Original paper: Identification of the honey bee swarming process by analysing the time course of hive vibrations

  • Authors:
  • Martin Bencsik;Joseph Bencsik;Michael Baxter;Andrei Lucian;Julien Romieu;Mathias Millet

  • Affiliations:
  • College of Science, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, United Kingdom;College of Science, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, United Kingdom;College of Science, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, United Kingdom;College of Science, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, United Kingdom;College of Science, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, United Kingdom;College of Science, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, United Kingdom

  • Venue:
  • Computers and Electronics in Agriculture
  • Year:
  • 2011

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Abstract

Honey bees live in groups of approximately 40,000 individuals and go through their reproductive cycle by the swarming process, during which the old queen leaves the nest with numerous workers and drones to form a new colony. In the spring time, many clues can be seen in the hive, which sometimes demonstrate the proximity to swarming, such as the presence of more or less mature queen cells. In spite of this the actual date and time of swarming cannot be predicted accurately, as we still need to better understand this important physiological event. Here we show that, by means of a simple transducer secured to the outside wall of a hive, a set of statistically independent instantaneous vibration signals of honey bees can be identified and monitored in time using a fully automated and non-invasive method. The amplitudes of the independent signals form a multi-dimensional time-varying vector which was logged continuously for eight months. We found that combined with specifically tailored weighting factors, this vector provides a signature highly specific to the swarming process and its build up in time, thereby shedding new light on it and allowing its prediction several days in advance. The output of our monitoring method could be used to provide other signatures highly specific to other physiological processes in honey bees, and applied to better understand health issues recently encountered by pollinators.