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Article

Floral Characteristics Alter the Abundance and Richness of Bees Captured in Passive Traps

by
Madison Mazur
1,
Christine Bell
1,2,
Michael E. Dillon
2,3 and
Lusha M. Tronstad
1,2,3,*
1
Wyoming Natural Diversity Database, University of Wyoming, Laramie, WY 82071, USA
2
Department of Zoology and Physiology, University of Wyoming, Laramie, WY 82071, USA
3
Program in Ecology and Evolution, University of Wyoming, Laramie, WY 82071, USA
*
Author to whom correspondence should be addressed.
Environments 2025, 12(9), 301; https://doi.org/10.3390/environments12090301
Submission received: 25 July 2025 / Revised: 22 August 2025 / Accepted: 27 August 2025 / Published: 28 August 2025
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Abstract

Bees are vital pollinators that maintain plant populations by transporting pollen among individuals; however, bees are declining, and information on how habitat characteristics alter the catch of bees in traps is needed to better assess monitoring. Few studies have measured how catch in passive traps may be altered by floral resources despite the well-known dependence of pollinators on forbs. We investigated the degree to which pollinating insects were attracted to vane traps and bee bowls placed at sites that varied in flower densities (0–800 flowers/m2). We also assessed if the catch of bees was better explained by flower characteristics directly around traps (subsite) or average flower characteristics at a site. Floral density, richness and surface area were measured in 1 m2 quadrats at each subsite. The surface area of flowers explained more variance in bees captured compared to the density or richness of flowers. Traps placed in areas with lower flower surface area captured the more bees and a more diverse sample. Floral resources at the subsite and site explained a similar amount of variance in the number of bees captured, suggesting that pollinators respond to flowers at both scales. We provide a method of correcting pollinator abundance by flower surface area to make catch in passive traps more comparable among areas. We can select sites that minimize or maximize the catch of bees by understanding how floral resources change the effectiveness of passive traps.

1. Introduction

Wild bees are dominant pollinators of flowering plants in wild areas, croplands and cities by transporting pollen among individuals [1,2,3]; however, some bee species are declining [4,5,6]. While the decline of iconic bee genera (i.e., bumblebees) is often well studied, the decline of lesser-known bees is typically lacking, reiterating the importance of studying bee populations. The decline of bees has been attributed to narrow pollen diets [7], parasites and pathogens [8], agricultural pesticide use [9], habitat degradation and fragmentation [10], and climate change [11]. Regardless of the cause, monitoring bees is critical to assess declines and study the mechanisms [12]. Declines > 90% over 20 years have been reported for some bee species [13], which shows the dire need for monitoring. Standardized monitoring can lead to a clearer understanding of the status of bee species across broad areas. Knowing the strengths and weaknesses of sampling methods, as well as the factors that introduce more variance, is key when developing a monitoring program [5,14].
Pollinating insects are commonly collected using active and passive methods. The most common active method is target netting where a person walks through a flower patch and catches bees using an insect net, but this method is biased towards larger species [15]. Passive traps are left on the landscape for a known time, and their color generally attracts pollinators. The most commonly used passive traps are bee bowls, and vane traps are a newer tool. Vane traps collected more genera and a wider size range of bees compared to bee bowls, and capture rate varied among and within sites [16]. The type of passive trap influences the assemblage captured [16,17,18,19], but several methods are routinely used to collect information about the entire bee community [20,21,22,23]. Passive traps have been deployed to collect bees in a variety of ecosystems including prairies, grasslands, and forests [24]. High variability in passive trap collections may be attributed to the ecosystem they were deployed in, the duration they were deployed, methods of deployment, and flower characteristics [20]. Passive traps offer a convenient approach to unattended sampling, but more information is needed to understand how placement may alter the resulting catch. To track ongoing declines in wild bee populations, monitoring protocols must be developed that account for variation in bee catch, and floral traits likely explain much of the observed variation. The density of flowers (flowers/m2) changes the number of bees captured [23], but the relationship is only beginning to be understood for some methods [15,19]. For example, higher flower density equated to more bees captured using target netting and fewer bees captured in bee bowls [19]. Understanding the limitations of each sampling method and project goals is vital when choosing the collection method and interpreting results [14,20].
The structure of pollinator communities largely depends on the density, composition, and nutrition of floral resources [25,26]. Diverse bee communities require a variety of flowering plants that produce various amounts and compositions of nectar and pollen throughout the spring, summer, and fall. Adult bees need energy-rich nectar and females must provision larvae with protein- and lipid-rich pollen [27]. Plant density and richness often increases the number and richness of bees in an area [3,28,29], and native bees tend to gather in patches of flowering plants that are >30 m2 [3]. Pollinator presence can shape plant abundance and diversity, because plants and pollinators depend on one another [30]. Studies are only beginning to account for floral characteristics when sampling for bees despite the well-known mutualism between them [31,32]. For example, target netting captured a different bee assemblage compared to bee bowls, and bee bowls collected more bees in areas with lower flower richness [15]. Therefore, information is needed to assess the best methods to collect bees in different ecosystems and within microhabitats.
We investigated the degree to which the abundance and richness of bees captured in two types of passive traps were affected by flower density, richness, and surface area. We sampled pollinators in forests, prairie, and urban areas in eastern Wyoming, USA, at 37 sites that varied in the density, richness, and surface area of flowers within sites and among sites. Flower surface area (mm2) is the two-dimensional size of the visible and accessible area of an individual flower. We included the surface area of flowers to account for flower size and density among plant species in a single metric. For example, Phlox produces small flowers that are dense, but Calochortus produces one large flower. Our specific questions were as follows: (1) Did flower density, richness, and surface area affect the abundance and richness of bees collected in passive traps? (2) Did the abundance of bees captured in vane traps and bee bowls depend on the floral resources immediately surrounding traps (subsite) or in the larger area (site)? (3) Did ecosystem type affect the abundance and richness of bees collected in passive traps? We hypothesized that denser and richer flower assemblages would result in a lower and less diverse catch of bees in bee bowls and vane traps, and that higher floral area would also reduce catch. We predicted that floral characteristics at the site would explain more variation in catch than at the site. We expected the highest catch of bees (abundance and richness) in mountain ecosystems and the fewest in urban areas. Understanding where to place passive traps within a site will help us more efficiently capture bees and correct catch based on flower characteristics to ensure samples are representative and comparable.

