Effects of Suburbanization on Forest Bee Communities Author(s): Adrian L. Carper, Lynn S. Adler, Paige S. Warren, and Rebecca E. Irwin Source: Environmental Entomology, 43(2):253-262. Published By: Entomological Society of America URL: http://www.bioone.org/doi/full/10.1603/EN13078

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COMMUNITY AND ECOSYSTEM ECOLOGY

Effects of Suburbanization on Forest Bee Communities ADRIAN L. CARPER,1,2,3 LYNN S. ADLER,4 PAIGE S. WARREN,5

AND

REBECCA E. IRWIN1

Environ. Entomol. 43(2): 253Ð262 (2014); DOI: http://dx.doi.org/10.1603/EN13078

ABSTRACT Urbanization is a dominant form of land-use change driving species distributions, abundances, and diversity. Previous research has documented the negative impacts of urbanization on the abundance and diversity of many groups of organisms. However, some organisms, such as bees, may beneÞt from moderate levels of development, depending on how development alters the availability of foraging and nesting resources. To determine how one type of low-intensity human development, suburbanization, affects bee abundance and diversity and the mechanisms involved, we surveyed bees across suburban and natural forests in the Raleigh-Durham area of North Carolina. We sampled for bees using a combination of bee bowls and hand-netting from March through July of 2008 and 2009. We found higher bee abundance in suburban than natural forests, and although observed species richness was greater in suburban than natural forests, there were no signiÞcant differences in rareÞed richness or evenness estimates in either year. In addition, the effects of suburbanization were similar across bee species of varying ecological and life-history characteristics. At the local scale, bee abundance and species richness were both positively related to the abundance and richness of ßowering species within forests, while the proportion of surrounding developed open areas, such as yards and roadsides, was a strong positive predictor of both bee abundance and richness at the landscape scale. These results suggest that open habitats and the availability of ßoral resources in suburban sites can support abundant and diverse bee communities and underscore the potential for native bee conservation in urban habitats. KEY WORDS bee, land use, pollinator, suburban, urban

Land-use change is a leading component of humaninduced environmental change (Kalnay and Cai 2003) that has ecological consequences at both local and global scales (Foley et al. 2005). Urbanization (the expansion of developed residential, municipal, and industrial areas) is a dominant form of land-use change that has drastically expanded in recent history (Irwin and Bockstael 2007), reducing biodiversity through habitat loss, fragmentation, and competition with introduced, competitively dominant, and often synanthropic species (Shochat et al. 2010). Declines in species richness (Burkle et al. 2013) also raise concern over the stability of natural communities and the ecosystem services they provide (Eigenbrod et al. 2011). Bees are an important group of organisms in urban and natural areas, given their importance as pollinators. Approximately 87% of ßowering plants rely on pollinators, especially bees, to reproduce (Ollerton et al. 2011). A growing body of evidence points to a 1 Department of Biological Sciences, Dartmouth College, 78 College St., Hanover, NH 03755. 2 Present address: Department of Ecology and Evolutionary Biology, University of Colorado, Ramaley N122, Boulder, CO 80309. 3 Corresponding author, e-mail: [email protected]. 4 Biology Department, 221 Morrill Science Center South, 611 N. Pleasant St., University of Massachusetts, Amherst, MA 01003. 5 Department of Environmental Conservation, University of Massachusetts, 216 Holdsworth, Amherst, MA 01003.

decline in bee populations (Biesmeijer et al. 2006, Cameron et al. 2011, Bartomeus et al. 2013, Burkle et al. 2013), and habitat loss because of land-use changes is hypothesized as a major factor (Winfree et al. 2009). For example, bee abundance and diversity often decline with increasing metrics of urbanization, such as built landscape (Bates et al. 2011), a pattern suggested as a general trend (Hernandez et al. 2009). However, the effects of urbanization likely vary with the degree or type of anthropogenic disturbance (Winfree et al. 2009), with the potential costs and beneÞts likely dependent on a variety of factors, including the type of development, the surrounding natural habitat, and the availability of foraging and nesting resources. For example, urban gardens can promote diverse bee assemblages (Fetridge et al. 2008), and the number of gardens in the surrounding landscape has been shown to positively inßuence bumblebee colony survival (Goulson et al. 2010). Moderate levels of human disturbance, such as rural and suburban development, could represent intermediate levels of disturbance that promote species coexistence (Connell 1978) and therefore could hold potential for bee conservation. However, more studies are needed to document patterns and identify mechanisms driving bee abundance and species richness. To understand how one type of human development, suburbanization, affects bee abundance and

