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Riparian ecosystems comprise less than 2% of the landscape in the arid western United States, yet they provide habitat and resources to over half of arid‐land wildlife species, including a broad diversity of anurans (frogs and toads). Despite this, relatively few studies describe riparian herpetofaunal communities in the Southwest, and remote wilderness areas are particularly undersurveyed. We employed passive acoustic monitoring to capture anuran advertisement calls in wilderness area tributaries of the Verde River, Arizona, USA. In the spring and summer of 2021 and 2022, 13–29 autonomous recording units (ARUs) were deployed along perennial, intermittent and ephemeral tributary stream reaches. We characterized sites based on the per cent of pool, riffle, run and side channel habitat within 100 m of each ARU and quantified substrate, wetted channel width and canopy cover. To relate anuran occupancy to environmental and hydrological variables, we evaluated acoustic detection data using single‐species, single‐season occupancy models. Four species were detected in this study: canyon treefrog (Hyla arenicolor), red‐spotted toad (Anaxyrus punctatus), Woodhouse's toad (Anaxyrus woodhousii) and non‐native American bullfrog (Lithobates catesbeianus), with canyon treefrog being the most ubiquitous species observed. Vocal activity of canyon treefrog was higher at perennial and intermittent sites compared to ephemeral sites, and the presence of pools was most strongly associated with canyon treefrog occupancy. Persistent, slow‐moving water best predicted the presence of breeding canyon treefrogs. Notably, this study did not detect several species with historical records in the middle Verde River catchment, including Arizona toad (Anaxyrus microscaphus) and northern leopard frog (Lithobates pipiens). Given climate change‐related flow declines and intensifying demands for water in arid lands globally, maintaining stream flows that provide consistent and suitable hydroregimes for anuran breeding and larval development is of increasing importance. Determining habitat use and flow regimes necessary to support anuran populations can aid in prioritization of conservation actions related to water management and predict how changes in water availability may impact stream‐breeding anurans. [ABSTRACT FROM AUTHOR]

The use of autonomous recording units (ARUs) has become an increasingly popular and powerful method of data collection for biological monitoring in recent years. However, the large-scale recordings collected using these devices are often nearly impossible for human analysts to parse through, as they require copious amounts of time and resources. Automated recognition techniques have allowed for quick and efficient analysis of these recordings, and machine learning (ML) approaches, such as deep learning, have greatly improved recognition robustness and accuracy. We evaluated the performance of two deep-learning algorithms: 1. our own custom convolutional neural network (CNN) detector (specialist approach) and 2. BirdNET, a publicly available detector capable of identifying over 6,000 bird species (generalist approach). We used audio recordings of mountain chickadees (Poecile gambeli) collected from ARUs and directional microphones in the field as our test stimulus set, with our custom detector trained to identify mountain chickadee songs. Using confidence thresholds of 0.6 for both detectors, we found that our custom CNN detector yielded higher detection compared to BirdNET. Given both ML approaches are significantly faster than a human detector and the custom CNN detector is highly accurate, we hope that our findings encourage bioacoustics practitioners to develop custom solutions for targeted species identification, especially given the availability of open-source toolboxes such as Koogu. [ABSTRACT FROM AUTHOR]

Anurans exhibit a diverse acoustic repertoire, and the release call is emitted when an individual is improperly clasped, often by males mistakenly grasping other males or unreceptive females during amplexus. The release call also serves as a crucial source for taxonomic resolution. However, for certain widespread hylid genera like Trachycephalus, knowledge about their release calls is limited or non-existent. Trachycephalus comprises 18 species, yet only one has its release call described. This study presents the first description of the release call of T. typhonius from a population in the Cerrado of Central-Western Brazil. Its release call is composed of a multipulsed note, distinguishing it from the only other known call in the genus (T. 'vermiculatus'). Given the differences in the release call parameters between these species, such variations could serve as diagnostic criteria for distinguishing the two lineages. Additional descriptions of release calls, including from more T. typhonius and T. 'vermiculatus' populations, are essential for species diagnosis and taxonomic classification. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Despite acoustic signals playing a fundamental role on animal behavior and reproduction, our understanding of the mechanisms driving their evolution is still scarce. Due to the high cost of signaling, several adaptive bioacoustics hypotheses have been proposed to explain call design. While Acoustic Adaptation Hypothesis (AAH) postulates the habitat influence on shaping acoustic signals, Morphological Adaptation Hypothesis (MAH) emphasizes the constraints that animal structure imposes to their calls. Yet, when testing those hypotheses, phylogenetic comparative methods have been scarcely applied so far. Here we evaluated the AAH and MAH in 163 anurans (i.e., Brazilian species of the family Hylidae), based on a broad review of the dominant frequency of their advertisement calls. Comparative methods revealed a negative relationship between dominant frequency and body size, supporting the MAH. AAH was partly rejected, as vegetation density, habitat type and calling microhabitat did not affect species' dominant frequency. However, species calling from higher perches had higher dominant frequencies, partially supporting habitat effect on call features. Our results not only highlight the role of morphology on shaping Hylidae call design, but also arise questions about scale effect, behavioral strategies and other sound propagation characteristics while testing AAH in frogs. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring is a promising method for surveying wildlife populations that are easier to detect acoustically than visually. When animal vocalisations can be uniquely identified on an array of sensors, the potential exists to estimate population density through acoustic spatial capture–recapture (ASCR). However, sound classification is imperfect, and in some situations, a high proportion of sounds detected on just a single sensor ('singletons') are not from the target species. We present a case study of bowhead whale calls (Baleana mysticetus) collected in the Beaufort Sea in 2010 containing such false positives. We propose a novel extension of ASCR that is robust to false positives by truncating singletons and conditioning on calls being detected by at least two sensors. We allow for individual-level detection heterogeneity through modelling a variable sound source level, model inhomogeneous call spatial density, and include bearings with varying measurement error. We show via simulation that the method produces near-unbiased estimates when correctly specified. Ignoring source-level variation resulted in a strong negative bias, while ignoring inhomogeneous density resulted in severe positive bias. The case study analysis indicated a band of higher call density approximately 30 km from shore; 59.8% of singletons were estimated to have been false positives. [ABSTRACT FROM AUTHOR]

