Bioacoustic Data Research Articles

Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.

Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step-by-step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field.

Active few-shot learning for rare bioacoustic feature annotation

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.

Birds, bats and beyond: evaluating generalization in bioacoustics models

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.

All thresholds barred: direct estimation of call density in bioacoustic data

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.

The fourth species of Leptobrachella (Anura, Megophryidae) found at Shiwandashan National Nature Reserve, Guangxi, China.

A new species of the genus Leptobrachella, L.guinanensissp. nov., is described in this study based on morphological, molecular, and bioacoustic data. The species was discovered in the Shiwandashan National Nature Reserve in Shangsi County, Guangxi, China. Phylogenetically, L.guinanensissp. nov. is closely related to L.ventripunctata. However, there are distinct morphological differences between L.guinanensissp. nov. and L.ventripunctata, as well as three other sympatric species (L.shangsiensis, L.shiwandashanensis, and L.sungi). These differences include body size (SVL 30.5-32.5 mm in males; 38.7-41.8 mm in females in the new species vs 25.5-28.0 mm in males, 31.5-35.0 mm in females in L.ventripunctata), the absence of brown spots on the ventral surface (vs chest and belly creamy white with many scattered brown spots in L.ventripunctata), 1/3 toe webbing and wide toe lateral fringes (vs no toe webbing and no lateral fringes in L.ventripunctata), and distinct dermal ridges under toes (vs absent in L.ventripunctata). Furthermore, the dominant vocal frequencies of the new species range from 7.3 to 8.3 kHz, which is unique compared to other Leptobrachella species and represents the highest dominant frequencies ever recorded. The Shiwandashan National Nature Reserve is now home to four known sympatric species of Leptobrachella.

Automatic bioacoustics noise reduction method based on a deep feature loss network

Acoustic sensors that collect acoustic data over extended periods and broad ranges are widely used in bioacoustics monitoring. However, in open environments, acoustic data collected using acoustic sensors can be subject to interference from various real-world noises, thereby influencing the subsequent analysis and processing of bioacoustic data. Existing bioacoustic noise reduction methods are limited in their application because of their low efficiency, unsuitability for non-stationary noise, generally unimproved signal-to-noise ratio (SNR) efficacy, and considerable amounts of residual noise. These limitations hinder the effective processing of recorded signals for which extraneous noise overlaps with bird vocalizations. In this study, we propose a bioacoustic noise reduction method based on a deep feature loss network for bird sounds. The method has a rapid denoising speed and can more effectively remove background noise from field recording signals without distorting the bird acoustic spectrum. The denoising effects of the proposed method were compared with those of a speech enhancement generative adversarial network, web real-time communications denoising, and other noise reduction methods. The denoising ability of these methods for different noises was evaluated using spectrograms and objective evaluation measures such as the SNR and perceptual evaluation of speech quality (PESQ). The experimental results revealed that our proposed noise reduction method can obtain higher SNRs and PESQ scores than other noise reduction methods, with the SNR increasing by up to 35.83 dB following denoising.

Development of the hyolaryngeal architecture in horseshoe bats: insights into the evolution of the pulse generation for laryngeal echolocation

BackgroundThe 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.ResultsWe 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.ConclusionsThe 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.

A novel deep learning‐based bioacoustic approach for identification of look‐alike white‐eye (Zosterops) species traded in wildlife markets

The songbird trade crisis in East and South East Asia has been fuelled by high demand, driving many species to the brink of extinction. This demand, driven by the desire for songbirds as pets, for singing competitions and for prayer animal release has led to the overexploitation of numerous species and the introduction and spread of invasive alien species and diseases to novel environments. The ability to identify traded species efficiently and accurately is crucial for monitoring bird trade markets, protecting threatened species and enforcing wildlife laws. Citizen scientists can make major contributions to these conservation efforts but may be constrained by difficulties in distinguishing ‘look‐alike’ bird species traded in markets. To address this challenge, we developed a novel deep learning‐based Artificial Intelligence (AI) bioacoustic tool to enable citizen scientists to identify bird species traded in markets. To this end, we used three major avian vocalization databases to access bioacoustic data for 15 morphologically similar White‐eye (Zosterops) species that are commonly traded in Asian wildlife markets. Specifically, we employed the Inception v3 pre‐trained model to classify the 15 White‐eye species and ambient sound (i.e. non‐bird sound) using 448 bird recordings we obtained. We converted recordings into spectrogram (i.e. image form) and used eight image augmentation methods to enhance the performance of the AI neural network through training and validation. We found that recall, precision and F1 score increased as the amount of data augmentation increased, resulting in up to 91.6% overall accuracy and an F1 score of 88.8% for identifying focal species. Through the application of bioacoustics and deep learning, this approach would enable citizen scientists and law enforcement officials efficiently and accurately to identify prohibited trade in threatened species, making important contributions to conservation.

