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Bird songs and morphometry are frequently used to distinguish bird species which are plumage-cryptic, or to delineate boundaries of subspecies. Rubigula flaviventris, Pycnonotus finlaysoni and Brachypodius melanocephalos have each recognized subspecies along the Thai-Malay Peninsula, however the range-limit of the subspecies distribution is still ambiguous and genetic data from a previous own study suggests a revision of the subspecies distribution and the subspecies barriers is warranted. To analyze the subspecies boundaries based on potential song and morphometric traits, we recorded, measured, and analyzed song characters and morphometry of the three bulbuls along the Thai-Malay Peninsula. The geographic variation of songs and morphometry differs significantly for specific traits; however, we could not find any trait discontinuities that would be in accordance with previously documented phylogeographic divides. Instead trait variation was mostly clinal and followed a North-South gradient in line with Bergmann’s rule. Due to the lack of distinctiveness of any subspecific taxon, we conclude that shallow genetic divides on the Thai-Malay Peninsula are not indicative of potential species-level splits in the three species of our study.

Abstract Biodiversity monitoring has entered an era of ‘big data’, exemplified by a near‐continuous collection of sounds, images, chemical and other signals from organisms in diverse ecosystems. Such data streams have the potential to help identify new threats, assess the effectiveness of conservation interventions, as well as generate new ecological insights. However, appropriate analytical methods are often still missing, particularly with respect to characterizing cyclical temporal patterns. Here, we present a framework for characterizing and analysing ecological responses that represent nonstationary, complex temporal patterns and demonstrate the value of using Fourier transforms to decorrelate continuous data points. In our example, we use a framework based on three approaches (spectral analysis, magnitude squared coherence, and principal component analysis) to characterize differences in tropical forest soundscapes within and across sites and seasons in Gabon. By reconstructing the underlying, cyclic behaviour of the soundscape for each site, we show how one can identify circadian patterns in acoustic activity. Soundscapes in the dry season had a complex diel cycle, requiring multiple harmonics to represent daily variation, while in the wet season there was less variance attributable to the daily cyclic patterns. Our framework can be applied to most continuous, or near‐continuous ecological data collected at a fine temporal resolution, allowing ecologists to explore patterns of temporal autocorrelation at multiple levels for biologically meaningful trends. Such methods will become indispensable as biological big data are used to understand the impact of anthropogenic pressures on biodiversity and to inform efforts to mitigate them.

n this work, the results of a detailed sound monitoring conducted during December 2023 in the mountain rainforestof Yungas (750 meters above sea level), in the vicinity of Piedras stream within the protected area of Sierra San JavierPark, are presented. The spectrograms obtained from the inner forest differ consistently from those of the riparian forest, distinguishable by their bioacoustic characteristics. It is conjectured that the stream, through its geophonic load and ecological imprint on the landscape, imposes restrictions on the occupation of the ecoacoustic niche by the fauna ensemble present in the area.

The collection and annotation of bioacoustic data present several challenges to researchers. Bioacoustic monitoring of rare (sparse) or cryptic species generally encounter two main issues. The cost of collecting and processing field data and a lack of labelled datasets for the target species. The detection of invasive species incursions and probability of absence testing is especially challenging due to these species having population densities at or close to zero. We present a methodology specifically designed to aid in the analysis of rare acoustic events within long-term field recordings. This approach combines a wavelet-based segmentation method that automatically extracts transient features from within-field recordings. A few-shot active learning recommender system in a human-in-the-loop process prioritises the annotation of low-certainty samples. This process combines the accuracy of human classification and the speed of computational tools to greatly reduce the presence of non-target features in field recordings. We evaluate this approach using an invasive species identification case study. This methodology achieves a test accuracy of 98.4% as well as 81.2% test accuracy using 2-shot, 2-way prototypical learning without fine-tuning, demonstrating high performance at varying data availability contexts. Active learning using low-certainty samples achieves >90% test accuracy using only 20 training samples compared to 80 samples without active learning. This approach allows users to train custom audio classification models for any application with rare features. The model can be easily exported for use in the field making real-time bioacoustic monitoring of less-vocal species a possibility. All code and data are available at https://github.com/Listening-Lab/Annotator.

Coral reefs are biodiverse marine ecosystems that are undergoing rapid changes, making monitoring vital as we seek to manage and mitigate stressors. Healthy reef soundscapes are rich with sounds, enabling passive acoustic recording and soundscape analyses to emerge as cost-effective, long-term methods for monitoring reef communities. Yet most biological reef sounds have not been identified or described, limiting the effectiveness of acoustic monitoring for diversity assessments. Machine learning offers a solution to scale such analyses but has yet to be successfully applied to characterize the diversity of reef fish sounds. Here we sought to characterize and categorize coral reef fish sounds using unsupervised machine learning methods. Pulsed fish and invertebrate sounds from 480 min of data sampled across 10 days over a 2-month period on a US Virgin Islands reef were manually identified and extracted, then grouped into acoustically similar clusters using unsupervised clustering based on acoustic features. The defining characteristics of these clusters were described and compared to determine the extent of acoustic diversity detected on these reefs. Approximately 55 distinct calls were identified, ranging in centroid frequency from 50 Hz to 1,300 Hz. Within this range, two main sub-bands containing multiple signal types were identified from 100 Hz to 400 Hz and 300 Hz–700 Hz, with a variety of signals outside these two main bands. These methods may be used to seek out acoustic diversity across additional marine habitats. The signals described here, though taken from a limited dataset, speak to the diversity of sounds produced on coral reefs and suggest that there might be more acoustic niche differentiation within soniferous fish communities than has been previously recognized.

IntroductionThis work presents an unsupervised learning-based methodology to identify and count unique manatees using underwater vocalization recordings.MethodsThe proposed approach uses Scattering Wavelet Transform (SWT) to represent individual manatee vocalizations. A Manifold Learning approach, known as PacMAP, is employed for dimensionality reduction. A density-based algorithm, known as Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), is used to count and identify clusters of individual manatee vocalizations. The proposed methodology is compared with a previous method developed by our group, based on classical clustering methods (K-Means and Hierarchical clustering) using Short-Time Fourier Transform (STFT)-based spectrograms for representing vocalizations. The performance of both approaches is contrasted by using a novel vocalization data set consisting of 23 temporally captured Greater Caribbean manatees from San San River, Bocas del Toro, in western Panama as input.ResultsThe proposed methodology reaches a mean percentage of error of the number of individuals (i.e., number of clusters) estimation of 14.05% and success of correctly grouping a manatee in a cluster of 83.75%.DiscussionThus having a better performances than our previous analysis methodology, for the same data set. The value of this work lies in providing a way to estimate the manatee population while only relying on underwater bioacoustics.

There is growing evidence that studying aquatic acoustic communities can provide ecologically relevant information. Understanding these communities may offer unique insights into species behaviour and ecology, while consolidating passive acoustic monitoring as a tool for mapping the presence of target species or estimating changes in aquatic biodiversity. Fish can be significant soundscape contributors, but most soniferous fish species are yet to be identified. Here, we crossed information of three key fish acoustic communities in the Lusitanian Province of the Temperate Northern Atlantic (the Madeira archipelago, the Azores archipelago and Arrábida in mainland Portugal) to unveil potential sources of unidentified fish sounds. We found that the three communities shared various sound types and we were able to narrow down the list of possible fish sound sources. Several sound types were suggested to be produced by species of the Pomacentridae, Scorpaenidae and Serranidae families. We also observed that the sound type /kwa/, associated with Scorpaena spp., exhibited more variations in the geographic area where more species of this genus are known to be present. This study showcases that, as databases of unidentified fish sounds continue to grow, future comparisons of multiple acoustic communities may provide insights into unknown fish sound sources and sound types.

