DOI: 10.5061/dryad.cvdncjtdp
title: Planting native wildflowers improves vacant land as bee habitat in a post-industrial city

description: As people leave post-industrial cities, abandoned homes are demolished and
 transformed into vacant lots. These greenspaces have been demonstrated to
 provide habitat for urban wildlife and supply ecosystem services to
 communities. In the post-industrial city of Cleveland, Ohio, U.S.A.,
 approximately 37% of the state’s bee fauna has been collected within
 vacant lots. Our goal was to determine if planting native wildflowers
 (“pocket prairies”) on vacant land would improve these sites as bee
 habitat. We hypothesized that pocket prairies would support a greater
 proportion of the regional bee species pool, represented by Metropark
 grassland bee communities in the suburban landscape, compared to unaltered
 vacant lots. Using pan traps and hand vacuums, we sampled bees in each
 treatment from June to September 2019. We collected 1,087 bees
 representing 24 genera and 81 species. Bees visited over 30 floral
 species, including native wildflowers and urban spontaneous vegetation.
 Metropark grasslands supported a higher bee species richness and diversity
 than urban pocket prairies. Both Metropark grasslands and pocket prairies
 supported a higher bee abundance, diversity, and species richness than
 urban vacant lots. Synthesis and Applications: Despite the substantially
 smaller extent of the pocket prairies, these habitats supported a similar
 bee abundance to the Metropark grasslands. Bees foraged on intentionally
 planted wildflowers and non-native spontaneous vegetation, highlighting
 the importance of managing both components in urban greenspaces. Our
 results suggest that greening vacant land can improve post-industrial
 cities as bee habitat.
description: <em>Local bloom assessment  </em>
 Bloom assessments occurred monthly in 2019 (June 11, July 10,
 August 6, September 17). In the center of each pocket prairie and vacant
 lot, we created a 7 × 15m grid, randomly selected six
 1m<sup>2</sup> grid quadrats, and placed a
 0.5m<sup>2</sup> PVC pipe square in the center of each
 quadrat. We identified every unique flowering species present in a quadrat
 to calculate <em>bloom richness</em>. Then, we counted all
 floral units to determine <em>bloom abundance </em>of each
 flowering species (Turo et al., 2021).<em> </em>Afterwards, we
 took five random measurements (mm<sup>2</sup>) of individual
 floral units for each species. Average bloom size per species was
 calculated and multiplied by bloom abundance to quantify
 total <em>bloom area</em> at a site. In each Metropark
 grassland, we established a 20m transect, randomly selected six
 1m<sup>2</sup> grid quadrats to sample along the transect and
 placed a 0.5m<sup>2</sup> PVC pipe square in each quadrat’s
 center wherein the same bloom data were collected.  
 <em>Bee sampling</em> We used pan
 traps to assess the bee community present throughout the season. We
 sampled bees once a month in each treatment (June 11, July 10, August 6,
 August 12, September 17, September 19). Pan traps were plastic souffle
 cups (3.25 oz; Solo© Dart Container Corporation, Mason, Michigan, U.S.A.)
 2/3 full of 1% dish soap solution (Blue Dawn© Proctor and Gamble,
 Cincinnati, Ohio, U.S.A.) painted fluorescent blue, white, or yellow (©
 Guerra Paint & Pigment Corp.). Pan traps were deployed at prairie
 flower height on 1m elevated stands which consisted of a 15 × 20cm
 corrugated plastic platform secured to an L-bracket and fixed to a step-in
 fence post. 12 pan traps were deployed for 24 hours at each site and
 collected the following morning. Insect samples were stored in 70% ethanol
 solution (© ThermoFisher Scientific, Waltham, Massachusetts, U.S.A.) and
 processed in the laboratory. To evaluate patterns of
 bee foraging, we used hand vacuums (© Bioquip) to collect bees from July
 to September 2019 (July 15, August 12, September 20). Bees were actively
 sampled from blooming vegetation on sunny days with clear conditions
 between 10am and 4pm. During each observation period, blooming floral
 species were each observed for 4.5 minutes. Each time a bee landed on the
 cluster of blooms under observation, collection was attempted.  