Discovery and description of a new species of burrowing frog Sphaerotheca Gnther, 1859 (Anura: Dicroglossidae) from the suburban landscapes of Bengaluru, India.

Recent resolution of prevailing taxonomic ambiguities in the genus Sphaerotheca and new species discoveries from urban/suburban landscapes highlight the need for attention to non-forested habitats for amphibian conservation. In this paper, we review the status of the members of the genus Sphaerotheca and justify the synonymy of Sphaerotheca magadha as a junior synonym of Sphaerotheca swani. The prospects of resurrection of Sphaerotheca swani (herein preliminarily referred to as Sphaerotheca cf. breviceps [swani]) are discussed. In addition, we describe a new species Sphaerotheca varshaabhu sp. nov. from the suburban region of Bengaluru, India. We employ an integrative taxonomic approach to characterize the new species using molecular phylogeny, genetic distance, morphological characters, and geographical isolation as lines of evidence. We also provide a description of vocal repertoire of Sphaerotheca varshaabhu sp. nov. and provide comparative bioacoustics data for four species. This previously undescribed species from the suburban areas of Bengaluru described herein as Sphaerotheca varshaabhu sp. nov. forms a genetically divergent lineage and its genetic distance varied from 3.6% to 12.2% for 16S rRNA with respect to other species of Sphaerotheca. Our phylogenetic analysis for the genus including the new species confirms the synonymy of one recently described species, resulting in 10 valid species in the genus Sphaerotheca. These results emphasize the need for utilizing an integrative taxonomic approach for uncovering hidden diversity of suburban areas. Given these recent discoveries, we advocate for more robust surveys in human dominated areas, so that these amphibians may receive more attention.

When colors mislead: Genomics and bioacoustics prompt re-classification of Asian flycatcher radiation (Aves: Niltavinae)

Traditional classification of many animals, including birds, has been highly dependent on external morphological characters like plumage coloration. However, both bioacoustics and genetic or genomic data have revolutionized our understanding of the relationships of certain lineages and led to sweeping taxonomic re-organizations. In this study, we present a case of erroneous delimitation of genus boundaries in the species-rich flycatcher subfamily Niltavinae. Genera within this subfamily have historically been delineated based on blue versus brown male body plumage until recent studies based on a few mitochondrial and nuclear loci unearthed several cases of generic misclassification. Here we use extensive bioacoustic data from 43 species and genomic data from 28 species for a fundamental reclassification of species in the Niltavinae. Our study reveals that song is an important trait to classify these birds even at the genus level, whereas plumage traits exhibit ample convergence and have led to numerous historic misattributions. Our taxonomic re-organization leads to new biogeographic limits of major genera, such that the genus Cyornis now only extends as far east as the islands of Sulawesi, Sula, and Banggai, whereas Eumyias is redefined to extend far beyond Wallace’s Line to the islands of Seram and Timor. Our conclusions advise against an over-reliance on morphological traits and underscore the importance of integrative datasets.

