01704nas a2200181 4500008004100000245007400041210006900115260004800184520112800232653001501360653001501375653002201390653001401412653001601426653001201442100002701454856004101481 2019 eng d00aDoes body size predict the buzz-pollination frequencies used by bees?0 aDoes body size predict the buzzpollination frequencies used by b aNassaub University of The Bahamasc09/20193 a

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-Marín, 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).

10aAnthophila10abuzz-ratio10afloral vibrations10afrequency10aPedicularis10aSolanum1 aDe Luca, Paul, Anthony uhttps://bio.acousti.ca/ja/node/5800202857nas a2200181 4500008004100000245009900041210006900140520225500209100002302464700002002487700002202507700001902529700001702548700002002565700002702585700002202612856004102634 2016 eng d00aPreliminary Report on Long-term Detection of Arabian Sea Humpback Whale Vocalizations off Oman0 aPreliminary Report on Longterm Detection of Arabian Sea Humpback3 a

This report presents an overview of the progress to date on the humpback whale detection component of the Arabian Sea Humpback Whale Acoustic Research program off Oman. Three archival acoustic recorders were deployed at sites in two regions for approximately one year each, in Hallaniyats Bay during 2011/2012, and the Gulf of Masirah during 2012/2013. Performance of recorders varied among deployments, but despite some challenges due to equipment failures we succeeded in documenting spatiotemporal patterns in the presence of humpback whales in a large dataset. Several key findings broaden our understanding of the Arabian Sea humpback whale population off Oman. There appears a strong seasonal component to the use of song by this population, primarily from November through May, which is congruent with the Northern Hemisphere breeding cycle and singing season. Throughout this period, singing was heard in both Hallaniyats Bay and the Gulf of Masirah, separated by approximately 400km, indicating that the population utilizes both of these regions. However, there was a much stronger presence in Hallaniyats Bay, with song heard nearly 24 hours/day during the peak months, and song detected simultaneously at multiple sites across the Bay. Previous boat-based survey data indicated similar trends, however, given limitation in the duration of this fieldwork the current acoustic dataset provides a more reliable indication of this population’s breeding related activity. This suggests that the Hallaniyats Bay might serve as a more important habitat for breeding activity than the monitored region of the Gulf of Masirah. There also appeared to be a subtle northward shift in distribution of detections as the singing season progressed, both within Hallaniyats Bay, and from Hallaniyats Bay to the Gulf of Masirah, suggesting a seasonal shift in distribution for singing males. During the summer and autumn months, very little song was detected, however sparse detections indicate that whales are present in both regions during at least some of this period. It is recommended that similar acoustic monitoring be conducted in all range states of the population to elucidate spatiotemporal distribution throughout the Arabian Sea.

1 aCerchio, Salvatore1 aWillson, Andrew1 aMuirhead, Charles1 aMinton, Gianna1 aCollins, Tim1 aBaldwin, Robert1 aWillson, Maia, Sarrouf1 aHarthi, Suaad, Al uhttps://bio.acousti.ca/ja/node/5791000407nas a2200085 4500008004100000245015300041210006900194100001700263856004100280 2019 eng d00aAssessing bioacoustics of middle spotted woodpeckers for non-invasive assessments of population structure and habitat quality in fragmented habitats0 aAssessing bioacoustics of middle spotted woodpeckers for noninva1 aRobles, Hugo uhttps://bio.acousti.ca/ja/node/5764701461nas a2200097 4500008004100000245008700041210006900128520088200197100002401079856026001103 2018 eng d00aBird vocalizations: seven different types of Great tit (Parus major) vocalizations0 aBird vocalizations seven different types of Great tit Parus majo3 a

The Great tit (Parus major) produces a large variety in calls and songs. Seven different types of vocalizations are described in this paper. Interesting is that those types differ in (acous- tic) structure. Three structures can be observed: 1) a vocalization can be 'tonal'; a pure tone, usually in time varying in frequency (often a so-called 'upsweep'); 2) a vocalization can be 'pulsed', a series of pulses consisting of a fundamental frequency on which the pulse repetition frequency is superimposed; the pulse repetition rate is between 210 and 240 pulses/s; 3) a vo- calization can be pulsed, however, with a ‘semi-broadband’ spectrum. Great tit vocalizations are concentrated around two frequency ranges: around 4 kHz and around 8 kHz. The duration of vocalization components is rather limited, usually between 95 and 130 ms.

1 aVerboom, Willem, C. uhttps://www.researchgate.net/profile/Willem_Verboom2/publication/329390530_Bird_vocalizations_seven_different_types_of_Great_tit_Parus_major_vocalizations/links/5c1618f34585157ac1c5cbaa/Bird-vocalizations-seven-different-types-of-Great-tit-Parus-major-voc02033nas a2200169 4500008004100000245007100041210006900112520149800181100001701679700002901696700002001725700001801745700002001763700001801783700002101801856004101822 2017 eng d00aMosquito Detection with Neural Networks: The Buzz of Deep Learning0 aMosquito Detection with Neural Networks The Buzz of Deep Learnin3 a

Many real-world time-series analysis problems are charac- terised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied with classification or regression engines which condition on this (often low-dimensional) fea- ture vector. The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets. This paper presents an application of deep learning for acoustic event detection in a challenging, data-scarce, real- world problem. Our candidate challenge is to accurately detect the pres- ence of a mosquito from its acoustic signature. We develop convolutional neural networks (CNNs) operating on wavelet transformations of audio recordings. Furthermore, we interrogate the network’s predictive power by visualising statistics of network-excitatory samples. These visualisa- tions offer a deep insight into the relative informativeness of components in the detection problem. We include comparisons with conventional clas- sifiers, conditioned on both hand-tuned and generic features, to stress the strength of automatic deep feature learning. Detection is achieved with performance metrics significantly surpassing those of existing al- gorithmic methods, as well as marginally exceeding those attained by individual human experts. The data and software related to this paper are available at http://humbug.ac.uk/kiskin2017/.

1 aKiskin, Ivan1 aOrozco, Bernardo, PĂ©rez1 aWindebank, Theo1 aZilli, Davide1 aSinka, Marianne1 aWillis, Kathy1 aRoberts, Stephen uhttps://bio.acousti.ca/ja/node/4765101020nas a2200121 4500008004100000245005500041210005300096520060400149100001700753700002300770700001800793856008700811 2015 eng d00aSpeckled Bush Cricket Data Logger - Project Report0 aSpeckled Bush Cricket Data Logger Project Report3 a

This report summarises work carried out on the Speckled Bush Cricket data logger project by Wil Bennett and Dave Chesmore at the University of York and Ed Baker at the Natural History Museum, who together designed and constructed - at the time of writing - a total of ten data logging devices to monitor the presence of Leptophyes punctatissima and other environmental variables such as temperature and humidity. This document summarises the capabilities and functionality of these devices, and comprehensively documents correct operational procedure for deploying the devices in the field.

1 aBennett, Wil1 aChesmore, E. David1 aBaker, Edward uhttps://bio.acousti.ca/ja/content/speckled-bush-cricket-data-logger-project-report