The Caatinga is the largest patch of Seasonal Dry Tropical Forest in the Neotropics, located in northeastern Brazil and characterized mainly by deciduous vegetation and extreme rainfall seasonality. It has historically been treated as a biologically impoverished domain, but recent studies uncovered new diversification patterns and several new taxa of frogs, mammals, insects, and fishes. Here we employed a dense sampling regime to evaluate whether the São Francisco River (SFR) would have promoted genetic diversification and fixed phenotypic differences and how Quaternary climatic oscillations shaped distribution and population sizes in a Caatinga endemic species, the Silvery‐cheeked Antshrike (Sakesphoroides cristatus). We adopted an integrative approach using multilocus genetic, plumage, vocal data, and ecological niche modelling (ENM) to characterize evolutionary units and niche suitability in past scenarios. We recovered strong genetic structure across the SFR that was congruent with plumage and vocal variation, revealing a yet undescribed species named herein as Sakesphoroides niedeguidonae, sp. nov. (urn:lsid:zoobank.org:act:2B9FC637‐008A‐4E9A‐B92B‐69ED7FE23823s). The splitting time estimated between the newly described species and S. cristatus is consistent with the establishment of the modern course of SFR, with a more recent course shift apparently promoting the secondary contact between the two species in the Raso da Catarina region. After their split, both species experienced increases in population sizes and range sizes at similar times during the Last Glacial Maximum. We expect other Caatinga avian endemic lineages to show similar patterns of genetic differentiation across the SFR that were enhanced by Quaternary climatic oscillations. [ABSTRACT FROM AUTHOR]

As part of the ecosystem, the western black-crested gibbon (Nomascus concolor) is important for ecological sustainability. Calls are an important means of communication for gibbons, so accurately recognizing and categorizing gibbon calls is important for their population monitoring and conservation. Since a large amount of sound data will be generated in the process of acoustic monitoring, it will take a lot of time to recognize the gibbon calls manually, so this paper proposes a western black-crested gibbon call recognition network based on SA_DenseNet-LSTM-Attention. First, to address the lack of datasets, this paper explores 10 different data extension methods to process all the datasets, and then converts all the sound data into Mel spectrograms for model input. After the test, it is concluded that WaveGAN audio data augmentation method obtains the highest accuracy in improving the classification accuracy of all models in the paper. Then, the method of fusion of DenseNet-extracted features and LSTM-extracted temporal features using PCA principal component analysis is proposed to address the problem of the low accuracy of call recognition, and finally, the SA_DenseNet-LSTM-Attention western black-crested gibbon call recognition network proposed in this paper is used for recognition training. In order to verify the effectiveness of the feature fusion method proposed in this paper, we classified 13 different types of sounds and compared several different networks, and finally, the accuracy of the VGG16 model improved by 2.0%, the accuracy of the Xception model improved by 1.8%, the accuracy of the MobileNet model improved by 2.5%, and the accuracy of the DenseNet network model improved by 2.3%. Compared to other classical chirp recognition networks, our proposed network obtained the highest accuracy of 98.2%, and the convergence of our model is better than all the compared models. Our experiments have demonstrated that the deep learning-based call recognition method can provide better technical support for monitoring western black-crested gibbon populations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aim Location Time Period Major Taxa Studied Methods Results Main Conclusions There is an urgent need to explore, characterize, describe and preserve as many species as possible to prevent their decline. Tropical biodiversity hotspots harbour most of the known land diversity and vast amounts of undiscovered and undescribed species. Here, we quantify the taxonomically unassessed amphibian species richness in Madagascar, one of the best‐studied and explored tropical hotspots worldwide, to identify knowledge gaps and conservation implications.Madagascar.Present.Amphibians.We used the Madagascar amphibian fauna as a model to unveil neglected diversity by analysing 10,873 mitochondrial sequences using species delimitation algorithms and incorporating all previously published bioacoustics, distributional, morphological and nuclear data with an integrative approach.Besides the currently described 413 species, we identified 408 divergent lineages. Among this, 310 fit the category of candidate species pending a taxonomic assessment, while 98 are considered deep conspecific lineages. These figures suggest that species richness could be twice as high as represented in the current taxonomy. Geographically, most of these candidate species occur in well‐studied areas within the island.Despite being one of the best‐studied and explored tropical countries worldwide for amphibians, we found that many species are awaiting a taxonomic assessment in Madagascar. Paradoxically, this unassessed diversity concentrates on highly explored regions, emphasizing the importance of exploring and inventorying new areas. Our results highlight the magnitude of the Linnean and Wallacean shortfalls, affecting both species richness estimates and the distribution ranges and biogeographic setting known for this fauna. Current conservation efforts should consider this novel diversity and unexplored areas as they will likely harbour yet many new species to be discovered. We expect similar patterns across less studied tropical realms and encourage researchers to perform such studies in different clades before this neglected biodiversity becomes irremediably lost. [ABSTRACT FROM AUTHOR]