2. Materials and Methods

2.1. Study Sites

We sampled a variety of sites across eastern Wyoming, USA, in 2018 that varied from mountain (22 sites), prairie (11 sites), and urban ecosystems (4 sites; Figure 1). The mountains were dominated by Engelmann spruce (Picea engelmannii), subalpine fir (Abies lasiocarpa) forests, sub-alpine meadows, and lodgepole pine (Pinus contorta) forests. The mountain ecosystems receive 24–46 cm of precipitation annually and average summer temperatures range between 12 and 34 °C. Lower-elevation ecosystems consisted of shortgrass and sagebrush-steppe prairies dominated by woody sagebrush (Artemisia), ≤50 cm tall cushion plants and grass species. Prairie and urban ecosystems receive 6–20 cm of precipitation annually and average summer temperatures range between 24 and 32 °C. We categorized mountain ecosystems as those within the Southern and Middle Rockies Ecoregions, prairie ecosystems as lower-elevation ecosystems dominated by graminoids (e.g., shortgrass prairie) or sagebrush (sagebrush steppe) within the Wyoming Basins, Northwestern Great Plains and High Plains Ecoregions, and urban ecosystems as those within a town. Surveys in urban areas included turf grass, patches of native wildflowers, or driveways lined with native and ornamental flowers. Precipitation was near average during 2018 compared to mean values over the previous 20 years (www.wrds.uwyo.edu).

