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Species conservation depends on robust population assessment. Data on population abundance, distribution, and connectivity are critical for effective management, especially as baseline information for newly documented populations. We describe a pygmy blue whale Balaenoptera musculus brevicauda population in New Zealand waters with year-round presence that overlaps with industrial activities. This population was investigated using a multidisciplinary approach, including analysis of survey data, sighting records, acoustic data, identification photographs, and genetic samples. Blue whales were reported during every month of the year in the New Zealand Exclusive Economic Zone, with reports concentrated in the South Taranaki Bight (STB) region, where foraging behavior was frequently observed. Five hydrophones in the STB recorded the New Zealand blue whale call type on 99.7% of recording days (January to December 2016). A total of 151 individuals were photo-identified between 2004 and 2017. Nine individuals were resighted across multiple years. No matches were made to individuals identified in Australian or Antarctic waters. Mitochondrial DNA haplotype frequencies differed significantly between New Zealand (n = 53 individuals) and all other Southern Hemisphere blue whale populations, and haplotype diversity was significantly lower than all other populations. These results suggest a high degree of isolation of this New Zealand population. Using a closed capture-recapture population model, our conservative abundance estimate of blue whales in New Zealand is 718 (SD = 433, 95% CI = 279-1926). Our results fill critical knowledge gaps to improve management of blue whale populations in New Zealand and surrounding regions.

The article discusses results of a study that examines whether the acoustic characteristics of leopard seal Hydrurga leptonyx high double trills (HDT) differed between three Antarctic locations, Drescher Inlet (DI), Atka Bay (AB) and Davis Sea (DS). It notes that leopard seal is known to produce underwater vocalizations during the breeding season in austral summer. The study showed notable differences in HDT recordings, with lower pulse repetition rates and narrower bandwidths at DI compared to AB and DS. It concludes that the results support genetic studies and showed a high level of similarity in leopard seal vocalizations between recording locations.

Wildlife researchers must balance the need to safely capture and handle their study animals to sample tissues, collect morphological measurements, and attach dataloggers while ensuring their results are not confounded by stress artifacts caused by handling. To determine the physiological effects of research activities including chemical immobilization, transport, instrumentation with biologgers, and overnight holding on a model marine mammal species, we collected hormone, blood chemistry, hematology, and heart rate data from 19 juvenile northern elephant seals (Mirounga angustirostris) throughout a translocation experiment. Across our six sampling timepoints, cortisol and aldosterone data revealed a moderate hormonal stress response to handling accompanied by minor changes in hematocrit and blood glucose, but not ketone bodies or erythrocyte sedimentation rate. We also examined heart rate as a stress indicator and found that interval heart rate, standard deviation of heart rate, and apnea-eupnea cycles were influenced by handling. However, when seals were recaptured after several days at sea, all hormonal and hematological parameters had returned to baseline levels. Furthermore, 100 % of study animals were resighted in the wild post-translocation, with some individuals observed over four years later. Together, these findings suggest that while northern elephant seals exhibit measurable physiological stress in response to handling, they recover rapidly and show no observable long-term deleterious effects, making them a robust species for ecological and physiological research., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Passive acoustic monitoring (PAM) is a powerful tool for studying ecosystems. However, its effective application in tropical environments, particularly for insects, poses distinct challenges. Neotropical katydids produce complex species-specific calls, spanning mere milliseconds to seconds and spread across broad audible and ultrasonic frequencies. However, subtle differences in inter-pulse intervals or central frequencies are often the only discriminatory traits. These extremities, coupled with low source levels and susceptibility to masking by ambient noise, challenge species identification in PAM recordings. This study aimed to develop a deep learning-based solution to automate the recognition of 31 katydid species of interest in a biodiverse Panamanian forest with over 80 katydid species. Besides the innate challenges, our efforts were also encumbered by a limited and imbalanced initial training dataset comprising domain-mismatched recordings. To overcome these, we applied rigorous data engineering, improving input variance through controlled playback re-recordings and by employing physics-based data augmentation techniques, and tuning signal-processing, model and training parameters to produce a custom well-fit solution. Methods developed here are incorporated into Koogu, an open-source Python-based toolbox for developing deep learning-based bioacoustic analysis solutions. The parametric implementations offer a valuable resource, enhancing the capabilities of PAM for studying insects in tropical ecosystems. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Automated bioacoustic analysis aids understanding and protection of both marine and terrestrial animals and their habitats across extensive spatiotemporal scales, and typically involves analyzing vast collections of acoustic data. With the advent of deep learning models, classification of important signals from these datasets has markedly improved. These models power critical data analyses for research and decision-making in biodiversity monitoring, animal behaviour studies, and natural resource management. However, deep learning models are often data-hungry and require a significant amount of labeled training data to perform well. While sufficient training data is available for certain taxonomic groups (e.g., common bird species), many classes (such as rare and endangered species, many non-bird taxa, and call-type) lack enough data to train a robust model from scratch. This study investigates the utility of feature embeddings extracted from audio classification models to identify bioacoustic classes other than the ones these models were originally trained on. We evaluate models on diverse datasets, including different bird calls and dialect types, bat calls, marine mammals calls, and amphibians calls. The embeddings extracted from the models trained on bird vocalization data consistently allowed higher quality classification than the embeddings trained on general audio datasets. The results of this study indicate that high-quality feature embeddings from large-scale acoustic bird classifiers can be harnessed for few-shot transfer learning, enabling the learning of new classes from a limited quantity of training data. Our findings reveal the potential for efficient analyses of novel bioacoustic tasks, even in scenarios where available training data is limited to a few samples., (© 2023. The Author(s).)