Quantification of bat communities and habitat heavily rely on non-invasive acoustic bat surveys the scope of which has greatly amplified with advances in remote monitoring technologies. Despite the unprecedented amount of acoustic data being collected, analysis of these data is often limited to simple species classification which provides little information on habitat function. Feeding buzzes, the rapid sequences of echolocation pulses emitted by bats during the terminal phase of prey capture, have historically been used to evaluate foraging habitat quality. Automated identification of feeding buzzes in recordings could benefit conservation by helping identify critical foraging habitat. I tested if detection of feeding buzzes in recordings could be automated with bat recordings from Ontario, Canada. Data were obtained using three different recording devices. The signal detection method involved sequentially scanning narrow frequency bands with the "Bioacoustics" R package signal detection algorithm, and extracting temporal and signal strength parameters from detections. Buzzes were best characterized by the standard deviation of the time between consecutive pulses, the average pulse duration, and the average pulse signal-to-noise ratio. Classification accuracy was highest with artificial neural networks and random forest algorithms. I compared each model's receiver operating characteristic curves and random forest provided better control over the false-positive rate so it was retained as the final model. When tested on a new dataset, buzzfindr's overall accuracy was 93.4% (95% CI: 91.5%– 94.9%). Overall accuracy was not affected by recording device type or species frequency group. Automated detection of feeding buzzes will facilitate their integration in the analytical workflow of acoustic bat studies to improve inferences on habitat use and quality. [ABSTRACT FROM AUTHOR]

Introduction: This work presents an unsupervised learning-based methodology to identify and count unique manatees using underwater vocalization recordings. Methods: The 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. Results: The 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%. Discussion: Thus 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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

We describe two new species of the microhylid frog genus Choerophryne Van Kampen, 1914 from the mountains of Papua New Guinea. The first is a scansorial species known only from foothill forest at altitudes of 440-950 m a.s.l. on the northern slopes of Papua New Guinea's Central Cordillera. It is a moderately large member of the genus (males 16.8-20.6 mm snout-urostyle length) that utters long series of short whistling calls each lasting 77-125 ms. The second species is arboreal and known only from a small area of montane forest at altitudes of 2060-2300 m a.s.l. on the southern edge of the Central Cordillera. It is also moderately large for the genus (three males with snout-urostyle lengths of 19.3-20.6 mm) and produces long series of short buzzing calls, each lasting 124-173 ms. Descriptions of these two species bring the number of recognised Choerophryne to 39, and at least 18 additional species in the genus await formal description. [ABSTRACT FROM AUTHOR]