2.2. Field Sampling

We sampled bees in eastern Wyoming from May to September 2018 to estimate whether the density of flowers altered the pollinators we captured in traps. This project was part of a larger study surveying bees in eastern Wyoming, and flowers were assessed to enhance and interpret our data. A detailed comparison of the bee assemblages captured in vane traps and bee bowls across the state of Wyoming can be found in Bell et al. [16]. At 37 sites, we set out three blue vane traps [21] and three sets of bee bowls for 24–48 h [33], and performed three floral surveys differing in relative flower density (Figure 2a) equating to 165 subsite measurements (includes sites visited twice over the summer). We noted ecosystem type (forest, prairie or urban) at each site. Vane traps have two blue cross vanes attached to a cone that funnels insects into a yellow collection container. Vane traps (~37 cm × ~14 cm; SpringStar, Woodinville, WA, USA) were attached to rebar and hung ~1 m above the ground and we did not add liquid or preservative to the traps (Figure 2b). In some settings, these traps are effective at collecting most pollinating insects [16]. Bee bowls were 10 cm in height by 6 cm in diameter polystyrene containers (Thornton Plastic Co., Salt Lake City, UT, USA) painted blue, yellow, or white [fluorescent blue, fluorescent yellow, and white; Royal Exterior Latex Flat House Paint, Ace Hardware Corp., Oak Brook, IL, USA]. Our bee bowls were attached to a 30 cm length of rebar, hung ~1 cm above the ground, and were filled with soapy water to break the surface tension (Figure 2c). Bee bowls mostly collect small pollinators, especially sweat bees [15,16,31]. Vane traps and bee bowls attract pollinators by color alone and no other attractants were used. Vane traps and bee bowls at each subsite were placed a mean of 33 m apart (9–88 m) so we assumed samples were independent [33] and sites were 362 m apart on average (23–1846 m). The area of each sites averaged 0.37 km2 (0.01–8.0 km2). We visited 54% of sites twice over the summer to gather pollinators at different flowering times to account for differences in floral characteristics. Bees were transported to the laboratory in a cooler and frozen until processed. Bees were washed, dried (bee bowls only), pinned, identified to genus [34], and stored in a permanent collection. We calculated the abundance of bees as individuals captured per unit time in individual traps (individuals/day).
We measured the density, richness, and flower surface area of flowering plants to estimate how flower characters altered the bees we captured in traps. We randomly placed a 1 m2 quadrat centrally at each subsite (one vane trap and a set of 3 bee bowls) to quantify flower characteristics (Figure 2d). The landscape and general plant composition within a site were similar. We recorded all flowering plant genera (excluding wind pollinated plants) in the quadrat and counted the number of flowers by genus. If flowers were too numerous to count, we calculated the average number of flowers on three inflorescences of the same genus and multiplied by the number of inflorescences in the quadrat. If plants of the Asteraceae family were in the quadrat, we counted each flowerhead as one flower instead of counting all disk and ray flowers, because bees likely use the entire flowerhead to guide foraging. Flowers born in clusters (i.e., cyme, umbel, panicle, etc.) were individually counted because flowers were often larger and spaced apart. We also measured flower surface area and calculated an average value for each genus. Using a millimeter ruler, we measured the diameter for symmetric flowers, and the length and width for irregular flowers. For symmetric, round flowers, we estimated flower surface area as the area of a circle. We measured from the base to the tip of the corolla of irregular flowers (including visible floral tubes), and from the widest part of the corolla. Irregular, oblong flower surface area was estimated by multiplying flower length by width. A voucher specimen was collected and pressed for each genus observed in the quadrat. All plants were identified to genus [35] and verified using the reference collection in the Rocky Mountain Herbarium, University of Wyoming.

2.3. Statistical Analyses

We investigated the degree to which flower characteristics at the subsite (area immediately around passive traps) or site (area encompassing all three subsites) explained more variation in bee abundance (individual/day) and richness. One set of bee bowls (yellow, blue and white) were analyzed together because of their proximity to each other. To characterize flowers at each subsite, we calculated the density of flowers (flowers/m2), richness of flowers (number of genera flowering), and the surface area of flowers (mm2/m2) in the quadrat during each visit. To characterize flowers at each site, we calculated the mean density and surface area of flowers, and cumulative species richness among the three quadrats during each visit. All tests were conducted in Program R, version 4.0.0 [36] using the packages plyr [37], vegan [38], matrix [39], and lme4 [40].
We used mixed effects models (lmer) to estimate which variables best explained the abundance and richness of bees captured in traps. We checked for normality (qqnorm and qqline), homogeneity of variance (fligner.test), outliers (outlier test), correlation (cor), and variance inflation factors (vif) using Program R [36]. We created 34 a priori models to explain bees captured using flower characteristics, ecosystem, and trap type. Fixed effects were flower density, flower richness, flower surface area, ecosystem, and trap type, and random effects were site and date. We removed outlying flower abundances when flowers were >1500/m2 as these values highly influenced the analysis altering the relationships (only occurred at 3 sites; outliers ranged from 1596 to 22,197 flowers/m2). The models used all explanatory variables to estimate which characteristics explained the most variance about bee catch and the best models were selected based on the lowest AIC values [41]. We considered models to be competitive when ΔAIC values were <2. The same number of observations (n = 111 for each trap type) were used for subsite and site variables so that AIC values were comparable (all data excluding outliers). We did not use predictor variables that were highly correlated (>0.8) or had high variance inflation factors (>5) in the same model. We analyzed the degree to which trap type (vane trap and bee bowl) and ecosystem (mountain, prairie, and urban) altered bee abundance and richness in traps using analysis of variance (ANOVA).