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ENVIRONMENTAL ENTOMOLOGY

richness and the mechanisms involved, we surveyed bees across paired suburban and natural forests in a metropolitan area of the southeastern United States. We deÞned suburban development as single-family, residential neighborhoods encompassed within or on the edge of more intensively incorporated metropolitan areas. We asked the following questions: 1) How does suburbanization affect forest bee abundance, richness, and composition? We predicted that suburban sites would have higher bee abundance and richness given that suburbs typically have gardens and green spaces and are often esthetically managed to provide ßowers throughout the growing season, which could beneÞt bees. However, the response of individual bee species to suburbanization may vary with ecological or life-history characteristics (Williams et al. 2010). Therefore, we predicted that suburban forests would have more polylectic bees because of increased availability of exotic ßowers in suburban gardens on which generalist bees could forage, and more soil and hive-nesting bees because of increased availability of open areas, such as yards and roadsides, and bare ground from human activities, such as gardening and erosion. Conversely, we expected natural forests to have greater abundances of oligolectic, pith, and stemnesting bees because larger forests likely retain more native ßowers and Þne woody nesting substrates. 2) What local and landscape factors affect bee communities? Bee abundance and richness can be driven by both the abundance and richness of ßoral resources (Potts et al. 2003) and the availability of suitable nesting substrates in the landscape (Cane et al. 2006). We predicted bee abundance and richness would increase with local ßoral abundance and richness. However, bees can forage widely, and some patterns of abundance or richness may not be evident at local scales. At the landscape scale, we predicted open and lowintensity developed land covers would be strong positive predictors of bee abundance and species richness because open surroundings likely provide superior foraging and nesting resources (McFrederick and LeBuhn 2006). Materials and Methods Study Area. We conducted this study in RaleighDurham, NC (hereafter RDU). RDU is one of the fastest growing urban areas in the United States (U.S. Census Bureau 2009) and is located in a region undergoing some of the fastest rates of land-cover change in the eastern United States (Brown et al. 2005). The two counties surrounding RDU (Durham and Wake) include ⬇737,000 single-family detached homes (62.5% of residences; NC OfÞce of State Budget and Management), creating a matrix of suburban developments in proximity to large tracts of naturally occurring forest. Using a paired approach, we identiÞed Þve sites within suburban forests and Þve sites in managed natural areas (hereafter, suburban forests and natural forests, respectively). We paired sites and located each pair at least 5 km apart. Natural forests consisted of contiguous tracts of mixed pine and hard-

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wood forest that were ⬎10 ha and primarily managed for the conservation of their natural resources, such as county and state parks. Suburban forests consisted of persistent wooded patches in single-family housing developments located ⬍5 km from each corresponding forested site (Supp. Table 1 [online only]). We established equal-sized sampling plots within each site pair ranging from 0.22 to 0.57 ha (Supp. Table 1 [online only]). The paired approach allowed us to control for landscape-scale differences in site characteristics while minimizing potential overlap in bee foraging within pairs. Bee Abundance, Richness, and Composition. We sampled bees over two ßowering seasons: May through July of 2008 and April through July of 2009. Sampling occurred approximately once per month in 2008 (three sampling dates) and twice per month in 2009 (six sampling dates). Bees were sampled using pan traps and hand-netting following a standard protocol (LeBuhn et al. 2003). Pan traps were made from 9-cm diameter plastic bowls (Fisherbrand Hexagonal Polystyrene Weighing Dishes, Pittsburgh, PA) that were left white or painted ßuorescent blue or yellow. In each site, we arranged 15 pan traps (5 of each color) Þlled with soapy water along a single 70-m transect, with 5 m between each trap. Transects were laid haphazardly within contiguous sampling plots except in two of the suburban sites, where two smaller forest patches were bisected by roads or driveways (RV and SC; Supp. Table 1 [online only]). We randomly assigned the color of pan traps to their positions along each transect and left the traps on the forest ßoor from 0800 to 1800 hours EDT. Captured bees were strained, rinsed, and dried before sorting. Hand-netting was conducted for 1 h per plot per sampling period to capture bees not attracted to pan traps. We conducted netting between 1000 and 1400 hours, during peak bee activity, and euthanized bees in ethyl acetate collecting jars. Suburban and natural forests were consecutively sampled within each site pair, with the order (suburban or natural forest Þrst) randomly chosen. In 2008, hand-netting corresponded to pan-trapping sessions by sampling one site pair per day. In 2009, pan trapping was done simultaneously across all sites, and hand-netting was conducted in each site pair within 24 Ð 48 h of pan-trapping events. All bees were pinned, labeled, identiÞed to either species or morphospecies, and housed in a collection at Dartmouth College. We compiled ecological and life-history information for each bee species from the primary literature, historical accounts, Discover Life (Ascher and Pickering 2012), and consultation with experts (J. Ascher, personal communication). For each bee species, we characterized nesting substrate (soil, wood, pithy stems, cavity, or hive), ßoral specialization (oligolectic vs. polylectic), and sociality (solitary, subsocial, eusocial, or parasitic). Only those species that foraged on a single plant family or a subset of ßowering genera were considered oligolectic. We separated bees that nest in existing cavities in wood as cavity instead of wood nesters. Lumping cavity and wood-nesting species had no effect on the interpretation of results. We

