Search

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]

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. Highlights: Northern white‐cheeked crested gibbons were found to sing less on mornings with rain, with fog, and on cooler mornings.Songs of the northern white‐cheeked gibbon in Nam Et‐Phou Louey National Park, Lao PDR, were observed to start later on mornings with fog but earlier on mornings with fog when the temperature was warmer.Cloud cover was found to correlate positively with detection of gibbon songs.Gibbon songs were observed to last longer on mornings with fog and on warmer mornings. [ABSTRACT FROM AUTHOR]

Amphibians such as frogs, toads, and salamanders provide important services in aquatic and terrestrial ecosystems and have been proposed as useful indicators of progress and success for ecological restoration projects. Limited guidance is available, however, on the costs and benefits of different amphibian monitoring techniques that might be applied to sites restored in compensation for contaminant injury. We used a variety of methods to document the amphibian communities present at 4 restored bottomland hardwood sites in Indiana, USA, and to compare the information return and cost of each method. For 1 method—automated recording units—we also modeled the effect of varying levels of sampling effort on the number of species detected, using sample‐based rarefaction and Bayesian nonlinear (Michaelis–Menten) mixed effects models. We detected 13 amphibian species across the restored sites, including 2 species of conservation concern in Indiana—northern leopard frogs (Lithobates pipiens) and Blanchard's cricket frogs (Acris blanchardi). Sites across a range of restoration ages demonstrated encouraging returns of amphibian communities. Although more mature sites showed greater species richness, recently restored sites still provided important habitat for amphibians, including species of conservation concern. Among the 4 methods compared, amphibian rapid assessment yielded the highest number of species detected and the greatest catch per unit effort, with the lowest per‐site cost. Our analysis of level‐of‐effort effects in the rarefied acoustic data found that number of nights sampled was a better predictor of observed species richness than the number of hours sampled within a night or minutes sampled within an hour. These data will assist restoration practitioners in selecting amphibian monitoring methods appropriate for their site characteristics and budget. Integr Environ Assess Manag 2024;20:1939–1953. Published 2019. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC) Key Points: Incorporating amphibians into restoration monitoring can improve accounting of ecosystem service returns on sites restored in compensation for contaminant injury. Amphibian rapid assessments detected the greatest number of species at the lowest per‐site cost. Sample‐based rarefaction of acoustic data from automated recording units indicated that increasing the number of nights sampled was the most efficient way to increase observed species richness. [ABSTRACT FROM AUTHOR]

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

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. Research highlights: Indri indri, the only singing lemur, emits songs daily, a behavior strictly linked to intergroup territory spacing.We explored the influence of weather conditions on indris' songs in the Maromizaha New Protected Area using Passive Acoustic Monitoring (PAM) and an automated detection algorithm.Indris adjusted their singing behavior based on weather conditions: rain and low temperatures are associated with decreased frequency. Rain determined delayed song emission in the morning. This suggests this behavior's plasticity is linked to energetic costs.Our study widens the knowledge of the behavior of an endangered species of nonhuman primates. It also showcases the strength of PAM in the conservation and behavioral study of different species. [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. Research Highlights: ‐Recent machine learning and deep learning methods provide innovative tools for the study of primate vocal communication.‐Applied to massive acoustic recordings, they offer new perspectives for better identifying and studying primate vocal productions.‐We provide a comprehensive review of established approaches and latest advances in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

The island biogeography theory predicts that species richness in islands and island-like systems is the ultimate result of island isolation and area. Species with high dispersal capabilities are predicted to be less affected by these factors because of their capacity to move more efficiently between islands or habitats, and here we test this idea in bats, the only mammals capable of flight. We conducted mist net and acoustic surveys across 21 islands in the San Juan Archipelago (Washington State, United States) and adjacent northwest mainland to: (i) investigate the effects of island area, distance from mainland, and habitat on bat diversity; and (ii) evaluate whether differences in morphological (body mass, forearm length, wing loading) and ecological (dietary niche breadth, foraging guild) traits among species influence their prevalence across islands. We found that island size strongly influenced patterns of species richness, with larger islands having a greater number of bat species. However, neither island distance from mainland nor any measure of habitat availability was a significant predictor of species richness at the scale of this study. Additionally, we found that dietary niche breadth, as opposed to any morphological trait, best predicted the prevalence of species across the islands. Our results suggest that species with more specialized diets may be more vulnerable to habitat fragmentation, and provide insight into how geographic and ecological factors affect the diversity of insular bat communities, adding to growing knowledge about the role of species traits as mediators of their responses to large-scale landscape structure. [ABSTRACT FROM AUTHOR]