3. Results

We observed 57 genera of flowering plants in 26 families (Table A1). Asteraceae was the most abundant family comprising 32% of plants in our surveys. We encountered 18 genera of asters and Achillea sp. (Yarrow), Antennaria sp. (Pussy toes), and Heterotheca sp. (Goldenaster) were the most common genera observed. Fabaceae (5 genera), Polygonaceae (2 genera), and Schrophulariaceae (6 genera) families followed in decreasing abundance. On average, we recorded 4 plant genera per site (4 ± 3.1) and 2 genera per subsite (2 ± 1.5). The density of flowers at a subsite ranged from 0 to 594 flowers/m2 (mean = 76 flowers/m2; outliers excluded) and the density of flowers at a site ranged from 0 to 758 flowers/m2 (mean = 210 flowers/m2; Table A1). The individual surface area of a flower for plant genera ranged from 0.8 mm2 (Cymopterus sp.) to 4418 mm2 (Opuntia sp.) and the average surface area of a flower was 120 mm2. The total surface area of flowers at a subsite ranged from 0 to 57,434 mm2/m2 (mean = 3683 mm2/m2) and the surface area of flowers at a site ranged from 0 to 65,957 mm2/m2 (mean = 10,808 mm2/m2). The number of flowers did not differ among ecosystems (F = 1.3, p = 0.27) except for urban ecosystems which typically contained fewer plant genera (richness; F = 7.2, p < 0.001; Figure 3a) and had lower flower surface area (F = 4.6, p < 0.01; Figure 3b).
We captured 3957 bees from 5 families in vane traps and bee bowls in forest, prairie, and urban ecosystems (Table A2). Apidae was the most abundant and diverse family, encompassing 63% of all bees collected. We observed 12 Apidae genera, and Bombus (44%), Eucera (29%), Anthophora (10%), and Mellissodes (8%) were the most abundant. Six genera of Halictidae, nine genera of Megachilidae, two genera of Andrenidae, and two genera of Colletidae were collected. We consistently captured more bees (F = 62.6, p < 0.001; Figure 4a) and more bee genera (F = 106.5, p < 0.001; Figure 4b) in vane traps than in bee bowls. We captured more bees (F = 21.5, p < 0.001; Figure 4c) and more bee genera (F = 7.0, p < 0.001; Figure 4d) in prairie and urban ecosystems than mountain ecosystems (Tukey HSD, p < 0.05).
Flower surface area explained more variance in bee abundance than flower density or richness (Table 1). The abundance of bees collected in passive traps was inversely related to total flower surface area at sites and subsites indicating that we captured more bees in traps surrounded by lower floral area (Figure 5a,b). The best model that explained bee abundance used flower surface area at sites (Figure 5a), but flower surface area at subsites was a close second (ΔAIC = 3.85; Table 1; Figure 5b). The top two models both included trap type, likely because vane traps consistently caught 4 times more bees (Figure 3a). None of the top five models included flower richness as it explained much less variance in bee abundance compared to flower surface area. The fourth model included flower density to explain bee abundance; however, flower surface area explained more variance. Ecosystem was included in the 3rd, 4th, and 5th best models explaining bee abundance in traps showing that catch varied among mountain, prairie and urban areas.
Flower area explained the most variance in the richness of bees captured in passive traps, and flower density and richness explained much less (Table 2). Bee richness was inversely related to the total surface area of flowers (Figure 5c,d) showing that we collected more bee genera in areas with lower flower surface area. The best model that explained bee richness was flower surface area at subsites (Figure 5c), but flower surface area at sites was a close second (ΔAIC = 1.5; Table 2; Figure 5d). The top two models both included trap type because vane traps consistently captured twice as many bee genera as bee bowls (Figure 3b). None of the top five models included flower density or richness as these appeared to explain much less variance compared to flower surface area and subsequent models had changes in AIC that were >125 (Table 2).

