Ecology of Urban Bees a Review of Current Knowledge and Directions for Future Study
PLoS I. 2019; xiv(12): e0225852.
The furnishings of urbanization on bee communities depends on floral resource availability and bee functional traits
Caleb J. Wilson
Department of Biological Sciences, Oakland University, Rochester, Michigan, U.s. of America
Mary A. Jamieson
Section of Biological Sciences, Oakland University, Rochester, Michigan, Usa of America
Maura (Gee) Geraldine Chapman, Editor
Received 2019 Aug 10; Accepted 2019 November 13.
- Supplementary Materials
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S1 Fig: Map of study sites in southeastern Michigan, USA. (DOCX)
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S2 Fig: Rank abundance of bee genera ordered past total number of specimens observed across sites. (DOCX)
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S3 Fig: Bee abundance and species richness beyond sites and sampling periods. (DOCX)
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S4 Fig: The most abundant wild bee species collected across sites. (DOCX)
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S5 Fig: Effect of urbanization on Bombus and Lasioglossum (Dialictus) affluence. (DOCX)
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S1 Table: Site-level bee community data. (DOCX)
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S2 Tabular array: Summary of collected bee specimens with functional trait data. (DOCX)
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S3 Table: The species number and bloom embrace provided plants of different geographic origin and plant category. (DOCX)
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S4 Table: Summary of AICc values for model choice. (DOCX)
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S5 Table: Correlation tests betwixt minimum site level temperature and impervious surface area. (DOCX)
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- Information Availability Statement
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All data will be publicly available from Mendeley data at the following DOI after acceptance of this article. http://dx.doi.org/10.17632/5vssx32pk2.1.
Abstract
Wild bees are important pollinators in many ecosystems threatened past anthropogenic disturbance. Urban evolution can reduce and degrade natural habitat for bees and other pollinators. All the same, some researchers suggest that cities could also provide refuge for bees, given that agronomical intensification may pose a greater run a risk. In this report, we surveyed bee communities at xv farms and gardens across an urban-rural gradient in southeastern Michigan, USA to evaluate the effect of urbanization on bees. We examined how floral resource, bee functional traits, temperature, farm size, and the spatial calibration of analysis influence bee response to urbanization. We found that urbanization positively affected bee diversity and evenness but had no result on total affluence or species richness. Additionally, urbanization altered bee customs limerick via differential furnishings on bee species and functional groups. More than urbanized sites supported a greater number of exotic, above-basis nesting, and solitary bees, but fewer eusocial bees. Blooming plant species richness positively influenced bee species diversity and richness. Furthermore, the amount of available floral resources was positively associated with exotic and eusocial bee abundances. Across sites, well-nigh 70% of floral resources were provided by exotic plants, most of which are characterized as weedy but non invasive. Our report demonstrates that urbanization can benefit some bee species and negatively touch on others. Notably, Bombus and Lasioglossum (Dialictus), were two of import pollinator groups negatively affected past urbanization. Our study supports the thought that urban environments tin can provide valuable habitat for diverse bee communities, but demonstrates that some bees are vulnerable to urbanization. Finally, while our results indicate that increasing the abundance and richness of floral resource could partially recoup for negative effects of urbanization on bees, the effectiveness of such measures may exist express past other factors, such as urban warming.
Introduction
Managed and wild bee species are threatened by multiple stressors, which have resulted in significant population declines and the extinction of certain species [1–3]. Little is known, notwithstanding, near the specific population trends of about wild bee species [2]. Because bees are the primary pollinators of most flowering plants, bee declines are predicted to exert economic impacts on farm production [3,4] coupled with declines in wild establish variety and plant community persistence [v,6]. One major cause of bee decline is habitat loss due to anthropogenic disturbance [vii,8], and urbanization is a rapidly growing class of such disturbance. Globally, over a million square kilometers of urban land are expected to be added to the globe from 2000–2030 [9].
Some studies accept demonstrated negative effects of urbanization on wild bee communities—showing reduced bee affluence, richness, and multifariousness in urban environments compared to more than natural environments [10–14]. Still, other studies indicate urbanization may raise bee abundance and/or species richness [15–17], or that such effects vary across unlike land uses inside a urban center [18,19]. In certain contexts, cities could back up pollinators by providing refuge from agricultural intensification [20]. The influence of urbanization on bee communities, yet, is variable and not well-understood [21,22]. Urban bee communities may correspond a subset of the regional species pool, dominated past species that thrive in cities [23,24]. Conversely, the increased habitat heterogeneity in cities could support more various bee communities than those in more homogenous rural environments [15]. Consideration of how bee abundance, variety, community composition, and species richness vary beyond an urban slope will help explicate such contrasting responses.
