology, University of Minnesota, Saint Paul, Minnesota, United States of America, 2 Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, New Jersey, United States of America, 3 Departamento Ecología Integrativa, Estación Biológica de Doñana (EDB-CSIC), Avda. Américo Vespucio s/n, Isla de la Cartuja, 41092, Sevilla, Spain, 4 Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543, Singapore, 5 Department of Entomology, Michigan State University, East Lansing, Michigan, United States of America

* [email protected]

Abstract

OPEN ACCESS Allometric relationships among morphological traits underlie important patterns in ecology. These relationships are often phylogenetically shared; thus quantifying allometric relation- Citation: Cariveau DP, Nayak GK, Bartomeus I, Zientek J, Ascher JS, Gibbs J, et al. (2016) The ships may allow for estimating difficult-to-measure traits across species. One such trait, pro- Allometry of Bee Proboscis Length and Its Uses in boscis length in , is assumed to be important in structuring bee communities and plant- Ecology. PLoS ONE 11(3): e0151482. doi:10.1371/ pollinator networks. However, it is difficult to measure and thus rarely included in ecological journal.pone.0151482 analyses. We measured intertegular distance (as a measure of body size) and proboscis Editor: Olav Rueppell, University of North Carolina, length (glossa and prementum, both individually and combined) of 786 individual bees of Greensboro, UNITED STATES 100 species across 5 of the 7 extant bee families (: Apoidea: Anthophila). Received: August 10, 2015 Using linear models and model selection, we determined which parameters provided the Accepted: February 29, 2016 best estimate of proboscis length. We then used coefficients to estimate the relationship

Published: March 17, 2016 between intertegular distance and proboscis length, while also considering family. Using allometric equations with an estimation for a scaling coefficient between intertegular dis- Copyright: © 2016 Cariveau et al. This is an open access article distributed under the terms of the tance and proboscis length and coefficients for each family, we explain 91% of the variance Creative Commons Attribution License, which permits in species-level means for bee proboscis length among bee species. However, within spe- unrestricted use, distribution, and reproduction in any cies, individual-level intertegular distance was a poor predictor of individual proboscis medium, provided the original author and source are length. To make our findings easy to use, we created an R package that allows estimation credited. of proboscis length for individual bee species by inputting only family and intertegular dis- Data Availability Statement: The data are found in tance. The R package also calculates foraging distance and body mass based on previ- two places. First, they are part of the supplemental material. Second, they are included in the R package ously published equations. Thus by considering both and intertegular distance that was created for this manuscript. The R package we enable accurate estimation of an ecologically and evolutionarily important trait. can be downloaded at: https://github.com/ibartomeus/ BeeIT.

Funding: This work was supported by the United States Department of Agriculture, Natural Resources Conservation Service Conservation Innovation Grant, Introduction NRCS Agreement #69-3A75-10-163 (to RW) (URL: nrcs.usda.gov/wps/portal/nrcs/main/national/ Allometric relationships underlie many important patterns in ecology and evolution. Body size programs/financial/cig/); a Fulbright-Nehru has been shown to scale with metabolic rate and this may in turn explain variation in life his- Postdoctoral Fellowship (to GN) (URL: cies.org/ tory traits, species interactions and the rate of ecological processes [1]. Allometric relationships

PLOS ONE | DOI:10.1371/journal.pone.0151482 March 17, 2016 1/13 Bee Proboscis Length and Its Uses in Ecology grantee/geetha-nayak); and a Rutgers University can reveal interesting patterns in life history evolution [2–4]. For example in mammals, the Aresty Undergraduate Research Award (to JZ) (URL: allometric relationship between body size and growth rate differs among species based on life https://aresty.rutgers.edu/our-programs/funding/ history traits such dietary specialization and death rate [2]. Because allometric relationships are undergraduate-research-fellowships). The funders had no role in study design, data collection and often phylogenetically conserved, knowledge of allometric relationships can be used to identify analysis, decision to publish, or preparation of the taxa that diverge from the expected pattern. These taxa often have atypical life histories or manuscript. exaggerated traits that may be the result of unique patterns of selection [5, 6]. Within commu- Competing Interests: The authors have declared nity ecology, allometric relationships can underlie key traits involved in resource acquisition, that no competing interests exist. and are used to predict competitive interactions [7]. The predictive nature of allometric relationships makes them a potentially useful tool for estimating ecologically important traits that are otherwise difficult to measure. Here we take this approach to develop a predictive allometric equation for proboscis length in bees (Hyme- noptera: Apoidea: Anthophila). Proboscis length mediates multiple aspects of bee ecology and evolution including flower choice [8, 9], efficiency in acquiring floral resources [10–12], polli- nation effectiveness [13, 14], community assembly ([15], but see [16]), the structure of plant- pollinator networks [17], and possibly even plant speciation [14, 18, 19]. Proboscis length may predict which bee species undergo population declines due to global change [20, 21], and which species benefit most from pollinator restoration programs [22]. However, few studies have examined proboscis length outside of the genus Bombus, thus limiting its applicability in community ecology and macroecology (but see [17]). Proboscis length in bees can be difficult to measure, requiring dissection of the mouthparts (glossa and prementum), ideally in fresh specimens, which must first be identified to the spe- cies level because dissection may damage specimens. Measurement of proboscis length is par- ticularly difficult for small bees. For these reasons, the few studies that have measured proboscis length for multiple bee species have focused on large-bodied species, such as bumble bees (Bombus), because their proboscis length is relatively easy to measure (e.g. [16, 20, 23] but see [17]). A common practice in pollination ecology is to use family or other phylogenetic groupings as a proxy for proboscis length [24–26]. In particular, the sister families Megachili- dae and comprise the long-tongued families [27], a monophyletic group diagnosed in part by homologous modifications of the proboscis, notably elongate and flattened labial palpi and an elongate glossa [28]. These are contrasted with short-tongued bees, comprising all other bee families, which are considered a paraphyletic assemblage, although the nature of this para- phyly is controversial due to conflict between and among morphological and molecular phylo- genetic studies [29, 30]. Bumble bees (genus Bombus) are likewise often divided into subgenera that have long and short proboscises [31], among which the phylogenetic relationships have recently been resolved through molecular phylogenetic analyses [32, 33]. While it is clear that proboscis length varies in general among bees, a central interest in many studies is to infer absolute or functional length of the proboscis to address ecological hypotheses that are related to the ability of pollinators to access floral rewards. Further, there is tremendous variation in functional length with these groups. However, a quantification of the variation in functional length of bee tongues has not been done outside of Bombus. Most importantly, allometric scal- ing of functional tongue length may be modified by phylogenetic relationships, but this has not yet been explored for pollinators. In this study, we develop a predictive equation for estimating bee proboscis length as a func- tion of family (taxonomy) and body size (allometry). We fit the equation and estimate coeffi- cients based on almost 800 empirical measurements of proboscis length from 100 bee species of 5 families. We provide an R package that allows users to input intertegular span and family to estimate proboscis length for any species of bee that belongs to five of the seven extant bee families. These five families include 99% of the world's bee species.

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Fig 1. Photographs of intertegular distance (IT), glossa, prementum and proboscis. (A) Photographs of IT for wilkella Kirby () (B) Glossa, prementum and proboscis length in the long-tongued bee Nomada illinoensis Robertson (Apidae), and two short-tongued bees (C) Andrena bradleyi Viereck (Andrenidae) a long-faced Andrena, and (D) Colletes inaequalis Say (Colletidae). doi:10.1371/journal.pone.0151482.g001 Material and Methods Data collection and morphological measurements To measure proboscis length on a diverse set of native, wild bees, we collected bees at 35 sites throughout the state of New Jersey, USA from 2012 to 2014. Specimens were collected on pri- vate property with permission from private land owners. All measurements were done on fresh specimens, using a Nikon SMZ800 stereoscope. To measure proboscis length and body size, we used Nikon NIS-Elements software (Ver. 3.07). We measured proboscis length as follows. The prementum was measured as the distance from the base of the mentum to the distal point of the basiglossal sclerite ([27], Fig 1B–1D). The glossa was measured from the basiglossal sclerite to the distal point of the labellum as in Harder 1982 ([23] Fig 1B–1D). It was common for glossae to be retracted in or folded along

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the prementum. In these cases, special care was taken to remove or extend the glossa. All mea- surements were conducted only after glossae were completely extended from the prementum [23]. We defined proboscis length as the length of the glossa plus the prementum. We measured bee body size as the distance between the tegulae, or wing bases (intertegular distance; hereafter, IT, Fig 1A). This is the standard measure of body size in bees as it is highly correlated with dry body mass ([34], R2 = 0.97, N = 20) which is itself problematic to measure particularly for pinned specimens. Ecological studies of bees have used IT to predict flight dis- tance [35] and other outcomes related to organism mobility such as response to land cover at various scales [36, 37].