4. Discussion

The abundance and richness of bees we captured in passive traps was inversely related to floral characteristics (i.e., flower density, richness and surface area). Our results showed that traps placed in areas with lower surface area of flowers captured more bees, due to fewer floral resources. Lower flower surface area was generally calculated when few large flowers were within the quadrat or when small flowers had low to moderate densities. These results agreed with a recent study showing that bee bowls captured fewer bees in areas with higher flower richness [15,19], and our data showed that vane traps were more effective than bee bowls at sites with higher flower surface areas. Passive traps are especially useful at sampling the bee assemblage in areas with few flowers where active methods yield few to no individuals. In such cases, passive traps may have captured bees traveling between floral patches, but the distance traveled depends on bee size and available nesting habitat. Interestingly, neither the site nor subsite scales were superior when using flower characteristics to describe the bee assemblage captured. We hypothesized that bees would select foraging areas based on a larger area because many bees use the landscape to navigate; however, flower characteristics directly around traps explained the abundance and richness of bees we captured as well as flowers in the larger area. We suggest managers and scientists measure the surface area of flowers near and around traps to account for differences in the catch of bees.
How bees choose foraging sites likely depends on the bee and floral conditions at varying scales. Some bees, such as bumble bees, are highly mobile animals and can fly ≤ 5 km from their nests for opportunistic foraging [42,43] and often choose large areas with dense and rich floral resources [3,28]. Conversely, small bees may only forage ≤ 100 m from nests [44]. Large bees commonly use floral density across the entire landscape to direct foraging, while small bees search smaller areas for high flower richness [45,46]. Regardless of body size, all pollinators use visual and chemical (i.e., scent) cues to navigate their environment. When bees navigate, they may use subsite characteristics to further guide flower and specific trap selection because our subsites were 33 m apart on average and likely allowed bees of many sizes to access the entire site. Our traps may have captured high abundance and richness of bees because a variety of pollinators were using different scales to navigate their environment.
Areas with many floral resources are excellent places to actively net for bees; however, dense and diverse stands of flowers are not ideal places to collect bees with passive traps if your goal is to collect a representative sample [15,19]. Pollinators gather in areas with dense floral resources to increase the probability of gathering energy-rich nectar and pollen [3,28], and previous studies found a positive relationship between floral density and bee abundance. For example, more bees were target netted in areas with more flowers [19]. When solely using passive traps, we found a negative relationship between floral characteristics and the number of bees captured in traps, which was also found by Pei et al. [19] and Kuhlman et al. [15]. We likely found this relationship because bees have fewer foraging options in places with few floral resources. With limited options, our traps acted as bright, large flowers that appealed to a variety of pollinators [22]. In contrast, bees have many choices when traps are deployed in dense stands of flowers and the chance of collecting a bee depends on the probability based on the number and variety of flower choices, as well as bee preference. The size, shape, scent, and species of flower can be critical for some bees [47,48]. For example, bumble bees prefer larger flowers [49] and Dufourea species often specialize on one or a few plant species [50]. Therefore, the density, richness, and area of flowers likely explained bee selection along with flower preferences.
The total surface area of flowers in an area was the best predictor of the number and richness of bees captured. In our study, we encountered a broad range of plant genera with various flower sizes. For example, a sego lily (Calochortus nuttallii) produced one flowerhead with a large surface area and was recorded as one flower. In contrast, buckwheat (Polygonum sp.) produced many flowers with small individual surface areas resulting in much higher estimates of flower density. Compared to one sego lily, a single buckwheat had higher flower surface area and denser flowers. We likely observed a strong trend between flower surface area and pollinators captured because flower surface area provided more information about flowers than flower density, flower richness or both. While flower density and richness were lesser models explaining bee capture, these floral characteristics are essential for calculating flower surface area. We urge others to measure flower surface area as this parameter explained more variance in passively collecting bees and may be applied in a variety of settings.
Trap type was included in all the best models explaining bee abundance and richness. We found that vane traps attracted higher abundance and richness of bees than bee bowls despite being deployed in the same subsites and for the same duration. While each method has biases, vane traps collected more bees, 97% of the taxa, the largest range in body sizes and a broader assemblage of bees compared to bee bowls [16], likely because these traps are larger [20] and were placed higher above the ground, making them more visible. In contrast, bee bowls are smaller, placed close to ground level, and are less visible. Smaller bees tend to fly close to the ground searching for cushion plants [24] which may partially explain why bee bowls tend to collect primarily sweat bees [51]. Previous studies stated that vane traps may over sample bee populations by being highly attractive [31]; however, this may occur with any trap and likely depends more on the floral resources in the area, the length of deployment [16], and the types of bees in an area, but more information is needed to understand differences between these trap types.