Urbanization affects pollinators through interrelated direct and indirect furnishings, including habitat loss and modification, urban warming, and increased exposure to environmental contaminants [25]. In detail, habitat modification reduces floral resource availability and is therefore a key driver of bee response to anthropogenic disturbance [26]. Bee response to such disturbances, however, may differ across functional groups. For instance, removal of floral resources due to urbanization may disproportionately touch bees which nest in the stems of plants by removing both food and nesting resource. Because bees vary in their nesting beliefs, sociality, and foraging preferences, their response to urbanization likely depends on their functional traits.
In this study, we investigated how urbanization at the landscape scale and floral resource availability at the local calibration influenced wild bee communities at farms and gardens beyond an urban-rural gradient in southeast Michigan, U.s.a.. A key aim of this report was to assistance inform farmers and gardeners in the metropolitan Detroit surface area about strategies for supporting pollinators.
In recent decades, urban agriculture has grown in prevalence beyond the United States and globally [27]. Urban farms and gardens provide numerous social, economic, and public wellness benefits to their surrounding communities [27–29] and may provide habitat for pollinators [xxx,31]. Many vegetable and fruit crops grown in urban farms and gardens depend on pollination services provided by bees [32]. Understanding the influence of urbanization on bees and how to mitigate potential negative effects is essential for conserving bee diversity and pollination services in urban environments.
Our master research objectives were: (i) to examine the effects of urbanization on bee communities, (ii) to investigate how functional traits mediate bee response to urbanization, (iii) to characterize floral resource availability across sites and evaluate the influence of these resources on bee communities, and (4) to identify which measures of urbanization and floral resources best predict bee response. We hypothesized that urbanization would negatively affect bee communities due to associated habitat deposition and urban warming. Specifically, we expected bee species diversity, richness, and abundance to be lower in more urban areas and that community composition would be simplified. We expected that bee functional traits, including nesting strategy, nutrition breadth, native or exotic status, and sociality would differentially mediate the result urbanization had on wild bees. Further, we hypothesized that bloom encompass and constitute species richness at a site-level would positively influence bee communities, potentially ameliorating the negative effects of urbanization.
Materials and methods
Study site characterization
We randomly selected xv farms and gardens across three counties in southeast Michigan, U.s.a. for data collection (S1 Fig, S1 Table). The population estimate for these counties in 2017 was 3.8 million people, making this region the near populous in Michigan [33]. At all sites, farmers and gardeners primarily grew diverse food crops, mostly vegetables and fruits. Wildflowers and/or ornamental flowers were also nowadays at all sites. Our written report sites spanned a gradient of urbanization, characterized by the amount of impervious surface in the surrounding mural and were spaced at least 1500 yard apart from each other. Overall, sites were surrounded by 4% to 59% impervious surface expanse at a 1000 thou calibration (hateful ± SD = 28 ± 19%) and ranged in size from 1,611 to nineteen,211 1000two (hateful ± SD = 6,979 ± 4,719 m2, S1 Table). 4 of our sites were small-scale-scale, for-profit farms (size: mean ± SD = xi,808 ± 5,331 mtwo) and 11 were customs gardens (size: hateful ± SD = 5,223 ± 3,152 grand2). Community gardens were characterized every bit sites where crop cultivation involved community members, who either individually or collectively managed crop production for consumption rather than profit. Our data had a natural pause in which approximately half of our written report sites had > twoscore% impervious surface area within a 1000 chiliad radius surrounding each site (mean ± SD = 49 ± 6%) and half the sites had < forty% impervious surface in the surrounding expanse (mean ± SD = 13 ± ix%, S1 Fig, S1 Table). This relationship was consistent at 1500 thousand and 2000 k radii and allowed for comparing between less urbanized (< 40% impervious surface) and more urbanized (> 40% impervious) sites at a landscape scale.
To determine the spatial extent of urbanization which best explained variation in bee community and functional group responses at a landscape scale, we measured the proportion of impervious surface at four radii (500 thousand, 1000 chiliad, 1500 m, and 2000 thou) surrounding each study site. We used one-meter resolution aerial imagery from the Us Section of Agriculture's (USDA) National Agronomics Imagery Program (NAIP) database from 2014 to estimate urbanization. We established site polygons in Google Globe Pro seven.1.8.3036 using aeriform imagery from 2017 [34] and created state cover classifications in ArcMap 10.v.ane [35]. Specifically, nosotros created supervised classifications using a maximum likelihood algorithm to delineate impervious surface surface area from all other state cover classes using fake colour imagery (ring order: brusque wave infrared, ruddy, bluish). For each classified image, we performed mail-processing to smooth the resultant nomenclature's edges and to remove small isolated pixel regions (protocol recommended by 35). We then assessed the accuracy of each classification at 50 randomly stratified accuracy cess points to ensure right land cover class assignment. All final kappa accuracy values (a mensurate of overall classification accuracy) for confusion matrices were ≥ 0.fourscore [36]. Finally, nosotros estimated urbanization at each spatial calibration by dividing impervious surface pixel counts past total pixel counts to calculate the proportion of surrounding impervious surface.
