4.6 Article

The Bees among Us: Modelling Occupancy of Solitary Bees

Journal

PLOS ONE
Volume 11, Issue 12, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0164764

Keywords

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Funding

  1. Canadian National Science and Engineering Council [CGS D 408565]
  2. NSERC discovery grant
  3. Natural Sciences and Engineering Research Council of Canada [CGS D 408565]

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Occupancy modelling has received increasing attention as a tool for differentiating between true absence and non-detection in biodiversity data. This is thought to be particularly useful when a species of interest is spread out over a large area and sampling is constrained. We used occupancy modelling to estimate the probability of three phylogenetically independent pairs of native-introduced species [Megachile campanulae (Robertson)-Megachile rotundata (Fab.), Megachile pugnata Say-Megachile centuncularis (L.), Osmia pumila Cresson-Osmia caerulescens (L.)] (Apoidea: Megachilidae) being present when repeated sampling did not always find them. Our study occurred along a gradient of urbanization and used nest boxes (bee hotels) set up over three consecutive years. Occupancy modelling discovered different patterns to those obtained by species detection and abundance-based data alone. For example, it predicted that the species that was ranked 4th in terms of detection actually had the greatest occupancy among all six species. The native M. pugnata had decreased occupancy with increasing building footprint and a similar but not significant pattern was found for the native O. pumila. Two introduced bees (M. rotundata and M. centuncularis), and one native (M. campanulae) had modelled occupancy values that increased with increasing urbanization. Occupancy probability differed among urban green space types for three of six bee species, with values for two native species (M. campanulae and O. pumila) being highest in home gardens and that for the exotic O. caerulescens being highest in community gardens. The combination of occupancy modelling with analysis of habitat variables as an augmentation to detection and abundance-based sampling is suggested to be the best way to ensure that urban habitat management results in the desired outcomes.

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