 Using dichotomous keys, we identified bees to species whenever
 possible (Ascher and Pickering, 2018; Gibbs, 2011; Gibbs et al., 2013).
 These identifications were verified by Sam Droege at the USGS Native Bee
 Inventory and Monitoring Lab in Laurel, Maryland, U.S.A. Then, we
 classified bees by functional traits, including body size, lecty
 (generalist/specialist), nesting guild, origin (native/alien), and
 sociality. Community-weighted means were used in functional trait analyses
 of bee body size (Ascher and Pickering, 2018; Sivakoff et al., 2018; Turo
 et al. 2021). Nesting guild categories included cavity nesting, colony
 nesting, pith nesting, soil nesting, wood nesting, and parasitic bees that
 do not use nests. Bee sociality was categorized as eusocial, parasitic,
 solitary, or subsocial.  <em>Statistical
 methods</em> <em>Bee community
 assessment</em> To assess bee community
 composition differences by treatment, we conducted a non-metric
 multidimensional scaling (NMDS) analysis using the ‘vegan’ package
 (Oksanen et al., 2019) in R (R Core Team, 2022), applying a Bray-Curtis
 distance matrix (Bray and Curtis, 1957) to our compiled bee abundance
 data. We pooled bee abundance by site and treatment across the season.
 Using the ‘pairwiseAdonis’ package (Martinez Arbizu, 2020), we then
 performed a pairwise permutational multivariate analysis of variation to
 assess the significance of differences in bee community
 composition.   To evaluate the effect of habitat
 establishment on bee communities, we used generalized linear models with
 bee abundance, Shannon-Weiner diversity, and species richness of bee
 samples collected from pan traps as response variables (Bates et al.,
 2015). We also used pan trap collections to compare body size and
 functional trait abundance between treatments. In our analyses, we
 screened three explanatory bloom variables
 (<em>abundance</em>, <em>area</em>,
 <em>richness</em>) for normality and variance inflation factor
 (Peterson, 2020). We also included a fixed effect for
 <em>treatment</em> and an offset term for the number of viable
 <em>pan traps</em>. Lastly, we considered mixed effects,
 including a random intercept for <em>month</em>. We used a
 stepwise backwards model selection approach to select the best fit model
 for each response variable (Fox and Weisberg, 2019).
 <em>Plant-bee network analysis</em>
 To assess to what extent bees used the seeded native prairie
 plants, we characterized bee foraging by generating plant-bee networks of
 vacuumed bee samples. Using the ‘bipartite’ package (Dormann et al.,
 2008), we constructed networks of bee foraging by treatment and site
 (<em>Metropark Grassland</em>, <em>Pocket
 Prairie</em>, and <em>Vacant Lot</em>). We used three
 indices in the ‘grouplevel’ function to assess the structure of these
 networks: <em>generality</em> (mean number of plant species a
 bee visits), <em>mean number of shared partners</em> (mean
 number of plant species any two bee species both interact with), and
 <em>niche overlap</em> (mean similarity of plant interactions
 between bee species). We also calculated three indices in the
 ‘networklevel’ function, including <em>edge betweenness
 centrality</em> (measure of centrality of a bee or plant species in
 a network), <em>linkage density</em> (ratio of realized
 plant-bee interactions to all possible interactions), and
 <em>H2’</em> (measure of network specialization). After we
 calculated these indices, we generated 1,000 null models for each network
 using the ‘nullmodel’ function and the ‘r2dtable’ method (Dormann et al.,
 2008), calculating the same indices for each null model. Then, we
 calculated mean index scores from each null model for all three indices,
 divided them by the standard deviation of the null model scores, and used
 the resulting values to correct raw index values as z-scores. This was
 done to ensure that our descriptions of plant-bee networks were not an
 artifact of sampling intensity or web dimension (Dormann et al.,
 2009).