Biodiversity is in crisis globally, and we consistently fail to hit global targets to stem its loss. Inspired by the Kunming‐Montreal Global Biodiversity Framework, the biodiversity credit market offers an avenue for vital funding for biodiversity conservation projects around the world. Various biodiversity credit methodologies and standards are becoming available, and most will require the measurement and monitoring of biodiversity at scale. Private investment in conservation through biodiversity credits entails specific needs for biodiversity data collection, including the need for biodiversity claims to be verifiable. We conceptualise these requirements around ‘SAGED’ criteria: Scalable, Accessible, Granular (data of appropriate spatial, taxonomic and temporal resolution), Evidenceable and Directly measured (where possible). Measuring and monitoring biodiversity across ecosystems, ecoregions and taxa is expensive and time‐consuming with traditional survey methods. These methods often rely on access to experts with sufficient taxonomic and survey expertise, which is challenging in many parts of the world. Accordingly, we review biodiversity monitoring technologies and assess their readiness to fulfil key requirements of assessments for the purpose of nature accounting for biodiversity credits (particularly SAGED criteria). We focus on monitoring technologies that are commonly cited by biodiversity credit methodologies, including (e)DNA metabarcoding, passive acoustic monitoring and various other remote sensing methods. We also explore the current limits of these techniques in obtaining appropriate biodiversity measures and metrics for biodiversity finance. Synthesis and applications. Technological solutions for biodiversity monitoring are not (yet) a panacea but are key for evidenceable monitoring at scale. For current use in biocredit markets, we advise these are combined with ground validation and human‐collected ecological data. Developments in automation and machine learning will rapidly make these technologies more accessible and efficient. [ABSTRACT FROM AUTHOR]

Restoring and monitoring soil biodiversity has never been more important. Ecoacoustics is emerging as a promising tool to detect and monitor soil biodiversity and was recently effective in a temperate forest context. However, there is a need to investigate the efficacy of soil ecoacoustics in other ecosystems and bioregions. Here, we applied ecoacoustics tools and indices (Acoustic Complexity Index, Bioacoustic Index, Normalised Difference Soundscape Index) to measure soil biodiversity in an Australian grassy woodland restoration chronosequence, spanning three age classes. We collected n = 240 soil acoustic samples from two cleared plots (continuously cleared through active management), two woodland plots undergoing restoration (revegetated 14–15 years ago) and two plots of remnant vegetation over 5 days in Mount Bold, South Australia. We used a below‐ground sampling device and sound attenuation chamber to record soil invertebrate communities, which were also manually counted. We found that acoustic complexity and diversity were significantly higher in revegetated and remnant plots than in cleared plots, both in‐situ and in sound attenuation chambers. The acoustic complexity and diversity also significantly associated with soil invertebrate abundance and richness. Synthesis and applications. Our results provide new support that ecoacoustics can help monitor soil biodiversity in different forest restoration contexts, including in UK temperate and Australian grassy woodlands. This technology holds promise in addressing the global need for effective soil biodiversity monitoring methods and protecting our planet's most diverse ecosystems. [ABSTRACT FROM AUTHOR]

Tracking biodiversity and its dynamics at scale is essential if we are to solve global environmental challenges. Detecting animal vocalizations in passively recorded audio data offers an automatable, inexpensive, and taxonomically broad way to monitor biodiversity. However, the labor and expertise required to label new data and fine-tune algorithms for each deployment is a major barrier. In this study, we applied a pretrained bird vocalization detection model, BirdNET, to 152,376 h of audio comprising datasets from Norway, Taiwan, Costa Rica, and Brazil. We manually listened to a subset of detections for each species in each dataset, calibrated classification thresholds, and found precisions of over 90% for 109 of 136 species. While some species were reliably detected across multiple datasets, the performance of others was dataset specific. By filtering out unreliable detections, we could extract species and community-level insight into diel (Brazil) and seasonal (Taiwan) temporal scales, as well as landscape (Costa Rica) and national (Norway) spatial scales. Our findings demonstrate that, with relatively fast but essential local calibration, a single vocalization detection model can deliver multifaceted community and species-level insight across highly diverse datasets; unlocking the scale at which acoustic monitoring can deliver immediate applied impact. [ABSTRACT FROM AUTHOR]

Aim Location Methods Results Main Conclusions Mountain ecosystems are hotspots of biodiversity due to their high variation in climate and habitats. Yet, above average rates of climate change and enhanced forest disturbance regimes alter local climatic conditions and vegetation structure, which should impact biodiversity. We here investigated the impact of vegetation and elevation as well as their interactions on bird communities to improve our ability to predict climate change effects on bird communities.European Alps, Germany.We studied patterns and drivers of bird communities at 213 plots along gradients in vegetation density and elevation using autonomous sound recorders. Bird species were identified from soundscapes by Convolutional Neural Networks (BirdNET) and taxonomists.Bird diversity and community metrics were moderately to strongly correlated for data based on either identification by BirdNET or taxonomists (Pearson's r = .47–.94), and ecological findings were overall similar for both datasets. Vegetation density 1–2 m and >2 m above ground strongly affected bird diversity and community composition and mediated effects of elevation. Community composition changed with elevation more strongly in habitats with low than high vegetation density >2 m. Species numbers decreased with elevation in habitats with low vegetation density 1–2 m and >2 m above ground, but increased in habitats with high vegetation density. Overall, functional and phylogenetic diversity increased with elevation indicating lower habitat filtering, but patterns were also mediated by vegetation density.Our results indicate that bird communities in the German Alps are determined by strong interactive effects of elevation and vegetation, underlining the importance to consider variation in vegetation in studies of biodiversity patterns along elevational gradients and under climate change. Combining remote sensing data and biodiversity monitoring based on autonomous sampling and AI‐based species identification opens new avenues for bird monitoring and research in remote areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Codas are vocalisations produced by sperm whales (Physeter macrocephalus) in social settings consisting of a short series of clicks with a stereotyped time pattern and are identified as cultural markers for different sperm whale populations across the globe. The current study analysed data from the OνDE acoustic station, part of the NEMO-SN1, between 2005 and 2006 at 2000 m depth off the coasts of Catania, Italy, to monitor the soundscape. Codas were classified using parsimonious mixtures of multivariate contaminated normal distributions based on their absolute inter-click interval and number of clicks per coda. The models found 3 clusters for three-click codas, 4 for four-click patterns, and 1 for the five-click pattern, with 8 clusters divided into three families. Codas of the '3 + 1' and '2 + 1' families were the most represented for all years and months, and the summer months had the highest number of detections. This study integrates well within the current knowledge of the coda repertoire of Mediterranean sperm whales and offers a new study location placed in the Ionian Sea, a strategic point where individuals could pass from the Western to the Eastern basin. [ABSTRACT FROM AUTHOR]