Background: Current protocols for bedside swallow evaluation have high rates of false negative results. Though experts are not consistently able to screen for aspiration risk by assessing vocal quality, there is emerging evidence that vocal acoustic parameters are significantly different in patients at risk of aspiration. Herein, we aimed to determine whether the presence of material on the vocal folds in an excised canine laryngeal model may have an impact on acoustic and aerodynamic measures., Methods: Two ex vivo canine larynges were tested. Three liquids of different viscosities (1:100 diluted glycerin, pure glycerin, and honey-thick Varibar) were placed on the vocal folds at a constant volume. Acoustic and aerodynamic measures were obtained in both adducted and abducted vocal fold configurations. Intraglottal high-speed imaging was used to approximate the maximum divergence angle of the larynges in the studied conditions and examine its relationship to vocal efficiency (VE) and acoustic measures., Results: In glottic insufficiency conditions only, we found that several acoustic parameters could predict the presence of material on the vocal folds. Based on the combination of the aerodynamic and acoustic data, we found that decreased spectral energy in the higher harmonics was associated with decreased VE in the presence of material on the vocal folds and/or glottic insufficiency., Conclusion: Decreased spectral energy in the higher harmonics of the voice was found to be a potential biomarker of swallowing dysfunction, as it correlates with decreased vocal efficiency due to material on the vocal folds and/or glottic insufficiency, both of which are known risk factors for aspiration., Level of Evidence: NA Laryngoscope, 133:2517-2524, 2023., (© 2022 The American Laryngological, Rhinological and Otological Society, Inc.)

Monitoring on the basis of sound recordings, or passive acoustic monitoring, can complement or serve as an alternative to real-time visual or aural monitoring of marine mammals and other animals by human observers. Passive acoustic data can support the estimation of common, individual-level ecological metrics, such as presence, detection-weighted occupancy, abundance and density, population viability and structure, and behaviour. Passive acoustic data also can support estimation of some community-level metrics, such as species richness and composition. The feasibility of estimation and certainty of estimates is highly context dependent, and understanding the factors that affect the reliability of measurements is useful for those considering whether to use passive acoustic data. Here, we review basic concepts and methods of passive acoustic sampling in marine systems that often are applicable to marine mammal research and conservation. Our ultimate aim is to facilitate collaboration among ecologists, bioacousticians, and data analysts. Ecological applications of passive acoustics require one to make decisions about sampling design, which in turn requires consideration of sound propagation, sampling of signals, and data storage. One also must make decisions about signal detection and classification and evaluation of the performance of algorithms for these tasks. Investment in the research and development of systems that automate detection and classification, including machine learning, are increasing. Passive acoustic monitoring is more reliable for detection of species presence than for estimation of other species-level metrics. Use of passive acoustic monitoring to distinguish among individual animals remains difficult. However, information about detection probability, vocalisation or cue rate, and relations between vocalisations and the number and behaviour of animals increases the feasibility of estimating abundance or density. Most sensor deployments are fixed in space or are sporadic, making temporal turnover in species composition more tractable to estimate than spatial turnover. Collaborations between acousticians and ecologists are most likely to be successful and rewarding when all partners critically examine and share a fundamental understanding of the target variables, sampling process, and analytical methods., (© 2023 Cambridge Philosophical Society.)