Abstract The study of animal sounds in biology and ecology relies heavily upon time–frequency (TF) visualisation, most commonly using the short‐time Fourier transform (STFT) spectrogram. This method, however, has inherent bias towards either temporal or spectral details that can lead to misinterpretation of complex animal sounds. An ideal TF visualisation should accurately convey the structure of the sound in terms of both frequency and time, however, the STFT often cannot meet this requirement. We evaluate the accuracy of four TF visualisation methods (superlet transform [SLT], continuous wavelet transform [CWT] and two STFTs) using a synthetic test signal. We then apply these methods to visualise sounds of the Chagos blue whale, Asian elephant, southern cassowary, eastern whipbird, mulloway fish and the American crocodile. We show that the SLT visualises the test signal with 18.48%–28.08% less error than the other methods. A comparison between our visualisations of animal sounds and their literature descriptions indicates that the STFT's bias may have caused misinterpretations in describing pygmy blue whale songs and elephant rumbles. We suggest that use of the SLT to visualise low‐frequency animal sounds may prevent such misinterpretations. Finally, we employ the SLT to develop ‘BASSA’, an open‐source, GUI software application that offers a no‐code, user‐friendly tool for analysing short‐duration recordings of low‐frequency animal sounds for the Windows platform. The SLT visualises low‐frequency animal sounds with improved accuracy, in a user‐friendly format, minimising the risk of misinterpretation while requiring less technical expertise than the STFT. Using this method could propel advances in acoustics‐driven studies of animal communication, vocal production methods, phonation and species identification.

Acoustic signals are vital in animal communication, and quantifying them is fundamental for understanding animal behaviour and ecology. Vocalizations can be classified into acoustically and functionally or contextually distinct categories, but establishing these categories can be challenging. Newly developed methods, such as machine learning, can provide solutions for classification tasks. The plains zebra is known for its loud and specific vocalizations, yet limited knowledge exists on the structure and information content of its vocalzations. In this study, we employed both feature-based and spectrogram-based algorithms, incorporating supervised and unsupervised machine learning methods to enhance robustness in categorizing zebra vocalization types. Additionally, we implemented a permuted discriminant function analysis to examine the individual identity information contained in the identified vocalization types. The findings revealed at least four distinct vocalization types—the ‘snort’, the ‘soft snort’, the ‘squeal’ and the ‘quagga quagga’—with individual differences observed mostly in snorts, and to a lesser extent in squeals. Analyses based on acoustic features outperformed those based on spectrograms, but each excelled in characterizing different vocalization types. We thus recommend the combined use of these two approaches. This study offers valuable insights into plains zebra vocalization, with implications for future comprehensive explorations in animal communication.

In the context of passive acoustic monitoring (PAM) better models are needed to reliably gain insights from large amounts of raw, unlabeled data. Bioacoustics foundation models, which are general-purpose, adaptable models that can be used for a wide range of downstream tasks, are an effective way to meet this need. Measuring the capabilities of such models is essential for their development, but the design of robust evaluation procedures is a complex process. In this review we discuss a variety of fields that are relevant for the evaluation of bioacoustics models, such as sound event detection, machine learning metrics, and transfer learning (including topics such as few-shot learning and domain generalization). We contextualize these topics using the particularities of bioacoustics data, which is characterized by large amounts of noise, strong class imbalance, and distribution shifts (differences in the data between training and deployment stages). Our hope is that these insights will help to inform the design of evaluation protocols that can more accurately predict the ability of bioacoustics models to be deployed reliably in a wide variety of settings.

The mechanoreceptors of land-dwelling juveniles of the family Coenobitidae were studied using scanning electron microscopy to determine how their morphology varied from the mechanoreceptors of the marine dwelling zoea stage. Archived specimens of Birgus latro Leach, 1816 and Coenobita clypeatus Latreille, 1829 from the collections maintained in the National Museum of Natural History, Smithsonian Institution were examined. The mechanoreceptors on the antennules of the planktonic marine larval stage, zoea, are potentially capable of detecting near field sound energy at frequencies two orders of magnitude lower than those of the antennules of the land-dwelling juveniles in both genera. This sensitivity to lower frequencies potentially enables the larval stages to detect sound energy sources at much greater distances.

Abstract Background The hyolaryngeal apparatus generates biosonar pulses in the laryngeally echolocating bats. The cartilage and muscles comprising the hyolarynx of laryngeally echolocating bats are morphologically modified compared to those of non-bat mammals, as represented by the hypertrophied intrinsic laryngeal muscle. Despite its crucial contribution to laryngeal echolocation, how the development of the hyolarynx in bats differs from that of other mammals is poorly documented. The genus Rhinolophus is one of the most sophisticated laryngeal echolocators, with the highest pulse frequency in bats. The present study provides the first detailed description of the three-dimensional anatomy and development of the skeleton, cartilage, muscle, and innervation patterns of the hyolaryngeal apparatus in two species of rhinolophid bats using micro-computed tomography images and serial tissue sections and compares them with those of laboratory mice. Furthermore, we measured the peak frequency of the echolocation pulse in active juvenile and adult individuals to correspond to echolocation pulses with hyolaryngeal morphology at each postnatal stage. Results We found that the sagittal crests of the cricoid cartilage separated the dorsal cricoarytenoid muscle in horseshoe bats, indicating that this unique morphology may be required to reinforce the repeated closure movement of the glottis during biosonar pulse emission. We also found that the cricothyroid muscle is ventrally hypertrophied throughout ontogeny, and that the cranial laryngeal nerve has a novel branch supplying the hypertrophied region of this muscle. Our bioacoustic analyses revealed that the peak frequency shows negative allometry against skull growth, and that the volumetric growth of all laryngeal cartilages is correlated with the pulse peak frequency. Conclusions The unique patterns of muscle and innervation revealed in this study appear to have been obtained concomitantly with the acquisition of tracheal chambers in rhinolophids and hipposiderids, improving sound intensity during laryngeal echolocation. In addition, significant protrusion of the sagittal crest of the cricoid cartilage and the separated dorsal cricoarytenoid muscle may contribute to the sophisticated biosonar in this laryngeally echolocating lineage. Furthermore, our bioacoustic data suggested that the mineralization of these cartilages underpins the ontogeny of echolocation pulse generation. The results of the present study provide crucial insights into how the anatomy and development of the hyolaryngeal apparatus shape the acoustic diversity in bats.

