01347nas a2200121 4500008004100000245011600041210006900157300001000226490000700236520091400243100002401157856004401181 2021 eng d00aWhat's the Buzz About? Progress and Potential of Acoustic Monitoring Technologies for Investigating Bumble Bees0 aWhats the Buzz About Progress and Potential of Acoustic Monitori a21-290 v243 a

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.

1 aMiller, Zachary, J. uhttps://bio.acousti.ca/pt-pt/node/5844601631nas a2200181 4500008004100000022001400041245009500055210006900150260001600219520103000235653002201265653001601287653002101303653002701324100002201351700002101373856005501394 2019 eng d a0044-843500aAutomated classification of bees and hornet using acoustic analysis of their flight sounds0 aAutomated classification of bees and hornet using acoustic analy cJul-01-20193 a

To investigate how to accurately identify bee species using their sounds, we conducted acoustic analysis to identify three pollinating bee species (Apis mellifera, Bombus ardens, Tetralonia nipponensis) and a hornet (Vespa simillima xanthoptera) by their flight sounds. Sounds of the insects and their environment (background noises and birdsong) were recorded in the field. The use of fundamental frequency and mel-frequency cepstral coefficients to describe feature values of the sounds, and supported vector machines to classify the sounds, correctly distinguished sound samples from environmental sounds with high recalls and precision (0.96–1.00). At the species level, our approach could classify the insect species with relatively high recalls and precisions (0.7–1.0). The flight sounds of V.s. xanthoptera, in particular, were perfectly identified (precision and recall 1.0). Our results suggest that insect flight sounds are potentially useful for detecting bees and quantifying their activity.

10aacoustic analysis10aHymenoptera10amachine learning10aspecies classification1 aKawakita, Satoshi1 aIchikawa, Kotaro uhttp://link.springer.com/10.1007/s13592-018-0619-601000nas a2200145 4500008004100000245009800041210006900139520047100208653003000679653004500709653001700754100001900771700002300790856004100813 2018 eng d00aTo bee or not to bee: Investigating machine learning approaches for beehive sound recognition0 aTo bee or not to bee Investigating machine learning approaches f3 a

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.

10abeehive sound recognition10aComputational bioacoustic scene analysis10aecoacoustics1 aNolasco, Inês1 aBenetos, Emmanouil uhttps://arxiv.org/pdf/1811.06016.pdf01412nas a2200157 4500008004100000245004900041210004800090520095200138100001901090700002401109700002101133700002001154700002001174700002301194856003701217 2018 eng d00aAudio-based identification of beehive states0 aAudiobased identification of beehive states3 a

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.

1 aNolasco, Inês1 aTerenzi, Alessandro1 aCecchi, Stefania1 aOrcioni, Simone1 aBear, Helen, L.1 aBenetos, Emmanouil uhttps://arxiv.org/abs/1811.0633001749nas a2200253 4500008004100000022001300041245011600054210006900170260001600239300001400255490000800269520097200277653002701249653000901276653001501285653002101300653002101321100002001342700001601362700001801378700001601396700001701412856006601429 2017 eng d a0003347200aAcoustic communication within ant societies and its mimicry by mutualistic and socially parasitic myrmecophiles0 aAcoustic communication within ant societies and its mimicry by m cJan-12-2017 a249 - 2560 v1343 a

This review focuses on the main acoustic adaptations that have evolved to enhance social communi- cation in ants. We also describe how other invertebrates mimic these acoustic signals in order to coexist with ants in the case of mutualistic myrmecophiles, or, in the case of social parasites, corrupt them in order to infiltrate ant societies and exploit their resources. New data suggest that the strength of each antemyrmecophile interaction leads to distinctive sound profiles and may be a better predictor of the similarity of sound between different myrmecophilous species than their phylogenetic distance. Finally, we discuss the evolutionary significance of vibrations produced by specialized myrmecophiles in the context of ant multimodal communication involving the use of chemical and acoustic signals in com- bination and identify future challenges for research including how new technology might allow a better understanding of the study systems.

10aacoustic communication10aants10amutualists10asocial parasites10asocial structure1 aSchönrogge, K.1 aBarbero, F.1 aCasacci, L.P.1 aSettele, J.1 aThomas, J.A. uhttp://linkinghub.elsevier.com/retrieve/pii/S000334721630282202629nas a2200193 4500008004100000245006600041210006500107260001600172300001300188490000700201520204000208100003402248700001702282700002002299700002302319700001902342700002202361856005202383 2017 eng d00aFlight of the bumble bee: Buzzes predict pollination services0 aFlight of the bumble bee Buzzes predict pollination services cJul-06-2017 ae01792730 v123 a

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.