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also classiÞed bee species according to body size by measuring intertegular span for up to three females per species and calculating body mass through a known relationship between intertegular span and dry body mass (Cane 1987). We classiÞed species as small (⬍4 mg), medium (4 Ð16 mg), or large (⬎16 mg; Winfree et al. 2007), reßecting the quantiles of the size distribution. Data Analysis. All statistical analyses (throughout the study) were conducted using SAS version 9.2 (SAS Institute 2009) unless otherwise noted. The number of bees captured using pan traps and hand-netting was combined within each site for each sampling date. To estimate bee abundance, we calculated the mean number of bees per sampling event per site for each year. Analyzing total bees across sampling events per site yielded similar results (data not shown). To estimate bee diversity, we used species richness (the combined number of species captured across all sampling events at each site across both years) and SimpsonÕs Evenness index. To account for differences in bee abundance on estimates of species richness, we rareÞed samples to the lowest common abundance using ECOSIM software (Gotelli and Entsminger 2001). To account for the paired approach, we used one-tailed paired t-tests (JMP version 8.0.2, SAS Institute) to test whether suburban forests had higher bee abundance and species richness than natural forests. We had no a priori prediction about the effects of suburbanization on species evenness, so we used a two-tailed paired t-test to assess whether suburbanization affected evenness. To compare species composition between suburban and natural forests, we used a series of approaches. First, we calculated MorisitaÕs C and percentage similarity for each site pair and for pooled captures across the Þve suburban and Þve natural sites. We used MorisitaÕs C because it is less inßuenced by sample size and species richness than other similarity indices (Wolda 1981). Second, we tested for spatial autocorrelation in bee abundance and species richness between sites by computing MoranÕs I. Third, we used nonmetric multidimensional scaling (NMDS), via the “vegan” package (Oksanen et al. 2012) in R version 2.14, to visualize any dissimilarity in bee community composition between suburban and forested sites. We conducted the NMDS using the Bray-Curtis dissimilarity measure, which takes into account the number of unique species between two sites relative to the total number of species sampled. The relative Þt of the NMDS was evaluated by computing the Þnal stress of the ordination. Stress is a measure of mismatch between the rankÐ order dissimilarities and the ordination distance, often referred to as a badness-of-Þt test, with values ⬍0.05 indicating strong support for the ordination distances. To determine whether the effects of suburbanization varied with bee ecology or life-history, we used generalized linear models (PROC GLIMMIX). We included forest type (suburban or natural forest) along with nesting habit, lecty, sociality, and body size as Þxed factors in four separate analyses, including

255

interactions, and site as a random effect. We used the combined bee abundance across both years as response variables, specifying a negative binomial distribution to reduce overdispersion. A signiÞcant interaction between forest type and nesting habit, lecty, sociality, or body size would suggest that the effects of suburbanization on bee abundance are driven by groups of species with particular ecological or lifehistory characteristics. Factors Driving Bee Abundance and Composition. At the local scale we estimated both foraging and nesting resources. We conducted ßower censuses over the entire area sampled for bees in each site, corresponding to each bee sampling event, in 2008 and 2009. We recorded the total number of individuals of each ßowering species and counted the number of ßowers on a subset of 10 haphazardly chosen individuals per species. For plants in the Asteraceae, we recorded the number of ßowering heads containing open disc ßowers on each plant. On large shrubs and trees with ⬎500 ßowers, we counted the number of ßowers on three separate branches and multiplied the average number of ßowers per branch by the total number of ßowering branches to estimate the total number of ßowers per plant. We then multiplied the mean number of ßowers per plant by the total number of individuals to estimate the total number of ßowers per species per plot, and calculated ßower density by dividing by the area of each plot. Nesting resources may also be important in driving bee abundance and diversity (Cane et al. 2006). To estimate wood-nesting resources, we used three 20-m line-intercept transects in each site to quantify coarse woody debris (CWD) in 2009. We recorded the length and width of dead wood ⱖ7.5 cm in diameter intersecting each transect (Woodall and Monleon 2008). We then calculated the volume of CWD per square meter using a standard formula, V ⫽ ␲2兺d2/8L, where V is the volume of woody debris per unit area, d is the diameter of each piece of wood, and L is the length of the transect (Harmon et al. 2004). We observed no bare ground along any transect and thus were unable to quantify it as substrate for soil nesting. Lastly, we incorporated measures of other abiotic factors that could directly or indirectly affect bee activity or abundance. Changes in climate associated with urban development have been shown to alter animal distributions (Parris and Hazell 2005). Differences in temperature or relative humidity (RH) could affect bee activity and the likelihood of capture, or the distribution or phenology of suitable host plants. To characterize temperature and RH, two iButton data loggers (Embedded Data Systems LLC, Lawrenceburg, KY) were haphazardly placed in each site to record temperature and RH hourly over the ßowering season in 2009. iButtons were mounted beneath lowlying branches and shielded with a small plastic rain guard. At the landscape scale, we used data from the 2006 National Land Cover Database (Fry et al. 2011) to explore how land-use cover affected bee abundance. The National Land Cover Database uses satellite im-