Abstract Animal migration is one of nature's most spectacular phenomena, but migratory animals and their journeys are imperilled across the globe. Migratory birds are among the most well‐studied animals on Earth, yet relatively little is known about in‐flight behaviour during nocturnal migration. Because many migrating bird species vocalize during flight, passive acoustic monitoring shows great promise for facilitating widespread monitoring of bird migration. Here, we present Nighthawk, a deep learning model designed to detect and identify the vocalizations of nocturnally migrating birds. We trained Nighthawk on the in‐flight vocalizations of migratory birds using a diverse dataset of recordings from across the Americas. Our results demonstrate that Nighthawk performs well as a nocturnal flight call detector and classifier for dozens of avian taxa, both at the species level and for broader taxonomic groups (e.g. orders and families). It achieves an average precision score above 0.80 for 50 species and a mean average precision of 0.96 across 4 orders. The model accurately quantified nightly nocturnal migration intensity (80% variation explained) and species phenology (78% variation explained) and performed well on data from across North America. Incorporating modest amounts of additional annotated audio (50–120 h) into model training yielded high performance on target datasets from both North and South America (average precision on order Passeriformes >0.99). By monitoring the vocalizations of actively migrating birds, Nighthawk provides a detailed window onto nocturnal bird migration that is not presently attainable by other means (e.g. radar or citizen science). Scientists, managers and practitioners could use acoustic monitoring with Nighthawk for a number of applications, including: monitoring migration passage at wind farms; studying airspace usage during migratory flights; monitoring the changing migrations of species susceptible to climate change; and revealing previously unknown migration routes and behaviours. Overall, this work will empower diverse stakeholders to efficiently monitor migrating birds across the Western Hemisphere and collect data in aid of science and conservation.

Automated remote sensing has revolutionized the fields of wildlife ecology and environmental science. Yet, a cost-effective and flexible approach for large scale monitoring has not been fully developed, resulting in a limited collection of high-resolution data. Here, we describe BioSense, a low-cost and fully programmable automated sensing platform for applications in bioacoustics and environmental studies. Our design offers customization and flexibility to address a broad array of research goals and field conditions. Each BioSense is programmed through an integrated Raspberry Pi computer board and designed to collect and analyze avian vocalizations while simultaneously collecting temperature, humidity, and soil moisture data. We illustrate the different steps involved in manufacturing this sensor including hardware and software design and present the results of our laboratory and field testing in southwestern United States.

Anurans are among the most vocally active vertebrate animals and emit calls with different functions. In order to attract a mate, during the breeding season male frogs produce mating calls which have species-specific structure and parameters, and have been successfully used to resolve issues in taxonomy and phylogenetic relations. This is particularly useful when closely related taxa are concerned, as many species are morphologically almost identical, but still their status is well-supported by molecular and genetic data, suggesting the existence of mechanisms for reproductive isolation. Such is the case for treefrogs from the Hyla arborea group, which are now recognized as several distinct species. The present study aims to establish differences in call parameters between the European tree frog, Hyla arborea, and the Eastern tree frog, Hyla orientalis, which both occur on the territory of Bulgaria. Using autonomous audio loggers, calls from six sites (three in the range of H. arborea and three in the range of H. orientalis) were recorded between 7 p.m. and 12 a.m. during the breeding season in 2020–2023. The following parameters in a total of 390 mating calls were analyzed: call count, pulse count, call series duration, call period, peak (dominant) frequency, entropy. Results indicated that sites formed two distinct groups, which corresponded to the known distribution ranges of H. arborea and H. orientalis. The first two components of the PCA explained 71% of the total variance, with variables call count, call series duration, peak frequency and entropy being most important for differentiation between the sites. This study presents the first attempt to differentiate between the calls of these two sister taxa, which both fall within the “short-call treefrogs” group, and results are discussed in terms of known data for mating calls in Hyla sp., as well as limitations and future perspectives.

Abstract Passive acoustic recorders have emerged as powerful tools for ecological monitoring. However, effective monitoring is not simply an act of recording sounds. To have meaning for conservation and management, acoustic monitoring needs to be properly planned and analyzed to yield high quality information. Here, we provide a set of considerations for the design of an effective acoustic monitoring program. We argue that such a program, has the following attributes: (1) has established appropriate partnerships with landowners, Traditional Owners, researchers, or other relevant stakeholders, (2) is based on clear objectives and questions, (3) is explicit in its target sound signals, (4) has considered in‐field sensor placement for a range of factors, including experimental design, statistical power, background noise, and potential impacts on human privacy and animal disturbance, (5) has a justified recording schedule and periodicity, (6) has methods to process sound data in line with objectives, and (7) has protocols for permanent data storage and access. Acoustic monitoring is increasingly used in large‐scale programs and will be important in addressing global biodiversity targets and new biodiversity markets. It is critical that new monitoring programs are designed to effectively and efficiently capture data that address pertinent and emerging issues in conservation.

The population sizes of manatees in many regions remain largely unknown, primarily due to the challenging nature of conducting visual counts in turbid and inaccessible aquatic environments. Passive acoustic monitoring has shown promise for monitoring manatees in the wild. In this study, we present an innovative approach that leverages a convolutional neural network (CNN) for the detection, isolation and classification of manatee vocalizations from long-term audio recordings. To improve the effectiveness of manatee call detection and classification, the CNN works in two phases. First, a long-term audio recording is divided into smaller windows of 0.5 seconds and a binary decision is made as to whether or not it contains a manatee call. Subsequently, these vocalizations are classified into distinct vocal classes (4 categories), allowing for the separation and analysis of signature calls (squeaks). Signature calls are further subjected to clustering techniques to distinguish the recorded individuals and estimate the population size. The CNN was trained and validated using audio recordings from three different zoological facilities with varying numbers of manatees. Three different clustering methods (community detection with two different classifiers and HDBSCAN) were tested for their suitability. The results demonstrate the ability of the CNN to accurately detect manatee vocalizations and effectively classify the different call categories. In addition, our study demonstrates the feasibility of reliable population size estimation using HDBSCAN as clustering method. The integration of CNN and clustering methods offers a promising way to assess manatee populations in visually challenging and inaccessible regions using autonomous acoustic recording devices. In addition, the ability to differentiate between call categories will allow for ongoing monitoring of important information such as stress, arousal, and calf presence, which will aid in the conservation and management of manatees in critical habitats.

During recent decades, increasing research of the taxonomy of the genus Isophya resulted in taxonomic descriptions of several new species. The delimitation of these species is mainly based on oscillographic song analysis of the male song in combination with morphological characters, such as the shape of male cerci, tegmina and ovipositor. In the present paper, we used an integrative taxonomic approach in order to resolve the status of four recently described Isophya taxa from Serbia. Based on our molecular and bioacoustic analyses, all analyzed taxa belong to a single species: Isophya modestior. The majority of the morphological characters used to differentiate the taxa showed strong intra- and interpopulational variability, proving that describing new species within the genus Isophya should not rely on morphological characters alone, but needs to consider bioacoustic analyses as the main tool and a larger series of specimens.

IntroductionWhen studying bird intra- and inter-specific interactions it is crucial to accurately track which individual emits which vocalization. However, locating sounds of free moving birds (and other animals) in nature can be challenging, especially in situations when many individuals call in spatial and temporal vicinity.MethodsIn this paper, we will introduce the use of a hand-held acoustic camera which allows solving this problem. The system comprises 64 microphones each sampling a wide angle at a rate of 200kHz. The system can localize up to dozens of nearby callers and it displays them on a visual image. We applied the system to localize and record vocalizations of Arabian babblers (Argya squamiceps) during snake-mobbing behavior to demonstrate its potential. As the recorded babblers are ringed and therefore identifiable, we could use the calling order to analyze the vocalizing mob and to reconstruct a vocal social network depicting which individuals call after which individuals.ResultsAcoustic analysis revealed that Arabian babblers call in a periodic pattern and with age specific pause lengths between different individuals. Mobbing events with young babblers display a lower vocal network density compared to only adult groups.DiscussionOur approach demonstrates how this novel technology can be applied to reveal new insight about vocal events in group living wild animals. The approach can be elaborated to study intra- and inter-specific animal vocalizations.