Data analysis To examine the interspecific relationship between intertegular distance, taxonomy and probos- cis length, we used a power function, as is commonly done in allometric studies [23, 38]: lnðYÞ¼lnðaÞþb lnðITÞðEquation 1Þ

Where Y = length of proboscis, glossa, or prementum in mm, a = a coefficient specific to the bee family, IT = intertegular distance (IT) in mm, and b = the allometric scaling coefficient. While overall proboscis length was the primary response variable of interest, the prementum and glossa individually may also be important for bee foraging [11]. Therefore we also used this equation separately for analysis of prementum and glossa lengths. The family-specific coef- ficient (a) allows for different intercepts for each family. The scaling constant (b) reflects the slope of the relationship between IT and proboscis length. Due to sample size limitations for some of the rarer species, we were limited in our ability to test for differences between sexes. However, analyses run with only females (which constitute the majority of individuals collected in most studies) versus with both females and males yielded similar results; thus we combined the sexes in further analyses. We then used model selection to determine which predictor variables provided the best-fit- ting model based on lowest AIC value. The presence of the family coefficient (a) in the best-fit- ting model indicates that intercepts differ among families. The presence of the scaling coefficient (b) in the best-fitting model indicates that proboscis, glossa or prementum scales with intertegular distance. An interaction between family and IT indicates that the scaling coef- ficient (b) differs among families. In addition, to determine whether simply long- or short-ton- gued grouping predicted proboscis length, we also conducted model selection using IT, tongue-type (short- versus long-tongued bees), and their interaction. The model that includes only tongue-type is equal to assumptions made in pollination studies that use this category without considering allometry. We used values from the best fitting model as parameter values for Eq 1. In addition to OLS regression that is best suited to our predictive purposes [39], we also used standardized major axis estimation (SMA) to test whether the family coefficients (a) and scaling coefficients (b) differed among models. This analysis is commonly used in allometry studies to summarize the relationships among variables as it incorporates error in the X vari- able (in our case IT) as well as the Y variable [39]. For the glossa, prementum and proboscis, we tested for differences in slopes and intercepts among families using the sma function in the smatr package in R [40]. As SMA approaches are highly sensitive to outliers, we used the robust SMA estimation [40, 41]. To determine whether this allometric relationship could explain within-species variation in proboscis length for bees, we also ran a set of intraspecific models, one for each bee species that had 10 or more individuals measured. Individual-level IT was the predictor. Because males,

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females, and in social bees, queens, can vary in morphology, we included sex or caste as a pre- dictor for those species for which we had representatives from each sex or caste. Lastly, we tested the generality of the model we developed above for bees, using data on but- terfly body size and proboscis length as reported by Kunte [7]. These data include species-level means for body length (as a proxy for body size) and proboscis length for 117 species of butter- flies in 6 different families in Costa Rica. We conducted this step for the interspecific model only because the butterfly data set contained only species-level means for body length and pro- boscis length.

Results We measured 786 specimens of 100 species belonging to 28 genera of 5 of the 7 extant bee fam- ilies. We lacked data for two families: Melittidae and Stenotritidae. Melittidae is limited to one relatively common species in our region, with the others being rarely collected. Therefore we were unable to predict values across Melittidae. Stenotritidae are restricted to Australia. Spe- cies-level means for IT, glossa, prementum, and proboscis are included as supplementary infor- mation (S1 Table). In the interspecific models using OLS regression, the combination of family and mean IT strongly predicted the mean length of the proboscis, glossa and prementum. Family and IT were retained in the best model selected by AIC in all cases (Table 1), and model R2 values were 0.91, 0.85, 0.91 for glossa, prementum and proboscis, respectively. Including the interac- tion between family and IT did not provide a better fit for the proboscis or glossa model while the interaction improved the fit of the prementum model (Table 1). The robust SMA analysis

Table 1. AIC and R2 values for interspecific models. Models are presented in ascending order of AIC value. Top models used for estimates are in bold. Models with + do not include an interaction term, whereas models with x do include the interaction.

Response Variable Model R2 AIC Proboscis Family + IT 0.91 -41.91 Family x IT 0.91 -38.96 Short- vs. Long-Tongued + IT 0.87 -11.1 Short- vs. Long-Tongued x IT 0.87 -10.6 IT Only 0.73 61.7 Short- vs. Long- Tongued Only 0.61 99.06 Family Only 0.62 102.6 Glossa Family + IT 0.91 67.43 Family x IT 0.91 71.06 Short- vs. Long-Tongued x IT 0.87 98.7 Short- vs. Long-Tongued + IT 0.86 102.2 Family Only 0.79 149.7 Short- vs. Long-Tongued Only 0.77 151.14 IT Only 0.53 222.9 Prementum Family x IT 0.85 -100.11 Family + IT 0.82 -92.1 Short- vs. Long-Tongued x IT 0.74 -56.2 Short- vs. Long-Tongued + IT 0.75 -55.9 IT Only 0.69 -41.9 Family Only 0.16 66.9 Short- vs. Long-Tongued Only 0.08 69.53 doi:10.1371/journal.pone.0151482.t001

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Table 2. Estimates for the coefficients a and b for fitting Eq 1, log(Y) = log(a) + log(I) * b, where Y = length of proboscis, glossa, or prementum in mm, a = a coefficient specific to the bee family, I = intertegular distance (IT) in mm, and b = the allometric scaling constant, and logs are base e. Coefficients are from the interspecific OLS regression models with the lowest AIC values (Table 1).

Response Variable Family Family Coefficient (a) IT Coefficient (b) Proboscis Andrenidae 1.06 Apidae 2.13 Colletidae 0.86 1.38 1.87 ---- 0.96 Prementum Andrenidae 0.88 0.83 Apidae 0.91 0.73 Colletidae 0.56 1.14 Halictidae 0.89 1.04 Megachilidae 0.77 0.68 Glossa Andrenidae 0.23 Apidae 1.27 Colletidae 0.21 Halictidae 0.43 Megachilidae 1.16 ---- 1.04 doi:10.1371/journal.pone.0151482.t002

obtained results qualitatively similar to those found by OLS regression. We found no evidence for an interaction between family and IT for either the glossa or proboscis model (glossa: likeli- hood ratio = 7.3, df = 4, P = 0.12; proboscis: likelihood ratio = 4.5, df = 4, P = 0.34). There was evidence for an interaction between family and IT in the prementum SMA model (likelihood ratio = 15.3, df = 4, P = 0.004). In all the OLS regression models, family was retained in the best- fit models indicating that mean length of mouthparts differed among families (Table 1). Simi-

larly, in all three SMA models, intercepts differed among families (glossa: Wc =465.1,df=4,P = < < < 0.001; prementum: Wc = 465.1, df = 4, P = 0.001l proboscis: Wc =465.1,df=4,P = 0.001). In general, long- and short-tongued families differed in proboscis and glossa length, with the two long-tongued families having the longest proboscises and glossae, although prementum length was more similar (Table 2, Fig 2). The R2 for the OLS regression models were lower for family alone (glossa = 0.53, prementum = 0.69, proboscis = 0.73, Table 1) or only long vs. short-tongued family groups (glossa = 0.77, prementum = 0.08, proboscis = 0.61, Table 1), or for IT alone (glossa = 0.79, prementum = 0.16, proboscis = 0.62, Table 1). Using the butterfly data from Kunte (2007), we found that the combination of family and body length (AIC = 24.3 with a body length by family interaction term) compared favorably to either family (AIC = 105.8) or body length (AIC = 101.8) alone. The R2 values were also higher when combining body length and family (R2 = 0.77 for both factors combined, versus R2 = 0.47 for body length and R2 = 0.25 for family). We used the estimates of the OLS regression models with the lowest AIC value for the parameter values in the allometric power function (Eq 1). As the slopes do not differ among families for the proboscis and glossa models (no interaction between IT and family, Table 1), the value for b is the same across families. The interaction was retained in the best model for prementum and therefore, each family has a specific IT scaling constant. Model-estimated val- ues for a and b are in Table 2. Fig 3 shows the family-specific allometric relationship between IT and proboscis length. The IT coefficient was nearly 1 and thus indicates a linear relationship between IT and proboscis length (Table 2).

