Potential declines in native pollinator communities and increased reliance on pollinator-dependent crops have raised concerns about native pollinator conservation and dispersal across human-altered landscapes. Bumble bees are one of the most effective native pollinators and are often the first to be extirpated in human-altered habitats, yet little is known about how bumble bees move across fine spatial scales and what landscapes promote or limit their gene flow. In this study, we examine regional genetic differentiation and fine-scale relatedness patterns of the yellow-faced bumble bee, Bombus vosnesenskii, to investigate how current and historic habitat composition impact gene flow. We conducted our study across a landscape mosaic of natural, agricultural and urban/suburban habitats, and we show that B. vosnesenskii exhibits low but significant levels of differentiation across the study system (F(ST) = 0.019, D(est) = 0.049). Most importantly, we reveal significant relationships between pairwise F(ST) and resistance models created from contemporary land use maps. Specifically, B. vosnesenskii gene flow is most limited by commercial, industrial and transportation-related impervious cover. Finally, our fine-scale analysis reveals significant but declining relatedness between individuals at the 1-9 km spatial scale, most likely due to local queen dispersal. Overall, our results indicate that B. vosnesenskii exhibits considerable local dispersal and that regional gene flow is significantly limited by impervious cover associated with urbanization.

Humans are dependent on pollinators for food security and for a vast number of services mediated by wild and cultivated plants, yet we know little about how pollination services could be affected by changes in local and regional climate. In this chapter, we review the current literature on pollination services and environmental change and provide ecologically informed suggestions for managing pollination services, focusing on native pollinators. Our management suggestions are intended to be generalizable for scientists and policy makers across communities and ecoregions. © 2013 Elsevier Inc. All rights reserved.

Field edge habitat in homogeneous agricultural landscapes can serve multiple purposes including enhanced biodiversity, water quality protection, and habitat for beneficial insects, such as native bees and natural enemies. Despite this ecosystem service value, adoption of field border plantings, such as hedgerows, on large-scale mono-cropped farms is minimal. With profits primarily driving agricultural production, a major challenge affecting hedgerow plantings is linked to establishment costs and the lack of clear economic benefits on the restoration investment. Our study documented that hedgerows are economically viable to growers by enhancing beneficial insects and natural pest control and pollination on farms. With pest control alone, our model shows that it would take 16 yr to break even from insecticide savings on the US$4,000 cost of a typical 300-m hedgerow field edge planting. By adding in pollination benefits by native bees, where honey bees ( Apis mellifera L.) may be limiting, the return time is reduced to 7 yr. USDA cost share programs allow for a quicker return on a hedgerow investment. Our study shows that over time, small-scale restoration can be profitable, helping to overcome the barrier of cost associated with field edge habitat restoration on farms.

The delivery of ecosystem services by mobile organisms depends on the distribution of those organisms, which is, in turn, affected by resources at local and landscape scales. Pollinator-dependent crops rely on mobile animals like bees for crop production, and the spatial relationship between floral resources and nest location for these central-place foragers influences the delivery of pollination services. Current models that map pollination coverage in agricultural regions utilize landscape-level estimates of floral availability and nesting incidence inferred from expert opinion, rather than direct assessments. Foraging distance is often derived from proxies of bee body size, rather than direct measurements of foraging that account for behavioral responses to floral resource type and distribution. The lack of direct measurements of nesting incidence and foraging distances may lead to inaccurate mapping of pollination services. We examined the role of local-scale floral resource presence from hedgerow plantings on nest incidence of ground-nesting bees in field margins and within monoculture, conventionally managed sunflower fields in California's Central Valley. We tracked bee movement into fields using fluorescent powder. We then used these data to simulate the distribution of pollination services within a crop field. Contrary to expert opinion, we found that ground-nesting native bees nested both in fields and edges, though nesting rates declined with distance into field. Further, we detected no effect of field-margin floral enhancements on nesting. We found evidence of an exponential decay rate of bee movement into fields, indicating that foraging predominantly occurred in less than 1% of medium-sized bees' predicted typical foraging range. Although we found native bees nesting within agricultural fields, their restricted foraging movements likely centralize pollination near nest sites. Our data thus predict a heterogeneous distribution of pollination services within sunflower fields, with edges receiving higher coverage than field centers. To generate more accurate maps of services, we advocate directly measuring the autecology of ecosystem service providers, which vary by crop system, pollinator species, and region. Improving estimates of the factors affecting pollinator populations can increase the accuracy of pollination service maps and help clarify the influence of farming practices on wild bees occurring in agricultural landscapes.

