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A Quantitative Model of Honey Bee Colony Population Dynamics

Overview of attention for article published in PLOS ONE, April 2011
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Title
A Quantitative Model of Honey Bee Colony Population Dynamics
Published in
PLOS ONE, April 2011
DOI 10.1371/journal.pone.0018491
Pubmed ID
Authors

David S. Khoury, Mary R. Myerscough, Andrew B. Barron

Abstract

Since 2006 the rate of honey bee colony failure has increased significantly. As an aid to testing hypotheses for the causes of colony failure we have developed a compartment model of honey bee colony population dynamics to explore the impact of different death rates of forager bees on colony growth and development. The model predicts a critical threshold forager death rate beneath which colonies regulate a stable population size. If death rates are sustained higher than this threshold rapid population decline is predicted and colony failure is inevitable. The model also predicts that high forager death rates draw hive bees into the foraging population at much younger ages than normal, which acts to accelerate colony failure. The model suggests that colony failure can be understood in terms of observed principles of honey bee population dynamics, and provides a theoretical framework for experimental investigation of the problem.

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Mendeley readers

The data shown below were compiled from readership statistics for 304 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 1%
France 3 <1%
Canada 3 <1%
New Zealand 2 <1%
Australia 1 <1%
Brazil 1 <1%
Colombia 1 <1%
Italy 1 <1%
Mexico 1 <1%
Other 1 <1%
Unknown 286 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 57 19%
Researcher 52 17%
Student > Bachelor 42 14%
Student > Master 35 12%
Student > Doctoral Student 16 5%
Other 57 19%
Unknown 45 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 135 44%
Environmental Science 36 12%
Mathematics 14 5%
Veterinary Science and Veterinary Medicine 12 4%
Biochemistry, Genetics and Molecular Biology 10 3%
Other 40 13%
Unknown 57 19%