Behavior represents animals' primary means of responding to environmental variation and adapting to rapid environmental change. Many animals' presence, let alone behavior, is highly cryptic to human observers, presenting a significant barrier in both theoretical and applied behavioral ecology. Bioacoustic signals not only reveal animals' presence, but also encode detailed information about the behaviors in which they are engaging. The study of behavioral bioacoustics has emerged to decipher the context and function of animal sounds and to apply this comprehension to understanding animal behavior across ecological scales and levels of biological organization. Growing capacity for behavioral bioacoustics represents a profound opportunity to understand animal behavior and steward rapidly changing ecosystems in the Anthropocene. 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. [ABSTRACT FROM AUTHOR]

Individual acoustic signatures, if present within animal species, can provide insights into evolution and behavior, and have great potential for differentiating individuals during monitoring and research. Recent advances in bioacoustic technology combined with acoustic individual identification (AIID) have the potential to revolutionize the study of sound-producing animals; however, methods and application of AIID remain in their infancy. Evidence of individual acoustic signatures across taxa and successes in adjacent acoustic disciplines suggest that opportunities exist for developing AIID. Research and development into AIID should combine deep learning methods with the construction and sharing of labeled training datasets, and should focus on recording and classification methods that maximize the potential applications of AIID. Broadscale implementation of AIID should be achievable in the near future and will allow biologists to answer important ecological and evolutionary questions with less bias and fewer negative population effects and resources than the current approaches. 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. [ABSTRACT FROM AUTHOR]

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]

Passive acoustic monitoring (PAM) is commonly used to obtain year‐round continuous data on marine soundscapes harboring valuable information on species distributions or ecosystem dynamics. This continuously increasing amount of data requires highly efficient automated analysis techniques in order to exploit the full potential of the available data. Here, we propose a benchmark, which consists of a public dataset, a well‐defined task and evaluation procedure to develop and test automated analysis techniques. This benchmark focuses on the special case of detecting animal vocalizations in a real‐world dataset from the marine realm. We believe that such a benchmark is necessary to monitor the progress in the development of new detection algorithms in the field of marine bioacoustics. We ultimately use the proposed benchmark to test three detection approaches, namely ANIMAL‐SPOT, Koogu and a simple custom sequential convolutional neural network (CNN), and report performances. We report the performance of the three detection approaches in a blocked cross‐validation fashion with 11 site‐year blocks for a multi‐species detection scenario in a large marine passive acoustic dataset. Performance was measured with three simple metrics (i.e., true classification rate, noise misclassification rate and call misclassification rate) and one combined fitness metric, which allocates more weight to the minimization of false positives created by noise. Overall, ANIMAL‐SPOT performed the best with an average F metric of 0.83, followed by the custom CNN with an average fitness metric of 0.79 and finally Koogu with an average fitness metric of 0.59. The presented benchmark is an important step to advance in the automatic processing of the continuously growing amount of PAM data that are collected throughout the world's oceans. To ultimately achieve usability of developed algorithms, the focus of future work should be laid on the reduction of the false positives created by noise. [ABSTRACT FROM AUTHOR]