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Fig 2. Proboscis, glossa, and prementum length. Boxplots of (A) proboscis, (B) glossa, and (C) prementum length for the five families of bees measured. Grey boxplots are long-tongued families while clear boxplots represent short-tongued families. Dots represent outliers. Figures are drawn using raw data. doi:10.1371/journal.pone.0151482.g002

We focused on the relationship between IT and proboscis length for practical reasons, because it is easy to measure this trait in live and preserved bees. However, body size is a stan- dard measure in the allometry literature so we investigated the body mass–proboscis length relationship as well. IT itself is known to have a negative allometric relationship with dry body mass such that IT does not increase as fast, in a proportional sense, with body mass (34). For this part of the paper, we used an additional analysis, SMA, which is best used to test for posi- tive or negative allometry. SMA analyses for all mouthparts indicated a negative allometric relationship with estimated body mass, with the exception of glossa in Andrenidae in which the scaling coefficient (b) of one was within the 95% confidence interval (S2 Table). Thus over- all proboscis length did not increase as quickly as body mass. We also provide an R package, BeeIT, that estimates proboscis, glossa and prementum length based on IT and family for most bee species in the five families for which we have data. The R package can be downloaded at https://github.com/ibartomeus/BeeIT. These five families

Fig 3. Relationship between IT and proboscis length. The relationship between intertegular distance (IT) and proboscis length in 101 species of bees. Each point represents the mean IT and proboscis length for a bee species. Colors are bee families. Lines are fit using regression coefficients from model outputs. Both IT and proboscis length are ln transformed. doi:10.1371/journal.pone.0151482.g003

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make up approximately 99% of all bee species, allowing for general use of this estimation. How- ever, it is important to note that some groups such as euglossine bees, are widely known to have extremely long tongues [18]. These groups will likely have higher intercepts than other species in Apidae. The R package, BeeIT, also uses IT to estimate body mass ([34] S1 Fig) and typical foraging distance ([35] Table 1) using previously published equations [34, 35]. We included these equations and coefficients from the OLS regression models in the R package, BeeIT, as many users may wish to calculate all three quantities simultaneously. In contrast to the interspecific models above, we found little evidence in the intraspecific mod- els that IT strongly predicted length of mouthparts across the individuals of a species. For the 24 species analyzed, we did find that the relationship was statistically significant in 10 (glossa), 16 (prementum), and 16 (proboscis) cases. However, R2 values were low for most species with mean values being 0.14, 0.39 and 0.36 for glossa, prementum and proboscis respectively (S3 Table, S2 Fig). Mean R2 values were low even for species with a statistically significant relationship between IT and length of mouthparts (P < 0.05; glossa = 0.36; prementum = 0.53, proboscis = 0.50).

Discussion We show that proboscis length in bees, a trait that is ecologically important but difficult to measure, can be predicted with an R2 of 0.91 using an allometric equation that requires only information on family and IT (Figs 1 and 3). Our use of body size allometry represents a dra- matic improvement over existing methods of estimating pollinator proboscis length, which used only taxonomic information. Ecologists and evolutionary biologists interested in func- tional proboscis length in bees have long relied on the long- vs. short-tongued family categories (e.g. [21, 24, 26]) without considering body size allometry; but this method explained only 61% of the variance in proboscis length in our large data set. The equations and coefficients we pres- ent here will increase the precision and quality of the many studies investigating the important of functional proboscis length ecology of native bee foraging behavior, resource competition, or mutualist partner choice, as well as evolutionary studies of plant-pollinator mutualisms. Our approach might generalize well to other pollinator taxa, based on our test with a data set on tropical butterflies, in which the combination of family and body size explained 77% of the var- iance in proboscis length, an improvement over the 47% explained by family alone. Our estimation tool should be useful for pollinator community ecology because proboscis length is such an important trait for structuring pollinator communities and plant-pollinator networks. For example, at a single study site Stang and others [42] measured proboscis length across multiple pollinator taxa and found trait matching between proboscis length and corolla length of flowers visited by those pollinators. Proboscis length and its matching with corolla length is likewise an important factor structuring plant-pollinator networks involving hummingbirds [43, 44] and Lepidoptera [45]. A frequent use of proboscis length in the pollina- tion ecology literature is to measure floral specialization with respect to nectar gathering; that is, pollinator species with long tongues are assumed to specialize on flowers with long corollas [20, 26]. However, measuring proboscis length is often not feasible for all species in a commu- nity. Our allometric equation will make these proboscis estimates more accurate and facilitate stronger community analyses. For instance, in our data set the smallest long-tongued bee spe- cies weighs approximately 2 mg while the largest is 162 mg, which leads to a nearly 6-fold empirical difference in estimated proboscis length (2.1 versus 12.1 mm). If studies examining the functional importance of proboscis length did not consider the scaling of proboscis length with body size, these studies would consider these species to have equivalent proboscis length. The relationship we present here will also be useful in exploring why particular species and genera deviate from the predicted relationship. Deviations from allometric relationships have

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revealed interesting patterns in other . For example in Odonata, species with relatively lon- ger wings for a given body length were migratory species [46]. In ants, controlling for body size, longer legs of some desert genera reduce foraging time and increase cooling [47]. In bees, probos- cis lengths that diverge from the predicted relationship, such as those that are longer than pre- dicted given their body size, may indicate selection pressures for flower specialization or other life history traits. For example, of the more than 1500 or more species of Andrena have short pro- boscises. Two long-tongued species, the sister species Andrena (Stenomelissa) lonicera Warncke and A.(S.)halictoides Smith are nectar specialists and likely evolved long proboscises from a common ancestor and now exploit the long corollas of their host flowers, Lonicera gracilipes Miq. (Caprifoliaceae) and Weigela hortensis (Siebold. & Zucc.) K. Koch. (Caprifoliaceae), respec- tively [14]. Similar patterns are found in other bee taxa and insect orders [48]. As some species and genera may deviate from the predicted allometric relationship, the equation we present here should not substitute for detailed measurements when warranted and practical. We found differences among the glossa, prementum, and overall proboscis length. In partic- ular, family alone explained much more variance in glossa length than in prementum length. This indicates that the result for the differences in proboscis length among families are driven primarily by glossa length. Estimating length of different mouthparts may be warranted depending on which families of bees are being studied. For example, some species of short-ton- gued bees extend the basal segments of their mouthparts including the prementum during feeding [11, 14]. In contrast, long-tongued bees such as Bombus may keep their prementum static and thus use it very little during feeding, instead extending their glossae [23]. In contrast to the strong predictive relationship that we found at the species level, we did not find strong evidence the IT consistently predicts the variation in proboscis length among individuals of the same species. This differs from earlier work showing that IT or other easy-to- measure morphological characters such as wing size and length of the radial cell of the wing showed strong relationships with proboscis length (R2 > 0.90), at least within Bombus species [9, 23, 49, 50]. Our results may differ from these previous studies as they often used specimens from single sampling locations ([9, 23, 49] but see [50]) or even a single colony [23]. Therefore, groups of closely related individuals may have more similar relationships between body size and proboscis length. In contrast, our study included bees collected across a wide geographic area, and allometric relationships can vary geographically and within species [51, 52]. Proboscis length is an important trait affecting plant-pollinator interactions but has not been routinely included as a variable in pollinator studies due to the difficulty of measuring these mouthparts, particularly in small, short-tongued bees, and it may conflict with other research goals such as specimen identification. The equation and coefficients presented here and in the accompanying R package (https://github.com/ibartomeus/BeeIT) will facilitate rapid estimation of proboscis length using only family information plus a simple body size measurement.

