Acoustic behavior of melon-headed whales varies on a diel cycle.

Many terrestrial and marine species have a diel activity pattern, and their acoustic signaling follows their current behavioral state. Whistles and echolocation clicks on long-term recordings produced by melon-headed whales (Peponocephala electra) at Palmyra Atoll indicated that these signals were used selectively during different phases of the day, strengthening the idea of nighttime foraging and daytime resting with afternoon socializing for this species. Spectral features of their echolocation clicks changed from day to night, shifting the median center frequency up. Additionally, click received levels increased with increasing ambient noise during both day and night. Ambient noise over a wide frequency band was on average higher at night. The diel adjustment of click features might be a reaction to acoustic masking caused by these nighttime sounds. Similar adaptations have been documented for numerous taxa in response to noise. Or it could be, unrelated, an increase in biosonar source levels and with it a shift in center frequency to enhance detection distances during foraging at night. Call modifications in intensity, directionality, frequency, and duration according to echolocation task are well established for bats. This finding indicates that melon-headed whales have flexibility in their acoustic behavior, and they collectively and repeatedly adapt their signals from day- to nighttime circumstances

An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing

In recent decades, the cost of acoustic technologies has declined dramatically. Advances in networks, storage devices, and power management have made it practical to consider the remote location of sensors. Nonetheless, many challenges remain for the fabrication, deployment, and use of remote sensors.This paper provides an overview of the issues involved in developing remote acoustic sensors. We discuss physical design and the integration of components, data storage and communication issues, signal acquisition and classification, and the relationship of these issues to power usage requirements

Spatio-temporal patterns of beaked whale echolocation signals in the North Pacific.

At least ten species of beaked whales inhabit the North Pacific, but little is known about their abundance, ecology, and behavior, as they are elusive and difficult to distinguish visually at sea. Six of these species produce known species-specific frequency modulated (FM) echolocation pulses: Baird's, Blainville's, Cuvier's, Deraniyagala's, Longman's, and Stejneger's beaked whales. Additionally, one described FM pulse (BWC) from Cross Seamount, Hawai'i, and three unknown FM pulse types (BW40, BW43, BW70) have been identified from almost 11 cumulative years of autonomous recordings at 24 sites throughout the North Pacific. Most sites had a dominant FM pulse type with other types being either absent or limited. There was not a strong seasonal influence on the occurrence of these signals at any site, but longer time series may reveal smaller, consistent fluctuations. Only the species producing BWC signals, detected throughout the Pacific Islands region, consistently showed a diel cycle with nocturnal foraging. By comparing stranding and sighting information with acoustic findings, we hypothesize that BWC signals are produced by ginkgo-toothed beaked whales. BW43 signal encounters were restricted to Southern California and may be produced by Perrin's beaked whale, known only from Californian waters. BW70 signals were detected in the southern Gulf of California, which is prime habitat for Pygmy beaked whales. Hubb's beaked whale may have produced the BW40 signals encountered off central and southern California; however, these signals were also recorded off Pearl and Hermes Reef and Wake Atoll, which are well south of their known range

Silbido profundo : an open source package for the use of deep learning to detect odontocete whistles

The authors wish to thank Dr. Michael Weise of the Office of Naval Research (N00014-17-1-2867, N00014-17-1-2567) for supporting this project. We also thank Anu Kumar and Mandy Shoemaker of U.S. Navy Living Marine Resources for supporting development of the data management tools used in this work (N3943020C2202).This work presents an open-source matlab software package for exploiting recent advances in extracting tonal signals from large acoustic data sets. A whistle extraction algorithm published by Li, Liu, Palmer, Fleishman, Gillespie, Nosal, Shiu, Klinck, Cholewiak, Helble, and Roch [(2020). Proceedings of the International Joint Conference on Neural Networks, July 19–24, Glasgow, Scotland, p. 10] is incorporated into silbido, an established software package for extraction of cetacean tonal calls. The precision and recall of the new system were over 96% and nearly 80%, respectively, when applied to a whistle extraction task on a challenging two-species subset of a conference-benchmark data set. A second data set was examined to assess whether the algorithm generalized to data that were collected across different recording devices and locations. These data included 487 h of weakly labeled, towed array data collected in the Pacific Ocean on two National Oceanographic and Atmospheric Administration (NOAA) cruises. Labels for these data consisted of regions of toothed whale presence for at least 15 species that were based on visual and acoustic observations and not limited to whistles. Although the lack of per whistle-level annotations prevented measurement of precision and recall, there was strong concurrence of automatic detections and the NOAA annotations, suggesting that the algorithm generalizes well to new data.Publisher PDFPeer reviewe