Detection and identification of species, subspecies or stocks of whales, dolphins and porpoises at sea remain challenging, particularly for cryptic or elusive species like beaked whales (Family: Ziphiidae). Here we investigated the potential for using an acoustically assisted sampling design to collect environmental (e)DNA from beaked whales on the U.S. Navy's Atlantic Undersea Test and Evaluation Center (AUTEC) in The Bahamas. During 12 days of August 2019, we conducted 9 small-boat surveys and collected 56 samples of seawater (paired subsamples of 1L each, including controls) using both a spatial collection design in the absence of visual confirmation of whales, and a serial collection design in the proximity of whales at the surface. There were 7 sightings of whales, including 11 Blainville's beaked whales (Mesoplodon densirostris). All whales were located initially with the assistance of information from a bottom-mounted acoustic array available on the AUTEC range. Quantification by droplet digital (dd)PCR from the four spatial design collections showed no samples of eDNA above the threshold of detection and none of these 20 samples yielded amplicons for conventional or next-generation sequencing. Quantification of the 31 samples from four serial collections identified 11 likely positive detections. eDNA barcoding by conventional sequencing and eDNA metabarcoding by next-generation sequencing confirmed species identification for 9 samples from three of the four serial collections. We further resolved five intra-specific variants (i.e., haplotypes), two of which showed an exact match to previously published haplotypes and three that have not been reported previously to the international repository, GenBank. A minimum spanning network of the five eDNA haplotypes, with all other published haplotypes of Blainville's beaked whales, suggested the potential for further resolution of differences between oceanic populations., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Baker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Although eco-acoustic monitoring has the potential to deliver biodiversity insight on vast scales, existing analytical approaches behave unpredictably across studies. We collated 8,023 audio recordings with paired manual avifaunal point counts to investigate whether soundscapes could be used to monitor biodiversity across diverse ecosystems. We found that neither univariate indices nor machine learning models were predictive of species richness across datasets but soundscape change was consistently indicative of community change. Our findings indicate that there are no common features of biodiverse soundscapes and that soundscape monitoring should be used cautiously and in conjunction with more reliable in-person ecological surveys., (© 2023. The Author(s).)

Many odontocetes produce whistles that feature characteristic contour shapes in spectrogram representations of their calls. Automatically extracting the time × frequency tracks of whistle contours has numerous subsequent applications, including species classification, identification, and density estimation. Deep-learning-based methods, which train models using analyst-annotated whistles, offer a promising way to reliably extract whistle contours. However, the application of such methods can be limited by the significant amount of time and labor required for analyst annotation. To overcome this challenge, a technique that learns from automatically generated pseudo-labels has been developed. These annotations are less accurate than those generated by human analysts but more cost-effective to generate. It is shown that standard training methods do not learn effective models from these pseudo-labels. An improved loss function designed to compensate for pseudo-label error that significantly increases whistle extraction performance is introduced. The experiments show that the developed technique performs well when trained with pseudo-labels generated by two different algorithms. Models trained with the generated pseudo-labels can extract whistles with an F1-score (the harmonic mean of precision and recall) of 86.31% and 87.2% for the two sets of pseudo-labels that are considered. This performance is competitive with a model trained with 12 539 expert-annotated whistles (F1-score of 87.47%)., (© 2023 Acoustical Society of America. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).)