This critical review examines four prominent active models of speech perception: quantal theory, motor theory, hyper-hypoarticulation theory, and exemplar theory. Each of these models offers distinctive insights into how listeners perceive, interpret, and categorize speech sounds. Quantal theory explores the non-linear relationship between articulation and acoustic output, emphasizing stable and unstable regions in speech production. Motor theory posits that speech perception is grounded in articulatory gestures, suggesting a direct link between production and perception. The hyper-hypo-articulation theory focuses on the speaker's adaptive articulation, which ranges from hyper- to hypo-articulation depending on the listener's needs and the communicative context. Exemplar theory, rooted in cognitive psychology, highlights the role of memory and experience, with individual speech sounds compared to stored exemplars in memory to aid perception. Despite their contributions, none of these models offers a comprehensive solution to all speech perception challenges. Each theory is suited to specific contexts, speech features, or tasks, making it crucial for researchers to understand their limitations. Furthermore, the review underscores the importance of selecting the appropriate model for particular research or practical applications, given the variability in their functionality. Finally, it calls for future research to delve deeper into passive models of speech perception and suggests that comparative studies between active and passive models could yield valuable insights. [ABSTRACT FROM AUTHOR]

Urban natural parks represent a remarkable concept that evokes the coexistence of human habitation with a wild environment, and the associated interactions between human and natural territories. In this context, urban noise infringes upon the natural soundscape, leading to various consequences for both realms. This study seeks to characterize the impact of anthropic noise levels on biodiversity in the urban natural Văcărești Park (Bucharest, Romania), utilizing on-site measurements and software simulation techniques. The study seeks to develop a method for evaluating integrative strategies to mitigate the impact of traffic noise on wildlife in an urban wild park, without addressing the specific effects of noise on the perception and communication of individual species. By calibrating field measurements with laboratory results, a more reliable data set will be used to identify areas where the biophonic environment is impacted by anthropogenic noise. Since human-generated noise in an urban natural park predominantly originates from road traffic and industrial sites, managing traffic noise and its propagation pathways could substantially improve the park's soundscape. Additionally, this study will apply software simulations for noise reduction strategies, such as vegetation planting and earthen embankments, to obtain suitable solutions and propose plausible and effective actions to authorities for improving the biophonic environment. This research could also serve as the basis for long-term monitoring, allowing for the assessment of the evolution and impact of implemented measures over time. [ABSTRACT FROM AUTHOR]

Humpback whale (Megaptera novaeangliae) nonsong vocalizations, or social calls, are much more poorly understood than humpback song. We examined humpback whale social calls from a foraging ground in southern British Columbia (BC) and developed a catalog for humpback social calls in BC. We tagged four humpback whales on the eastern edge of Swiftsure Bank, BC, in early September 2020, with a passive acoustic and movement tag. We manually classified 32 call types in our data set based on comparisons with published classifications of humpback social calls. Many of the calls identified in our data set had similar characteristics to calls from other locations. We also used two statistical classification methods, a cluster analysis and a random forest. The cluster analysis grouped 20 of these call types into four categories, and the random forest classifier was able to accurately classify all 20 call types 87.6% of the time. This study fills a geographical gap of humpback whale social calls on foraging grounds and is a first step towards categorizing the social calls of humpback whales in BC. [ABSTRACT FROM AUTHOR]

Assessing habitat quality is critical for migrating birds, with implications for fuel loading, predation risk and timing of departure. To efficiently distribute within stopover sites, migrants rely on various cues that facilitate habitat quality assessment. In this study, we conducted a playback experiment at a mid‐route stopover site to test the effects of vocal cues, specifically bird songs, on habitat redistribution during spring migration. To estimate the effect of bird songs on bird abundance we mist‐netted and ringed birds for 60 days on alternating playback treatment (playing songs of three sylviid warbler species) and no playback (control) days. A nearby site was used to control for fluctuations caused by migration waves. Overall, total bird abundance was significantly increased by the playback treatment. This increase resulted from the attraction of conspecifics (species whose songs were played) rather than of other species – sylviids or others. Correspondingly, species richness was seemingly unaffected. To investigate the relationship between internal‐state factors and the attraction to playback in the most abundantly attracted species, Eurasian Blackcap Sylvia atricapilla, we tested associations with age, sex and body condition index but found no significant effects, possibly because of the limited sample size. Our findings challenge established ideas regarding heterospecific attraction and contradict previous studies. Attraction to bird song is discussed in the context of geographical location, timing and internal drivers. Vocal cues, specifically bird songs, may affect micro‐habitat selection by migrating songbirds during stopovers. Consequently, we call for consideration of the influence of the acoustic environment on birds during migratory stopover in future studies, habitat management and conservation efforts. [ABSTRACT FROM AUTHOR]