Supporting Information S1 Fig. The relationship between body mass and proboscis length in 101 species of bees. ÀÁ1 Body Mass ¼ IT 0:405 Body mass is estimated using equation: in Cane 1987, Fig 1 where 0:77 . Each point represents the mean dry body mass and mean proboscis length for a bee species. (EPS) S2 Fig. Scatterplots of intraspecific relationships between IT and proboscis length. Species with 10 or more individuals are represented. (EPS)

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S1 Table. Table of species-level means for IT, glossa, prementum and proboscis length. (XLSX) S2 Table. Table of family-level slopes and lower and upper 95% confidence intervals from robust SMA. (CSV) S3 Table. Table of intraspecific R2 values for OLS regression models of IT as a function of glossa, prementum and proboscis length. Predictor variables were IT and sex. Response vari- ables were either glossa, prementum and proboscis length. (XLSX)

Acknowledgments Funding was provided to GN through a Fulbright-Nehru Postdoctoral Fellowship, to JZ from a Rutgers University Aresty Undergraduate Research Award, and to RW from a USDA Conser- vation Innovation Grant (Co-PI, with N Williams PI). We thank D. Inouye and one anony- mous reviewer who greatly improved this manuscript.

Author Contributions Conceived and designed the experiments: RW. Performed the experiments: GKN JZ. Analyzed the data: DPC IB. Wrote the paper: DPC GKN IB JZ JSA JG RW. Identified the bee specimens: JSA JG.

References 1. Brown JH, Gillooly JF, Allen AP, Savage VM, West GB. Toward a metabolic theory of ecology. Ecology. 2004; 85(7):1771–89. doi: 10.1890/03-9000 PMID: WOS:000223113500001. 2. Sibly RM, Brown JH. Effects of body size and lifestyle on evolution of mammal life histories. Proc Natl Acad Sci USA. 2007; 104(45):17707–12. doi: 10.1073/pnas.0707725104 PMID: WOS:000250897600031. 3. Danforth BN. The evolution of hymenopteran wings—The importance of size. J Zool. 1989; 218:247– 76. doi: 10.1111/j.1469-7998.1989.tb02536.x PMID: WOS:A1989AF93900006. 4. Danforth BN, Desjardins CA. Male dimorphism in Perdita portalis (Hymenoptera, Andrenidae) has arisen from preexisting allometric patterns. Insectes Soc. 1999; 46(1):18–28. doi: 10.1007/ S000400050107 PMID: WOS:000079081700004. 5. Iwaniuk AN, Dean KM, Nelson JE. Interspecific allometry of the brain and brain regions in parrots (Psit- taciformes): comparisons with other birds and primates. Brain Behav Evol. 2005; 65(1):40–59. doi: 10. 1159/000081110 PMID: 15467290. 6. Emlen DJ, Nijhout HF. The development and evolution of exaggerated morphologies in insects. Annu Rev Entomol. 2000; 45:661–708. doi: 10.1146/annurev.ento.45.1.661 PMID: 10761593. 7. Kunte K. Allometry and functional constraints on proboscis lengths in butterflies. Funct Ecol. 2007; 21 (5):982–7. doi: 10.1111/j.1365-2435.2007.01299.x 8. Goulson D, Lye GC, Darvill B. Diet breadth, coexistence and rarity in bumblebees. Biodivers Conserv. 2008; 17(13):3269–88. doi: 10.1007/s10531-008-9428-y PMID: ISI:000260509200014. 9. Peat J, Tucker J, Goulson D. Does intraspecific size variation in bumblebees allow colonies to effi- ciently exploit different flowers? Ecol Entomol. 2005; 30:176–81. doi: 10.1111/j.0307-6946.2005. 00676.x 10. Harder LD. Flower handling efficiency of bumble bees: Morphological aspects of probing time. Oecolo- gia. 1983; 57(1–2):274–80. doi: 10.1007/Bf00379591 PMID: WOS:A1983QG36400039. 11. Harder LD. Functional differences of the proboscides of short-tongued and long-tongued bees (Hyme- noptera, Apoidea). Can J Zool/Rev Can Zool. 1983; 61(7):1580–6. doi: 10.1139/Z83-212 PMID: WOS: A1983RD15800020. 12. Borrell BJ. Scaling of nectar foraging in orchid bees. Am Nat. 2007; 169:569–80. doi: 10.1086/512689 PMID: 17427129

PLOS ONE | DOI:10.1371/journal.pone.0151482 March 17, 2016 11 / 13 Bee Proboscis Length and Its Uses in Ecology

13. Stout JC, Allen JA, Goulson D. Nectar robbing, forager efficiency and seed set: Bumblebees foraging on the self incompatible plant Linaria vulgaris (Scrophulariaceae). Acta Oecol. 2000; 21(4–5):277−83. doi: 10.1016/s1146-609x(00)01085-7 14. Shimizu A, Dohzono I, Nakaji M, Roff DA, Miller DG 3rd, Osato S, et al. Fine-tuned bee-flower coevolu- tionary state hidden within multiple pollination interactions. Sci Rep. 2014; 4:3988. doi: 10.1038/ srep03988 PMID: 24496444; PubMed Central PMCID: PMC3913927. 15. Inouye DW. Resource partitioning in bumblebees—Experimental studies of foraging behavior. Ecology. 1978; 59(4):672–8. doi: 10.2307/1938769 PMID: ISI:A1978GB33300007. 16. Harmon-Threatt AN, Ackerly DD. Filtering across spatial scales: phylogeny, biogeography and commu- nity structure in bumble bees. PLoS One. 2013; 8(3):e60446. doi: 10.1371/journal.pone.0060446 PMID: 23544141; PubMed Central PMCID: PMC3609857. 17. Stang M, Klinkhamer PGL, van der Meijden E. Size constraints and flower abundance determine the number of interactions in a plant-flower visitor web. Oikos. 2006; 112:111–21. doi: 10.1111/j.0030- 1299.2006.14199.x 18. Borrell BJ. Long tongues and loose niches: Evolution of euglossine bees and their nectar flowers. Bio- tropica. 2005; 37(4):664–9. doi: 10.1111/j.1744-7429.2005.00084.x PMID: ISI:000233766800019. 19. Rodriguez-Girones MA, Santamaria L. Resource competition, character displacement, and the evolu- tion of deep corolla tubes. Am Nat. 2007; 170(3):455–64. doi: 10.1086/520121 PMID: 17879195. 20. Bommarco R, Lundin O, Smith HG, Rundlof M. Drastic historic shifts in bumble-bee community compo- sition in Sweden. Proc R Soc B. 2012; 279(1727):309–15. doi: 10.1098/rspb.2011.0647 PMID: 21676979; PubMed Central PMCID: PMC3223670. 21. Biesmeijer JC, Roberts SPM, Reemer M, Ohlemuller R, Edwards M, Peeters T, et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science. 2006; 313 (5785):351–4. doi: 10.1126/science.1127863 PMID: ISI:000239154300044. 22. Carvell C, Osborne JL, Bourke AF, Freeman SN, Pywell RF, Heard MS. Bumble bee species' responses to a targeted conservation measure depend on landscape context and habitat quality. Ecol Appl. 2011; 21(5):1760–71. doi: 10.1890/10-0677.1 PMID: 21830716. 23. Harder LD. Measurement and estimation of functional proboscis length in bumblebees (Hymenoptera: Apidae). Can J Zool/Rev Can Zool. 1982; 60(5):1073–9. doi: 10.1139/Z82-148 PMID: WOS: A1982NS95100046. 24. Fortel L, Henry M, Guilbaud L, Guirao AL, Kuhlmann M, Mouret H, et al. Decreasing abundance, increasing diversity and changing structure of the wild bee community (Hymenoptera: Anthophila) along an urbanization gradient. PLoS One. 2014; 9(8):e104679. doi: 10.1371/journal.pone.0104679 PMID: 25118722; PubMed Central PMCID: PMC4131891. 25. Dorchin A, Filin I, Izhaki I, Dafni A. Movement patterns of solitary bees in a threatened fragmented habi- tat. Apidologie. 2012; 44(1):90–9. doi: 10.1007/s13592-012-0159-4 26. Clough Y, Ekroos J, Baldi A, Batary P, Bommarco R, Gross N, et al. Density of insect-pollinated grass- land plants decreases with increasing surrounding land-use intensity. Ecol Lett. 2014; 17(9):1168–77. doi: 10.1111/ele.12325 PMID: 25040328. 27. Michener CD. The Bees of the World, 2nd edition. Baltimore and London: Johns Hopkins University Press; 2007. 28. Michener CD, Brooks RW. Comparative study of the glossa of bees. Contrib Am Entomol Inst. 1984; 22:1–74. 29. Hedtke SM, Patiny S, Danforth BN. The bee tree of life: a supermatrix approach to apoid phylogeny and biogeography. BMC Evol Biol. 2013; 13:138. doi: 10.1186/1471-2148-13-138 PMID: 23822725; PubMed Central PMCID: PMC3706286. 30. Danforth BN, Cardinal S, Praz C, Almeida EA, Michez D. The impact of molecular data on our under- standing of bee phylogeny and evolution. Annu Rev Entomol. 2013; 58:57–78. doi: 10.1146/annurev- ento-120811-153633 PMID: 22934982. 31. Inouye DW. Species Structure of Bumblebee Communities in North America and Europe. In: Mattson WJ, editor. The role of in forest ecosystems. New York: Springer-Verlag; 1977. p. 35–40. 32. Cameron SA, Hines HM, Williams PH. A comprehensive phylogeny of the bumble bees (Bombus). Biol J Linn Soc. 2007; 91(1):161–88. doi: 10.1111/J.1095-8312.2007.00784.X PMID: WOS:000245938100013. 33. Kawakita A, Sota T, Ito M, Ascher JS, Tanaka H, Kato M, et al. Phylogeny, historical biogeography, and character evolution in bumble bees (Bombus: Apidae) based on simultaneous analysis of three nuclear gene sequences. Mol Phylogen Evol. 2004; 31(2):799–804. doi: 10.1016/j.ympev.2003.12.003 PMID: 15062814.