Ecosystem type was not in the top models explaining bee abundance and richness; however, the variable appeared in several of the top 5 models, especially for bee abundance. This may be attributed to our uneven sample sizes of ecosystems, and we encourage others to investigate trap effectiveness in various ecosystems. Land use should also be assessed in future studies. Despite mountain and prairie ecosystems having a similar richness and surface area of flowers, we captured fewer bees in the mountains. Lower bee abundance in mountains may at least be partially explained by the difference in elevation between our higher mountain sites, and prairie sites which were located in the lower-elevation basins. For example, bumble bees reach peak abundance and richness at 2500 m elevation perhaps because they have more insulation to protect them from cooler temperatures and non-Bombus bees have higher abundance and richness at lower elevations [14,52], but elevation was not used in our models. Nesting requirements may also explain variance. While most bees are ground-nesting, some are cavity-nesting and require dead wood or stems to complete a life cycle; therefore, areas with high floral resources and hollow stems may catch a different assemblage of bees than other areas. We know from previous literature that the assemblage of bees in mountain, prairie, and urban ecosystems may differ, and we suggest researchers and managers consider including ecosystem type when analyzing bee assemblages from large areas to account for variance and produce comparable results.
Differing floral resources make the abundance and richness of bees incomparable among sites; however, our models may be used to account for differences so that the number of bees captured can be standardized by flower characteristics. Managers and researchers can collect information on the density, richness, and individual flower area of plants to calculate flower surface area and adjust the number and richness of bees captured. For example, you can correct the number or richness of bees captured in conditions where no flowers are flowering (flower surface area = 0) as compared to bees captured in an area with many flowers so that samples are comparable. You can create an empirical relationship for traps in your area by including flower characteristics in a generalized linear model or you can use the relationship we measured in our study, though researchers should be aware that our study is based on one season of data and we expect the relationship varies among areas. To calculate the corrected number of bees captured at zero flowers in a single trap type using the relationship measured in our study, use A C = ( 0.19 × F S ) + A , where A is the abundance of bees captured in the trap, AC is the abundance of bees after being corrected by flower surface area and FS is the surface area of flowers (mm2/m2) you measured. The same procedure can be used to correct bee richness ( R C = ( 1.27 × F S ) + R ) where R is the richness of bees and Rc is the corrected richness of bees after accounting for floral surface area. We recommend correcting bee catch by flower surface area to account for some of the variance in bee abundance and richness in passive traps.
We suggest placing passive traps in areas with lower floral resources to catch a representative bee assemblage. We recognize that bee may be traveling through such areas in search of flower patches which is a consideration when using any passive trap. Flower density and individual flower surface area for each plant should be measured during deployment to calculate total flower surface area and account for differences in floral resources and ultimately the catch in passive traps. Models including flower density and richness were far inferior to flower surface area; thus we suggest always calculating flower surface area to predict and standardize pollinator capture. If oversampling is a concern because of rare or protected species, place traps in areas with denser floral resources; however, be aware that traps in such conditions will collect fewer bees and fewer bee genera, and a less complete snapshot of the bee community. The placement of vane traps and bee bowls may be tailored to the researcher’s and manager’s needs.
Flower characteristics play a role in the probability that a bee will be captured using active and passive methods [15,19]. Using both methods in combination will provide a fuller picture of the bee community than either method alone [14] because sites can vary widely in flower abundance. Areas of dense flowers are excellent locations to target net bees, but fewer bees are captured in passive traps [19]. Managers may measure simple flower characteristics (flower density and richness) to save money, time, and effort; however, calculating flower surface area is relatively easy and was far superior in our models. We suggest making a dataset composed of flower surface area for each plant taxon to ease calculations, which can be updated seasonally. We measured floral characteristics in 1 m2 quadrats adjacent to each sampling station, but we encourage others to estimate how many quadrats should be measured to minimize variance yet conserve time in the field. Setting out 30 bee bowls is common practice [53,54] and we suggest measuring floral resources at the beginning, middle, and end of the transect. Measuring flower characteristics more frequently will yield better estimates of floral resources, but time to estimate these measurements must be balanced with what is practical given limited time and funding. We hope our study provides information to bee researchers and managers so that they consider where they place traps leading to more tailored data collection. Understanding where to place traps in microhabitats will help identify taxa of interest, collect the most diverse assemblage of bees, correct for differences in flower characteristics among sites, and sample more conservatively when desired.