We measured temperature at field sites using two HOBO Pendant temperature loggers (UA-002-08, Onset Figurer Corporation, Bourne, MA) per site during the study menses from June-Baronial. HOBO loggers recorded temperature measurements every iv hours. We examined minimum nighttime temperature measurements recorded at each site (before 8:00 and after 20:00) to avoid issues with measurement fault associated with the influence of solar radiation on data loggers [37–39]
Floral resource surveys
Nosotros conducted floral and bee community surveys across our 15 study sites in June, July and August of 2017. To evaluate the influence of floral resources availability on bee response variables, we examined two metrics of floral resources at the site level: bloom embrace and blooming constitute species richness. To obtain these floral metrics, we inventoried plants forth four 25 thou transects, which were haphazardly placed to capture a representative sample of the abundance and diversity of available floral resources. We inventoried floral resources provided by trees or shrubs only when they intersected transects, which was infrequent for shrubs and did not occur for trees. Thus, floral resources described in our study are largely herbaceous species. All surveys were conducted within 100 m of the center of each site. Within one meter from the centerline of each survey transect, we identified plants in bloom to the lowest taxonomic level possible. Nosotros estimated flower cover for each species equally a pct of the entire transect surface area (l yard2), using a 1 chiliad2 and 0.05 1000two PVC quadrat. And then, nosotros converted bloom cover percentages to square meter estimates and summed the total bloom cover recorded along transects to generate a site-level estimate of flower cover. We estimated blooming plant species richness at the site-level by summing the number of species in blossom across transects. Across sites, bloom comprehend and blooming plant species richness were not influenced past urbanization (east.yard., at a 1000m radius, blossom cover: R2 = 0.x, txiii = -one.262, P = 0.229 and establish species richness: R2 = 0.02, txiii = -0.474, P = 0.643).
Plants were categorized based on species origin (native, exotic, or uncertain) using the United States Department of Agriculture's Plants database [40]. Nosotros further classified plants into one of iii groups: baneful weeds, wildflowers, and cultivated plants (ornamentals and crops). We classified baneful weeds co-ordinate to definitions and classifications provided past the USDA Plants Database and the USDA Natural Resources Conservation Service (NRCS) [40,41]. Cultivated plants include cultivated varieties of native or exotic plants and all crops. Wildflowers were non-cultivated native or exotic plants that were not classified as baneful weeds by referenced USDA sources.
Bee community surveys
To sample bees across study sites, nosotros used a combination of pan traps and mitt-netting to reduce bias from utilizing either capture method solitary [42]. We sampled bees 3 times across the summertime of 2017. The sampling periods ran from June seven—June 14, from July three—July 8, and from July 31—Aug 8. We placed iv pan trap triplets at each study site during the beginning survey period, and and so half-dozen pan traps for the concluding ii survey periods due to initially depression numbers of nerveless bees. During each survey menstruation triplets were nerveless after 48 hours. Pan trap triplets consisted of iii 150ml plastic cylinders colored fluorescent white, yellowish, and blue held in identify with a PVC base and a bamboo stake. Each trap was filled 3/4ths full of soapy water (Dawn Ultra Liquid Dish Soap, Procter & Adventure, Cincinnati, OH.). We placed pan traps near patches of blooming flowers at each site so that each bowl was at least 5 grand autonomously from all others. Traps were placed to sample bees near all major patches of floral resources while also roofing the entire extent of each site.
During each survey period, we sampled bees via mitt-netting forth floral survey transects. We walked along each transect for seven and a one-half minutes, thus sampling for thirty minutes at each field site during each sampling menstruation, per recommended guidelines [43]. During each survey catamenia, sampling was split up between ii observers so that each person sampled along two transects. We did non count handling fourth dimension spent transferring bees into collection vials. During the final survey menstruum, we counted rather than collected dear bees (Apis mellifera Linn.). We sampled bees only on days that met the following criteria: (1) ambient temperatures were greater than 15.five degrees C, (2) wind speeds less than 3.9 mps for thirty seconds, and (3) plenty continuous sunlight to bandage a shadow [guidelines modified from 44]. Sites were sampled in different club by different surveyors across survey periods. All collected bees were identified to species or morphospecies. Specimens of taxonomically challenging genera were identified or verified by Rob Jean (Environmental Solutions & Innovations), Jason Gibbs (University of Manitoba), and Karen Wright (Texas A&G University). Encounter S2 Tabular array for a consummate list of collected bee species.