PLOS ONE | DOI:10.1371/journal.pone.0151482 March 17, 2016 12 / 13 Bee Proboscis Length and Its Uses in Ecology

34. Cane J. Estimation of bee size using intertegular span (Apoidea). J Kans Entomol Soc. 1987; 60:145– 7. 35. Greenleaf SS, Williams NM, Winfree R, Kremen C. Bee foraging ranges and their relationship to body size. Oecologia. 2007; 153(3):589–96. doi: 10.1007/s00442-007-0752-9 PMID: ISI:000248812000010. 36. Benjamin FE, Reilly JR, Winfree R. Pollinator body size mediates the scale at which land use drives crop pollination services. J Appl Ecol. 2014; 51(2):440–9. doi: 10.1111/1365-2664.12198 37. Kennedy CM, Lonsdorf E, Neel MC, Williams NM, Ricketts TH, Winfree R, et al. A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems. Ecol Lett. 2013; 16 (5):584–99. doi: 10.1111/ele.12082 PMID: WOS:000318077200003. 38. Huxley J. Problems of relative growth. New York City: Dial Press; 1932. 39. Warton DI, Wright IJ, Falster DS, Westoby M. Bivariate line-fitting methods for allometry. Biological Reviews. 2006; 81:259–91. PMID: 16573844 40. Warton DI, Duursma RA, Falster DS, Taskinen S. smatr 3- an R package for estimation and inference about allometric lines. Methods in Ecology and Evolution. 2012; 3(2):257–9. doi: 10.1111/j.2041-210X. 2011.00153.x 41. Taskinen S, Warton DI. Robust tests for one or more allometric lines. J Theor Biol. 2013; 333:38–46. doi: 10.1016/j.jtbi.2013.05.010 PMID: 23688823. 42. Stang M, Klinkhamer PG, Waser NM, Stang I, van der Meijden E. Size-specific interaction patterns and size matching in a plant-pollinator interaction web. Ann Bot. 2009; 103(9):1459–69. doi: 10.1093/aob/ mcp027 PMID: 19228701; PubMed Central PMCID: PMC2701768. 43. Vizentin-Bugoni J, Maruyama PK, Sazima M. Processes entangling interactions in communities: for- bidden links are more important than abundance in a hummingbird-plant network. Proc R Soc B. 2014; 281(1780):20132397. doi: 10.1098/rspb.2013.2397 PMID: 24552835. 44. Maglianesi MAA, Blüthgen N, Böhning-Gaese K, Schleuning M. Morphological traits determine special- ization and resource use in plant–hummingbird networks in the neotropics. Ecology. 2014; 95 (12):3325–34. doi: 10.1890/13-2261.1 45. Corbet SA. Butterfly nectaring flowers: butterfly morphology and flower form. Entomol Exp Appl. 2000; 96(3):289–98. doi: 10.1046/J.1570-7458.2000.00708.X PMID: WOS:000088587800012. 46. Sacchi R, Hardersen S. Wing length allometry in Odonata: differences between families in relation to migratory behaviour. Zoomorphology. 2012; 132(1):23–32. doi: 10.1007/s00435-012-0172-1 47. Sommer S, Wehner R. Leg allometry in ants: extreme long-leggedness in thermophilic species. Arthro- pod Struct Dev. 2012; 41(1):71–7. doi: 10.1016/j.asd.2011.08.002 PMID: 21992805. 48. Krenn HW, Plant JD, Szucsich NU. Mouthparts of flower-visiting insects. Structure & Devel- opment. 2005; 34(1):1–40. doi: 10.1016/j.asd.2004.10.002 PMID: ISI:000228309700001. 49. Morse DH. Estimating proboscis length from wing length in bumblebees (Bombus spp.). Ann Entomol Soc Am. 1977; 70:311–5. doi: 10.1093/aesa/70.3.311 50. Medler JT. Morphometric studies on bumble bees. Ann Entomol Soc Am. 1962; 55:212–8. doi: 10. 1093/aesa/55.2.212 51. Owen RE, Harder LD. Heritable allometric variation in bumble bees: opportunities for colony-level selection of foraging ability. J Evol Biol. 1995; 8:725–38. doi: 10.1046/j.1420-9101.1995.8060725.x 52. Gibson RL. Soldier production in Camponotus novaeboracensis during colony growth. Insectes Soc. 1989; 36(1):28–41. doi: 10.1007/BF02225878

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Agapostemon_virescens Andrena_bradleyi Andrena_imitatrix Andrena_wilkella

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ln(proboscis) ● ● ● ●

0.30 0.40 0.50 ●

● 0.60 0.70 0.80 0.90 ●

1.0 1.1 1.2 1.3 1.4 ● ● 0.75● 0.85 0.95 1.05

0.55 0.65 0.75 0.0 0.2 0.4 0.6 0.8 0.45 0.50 0.55 0.60 0.65 0.70 0.6 0.7 0.8 0.9 Augochlora_pura Augochlorella_aurata Bombus_bimaculatus Bombus_griseocollis

● ● ● ● ●● ● ● ● ●● ● ● ● ● ●●●● ●●● ● ● ● ● ●● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ●●● ●●● ● ●●●● ● ● ● ● ●●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

ln(proboscis) ●● ● ● ●● ● ● ● ● ● ● ● 1.6 1.8 2.0 2.2 2.4 2.0 2.2 2.4 0.7 0.8 0.9 1.0 1.1 ● ● ● ● 0.70 0.80 0.90 0.35 0.40 0.45 0.50 0.55 0.60 0.20 0.25 0.30 0.35 0.40 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.3 1.4 1.5 1.6 1.7 1.8 Bombus_impatiens Bombus_perplexus Colletes_inaequalis Habropoda_laboriosa