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Fig. 1. Suburban forests exhibited greater bee abundance (a) and observed species richness (b) than natural forests in 2008 and 2009, but no difference in rareÞed species richness (c). Bars are means ⫾ SE. Asterisks indicate signiÞcant differences (P ⬍ 0.05).

agery to characterize land cover at a 30-m resolution. We calculated the proportions of surrounding land covers within a 1-km radius of the center of each site using ArcGis (version 9.3.1, ESRI, Redlands, CA) and identiÞed the four most dominant land covers: evergreen (pine) forest, deciduous forest, developed open space (⬍20% impervious surface), and low-intensity developed (20 Ð 49% impervious surface). We chose 1 km because it likely encompasses the foraging distance of most of the species sampled while leaving little overlap within site pairs. Data Analyses. At the local scale, we compared ßower species richness, ßoral density, and CWD between forest types in each year using one-tailed paired t-tests to test the hypothesis that suburban forests have greater ßoral resources but fewer nesting resources compared with natural forests. We also used linear regression to determine whether ßoral density and richness predicted bee abundance and richness. We calculated mean, minimum, and maximum daily temperature, as well as RH, averaging values from the two iButtons in each site. We compared each metric of temperature and RH between forest types using separate repeated measures analyses of variance. At the landscape scale, we compared land cover types between suburban and natural forests using multivariate analysis of variance, with site pair and forest type as Þxed factors and the proportions of the four dominant land covers as responses. We used multiple regressions (JMP version 8.0.2, SAS Institute) to determine whether land cover predicted bee abundance and species richness. Each proportion of land cover was log-transformed to meet the assumptions of homoscedasticity, and multicollinearity was assessed from the variance inßation factors. Results In 2008, we captured 258 bees (mean: 86 bees per sampling event, pooled across sites) and in 2009, 365 bees (mean: 61 bees per sampling event), belonging to six families, 26 genera, and 71 species or morphospecies. All but Þve individuals were identiÞed to species. One Lasioglossum sp. male, one Ceratina sp., and three Nomada sp. males were identiÞed as morphospecies. In addition, we combined individuals in two taxonomic groupings currently being revised: 11 Hylaeus affinis Smith/modestus Say and 19 Halictus ligatus Say/