An improved computational bioacoustic monitoring approach for detecting sparse features

The collection and annotation of bioacoustic data presents a number of challenges to researchers, often constraining analysis to highly vocal species. Computational tools allow monitoring to be extended to less vocal and more challenging species but data limitations remain an issue. We present a human-in-the-loop approach that combines the efficiency of computational tools with the accuracy of human analysis. We use a wavelet-based segmentation method that automatically extracts transient features within field recordings which can reduce data by up to 90% and requires as few as one reference feature. Segmented features are then used to fine-tune a transformer-based model, audio spectrogram transformer (AST), the output of which is verified by a human and the adjusted data fed back into the model to improve performance over time. We also present an outlier detection approach based on Mel-frequency Cepstral Coefficients. Coefficients are projected to 2-D and outliers are detected using silhouette score. This approach was able to achieve 98.8% validation accuracy on a binary classification task using a limited dataset of 200 5-min recordings with sparse features (occurrence rates of less than 1%). This approach makes real-time bioacoustic monitoring of less-vocal species a possibility.

Where are the whales? Continued acoustic monitoring efforts of California’s Central Coast

Understanding and conserving ocean ecosystems is a difficult, but important task made easier with bioacoustic technology. Although marine mammal monitoring efforts can be done with visual surveys, they often lack temporal information on a finer scale. Long-term passive acoustic monitoring efforts of critical migratory species can provide detailed information about the region’s marine soundscape that is key to conservation efforts. The aim of our project is to establish a long-term bioacoustic monitoring station off the coast of central California and help close the gap in our understanding of marine mammal presence along the Pacific coastline. The goals put forth by the bioacoustic lab for this academic year include (1) determining probable species presence, (2) ascertaining temporal (day/night) variations of vocalizations, and (3) examining key differences in species and call type between two deployments from Fall 2022 and Winter 2023. These comparisons will be crucial to help develop a baseline understanding of seasonal and temporal variations of the soundscape in our region. We aim to implement various long-term deployments in order to gather more complete and consistent bioacoustic data in our local waters. These deployments and the subsequent analysis will contribute to large scale marine mammal monitoring and conservation efforts. [This project was made possible by a grant from the Santa Rosa Creek Foundation and the COAST undergraduate research fund.]

A new species of the genus Leptobrachella Smith 1925 (Amphibia, Anura, Megophryidae) from Guangxi, China.

A new species of Leptobrachella, L.wumingensissp. nov., was described from the Damingshan National Nature Reserve, Wuming District, Nanning City, Guangxi, China based on morphological, molecular and bioacoustic data. Phylogenetic analysis of 16S mtDNA fragments revealed that the new species is closely related to L.damingshanensis. Uncorrected p-distances between the new species and all homologous DNA sequences available for the 16S gene of Leptobrachella are greater than 7.1%. Morphologically, L.wumingensissp. nov. differs from its congeners in several ways, including a medium body size (SVL 26.0-26.7 mm in males, 30.6-34.8 mm in females), lack of toe webbing and lateral fringes, shagreened and granular dorsal surface, pale brown dorsum with darker brown markings, iris bicolored, with the upper half copper and fading to silver in the lower half, and the presence of small irregular black spots and tangerine tubercles on the flanks. Furthermore, we found the new species to have two types of advertisement calls and relatively high dominant frequencies, making it distinct from its congeners.

OpenSoundscape: An open‐source bioacoustics analysis package for Python

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.

Bioacoustics of Tettigoniidae (Insecta, Orthoptera) Distributed in Hakkari (Eastern Anatolia) Province

In this study, the male calling songs of sixteen species belonging to the Tettigoniidae family distributed in Hakkari were recorded in the field studies and bioacoustic analyses were performed. The bioacoustic analysis of two genera (Kurdia and Novadrymadusa) documented first time with present study and in total six species (Bradyporus (Callimenus) latipes, Isophya hakkarica, Kurdia uvarovi, Novadrymadusa karabagi, Psorodonotus hakkari, and Squamiana supericola) were presented first time. The bioacoustic analyses of seven species (Conocephalus (Anisoptera) fuscus, Pezodrymadusa indivisa, Polysarcus zacharovi, Tettigonia armeniaca, Tettigonia caudata, Tettigonia viridissima, and Uvarovistia satunini) were examined for the first time from Hakkari populations. In addition, in this study, the bioacoustic analyses of three species (Apholidoptera kurda, Saga hakkarica, and Uvarovistia zebra) distributed in Hakkari were re-evaluated in this study on new individuals. As a result of the findings, it has been determined that the male calling song of sixteen species belonging to the Tettigoniidae family are species-specific, have a simple calling song type, and morphological species hypotheses are also supported by bioacoustic data (male calling song).