Some acoustic parameters of animal vocalisations have been shown to reliably indicate male quality and play a role in mate and rival assessment. Reindeer possess a peculiar vocal tract anatomy involving a laryngeal air sac which probably acts as an additional filter, making it a candidate species for novel investigations in the field of bioacoustics. We investigated whether some acoustic parameters of male rutting vocalisations were good indicators of age and body weight (used as an index for body size). We did this by performing acoustic analyses using recordings collected from a semi-domesticated reindeer population in northern Finland. We found the age of subadult males (aged 2.5–4.5 years) to be negatively correlated with formant F3 and formant spacing, suggesting that their vocalisations convey information on the caller's age. Individual formant frequencies were not affected by male body weight, but formant spacing was lower in heavier males. Despite the presence of the laryngeal air sac, formant spacing seems to be an acoustic parameter influencing mate and rival assessment in reindeer as it gives an honest indication of male body size. We discuss the importance of reliable acoustic cues to quality indices in sexual selection contexts. [ABSTRACT FROM AUTHOR]

The acoustics of small mammals, particularly dormice species, are generally understudied. During this study, we explored the vocalisations and acoustic behaviour of the edible dormouse (Glis glis) in various environments in Catalonia using ultrasound recorders. Up to five different types of calls were identified in various environmental conditions (captivity and free-ranging animals) and developmental stages, from pups to adults. Additionally, one new call type was described. Significant differences were observed between captive and wild dormice (e.g. length of the calls and position of the peak harmonic), enhancing the plasticity of their vocalisations. In addition, ultrasonic sound production was discovered in pups, suggesting ontogenetic changes in the vocal repertoire. The lack of differences in vocalisations between sexes reported in captivity highlight the importance of traditional monitoring methods (i.e. nest boxes) for population monitoring in the case of the edible dormouse. The potential of the acoustic method as a non-invasive tool for studying ecological behaviours and interactions, or early detection of the species in the natural environment is emphasised. This study provides an open call reference library, laying the groundwork for a better understanding of edible dormouse acoustics, and focuses on the importance of future research to explore the acoustic social behaviour of the species. [ABSTRACT FROM AUTHOR]

Anurans have several characteristics in their acoustic communication that distinguish them. These characteristics are influenced by body-related, environmental and social variables. However, the complexity of anuran repertoire and the effect of such variables on it are still understudied. Dendropsophus minutus is a Neotropical treefrog that has a wide repertoire composed of three notes, grouped in at least 71 unique combinations. To quantify complexity, we used the entropy measurement, since it measures the randomness of a sequence. Therefore, we could test if repertoire complexity in D. minutus is related with body condition, social context, and environmental variables. We recorded vocalisations of 80 males in three sites and found 44 unique note combinations not described before, increasing the species' repertoire to 115 combinations. We observed that the species has near maximum entropy for note frequency, but a low entropy for note sequencies, showing a clear a bias for some note combinations. We found a positive relationship between repertoire complexity and air temperature. Thus, based on our results, the complexity of this species' repertoire is an additional trait influenced by climate, and should be considered in future studies. [ABSTRACT FROM AUTHOR]

