RESUMEN

Turbulence and sound are important cues for oyster reef larval recruitment. Numerous studies have found a relationship between turbulence intensity and swimming behaviors of marine larvae, while others have documented the importance of sounds in enhancing larval recruitment to oyster reefs. However, the relationship between turbulence and the reef soundscape is not well understood. In this study we made side-by-side acoustic Doppler velocimeter turbulence measurements and hydrophone soundscape recordings over 2 intertidal oyster reefs (1 natural and 1 restored) and 1 adjacent bare mudflat as a reference. Sound pressure levels (SPL) were similar across all three sites, although SPL > 2000 Hz was highest at the restored reef, likely due to its larger area that contained a greater number of sound-producing organisms. Flow noise (FN), defined as the mean of pressure fluctuations recorded by the hydrophone at f < 100 Hz, was significantly related to mean flow speed, turbulent kinetic energy, and turbulence dissipation rate (ε), agreeing with theoretical calculations for turbulence. Our results also show a similar relationship between ε and FN to what has been previously reported for ε vs. downward larval swimming velocity (w b ), with both FN and w b demonstrating rapid growth at ε > 0.1 cm2 s-3. These results suggest that reef turbulence and sounds may attract oyster larvae in complementary and synergistic ways.

RESUMEN

Ecoacoustics-or acoustic ecology-aids in monitoring elusive and protected species in several ecological contexts. For example, passive acoustic monitoring (PAM), which involves autonomous acoustic sensors, is widely used to detect various taxonomic groups in terrestrial and aquatic ecosystems, from birds and bats to fish and cetaceans. Here, we illustrate the potential of ecoacoustics to monitor soil biodiversity (specifically fauna)-a crucial endeavour given that 59% of species live in soil yet 75% of soils are affected by degradation. We describe the sources of sound in the soil (e.g. biological, geological and anthropogenic) and the ability of acoustic technology to detect and differentiate between these sounds, highlighting opportunities and current gaps in knowledge. We also propose a roadmap for the future development of optimized hardware, analytical pipelines and experimental approaches. Soil ecoacoustics is an emerging field with considerable potential to improve soil biodiversity monitoring and 'soil health' diagnostics. Indeed, early studies suggest soil ecoacoustics can be successfully applied in various ecosystems (e.g. grasslands, temperate, tropical and arid forests) and land uses (e.g. agriculture, viticulture, natural and restored ecosystems). Given the low cost, minimal intrusiveness, and effectiveness in supporting soil biodiversity assessments and biosecurity risks, we advocate for the advancement of soil ecoacoustics for future land management applications.

Asunto(s)

RESUMEN

In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.

RESUMEN

The alarm calls of vervet monkeys (Chlorocebus pygerythrus) have been the subject of considerable focus by researchers, owing primarily to the purported referential qualities of different alarm call types. With this focus on reference, acoustic variation among calls elicited by the same range of predators has typically been overlooked. Specifically, at least one type of alarm call-the terrestrial alarm-was described over 50 years ago as being acoustically distinct between males and females-a description that has largely eluded more systematic scrutiny. Here, we provide a quantitative acoustic analysis and comparison of terrestrial alarm calls produced by adult male and female vervet monkeys. We use a random forest model to determine which acoustic variables best distinguish between the calls of males and females, and use an unsupervised clustering technique to objectively determine whether alarms produced by each sex fall into discrete types. We found that the calls of males and females differed most in frequency-based parameters, with male alarms containing more energy at lower frequencies relative to females. Calls produced by males were also of longer duration, and consisted of longer individual call elements relative to female calls. While calls generally fell into clusters associated with either male or female alarms, we found that some fell into atypical clusters given the caller's sex, and that the clusters themselves showed evidence of intergradation. We discuss these results in terms of potential differences in the function of, and motivation for, calling by males and females. We emphasize the need for a more holistic approach to the classification of vocal signals that considers contextual, functional, and structural variation.