Author Contributions

M.M., C.B., M.E.D. and L.M.T. designed the project. M.M. collected field data, prepared models, and wrote the manuscript. C.B. collected field data, identified the bees, and edited various versions of the manuscript. L.M.T. assisted with modeling and exhaustively edited the manuscript. M.E.D. edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This undergraduate research project of Madison Mazur was funded by the Wyoming NASA Space Grant Consortium, the Wyoming Research Scholars Program, and the Wyoming Bureau of Land Management.

Data Availability Statement

The data presented in this study are openly available in Dryad at 10.5061/dryad.7sqv9s4wf.

Acknowledgments

We thank Delina Dority, Alexis Lester, Dominique Lujan, and Katrina Cook for endless support in writing this manuscript. We also thank Joy Handling and Ernie Nelson for helping in the identification of Wyoming flora. Zach Wallace was instrumental in planning data analysis.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Plant taxa and abundance in eastern Wyoming, collected from May to September 2018.
Table A1. Plant taxa and abundance in eastern Wyoming, collected from May to September 2018.
TaxaIndividual Flower Surface Area (mm2/m2)Number of FlowersNumber of Specimens
Alliaceae 30710
Allium14.930710
Anthericaceae 94
Leucocrinum572.694
Apiaceae 1245
Cymopterus5.81245
Asteraceae 176075
Achillea9.533919
Anaphalis12.62802
Antennaria20.752111
Cirsium641.443
Erigeron189.464
Grindelia323.4287
Gutierrezia18.74848
Heterotheca147.28210
Senecio328113
Solidago115.513
Taraxacum423.633
Townsendia207.812
Boraginaceae 48313
Cryptantha15.53925
Cynoglossum19.6281
Mertensia201.5596
Myosotis19.641
Brassicaceae 725
Physaria45.1725
Cactaceae 46
Opuntia1943.946
Calochortaceae 12
Calochortus353.512
Campanulaceae 113
Campanula645.3113
Caryophyllaceae 56018
Cerastium122.2368
Eremogone56.752410
Crassulaceae 263
Sedum100263
Euphorbiaceae 7043
Chamaesyce12.65051
Euphorbia78.51992
Fabaceae 335331
Astragalus204053
Lupinus35.551917
Melilotus7.35775
Oxytropis7521
Trifolium2418005
Linaceae 62
Linum304.762
Melanthiaceae 193
Zigadenus22.2193
Onagraceae 145
Calylophus865.972
Chamerion176.761
Oenothera628.312
Plantaginaceae 18763
Plantago7.118763
Polemoniaceae 1514
Phlox136.71473
Polemonium153.941
Polygonaceae 10368
Erigonum11.34303
Polygonum3.56065
Portulanceae 32
Lewisii39.332
Ranunculaceae 489
Anemone314.211
Delphinium227.3478
Rosaceae 8515
Pentaphylloides128.874
Potentilla175.77811
Rubiaceae 2041
Galium12.62041
Santalaceae 1262
Comandra241262
Scrophulariaceae 42421
Castilleja125.5494
Collinsia871
Linaria13211
Orthocarpus8811
Pedicularis129621
Penstemon84.230413
Violaceae 21
Viola22721
TotalNA11,406254
Table A2. Bee taxa and abundance collected during 2018 surveys in eastern Wyoming.
Table A2. Bee taxa and abundance collected during 2018 surveys in eastern Wyoming.
TaxaNo. Specimens
Vane		No. Specimens
Bowl
Andrenidae3323
Andrena2310
Perdita1010
Halictidae904521
Agapostemon22159
Halictus10736
Lasioglossum547420
Augochlorella104
Dufourea81
Sphecodes111
Megachilidae35540
Anthidium193
Ashmeadiella01
Dianthidium205
Hoplitis619
Megachile472
Osmia20220
Coelioxys10
Heriades10
Stelis40
Apidae1944114
Anthophora25029
Apis101
Bombus55412
Eucera74751
Melissodes19910
Ceratina394
Diadasia1086
Habropoda50
Melecta60
Nomada51
Svastra170
Tetraloniella40
Colletidae167
Colletes61
Hylaeus106

