Knauer Anina C, Gallmann Johannes, Albrecht Matthias
Agroscope, Agroecology and Environment Zürich Switzerland.
Ubique Innovations AG Zürich Switzerland.
Ecol Evol. 2022 Mar 7;12(3):e8575. doi: 10.1002/ece3.8575. eCollection 2022 Mar.
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 radiofrequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honeybees, 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 machine learning-based software. This 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 and 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 according to a 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.
蜜蜂的觅食和筑巢行为表现可为蜜蜂健康状况提供重要信息，对环境压力源的风险和影响评估具有重要意义。虽然射频识别（RFID）技术是一种越来越高效的工具，常用于收集诸如蜜蜂等社会性蜜蜂物种的行为数据，但对于独居蜜蜂的行为研究仍主要依赖直接观察，这非常耗时。在此，我们提出一种新颖的自动化方法，可单独且同时追踪和分析众多在洞穴中筑巢的独居蜜蜂的觅食和筑巢行为。该方法包括通过视频记录监测筑巢单元，并使用基于机器学习的软件对视频进行自动分析。此软件由四个经过训练的深度学习网络组成，用于检测进出巢穴的蜜蜂，并根据蜜蜂在筑巢单元中的位置识别其胸部的个体ID及其巢穴的ID。该软件能够识别每只筑巢独居蜜蜂的每个巢穴，从而可以衡量基于个体的繁殖成功率。此外，该软件会量化雌性蜜蜂在找到巢穴之前进入的洞穴数量，以此作为巢穴识别能力的指标，并且它还能提供觅食飞行的次数和持续时间的信息。通过在8个视频上训练该软件，每个视频记录24只筑巢雌性蜜蜂，该软件在这些参数的测量精度上达到了96%的正确率。通过根据一组视频对软件进行训练，该软件可适用于各种实验设置。所提出的方法能够高效收集大量关于在洞穴中筑巢的独居蜜蜂物种的数据，是在实验室、半野外和野外条件下监测和评估行为及繁殖成功率的一种很有前景的新工具。
