Otesbelgue Alex, Orth Amara Jean, Fong Chandler David, Fassbinder-Orth Carol Anne, Blochtein Betina, Pereira Maria João Ramos
Graduate Program in Ecology, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
Department of Earth and Planetary Sciences, Stanford University, Stanford, California, United States of America.
PLoS One. 2025 Jun 18;20(6):e0325732. doi: 10.1371/journal.pone.0325732. eCollection 2025.
Pollinator populations are declining globally at an unprecedented rate, driven by factors such as pathogens, habitat loss, climate change, and the widespread application of pesticides. Although colony losses remain difficult to prevent, precision beekeeping has introduced non-invasive strategies for monitoring hive conditions. Acoustic data, combined with machine learning techniques, has proven effective in detecting stressors and specific events in honeybee colonies; however, such methodologies remain underexplored for stingless bees, a group of native pantropical pollinators. Meliponiculture, the practice of keeping stingless bees, is an expanding field that offers significant economic and conservation benefits. Stingless bees are particularly susceptible to pesticide toxicity, even at residual concentrations, underscoring the critical need to prevent hive losses and to understand the impacts of sub-lethal pesticide exposure on these species. This study addresses the challenge of detecting airborne pesticide exposure by aiming to identify stress responses in hives of the stingless bee Tetragonisca fiebrigi when exposed to chlorpyrifos, a commonly used insecticide. We employed a Hidden Markov Model (HMM) with MATLAB's Hidden Markov Model Toolkit (MATLABHTK) to analyze acoustic data from eight hives under both exposed and unexposed conditions, assessing the potential of acoustic monitoring as an indicator of pesticide-related stress. Initial analysis across multiple hives indicated moderate model performance. However, hive-specific analyses yielded higher performance in detecting pesticide exposure. Furthermore, the model accurately classified individual hives, suggesting the presence of a distinct acoustic 'fingerprint' for each hive. These findings advance the field of stingless bee bioacoustics and provide initial evidence that acoustic monitoring of stingless bee hives could be a useful and non-invasive tool to detect airborne pesticide contamination.
受病原体、栖息地丧失、气候变化以及农药广泛使用等因素影响，全球传粉者种群正以前所未有的速度减少。尽管蜂群损失仍然难以预防，但精准养蜂引入了监测蜂箱状况的非侵入性策略。声学数据与机器学习技术相结合，已被证明在检测蜜蜂蜂群中的应激源和特定事件方面有效；然而，对于无刺蜂（一类泛热带本土传粉者），此类方法仍未得到充分探索。无刺蜂养殖，即饲养无刺蜂的实践，是一个不断发展的领域，具有显著的经济和保护效益。无刺蜂对农药毒性特别敏感，即使是残留浓度，这凸显了预防蜂箱损失以及了解亚致死剂量农药暴露对这些物种影响的迫切需求。本研究旨在通过识别无刺蜂Tetragonisca fiebrigi蜂箱在接触常用杀虫剂毒死蜱时的应激反应，应对检测空气中农药暴露的挑战。我们使用带有MATLAB隐马尔可夫模型工具包（MATLABHTK）的隐马尔可夫模型（HMM）来分析八个蜂箱在暴露和未暴露条件下的声学数据，评估声学监测作为农药相关应激指标的潜力。对多个蜂箱的初步分析表明模型性能中等。然而，针对单个蜂箱的分析在检测农药暴露方面表现出更高的性能。此外，该模型准确地对各个蜂箱进行了分类，表明每个蜂箱都存在独特的声学“指纹”。这些发现推动了无刺蜂生物声学领域的发展，并提供了初步证据，表明对无刺蜂蜂箱进行声学监测可能是检测空气中农药污染的一种有用且非侵入性的工具。
