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The oceanographic conditions of the Southern California Bight (SCB) dictate the distribution and abundance of prey resources and therefore the presence of mobile predators, such as goose-beaked whales (Ziphius cavirostris). Goose-beaked whales are deep-diving odontocetes that spend a majority of their time foraging at depth. Due to their cryptic behavior, little is known about how they respond to seasonal and interannual changes in their environment. This study utilizes passive acoustic data recorded from two sites within the SCB to explore the oceanographic conditions that goose-beaked whales appear to favor. Utilizing optimum multiparameter analysis, modeled temperature and salinity data are used to identify and quantify these source waters: Pacific Subarctic Upper Water (PSUW), Pacific Equatorial Water (PEW), and Eastern North Pacific Central Water (ENPCW). The interannual and seasonal variability in goose-beaked whale presence was related to the variability in El Niño Southern Oscillation events and the fraction and vertical distribution of the three source waters. Goose-beaked whale acoustic presence was highest during the winter and spring and decreased during the late summer and early fall. These seasonal increases occurred at times of increased fractions of PEW in the California Undercurrent and decreased fractions of ENPCW in surface waters. Interannual increases in goose-beaked whale presence occurred during El Niño events. These results establish a baseline understanding of the oceanographic characteristics that correlate with goose-beaked whale presence in the SCB. Furthering our knowledge of this elusive species is key to understanding how anthropogenic activities impact goose-beaked whales.

Introduction: Acoustic monitoring and soundscape analysis provide valuable data for the conservation and restoration of underwater habitats. However, before these methods can be widely implemented for management purposes, it is crucial to validate the ecological relevance of different sampling methodologies and quantify potential biases. Methods: We investigated how the distance and orientation of an acoustic sensor relative to a target habitat influence the received soundscape. Using a spatial array of hydrophones, we recorded sound at different distances (1 m, 2 m, 5 m) and orientations (vertical vs. horizontal) from a shallow coral reef. Results: Hydrophones oriented horizontally toward the reef exhibited the expected decrease in sound levels with increasing distance. In contrast, hydrophones oriented vertically showed an inverse trend, with lower sound pressure levels at closer distances and higher levels further away. Discussion: These findings indicate that sensor directivity significantly influences the received soundscape, introducing a potential methodological bias within and across acoustic datasets. To improve the accuracy and comparability of acoustic sampling in coastal habitats, sensor beam patterns should be carefully considered in experimental design. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring has emerged as a useful technique for monitoring vocal species and contributing to biodiversity monitoring goals. However, finding target sounds for species without pre-existing recognisers still proves challenging. Here, we demonstrate how the embeddings from the large acoustic model BirdNET can be used to quickly and easily find new sound classes outside the original model's training set. We outline the general workflow, and present three case studies covering a range of ecological use cases that we believe are common requirements in research and management: monitoring invasive species, generating species lists, and detecting threatened species. In all cases, a minimal amount of target class examples and validation effort was required to obtain results applicable to the desired application. The demonstrated success of this method across different datasets and different taxonomic groups suggests a wide applicability of BirdNET embeddings for finding novel sound classes. We anticipate this method will allow easy and rapid detection of sound classes for which no current recognisers exist, contributing to both monitoring and conservation goals. [ABSTRACT FROM AUTHOR]

Monitoring the farm acoustic environment can provide important information about processes affecting crop production. This paper assesses farmers' knowledge and attitudes toward acoustic technology for farm monitoring in Rwanda. Stratified random sampling was used to select 430 farmers from 3 sectors. Demographic, farm, and technical knowledge data were collected from farmers with a survey. Cross-tabulation results show farmers with secondary and university education are more interested in acoustic technology than those with a primary education and farmers engaged in commercial farming are more interested in technology for monitoring acoustic sources than subsistence farmers. However, nearly all farmers are willing to deploy a listening technology to monitor the acoustic environment. Farmers have a clear priority to listen to animals on the farm, whether they are pests (specifically, birds) or livestock. A chi-square test of independence indicates a significant relationship between farmers' consideration of technology for farm monitoring and their attitudes toward sound monitoring. Farmers want a technological solution, but demand that the solution be low-cost and provide a simple alert. The results inform system requirements for an Internet of Things acoustic network that can deliver this information to the farmer. [ABSTRACT FROM AUTHOR]

Acoustic indices allow time efficient analysis of large acoustic datasets obtained from passive acoustic monitoring, but results regarding their effectiveness in assessing biodiversity are inconsistent. We evaluated the efficacy of six acoustic indices (ACI, ADI, AEI, H, BI, NDSI) for studying bird and structural diversity in 51 cocoa plantations, 24 of which were certified by Rainforest Alliance, in Luwu Timur, Sulawesi, Indonesia. We used linear models to assess the correlation of index values with bird species richness, and linear mixed models to test the influence of canopy closure, shade tree basal area, distance to primary forest and tree cover in a 200-m buffer on index values. Bird species richness was positively correlated with BI (p = 0.02) and negatively with H (p = 0.03), yet predictive power was low (R2 = 0.10 and 0.09, respectively). Acoustic indices did not differ significantly for certified cocoa plantations. Tree cover within the 200-m buffer moderately well predicted ACI values (marginal R2 = 0.37) while for the other indices effect sizes were low or correlations were not significant. Comparing our results to other studies, acoustic indices may reflect biodiversity across land uses, but were of limited value for tracking subtle differences in cocoa plantations in Sulawesi. Future studies may include more land uses (i.e. rice paddies, secondary forest, oil palm) as well as more taxa (i.e. insects). More research is needed on the comparability of acoustic indices, as we found them to be influenced by recording equipment and calculation settings. [ABSTRACT FROM AUTHOR]

