Original database from Towards acoustic monitoring of bees: wingbeat sounds are related to species and individual traits

Global pollinator decline urgently requires effective methods to assess their trends, distribution and behaviour. Passive acoustics is a non-invasive and cost-efficient monitoring tool increasingly employed for monitoring animal communities. However, insect sounds remain highly unexplored, hindering the application of this technique for pollinators. To overcome this shortfall and support future developments, we recorded and characterized wingbeat sounds of a variety of Iberian domestic and wild bees and tested their relationship with taxonomic, morphological, behavioural and environmental traits at inter- and intra-specific levels. Using directional microphones and machine learning, we shed light on the acoustic signature of bee wingbeat sounds and their potential to be used for species identification and monitoring. Our results revealed that frequency of wingbeat sounds is negatively related with body size and environmental temperature (between-species analysis), while positively related with experimentally induced stress conditions (within-individual analysis). We also found a characteristic acoustic signature in the European honeybee that supported automated classification of this bee from a pool of wild bees, paving the way for passive acoustic monitoring of pollinators. Overall, these findings confirm that insect sounds during flight activity can provide insights on individual and species traits, and hence suggest novel and promising applications for this endangered animal group.This article is part of the theme issue ‘Towards a global toolkit for insect biodiversity monitoring’.

全球传粉昆虫（pollinator）种群衰减的现状亟需有效方法，以评估其种群趋势、分布格局与行为特征。被动声学（passive acoustics）是一种非侵入式且成本效益优异的监测手段，目前正越来越多地被应用于动物群落监测。然而，昆虫鸣声的研究仍处于高度未探索的状态，这阻碍了该技术在传粉昆虫监测中的应用。为弥补这一短板并支撑后续研究发展，我们记录并表征了伊比利亚半岛多种家养与野生蜜蜂的振翅鸣声，并在种间与种内层面检验了其鸣声与分类学、形态学、行为学及环境特征之间的关联。本研究借助定向麦克风与机器学习技术，阐明了蜜蜂振翅鸣声的声学特征（acoustic signature），并验证了其在物种识别与监测中的应用潜力。研究结果显示，振翅鸣声的频率与个体体型及环境温度呈负相关（种间分析），而与实验诱导的应激状态呈正相关（个体内分析）。此外，我们在欧洲蜜蜂（European honeybee）中发现了独特的声学特征，可实现该蜂类与野生蜂类群落的自动化分类，为传粉昆虫的被动声学监测铺平了道路。综上，本研究证实，昆虫飞行活动期间的鸣声可反映个体与物种层面的特征，从而为这一濒危动物类群开辟了全新且极具前景的应用方向。本文属于"面向昆虫生物多样性监测的全球工具箱（Towards a global toolkit for insect biodiversity monitoring）"专题议题的组成部分。