poeyi Lepeletier. Each of these two groupings was treated as a single species in subsequent analyses. The two most dominant bee species, Augochlorella aurata Smith (Halictidae) and Lasioglossum bruneri Crawford (Halictidae), represented 17 and 14% of all bees collected, respectively. Exotic bee species were rare and made up ⬍3% of the total number of captures. We captured eight honeybees (Apis mellifera L.) in suburban forests and two in natural forests. We also captured two giant resin bees (Megachile sculpturalis Smith) and Þve alfalfa leafcutting bees (Megachile rotundata F.) in suburban forests. In addition, we captured 38 species that were each represented by a single specimen, including 28 from suburban and 10 from natural forests (Supp. Table 2 [online only]). Bee Abundance, Richness, and Composition. Suburban forests had 2.2 times more bees captured per sampling event than natural forests in 2008 (t4 ⫽ 1.99, P ⫽ 0.059) and 1.8 times more bees in 2009 (t4 ⫽ 2.88, P ⫽ 0.022, Fig. 1a). In suburban forests, bee richness ranged from 7 to 19 species compared with natural forests, where bee richness ranged from 6 to 16 species. Although observed species richness was signiÞcantly higher in suburban forests in both years (2008: t4 ⫽ 2.87, P ⫽ 0.027; 2009: t4 ⫽ 4.78, P ⫽ 0.004; Fig. 1b), rarefaction yielded no signiÞcant difference in species richness between suburban and natural forests in either year (2008: t4 ⫽ ⫺1.45, P ⫽ 0.09; 2009: t4 ⫽ ⫺0.52, P ⫽ 0.31; Fig. 1c). We also detected no spatial autocorrelation in either year (MoranÕs I ⫽ ⫺0.11, P ⫽ 0.808, MoranÕs I ⫽ ⫺0.12, P ⫽ 0.420, respectively). Percentage similarity between paired suburban and natural forests ranged from 21.8 to 45.2%. Pooled across site pairs and years, suburban and natural forests were characterized by 59.3% similarity (MorisitaÕs C ⫽ 0.85). The most abundant genus sampled was Lasioglossum, with 19 total species, 7 of which were unique to suburban forests, compared with only 3 species unique to natural forests. Suburban forests also yielded nearly three times as many singletons (species represented by a single captured individual) as natural forests (28 vs. 10 species, respectively). However, there was no signiÞcant difference in SimpsonÕs Evenness between suburban and natural forests (t4 ⫽ 1.32, P ⫽ 0.258). NMDS indicated overlap in bee composition between suburban and natural forests, driven mainly by one natural forest site (Fig. 2). The Þnal

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CARPER ET AL.: EFFECTS OF SUBURBANIZATION ON FOREST BEE COMMUNITIES

Fig. 2. NMDS indicated overlap in bee communities between suburban and natural forests driven primarily by one natural forest, Lake Crabtree County Park, overlapping with suburban forests.

stress of the NMDS with two dimensions was 0.11, indicating fair conÞdence in the ordination distances. Bee abundance varied as a function of ecological and life-history traits and suburbanization; however, there were no signiÞcant interactions between any of the ecological traits considered and suburbanization (Table 1), suggesting that higher bee abundance in suburban sites was not driven by bees with particular ecological or life-history characters. Generalist bees were more abundant than specialist bees, with polylectic species making up ⬎98% of bees captured, compared with ⬍2% that were oligolectic. Although suburban forests had 2.3 times more polylectic bees and 1.98 times more oligolectic bees (Fig. 3a), the effect of suburbanization was not statistically signiÞTable 1. Generalized linear models of bee abundance by four ecological characteristics (lecty, sociality, body size, and nesting substrate) and forest type (suburban vs. natural) Ecological trait Lecty Lecty Forest type Lecty*forest type Sociality Sociality Forest type Sociality*forest type Body size Body size Forest type Body size*forest type Nesting substrate Nesting Forest type Nesting*forest type *

df

F

P

1,12 1,12 1,12

122.03 4.15 0.05

⬍0.001 0.0642 0.8281

3,28 1,28 3,28

33.87 17.57 1.96

⬍0.001 ⬍0.001 0.143

2,20 1,20 1,20

26.92 25.03 1.7

⬍0.001 ⬍0.001 0.208

4,36 1,36 4,36

57.92 8.12 0.99

⬍0.001 0.007 0.424

represents an interaction between the main effects.