Background: In ecosystems influenced by strong seasonal variation in insolation, the fitness of diverse taxa depends on seasonal movements to track resources along latitudinal or elevational gradients. Deep pelagic ecosystems, where sunlight is extremely limited, represent Earth's largest habitable space and yet ecosystem phenology and effective animal movement strategies in these systems are little understood. Sperm whales (Physeter macrocephalus) provide a valuable acoustic window into this world: the echolocation clicks they produce while foraging in the deep sea are the loudest known biological sounds on Earth and convey detailed information about their behavior. Methods: We analyze seven years of continuous passive acoustic observations from the Central California Current System, using automated methods to identify both presence and demographic information from sperm whale echolocation clicks. By integrating empirical results with individual-level movement simulations, we test hypotheses about the movement strategies underlying sperm whales' long-distance movements in the Northeast Pacific. Results: We detect foraging sperm whales of all demographic groups year-round in the Central California Current System, but also identify significant seasonality in frequency of presence. Among several previously hypothesized movement strategies for this population, empirical acoustic observations most closely match simulated results from a population undertaking a "seasonal resource-tracking migration", in which individuals move to track moderate seasonal-latitudinal variation in resource availability. Discussion: Our findings provide evidence for seasonal movements in this cryptic top predator of the deep sea. We posit that these seasonal movements are likely driven by tracking of deep-sea resources, based on several lines of evidence: (1) seasonal-latitudinal patterns in foraging sperm whale detection across the Northeast Pacific; (2) lack of demographic variation in seasonality of presence; and (3) the match between simulations of seasonal resource-tracking migration and empirical results. We show that sperm whales likely track oceanographic seasonality in a manner similar to many surface ocean predators, but with dampened seasonal-latitudinal movement patterns. These findings shed light on the drivers of sperm whales' long-distance movements and the shrouded phenology of the deep-sea ecosystems in which they forage. [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]

Diel vertical migration (DVM) is a behaviour observed across zooplankton taxa in marine and limnetic systems worldwide. DVM influences biogeochemical cycling and carbon drawdown in oceanic systems and alters prey availability for zooplanktivorous species. DVM has been well studied among zooplankton, and many exogenous and endogenous triggers as well as adaptive significances have been hypothesized. However, second-order variability in DVM timing, the deviation of DVM times to respective dawn and dusk times throughout the year, is a less-studied phenomenon that can help identify the factors influencing migration timing as well as demonstrate the changes of DVM behaviours within and across systems. Here, we quantified seasonal trends in second-order variability of DVM timing of euphausiids at Brooks Peninsula, Clayoquot Canyon, and Saanich Inlet near Vancouver Island, British Columbia, Canada, over multiple years using upward-facing moored echosounders. We used generalized additive mixed models to characterize this seasonality. DVM timing relative to civil twilight times showed strong seasonality at all locations, with euphausiids remaining near the surface longer than expected in spring and summer, and shorter than expected in winter. Euphausiids spent less time near the surface at Brooks Peninsula and Clayoquot Canyon than at Saanich Inlet throughout the year. Increased primary productivity in Saanich Inlet, which reduced light penetration and hid euphausiids from visual predators, likely drove this difference. Our findings confirm that proper understanding of DVM behaviours must account for seasonal variability due to context-specific oceanographic and ecological parameters. This is particularly pertinent when attempting to model the biogeochemical or predator–prey interactions influenced by DVM behaviours. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring is a promising tool for monitoring at-risk populations of vocal species, yet, extracting relevant information from large acoustic datasets can be time-consuming, creating a bottleneck at the point of analysis. To address this, an open-source framework for deep learning in bioacoustics to automatically detect Bornean white-bearded gibbon (Hylobates albibarbis) "great call" vocalizations in a long-term acoustic dataset from a rainforest location in Borneo is adapted. The steps involved in developing this solution are described, including collecting audio recordings, developing training and testing datasets, training neural network models, and evaluating model performance. The best model performed at a satisfactory level (F score = 0.87), identifying 98% of the highest-quality calls from 90 h of manually annotated audio recordings and greatly reduced analysis times when compared to a human observer. No significant difference was found in the temporal distribution of great call detections between the manual annotations and the model's output. Future work should seek to apply this model to long-term acoustic datasets to understand spatiotemporal variations in H. albibarbis' calling activity. Overall, a roadmap is presented for applying deep learning to identify the vocalizations of species of interest, which can be adapted for monitoring other endangered vocalizing species. [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]