Navigation through dense foliage presents a fundamental challenge to autonomous systems, and achieving a performance level similar to echolocating bats could have important applications in areas such as forestry and farming. However, the clutter echoes originating from such environments have been difficult to analyze. To study the problem of sonar‐based navigation in dense foliage in simulation, an artificial generation system for leaf impulse responses (IRs) based on variational auto‐encoders is proposed. The system is to aid the construction of artificial foliage echo environments. A dataset of leaf echoes was collected in an anechoic chamber and convolved with the original signal to estimate the IR of each leaf. A modified version of the conditional variational autoencoder ‐ generative adversarial network (cVAE‐GAN) architecture was trained successfully on this dataset to produce a generative model that was conditional on leaf viewing angles, size, and species. The IRs generated by the model capture quantitative and qualitative similarity to the measured IRs. It surpasses the previous state of the art foliage echo model based on reflecting disks. The model's computational efficiency and its success suggest its potential use for simulating large environments of foliage to study bat biosonar and aid in engineering biomimetic sonar devices.

Fin whale song is a ubiquitous low-frequency pulsing that has been recorded in every ocean basin. In recent years, the complexity of fin whale song has been realized. In the North Atlantic, this song is made up of two low-frequency note types (A and B notes) and one higher-frequency note type (upsweeps). The A and B notes are produced with both singlet and doublet inter-note intervals. These song patterns are interwoven throughout an individual’s song and have been observed to shift over time, both gradually and suddenly. Like many baleen whales, the population size of fin whales is poorly defined. Passive acoustic monitoring has been suggested as a way to estimate abundance if a cue rate or calling rate can be determined. In this study, we used recordings from 119 fin whale tracks from 2013–2023 generated from passive acoustic recorders in the North Atlantic to define fin whale song patterns in this ocean basin, identify changes in these song patterns over time, and calculate cue rates. The fin whales recorded sang in 4 distinct patterns. Inter-note intervals were 25 s for A-A singlets, 13 s for B-B singlets, 12/20 s for B-B doublets, and 10/11 s for A-B doublets. B-B doublet inter-note intervals significantly increased from May 2019–April 2023 and A-B inter-note intervals showed an intra-annual increasing trend that reset each summer. Fin whale song in the North Atlantic also often includes higher frequency upsweep notes immediately prior to B notes. While A and B note peak frequency was steady over these years, upsweep peak frequency significantly decreased, continuing a similar trend that has been observed over 30 years. Despite these changes, the dominant song pattern remained consistent and cue rate for actively singing whales was relatively stable, which is in contrast to patterns observed in the North Pacific. Many of these nuances in fin whale singing behavior are newly described herein, and these findings are vital for monitoring baseline behavior, behavioral trends over time, and ultimately estimating abundance of a poorly understood species.

Population monitoring is an essential component of biodiversity conservation and management, but low detection probabilities for rare and/or cryptic species makes estimating abundance and occupancy challenging. Passive acoustic monitoring combined with machine learning algorithms represents a potential path forward to effectively and efficiently monitor the occurrence of rare vocalizing species across entire forest landscapes. Our objectives were to develop and implement a convolutional neural network (PNW-Cnet) to identify vocalizations of a rare and threatened forest nesting bird species – the marbled murrelet (Brachyramphus marmoratus) – in the Pacific Northwest, U.S.A., 2018–2021. We used PNW-Cnet predictions from broadscale passive acoustic monitoring data to examine spatiotemporal patterns in the distribution of murrelets. PNW-Cnet showed sufficiently high prediction accuracy (overall precision > 0.9) to enable broadscale population monitoring. Spatiotemporal analysis showed that annual peak murrelet call abundance occurs in ordinal weeks 28–32 (late July–Mid August) but this varied by study area. The greatest number of detections typically occurred in the Olympic Peninsula and Oregon Coast Range where late-successional forest dominates and nearer to ocean habitats. We demonstrate that passive acoustic monitoring can be used to understand intensity of use across broad scales for a rare and cryptic species in addition to the typical detection/non-detection data that are often collected. Passive acoustic monitoring combined with PNW-Cnet offers considerable promise for species distribution modeling and long-term population monitoring for rare species.

Passive acoustic monitoring (PAM) studies generate thousands of hours of audio, which may be used to monitor specific animal populations, conduct broad biodiversity surveys, detect threats such as poachers, and more. Machine learning classifiers for species identification are increasingly being used to process the vast amount of audio generated by bioacoustic surveys, expediting analysis and increasing the utility of PAM as a management tool. In common practice, a threshold is applied to classifier output scores, and scores above the threshold are aggregated into a detection count. The choice of threshold produces biased counts of vocalizations, which are subject to false positive/negative rates that may vary across subsets of the dataset. In this work, we advocate for directly estimating call density: The proportion of detection windows containing the target vocalization, regardless of classifier score. We propose a validation scheme for estimating call density in a body of data and obtain, through Bayesian reasoning, probability distributions of confidence scores for both the positive and negative classes. We use these distributions to predict site-level call densities, which may be subject to distribution shifts (when the defining characteristics of the data distribution change). These methods may be applied to the outputs of any binary detection classifier operating on fixed-size audio input windows. We test our proposed methods on a real-world study of Hawaiian birds and provide simulation results leveraging existing fully annotated datasets, demonstrating robustness to variations in call density and classifier model quality.

Bird sound recognition is crucial for bird protection. As bird populations have decreased at an alarming rate, monitoring and analyzing bird species helps us observe diversity and environmental adaptation. A machine learning model was used to classify bird sound signals. To improve the accuracy of bird sound recognition in low-cost hardware systems, a recognition method based on the adaptive frequency cepstrum coefficient and an improved support vector machine model using a hunter-prey optimizer was proposed. First, in sound-specific feature extraction, an adaptive factor is introduced into the extraction of the frequency cepstrum coefficients. The adaptive factor was used to adjust the continuity, smoothness and shape of the filters. The features in the full frequency band are extracted by complementing the two groups of filters. Then, the feature was used as the input for the following support vector machine classification model. A hunter-prey optimizer algorithm was used to improve the support vector machine model. The experimental results show that the recognition accuracy of the proposed method for five types of bird sounds is 93.45%, which is better than that of state-of-the-art support vector machine models. The highest recognition accuracy is obtained by adjusting the adaptive factor. The proposed method improved the accuracy of bird sound recognition. This will be helpful for bird recognition in various applications.

