Data from: Flight of the bumble bee: buzzes predict pollination services

Multiple interacting factors drive recent declines in wild and managed bees, threatening their pollination services. Widespread and intensive monitoring could lead to more effective management of wild and managed bees. However, tracking their dynamic populations is costly. We tested the effectiveness of an inexpensive, noninvasive and passive acoustic survey technique for monitoring bumble bee behavior and pollination services. First, we assessed the relationship between the first harmonic of the flight buzz (characteristic frequency) and pollinator functional traits that influence pollination success using flight cage experiments and a literature search. We analyzed passive acoustic survey data from three locations on Pennsylvania Mountain, Colorado to estimate bumble bee activity. We developed an algorithm based on Computational Auditory Scene Analysis that identified and quantified the number of buzzes recorded in each location. We then compared visual and acoustic estimates of bumble bee activity. Using pollinator exclusion experiments, we tested the power of buzz density to predict pollination services at the landscape scale for two bumble bee pollinated alpine forbs (Trifolium dasyphyllum and T. parryi). We found that the characteristic frequency was correlated with traits known to affect pollination efficacy, explaining 30-52% of variation in body size and tongue length. Buzz density was highly correlated with visual estimates of bumble bee density (r = 0.97), indicating that acoustic signals are predictive of bumble bee activity. Buzz density predicted seed set in two alpine forbs when bumble bees were permitted access to the flowers, but not when they were excluded from visiting. Our results indicate that acoustic signatures of flight can be deciphered to monitor bee activity and pollination services to bumble bee pollinated plants. We propose that applications of this technique could assist scientists and farmers in rapidly detecting and responding to bee population declines.

多种交互作用的因子共同导致了野生与驯养蜜蜂近期的种群衰退，对其授粉服务（pollination services）构成了威胁。大范围且精细化的监测可实现野生与驯养蜜蜂的更高效管理，但对其动态种群进行追踪的成本高昂。我们针对一种低成本、无创且被动式的声学监测技术展开有效性测试，该技术可用于监测熊蜂（bumble bee）的行为与授粉服务。首先，我们通过飞行笼实验（flight cage experiments）与文献检索，评估了飞行鸣音的一次谐波（即特征频率，characteristic frequency）与影响授粉成功率的传粉者功能性状之间的关联。我们分析了取自科罗拉多州宾夕法尼亚山三处点位的被动声学监测数据，以估算熊蜂的活动情况。我们开发了一种基于计算听觉场景分析（Computational Auditory Scene Analysis）的算法，可识别并量化各点位记录到的鸣音数量。随后我们对比了熊蜂活动的目视估算结果与声学估算结果。我们借助传粉者排除实验（pollinator exclusion experiments），针对两种由熊蜂授粉的高山非禾本科草本植物（alpine forbs）Trifolium dasyphyllum与T. parryi，测试了鸣音密度在景观尺度上预测授粉服务的能力。我们发现，特征频率（characteristic frequency）与已知会影响授粉效率的性状存在关联，可解释30%至52%的体型与喙长变异。鸣音密度与熊蜂密度的目视估算结果呈高度相关（相关系数r=0.97），表明声学信号可有效预测熊蜂的活动情况。当熊蜂可接触花朵时，鸣音密度可预测两种高山非禾本科草本植物的结籽率（seed set）；但当熊蜂被禁止访花时，该相关性并不存在。我们的研究结果表明，可通过解析飞行的声学特征来监测蜜蜂活动以及熊蜂授粉植物的授粉服务。我们认为，该技术的应用可帮助科研人员与农户快速察觉蜜蜂种群衰退并做出应对。