Because the goal of our study was to evaluate the influence of urbanization and floral resources on wild bee species, we excluded the European honey bee (Apis mellifera) from analyses as has been done in other studies [13,45,46]. Since A. mellifera is a managed species, the distribution and abundance of individuals is influenced by placement and management of hives, which could non be adequately assessed across our report sites. While research indicates that honey bees can have competitive effects of on wild bees [47], we institute no significant relationship between love bee and wild bee abundance (R2 = 0.058, t13 = 1.065, P = 0.306). Additionally, nosotros constitute no relationship between urbanization and honey bee affluence (at a 1000 m radius: Rtwo = 0.058, t13 = -1.742, P = 0.389). Descriptive data on love bee abundance in comparison to wild bee affluence across sites are presented for beloved bees, simply analyses focus on wild bee data.
Bee community traits and functional groups
To evaluate the influence of urbanization and floral resources on bee communities across sites, we examined four bee community response variables: total affluence, species richness, diversity, and evenness. We used individual-based rarefaction to judge species richness based on 100 randomizations using EstimateS 9.1.0 [48]. This statistical interpolation method accounts for uneven sample size and standardizes estimates of species richness across samples [49]. From rarefied species richness data, nosotros calculated the exponential form of Shannon entropy. This species diversity metric produces an judge that transforms Shannon entropy into an "effective number of species" and provides a more than stable and interpretable measure of variety [50]. Finally, we calculated Pielou's evenness index (J) to compare the relative evenness of species abundances across sites [51].
To examine how functional groups mediate bee response to urbanization, we sorted species based on the following traits: nesting strategy (footing, cavity, stem, soft wood, difficult woods, managed hives), diet latitude (generalist—collecting pollen from many plant families, specialist—collecting or preferring pollen from one plant family unit), native or exotic status, and sociality (lonely, communal, subsocial, eusocial, cleptoparasite). We assigned specimens to functional group categories using information sourced primarily from Gibbs et al. [52] and references therein. Species that utilise multiple nesting substrates or social strategies were assigned to the category they virtually frequently use based on regional records. For a complete list of species collected and functional group assignments, see S2 Table. For analyses, we combined bees that nest in stems, cavities, and forest into an above-ground nesting group. Similarly, lone, communal, subsocial, and cleptoparasitic bees were combined into a single category—alone bees. Primitively eusocial Halictidae and eusocial bumble bees were grouped as eusocial bees. Lasioglossum (Dialictus) species were treated as eusocial when records were lacking because this grouping is ancestrally eusocial and almost studied species accept been shown to make eusocial nests [53–55]. In instances where no functional trait information was available, specimens were assigned to functional groups based on information known for closely related taxa (i.e. known traits for species within a morphospecies group, east.chiliad. Hylaeus modestus group, or assumed genus-level traits). For example, many Andrena species practise not take published records of nesting beliefs, simply most studied species are known to nest in the ground. Therefore, basis-nesting behavior was inferred for all Andrena when no published records existed. In full, nosotros used this approach to infer functional traits for 29 species and 8 morphospecies. When data was lacking and could not be fairly assessed, we excluded species from analyses. Functional group assignments, including instances of inferred traits and exclusions are listed in S2 Table.
Statistical analyses
Statistical analyses were performed in R v. iii.v.2 [56]. Nosotros used general linear models to examine the influence of urbanization and floral resources on bee community response variables and bee functional group abundances at the site level with temperature and subcontract size included every bit covariates. We ln(x+1) transformed bee response variables to meet assumptions of statistical tests. We transformed information for model fitting instead of modelling relationships with a count distribution to prevent inflating type-I error given our pocket-sized sample size [57].
We used a frontward selection arroyo to choose predictor variables and models with the everyman AICc. Commencement, we selected the urbanization radius which best explained bee response to urbanization (impervious surface from 500 m, 1000 m, 1500 yard, 2000 m) by plumbing equipment a model that included each variable every bit a single predictor. Next, we compared models which independent the all-time urbanization predictor from the first stride and all combinations of our floral resource predictors (total blossom cover & plant spp. richness together, and separately). We then took the resultant best model and assessed whether the addition of farm size and temperature improved the fit of each model. This process was repeated for all bee response variables. We evaluated the significance of predictors using type-Two F-tests.
