For beekeepers, swarming provides an opportunity to capture the departing bees and establish a new hive.
To forecast a swarm, beekeepers regularly inspect their hives for the presence of larger honeycomb cells that host developing future queens.
Honeybees have a complex language encoded in their buzzing—for instance, newly emerged virgin queens vibrate their folded wings and send short pulses through the honeycomb to alert the hive.
Using the resulting vibrational spectra from swarming and non-swarming colonies, the researchers trained two machine-learning algorithms to search for buzzing patterns that might indicate a swarm.
Each hour, the algorithms would evaluate whether the colony was preparing to swarm, and if they determined that it was, they would alert the researchers.
The first algorithm-based its decision on an hour of data at a time. During the swarming season of late spring to early summer, the algorithm correctly differentiated between swarming and non-swarming colonies 91% of the time, but it was ineffective at predicting off-month swarms.
The second algorithm analyzed 10 days of successive buzzing to make its hourly predictions. It had an 80% success rate during the swarming season but performed better than the other algorithm year-round.
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