@article {58446, title = {What{\textquoteright}s the Buzz About? Progress and Potential of Acoustic Monitoring Technologies for Investigating Bumble Bees}, journal = { IEEE Instrumentation \& Measurement Magazine}, volume = {24}, year = {2021}, pages = {21-29}, chapter = {21}, abstract = {

Pollination is a vital ecosystem service for both natural and agricultural ecosystems. Most flowering plants-including many staples in the human diet such as fruits, vegetables and nuts- require animals for pollination. The majority is done by over 20,000 species of bees, some of which are managed commercially to provide these services. The global industry of pollination is valued at US $153 billion annually and is increasing in demand to meet the needs of a growing human population [1]. However, both managed and wild bees, especially the ecologically and economically important bumble bees (Bombus spp.), are suffering alarming declines worldwide, presenting serious implications for food security and biodiversity. Despite numerous pleas by farmers and scientists for improved management and monitoring methods [2], [3], precision techniques for these essential pollinators are still lacking.

}, doi = {10.1109/MIM.2021.9549234}, author = {Zachary J. Miller} } @article {58002, title = {Does body size predict the buzz-pollination frequencies used by bees?}, number = {24}, year = {2019}, month = {09/2019}, institution = { University of The Bahamas}, address = {Nassau}, abstract = {

We investigated the influence of body size in a specialized pollination system (buzz- pollination) where bees vibrate flowers to release pollen concealed within poricidal stamens (Buchmann, 1983; De Luca \& Vallejo-Mar\ín, 2013). Specifically, we explored how body size influences the frequency of buzz-pollination vibrations. Body size is expected to affect frequency as a result of the physical constraints it places on the indirect flight muscles that control the production of floral vibrations (King \& Buchmann, 2003). Larger insects beat their wings less rapidly than smaller-bodied insects when flying (Unwin \& Corbet, 1984), but whether similar scaling relationships exist with floral vibrations has not been widely explored. This is important because buzz pollinated flowers are hypothesized to be sensitive to certain frequencies that maximize the release of pollen from anthers (Harder \& Barclay, 1994). Therefore, if body size affects the range of frequencies a bee may generate this could limit the types of potential floral hosts that could be visited (Corbet \& Huang, 2014).

}, keywords = {Anthophila, buzz-ratio, floral vibrations, frequency, Pedicularis, Solanum}, author = {Paul Anthony De Luca} } @article {53337, title = {To bee or not to bee: Investigating machine learning approaches for beehive sound recognition}, journal = {arXiv}, year = {2018}, abstract = {

In this work, we aim to explore the potential of machine learning methods to the problem of beehive sound recognition. A major contribution of this work is the creation and release of annotations for a selection of beehive recordings. By experimenting with both support vector machines and convolutional neural networks, we explore important aspects to be considered in the development of beehive sound recognition systems using machine learning approaches.

}, keywords = {beehive sound recognition, Computational bioacoustic scene analysis, ecoacoustics}, url = {https://arxiv.org/pdf/1811.06016.pdf}, author = {In{\^e}s Nolasco and Emmanouil Benetos} } @article {53336, title = {Audio-based identification of beehive states}, journal = {arXiv}, year = {2018}, abstract = {

The absence of the queen in a beehive is a very strong indicator of the need for beekeeper intervention. Manually searching for the queen is an arduous recurrent task for beekeepers that disrupts the normal life cycle of the beehive and can be a source of stress for bees. Sound is an indicator for signalling different states of the beehive, including the absence of the queen bee. In this work, we apply machine learning methods to automatically recognise different states in a beehive using audio as input. \% The system is built on top of a method for beehive sound recognition in order to detect bee sounds from other external sounds. We investigate both support vector machines and convolutional neural networks for beehive state recognition, using audio data of beehives collected from the NU-Hive project. Results indicate the potential of machine learning methods as well as the challenges of generalizing the system to new hives.

}, url = {https://arxiv.org/abs/1811.06330}, author = {In{\^e}s Nolasco and Alessandro Terenzi and Stefania Cecchi and Simone Orcioni and Helen L. Bear and Emmanouil Benetos} } @article {47919, title = {Flight of the bumble bee: Buzzes predict pollination services}, journal = {PLOS ONE}, volume = {12}, year = {2017}, month = {Jul-06-2017}, pages = {e0179273}, 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.

}, doi = {10.1371/journal.pone.0179273}, url = {http://dx.plos.org/10.1371/journal.pone.0179273}, author = {Miller-Struttmann, Nicole E. and Heise, David and Schul, Johannes and Geib, Jennifer C. and Galen, Candace}, editor = {Borges, Renee M.} }