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cant. Bee abundance varied by sociality, with eusocial bees representing 65% of bees sampled, followed by solitary bees (21%), subsocial (9%), and parasitic bees (5%). Suburban forests had more bees in each sociality category compared with natural forests (Fig. 3b). Medium-sized bees were the most common bees captured, representing 51% of individuals, followed by small and large bees making up 36 and 13%, respectively. Suburban forests had more bees in each size class compared with natural forests (Fig. 3c). Finally, soil-nesting bees were the most abundant, comprising ⬇70% of individuals, with cavity nesters making up 11%, hive and stem nesters each making up ⬇9%, and wood nesters making up ⬍1% (Fig. 3d). Factors Driving Bee Abundance and Composition. Bee communities were affected by both local and landscape scale factors. At the local scale, ßoral resources were the strongest factor affecting bees. Flowering tree species were the dominant ßoral resource in suburban and natural forests. Oxydendrum arboreum (L.) (Ericaceae) was the most abundant ßower resource within sampling plots, constituting 47% of all ßowers surveyed, followed by Ilex opaca (Alton) (Aquifoliaceae) at 9% of ßowers. Aside from trees, the three most abundant ßowering species were Erigeron annuus (L.) (Asteraceae), Lonicera japonica (Thunb.) (Caprifoliaceae), and Rubus argutus (Link) (Rosaceae), making up 8, 4, and 3% of surveyed ßowers, respectively. The remaining 68 species recorded made up ⬍2% of total ßower abundance each. Suburban forests averaged 3.4 ⫾ 2.8 (mean ⫾ SE) and 6.2 ⫾ 3.3 more ßowering species than natural forests in 2008 and 2009, respectively, but these differences were not statistically signiÞcant in 2008 (t4 ⫽ ⫺1.22, P ⫽ 0.143) or 2009 (t4 ⫽ ⫺1.87, P ⫽ 0.067). Suburban forests also had 14.3% greater density of ßowers than natural forests in 2008 (1.09 ⫾ 0.57 vs. 1.25 ⫾ 0.46 ßowers per square meter). However, the opposite pattern occurred in 2009 when natural forests had 13.8% more ßowers per square meter than suburban sites (0.95 ⫾ 0.61 vs. 0.83 ⫾ 0.16 ßowers per square meter), but in neither year were these differences in ßoral density statistically signiÞcant (2008: t4 ⫽ ⫺0.26, P ⫽ 0.403; 2009: t4 ⫽ 0.17, P ⫽ 0.436). There was no relationship between the total number of ßowers and total bee abundance in either year (2008: r2 ⫽ 0.23, P ⫽ 0.164; 2009: r2 ⫽ 0.002, P ⫽ 0.905). However, observed bee richness and total bee abundance increased signiÞcantly with ßower richness in suburban sites in 2008 (richness: r2 ⫽ 0.61, P ⬍ 0.005; abundance: r2 ⫽ 0.65, P ⫽ 0.005; Fig. 4a and b), though neither relationship remained signiÞcant in 2009 (richness: r2 ⫽ 0.091, P ⫽ 0.396; abundance: r2 ⫽ 0.07, P ⫽ 0.46). The total volume of CWD ranged from 0.011 to 0.041 m3/m2 in natural forests and 0.006 Ð 0.021 m3/m2 in suburban forests; however, natural forests showed no signiÞcant difference in the volume of CWD from suburban forests (t4 ⫽ 0.80, P ⫽ 0.24). Given the low representation of wood-nesting species in our samples (⬍1% of total bee captures), we did not further pursue CWD as a mechanism driving bee abundance or richness. Neither mean, maximum, or minimum daily tem-

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Fig. 3. There were no signiÞcant interactions between forest type and ecological categories studied. Compared with natural forests, suburban forests supported a signiÞcantly greater abundance of bees across all foraging habits (a), socialities (b), body sizes (c), and nesting types (d). Bars are means ⫾ SE.

peratures nor RHs signiÞcantly differed between forest types in 2009 (in all cases: F1,8 ⬍ 1.6, P ⬎ 0.24). At the landscape scale, land cover varied across sites, with deciduous and evergreen forests, developed open space, and hay or pasture the most dominant land covers (Fig. 5). Suburban forests were surrounded by four times more low intensity development and nearly Þve times more developed open space as natural forests

(WilkÕs ␭ ⫽ 0.198, F4,5 ⫽ 5.07, P ⫽ 0.052). The proportion of developed open space in the surrounding landscape was signiÞcantly and positively associated with both bee abundance and species richness (Table 2). However, bee abundance and richness were negatively associated with the proportion of low-intensity development in the landscape, although the relationship was only statistically signiÞcant for bee abundance (Table 2). All variance

Fig. 4. Flower richness within suburban forests in 2008 was a signiÞcant predictor of suburban bee abundance (a) and species richness (b), though no relationship existed in natural forests.

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259

Fig. 5. The dominant land cover types surrounding suburban and natural forests were calculated using a 1-km buffer around each site center. Land cover types in the “Other” category include cultivated crops, developed high intensity, shrub/scrub, barren land, emergent herbaceous, and wetlands.

inßation factors were small (⬍5), suggesting multicollinearity had little effect on results. Discussion Suburbanization can have powerful effects on the abundance and richness of both plants and animals (Shochat et al. 2006). Here, we report that forests within suburban development in a metropolitan area of the southeastern United States have higher bee abundance than natural forests. Moreover, suburban forests had more bees of all ecological and life-history characteristics that we sampled. At the local scale, bee abundance and richness were positively associated with ßower abundance and richness, though the relationship varied between years. At the landscape level, bee abundance and richness were positively associated with the amount of open space, and negatively associated with low-intensity development. Taken together, these results suggest that the combination of open habitats and availability of diverse ßoral resources in suburban sites are capable of sustaining abundant and diverse bee communities. These results add to a growing body of literature demonstrating that urban and suburban areas can supTable 2. Least squares multiple regressions of bee abundance and observed species richness against the proportion of four dominant land-cover types surrounding each of 10 study sites Land-cover type Abundance Log (deciduous forest) Log (evergreen forest) Log (developed, low intensity) Log (developed, open space) Species richness Log (deciduous forest) Log (evergreen forest) Log (developed, low intensity) Log (developed, open space)