Pollination is critical to fruit production, but the interactions of pollination with plant resources on a plant's reproductive and vegetative features are largely overlooked. We examined the influences of pollination, irrigation and fertilisation on the performance of almond, Prunus dulcis, in northern California. We used a full-factorial design to test for the effects of pollination limitation on fruit production and foliage variables of whole trees experiencing four resource treatments: (i) normal water and nutrients, (ii) reduced water, (iii) no nutrients, and (iv) reduced water and no nutrients. In each of these combinations, we applied three pollination treatments: hand-cross pollination, open-pollination and pollinator exclusion. Pollination strongly affected yield even under reduced water and no nutrient applications. Hand-cross pollination resulted in over 50% fruit set with small kernels, while open-pollinated flowers showed over 30% fruit set with moderate-sized kernels. Pollinator-excluded flowers had a maximum fruit set of 5%, with big and heavy kernels. Reduced water interacted with the open- and hand-cross pollination treatments, reducing yield more than in the pollinator exclusion treatment. The number of kernels negatively influenced the number of leaves, and reduced water and no nutrient applications interacted with the pollination treatments. Overall, our results indicate that the influences of pollination on fruit tree yield interact with the plant availability of nutrients and water and that excess pollination can reduce fruit quality and the production of leaves for photosynthesis. Such information is critical to understand how pollination influences fruit tree performance.

Understanding the relationships between trait diversity, species diversity and ecosystem functioning is essential for sustainable management. For functions comprising two trophic levels, trait matching between interacting partners should also drive functioning. However, the predictive ability of trait diversity and matching is unclear for most functions, particularly for crop pollination, where interacting partners did not necessarily co-evolve. World-wide, we collected data on traits of flower visitors and crops, visitation rates to crop flowers per insect species and fruit set in 469 fields of 33 crop systems. Through hierarchical mixed-effects models, we tested whether flower visitor trait diversity and/or trait matching between flower visitors and crops improve the prediction of crop fruit set (functioning) beyond flower visitor species diversity and abundance. Flower visitor trait diversity was positively related to fruit set, but surprisingly did not explain more variation than flower visitor species diversity. The best prediction of fruit set was obtained by matching traits of flower visitors (body size and mouthpart length) and crops (nectar accessibility of flowers) in addition to flower visitor abundance, species richness and species evenness. Fruit set increased with species richness, and more so in assemblages with high evenness, indicating that additional species of flower visitors contribute more to crop pollination when species abundances are similar. Synthesis and applications. Despite contrasting floral traits for crops world-wide, only the abundance of a few pollinator species is commonly managed for greater yield. Our results suggest that the identification and enhancement of pollinator species with traits matching those of the focal crop, as well as the enhancement of pollinator richness and evenness, will increase crop yield beyond current practices. Furthermore, we show that field practitioners can predict and manage agroecosystems for pollination services based on knowledge of just a few traits that are known for a wide range of flower visitor species. Despite contrasting floral traits for crops world-wide, only the abundance of a few pollinator species is commonly managed for greater yield. Our results suggest that the identification and enhancement of pollinator species with traits matching those of the focal crop, as well as the enhancement of pollinator richness and evenness, will increase crop yield beyond current practices. Furthermore, we show that field practitioners can predict and manage agroecosystems for pollination services based on knowledge of just a few traits that are known for a wide range of flower visitor species.