@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 {57910, title = {Preliminary Report on Long-term Detection of Arabian Sea Humpback Whale Vocalizations off Oman}, year = {2016}, abstract = {

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.

}, author = {Salvatore Cerchio and Andrew Willson and Charles Muirhead and Gianna Minton and Tim Collins and Robert Baldwin and Maia Sarrouf Willson and Suaad Al Harthi} } @article {57647, title = {Assessing bioacoustics of middle spotted woodpeckers for non-invasive assessments of population structure and habitat quality in fragmented habitats}, year = {2019}, author = {Hugo Robles} } @article {57602, title = {Sounding Soil: An Acoustic, Ecological \& Artistic Investigation of Soil Life}, year = {2019}, abstract = {

The interdisciplinary Sounding Soil project explores the soil\’s soundscape and renders experienceable and investigable the activity and diversity of soil life in an artistic-acoustic observatory. The main aim is to increase soil awareness in the general population as well as among decision-makers involved in soil policies and food producers. While moving through or digging the soil matrix, the soil fauna produces noises. Moreover, some animals seem to use the soil as a communication medium, forming a complex soundscape. Land use and agricultural management may have marked effects on the soil soundscape. Thus, the (acoustic) richness of a local soil animal community may serve as an indicator of the functioning of a soil ecosystem. In the scientific module of the Sounding Soil project, we implement and test acoustic indices to assess soil biodiversity and community composition. In the art and citizen science module, several Swiss farmers and subsequently the broader public record the soundscapes of their soils with a low-cost recording device. The result is a sound art installation as a publicly accessible observatory, integrating our scientific findings with recordings by participants in the citizen science module. This article describes the character of soil soundscapes in agricultural land and forests and reports on our research design and first insights into the relations between land use, soil types, biodiversity, and soil soundscapes.

}, keywords = {acoustic ecology, Artistic Research, bioacoustics, ecoacoustics, Soil Ecology}, author = {Marcus Maeder and Martin M. Gossner and Armin Keller and Martin Neukom} } @article {53523, title = {The Ocean as a Living Sensor: Environmental DNA and Acoustics for Detecting Marine Life}, year = {2018}, type = {Discussion Paper}, author = {Alison W. Watts and Jennifer L. Miksis-Olds} } @article {53437, title = {Bird vocalizations: seven different types of Great tit (Parus major) vocalizations}, number = {201811}, year = {2018}, abstract = {

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 \&$\#$39;tonal\&$\#$39;; a pure tone, usually in time varying in frequency (often a so-called \&$\#$39;upsweep\&$\#$39;); 2) a vocalization can be \&$\#$39;pulsed\&$\#$39;, 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.

}, url = {https://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-voc}, author = {Willem C. Verboom} } @article {51243, title = {Passive acoustic monitoring in ecology and conservation}, number = {1(2)}, year = {2017}, institution = {WWF-UK}, address = {Woking, United Kingdom}, author = {Ella Browning and Rory Gibb and Paul Glover-Kapfer and Kate Elizabeth Jones} } @article {50822, title = {The Clustering of Acoustic Indices derived from Long-duration Recordings of the Environment}, year = {2017}, abstract = {

This paper outlines the recording dataset and methods used to choose a clustering algorithm for a large twenty-six month acoustic dataset. The recordings were of the natural environment and consist of thirteen months of recording from each of two sites in two national parks 160 km north of Brisbane, Queensland. This paper also explains the calculation and use of the intra-three-day-distance (I3DD) error measure used to determine the optimum clustering result. Site maps and photos are provided at the end of this document.

}, keywords = {acoustic environment, acoustic indices, hierarchical clustering, k-means clustering}, url = {https://eprints.qut.edu.au/110659/}, author = {Yvonne F. Phillips} } @article {50821, title = {The calculation of acoustic indices derived from long-duration recordings of the natural environment}, year = {2017}, abstract = {

The work described in this technical report is part of ongoing research within the QUT Ecoacoustics Research Group to build practical tools for the manipulation, analysis and visualisation of recordings of the natural environment. This report describes methods we use to calculate spectral and summary acoustic indices derived from recordings of the environment. A spectral index is a vector of N values, one for each frequency bin of the spectrogram. A summary index is a scalar value, in some cases derived directly from the waveform envelope and in other cases from the corresponding spectral index.

}, keywords = {acoustic environment, acoustic indices, eco-acoustics, spectral indices, summary indices}, url = {https://eprints.qut.edu.au/110634/} } @article {47651, title = {Mosquito Detection with Neural Networks: The Buzz of Deep Learning}, year = {2017}, abstract = {

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/.

}, author = {Ivan Kiskin and Bernardo P{\'e}rez Orozco and Theo Windebank and Davide Zilli and Marianne Sinka and Kathy Willis and Stephen Roberts} } @article {12075, title = {Speckled Bush Cricket Data Logger - Project Report}, year = {2015}, abstract = {

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.

}, doi = {10.6084/m9.figshare.1430094}, author = {Bennett, Wil and E. David Chesmore and Edward Baker} } @article {11166, title = {Insect Sounds in Taxonomy}, year = {1974}, pages = {49-50}, institution = {British Museum (Natural History)}, address = {London, UK}, author = {BMNH} }