Citizen science is rapidly advancing as a field, engaging the public in scientific research and outcomes, while obtaining large quantities of data. Within the natural sciences, identification of organisms by citizen scientists is an important and increasing source of biodiversity records. However, to ensure the reliability and quality of citizen science data, including species identifications, various strategies have been employed. Crowdsourcing is commonly used to increase confidence in data quality, where results are an aggregate of multiple responses. Projects utilising crowdsourcing techniques have focused on image-based datasets, and are taxonomically biased towards large and charismatic taxa. We explored the utility of crowdsourcing species identification from audio files of calling frogs. We developed a series of audio analysis trials, inviting members of the public to identify frog species from audio recordings submitted to the FrogID project. By comparing the identifications made by citizen scientists with those made by experts in frog identification, we evaluated the suitability of using crowdsourcing to identify frog species calling in 20–60-second audio files. Citizen scientists identified frog species with 57% accuracy, although this varied depending on species, task complexity, and the individual volunteer. By aggregating responses, the accuracy of citizen scientist responses increased to up to 66% when we required a consensus of two volunteers from five responses. Our results indicate that crowdsourcing the identification of multiple frog species from audio recordings is challenging, requiring further exploration into the benefits of training volunteers, providing detailed feedback, and weighting volunteer responses according to individual skill.

The western black-crested gibbon (Nomascus concolor) is a rare and endangered primate that inhabits southern China and northern Vietnam, and has become a key conservation target due to its distinctive call and highly endangered status, making its identification and monitoring particularly urgent. Identifying calls of the western black-crested gibbon using passive acoustic monitoring data is a crucial method for studying and analyzing these gibbons; however, traditional call recognition models often overlook the temporal information in audio features and fail to adapt to channel-feature weights. To address these issues, we propose an innovative deep learning model, VBSNet, designed to recognize and classify a variety of biological calls, including those of endangered western black-crested gibbons and certain bird species. The model incorporates the image feature extraction capability of the VGG16 convolutional network, the sequence modeling capability of bi-directional LSTM, and the feature selection capability of the SE attention module, realizing the multimodal fusion of image, sequence and attention information. In the constructed dataset, the VBSNet model achieved the best performance in the evaluation metrics of accuracy, precision, recall, and F1-score, realizing an accuracy of 98.35%, demonstrating high accuracy and generalization ability. This study provides an effective deep learning method in the field of automated bioacoustic monitoring, which is of great theoretical and practical significance for supporting wildlife conservation and maintaining biodiversity.

The acoustic communication system (ACS) in bark beetles has been studied mainly in species of the genera Dendroctonus, Ips and Polygraphus. Specifically, ACS of the roundheaded pine beetle, Dendroctonus adjunctus, has been little studied. In this study, we described the stridulatory apparatus of this beetle using optical and scanning electron microscopy and recorded the call types produced by males in three behavioral contexts: stress, female–male–, and male–male interactions. From the spectrograms and waveforms, call types, as well as temporal (tooth strike, tooth strike rate, and intertooth strike interval) and spectral features (minimum, maximum and dominant frequency), were determined. Males have a functional elytro–tergal stridulatory apparatus—females do not—consisting of a file for the pars stridens and two lobes for the plectrum. Most of spectro–temporal features were statistically different between single– and multi–noted calls and across the three behavioral contexts. In the male–male interaction, a new type of call named “withdrawal” was produced by the male withdrawing or fleeing. Our results suggest that the spectro–temporal features of single– and multiple–noted calls in the three behavioral conditions are specific and different from each other. Yet, the combination of single and multiple calls determines an overall calling pattern characteristic of the tested behaviors and, therefore, is species–specific.

Advertisement calls in frogs have evolved to be species-specific signals of recognition and are therefore considered an essential component of integrative taxonomic approaches to identify species and delineate their distribution range. The species rich genus Microhyla is a particularly challenging group for species identification, discovery and conservation management due to the small size, conserved morphology and wide distribution of its members, necessitating the need for a thorough description of their vocalization. In this study, we provide quantitative description of the vocal behaviour of Microhyla nilphamariensis, a widely distributed south Asian species, from Delhi, India, based on call recordings of 18 individuals and assessment of 21 call properties. Based on the properties measured acrossed 360 calls, we find that a typical advertisement call of M. nilphamariensis lasts for 393.5 ± 57.5 ms, has 17 pulses on average and produce pulses at rate of 39 pulses/s. The overall call dominant frequency was found to be 2.8 KHz and the call spectrum consisted of two dominant frequency peaks centered at 1.6 KHz and 3.6 KHz, ranging between 1.5–4.1 KHz. Apart from its typical advertisement call, our study also reveals the presence of three ‘rare’ call types, previously unreported in this species. We describe variability in call properties and discuss their relation to body size and temperature. We found that overall dominant frequency 1 (spectral property) was found to be correlated with body size, while first pulse period (temporal property) was found to be correlated with temperature. Further, we compare the vocal repertoire of M. nilphamariensis with that of the congener Microhyla ornata from the western coast of India and Sri Lanka and also compare the call properties of these two populations of M. ornata to investigate intra-specific call variation. We find statistically significant differentiation in their acoustic repertoire in both cases. Based on 18 call properties (out of 20), individuals of each locality clearly segregate on PCA factor plane forming separate groups. Discriminant function analysis (DFA) using PCA factors shows 100% classification success with individuals of each locality getting classified to a discrete group. This confirms significant acoustic differentiation between these species as well as between geographically distant conspecifics. The data generated in this study will be useful for comparative bioacoustic analysis of Microhyla species and can be utilized to monitor populations and devise conservation management plan for threatened species in this group.

Abstract In a rapidly changing Arctic, multiple lines of evidence suggest that bowhead whale migration is changing. To explore these changes further, we used passive acoustic data to examine bowhead whale presence in the western Beaufort Sea (12 years) and Chukchi Plateau (11 years) spanning 2008 to 2022. Departure from the western Beaufort Sea shifted 45 days later over the 12‐year period. Summer presence increased at both sites, suggesting feeding areas within the Chukchi Sea are becoming more favorable. Likewise, findings from the Bering Strait suggest that some whales are remaining north of the Bering Strait for the winter instead of in the Bering Sea. These Pacific Arctic‐wide changes to migration have occurred over only one decade. Questions remain about prey availability in the Chukchi Sea, implications of migratory changes, such as a northward shift in the core overwintering area, and impact to communities south of the Bering Strait.

At present, there are still populations of different balaenopterids that have never been acoustically recorded and observed simultaneously. In an opportunistic sighting of dwarf minke whales during winter in southeastern Brazil, we have registered six individuals and recorded over 200 acoustic signals. Signals were quantified and had their acoustic parameters extracted; the calling rate was estimated as the number of sounds per minute, and the repetition rate was estimated as the number of calls of the same type emitted per minute. Four call types were described: Ba1, Ba2, Ba3 and Ba4. The most common and distinctive call was the Ba1, composed of three components: one had a peak frequency of 615.0 ± 189.8 Hz, and the other had peak frequencies of 1632.0 ± 191.5 and 5038.2 ± 195.1 Hz. Ba1 repetition varied from 2.4 to 11.1 repetitions/min. Ba2 was the second most common call with a peak frequency of 485.2 ± 421.9 Hz. The total calling rate varied from 4.4 to 11.1 calls/min. The signals are in the same frequency range reported for other areas but contain different structures.