Copyright of Avian Conservation & Ecology is the property of Resilience Alliance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Abstract Background In ecosystems influenced by strong seasonal variation in insolation, the fitness of diverse taxa depends on seasonal movements to track resources along latitudinal or elevational gradients. Deep pelagic ecosystems, where sunlight is extremely limited, represent Earth’s largest habitable space and yet ecosystem phenology and effective animal movement strategies in these systems are little understood. Sperm whales (Physeter macrocephalus) provide a valuable acoustic window into this world: the echolocation clicks they produce while foraging in the deep sea are the loudest known biological sounds on Earth and convey detailed information about their behavior. Methods We analyze seven years of continuous passive acoustic observations from the Central California Current System, using automated methods to identify both presence and demographic information from sperm whale echolocation clicks. By integrating empirical results with individual-level movement simulations, we test hypotheses about the movement strategies underlying sperm whales’ long-distance movements in the Northeast Pacific. Results We detect foraging sperm whales of all demographic groups year-round in the Central California Current System, but also identify significant seasonality in frequency of presence. Among several previously hypothesized movement strategies for this population, empirical acoustic observations most closely match simulated results from a population undertaking a “seasonal resource-tracking migration”, in which individuals move to track moderate seasonal-latitudinal variation in resource availability. Discussion Our findings provide evidence for seasonal movements in this cryptic top predator of the deep sea. We posit that these seasonal movements are likely driven by tracking of deep-sea resources, based on several lines of evidence: (1) seasonal-latitudinal patterns in foraging sperm whale detection across the Northeast Pacific; (2) lack of demographic variation in seasonality of presence; and (3) the match between simulations of seasonal resource-tracking migration and empirical results. We show that sperm whales likely track oceanographic seasonality in a manner similar to many surface ocean predators, but with dampened seasonal-latitudinal movement patterns. These findings shed light on the drivers of sperm whales’ long-distance movements and the shrouded phenology of the deep-sea ecosystems in which they forage.

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.

For over a century, the role of acoustic communication in the sensory ecology of bark beetles (Scolytinae) has been recognized. However, their 'world of sound' remains largely unexplored. Here, we review 153 years of bark beetle bioacoustics publications to summarize current knowledge, identify gaps and suggest future research directions. Our survey identified 117 publications covering 170 species. Morphological reports revealed five stridulatory organs across 125 species, with elytro‐tergal, gular‐prosternal and vertex‐pronotum mechanisms being the most prevalent for sound production. However, confirmed sound recordings exist for only 40 species. Acoustic signalling in adults is proposed to function in avoiding enemies, pair formation, sexual selection and spacing, while in juveniles, vibratory communication is proposed for gallery spacing. However, experimental evidence supporting these functions is lacking. Acoustic sensory organs remain unidentified, and comprehension of signal transmission—whether through airborne sounds or solid‐borne vibrations (or both)—is limited. Bioacoustic technologies have emerged as tools for potential management practices and are also discussed. Based on these findings, we recommend three directions for future research: (1) characterize acoustic morphology and behaviours in more species, particularly unrepresented taxa, with recordings in various contexts, preferably under natural conditions; (2) test hypotheses to explain the functions of acoustic communication through experimental and comparative phylogenetic methods and (3) investigate how sounds or vibrations are transmitted and received through behavioural and neurophysiological experiments. Advancements in bark beetle acoustic sensing and communication research will enhance our understanding of their sensory ecology and facilitate potential control measures of these fascinating insects. [ABSTRACT FROM AUTHOR]

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.

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]

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]

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]

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]

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]