This study aims to detect the bioacoustics signal in the underwater soundscape, specifically those produced by snapping shrimp, using adaptive iterative transfer learning. The proposed network is initially trained with pre-classified snapping shrimp sounds and Gaussian noise, then applied to classify and remove snapping-free noise from field data. This separated ambient noise is subsequently used for transfer learning. This process was iterated to distinguish more effectively between ambient noise and snapping shrimp sounds characteristics, resulting in improved classification. Through iterative transfer learning, significant improvements in precision and recall were observed. The application to field data confirmed that the trained network could detect signals that were difficult to identify using existing threshold classification methods. Furthermore, it was found that the rate of false detection decreased, and detection probability improved with each stage. This research demonstrates that incorporating the noise characteristics of field data into the trained network via iterative transfer learning can generate more realistic training data. The proposed network can successfully detect signals that are challenging to identify using existing threshold classification methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Despite the great diversity of Brazilian anuran species, there are still many knowledge gaps about the acoustic communication of these animals. Among the species, those classified as 'Data Deficient' have the greatest gaps in knowledge on their behavior. This is the case of Boana ericae, a species belonging to the Boana pulchella group, endemic to the Chapada dos Veadeiros National Park region, in the northeast of the state of Goiás, Central Brazil. Three distinct call types (A, B, and C) are currently known for B. ericae, with the type A call associated with attracting reproductive partners and the type C call presenting a territorial function. We recorded the vocalizations of 10 individuals of this species and found the presence of vocalizations composing three new note types (D, E, and F). Our results indicate that air temperature and body condition influenced the acoustic parameters of the advertisement and aggressive calls of this species. We also observed that some acoustic parameters of the advertisement calls can be used for individual recognition for showing greater variation among individuals than intra-individually. Our results add new information regarding the vocal repertoire in this species, expanding their known intra-individual classification of the acoustic parameters. [ABSTRACT FROM AUTHOR]

Impacts of urbanization can affect bat species differently, some bat assemblages demonstrated differences in their activity and richness between forest environments and urban areas. Bats species can seek refuge in green areas (urban forest remnants, parks and groves) or in buildings within the urban landscape. Using bioacoustics, we examined habitat use of by non-phyllostomid bats in a large Metropolitan Region of João Pessoa-PB, northeastern Brazil, comparing the activity and species/sonotypes composition of bat assemblages documented in Atlantic Forest remnants (FF) with those in the urbanized matrix areas (UM). Fifteen species belonging to four families were recorded. Out of these records, 11 were documented in both UM and FF, while Neoplatymops mattogrossensis, Myotis cf. riparius, and Rhynchonycteris naso were exclusively found in FF, and Promops nasutus (first record for the state) was only found in UM. The richness and activity of bats differed between FF and UM. Molossops temminckii and Promops nasutus were observed solely in arboreal habitats, whether in the forest fragments or in the UM areas. Only Cynomops planirostris, Eumops sp., Molossus molossus, Molossus rufus, and Promops centralis were recorded in strictly urban habitats, and they did not show differences in activities between FF and UM. These results indicate the ability of these bats to adapt to structural habitat changes within an urban matrix, reaching the highest levels of synanthropy. Our results demonstrate that the impact of urbanization on bat assemblages can be mitigated by maintaining green areas within an urban matrix. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Insect-eating bats play a crucial role in agriculture and public health by suppressing populations of agricultural pests and disease vectors. These ecosystem services promoted by insect-eating bats are essential in a world that seeks sustainability in agricultural production and the management of urban areas. Despite Brazil's status as one of the largest agricultural producers globally, research using insect-eating bats for pest control is lacking. This review underscores the importance of filling this knowledge gap and provides guidelines for future research. Bats exhibit diverse feeding habits and consume significant quantities of insects, including agricultural pests. While studies in the USA and Europe have highlighted insect-eating bats' role in reducing agricultural losses, research in Brazil is limited. Challenges include the need for more advanced diet analysis techniques, such as DNA metabarcoding, and understanding bat habitat use in agricultural and urban landscapes. Research on natural and artificial roosts' effectiveness and population dynamics is also essential. Integrating bat conservation into agricultural sustainability requires investment in technological methods, valuation of ecosystem services, population monitoring, habitat use studies and environmental education. Financial incentives and collaboration between sectors are crucial for advancing research and implementing bat-friendly practices in agriculture, ultimately enhancing biodiversity conservation and production sustainability in Brazil. [ABSTRACT FROM AUTHOR]

Training complex deep neural networks can result in overfitting when the networks are trained from random weight initialization on small datasets. Augmentation helps to reduce the negative effects of overfitting. The findings in computer vision and audio recognition research reveals that the performance of machine learning classifiers is significantly improved when augmentation is used. In the context of ecology, researchers conduct field surveys whereby microphones are placed in some location and audio data is recorded over a period of time. There is however no guarantee that the particular species of interest in the field survey will vocalize frequently near the microphone. Thus, the amount of data captured for the species of interest might be limited, and it may then be the source of overfitting. The main contribution of this paper is in performing experiments with time and frequency masking, and noise addition augmentation techniques in training a visual convolutional neural networks (CNN) repurposed for pattern recognition in acoustic spectrograms. These techniques increased the audio examples for the pin-tailed whydah and the Cape robin-chat to create a robust audio vocalization classifiers. To evaluate the performance of the augmentation techniques we conducted a comparison between experiments run with and without augmentation. We chose to use CNN as our classifier given that they are state-of-the-art in audio recognition tasks and they have revealed good performance. In the used augmentation techniques; time masking achieved 90.2% as the highest testing accuracy while pink noise is the most successful best classifier. [ABSTRACT FROM AUTHOR]