description: # Planting native wildflowers improves vacant land as bee habitat in a
 post-industrial city
 [https://doi.org/10.5061/dryad.cvdncjtdp](https://doi.org/10.5061/dryad.cvdncjtdp) ## Description of the data and file structure In June-August 2019, we used pan traps to assess the bee community present throughout the season in urban vacant lots, pocket prairies, and surrounding Metropark grasslands in the greater Cleveland metropolitan area. We sampled bees once a month in each treatment (June 11, July 10, August 6, August 12, September 17, September 19). Pan traps were plastic souffle cups (3.25 oz; Solo© Dart Container Corporation, Mason, Michigan, U.S.A.) 2/3 full of 1% dish soap solution (Blue Dawn© Proctor and Gamble, Cincinnati, Ohio, U.S.A.) painted fluorescent blue, white, or yellow (© Guerra Paint & Pigment Corp.). Pan traps were deployed at prairie flower height on 1m elevated stands which consisted of a 15 × 20cm corrugated plastic platform secured to an L-bracket and fixed to a step-in fence post. 12 pan traps were deployed for 24 hours at each site and collected the following morning. Insect samples were stored in 70% ethanol solution (© ThermoFisher Scientific, Waltham, Massachusetts, U.S.A.) and processed in the laboratory. To evaluate patterns of bee foraging, we used hand vacuums (© Bioquip) to collect bees from July to September 2019 (July 15, August 12, September 20). Bees were actively sampled from blooming vegetation on sunny days with clear conditions between 10am and 4pm. During each observation period, blooming floral species were each observed for 4.5 minutes. Each time a bee landed on the cluster of blooms under observation, collection was attempted.   Using dichotomous keys, we identified bees to species whenever possible (Ascher and Pickering, 2018; Gibbs, 2011; Gibbs et al., 2013). These identifications were verified by Sam Droege at the USGS Native Bee Inventory and Monitoring Lab in Laurel, Maryland, U.S.A. Then, we classified bees by functional traits, including body size, lecty (generalist/specialist), nesting guild, origin (native/alien), and sociality. Community-weighted means were used in functional trait analyses of bee body size (Ascher and Pickering, 2018; Sivakoff et al., 2018; Turo et al. 2021). Nesting guild categories included cavity nesting, colony nesting, pith nesting, soil nesting, wood nesting, and parasitic bees that do not use nests. Bee sociality was categorized as eusocial, parasitic, solitary, or subsocial.  Bloom assessments also occurred monthly in 2019 (June 11, July 10, August 6, September 17). In the center of each pocket prairie and vacant lot, we created a 7 × 15m grid, randomly selected six 1m2 grid quadrats, and placed a 0.5m2 PVC pipe square in the center of each quadrat. We identified every unique flowering species present in a quadrat to calculate *bloom richness*. Then, we counted all floral units to determine *bloom abundance *of each flowering species (Turo et al., 2021).* *Afterwards, we took five random measurements (mm2) of individual floral units for each species. Average bloom size per species was calculated and multiplied by bloom abundance to quantify total *bloom area* at a site. In each Metropark grassland, we established a 20m transect, randomly selected six 1m2 grid quadrats to sample along the transect and placed a 0.5m2 PVC pipe square in each quadrat’s center wherein the same bloom data were collected.   ### Files and variables ## File: 2019pans.csv **Description:** CSV file of pan trap bee collections which includes information about bee species, collection sites, and local and landscape variation. Landscape data were extracted using Fragstats from the 2009 National Land Cover Database. All missing values are indicated as NA.  ##### Variables * abund: Number of bees at a site * richness: Number of bee species at a site * div: Shannon-Weiner diversity of bee species at a site * site: A specific habitat (pocket prairie/vacant lot/Metropark grassland) within a neighborhood (e.g., Hough)  * neighbor: A neighborhood where a site was located * trt: Habitat treatments (pocket prairie - UP, vacant lot - VL, and Metropark grassland - OF) * trt1: Habitat treatments written out with more specific information about vacant lot greening treatments (T1/6/7/8) * day: Day data were collected * month: Month data were collected * season: Early or late season * pans: Number of pan traps deployed at a site * bloom.rich: Number of bloom species within a randomly sampled quadrat  * bloom.abund: Number of blooms within a randomly sampled quadrat * bloom.area: Average bloom size per species was calculated and multiplied by bloom abundance to quantify total *bloom area* at a site. In each Metropark grassland, we established a 20m transect, randomly selected six 1m2 grid quadrats to sample along the transect and placed a 0.5m2 PVC pipe square in each quadrat’s center wherein the same bloom data were collected.   - ca500: class area of greenspace at 500m - pland500: percent land cover of greenspace at 500m - lpi500: largest patch index at 500m - enn_mn500: mean Euclidian nearest neighbor at 500m - enn_am500: average Euclidian nearest neighbor at 500m - enn_md500: median Euclidian nearest neighbor at 500m - shdi500: Shannon's diversity index at 500m - ca1500: class area of greenspace at 1500m - pland1500: percent land cover of greenspace at 1500m - lpi1500: largest patch index at 1500m - area1500: greenspace area at 1500m - enn_mn1500: mean Euclidian nearest neighbor at 500m ## File: 2019traits.csv **Description:** CSV file of bee functional traits, collection sites, and local and landscape variation. Landscape data were extracted using Fragstats from the 2009 National Land Cover Database. All missing values are indicated as NA.  ##### Variables * generalist: bee functional trait in regard to floral preferences * specialist: bee functional trait in regard to floral preferences * cleptoparasite: bee functional trait in regard to nesting preferences (using other hosts)  * soil: bee functional trait in regard to nesting preferences * cavity: bee functional trait in regard to nesting preferences * hive: bee functional trait in regard to nesting preferences * pith: bee functional trait in regard to nesting preferences * wood: bee functional trait in regard to nesting preferences * subsocial: bee functional trait in regard to sociality * solitary: bee functional trait in regard to sociality * eusocial: bee functional trait in regard to sociality * parasite: bee functional trait in regard to parasitic behavior * native: bee functional trait in regard to origin * alien: bee functional trait in regard to origin * site: A specific habitat (pocket prairie/vacant lot/Metropark grassland) within a neighborhood (e.g., Hough)  * neighbor: A neighborhood where a site was located - trt: Habitat treatments (pocket prairie - UP, vacant lot - VL, and Metropark grassland - OF) - trt1: Habitat treatments written out with more specific information about vacant lot greening treatments (T1/6/7/8) - day: Day data were collected - month: Month data were collected - season: Early or late season * pans: Number of pan traps deployed at a site * bloom.rich: Number of bloom species within a randomly sampled quadrat  * bloom.abund: Number of blooms within a randomly sampled quadrat * bloom.area: Average bloom size per species was calculated and multiplied by bloom abundance to quantify total *bloom area* at a site. In each Metropark grassland, we established a 20m transect, randomly selected six 1m2 grid quadrats to sample along the transect and placed a 0.5m2 PVC pipe square in each quadrat’s center wherein the same bloom data were collected.   - ca500: class area of greenspace at 500m - pland500: percent land cover of greenspace at 500m - lpi500: largest patch index at 500m - enn_mn500: mean Euclidian nearest neighbor at 500m - enn_am500: average Euclidian nearest neighbor at 500m - enn_md500: median Euclidian nearest neighbor at 500m - shdi500: Shannon's diversity index at 500m - ca1500: class area of greenspace at 1500m - pland1500: percent land cover of greenspace at 1500m - lpi1500: largest patch index at 1500m - area1500: greenspace area at 1500m - enn_mn1500: mean Euclidian nearest neighbor at 500m ## File: CWMlength.csv **Description:** CSV file of community weighted means of average body length of collected bee species, collection sites, and local and landscape variation. Landscape data were extracted using Fragstats from the 2009 National Land Cover Database. All missing values are indicated as NA.  ##### Variables * site: A specific habitat (pocket prairie/vacant lot/Metropark grassland) within a neighborhood (e.g., Hough)  - neighbor: A neighborhood where a site was located * trt: Habitat treatments (pocket prairie - UP, vacant lot - VL, and Metropark grassland - OF) * trt1: Habitat treatments written out with more specific information about vacant lot greening treatments (T1/6/7/8) * day: Day data were collected * month: Month data were collected * season: Early or late season - length: Average body length of a given species * pans: Number of pan traps deployed at a site * bloom.rich: Number of bloom species within a randomly sampled quadrat  * bloom.abund: Number of blooms within a randomly sampled quadrat * bloom.area: Average bloom size per species was calculated and multiplied by bloom abundance to quantify total *bloom area* at a site. In each Metropark grassland, we established a 20m transect, randomly selected six 1m2 grid quadrats to sample along the transect and placed a 0.5m2 PVC pipe square in each quadrat’s center wherein the same bloom data were collected.   - ca500: class area of greenspace at 500m - pland500: percent land cover of greenspace at 500m - lpi500: largest patch index at 500m - enn_mn500: mean Euclidian nearest neighbor at 500m - enn_am500: average Euclidian nearest neighbor at 500m - enn_md500: median Euclidian nearest neighbor at 500m - shdi500: Shannon's diversity index at 500m - ca1500: class area of greenspace at 1500m - pland1500: percent land cover of greenspace at 1500m - lpi1500: largest patch index at 1500m - area1500: greenspace area at 1500m - enn_mn1500: mean Euclidian nearest neighbor at 500m ## File: 2019length.csv **Description:** CSV file of average body length of collected bees, collection sites, and local and landscape variation. Landscape data were extracted using Fragstats from the 2009 National Land Cover Database. All missing values are indicated as NA.  ##### Variables * species: bee species * length: Average body length of a given species * lecty: generalist or specialist * nest: cavity nesting, colony nesting, pith nesting, soil nesting, wood nesting, or parasitic bees that do not use nests * sociality: eusocial, parasitic, solitary, or subsocial * origin: native or alien * site: A specific habitat (pocket prairie/vacant lot/Metropark grassland) within a neighborhood (e.g., Hough)  - neighbor: A neighborhood where a site was located - trt: Habitat treatments (pocket prairie - UP, vacant lot - VL, and Metropark grassland - OF) - month: Month data were collected - season: Early or late season * ca500: class area of greenspace at 500m * pland500: percent land cover of greenspace at 500m * lpi500: largest patch index at 500m * enn_mn500: mean Euclidian nearest neighbor at 500m * enn_am500: average Euclidian nearest neighbor at 500m * enn_md500: median Euclidian nearest neighbor at 500m * shdi500: Shannon's diversity index at 500m * ca1500: class area of greenspace at 1500m * pland1500: percent land cover of greenspace at 1500m * lpi1500: largest patch index at 1500m * area1500: greenspace area at 1500m * enn_mn1500: mean Euclidian nearest neighbor at 500m - pans: Number of pan traps deployed at a site - bloom.rich: Number of bloom species within a randomly sampled quadrat  - bloom.abund: Number of blooms within a randomly sampled quadrat - bloom.area: Average bloom size per species was calculated and multiplied by bloom abundance to quantify total *bloom area* at a site. In each Metropark grassland, we established a 20m transect, randomly selected six 1m2 grid quadrats to sample along the transect and placed a 0.5m2 PVC pipe square in each quadrat’s center wherein the same bloom data were collected.   ## File: 19BFnetworkAnalysis1.csv **Description:** CSV file of plant-bee bipartite data for network analysis presented in manuscript, separated by treatment, floral taxa, and bee taxa. All missing values are indicated as NA.  ##### Variables * lower: floral taxa * upper:  bee taxa * webID: treatment (pocket prairie - UP, vacant lot - VL, and Metropark grassland - OF) * freq: number of times this interaction was recorded ## File: 19BFnetworkAnalysis2.csv **Description:** CSV file of plant-bee bipartite data for generalized networks displayed in manuscript, separated by treatment, floral taxa, and bee taxa. All missing values are indicated as NA.  ##### Variables * lower: floral taxa * upper:  bee taxa * webID: treatment (pocket prairie - UP, vacant lot - VL, and Metropark grassland - OF) * freq: number of times this interaction was recorded ## File: 19BFnetworkAnalysis3.csv **Description:** CSV file of plant-bee bipartite data for network analysis presented in manuscript, separated by site, treatment, floral taxa, and bee taxa. All missing values are indicated as NA.  ##### Variables * lower: floral taxa * upper: bee taxa * webID: treatment within a neighborhood * freq: number of times this interaction was recorded ## File: specimenDatabase.xlsx **Description:** This database includes information on species identifications, (confirmedID/allID), local bloom variables (bloomdata), site specific information (sites), and zeros in the dataset (zeros) where flowers were observed but no bees were collected using vacuums. All missing values are indicated as NA.  ##### Variables **confirmed ID:** sheet includes all identifications verified by Sam Droege at the USGS Native Bee Monitoring Lab to species level   **allID:** sheet includes all identifications verified by Sam Droege at the USGS Native Bee Monitoring Lab * database: database name (beeforage19) * uniqueID: unique ID for each bee specimen * code: number denoting unique # for each bee specimen * det_date: when bee species was determined * longitude: Longitudinal coordinate of site  * latitude: Latitudinal coordinate of site * site: treatment (T6-8 pocket prairie - UP,  T1 vacant lot - VL, and Metropark grassland - OF) followed by an abbreviation for each site (Bedford - BF, Brookside - BS, Tremont - TM, Fairfax - F, Detroit Shoreway - DS, Glenville - G, Slavic Village - SV, Brecksville - BV, W.J. Green - WJG, Detroit - D, Case Western - CW, Central - C, Hough - H (only UP/VL), Hinckley - H (only OF)) - neighbor: A neighborhood where a site was located - trt: Habitat treatments (pocket prairie - UP, vacant lot - VL, and Metropark grassland - OF) - trt1: Habitat treatments written out with more specific information about vacant lot greening treatments (T1/6/7/8) - fdate: Date data were collected in month/day/year format  - day: Day data were collected - month: Month data were collected  - season: early (June-July) or late (August-September)  - year: Year data were collected  - method: Method used to collected bees (bee bowl/vacuum)  - flower: Flower bee was collected from for vacuum sample or set of pan traps bees were associated with at a given site (1/2/3/4) - collector: Individual who collected samples (Katherine J. Turo - KJT)  - sex: Determined sex of bee specimen (male/female)  - county: County data was collected in (Cuyahoga)  - city: City data was collected in (Cleveland)  - state: State data was collected in (Ohio)  **bloomdata:** sheet includes information on local bloom variables measured using 6 randomly selected quadrats at each site, once a month * Name: site information, including name of the neighborhood and indication of whether it was an urban vacant lot (low diversity - LD), pocket prairie (high diversity - HD), or a Metropark grassland (only neighborhood listed no acronym following)  * Neighborhood: Neighborhood where data were collected * tif.id: Name of field site in geospatial dataset used to extract landscape data from the 2009 National Land Cover Database  * USID: Unique ID number for each site  * trt: Urban vacant lot (low diversity - LD), pocket prairie (high diversity - HD), or a Metropark grassland (OF)  * Month: Month data were collected  * n.pans: Number of pans deployed at a given site to collect bees * bloom.rich: Number of unique floral species identified at a site from 6 randomly selected quadrats * bloom.abundance: Number of blooming clusters of flowers identified at a site from 6 randomly selected quadrats * bloom.area: Five random measurements (mm2) of individual floral units were taken for each species at a site. Average bloom size per species was calculated and multiplied by bloom abundance to quantify total *bloom area* at a site **sites** * treatment: Habitat treatments (pocket prairie - UP, vacant lot - VL, and Metropark grassland - OF) * neighborhood: Neighborhood where data were collected * longitude: Longitudinal coordinate of site  * latitude: Latitudinal coordinate of site **zeros** * plant: Plant bees were not collected from * site: neighborhood where data were collected * treatment: Vacant lot (T1), pocket prairie (T6-8), Metropark grassland (Old Field), or Not listed  * date: data when data were collected  * notes: particular notes on different zeros in the dataset  ## Code/software We used R to conduct our statistical analyses. In the R files included, you will find the packages needed to replicate our analyses at the beginning of each file. bfAnalysis.R is the file which contains the majority of our analyses, while bf19_NMDS_1.R contains the code used for creating a nonmetric multidimensional scaling analysis of the data. Below we describe the specific workflow we followed.