RESUMEN

The study of how animals adapt their behaviors depending on weather variables has gained particular significance in the context of climate change. This exploration offers insights into endangered species' potential threats and provides information on the direction to take in conservation activities. In this context, noninvasive, cost-effective, and potentially long-term monitoring systems, such as Passive Acoustic Monitoring (PAM), become particularly appropriate. Our study investigates the relationship between weather variables and the vocal behavior of Indri indri, the sole singing lemur species, within Madagascar's Maromizaha New Protected Area. Using PAM, we explore the factors shaping the vocalization patterns of this primate species in response to some environmental factors in their natural habitat. Analysis of an extensive audio data set collected across different years revealed the differential influence of temperature and precipitation on Indri indri vocal activity. We found that rainfall negatively influenced the emission of the vocalizations while warmer temperatures correlated with a greater emission of songs. The various environmental factors we considered also affected the timing of vocal emissions, showing the same pattern. Furthermore, our study confirms, once again, the strength of PAM as a valuable tool for studying vocal animal communication quickly, giving us information about long-term behavioral patterns that would be difficult to get in other ways. This research gives us further valuable information about how indris use vocalizations in their environment and how they adjust to environmental changes.

RESUMEN

Anthropogenic noise has been identified as one of the most harmful forms of global pollutants impacting both terrestrial and aquatic ecosystems. As global populations continue to increase, coastlines are seeing substantial increases in the level of urbanisation. Although measures are in place to minimise stress on fauna, they rarely consider the impact of anthropogenic noise. In Australia, New South Wales (NSW) estuaries have seen extensive increases in urbanisation in recent years. Yet, there remains minimal baseline data on their soundscapes to determine if noise pollution is a threat. This research provides a first assessment of baseline sounds across a temporal and seasonal scale. Recreational boating was the primary soundscape contributor in estuaries, and estuaries with higher urbanisation levels contained higher sound levels. This research provides useful information for managers of NSW estuaries and is of global relevance in an era of increasing generation of anthropogenic noise in estuarine and coastal systems.

Asunto(s)

RESUMEN

Considering the conservation attention needed to keep viable the few remaining wild populations of the Critically Endangered northern white-cheeked gibbon (Nomascus leucogenys), there has been a serious paucity of research undertaken for the species. To improve the effectiveness of surveys of this and other gibbon species, and ultimately their conservation, it is important to better understand the variables that affect their singing behavior-the feature that is currently used in most gibbon population surveys. We collected singing and meteorological data from 320 days, between October 2020 and March 2021, at 80 different locations, during an auditory presence/non-detection survey of N. leucogenys in northeastern Lao PDR. Songs were detected on 100 of the 320 days, a total of 154 song bouts. We analyzed the differences in song bout frequency, song bout timing (in relation to sunrise), and song bout length in relation to the meteorological variables of temperature, relative humidity, cloud cover, rain, fog, and wind. We found song bout frequency to be significantly greater on mornings with cloud cover (as a function of improved detection), on mornings without fog, on mornings without rain, and on warmer mornings. We found song bouts to start significantly earlier on mornings without fog and on warmer mornings when fog was present. Finally, we found song bouts lasted longer on mornings with fog and on warmer mornings. We did not find any significant relationships with relative humidity or wind. These patterns fit with prior research on behavioral responses of gibbons to weather and improve the understanding of gibbon vocal behavior to better prepare researchers for designing auditory surveys of Nomascus and other gibbon species.

RESUMEN

This paper provides a comprehensive review of the use of computational bioacoustics as well as signal and speech processing techniques in the analysis of primate vocal communication. We explore the potential implications of machine learning and deep learning methods, from the use of simple supervised algorithms to more recent self-supervised models, for processing and analyzing large data sets obtained within the emergence of passive acoustic monitoring approaches. In addition, we discuss the importance of automated primate vocalization analysis in tackling essential questions on animal communication and highlighting the role of comparative linguistics in bioacoustic research. We also examine the challenges associated with data collection and annotation and provide insights into potential solutions. Overall, this review paper runs through a set of common or innovative perspectives and applications of machine learning for primate vocal communication analysis and outlines opportunities for future research in this rapidly developing field.