This review discusses how welfare scientists can examine vocalizations to gain insight into the affective states of individual animals. In recent years, researchers working in professionally managed settings have recognized the value of monitoring the types, rates, and acoustic structures of calls, which may reflect various aspects of welfare. Fortunately, recent technological advances in the field of bioacoustics allow for vocal activity to be recorded with microphones, hydrophones, and animal-attached devices (e.g., collars), as well as automated call recognition. We consider how vocal behavior can be used as an indicator of affective state, with particular interest in the valence of emotions. While most studies have investigated vocal activity produced in negative contexts (e.g., experiencing pain, social isolation, environmental disturbances), we highlight vocalizations that express positive affective states. For instance, some species produce vocalizations while foraging, playing, engaging in grooming, or interacting affiliatively with conspecifics. This review provides an overview of the evidence that exists for the construct validity of vocal indicators of affective state in non-human mammals. Furthermore, we discuss non-invasive methods that can be utilized to investigate vocal behavior, as well as potential limitations to this line of research. In the future, welfare scientists should attempt to identify reliable, valid species-specific calls that reflect emotional valence, which may be possible by adopting a dimensional approach. The dimensional approach considers both arousal and valence by comparing vocalizations emitted in negative and positive contexts. Ultimately, acoustic activity can be tracked continuously to detect shifts in welfare status or to evaluate the impact of animal transfers, introductions, and changes to the husbandry routine or environment. We encourage welfare scientists to expand their welfare monitoring toolkits by combining vocal activity with other behavioral measures and physiological biomarkers.

Identical twins challenge individual voice recognition applied in communication, criminal investigation, and health technologies. The similarities between siblings are due to the genetic heritage that produced either their vocal tract or the psychological processes acting since birth. To contribute to a precise individual identification by voice, we tested the hypothesis that even MZ siblings would differ in the spectral distribution of formants and sound quality. We recorded 86 same-sex twin pairs (71 MZ, 15 DZ 30 ± 12 years old) saying the Portuguese verse of “Hi, my name is (Pedro or Ana).” We trimmed the diphthong Ój (“Hi”) and measured f0, F1-F4 formants, jitter, shimmer, and Harmonic-to-noise ratio (HNR). As the male sampling was small, we compared only female twins using the Wilcoxon-matched test, the intrapair Correlation Coefficient (ICC), and GLMM models. On average, [Ój] lasted 0.24 ± 0.07 s, and f0 ranged from 469 to 510 kHz according to sex. We found no differences between the MZ or DZ sisters in any acoustic parameter, besides F1 for MZ ones. Also, MZ sisters were more alike (F1, jitter, HNR, weak; F3, F4 moderate) than DZ (only F1 high ICC). The interaction zygosity × age affected intrapair similarity, the older the sisters, the greater their differences. Short voice segments, like the diphthong [Ój], may not be good for differentiating identical twins but provide insights to discussion; F2, F4, and shimmer should be investigated in future studies considering males, younger participants, and other vocal expressions.

Acoustic monitoring has proven to be an effective tool for monitoring biotic soundscapes in the marine, terrestrial, and aquatic realms. Recently it has been suggested that it could also be an effective method for monitoring soil soundscapes, but has been used in very few studies, primarily in temperate and polar regions.We present the first study of soil soundscapes using passive acoustic monitoring in tropical forests, using a novel analytical pipeline allowing for the use of in-situ recording of soundscapes with minimal soil disturbance. We found significant differences in acoustic index values between burnt and unburnt forests and the first indications of a diel cycle in soil soundscapes.These promising results and methodological advances highlight the potential of passive acoustic monitoring for large-scale and long-term monitoring of soil biodiversity. We use the results to discuss research priorities, including relating soil biophony to community structure and ecosystem function, and the use of appropriate hardware and analytical techniques.

Vocal communication is widespread in animals, with vocal repertoires of varying complexity. The social complexity hypothesis predicts that species may need high vocal complexity to deal with complex social organization (e.g. have a variety of different interindividual relations). We quantified the vocal complexity of two geographically distant captive colonies of rooks, a corvid species with complex social organization and cognitive performances, but understudied vocal abilities. We quantified the diversity and gradation of their repertoire, as well as the inter-individual similarity at the vocal unit level. We found that males produced call units with lower diversity and gradation than females, while song units did not differ between sexes. Surprisingly, while females produced highly similar call repertoires, even between colonies, each individual male produced almost completely different call repertoires from any other individual. These findings question the way male rooks communicate with their social partners. We suggest that each male may actively seek to remain vocally distinct, which could be an asset in their frequently changing social environment. We conclude that inter-individual similarity, an understudied aspect of vocal repertoires, should also be considered as a measure of vocal complexity.

Abstract Animal acoustic signals are widely used in diverse research areas due to the relative ease with which sounds can be registered across a wide range of taxonomic groups and research settings. However, bioacoustics research can quickly generate large data sets, which might prove challenging to analyse promptly. Although many tools are available for the automated detection of sounds, choosing the right approach can be difficult and only a few tools provide a framework for evaluating detection performance. Here, we present ohun, an R package intended to facilitate automated sound event detection. ohun provides functions to diagnose and optimize detection routines, compare performance among different detection approaches and evaluate the accuracy in inferring the temporal location of events. The package uses reference annotations containing the time position of target sounds in a training data set to evaluate detection routine performance using common signal detection theory indices. This can be done both with routine outputs imported from other software and detections run within the package. The package also provides functions to organize acoustic data sets in a format amenable to detection analyses. In addition, ohun includes energy‐based and template‐based detection methods, two commonly used automatic approaches in bioacoustics research. We show how ohun can be used to automatically detect vocal signals with case studies of adult male zebra finch Taenopygia gutata songs and Spix's disc‐winged bat Thyroptera tricolor ultrasonic social calls. We also include examples of how to evaluate the detection performance of ohun and external software. Finally, we provide some general suggestions to improve detection performance.

Abstract Landscape‐scale bioacoustic projects have become a popular approach to biodiversity monitoring. Combining passive acoustic monitoring recordings and automated detection provides an effective means of monitoring sound‐producing species' occupancy and phenology and can lend insight into unobserved behaviours and patterns. The availability of low‐cost recording hardware has lowered barriers to large‐scale data collection, but technological barriers in data analysis remain a bottleneck for extracting biological insight from bioacoustic datasets. We provide a robust and open‐source Python toolkit for detecting and localizing biological sounds in acoustic data. OpenSoundscape provides access to automated acoustic detection, classification and localization methods through a simple and easy‐to‐use set of tools. Extensive documentation and tutorials provide step‐by‐step instructions and examples of end‐to‐end analysis of bioacoustic data. Here, we describe the functionality of this package and provide concise examples of bioacoustic analyses with OpenSoundscape. By providing an interface for bioacoustic data and methods, we hope this package will lead to increased adoption of bioacoustics methods and ultimately to enhanced insights for ecology and conservation.

