Bee Tracker – an open-source machine-learning based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees

The foraging and nesting performance of bees can provide important
information on bee health and is of interest for risk and impact
assessment of environmental stressors. While radio-frequency
identification (RFID) technology is an efficient tool increasingly used
for the collection of behavioral data in social bee species such as honey
bees, behavioral studies on solitary bees still largely depend on direct
observations, which is very time-consuming. Here, we present a novel
automated methodological approach of individually and simultaneously
tracking and analyzing foraging and nesting behavior of numerous
cavity-nesting solitary bees. The approach consists of monitoring nesting
units by video recording and automated analysis of videos by a machine
learning based software. This Bee Tracker software consists of four
trained deep learning networks to detect bees that enter or leave their
nest and to recognize individual IDs on the bees’ thorax as well as the
IDs of their nests according to their positions in the nesting unit. The
software is able to identify each nest of each individual nesting bee,
which permits to measure individual-based measures of reproductive
success. Moreover, the software quantifies the number of cavities a female
enters until it finds its nest as a proxy of nest recognition, and it
provides information on the number and duration of foraging trips. By
training the software on 8 videos recording 24 nesting females per video,
the software achieved a precision of 96% correct measurements of these
parameters. The software could be adapted to various experimental setups
by training it to an according set of videos. The presented method allows
to efficiently collect large amounts of data on cavity-nesting solitary
bee species and represents a promising new tool for the monitoring and
assessment of behavior and reproductive success under laboratory,
semi-field and field conditions.