RESUMEN

Passive Acoustic Monitoring (PAM), which involves using autonomous record units for studying wildlife behaviour and distribution, often requires handling big acoustic datasets collected over extended periods. While these data offer invaluable insights about wildlife, their analysis can present challenges in dealing with geophonic sources. A major issue in the process of detection of target sounds is represented by wind-induced noise. This can lead to false positive detections, i.e., energy peaks due to wind gusts misclassified as biological sounds, or false negative, i.e., the wind noise masks the presence of biological sounds. Acoustic data dominated by wind noise makes the analysis of vocal activity unreliable, thus compromising the detection of target sounds and, subsequently, the interpretation of the results. Our work introduces a straightforward approach for detecting recordings affected by windy events using a pre-trained convolutional neural network. This process facilitates identifying wind-compromised data. We consider this dataset pre-processing crucial for ensuring the reliable use of PAM data. We implemented this preprocessing by leveraging YAMNet, a deep learning model for sound classification tasks. We evaluated YAMNet as-is ability to detect wind-induced noise and tested its performance in a Transfer Learning scenario by using our annotated data from the Stony Point Penguin Colony in South Africa. While the classification of YAMNet as-is achieved a precision of 0.71, and recall of 0.66, those metrics strongly improved after the training on our annotated dataset, reaching a precision of 0.91, and recall of 0.92, corresponding to a relative increment of >28 %. Our study demonstrates the promising application of YAMNet in the bioacoustics and ecoacoustics fields, addressing the need for wind-noise-free acoustic data. We released an open-access code that, combined with the efficiency and peak performance of YAMNet, can be used on standard laptops for a broad user base.

Asunto(s)

RESUMEN

Temperate woodlands are biodiverse natural communities threatened by land use change and fire suppression. Excluding historic disturbance regimes of periodic groundfires from woodlands causes degradation, resulting from changes in the plant community and subsequent biodiversity loss. Restoration, through prescribed fire and tree thinning, can reverse biodiversity losses, however, because the diversity of woodland species spans many taxa, efficiently quantifying biodiversity can be challenging. We assessed whether soundscapes in an eastern North American woodland reflect biodiversity changes during restoration measured in a concurrent multitrophic field study. In five restored and five degraded woodland sites in Wisconsin, USA, we sampled vegetation, measured arthropod biomass, conducted bird surveys, and recorded soundscapes for five days of every 15-day period from May to August 2022. We calculated two complementary acoustic indices: Soundscape Saturation, which focuses on all acoustically active species, and Acoustic Complexity Index (ACI), which was developed to study vocalizing birds. We used generalized additive models to predict both indices based on Julian date, time of day, and level of habitat degradation. We found that restored woodlands had higher arthropod biomass, and higher richness and abundance of breeding birds. Additionally, soundscapes in restored sites had higher mean Soundscape Saturation and higher mean ACI. Restored woodland acoustic indices exhibited greater magnitudes of daily and seasonal peaks. We conclude that woodland restoration results in higher soundscape saturation and complexity, due to greater richness and abundance of vocalizing animals. This bioacoustic signature of restoration offers a promising monitoring tool for efficiently documenting differences in woodland biodiversity.

Asunto(s)

RESUMEN

Bioacoustic event detection is a demanding endeavor involving recognizing and classifying the sounds animals make in their natural habitats. Traditional supervised learning requires a large amount of labeled data, which are hard to come by in bioacoustics. This paper presents a few-shot learning (FSL) method incorporating transductive inference and data augmentation to address the issues of too few labeled events and small volumes of recordings. Here, transductive inference iteratively alters class prototypes and feature extractors to seize essential patterns, whereas data augmentation applies SpecAugment on Mel spectrogram features to augment training data. The proposed approach is evaluated by using the Detecting and Classifying Acoustic Scenes and Events (DCASE) 2022 and 2021 datasets. Extensive experimental results demonstrate that all components of the proposed method achieve significant F-score improvements of 27% and 10%, for the DCASE-2022 and DCASE-2021 datasets, respectively, compared to recent advanced approaches. Moreover, our method is helpful in FSL tasks because it effectively adapts to sounds from various animal species, recordings, and durations.

RESUMEN

Interpreting sound gives powerful insight into the health of ecosystems. Beyond detecting the presence of wildlife, bioacoustic signals can reveal their behavior. However, behavioral bioacoustic information is underused because identifying the function and context of animals' sounds remains challenging. A growing acoustic toolbox is allowing researchers to begin decoding bioacoustic signals by linking individual and population-level sensing. Yet, studies integrating acoustic tools for behavioral insight across levels of biological organization remain scarce. We aim to catalyze the emerging field of behavioral bioacoustics by synthesizing recent successes and rising analytical, logistical, and ethical challenges. Because behavior typically represents animals' first response to environmental change, we posit that behavioral bioacoustics will provide theoretical and applied insights into animals' adaptations to global change.