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Environments 12 00301 g001
Figure 1. We sampled sites in mountain (Middle and Southern Rockies Ecoregions), prairie (Wyoming Basin, Northwestern Great Plains and High Plains Ecoregions) and urban areas across eastern Wyoming. The colors show the ecoregions within the state. Multiple sites were sampled in some townships shown as squares (93 km2). The inset maps show the location of the state of Wyoming in the western USA.
Figure 1. We sampled sites in mountain (Middle and Southern Rockies Ecoregions), prairie (Wyoming Basin, Northwestern Great Plains and High Plains Ecoregions) and urban areas across eastern Wyoming. The colors show the ecoregions within the state. Multiple sites were sampled in some townships shown as squares (93 km2). The inset maps show the location of the state of Wyoming in the western USA.
Environments 12 00301 g001
Environments 12 00301 g002
Figure 2. (a) We collected bees using two passive trap types and measured floral resources at three subsites within each site. Subsites were chosen that had low, medium and high flower density relative to the site. (b) A blue vane trap and (c) a set of three bee bowls (blue, yellow, and white) were deployed at each subsite to collect bees. (d) We measured the density, richness and area of flowers using a 1 m2 quadrat placed adjacent to passive traps at each subsite.
Figure 2. (a) We collected bees using two passive trap types and measured floral resources at three subsites within each site. Subsites were chosen that had low, medium and high flower density relative to the site. (b) A blue vane trap and (c) a set of three bee bowls (blue, yellow, and white) were deployed at each subsite to collect bees. (d) We measured the density, richness and area of flowers using a 1 m2 quadrat placed adjacent to passive traps at each subsite.
Environments 12 00301 g002
Environments 12 00301 g003
Figure 3. Flower (a) richness and (b) flower surface area within each ecosystem. An asterisk (*) denotes that urban areas had significantly (alpha < 0.05) lower values compared to the other ecosystems.
Figure 3. Flower (a) richness and (b) flower surface area within each ecosystem. An asterisk (*) denotes that urban areas had significantly (alpha < 0.05) lower values compared to the other ecosystems.
Environments 12 00301 g003
Environments 12 00301 g004
Figure 4. Bee abundance (a,c) and richness (b,d) differed between passive traps (a,b) and ecosystem types (c,d). An asterisk (*) denotes that we captured fewer bees in bowls than vane traps and fewer bees in mountain ecosystems compared to prairie and urban area.
Figure 4. Bee abundance (a,c) and richness (b,d) differed between passive traps (a,b) and ecosystem types (c,d). An asterisk (*) denotes that we captured fewer bees in bowls than vane traps and fewer bees in mountain ecosystems compared to prairie and urban area.
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Environments 12 00301 g005
Figure 5. Bee abundance (a,b) and bee richness (c,d) collected in vane traps and bee bowls were related to the mean flower surface area at a site (a,c) and the flower surface area adjacent to traps at each subsite (b,d). Trend lines were drawn for the best model using unscaled variables for vane traps and bee bowls.
Figure 5. Bee abundance (a,b) and bee richness (c,d) collected in vane traps and bee bowls were related to the mean flower surface area at a site (a,c) and the flower surface area adjacent to traps at each subsite (b,d). Trend lines were drawn for the best model using unscaled variables for vane traps and bee bowls.
Environments 12 00301 g005
Table 1. The top five models explaining abundance of bees captured in passive traps (vane traps and bee bowls). Subsite measurements were recorded from the area immediately around traps whereas site estimates averaged the subsite measurements assessed within 1 km2. We measured flower density, richness and surface area in 1 m2 quadrats in three ecosystems (mountain, prairie and urban).
Table 1. The top five models explaining abundance of bees captured in passive traps (vane traps and bee bowls). Subsite measurements were recorded from the area immediately around traps whereas site estimates averaged the subsite measurements assessed within 1 km2. We measured flower density, richness and surface area in 1 m2 quadrats in three ecosystems (mountain, prairie and urban).
ModelAICΔAICKWeight
Site flower surface area + Trap type492.48 60.87
Subsite flower surface area + Trap type496.333.8560.13
Ecosystem560.9468.4660.00
Ecosystem + Site flower density564.4671.9870.00
Ecosystem + Site flower surface area564.6272.1470.00
Table 2. The top five models explaining the richness of bees captured in passive traps (vane traps and bee bowls). Subsite measurements were recorded from the area immediately around traps whereas site estimates averaged the subsite measurements assessed within 1 km2. We measured flower density, richness and surface area in 1 m2 quadrats in three ecosystems (mountain, prairie and urban).
Table 2. The top five models explaining the richness of bees captured in passive traps (vane traps and bee bowls). Subsite measurements were recorded from the area immediately around traps whereas site estimates averaged the subsite measurements assessed within 1 km2. We measured flower density, richness and surface area in 1 m2 quadrats in three ecosystems (mountain, prairie and urban).
ModelAICΔAICKWeight
Subsite flower surface area + Trap type 459.66 60.68
Site flower surface area + Trap type461.151.5060.32
Site flower surface area586.68127.0350.00
Subsite flower surface area587.15127.5050.00
Ecosystem587.22127.5660.00
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Mazur, M.; Bell, C.; Dillon, M.E.; Tronstad, L.M. Floral Characteristics Alter the Abundance and Richness of Bees Captured in Passive Traps. Environments 2025, 12, 301. https://doi.org/10.3390/environments12090301

AMA Style

Mazur M, Bell C, Dillon ME, Tronstad LM. Floral Characteristics Alter the Abundance and Richness of Bees Captured in Passive Traps. Environments. 2025; 12(9):301. https://doi.org/10.3390/environments12090301

Chicago/Turabian Style

Mazur, Madison, Christine Bell, Michael E. Dillon, and Lusha M. Tronstad. 2025. "Floral Characteristics Alter the Abundance and Richness of Bees Captured in Passive Traps" Environments 12, no. 9: 301. https://doi.org/10.3390/environments12090301

APA Style

Mazur, M., Bell, C., Dillon, M. E., & Tronstad, L. M. (2025). Floral Characteristics Alter the Abundance and Richness of Bees Captured in Passive Traps. Environments, 12(9), 301. https://doi.org/10.3390/environments12090301

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