To examine the composition of bee species and functional groups across sites and urbanization categories, we used non-metric multidimensional scaling (NMDS) via the R parcel "vegan" [58]. Bee species and functional group information were foursquare root transformed and then Wisconsin double standardized before calculating Bray-Curtis dissimilarity measures. This was done to reduce stress in the concluding ordination [59]. We constructed polygons on ordination plots to delineate sites belonging to each urbanization category. We conducted an Analysis of Similarities (ANOSIM) permutation test, set to 999 permutations, to determine if the composition of species and functional groups differed significantly for the two urbanization categories. Finally, we performed a Mantel exam using Pearson'southward correlation coefficient to determine if bee species and functional group composition were spatially autocorrelated. We used latitude and longitude measurements to summate geographic contrast of sites based on Euclidean distance which were compared to species and functional group Bray-Curtis dissimilarity matrices.
Results
In total, nosotros collected ane,844 bee specimens representing 106 species and 12 morphospecies from 26 genera inside v families (S1 and S2 Tables, Fig 1, S2 and S3 Figs). Of these specimens, 355 were dear bees (Apis mellifera Linn.). Side by side to A. mellifera, the well-nigh abundant bee species was Bombus impatiens Cresson with 140 specimens (ix%) followed by Lasioglossum imitatum (Smith), Halictus ligatus Say, Ceratina calcarata Robertson, and Hylaeus hyalinatus Smith (respectively vii%, 4%, 3%, and 3% of the bee community; S4 Fig). The number of wild bee species observed across sites ranged from xiv to 41 (mean ± SD = 29.4 ± 7.4; S1 Table).
Bee abundance (bars) and species richness (numbers above bars) across study sites.
Sites are ordered by increasing urbanization with values for % urbanization (measured equally impervious surface) shown along the bottom of the x-axis. Urbanization values are provided in S1 Table. For some analyses, sites were divided into two categories: less urbanized (hateful ± SD = 13 ± nine) and more than urbanized (mean ± SD = 49 ± 6). The dashed gray line illustrates how sites were divided with associated levels of urbanization.
The majority of collected bee species were ground-nesters (63.9%). Above-ground nesters (36% of all bees) consisted of crenel (22%), stalk (11%), and woods-nesting bees (3%). Most wild bee species were either eusocial (46%) or alone (43%). Subsocial and communal bees comprised 7% and iii% of all species, respectively. For the remaining functional groups, we found more generalist species (89%) compared with specialists and native bee species (87%) compared with exotic. Since bees were characterized by multiple traits, certain functional groups showed considerable overlap. In item, all exotic and crenel-nesting bees were solitary, 77% of exotic bees were crenel-nesting, and 98% percentage of eusocial bees were ground-nesting.
Most flowering species available at our sites were exotic species (68%), representing 66% of total blossom cover (S3A Table). Wildflowers were the most abundant plant category representing 56% of found species and 54% of total bloom cover. Twoscore ii pct of plant species were ornamentals or crops, constituting 35% of overall blossom cover. Noxious weeds comprised approximately 2% of species and 11% of the total bloom cover beyond sites and dates (S3B Tabular array). In order of affluence, the five nigh commonly recorded plant species were Rudbeckia hirta L., Daucus carota Fifty., Erigeron spp. L., Achillea millefolium L., and Trifolium repens L.. Two of these species (D. carota and T. repens) are naturalized wildflowers from Eurasia that are considered to be weedy species, simply non baneful or invasive.
Effects of urbanization and floral resources on bee community traits
Urbanization positively influenced bee species diversity and evenness but had no consequence on bee abundance or species richness (Fig 2A; Table 1A). Urbanization explained the most variation in bee multifariousness at a 500 m radius (Fig 2A) and in species evenness at a 1000 chiliad radius (Fig 2A). For these analyses, model fits were comparable across spatial scales (ΔAICc < 2; S4A Tabular array). Greater plant species richness positively influenced bee diversity and species richness (Fig 3). Withal, no floral resource metrics significantly afflicted bee species evenness or abundance (Table 1A).
Relationships betwixt wild bee response variables and urbanization predictors.
Ln(ten+i) transformed data are plotted on all y-axes except those with asterisks, which bespeak partial residuals are plotted instead. Fractional residuals evidence the remaining variation explained by the plotted predictor variable when the remaining variation explained by some other predictor is deemed for. Partial residuals are plotted in instances where terminal models had two significant predictors (Table 1). Asterisks next to R2 values indicate the significance of the plotted relationship: * P < 0.05, *** P< 0.001.
Influence of floral resources on wild bee response variables.
Ln(x+1) transformed data are plotted on all y-axes except those with asterisks, which indicate partial residuals are plotted instead. Partial residuals were plotted for groups which had two pregnant predictor variables in final models (Tabular array 1). Partial residuals testify the remaining variation explained by the plotted predictor variable. Asterisks next to R2 values indicate the significance of the plotted human relationship: * P < 0.05.
