ArticlePloS one2017
Flight of the bumble bee: Buzzes predict pollination services.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Article
- buzzdetect: an open-source deep learning tool for automated bioacoustic pollinator monitoring.Journal of insect science (Online) · 2025Article
- High-Res Acoustic and Environmental Data to Monitor Bombus dahlbomii Amid Invasive Species, Habitat Loss.Scientific data · 2025Article
- Science Literacy: Using Multimodal STEM Text Sets to Help Students With Disabilities Engage in Argumentation.Learning disability quarterly : journal of the Division for Children with Learning Disabilities · 2024Article
- Bioacoustic IoT Sensors as Next-Generation Tools for Monitoring: Counting Flying Insects through Buzz.Insects · 2023Article
- Automated detection of the yellow-legged hornet (Vespa velutina) using an optical sensor with machine learning.Pest management science · 2023Article
- Automating insect monitoring using unsupervised near-infrared sensors.Scientific reports · 2022Article
- "Flight of the Bumblebee": A Middle School STEM Text Set to Support Development of Disciplinary Literacy Skills.Science scope (Washington, D.C.)Article
- Multimodal Text Sets to Use Literature and Engage All Learners in the Science Classroom.Science scope (Washington, D.C.)Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.