Integrative revision of the Blommersia wittei complex, with description of a new species of frog from western and north-western Madagascar.

Frogs of the Blommersia wittei complex are widespread in western and northern Madagascar, and are one of two clades of the family Mantellidae that have colonized the Comoran island of Mayotte. Based on a comprehensive set of DNA sequences of the mitochondrial 16S rRNA gene and the nuclear-encoded RAG1 and SACS genes, integrated with morphological and bioacoustic data, we here analyze the genetic differentiation of populations of this complex across Madagascar. We confirm that a candidate species named B. sp. Ca5 in previous studies represents a genetically well-defined evolutionary lineage distributed over much of western Madagascar, which we describe herein as Blommeria bara sp. nov. based on its molecular and bioacoustic differentiation. Blommersia wittei occurs across northern Madagascar but its type locality Ambanja, at the lower Sambirano river, is very close to the range of another, newly discovered microendemic lineage that was only found at two sites along the upper Sambirano river (here named as candidate species B. sp. Ca12). The B. wittei complex thus provides an example of a clade of closely related Malagasy frogs that contains species widespread over hundreds of kilometers, as well as extreme microendemics. For a full resolution of this species complex, more data need to be collected on the geographical contact among these two lineages, on the morphology and bioacoustics of B. sp. Ca12, and on the north-eastern populations of B. wittei at Sambava, which are weakly differentiated in mitochondrial genes but differ in bioacoustics and possibly in the extent of foot webbing.

Artificial sound impact could put at risk hermit crabs and their symbiont anemones

The sea anemone Calliactis parasitica, which is found in the East Atlantic (Portugal to Senegal) and the Mediterranean Sea, forms a symbiotic relationship with the red hermit crab, Dardanus calidus, in which the anemone provides protection from predators such as the octopus while it gains mobility, and possibly food scraps, from the hermit crab. Acoustic pollution is recognised by the scientific community as a growing threat to ocean inhabitants. Recent findings on marine invertebrates showed that exposure to artificial sound had direct behavioural, physiological and ultrastructural consequences. In this study we assess the impact of artificial sound (received level 157 ± 5 dB re 1 μPa2 with peak levels up to 175 dB re 1 μPa2) on the red hermit crab and its symbiotic sea anemone. Scanning electron microscopy analyses revealed lesions in the statocyst of the red hermit crab and in the tentacle sensory epithelia of its anemone when exposed to low-intensity, low-frequency sounds. These ultrastructural changes under situations of acoustic stress in symbiotic partners belonging to different phyla is a new issue that may limit their survival capacity, and a new challenge in assessing the effects of acoustic disturbance in the oceanic ecosystem. Despite the lesions found in the red hermit crab, its righting reflex time was not as strongly affected showing only an increase in the range of righting times. Given that low-frequency sound levels in the ocean are increasing and that reliable bioacoustic data on invertebrates is very scarce, in light of the results of the present study, we argue that anthropogenic sound effects on invertebrates species may have direct consequences in the entire ecosystem.

An active learning framework and assessment of inter-annotator agreement facilitate automated recogniser development for vocalisations of a rare species, the southern black-throated finch (Poephila cincta cincta)