Table i
(A) Bee customs and (B) functional group response to urbanization, temperature, floral resources, and farm size.
| Response Variable | Parameters | Multiple R2 | Estimate | SE | F | DF | P |
|---|---|---|---|---|---|---|---|
| A. Community level | |||||||
| Full bee affluence | Urbanization (1500m) | 0.171 | 0.124 | 0.587 | 0.044 | (1,12) | 0.837 |
| Bloom cover | 0.021 | 0.013 | 2.432 | (1,12) | 0.145 | ||
| Bee species richness | Urbanization (500 g) | 0.363 | 0.254 | 0.174 | two.121 | (1,12) | 0.171 |
| Plant species richness | 0.006 | 0.003 | 5.468 | (1,12) | 0.038 * | ||
| Bee evenness | Urbanization (1000m) | 0.472 | 0.070 | 0.022 | 10.395 | (1,12) | 0.007 * |
| Institute species richness | 0.001 | 0.001 | one.425 | (1,12) | 0.339 | ||
| Bee diversity | Urbanization (500m) | 0.486 | 0.617 | 0.223 | seven.658 | (i,12) | 0.017 * |
| Plant species richness | 0.007 | 0.003 | 5.121 | (one,12) | 0.043 * | ||
| B. Functional groups | |||||||
| Native bees | Urbanization (1000m) | 0.186 | 0.244 | 0.125 | 0.169 | (one,12) | 0.688 |
| Total blossom encompass | 0.019 | 0.012 | 1.900 | (1,12) | 0.193 | ||
| Exotic Bees | Urbanization (500m) | 0.666 | 3.908 | 0.820 | 22.684 | (1,12) | < 0.001 *** |
| Total bloom cover | 0.039 | 0.018 | four.771 | (1,12) | 0.050 * | ||
| Footing-nesting bees | Urbanization (1000m) | 0.420 | 1.024 | 0.550 | 3.472 | (1,12) | 0.087 |
| Full bloom embrace | 0.019 | 0.013 | 2.378 | (1,12) | 0.149 | ||
| To a higher place-ground nesting bees | Urbanization (500m) | 0.345 | ii.643 | 1.161 | 5.177 | (one,12) | 0.042 * |
| Constitute species richness | 0.023 | 0.017 | ane.844 | (1,12 | 0.199 | ||
| Alone bees | Urbanization (1500m) | 0.361 | two.211 | 0.851 | six.743 | (ane,12) | 0.023 * |
| Total flower encompass | 0.009 | 0.020 | 0.218 | (1,12) | 0.649 | ||
| Eusocial bees | Urbanization (1000m) | 0.596 | ane.348 | 0.529 | 6.505 | (1,12) | 0.025 * |
| Total bloom embrace | 0.028 | 0.012 | five.380 | (1,12) | 0.039 * | ||
| Generalist bees | Urbanization (1500m) | 0.206 | 0.050 | 0.624 | 0.007 | (1,12) | 0.937 |
| Full bloom cover | 0.025 | 0.014 | 2.956 | (i,12) | 0.111 | ||
| Specialist bees | Urbanization (500m) | 0.171 | ane.290 | 0.860 | ii.248 | (1,12) | 0.160 |
| Plant species richness | 0.004 | 0.013 | 0.082 | (i,12) | 0.779 |
Bee species and functional group composition differed significantly between more urbanized and less urbanized sites (ANOSIM: R = 0.217, 0.352; P = 0.038, 0.001; Fig iv). More urbanized sites clustered closely together and thus were more similar in bee species and functional group limerick compared with less urbanized sites (Fig four). Species composition, yet, was spatially autocorrelated (Mantel test: r = 0.444, P = 0.001), while functional group composition was not (Mantel test: r = 0.084, P = 0.233).
Non-metric multi-dimensional scaling (NMDS) ordinations of (A) Bee species and (B) functional grouping limerick across study sites (Northward = 15).
Less urbanized sites take < 40% while more urbanized sites have > 40% surrounding impervious surface area at a 1000 m radius. Polygons show clustering of sites within each urbanization category in reduced ordination space. The composition of less urbanized and more than urbanized sites was significantly unlike for both ordinations. Stress was 0.166 for (A) and 0.033 for (B).
Effects of urbanization and floral resources on functional groups
Urbanization positively influenced the abundances of exotic, above-footing nesting, and solitary bees, whereas eusocial bees were negatively affected (Fig 2B; Table 1B). Urbanization at a 500 thousand radius provided a significantly amend model fit for exotic bees (S4B Table). For ground-nesting and eusocial bees, larger spatial scales of analysis (1000-2000m) provided meliorate model fits for examining the effect of urbanization on these functional groups (S4B Table).