Improve automatic detection of animal call sequences with temporal context

Funding: This work was supported by the US Office of Naval Research (grant no. N00014-17-1-2867).Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences of calls has implications for conservation and biological studies. We show that the performance of a convolutional neural network (CNN), designed to detect song notes (calls) in short-duration audio segments, can be improved by combining it with a recurrent network designed to process sequences of learned representations from the CNN on a longer time scale. The combined system of independently trained CNN and long short-term memory (LSTM) network models exploits the temporal patterns between song notes. We demonstrate the technique using recordings of fin whale (Balaenoptera physalus) songs, which comprise patterned sequences of characteristic notes. We evaluated several variants of the CNN + LSTM network. Relative to the baseline CNN model, the CNN + LSTM models reduced performance variance, offering a 9-17% increase in area under the precision-recall curve and a 9-18% increase in peak F1-scores. These results show that the inclusion of temporal information may offer a valuable pathway for improving the automatic recognition and transcription of wildlife recordings.Publisher PDFPeer reviewe

Signalling in groups: New tools for the integration of animal communication and collective movement

Investigations of collective movement and animal communication have often followed distinct, though complementary, trajectories. Both subfields are deeply concerned with how information flows between individuals and shapes subsequent behaviour. Collective movement has largely focused on the dynamics of passive, cue-mediated group coordination, while animal communication has primarily examined the content and function of active dyadic signal exchanges in sender–receiver frameworks. However, in many social groups, network-wide signalling and collective movement decisions are tightly linked. Here we discuss opportunities afforded by using multi-sensor tracking tags to simultaneously monitor the fine-scale movements and vocalisations of entire social groups. We highlight how such data can elucidate the role of vocal signals in individual and collective movement while illuminating the structures of entire vocal-interaction sequences at previously unexamined timescales and across entire communication networks. We identify practical and analytical challenges associated with these new tools and datasets, and present avenues for addressing them. We specifically address issues associated with the deployment and synchronisation of multiple tags, the processing and interpretation of resulting multidimensional datasets, and the benefits of combining tag-based data collection with experimental approaches. Finally, we argue that a comparative approach employing consistent methodologies across a range of environments, populations and systems is needed to shed light on the evolutionary ecology of communication and collective behaviour

Measuring organizational readiness for knowledge translation in chronic care

<p>Abstract</p> <p>Background</p> <p>Knowledge translation (KT) is an imperative in order to implement research-based and contextualized practices that can answer the numerous challenges of complex health problems. The Chronic Care Model (CCM) provides a conceptual framework to guide the implementation process in chronic care. Yet, organizations aiming to improve chronic care require an adequate level of organizational readiness (OR) for KT. Available instruments on organizational readiness for change (ORC) have shown limited validity, and are not tailored or adapted to specific phases of the knowledge-to-action (KTA) process. We aim to develop an evidence-based, comprehensive, and valid instrument to measure OR for KT in healthcare. The OR for KT instrument will be based on core concepts retrieved from existing literature and validated by a Delphi study. We will specifically test the instrument in chronic care that is of an increasing importance for the health system.</p> <p>Methods</p> <p>Phase one: We will conduct a systematic review of the theories and instruments assessing ORC in healthcare. The retained theoretical information will be synthesized in a conceptual map. A bibliography and database of ORC instruments will be prepared after appraisal of their psychometric properties according to the standards for educational and psychological testing. An online Delphi study will be carried out among decision makers and knowledge users across Canada to assess the importance of these concepts and measures at different steps in the KTA process in chronic care.</p> <p>Phase two: A final OR for KT instrument will be developed and validated both in French and in English and tested in chronic disease management to measure OR for KT regarding the adoption of comprehensive, patient-centered, and system-based CCMs.</p> <p>Discussion</p> <p>This study provides a comprehensive synthesis of current knowledge on explanatory models and instruments assessing OR for KT. Moreover, this project aims to create more consensus on the theoretical underpinnings and the instrumentation of OR for KT in chronic care. The final product--a comprehensive and valid OR for KT instrument--will provide the chronic care settings with an instrument to assess their readiness to implement evidence-based chronic care.</p

Spatio-Temporal Patterns of Beaked Whale Echolocation Signals in the North Pacific

Postprint (author's final draft