1 aMiller-Struttmann, Nicole, E.1 aHeise, David1 aSchul, Johannes1 aGeib, Jennifer, C.1 aGalen, Candace1 aBorges, Renee, M. uhttp://dx.plos.org/10.1371/journal.pone.017927303978nas a2200145 4500008004100000022001400041245010600055210006900161260001600230300001400246490000800260520349400268100002203762856004803784 1980 eng d a0340-759400aInsect disturbance stridulation: Characterization of airborne and vibrational components of the sound0 aInsect disturbance stridulation Characterization of airborne and cJan-01-1980 a259 - 2680 v1353 a

Some insects stridulate when attacked by a predator. This behavior has been interpreted as a defensive response, the sound being a warning to predators of the insect's noxiousness. Since to humans many such disturbance sounds are audibly similar, it is possible that they may in fact be mutually mimetic. This idea was investigated through analysis of the temporal and spectral characteristics of the disturbance sounds of a variety of insects that stridulate by a file- and -scraper device. Properties of both the airborne sound and the underlying cuticular vibration (detected by a special vibration measuring instrument) were examined, and four characteristic features found:

    1. The temporal pattern is simple. Bursts of toothstrike impulses are about 80 ms long, and are separated by pauses about 90 ms long. Bursts occur at a rate of about 5 to 10/s.
    
    2. The temporal pattern is irregular. For toothstrike interval, burst duration, pause duration and interburst interval, the standard deviation is usually >30% of the mean. Much of the irregularity is presumably caused by the insect struggling at the same time it stridulates. Some insects show less variability, and these appear to lack tight coupling between stridulatory movements and struggling movements, so struggling does not interfere with stridulation.
    
    3. The airborne sound pressure waveform is impulsive. The frequency coverage of the sounds is quite broad with an average 10-dB bandwidth of about 40 kHz centered at 25 kHz. The sounds are not intense, ranging from about 10 to 60 dB (re 20×10−6 Pa) at 10 cm.
    
    4. The cuticular vibration waveform is sharply peaked and contains maximum energy at a frequency determined by the tooth-strike rate, usually about 1 kHz. The average decrease in power above this frequency is about 12 dB/octave. The maximum peak-to-peak amplitude of cuticular motion is about 1 to 10 μm.
    

These common characteristics may lead predators to treat insects producing disturbance sounds similarly, although this possibility should be tested empirically. If acoustic mimicry exists, the communicatory interchange between predator and prey may be subtler than is commonly appreciated.

Supported in part by an NSF predoctoral fellowship and Bache Fund stipend from the National Academy of Sciences (Masters), and NIH grant AI-02908 and NSF grant PCM77-25807 (T. Eisner). I thank the insect identification service of the U.S. Department of Agriculture and E.R. Hoebeke for identification of insects. I am grateful to the Director and staff of the Archbold Biological Station, Lake Placid, Florida, for their hospitality while I was there. To my chairman, Dr. Thomas Eisner, I wish to acknowledge my great debt for his original proposal that I study insect disturbance sounds and the suggestion that the sounds might be mutually mimetic, and for his unfailing support throughout my graduate career. I also thank Drs. Eisner, R.R. Capranica, D.J. Aneshansley, W.L. Brown and J. Camhi for helpful criticism of the manuscript, Drs. Capranica and G. Hausfater for loan of equipment, and Dr. A. George for discussions of acoustics. Finally, for invaluable help with all phases of this research, I thank my wife, Anne Moffat.

1 aMasters, Mitchell uhttp://link.springer.com/10.1007/BF0065725402576nas a2200181 4500008004100000245010700041210006900148260001600217300001100233490000600244520190900250100002402159700002302183700002502206700002202231700001702253856012402270 2013 eng d00aCharacterization and Generation of Male Courtship Song in Cotesia congregata (Hymenoptera: Braconidae)0 aCharacterization and Generation of Male Courtship Song in Cotesi cOct-04-2014 ae620510 v83 a

Male parasitic wasps attract females with a courtship song produced by rapid wing fanning. Songs have been described for several parasitic wasp species; however, beyond association with wing fanning, the mechanism of sound generation has not been examined. We characterized the male courtship song of Cotesia congregata (Hymenoptera: Braconidae) and investigated the biomechanics of sound production. Courtship songs were recorded using high-speed videography (2,000 fps) and audio recordings. The song consists of a long duration amplitude-modulated “buzz” followed by a series of pulsatile higher amplitude “boings,” each decaying into a terminal buzz followed by a short inter-boing pause while wings are stationary. Boings have higher amplitude and lower frequency than buzz components. The lower frequency of the boing sound is due to greater wing displacement. The power spectrum is a harmonic series dominated by wing repetition rate ~220 Hz, but the sound waveform indicates a higher frequency resonance ~5 kHz. Sound is not generated by the wings contacting each other, the substrate, or the abdomen. The abdomen is elevated during the first several wing cycles of the boing, but its position is unrelated to sound amplitude. Unlike most sounds generated by volume velocity, the boing is generated at the termination of the wing down stroke when displacement is maximal and wing velocity is zero. Calculation indicates a low Reynolds number of ~1000. Acoustic pressure is proportional to velocity for typical sound sources. Our finding that the boing sound was generated at maximal wing displacement coincident with cessation of wing motion indicates that it is caused by acceleration of the wing tips, consistent with a dipole source. The low Reynolds number requires a high wing flap rate for flight and predisposes wings of small insects for sound production.

1 aBredlau, Justin, P.1 aMohajer, Yasha, J.1 aCameron, Timothy, M.1 aKester, Karen, M.1 aZeil, Jochen uhttps://bio.acousti.ca/pt-pt/content/characterization-and-generation-male-courtship-song-cotesia-congregata-hymenoptera