This study highlights the potential of aquariums as research platforms for bioacoustic research. Aquariums provide access to a wide variety of fish species, offering unique opportunities to characterize their acoustic features in controlled settings. In particular, we present a preliminary description of the acoustic characteristics of Myripristis jacobus, a soniferous species in the Holocentridae family, within a controlled environment at a zoological facility in the Canary Islands, Spain. Using two HydroMoth 1.0 hydrophones, we recorded vocalizations of the blackbar soldierfish in a glass tank, revealing a pulsed sound type with a peak frequency around 355 Hz (DS 64), offering a more precise characterization than previously available. The vocalizations exhibit two distinct patterns: short sequences with long pulse intervals and fast pulse trains with short inter-pulse intervals. Despite some limitations, this experimental setup highlights the efficacy of cost-effective methodologies in public aquariums for initial bioacoustic research. These findings contribute to the early stages of acoustic characterization of coastal fishes in the western central Atlantic, emphasizing the value of passive acoustic monitoring for ecological assessments and conservation efforts. Moreover, this study opens new avenues for considering the acoustic environment as a crucial factor in the welfare of captive fish, an aspect that has largely been overlooked in aquarium management.

Passive acoustic monitoring is a standard technique for studying bat ecology and behavior. However, an issue that has received little attention is how to appropriately analyze data within a long‐term acoustic monitoring dataset when the equipment has been replaced and updated. Equipment changes are often inevitable, especially for microphones, which need to be replaced regularly due to extended weather exposure and associated reductions in recording quality. We compared 2 ultrasonic microphone models (Wildlife Acoustics SMM‐U1 and SMM‐U2) by deploying them side‐by‐side with the same acoustic detector unit. We tested 9 or 10 microphones per model in field deployments lasting an average of 9 nights. We compared triggering frequency, species classification, detection rates, and echolocation call parameters (as indicators of signal quality) from both microphones. The vast majority (97%) of our 25,949 paired recordings were captured simultaneously by both microphones. Yet, the SMM‐U2 outperformed the SMM‐U1 in terms of proportion of files classifiable to the species level (70% versus 61%), rate of bat detections per night (1–6.5 more detections per night depending on species), and recording quality. Based on our results, we propose a correction factor to facilitate direct comparison of datasets collected with these 2 different microphones. Our study will assist bat researchers in selecting appropriate equipment and accounting for potential biases in long‐term acoustic monitoring programs. [ABSTRACT FROM AUTHOR]

In this study, we explored the fish acoustic community at two coralligenous sites in Sardinia (Tavolara and Santa Teresa, western Mediterranean Sea) during the summer 2023. Our goal was to understand spatial and temporal patterns of fish acoustic activity on different temporal scales to offer insights for optimizing acoustic monitoring of this crucial ecosystem. We identified seventeen distinct sound types, revealing a diverse acoustic community. Tavolara had higher acoustic richness and abundance compared to Santa Teresa, which may be attributed to site-specific factors such as habitat structure, species composition, or levels of protection. Temporal analysis revealed clear diel patterns, with certain sounds associated with nocturnal or diurnal periods, reflecting the daily rhythms of different species. The study also examined how recording duration influenced acoustic richness, finding that longer recordings (15 minutes per hour and 15 minutes per hour at night) provided a more comprehensive detection of acoustic activity. Additionally, the number of recording days required to detect species richness varied depending on the site. While extended recordings improve the likelihood of detecting rare or sporadic sounds, they also present challenges in data management and equipment maintenance. The study underscores the importance of carefully planning sampling strategies to optimize acoustic monitoring and ensure effective and sustainable ecological research in coralligenous ecosystems. [ABSTRACT FROM AUTHOR]

This study highlights the potential of aquariums as research platforms for bioacoustic research. Aquariums provide access to a wide variety of fish species, offering unique opportunities to characterize their acoustic features in controlled settings. In particular, we present a preliminary description of the acoustic characteristics of Myripristis jacobus, a soniferous species in the Holocentridae family, within a controlled environment at a zoological facility in the Canary Islands, Spain. Using two HydroMoth 1.0 hydrophones, we recorded vocalizations of the blackbar soldierfish in a glass tank, revealing a pulsed sound type with a peak frequency around 355 Hz (DS 64), offering a more precise characterization than previously available. The vocalizations exhibit two distinct patterns: short sequences with long pulse intervals and fast pulse trains with short inter-pulse intervals. Despite some limitations, this experimental setup highlights the efficacy of cost-effective methodologies in public aquariums for initial bioacoustic research. These findings contribute to the early stages of acoustic characterization of coastal fishes in the western central Atlantic, emphasizing the value of passive acoustic monitoring for ecological assessments and conservation efforts. Moreover, this study opens new avenues for considering the acoustic environment as a crucial factor in the welfare of captive fish, an aspect that has largely been overlooked in aquarium management. [ABSTRACT FROM AUTHOR]