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.' [ABSTRACT FROM AUTHOR]

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'. [ABSTRACT FROM AUTHOR]

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'. [ABSTRACT FROM AUTHOR]

It is nearly a century since the Buff-breasted Button-quail Turnix olivii was last definitively recorded, resulting in the species being recently classified as one of Australia's most imperilled species. However, conservation action to recover the species has been hampered by an inability to locate an extant population. To overcome this problem we conducted extensive surveys across the species' presumed distribution. We surveyed historical sites where the species was collected, and also where habitat deemed suitable for the species occurred on Cape York Peninsula. We also surveyed sites on the northern Atherton Tablelands where a contemporary population has been reported. Surveys were conducted from 2018 to 2022 and employed a variety of survey methods known to be suitable for detecting button-quail. No evidence of Buff-breasted Button-quail was detected. However, Painted Button-quail Turnix varius where found to be widespread at sites surveyed in the Wet Tropics and Einasleigh Uplands bioregions as well as in southern areas of Cape York Peninsula. We discuss the implications of these results including the likely effectiveness of the different survey methods for detecting the Buff-breasted Button-quail specifically, and button-quail generally. [ABSTRACT FROM AUTHOR]

The Pelophylax genus comprises two main hybridogenetic systems resulting from hybridization between Pelophylax ridibundus and other species: the P. lessonae-esculentus (LE) system and the P. perezi-grafi (PG) system, whose distribution ranges lie partly (LE) or entirely (PG) outside the natural distribution of P. ridibundus. The introductions and subsequent expansions of P. ridibundus in many areas naturally inhabited by these hybridogenetic systems have led to their decrease or local extinction. One of the major impediments to efficient population surveys and conservation measures for these systems is the difficulties of reliable species identification due to their similar morphology and poorly documented vocalizations, especially for the PG system. Our aim in this work was to design a reliable method of acoustic identification to distinguish the taxa composing the hybridogenetic systems from P. ridibundus and to assess the possibility of separating the PG and LE systems by their vocalizations. Our acoustic identification methods proved highly accurate to separate the PG and LE systems from P. ridibundus but only a fraction of the PG and LE calls can be identified. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Acoustic signaling among birds is central to intra-species communication, courtship, and reproductive success, and so habitat suitability is partially dependent upon the availability of a suitable acoustic niche. It is well documented that birds may modify their vocal behavior to avoid overlap with anthropogenic noise pollution, but responses to biotic signal making are less well understood. This study uses more than 50,000 h of audio recorded in tropical forest, and machine learning methods for the detection of the vocalizations of nine species of bird and tymbalizations of three species of cicada to examine patterns of signal masking and co-chorusing avoidance among species pairs. Among these focal species, no bird avoided co-chorusing with any other bird. Birds avoided co-chorusing with cicadas only and always when (1) the bird vocalized in a frequency band completely overlapped by the cicada tymbalization, and (2) the cicada tymbalization saturated the majority of that frequency band. These results indicate that avian behavioral modifications in response to biotic noise in longstanding species communities is similar to behavioral modifications observed in populations subjected to high levels of anthropogenic noise pollution—in all cases overlap avoidance is species-specific and dependent upon both frequency and intensity. [ABSTRACT FROM AUTHOR]

Machine learning tools capable of identifying animals by sound have proliferated, making the challenge of interpreting their outputs much more prevalent. These tools, like their predecessors, quantify prediction uncertainty with scores that tend to resemble probabilities but are actually unitless scores that are (generally) positively related to prediction accuracy in species-specific ways. BirdNET is one such tool, a freely available animal sound identification algorithm capable of identifying > 6,000 species, most of them birds. We describe two ways in which BirdNET "confidence scores"—and the output scores of other detector tools—can be used appropriately to interpret BirdNET results (reviewing them down to a user-defined threshold or converting them to probabilities), and provide a step-by-step tutorial to follow these suggestions. These suggestions are complementary to common performance metrics like precision, recall, and receiver operating characteristic. BirdNET can be a powerful tool for acoustic-based biodiversity research, but its utility depends on the careful use and interpretation of its outputs. [ABSTRACT FROM AUTHOR]