Objective/hypothesis: We explored the following hypotheses in a cohort of patients undergoing injection laryngoplasty: (1) glottic insufficiency affects voluntary cough airflow dynamics and restoring glottic competence may improve parameters of cough strength, (2) cough strength can be inferred from cough acoustic signal, and (3) glottic competence changes cough sounds and correlates with spectrogram morphology., Study Type/design: Prospective interventional study., Methods: Subjects with glottic insufficiency secondary to unilateral vocal fold paresis, paralysis, or atrophy, and scheduled for injection laryngoplasty completed an instrumental assessment of voluntary cough airflow using a pneumotachometer and a protocolized voluntary cough sound recording. A Wilcoxon signed-rank test was used to compare the differences between pre- and post-injection laryngoplasty in airflow and acoustic measures. A Spearman rank-order correlation was used to evaluate the association between airflow and acoustic cough measures., Results: Twenty-five patients (13F:12M, mean age 68.8) completed voluntary cough airflow measurements and 22 completed cough sound recordings. Following injection laryngoplasty, patients had a statistically significant decreased peak expiratory flow rise time (PEFRT) (mean change: -0.03 s, SD: 0.06, p = 0.04) and increased cough volume acceleration (mean change: 13.1 L/s 2 , SD: 33.9, p = 0.03), suggesting improved cough effectiveness. Correlation of cough acoustic measures with airflow measures showed a weak relationship between PEFRT and acoustic energy (coefficient: -0.31, p = 0.04) and peak power density (coefficient: -0.35, p = 0.02)., Conclusions: Our study thus indicates that injection laryngoplasty may help avert aspiration in patients with glottic insufficiency by improving cough effectiveness and that improved cough airflow measures may be tracked with cough sounds., Level of Evidence: 3 Laryngoscope, 133:S1-S14, 2023., (© 2022 The American Laryngological, Rhinological and Otological Society, Inc.)

Animal behavior is motivated by the fundamental need to feed and reproduce, and these behaviors can be inferred from spatiotemporal variations in biological signals such as vocalizations. Yet, linking foraging and reproductive effort to environmental drivers can be challenging for wide-ranging predator species. Blue whales are acoustically active marine predators that produce two distinct vocalizations: song and D calls. We examined environmental correlates of these vocalizations using continuous recordings from five hydrophones in the South Taranaki Bight region of Aotearoa New Zealand to investigate call behavior relative to ocean conditions and infer life history patterns. D calls were strongly correlated with oceanographic drivers of upwelling in spring and summer, indicating associations with foraging effort. In contrast, song displayed a highly seasonal pattern with peak intensity in fall, which aligned with the timing of conception inferred from whaling records. Finally, during a marine heatwave, reduced foraging (inferred from D calls) was followed by lower reproductive effort (inferred from song intensity)., Competing Interests: The authors declare that they have no competing interests., (© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

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.

A single-hydrophone ocean glider was deployed within a cabled hydrophone array to demonstrate a framework for estimating population density of fin whales (Balaenoptera physalus) from a passive acoustic glider. The array was used to estimate tracks of acoustically active whales. These tracks became detection trials to model the detection function for glider-recorded 360-s windows containing fin whale 20-Hz pulses using a generalized additive model. Detection probability was dependent on both horizontal distance and low-frequency glider flow noise. At the median 40-Hz spectral level of 97 dB re 1 μPa 2 /Hz, detection probability was near one at horizontal distance zero with an effective detection radius of 17.1 km [coefficient of variation (CV) = 0.13]. Using estimates of acoustic availability and acoustically active group size from tagged and tracked fin whales, respectively, density of fin whales was estimated as 1.8 whales per 1000 km 2 (CV = 0.55). A plot sampling density estimate for the same area and time, estimated from array data alone, was 1.3 whales per 1000 km 2 (CV = 0.51). While the presented density estimates are from a small demonstration experiment and should be used with caution, the framework presented here advances our understanding of the potential use of gliders for cetacean density estimation.