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Floral abundance, richness, and spatial distribution drive urban garden bee communities

Published online by Cambridge University Press:  01 March 2017

M. Plascencia
Affiliation:
Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA 95064, USA Department of Environmental Studies, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
S.M. Philpott*
Affiliation:
Department of Environmental Studies, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
*
*Author for correspondence Tel.: 831-459-1549 Fax: 831-459-5900 E-mail: sphilpot@ucsc.edu
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Abstract

In urban landscapes, gardens provide refuges for bee diversity, but conservation potential may depend on local and landscape features. Foraging and population persistence of bee species, as well as overall pollinator community structure, may be supported by the abundance, richness, and spatial distribution of floral resources. Floral resources strongly differ in urban gardens. Using hand netting and pan traps to survey bees, we examined whether abundance, richness, and spatial distribution of floral resources, as well as ground cover and garden landscape surroundings influence bee abundance, species richness, and diversity on the central coast of California. Differences in floral abundance and spatial distribution, as well as urban cover in the landscape, predicted different bee community variables. Abundance of all bees and of honeybees (Apis mellifera) was lower in sites with more urban land cover surrounding the gardens. Honeybee abundance was higher in sites with patchy floral resources, whereas bee species richness and bee diversity was higher in sites with more clustered floral resources. Surprisingly, bee species richness and bee diversity was lower in sites with very high floral abundance, possibly due to interactions with honeybees. Other studies have documented the importance of floral abundance and landscape surroundings for bees in urban gardens, but this study is the first to document that the spatial arrangement of flowers strongly predicts bee abundance and richness. Based on these findings, it is likely that garden managers may promote bee conservation by managing for floral connectivity and abundance within these ubiquitous urban habitats.

Information

Type
Research Papers
Copyright
Copyright © Cambridge University Press 2017 
Figure 0

Fig. 1. A map of the Central coast region of California showing the 18 urban garden sites in Monterey, Santa Clara, and Santa Cruz Counties, and land cover types in the study region and surrounding the garden study sites with three zoomed in panels to show (a) a garden surrounded primarily by urban and natural land, (b) a garden surrounded by natural, open, and urban land, and (c) a garden surrounded by primarily urban and agricultural land.

Figure 1

Fig. 2. Correlations showing relationships between percent urban land cover and (a) number of bee individuals and (b) number of Apis mellifera individuals and the nearest-neighbour ratio (NNR) for quadrats with ≥15 flowers and (c) number of A. mellifera for bees collected in urban gardens in the Central coast region of California. The lines show the best fit and the grey area cover confidence bands based on the generalized linear models. Smaller NNR values indicate stronger floral clustering.

Figure 2

Fig. 3. Correlations showing relationships between mean number of flowers per 2 × 2 m2 quadrat and (a) bee species richness and (c) bee diversity, and between the nearest-neighbour ratio (NNR) for quadrats with ≥15 flowers and (b) bee species richness and (d) bee diversity for bees collected in urban gardens in the Central coast region of California. The lines show the best fit and the grey area covers confidence bands based on the generalized linear models. Smaller NNR values indicate stronger floral clustering.

Figure 3

Fig. 4. Correlations showing relationships between the number of honeybees (Apis mellifera) and (a) bee species richness and (b) bee diversity for bees collected in urban gardens in the Central coast region of California. The lines show the best fit and the grey area covers confidence bands based on the generalized linear models.

Figure 4

Table 1. GLM results table showing all response variables, explanatory variables included in the best models, AICc values, residual degrees of freedom, and R2 values for general linear models.

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