␤

SE

t

P

11.07 ⫺13.59 ⫺21.33 44.30

12.92 17.27 7.85 12.02

0.86 ⫺0.79 ⫺2.72 3.69

0.431 0.467 0.042 0.014

5.53 ⫺2.58 ⫺3.11 7.30

2.32 3.10 1.41 2.16

2.38 ⫺0.83 ⫺2.21 3.39

0.063 0.444 0.079 0.020

port a diversity of native bees (Matteson et al. 2008, Banaszak-Cibicka and Zmihorski 2012). Although we found that observed species richness was higher in suburban forests across both years, the lack of difference between rareÞed species richness suggests that low richness in natural forest sites could be a result of low bee abundance and reduced likelihood of capture. Nonetheless, the Þnding that suburban areas harbor more abundant and at least as rich bee communities as nearby natural areas is consistent with some studies. For example, Winfree et al. (2007) found that both bee abundance and species richness were higher in suburban and urban development than in extensive forests in New Jersey. However, other studies comparing bee richness in desert and residential areas have found higher richness in the natural desert habitat (Hostetler and McIntyre 2001, Gotlieb et al. 2011). Thus, while some aspects of residential areas may beneÞt bees, the overall landscape context is likely important in driving species richness patterns (Ahrne et al. 2009). Contrary to our predictions, bee community composition exhibited little difference between suburban and natural forests. We found no interactions between forest type and ecological or life-history categories on bee abundance, implying that all types of bees in our study system responded similarly to suburbanization. This result contradicts empirical work that demonstrated guild-speciÞc responses of bees to disturbance (Williams et al. 2010). Without more precise measurements of characteristics, such as the distribution and availability of nesting substrates, it is difÞcult to speculate why no differences were observed. One explanation is that bees captured in the forest are primarily moving between widely distributed resources in the landscape, and the scale of our sampling did not adequately reßect the scale at which differences in community composition are evident. The 71 species captured are also only a subset of the regional

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bee fauna, and our sampling periods may have missed some species, such as early and late season specialists. Regardless, suburban bee community composition appears similar to that of natural forests, only more abundant. This pattern was reinforced by overlap in NMDS, which suggests that the bee community composition was fairly similar across forest types. However, it is important to note that the NMDS overlap was driven primarily by one natural forest, Crabtree County Park. Interestingly, this was the only natural forest site that actively planted ßowers along roadsides and park structures (A.C., unpublished data), and thus had much higher ßoral density than the other naturally forested sites. If such planted resources are important factors structuring bee communities, they could explain the potential overlap. Further work on the role of planted ßoral resources is needed to determine whether they can affect forest bee communities. The abundance and diversity of ßoral resources are dominant factors structuring bee communities (Potts et al. 2003), and ßoral resources within urban areas can support a variety of bee species (Tommasi et al. 2004). The relationships between bees and ßoral resources in our sites were highly variable between sites and years. At the local scale, bee abundance and richness were more strongly associated with ßower richness than ßower abundance in 2008. Floral richness has been experimentally demonstrated to affect bee abundance (Ebeling et al. 2008) and can be important in structuring pollinator communities in other systems (Moeller 2005). The lack of relationship between either bee abundance or richness and ßower richness in 2009 could be because of unmeasured factors obscuring any potential relationship, or be indicative of the stochastic nature of ßoral resources. Additionally, the scale of ßoral sampling may not have been adequate to accurately depict the availability of ßoral resources for bees, which are very mobile foragers. More extensive ßoral sampling from roadside surveys in 2011, including yards, gardens, and other green spaces, indicated that suburban landscapes tended to have more abundant and diverse ßoral resources than forested landscapes, which was not evident at local scales within forest patches (A.C., data not shown). Unfortunately, roadside surveys did not coincide with bee sampling, so we were unable to test for relationships between landscape ßoral resources and local bee abundance and diversity. One caveat is that the distribution of nectar and pollen resources may be a better predictor of bee abundance and richness than ßoral abundance and richness (Potts et al. 2004). Measuring nectar and pollen resources was beyond the scope of this research, but given the importance of conserving bee communities in urban and suburban areas and recent interest in pollinator-friendly gardens, more studies are needed that assess community-wide nectar and pollen resources in suburban versus natural habitats. The abundance and distribution of nesting resources also play key roles in the distribution of various nesting bees (Potts et al. 2005). Overall, suburban areas supported more bees of all nesting categories, suggesting that such areas either have more nesting