The temporal characteristics of whale song are considered to be amongst the most stable elements of song production, though very few studies have looked at the timing of discrete sounds or the silences between them. This study indicates that the temporal characteristics of the song of the southeast Indian Ocean pygmy blue (SEIOPB) whale are highly variable. While the song repetition interval, or inter-song interval (ISI), appeared to remain consistent within a song event, defined as a consistent bout of singing presumed to come from an individual animal, it was found to be variable between song events. Song structure was a confounding factor in the analysis of variability in ISI with songs comprised of longer phrases naturally having a longer song repetition interval. The songs of the SEIOPB whale are characteristic of the species with only three primary song types. The three-unit song (units I, II and III) has the longest mean ISI of ~ 200 s, approximately twice that of songs composed of only two units (II and III) at a mean of ~ 100 s ISI which is approximately twice the ISI of a one-unit song (unit II only). The results of this study suggest that variability in ISI cannot be attributed to change in one element of a song but is driven by temporal changes in all elements of the song including phrase structure, unit duration and the length of the intervals between song units. Thus, the temporal characteristics of blue whale song appear to be under the control of the animal and may vary between individual whales, and within songs of an individual. It is concluded that the temporal characteristics of song potentially encode contextual information about the singer and may be influenced by audience effects, including audience composition and singer density.

Abstract Studying the vocalisations of wild animals can be a challenge due to the limitations of traditional computational methods, which often are time‐consuming and lack reproducibility. Here, I present pykanto, a new software package that provides a set of tools to build, manage, and explore large sound databases. It can automatically find discrete units in animal vocalisations, perform semi‐supervised labelling of individual repertoires with a new interactive web app and feed data to deep learning models. pykanto can be used to streamline research on, for example, individual vocal signatures and acoustic similarity between individuals and populations. To demonstrate its capabilities, I put the library to the test on the vocalisations of male great tits in Wytham Woods, near Oxford, UK. The results show that the identities of individual birds can be accurately determined from their songs and that the use of pykanto improves the efficiency and reproducibility of the process.

Passerines are the most successfully diversified bird order (around 60% of all avian species). They have developed complicated songs to defend their territories and to attract females for mating that can evolve quickly due to cultural transmission. Complex singing as well as plumage coloration of male birds are honest signals for potential partners and provide information about the males’ quality. To function as honest signals, both traits must be costly for the males. Of course, not all passerine species are equally clever or beautiful. Even within a single family of 50 to 70 species, relevant traits may vary considerably. Tits and chickadees (Paridae) comprise species of similar size, varying a lot in coloration and plumage pattern. The territorial songs are relatively short and simple. We investigated the relationship between song complexity and plumage coloration, taking phylogenetic relationships into account. We studied 55 out of the 64 species with 1084 song recordings retrieved from an online database. In the best model, besides colorfulness, body size had a negative impact on song complexity. Large colorful species were found to sing less complex songs. This result supports the hypothesis of a trade-off between costly traits and their likely intense signal function. This study contributes to a better understanding of how sexual selection influences the diversification of traits. In addition, we found that despite the relatively uniform size, the general negative correlation between body size and song frequency can be recovered. Some song traits are further influenced by distribution and thus by interspecific differences in climate niche.

Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential for many bat studies. Specialized audio sensors can be used to record bat echolocation calls that can then be used to identify bat species. However, the complexity of bat calls presents a significant challenge, necessitating expert analysis and extensive time for accurate interpretation. Recent advances in neural networks can help identify bat species automatically from their echolocation calls. Such neural networks can be integrated into a complete end-to-end system that leverages recent internet of things (IoT) technologies with long-range, low-powered communication protocols to implement automated acoustical monitoring. This paper presents the design and implementation of such a system that uses a tiny neural network for interpreting sensor data derived from bat echolocation signals. A highly compact convolutional neural network (CNN) model was developed that demonstrated excellent performance in bat species identification, achieving an F1-score of 0.9578 and an accuracy rate of 97.5%. The neural network was deployed, and its performance was evaluated on various alternative edge devices, including the NVIDIA Jetson Nano and Google Coral.

As inhabitants of cavities, some owl species benefit from natural processes, different tree compositions and higher volumes of dead wood. We assumed that protected areas would have a positive impact on the owl community. We compared the abundance of calling owls on 22 lines in managed versus protected forests. Here, we recorded the acoustic communication of owls. The composition of the owl community did not differ between areas. However, we found the impact of forest landscape integrity index and altitude on the diversity of owls. Forests in protected areas probably need time to develop natural and heterogeneous habitat structures. The conservation priority should be to increase the integrity of the forests. Our results also confirmed that managed forests can have a high diversity of owls.

Abstract Rhythmicity in the millisecond to second range is a fundamental building block of communication and coordinated movement. But how widespread are rhythmic capacities across species, and how did they evolve under different environmental pressures? Comparative research is necessary to answer these questions but has been hindered by limited crosstalk and comparability among results from different study species. Most acoustics studies do not explicitly focus on characterising or quantifying rhythm, but many are just a few scrapes away from contributing to and advancing the field of comparative rhythm research. Here, we present an eight‐level rhythm reporting framework which details actionable steps researchers can take to report rhythm‐relevant metrics. Levels fall into two categories: metric reporting and data sharing. Metric reporting levels include defining rhythm‐relevant metrics, providing point estimates of temporal interval variability, reporting interval distributions, and conducting rhythm analyses. Data sharing levels are: sharing audio recordings, sharing interval durations, sharing sound element start and end times, and sharing audio recordings with sound element start/end times. Using sounds recorded from a sperm whale as a case study, we demonstrate how each reporting framework level can be implemented on real data. We also highlight existing best practice examples from recent research spanning multiple species. We clearly detail how engagement with our framework can be tailored case‐by‐case based on how much time and effort researchers are willing to contribute. Finally, we illustrate how reporting at any of the suggested levels will help advance comparative rhythm research. This framework will actively facilitate a comparative approach to acoustic rhythms while also promoting cooperation and data sustainability. By quantifying and reporting rhythm metrics more consistently and broadly, new avenues of inquiry and several long‐standing, big picture research questions become more tractable. These lines of research can inform not only about the behavioural ecology of animals but also about the evolution of rhythm‐relevant phenomena and the behavioural neuroscience of rhythm production and perception. Rhythm is clearly an emergent feature of life; adopting our framework, researchers from different fields and with different study species can help understand why.

Abstract Passive acoustic monitoring is usually presented as a complementary approach to monitoring wildlife communities and assessing ecosystem conditions. Automatic species detection methods support biodiversity monitoring and analysis by providing information on the presence–absence of species, which allows understanding the ecosystem structure. Therefore, different alternatives have been proposed to identify species. However, the algorithms are parameterized to identify specific species. Analysing multiple species would help to monitor and quantify biodiversity, as it includes the different taxonomic groups present in the soundscape. We present an unsupervised methodology for multi‐species call recognition from ecological soundscapes. The proposal is based on a clustering algorithm, specifically the learning algorithm for multivariate data analysis (LAMDA) 3pi algorithm, which automatically suggests the number of clusters associated with the sonotypes. Emphasis was made on improving the segmentation of the audio to analyse the whole soundscape without parameterizing the algorithm according to each taxonomic group. To estimate the performance of our proposal, we used four datasets from different locations, years and habitats. These datasets contain sounds from the four major taxonomic groups that dominate terrestrial soundscapes (birds, amphibians, mammals and insects) in audible and ultrasonic spectra. The methodology presents performances between 75% and 96% in presence–absence species recognition. Using the clusters proposed by our methodology, the whole soundscape biodiversity was measured and compared with the estimate of four acoustic indices (ACI, NP, SO and BI). Our approach performs biodiversity assessments similar to acoustic indices with the advantage of providing information about acoustic communities without the need for prior knowledge of the species present in the audio recordings.