The application of machine learning methods has led to major advances in the development of automated recognisers used to analyse bioacoustics data. To further improve the performance of automated call recognisers, we investigated the development of efficient data annotation strategies and how best to address uncertainty around ambiguous vocalisations. These challenges present a particular problem for species whose vocalisations are rare in field recordings, where collecting enough training data can be problematic and a species' vocalisations may be poorly documented.We provide an open access solution to address these challenges using two strategies. First, we applied an active learning framework to iteratively improve a convolutional neural network (CNN) model able to automate call identification for a target rare bird species, the southern black-throated finch (Poephila cincta cincta). We collected 9098 h of unlabelled audio recordings from a field study in the Desert Uplands Bioregion of Queensland, Australia, and used active learning to prioritise human annotation effort towards data that would best improve model fit. Second, we progressed methods for managing ambiguous vocalisations by applying machine learning methods more commonly used in medical image analysis and natural language processing. Specifically, we assessed agreement among human annotators and the CNN model (i.e. inter-annotator agreement) and used this to determine realistic performance outcomes for the CNN model and to identify areas where inter-annotator agreement may be improved. We also applied a classification approach that allowed the CNN model to classify sounds into an ‘uncertain’ category, which replicated a requirement of human-annotation and facilitated the comparison of human-model annotation performance.We found that active learning was an efficient strategy to build a CNN model where there was limited labelled training data available, and target calls were extremely rare in the unlabelled data. As few as five active learning iterations, generating a final labelled dataset of 1073 target calls and 5786 non-target sounds, were required to train a model to identify the target species with comparable performance to experts in the field.Assessment of inter-annotator agreement identified a bias in our model to align predictions most closely with those of the primary annotator and identified significant differences in inter-annotator agreement among subsets of our acoustic data. Our results highlight the use of inter-annotator agreement to understand model performance and identify areas for improvement in data annotation. We also show that excluding ambiguous vocalisations during data annotation results in an overestimation of model performance, an important consideration for datasets with inter-annotator disagreement.

A new glassfrog species of the genus Centrolene (Amphibia, Anura, Centrolenidae) from Cordillera del Cóndor, southern Ecuador.

Based on an integrative taxonomical approach, using molecular, morphological, and bioacoustics data, a new species of glassfrog of the genus Centrolene is described from Refugio de Vida Silvestre El Zarza, southern Ecuador. Centrolenezarzasp. nov. is a medium sized species, easily distinguished from all other glassfrogs by its unique combination of characters, such as a shagreen dorsum with elevated warts corresponding to white spots, an evident tympanum, half or more than half of the upper parietal peritoneum covered by iridophores, iridophores absent on all visceral peritonea, including the pericardium, a lobed liver lacking iridophores, males with small projecting humeral spines, the outer edges of forearms and tarsus with a row of enameled warts that often continue into the external edges of Finger IV and/or Toe V, and white or yellowish white iris with thick black reticulations. The new species is closely related to a currently undescribed species and superficially resembles C.condor, C.pipilata, C.solitaria, C.altitudinalis, and C.daidalea. The tadpole and advertisement and courtship calls are described, and the threats to the species survival, mainly represented by habitat loss and contamination due to mining activities, are briefly discussed.

Widespread passive acoustic monitoring reveals spatio-temporal patterns of blue and fin whale song vocalizations in the Northeast Pacific Ocean

The NOAA-NPS Ocean Noise Reference Station Network (NRS) is a passive acoustic monitoring effort to record the low-frequency (<2 kHz) sound field throughout the U.S. Exclusive Economic Zone. Data collection began in 2014 and spans 12 acoustic recording locations. To date, NRS datasets have been analyzed to understand spatial variation of large-scale sound levels, however, assessment of specific sound sources is an area where these datasets can provide additional insights. To understand seasonal patterns of blue whale,Balaenoptera musculus, and fin whale,B. physalus, sound production in the eastern North Pacific Ocean, this study explored data recorded between 2014 and 2020 from four NRS recording sites. A call index (CI) was used to quantify the intensity of blue whale B calls and fin whale 20 Hz pulses. Diel and seasonal patterns were then determined in the context of their migratory patterns. Most sites shared similar patterns in blue whale CI: persistent acoustic presence for 4–5 months starting by August and ending by February with a CI maximum in October or November. Fin whale patterns included persistent acoustic presence for 5–7 months starting by October and ending before April with a CI maximum between October and December. The diel patterning of blue whale song varied across the sites with the Gulf of Alaska, Olympic Coast, Cordell Bank, and Channel Islands (2014–2015) exhibiting a tendency towards nighttime song detection. However, this diel pattern was not observed at Channel Islands (2018–2020). Fin whale song detection was distributed evenly across day and night at most recording sites and months, however, a tendency toward nighttime song detection was observed in Cordell Bank during fall, and Gulf of Alaska and Olympic Coast during spring. Understanding call and migration patterns for blue and fin whales is essential for conservation efforts. By using passive acoustic monitoring and efficient detection methods, such as CI, it is possible to process large amounts of bioacoustic data and better understand the migratory behaviors of endangered marine species.