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resources or at least a superior combination of ßower and nesting resources. We observed no bare ground in our surveys, which was not surprising given that sampling took place under an extensive forest canopy. Still, a greater abundance of all bees in suburban sites, including soil-nesting species, indicates that suitable nest sites may be more abundant in suburban areas, despite there being more extensive impervious surface cover in the form of low-intensity development. Winfree et al. (2007) found a positive association between wood-nesting species and the extent of forested habitat in the landscape. Contrary to our predictions, CWD did not differ between forest types even though wood-nesting bees were more abundant in suburban forests; however, these made up ⬍5% of the total bees sampled. Studies considering mechanisms driving bee abundance at landscape scales often Þnd that bee abundance and richness are positively associated with the amount of “natural” habitat in the surrounding landscape, which is assumed to reßect higher quality habitat (Hines and Hendrix 2005). In urban areas, green spaces have been shown to increase bee abundance and diversity (Tonietto et al. 2011). At the landscape scale in our study, land cover surrounding our sites did predict bee abundance and diversity. The proportion of developed open space was a strong, positive predictor of both bee abundance and diversity, indicating that areas dominated by lawns and human structures, such as single-family housing developments, can harbor bees. There were no signiÞcant effects of either deciduous or evergreen forest on bee abundance, although both represent natural land covers. One explanation for these results is that open space may reßect the distribution of ßoral and nesting resources outside of forested patches and serve as important foraging and nesting areas. For example, bumblebee abundance in urban parks of San Francisco, CA was positively associated with the openness of the surrounding urban matrix, suggesting open areas either have more resources or are more easily navigated by foraging bees (McFrederick and LeBuhn 2006). Lowintensity development had a negative effect on abundance and diversity in our study, which was not surprising given that it has up to 50% impervious surface, and thus may provide low foraging and nesting resources for bees. The differences between bee response to developed open space and low-intensity development suggest that any beneÞts of human development decrease with increasing intensity of urbanization. In addition, the spatial scale of surveys and landscape context likely determine the effects of urbanization on bee communities (Steffan-Dewenter et al. 2002). Forested landscapes, such as in this study, could beneÞt from moderate levels of disturbance (Winfree et al. 2007), whereas open habitats, such as deserts (Hostetler and McIntyre 2001, Gotlieb et al. 2011), typically lose bees to disturbance through habitat loss. Historical land use could also be an important factor effecting contemporary bee communities. Comparative studies using museum records could provide context for temporal change in both land use and

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CARPER ET AL.: EFFECTS OF SUBURBANIZATION ON FOREST BEE COMMUNITIES

bee community composition, but was outside the scope of this study. Therefore, more detailed knowledge of the endemic bee fauna, historical land use, and land-use practices is crucial to interpreting the effects of urban development on bee communities. In conclusion, this work suggests that moderate levels of human disturbance, such as suburban development, can support abundant and diverse bee communities. With increasing concern over the status of pollinators and the sustainability of pollination services (Kearns et al. 1998), such Þndings underscore the need to incorporate ecological Þndings in both urban and conservation planning (Miller and Hobbs 2002). Given the challenges of urban and suburban areas for bees (Cane 2005), insights from such work could have broad implications for the conservation of native pollinators and the sustainability of pollination services in human-dominated environments. For example, planned development in forested areas could promote abundant and diverse bee communities through the establishment of ßower-rich open areas in concordance with the retention of natural forest habitat. Ultimately, comparative studies integrating both planned development and conservation strategies are needed to determine how to best manage for and conserve native pollinators. Acknowledgments This research was made possible by cooperation from landowners, homeownersÕ associations, private citizens, the North Carolina Division of Parks and Recreation, the Wake County Department of Parks, Recreation and Open Space, and North Carolina State University. We thank S. Droege for assistance in bee identiÞcation; J. Ascher for help with bee biology and life-history; and D. Bolger, Z. Gezon, R. Schaeffer, L. Richardson, and C. Urbanowicz for comments on the manuscript. Funding was provided by the National Science Foundation (DEB-0743535) and Dartmouth College (the R. Melville Cramer Fund and a Rockefeller Center Urban Studies Research Grant). Any opinions, Þndings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reßect the views of the funding sources.

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