RESUMEN

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.

RESUMEN

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.

Asunto(s)

RESUMEN

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.

RESUMEN

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.

RESUMEN

Recent advances in bioacoustics combined with acoustic individual identification (AIID) could open frontiers for ecological and evolutionary research because traditional methods of identifying individuals are invasive, expensive, labor-intensive, and potentially biased. Despite overwhelming evidence that most taxa have individual acoustic signatures, the application of AIID remains challenging and uncommon. Furthermore, the methods most commonly used for AIID are not compatible with many potential AIID applications. Deep learning in adjacent disciplines suggests opportunities to advance AIID, but such progress is limited by training data. We suggest that broadscale implementation of AIID is achievable, but researchers should prioritize methods that maximize the potential applications of AIID, and develop case studies with easy taxa at smaller spatiotemporal scales before progressing to more difficult scenarios.

RESUMEN

Acoustic signalling is a key mode of communication owing to its instantaneousness and rapid turnover, its saliency and flexibility and its ability to function strategically in both short- and long-range contexts. Acoustic communication is closely intertwined with both collective behaviour and social network structure, as it can facilitate the coordination of collective decisions and behaviour, and play an important role in establishing, maintaining and modifying social relationships. These research topics have each been studied separately and represent three well-established research areas. Yet, despite the close connection of acoustic communication with collective behaviour and social networks in natural systems, only few studies have focused on their interaction. The aim of this theme issue is therefore to build a foundation for understanding how acoustic communication is linked to collective behaviour, on the one hand, and social network structure on the other, in non-human animals. Through the building of such a foundation, our hope is that new questions in new avenues of research will arise. Understanding the links between acoustic communication and social behaviour seems crucial for gaining a comprehensive understanding of sociality and social evolution. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Asunto(s)

RESUMEN

Communication takes place within a network of multiple signallers and receivers. Social network analysis provides tools to quantify how an individual's social positioning affects group dynamics and the subsequent biological consequences. However, network analysis is rarely applied to animal communication, likely due to the logistical difficulties of monitoring natural communication networks. We generated a simulated communication network to investigate how variation in individual communication behaviours generates network effects, and how this communication network's structure feeds back to affect future signalling interactions. We simulated competitive acoustic signalling interactions among chorusing individuals and varied several parameters related to communication and chorus size to examine their effects on calling output and social connections. Larger choruses had higher noise levels, and this reduced network density and altered the relationships between individual traits and communication network position. Hearing sensitivity interacted with chorus size to affect both individuals' positions in the network and the acoustic output of the chorus. Physical proximity to competitors influenced signalling, but a distinctive communication network structure emerged when signal active space was limited. Our model raises novel predictions about communication networks that could be tested experimentally and identifies aspects of information processing in complex environments that remain to be investigated. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Asunto(s)

RESUMEN

Animal vocal communication research traditionally focuses on acoustic and contextual features of calls, yet substantial information is also contained in response selectivity and timing during vocalization events. By examining the spatiotemporal structure of vocal interactions, we can distinguish between 'broadcast' and 'exchange' signalling modes, with the former potentially serving to transmit signallers' general state and the latter reflecting more interactive signalling behaviour. Here, we tracked the movements and vocalizations of wild meerkat (Suricata suricatta) groups simultaneously using collars to explore this distinction. We found evidence that close calls (used for maintaining group cohesion) are given as signal exchanges. They are typically given in temporally structured call-response sequences and are also strongly affected by the social environment, with individuals calling more when they have more neighbours and juveniles responding more to adults than the reverse. In contrast, short note calls appear mainly in sequences produced by single individuals and show little dependence on social surroundings, suggesting a broadcast signalling mode. Despite these differences, both call categories show similar clustering in space and time at a group level. Our results highlight how the fine-scale structure of vocal interactions can give important insights into the usage and function of signals in social groups. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics.'

Asunto(s)