Because eusocial bees were the only functional group negatively affected by urbanization, nosotros conducted dissever post-hoc analyses on bees in the two nigh arable genera—Bombus and Lasioglossum (Dialictus)—which correspond 77% of eusocial bees in our study. Since Bombus and Lasioglossum (Dialictus) differ greatly in size, and thus foraging ability, we expected that these genera could respond differently to urbanization. Post-hoc analyses, notwithstanding, showed that both genera were negatively influenced past urbanization (S5 Fig), and that all urbanization radii provided similar model fits (S4C Tabular array). In general, statistical models using different urbanization radii were comparable for most functional groups (ΔAICc < 2), unless otherwise noted in a higher place (S4 Tabular array).
Overall, blooming plant cover was a better floral resource predictor for bee functional group response in comparison to plant species richness (Table 1B, S4B Table). However, this floral resource metric only positively influenced eusocial bee affluence and exotic bee abundance (Table 1B; Fig 3).
Neither site-level temperature nor subcontract size improved the fit of any community level or functional group models (S4 Table). Urbanization and minimum temperature, however, were positively correlated, and thus community level and functional group responses to urbanization cannot be disentangled from bee response to warmer temperatures associated with greater urbanization (S5 Tabular array).
Discussion
Because some bee species and functional groups respond differently to anthropogenic drivers, detecting effects on the overall customs is challenging [21]. A growing body of research, however, has found that bee functional traits can be used to aid understand how bees reply to the effects of anthropogenic changes, such as urbanization [threescore–64]. Across our study region, more urbanized sites supported a greater number of above-ground nesting, exotic, and alone bees. Ultimately, the positive response of these particular functional groups explain the overall positive effects of urbanization on bee diversity and evenness observed in our written report. Other studies have also shown that cavity-nesting and exotic bees are often more abundant in cities [reviewed in 22,25], which suggests that urbanization could homogenize bee communities by preferentially supporting these groups. Indeed, a contempo report showed that urban landscapes were associated with phylogenetic homogenization of bee communities across ecoregions in the northeastern U.s.a. [46]. Thus, although some urban areas, such equally our study region, can support various bee communities, these habitats may be dominated by certain functional or phylogenetic groups.
While some functional groups responded positively to urbanization in our study, many were unaffected and eusocial bees were negatively afflicted. The negative effect of urbanization on eusocial bees is especially concerning given their ecological and economic importance. Bumble bees, in item, are key pollinators for many plant species, including a number of fruit and vegetable food crops [65]. Given that many bumble bee species are experiencing widespread decline [1,66–68], conservation measures targeting these species are disquisitional for buffering against further losses.
In our written report, blossom cover was an important predictor of eusocial bee abundance, indicating that speciose and abundant urban wildflower plantings are needed to support these species. Bee reproductive success is influenced by the availability and proximity of floral resources [69,70], and bees adopt to nest shut to areas of ample bloom cover [71]. Thus, if floral resources are locally dense within cities, bees would non have to provender far to collect necessary resources for colony survival. Greater floral resource abundance and diversity has been shown to positively affect the fettle and reproductive output of eusocial bee colonies [72]. Furthermore, local scale floral resources additions to an urban area have been linked with increased bee density and abundance [73,74].
Our study supports previous inquiry showing that increased floral resources availability tin can heighten bee diversity and affluence in urban environments [23,63,75–78]. We found that bee diverseness and species richness were positively influenced by blooming plant species richness. These results suggest that increasing flowering plant multifariousness is an of import conservation measure. Many of the floral resource found across our sites were exotic species, mostly naturalized weedy wildflowers. Such species can provide important floral resources for bees in areas and at times when native species are lacking [79–81]. However, while wild bees volition fodder on exotic plants, they oftentimes prefer native species [81–83]. Given the value of native plants in supporting arthropod biodiversity [84–86] and the dominance of exotic flora in our study region and other urban ecosystems [77,87–89], we recommend conservation efforts encourage planting more native species to support urban bees.
Interestingly, some researchers take establish positive effects of urbanization on bumble bees [90–92], while others accept found declines with greater urbanization [xi,93]. Similarly, Harrison et al. [46] establish that Lasioglossum were more arable at urban study sites compared with agronomical and forested sites, whereas we found a negative effect of urbanization on Lasioglossum. Collectively, these context-dependent results indicate a demand for research beyond broad geographic regions besides as a better understanding of how contextual differences contribute to variable results. In some cases, variable results may arise due to differences in report methodologies or habitat surveyed. For case, Harrison et al. [46] compared Lasioglossum in 3 discrete state-encompass categories, while our report examined Lasioglossum across like habitats that varied in the amount of urbanization in the surrounding landscape. In Harrison et al., sites were characterized as at least 80% urban, agriculture, or wood, whereas the caste of urbanization ranged from 5–60% in our study. Furthermore, in our written report, rural sites were open habitats similar to urban and peri urban sites and were non predominately agricultural. Thus, rural sites in our study area may take been amend at supporting Lasioglossum in comparing to our urban sites, yielding results that differ from the findings of Harrison et al. Variation in sampling technique, survey catamenia, and spatial scale of analysis are other methodological differences that could underlie inconsistent results.