Simple Summary: Simple Summary: Acoustic communication is present in different groups of insects and is used in different contexts during their life cycle. Bark beetles are a group of insects of ecological importance, because they can kill a large number of trees and affect forest succession These insects communicate using chemical signals for long distances, but while on or in the tree, they use acoustic communication to interact between individuals during the colonization of their host trees. A series of sounds have been described for bark beetles, which are produced by specialized anatomical structures under different conditions, such as stress, courtship, and territoriality. In this study, we worked with males of Dendroctonus adjunctus, and we described the stridulatory structures using optical and electron microscopy. In addition, we recorded and characterized the types of calls and their spectral and temporal characteristics under stress conditions, female–male, and male–male interactions. Results showed that the shape of the stridulatory apparatus is like males of other beetle species in the genus, and we identified single– and multi–noted calls with differences in temporal and spectral characteristics under three behavioral contexts. Finally, a new type of withdrawal call was identified in male–male interactions. 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. [ABSTRACT FROM AUTHOR]

Orang-utans are well-known for their vocal flexibility. However, the described vocal repertoire of female orang-utans is relatively limited. This anecdotal report discusses the occurrence of long call-like vocalisations in two zoo-housed Bornean orang-utan females. While Temmy demonstrated these vocalisations in response to playbacks of male long calls, Eloise proactively produced vocalisations during her ovulation before her reproductive senescence. The vocalisations produced by Temmy and Eloise seem to differ in many aspects from known orang-utan calls, although they bear some similarities to male long calls. Thus, the vocal repertoire of female orang-utans may be more extensive than previously thought. We carefully suggest that these calls might indicate proceptivity, while also discussing methodological limitations and theoretical considerations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Some urbanization impacts on biodiversity are often underestimated such as noise pollution generated by anthropogenic activities. Birds inhabiting urban and peri-urban areas suffer anthropogenic noise that impact their communication, affecting fitness components as reproduction, distribution, density and diversity patterns. We evaluate how anthropogenic sounds (automobiles, airplanes, and helicopters) affect vocalizations of four functionally important bird species of the Andean-Patagonian Forest: The Thorn-tailed Rayadito (Aphrastura spinicauda, arthropod predator), the White-crested Elaenia (Elaenia albiceps, seed disperser), the Green-backed Firecrown (Sephanoides sephaniodes, pollinator) and the Austral Parakeet (Enicognathus ferrugineus, large seed disperser). During the spring–summer of 2021 and 2022 we placed audio recorders (AudioMoth v 1.2.0) in wooded areas of a city surrounded by a National Park, in Argentine Patagonia. We selected recordings where an individual was vocalizing before, during, and after an anthropogenic noise. From each recording, we analyzed frequency (minimum, maximum and dominant frequency) and temporal bioacoustic parameters (duration of vocalization and pause length between syllables). We found that anthropogenic noise affected all bird species, but responses varied between species. During anthropogenic noise, frequency bioacoustic parameters changed in two studied species, whereas all species modified at least one temporal bioacoustic parameter. Some impacts continued even after the noise ceased. Our results show noise produced by human activities affected bird communication potentially threatening birds' fitness and ecosystem functioning. Given the increase in urbanization over recent decades, and its associated anthropogenic noise increase, understanding the potential impacts of noise is essential for the conservation of bird populations and maintaining ecosystem functioning. [ABSTRACT FROM AUTHOR]

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.

Bats are typically nocturnal. However, daylight activity (ie between one hour after sunrise and one hour before sunset) is known for a small subset of species, often associated with small oceanic islands with no diurnal avian predators. Here, we describe numerous observations of diurnal activity of Horsfield's bat (Myotis horsfieldii), in Gunung Mulu National Park in Sarawak, Malaysia. Between 2015 and 2020, bats were repetitively observed flying along streams and rivers at 14 localities during various hours of the day. Daylight active bats exhibited a flight pattern concurrent with prey hunting strategies and, through bioacoustics, foraging was confirmed by the presence of feeding buzzes. Despite the occurrence of numerous sympatric avian diurnal predators known to hunt bats (eg bat hawk Macheiramphus alcinus) and avian competitors (eg cave swiftlet Aerodramus spp.), the relatively wide temporal and spatial coverage of the records suggests that diurnal activity of this water-associated bat might be a common phenomenon in the area. We speculate that this atypical behaviour might have arisen to circumvent night-time competition with the high numbers of bats roosting in nearby caves. [ABSTRACT FROM AUTHOR]

Audiovisual collections in traditional private or public institutions are crucial for preserving and safeguarding natural recordings of biodiversity. These archives represent a snapshot of invaluable moments, behaviours, and interactions among species, often representing the last or only existing recordings. Therefore, animal vocalisation recordings are essential for future generations to understand the past and preserve the remaining biodiversity. In this study, we examined the largest Latin American sound archive, the Fonoteca Neotropical Jacques Vielliard, in search for anuran sounds that are not yet formally described. Herein, we describe the calls of eight species, the advertisement call of seven of them, and the release call of two of them. These species, belonging to three different families, were recorded in Brazil over the past six decades and have different conservation status. Notably, two of them are threatened by extinction and one is extinct. Our study fills important knowledge gaps about rare and declining anuran populations, highlighting the value of public scientific audiovisual collections. [ABSTRACT FROM AUTHOR]