●●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● (p ) ● ● ●● ● ● ● ● ● ln(proboscis) ● ● ● ● ● ● ● ● ● ● ● ● ● 1.9 2.1 2.3 0.7 0.8 0.9 1.0 1.9 2.1 2.3 2.5 ● ● ● 1.7● 1.9 2.1 2.3

1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.2 1.3 1.4 1.5 1.6 1.7 0.8 0.9 1.0 1.1 1.2 1.15 1.20 1.25 1.30 1.35 1.40 1.45 Hoplitis_pilosifrons Lasioglossum_pilosum Lasioglossum_versatum Lasioglossum_vierecki

● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

ln(proboscis) ● ● ● ● ● ● ● ● ● ● ● 0.4 0.5 0.6 0.7 ● ●● 1.45 1.50 1.55 1.60 1.65 ● ● ● ● ● 0.3 0.5 0.7 0.9 0.15 0.20 0.25 0.30 0.40 0.45 0.50 0.55 0.60 0.65 0.05 0.15 0.25 0.05 0.15 0.25 0.35 −0.20 −0.10 0.00 0.05 Megachile_mendica Megachile_pugnata Megachile_xylocopoides Melissodes_denticulata

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ln(proboscis) ● ● ● ● ● ● ● ● ●● 1.50 1.60 1.70

● ● 1.76 1.78● 1.80 1.82 1.84 ● 1.60 1.65 1.70 1.75 1.88 1.92 1.96 2.00 0.95 1.00 1.05 1.10 1.15 1.10 1.15 1.20 1.25 1.30 1.10 1.15 1.20 1.25 1.30 1.35 0.8 0.9 1.0 1.1

Xylocopa_virginica ln(IT) ln(IT) ln(IT)

● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●

●

ln(proboscis) ● ●

1.85 1.90 1.95 2.00 2.05 ●

1.65 1.70 1.75 1.80 1.85 1.90

ln(IT) Family Subfamily Tribe genus species Full name tongue_type - Phylogenetic grouping sample size number of femalesnumber of malesmean proboscismean length IT length (mm)mean (mm) glossamean length prementum (mm)Comments length ontaxonomic (mm) exotic and notes parasitic status AndrenidaeAndreninae Andrenini Andrena alleghaniensisAndrena (Scrapteropsis) alleghaniensis Viereck, 1907 short 5 4 1 1.62 1.9 0.37 1.25 AndrenidaeAndreninae Andrenini Andrena banksi Andrena (Archiandrena) banksi Malloch, 1917 short 1 0 1 1.86 1.4 0.52 1.34 AndrenidaeAndreninae Andrenini Andrena bisalicis Andrena (Thysandrena) bisalicis Viereck, 1908 short 1 1 0 1.6 1.84 0.27 1.32 AndrenidaeAndreninae Andrenini Andrena bradleyi Andrena (Conandrena) bradleyi Viereck, 1907 short 14 8 6 2.27 1.75 0.49 1.78 AndrenidaeAndreninae Andrenini Andrena carlini Andrena (Melandrena) carlini Cockerell, 1901 short 3 2 1 2.36 2.13 0.48 1.88 AndrenidaeAndreninae Andrenini Andrena carolina Andrena (Andrena) carolina Viereck, 1909 short 1 1 0 2.17 2.09 0.5 1.67 AndrenidaeAndreninae Andrenini Andrena cressonii Andrena (Holandrena) cressonii cressonii Robertson, 1891 short 1 1 0 1.85 2.01 0.36 1.49 AndrenidaeAndreninae Andrenini Andrena fenningeri Andrena (Scrapteropsis) fenningeri Viereck, 1922 short 1 1 0 1.53 1.88 0.25 1.28 AndrenidaeAndreninae Andrenini Andrena imitatrix Andrena (Scrapteropsis) imitatrix Cresson, 1872 short 10 8 2 1.47 1.8 0.33 1.14 AndrenidaeAndreninae Andrenini Andrena morrisonellaAndrena (Scrapteropsis) morrisonella Viereck, 1917 short 4 3 1 1.41 1.69 0.34 1.07 AndrenidaeAndreninae Andrenini Andrena nasonii Andrena (Simandrena) nasonii Robertson, 1895 short 7 6 1 1.51 1.52 0.33 1.19 AndrenidaeAndreninae Andrenini Andrena perplexa Andrena (Tylandrena) perplexa Smith, 1853 short 1 1 0 2.51 2.44 0.63 1.88 AndrenidaeAndreninae Andrenini Andrena pruni Andrena (Melandrena) pruni Robertson, 1891 short 2 2 0 2.59 2.37 0.49 2.09 AndrenidaeAndreninae Andrenini Andrena robertsonii Andrena (Rhacandrena) robertsonii Dalla Torre, 1896 short 1 1 0 1.45 1.58 0.26 1.19 AndrenidaeAndreninae Andrenini Andrena rudbeckiae Andrena (Callandrena sensu lato) rudbeckiae Robertson, 1891 short 1 1 0 2.56 2.69 0.68 1.88 AndrenidaeAndreninae Andrenini Andrena tridens Andrena (Andrena) tridens Robertson, 1902 short 1 1 0 1.98 1.75 0.41 1.57 AndrenidaeAndreninae Andrenini Andrena vicina Andrena (Melandrena) vicina Smith, 1853 short 8 8 0 2.68 2.51 0.58 2.11 AndrenidaeAndreninae Andrenini Andrena wilkella Andrena (Taeniandrena) wilkella (Kirby, 1802) short 15 10 5 2.38 2.24 0.68 1.7 Exotic AndrenidaePanurginae andreniformisCalliopsis (Calliopsis) andreniformis Smith, 1853 short 1 0 1 3.12 1.45 1.62 1.5 Apidae Apini Apis mellifera Apis (Apis) mellifera Linnaeus, 1758 long 2 2 0 4.97 3.03 3.3 1.67 Exotic Apidae Apinae Bombini Bombus bimaculatusBombus (Pyrobombus) bimaculatus Cresson, 1863 long 54 33 21 9.99 4.36 6.94 3.05 Apidae Apinae Bombini Bombus citrinus Bombus (Psithyrus) citrinus (Smith, 1854) long 3 0 3 7.58 3.55 5.27 2.31 Parasitic Apidae Apinae Bombini Bombus fervidus Bombus (Thoracobombus) fervidus (Fabricius, 1798) long 3 2 1 10.72 3.5 7.68 3.04 Apidae Apinae Bombini Bombus griseocollis Bombus (Cullumanobombus) griseocollis (DeGeer, 1773) long 67 50 17 9.2 5 6.24 2.96 Apidae Apinae Bombini Bombus impatiens Bombus (Pyrobombus) impatiens Cresson, 1863 long 29 29 0 10.14 4.82 7.02 3.12 Apidae Apinae Bombini Bombus perplexus Bombus (Pyrobombus) perplexus Cresson, 1863 long 17 15 2 8.31 4.28 5.5 2.81 Apidae Apinae Bombini Bombus vagans Bombus (Pyrobombus) vagans vagans Smith, 1854 long 1 1 0 8.91 3.09 6.38 2.53 Apidae Xylocopinae Ceratinini Ceratina calcarata Ceratina (Zadontomerus) calcarata Robertson, 1900 long 36 34 2 3.67 1.42 2.48 1.18 Apidae Xylocopinae Ceratinini Ceratina strenua Ceratina (Zadontomerus) strenua Smith, 1879 long 7 7 0 2.68 1.02 1.79 0.9 Apidae Apinae Anthophorini Habropoda laboriosa Habropoda laboriosa (Fabricius, 1804) long 32 22 10 9.17 3.79 6.81 2.36 Apidae Apinae bimaculatusMelissodes (Melissodes) bimaculatus bimaculatus (Lepeletier, 1825) long 2 1 1 6.38 2.6 3.94 2.44 The genera Melissodes and Coelioxys are considered masculine and their changeable epithets cited accordingly. See Apidae Apinae Eucerini Melissodes denticulatusMelissodes (Eumelissodes) denticulatus Smith, 1854 long 24 14 10 5.29 2.44 3.36 1.93 The genera Melissodes and Coelioxys are considered masculine and their changeable epithets cited accordingly. See Apidae Apinae Eucerini Melissodes subillatus Melissodes (Eumelissodes) subillatus LaBerge, 1961 long 9 3 6 4.25 2.44 2.66 1.58 The genera Melissodes and Coelioxys are considered masculine and their changeable epithets cited accordingly. See Apidae Apinae Eucerini Melissodes (Eumelissodes) trinodis Robertson, 1901 long 2 2 0 5.31 2.78 3.62 1.69 Apidae Nomadinae Nomadini Nomada articulata Nomada articulata Smith, 1854 long 8 2 6 2.5 1.39 1.37 1.13 Parasitic Apidae Nomadinae Nomadini Nomada illinoensis Nomada illinoensis Robertson, 1900 long 1 1 0 2.11 1.33 1.07 1.04 Parasitic Apidae Nomadinae Nomadini Nomada imbricata Nomada imbricata Smith, 1854 long 1 1 0 3.11 1.88 1.64 1.47 Parasitic Apidae Nomadinae Nomadini Nomada pygmaea Nomada pygmaea Cresson, 1863 long 2 1 1 2.04 1.09 1.06 0.98 Parasitic Apidae Nomadinae Nomadini Nomada vegana Nomada vegana Cockerell, 1903 long 5 3 2 3.54 1.67 2.26 1.28 Parasitic Apidae Apinae Eucerini pruinosa Peponapis (Peponapis) pruinosa (Say, 1837) long 2 2 0 7.96 3.45 5.79 2.17 Apidae Xylocopinae Xylocopini Xylocopa virginica Xylocopa (Xylocopoides) virginica virginica (Linnaeus, 1771) long 40 20 20 7.31 6.03 4.97 2.33 Colletidae Colletinae Colletes inaequalis Colletes inaequalis Say, 1837 short 33 32 1 2.35 2.94 0.54 1.82 Colletidae Colletinae Colletes thoracicus Colletes thoracicus Smith, 1853 short 1 1 0 2.77 3.26 0.82 1.95 Colletidae Colletinae Colletes validus Colletes validus Cresson, 1868 short 9 4 5 2.83 2.76 0.71 2.12 Colletidae Hylaeinae Hylaeus affinis Hylaeus (Prosopis) affinis (Smith, 1853) short 4 3 1 0.97 1.21 0.31 0.66 Colletidae Hylaeinae Hylaeus sparsus Hylaeus (Metziella) sparsus (Cresson, 1869) short 1 0 1 0.76 1.02 0.17 0.59 Halictidae Agapostemonsericeus (Agapostemon) sericeus (Förster, 1771) short 1 1 0 3.1 1.89 1.15 1.94 Halictidae Halictinae Halictini Agapostemontexanus Agapostemon (Agapostemon) texanus Cresson, 1872 short 3 3 0 3.49 1.99 1.35 2.15 Halictidae Halictinae Halictini Agapostemonvirescens Agapostemon (Agapostemon) virescens (Fabricius, 1775) short 16 12 4 3.74 2.06 1.35 2.39 Halictidae Halictinae Augochlorini Augochlorapura Augochlora (Augochlora) pura pura (Say, 1837) short 21 20 1 2.75 1.66 1.14 1.62 Halictidae Halictinae Augochlorini Augochlorellaaurata Augochlorella aurata (Smith, 1853) short 10 10 0 2.43 1.42 0.89 1.54 Halictidae Halictinae Augochlorini Augochlorellapersimilis Augochlorella persimilis (Viereck, 1910) short 1 1 0 1.69 1.27 0.43 1.26 Halictidae Halictinae Augochlorini Augochloropsismetallica Augochloropsis (Paraugochloropsis) metallica (Fabricius, 1793) short 1 1 0 2.32 1.71 0.77 1.55 Halictidae Halictinae Halictini confusus Halictus (Seladonia) confusus confusus Smith, 1853 short 7 6 1 2.11 1.39 0.67 1.44 Halictidae Halictinae Halictini Halictus (Odontalictus) ligatus Say, 1837 short 30 21 9 2.85 1.7 1.13 1.72 Halictidae Halictinae Halictini Halictus (Nealictus) parallelus Say, 1837 short 7 6 1 2.92 2.26 1 1.92 Halictidae Halictinae Halictini Halictus rubicundusHalictus (Protohalictus) rubicundus (Christ, 1791) short 6 6 0 2.64 1.98 0.83 1.8 Halictidae Halictinae Halictini Lasioglossumcallidum (Dialictus) callidum (Sandhouse, 1924) short 2 2 0 1.5 1.31 0.51 0.98 Halictidae Halictinae Halictini Lasioglossumcoerulum Lasioglossum (Dialictus) coeruleum (Robertson, 1893) short 1 1 0 1.3 1.34 0.25 1.05 Halictidae Halictinae Halictini Lasioglossumcoreopsis Lasioglossum (Dialictus) coreopsis (Robertson, 1902) short 4 4 0 1.18 0.88 0.37 0.81 Halictidae Halictinae Halictini Lasioglossumcoriaceum Lasioglossum (Lasioglossum) coriaceum (Smith, 1853) short 3 3 0 2.56 1.88 0.93 1.63 Halictidae Halictinae Halictini Lasioglossumcressonii Lasioglossum (Dialictus) cressonii (Robertson, 1890) short 1 1 0 1.57 1.45 0.49 1.08 Halictidae Halictinae Halictini Lasioglossumephialtum Lasioglossum (Dialictus) ephialtum Gibbs, 2010 short 2 1 1 1.27 1.14 0.3 0.97 Halictidae Halictinae Halictini Lasioglossumfloridanum Lasioglossum (Dialictus) floridanum (Robertson, 1892) short 1 1 0 1.48 1.38 0.25 1.23 Halictidae Halictinae Halictini Lasioglossumfuscipenne Lasioglossum (Lasioglossum) fuscipenne (Smith, 1853) short 2 2 0 2.27 1.8 0.6 1.67 Halictidae Halictinae Halictini Lasioglossumhitchensi Lasioglossum (Dialictus) hitchensi Gibbs, 2012 short 3 3 0 1.28 0.98 0.45 0.82 Halictidae Halictinae Halictini Lasioglossumimitatum Lasioglossum (Dialictus) imitatum (Smith, 1853) short 1 1 0 1.23 0.85 0.43 0.81 Halictidae Halictinae Halictini LasioglossumleucocomumLasioglossum (Dialictus) leucocomum (Lovell, 1908) short 6 4 2 1.63 1.13 0.55 1.08 Halictidae Halictinae Halictini Lasioglossumpectorale Lasioglossum (Hemihalictus) pectorale (Smith, 1853) short 8 6 2 2 1.38 0.81 1.19 Halictidae Halictinae Halictini Lasioglossumpilosum Lasioglossum (Dialictus) pilosum (Smith, 1853) short 16 15 1 1.8 1.23 0.6 1.21 Halictidae Halictinae Halictini Lasioglossumtegulare Lasioglossum (Dialictus) tegulare (Robertson, 1890) short 4 2 2 1.31 0.92 0.44 0.87 Halictidae Halictinae Halictini LasioglossumtrigeminumLasioglossum (Dialictus) trigeminum Gibbs, 2011 short 6 6 0 1.45 1.19 0.47 0.98 Halictidae Halictinae Halictini Lasioglossumtruncatum Lasioglossum (Sphecodogastra) truncatum (Robertson, 1901) short 1 1 0 2.05 1.72 0.64 1.41 Halictidae Halictinae Halictini Lasioglossumversatum Lasioglossum (Dialictus) versatum (Robertson, 1902) short 19 5 14 1.72 1.25 0.64 1.08 Halictidae Halictinae Halictini Lasioglossumvierecki Lasioglossum (Dialictus) vierecki (Crawford, 1904) short 11 7 4 1.25 0.94 0.41 0.84 Halictidae Halictinae Halictini dichrous Sphecodes dichrous Smith, 1853 short 1 0 1 1.82 1.84 0.61 1.22 Parasitic MegachilidaeMegachilinae Anthidium manicatumAnthidium (Anthidium) manicatum manicatum (Linnaeus, 1758) long 1 0 1 4.62 3.48 2.61 2.01 Exotic MegachilidaeMegachilinae Megachilini Coelioxys hunteri Coelioxys (Synocoelioxys) hunteri Crawford, 1914 long 1 1 0 4.03 2.11 2.9 1.12 Parasitic MegachilidaeMegachilinae Megachilini Coelioxys octodentatusCoelioxys (Boreocoelioxys) octodentatus Say, 1824 long 1 0 1 3.71 2.17 2.64 1.06 Parasitic The genera Melissodes and Coelioxys are considered masculine and their changeable epithets cited accordingly. See MegachilidaeMegachilinae Megachilini Coelioxys sayi Coelioxys (Boreocoelioxys) sayi Robertson, 1897 long 1 0 1 4.09 2.18 3.03 1.05 Parasitic MegachilidaeMegachilinae Osmiini Heriades carinata Heriades (Neotrypetes) carinata Cresson, 1864 long 1 1 0 2.64 1.48 1.75 0.89 MegachilidaeMegachilinae Osmiini Heriades leavitti Heriades (Neotrypetes) leavitti Crawford, 1913 long 2 2 0 2.9 1.7 2.01 0.89 MegachilidaeMegachilinae Osmiini Hoplitis pilosifrons Hoplitis (Alcidamea) pilosifrons (Cresson, 1864) long 23 18 5 4.68 1.72 3.23 1.45 MegachilidaeMegachilinae Osmiini Hoplitis producta Hoplitis (Alcidamea) producta producta (Cresson, 1864) long 3 1 2 2.85 1.38 1.64 1.21 MegachilidaeLithurginae Lithurgini Lithurgus chrysurus Lithurgus chrysurus Fonscolombe, 1834 long 6 0 6 9.04 3.34 7.59 1.45 Exotic MegachilidaeMegachilinae Megachilini addenda Megachile (Xanthosarus) addenda Cresson, 1878 long 1 0 1 6.16 3.98 4.25 1.91 MegachilidaeMegachilinae Megachilini Megachile brevis Megachile (Litomegachile) brevis Say, 1837 long 1 1 0 5.05 2.87 3.49 1.56 MegachilidaeMegachilinae Megachilini Megachile centuncularisMegachile (Megachile) centuncularis (Linnaeus, 1758) long 1 1 0 4.21 3.15 2.73 1.48 Exotic? MegachilidaeMegachilinae Megachilini Megachile exilis Megachile () exilis parexilis Mitchell, 1937 long 1 0 1 3.3 1.93 1.92 1.38 MegachilidaeMegachilinae Megachilini Megachile frigida Megachile (Xanthosarus) frigida frigida Smith, 1853 long 3 0 3 4.31 3.05 2.43 1.88 MegachilidaeMegachilinae Megachilini Megachile inimica Megachile (Sayapis) inimica sayi Cresson, 1878 long 6 5 1 5.77 2.94 4.1 1.67 MegachilidaeMegachilinae Megachilini Megachile mendica Megachile (Litomegachile) mendica Cresson, 1878 long 28 14 14 5.04 2.81 3.55 1.49 MegachilidaeMegachilinae Megachilini Megachile petulans Megachile (Leptorachis) petulans Cresson, 1878 long 1 0 1 5.05 2.78 3.52 1.53 MegachilidaeMegachilinae Megachilini Megachile pugnata Megachile (Sayapis) pugnata pugnata Say, 1837 long 10 10 0 7.03 3.2 4.96 2.07 MegachilidaeMegachilinae Megachilini Megachile relativa Megachile (Megachile) relativa Cresson, 1878 long 1 1 0 4.39 2.54 3.01 1.39 MegachilidaeMegachilinae Megachilini Megachile sculpturalisMegachile () sculpturalis Smith, 1853 long 1 0 1 7.23 4.29 5.13 2.09 Exotic MegachilidaeMegachilinae Megachilini Megachile xylocopoidesMegachile (Melanosarus) xylocopoides Smith, 1853 long 10 9 1 6.07 3.52 4.35 1.73 MegachilidaeMegachilinae Osmiini Osmia pumila Osmia (Melanosmia) pumila Cresson, 1864 long 6 6 0 3.67 2.06 2.42 1.26 MegachilidaeMegachilinae Osmiini Osmia taurus Osmia (Osmia) taurus Smith, 1873 long 2 1 1 4.96 2.79 3.22 1.74 Exotic MegachilidaeMegachilinae Anthidiini Trachusa dorsalis Trachusa (Heteranthidium) dorsalis (Lepeletier, 1841) long 7 4 3 6.48 3.65 4.54 1.94 Glossa,,, Family,lower 95% CI,slope,upper 95% CI Andrenidae,0.53,0.78,1.13 Apidae,0.43,0.53,0.65 Colletidae,0.32,0.48,0.74 Megachilidae,0.51,0.64,0.8 Halictidae,0.34,0.45,0.6 ,,, ,,, Prementum,,, family,lower 95% CI,slope,upper 95% CI Andrenidae,0.35,0.48,0.65 Apidae,0.29,0.32,0.37 Colletidae,0.37,0.47,0.59 Megachilidae,0.4,0.46,0.53 Halictidae,0.28,0.36,0.45 ,,, ,,, Proboscis,,, family,lower 95% CI,slope,upper 95% CI Andrenidae,0.41,0.55,0.75 Apidae,0.38,0.44,0.52 Colletidae,0.37,0.46,0.57 Megachilidae,0.43,0.51,0.6 Halictidae,0.31,0.39,0.49 Genus species sample size R2_glossa p value glossaR2_prementump value prementumR2_proboscis Bombus impatiens 29 0.605 <0.001 0.958 <0.001 0.768 Megachile xylocopoides 10 0.527 0.03 0.347 0.093 0.512 Megachile mendica 28 0.519 <0.001 0.276 0.007 0.666 Lasioglossumpilosum 16 0.312 0.035 0.614 0.001 0.55 Bombus bimaculatus 54 0.286 <0.001 0.858 <0.001 0.536 Habropoda laboriosa 32 0.247 0.006 0.026 0.258 0.246 Melissodes denticulatus 24 0.207 0.034 0.432 0.001 0.42 Bombus griseocollis 67 0.168 0.002 0.338 <0.001 0.411 Agapostemonvirescens 16 0.119 0.173 0.298 0.04 0.197 Bombus perplexus 17 0.05 0.322 0.981 <0.001 0.58 Lasioglossumvierecki 11 0.031 0.361 0.123 0.242 0.229 Xylocopa virginica 40 0.031 0.21 0.314 <0.001 0.009 Megachile pugnata 10 0.027 0.296 0.201 0.109 0.475 Andrena wilkella 15 0.003 0.389 0.564 0.003 -0.027 Lasioglossumversatum 19 -0.004 0.401 0.469 0.002 0.256 Colletes inaequalis 33 -0.053 0.82 0.182 0.019 0.189 Augochlorapura 21 -0.058 0.646 0.417 0.003 0.089 Hoplitis pilosifrons 23 -0.068 0.743 0.031 0.282 0.792 Augochlorellaaurata 10 -0.099 0.672 -0.103 0.7 -0.092 Andrena bradleyi 14 -0.15 0.861 0.783 <0.001 0.475 Andrena imitatrix 10 -0.254 0.915 -0.099 0.578 -0.191 p value proboscis <0.001 0.034 <0.001 0.002 <0.001 0.006 0.001 <0.001 0.095 0.002 0.145 0.321 0.017 0.465 0.036 0.016 0.167 <0.001 0.638 0.011 0.765