Study area and sampling design
Madison, Wisconsin is an urban state capital surrounded by agricultural land in one of the fastest growing counties in the US. The primary transition type occurring in the Madison area for the past century is the conversion of agricultural to urban land around the city edge (Wegener 2001; Carpenter et al. 2007; Riera et al. 2001). The dominant urban area is typified by mixed residential and commercial zones with small forest patches and city parks. The 123 km2 central urban zone of Madison includes 46 km2 (37%) of impervious surface, 30 km2 (24%) of vegetated space, with the remaining landscape covered by lakes. The city receives semi-frequent rain and severe thunderstorms throughout the summer months that supports abundant flowering prairie plants in city parks or where native grasslands have been conserved or restored around the city.
Flower-visiting insects were sampled across Madison using a spatially stratified survey to account for changing regional species pools. To select sites, a grid of 2.5 ´ 2.5km squares was laid across Madison and cells dominated by lake or agriculture were excluded, leaving 19 cells dominated by high-density residential and urban land (Fig. 1). In each of these cells we used a paired design and selected two sites characterized by either (1) high (> 55%) or (2) low (< 30%) impervious surface area within the surrounding 200 m based on a lower resolution classified land cover surface (USDA-NASS 2013). Within each cell, paired sites with high or low impervious surface area were separated by at least 400 m. These 38 sites were selected in a stratified-random manner, and permission from property owners (identified from a city database) was requested until appropriate locations were identified. Sample sites included primarily residential properties, as well as commercial properties, urban storm water management areas, and city parks.
Bee community sampling
Bees were sampled six times between early June and late August 2013. Pan traps were distributed every two weeks during clear, sunny days when bees were foraging. All traps were distributed across the same evening to early morning period (after 17:00-dark and before dawn-8:00), and collected 4 d later. Six bee traps were placed at least 5 m apart within a 40 m area in each site, with two dark blue, two canary yellow, and two white; bees were also trapped in 0.5 L pan traps suspended 20 cm or 2.5 m from the ground to match the height of flowering vegetation. Bees were identified to species using the discover life online key and a comprehensive dichotomous key available for Lasioglossum (Ascher & Pickering 2013; Gibbs 2011).
We classified bee taxa as soil-nesting, below-ground cavity-nesting, and above-ground nesting bees, based on available observations. The below ground cavity-nesting bees included 7 species of bumble bees (Apidae: Bombus). Above-ground nesting bees included small carpenter bees (Ceratina spp.), yellow faced bees (Hylaeus spp.), carder, mason, and leafcutter bees (Megachilidae), and two sweat bees that nest in decaying wood, Lasioglossum cressonii (Mitchell 1960) and L. oblongum (Sakagami & Michener 1962). Above-ground nesting bees included 22 species. The rest of the bees were classified as soil nesting bees, which included 69 species across several groups: (i) long-horned bees (Tribe Eucerini), (ii) mining bees (Andrena spp.), (iii) green bees, (iv) all of the other sweat bees, and (v) any others were classified as soil nesting bees, although natural history observation of many species could not be located.
Measuring land cover and neighborhood development around study sites
Six-inch resolution digital aerial images were used to classify impervious surfaces such as roads, parking lots, and structures. Unsupervised classification was initiated with 30 classes that were clumped into land cover types. The impervious surface layer from this classification was added to the City of Madison building footprint and road layer to recover impervious surface obscured by tree canopy. Natural vegetation was identified visually within 1000 m of each site and included open canopy, perennial grasses and forbs in greenways, parks, or transportation corridors. Closed canopy forest was also digitized around sites. Each land cover variable was measured as a percent of the landscape, then variables were standardized with a mean of zero and standard deviation of 1 for comparison in analyses. The three land cover types were also consolidated in a distance matrix at each scale. To characterize neighborhood development history, publicly accessible tax assessment data was obtained and property development year was extracted for parcels located within a 200 m radius of each site, from which we extracted an area-weighted average development year for each site. A Bray-Curtis distance matrix was constructed to contrast sites based on the variability of the area-weighted average, median development year, and most recent property development year within the 200m buffer.
Data analysis
Individual-based rarefaction curves were constructed for each site using the ‘vegan’ R package, and rarefaction-based species richness estimates were compared to observed richness (Oksanen et al. 2018). Rarefied richness did not reach an asymptote, so raw abundance and richness values were used as sampling effort was standardized (Fig. 2). We used linear regression models to test whether land cover and neighborhood development (median property development year) affected bee species richness (α-diversity); separate analyses were conducted for the overall community and three bee guilds. All variables were scaled to a mean of 0 and standard deviation of 1 and top models were selected using stepwise AIC model selection using the ‘MASS’ R package (Ripley et al. 2018). For purposes of comparison we discuss “old” neighborhoods as those with a median development year prior to 1960 and “new” neighborhoods as those with a median development year after 1960. Bee abundance and richness seemed to drop off after this time point, reflecting a qualitative difference rather than a gradual, linear decline. The Moran’s I test was used to check for spatial autocorrelation in model fit for each full and final models, applied using the ‘car’ R package (Fox et al. 2018).
We used multiple regression on distance matrices (MRM) to assess effects of the various explanatory variables on bee community composition at the landscape scale (β-diversity), which was implemented through the R package ‘ecodist’ (Legendre and Legendre 1998; Goslee and Urban 2017). This allowed us to capture the various multifaceted explanatory variables reflecting heterogeneity of land cover and land use history. MRMs measure the effect magnitude of each explanatory distance matrix using a non-parametric framework and pseudo t-tests are used to assess significance of explanatory variables (Goslee and Urban 2017).