Mesophotic coral ecosystems (MCEs) are the deepest part of tropical coral reefs, ranging from depths of 30 to over 170 m. Despite their significance, MCEs remain largely unexplored due to the challenges associated with accessing these depths. However, the application of passive acoustic monitoring methods (PAM) is a suitable approach for studying fish communities within these unique habitats. In French Polynesia, recent PAM studies have unveiled a higher occurrence of frequency-modulated fish sounds in MCEs than in shallower reef environments. This study aims to further enhance our understanding of fish sounds in MCEs by examining their diel patterns, focusing specifically on the two most abundant frequency-modulated fish sounds that were recorded at depths of 60 and 120 m at six Polynesian islands. Both sound types occurred predominantly during the beginning and the end of nocturnal periods. The presence and abundance of these sounds exhibited variation between the islands, highlighting potential regional disparities in vocal activity or the bathymetric distribution of the sound-producing species. By characterizing the diel cycles and bathymetric differences in relation to their geographical distribution, this study offers preliminary insights into identifying the potential sound-producing species.

Background Animal communication consists of signal production and perception, which are crucial for social interactions. The main form used by anurans is auditory communication, in most cases produced as advertisement calls. Furthermore, sound perception happens mainly through an external tympanic membrane, and plays an important role in social behavior. In this study, we evaluated the influence of body and tympanic membrane sizes on call frequency across the phylogeny of anurans. Methods We use data on snout-vent length, tympanic membrane diameter, and dominant frequency of the advertisement call from the literature and from natural history museum collections. We mapped these traits across the anuran phylogeny and tested different models of diversification. Our final dataset includes data on body size, tympanic membrane size, and call dominant frequency of 735 anuran species. Results The best explanatory model includes body and tympanum size with no interaction term. Although our results show that call frequency is strongly constrained by body and tympanum size, we identify five evolutionary shifts in allometry from that ancestral constraint. We relate these evolutionary shifts to the background noise experienced by populations. Body size is important for myriad ecological interactions and tympanum size is strongly associated with female call frequency preferences. Thus, allometric escape in frog calls might arise through environmental selection such as breeding in fast flowing or soundscape competition, as well as sexual selection linked to tympanum size.

Tropical dry forests (TDFs) support endemic biodiversity, and the livelihoods of millions of people globally. Invasive species, such as Lantana camara, are a predominant cause of degradation of TDFs. We examined lesser-studied vocalizing fauna and social outcomes of TDF restoration through Lantana removal, focusing on a Central Indian TDF. We quantified biodiversity using acoustics in 55 locations in restored, unrestored, and Low Lantana Density (LLD) forest sites and surveyed 656 households across villages adjacent to these forest sites. Our ecological analysis showed that in comparison to unrestored and LLD sites, restoration was not significantly associated with a different acoustic space occupancy (ASO) in higher frequencies (9–24 kHz) during night time hours, meaning restoration does not impact nocturnal vocalising or stridulating species. However, restored and LLD sites had significantly lower ASO in the day time hours, potentially due to differences in the insect community when Lantana is absent. Through the household surveys, we found that the highest number of respondents across all the three types of sites valued the cash payment they received for participating in restoration efforts. Perceptions of lower amounts of crop raiding by wild ungulates were associated by villagers with a restored site. This perception was mediated by the total number of households in a village with a restored site. Focusing restoration efforts on forests surrounding villages, restoration planners could reduce potential negative human-wildlife interactions. Combining ecological and social outcomes, we found that there are immediate positive outcomes of restoration for people. However, in the short term (three years following restoration), there was no significant biodiversity ‘benefit’. Based on our results, we recommend that restoration planners (1) consult local people about their perception of forest degradation and restoration because people’s perceptions can accurately mirror the condition of the forest; (2) provide a cash income for participating in restoration activities and (3) anticipate potential changes in the faunal species community in the short term when large scale invasive species removal takes place.

The extraction of species-specific calls from passive acoustic recordings is a common preliminary step in ecological analysis. But for many species, especially those occupying noisy, acoustically variable habitats, the call extraction process remains largely manual, a time-consuming, and increasingly unsustainable process. Deep neural networks have been shown to provide excellent performance in a range of acoustic classification applications. We take as an example the recognition of four songs of one of the rarest mammals in the world, the western black-crested gibbon (Nomascus concolors).The process of recognition in this paper which includes distributed BIC ambient sound segmentation based on the OpenPAI platform; DNN-based western black-crested gibbon song enhancement processing; data pre-processing, labeling samples; proposed DNN + ResNet34 + CBAM + GRU + Attention recognition model; comparing other classical neural network models.Our best model converts acoustic recordings into spectrogram images on the mel frequency scale and uses these images to train convolutional neural networks.Our proposed model proved to be very accurate in predicting the segmented western black-crested gibbon songs with an accuracy of 99.8%, and almost a few western black-crested gibbon songs were incorrectly identified when all segmented data were recognized. In the four consecutive years of the acoustic monitoring system deployed in the Ailao Mountain National Nature Reserve, the western black-crested gibbon was most active near the monitoring site from March to August each year, and least active in January and February. Based on call sound intensity analysis, we monitored a total of two different western black-crested gibbon groups (G1 and G2) during the monitoring cycle. We demonstrate that passive acoustic monitoring combined with CNN classifiers is an effective tool for the remote detection of one of the rarest and most threatened species in the world.

Summary: Ultrasound focusing plays an important role in biomedical therapy and diagnosis. Acoustic metalens has showcased remarkable focusing performance but yet to be implemented to the practical ultrasound therapeutic applications. We design a planar metalens operating at megahertz and experimentally demonstrate the distinct thermal effect on biological tissues induced by the high-resolution focusing. A prominent temperature rise of 50°C is experimentally observed in the biological phantom, with a much lower input ultrasound power of 4 W compared with the traditional methods. We further study the thermal effect on fresh porcine liver and investigate the morphological changes under different physical parameters. Visible lesions are observed in in vitro tissues at the lowest input ultrasound power of 2.6 W within 10 s. This study facilitates the practical biomedical application of acoustic metalens, providing a feasible approach for the precise, safe, and reliable therapeutic ultrasound with the simple and compact metalens.

Monitoring population size at ecosystem scales is difficult for most species of conservation concern. While assessing site occupancy at broad scales has proven feasible, rigorous tracking of changes in population size over time has not – even though it can provide a stronger basis for assessing population status and conservation-decision making. Therefore, we demonstrate how relatively low-intensity, ecosystem-scale passive acoustic monitoring (PAM) can be linked to local-density monitoring to estimate the population size of native California spotted owls (Strix occidentalis occidentalis) and invasive barred owls (S. varia) across the western Sierra Nevada, California. Based on a PAM sampling grid with 400 ha cells (the approximate home range size of these species), we estimated site occupancy to be between 0.42 (SE = 0.02) and 0.30 (SE = 0.02) for California spotted owls using relatively liberal and strict criteria, respectively, for considering a cell occupied. PAM-based site occupancy estimates within local-scale density monitoring study areas (range = 0.41–0.78 and 0.28–0.76 for liberal and strict criteria, respectively) were strongly and positively correlated with local density (range = 0.08–0.31 owl/km2) for this species. In contrast, ecosystem-wide site occupancy of barred owls was very low based on PAM (0.034, SE