Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/29258
Title: Assessing the density of honey bee colonies at ecosystem scales
Contributor(s): Utaipanon, Patsavee (author); Schaerf, Timothy M  (author)orcid ; Oldroyd, Benjamin P (author)
Publication Date: 2019-06
Early Online Version: 2018-12-23
Open Access: Yes
DOI: 10.1111/een.12715Open Access Link
Handle Link: https://hdl.handle.net/1959.11/29258
Abstract: 1. Information about the density of wild honey bee (Apis spp.) colonies in an ecosystem is central to understanding the functional role of honey bees in that ecosystem, necessary for effective biosecurity response planning, and useful for determining whether pollination services are adequate. However, direct visual surveys of colony locations are not practical at ecosystem scales. Thus, indirect methods based on population genetic analysis of trapped males have been proposed and implemented.
2. In this review, indirect methods of assessment of honey bee colony densities are described, which can be applied at ecosystem scales. The review also describes how to trap males in the field using the Williams drone trap (or virgin queens) the appropriate genetic markers and statistical analyses, and discusses issues surrounding sample size.
3. The review also discusses some outstanding issues concerning the methods and the conversion of estimated colony number to colony density per km2. The appropriate conversion factor will require further research to determine the area over which a drone trap draws drones.
Publication Type: Journal Article
Source of Publication: Ecological Entomology, 44(3), p. 291-304
Publisher: Wiley-Blackwell Publishing Ltd
Place of Publication: United Kingdom
ISSN: 1365-2311
0307-6946
Fields of Research (FoR) 2008: 060207 Population Ecology
060808 Invertebrate Biology
010202 Biological Mathematics
Fields of Research (FoR) 2020: 310913 Invertebrate biology
310307 Population ecology
490102 Biological mathematics
Socio-Economic Objective (SEO) 2008: 970106 Expanding Knowledge in the Biological Sciences
970101 Expanding Knowledge in the Mathematical Sciences
Socio-Economic Objective (SEO) 2020: 280118 Expanding knowledge in the mathematical sciences
280102 Expanding knowledge in the biological sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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