In our written report, the spatial scale which explained the nigh variation in bee response varied across groups and between community metrics. Even so, all spatial scales explained like amounts of variation (ΔAICc < two), except in the case of exotic bees (best radius 500 m), basis nesting bees (best radii > 500 m), and eusocial bees (best radii > 500 m). Similarly, bee diversity was the but customs metric with large deviation in model fit from ane radius compared to all others (500 m). Some researchers have examined bee response to urbanization at smaller spatial scales [eastward.chiliad. thirteen], while other use several scales [e.k. 93], and responses across radii are often similar. Our results suggest that for most functional groups, impervious surface extent measured at big and small spatial scales will effectively approximate bee response to urbanization.
We found no effect of increased urban temperatures on bee response variables after accounting for the effects of impervious surface area, Oestrus island effects vary by latitude and are generally expected to produce positive effects on species at higher latitudes [94]. Given the location of our study area, the warmer temperatures of our urban study sites may all the same be within the range of optimal growth and development for many bee species. The effects of greater impervious surface on habitat loss and urban warming, notwithstanding, cannot exist disentangled in our study. Even so, some bee species are more than sensitive to hotter temperatures than others, and thus may be negatively affected past urban warming [39,95]. Thus, while efforts to increase floral resource abundance and variety will certainly benefit many urban bees, these conservation measures will probable not ameliorate other negative effects of urbanization—such as the effects of increased impervious surface on warming trends and the availability of nesting sites for ground-nesting bees.
For most of the globe'southward wild bee species, we know little about how species' ranges and populations are changing with urbanization, and what we practice know is limited in scope beyond geographic areas [96,97]. Our written report adds to the cognition of bees in southeastern Michigan, contributing information to an expanse that has not been historically well-studied. Overall, this projection added new state occurrence records for two exotic bee species as well as 74 new county-level species records [96]. Further research, especially multi-year studies across broad geographic regions, are needed to help monitor and mitigate the effects of urbanization and other human-caused ecology changes on bee communities.
Equally cities continue to grow worldwide, effective management strategies must be developed to back up bees and the of import pollination services they provide. City and state managers can work alongside conservationists, farmers, and gardeners to support urban biodiversity. Critical components of pollinator conservation in cities include the development and maintenance of habitats that provide diverse and arable floral resources in addition to nesting substrates while limiting detrimental effects of urban warming and environmental pollutants. Such considerations are essential in urban evolution and urban agriculture.
Supporting information
S1 Fig
Map of written report sites in southeastern Michigan, United states.
(DOCX)
S2 Fig
Rank affluence of bee genera ordered past full number of specimens observed beyond sites.
(DOCX)
S3 Fig
Bee abundance and species richness across sites and sampling periods.
(DOCX)
S4 Fig
The nearly abundant wild bee species collected across sites.
(DOCX)
S5 Fig
Event of urbanization on Bombus and Lasioglossum (Dialictus) abundance.
(DOCX)
S1 Table
Site-level bee community data.
(DOCX)
S2 Tabular array
Summary of nerveless bee specimens with functional trait information.
(DOCX)
S3 Table
The species number and bloom cover provided plants of unlike geographic origin and establish category.
(DOCX)
S4 Table
Summary of AICc values for model selection.
(DOCX)
S5 Tabular array
Correlation tests between minimum site level temperature and impervious surface expanse.
(DOCX)
Acknowledgments
We would like to thank Stephen D. Hendrix, Steven D. Frank, Thomas Raffel, Scott Tiegs, and 5 reviewers for their thoughtful feedback on earlier versions of this manuscript. We want to thank Rob Jean, Jason Gibbs, and Karen Wright for contributing their taxonomic expertise. Finally, we give thanks the students and technicians who helped collect data as well as the farmers and gardeners who gave us permission to utilize their land for our study.
Funding Statement
This work was funded by a Foundation for Food and Agricultural Research New Innovator Award to MAJ (FFAR Honor No.430876) (https://foundationfar.org/) and the Oakland Academy Provost Graduate Educatee Enquiry Honor to CJW (Oakland.edu). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886752/
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