In recent decades, climate change has significantly influenced the frequency and intensity of wildfires across Mediterranean pine forests. The loss of forest cover can bring long-term ecological changes that impact the overall biodiversity and alter species composition. Understanding the long-term impact of wildfires requires effective and cost-efficient methods for monitoring the postfire ecosystem dynamics. Passive acoustic monitoring (PAM) has been increasingly used to monitor the biodiversity of vocal species at large spatial and temporal scales. Using acoustic indices, where the biodiversity of an area is inferred from the overall structure of the soundscape, rather than the more labor-intensive identification of individual species, has yielded mixed results, emphasizing the importance of testing their efficacy at the regional level. In this study, we examined whether widely used acoustic indicators were effective at capturing changes in the avifauna diversity in Pinus halepensis forest stands with different fire burning histories (burnt in 2001, 2009, and 2018 and unburnt for >20 years) on the Sithonia Peninsula, Greece. We recorded the soundscape of each stand using two–three sensors across 11 days of each season from March 2022 to January 2023. We calculated for each site and season the following five acoustic indices: the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Normalized Difference Soundscape Index (NDSI), and Bioacoustic Index (BI). Each acoustic index was then assessed in terms of its efficacy at predicting the local avifauna diversity, as estimated via two proxies—the species richness (SR) and the Shannon Diversity Index (SDI) of vocal bird calls. Both the SR and SDI were calculated by having an expert review the species identification of calls detected within the same acoustic dataset by the BirdNET convolutional neural network algorithm. A total of 53 bird species were identified. Our analysis shows that the BI and NDSI have the highest potential for monitoring the postfire biodiversity dynamics in Mediterranean pine forests. We propose the development of regional-scale acoustic observatories at pine and other fire-prone Mediterranean habitats, which will further improve our understanding of how to make the best use of acoustic indices as a tool for rapid biodiversity assessments. [ABSTRACT FROM AUTHOR]

Since the Industrial Revolution, underwater soundscapes have become more complex and contaminated due to increased cumulative human activities. Anthropogenic underwater sources have been growing in number, and shipping noise has become the primary source of chronic acoustic exposure. However, global data on current and historic noise levels is lacking. Here, using the Listening to the Deep-Ocean Environment network, we investigated the baseline shipping noise levels in thirteen observatories (eight stations from ONC Canada, four from the JAMSTEC network, and OBSEA in the Mediterranean Sea) and, in five of them, animal presence. Our main results show yearly noise variability in the studied locations that is not dominated by marine traffic but by natural and biological patterns. The halt in transportation due to COVID was insignificant when the data were recorded far from shipping routes. In order to better design a legislative framework for mitigating noise impacts, we highlight the importance of using tools that allow for long-term acoustic monitoring, automated detection of sounds, and big data handling and management. [ABSTRACT FROM AUTHOR]

In 2014, a novel call was discovered in autonomous acoustic recordings from the Mariana Archipelago and designated a "Biotwang". It was assumed to be produced by a baleen whale, but without visual verification it was impossible to assign a species. Using a combination of visual and acoustic survey data collected in the Mariana Archipelago, we determined that Biotwangs are produced by Bryde's whales. Bryde's whales occur worldwide in tropical and warm temperate waters, but their population structure and movements are not well understood. Genetic and morphological data recognize two populations in the western North Pacific (WNP), separate from those elsewhere in the Pacific. We used a combination of manual and machine learning annotation methods to detect Biotwangs in our extensive historical passive acoustic monitoring datasets collected across the central and western North Pacific. We identified a consistent seasonal presence of Biotwangs in the Mariana Archipelago and to the east at Wake Island, with occasional occurrence as far away as the Northwestern Hawaiian Islands and near the equator (Howland Island). The seasonal occurrence of Biotwangs is consistent with Bryde's whales migrating between low and mid-latitudes, with a small peak in calling between February and April and a larger peak between August and November as the whales travel past the recording sites. Our results provide evidence for a pelagic WNP population of Bryde's whales with broad distribution, but with seasonal and inter-annual variation in occurrence that imply a complex range most likely linked to changing oceanographic conditions in this region. [ABSTRACT FROM AUTHOR]

Understanding and identifying population-specific acoustic features is crucial to passive acoustic monitoring-based remote sensing of population distributions. Fin whales are known to produce 20-Hz pulses, often accompanied by a simultaneous higher frequency (HF) component. The centre frequency of this component has been found to differ regionally, presumably representing a population-specific acoustic characteristic. Within the Southern Ocean, five distinct HF components have been identified so far, two of which are present in the Atlantic Sector of the Southern Ocean (ASSO) with peak frequencies around 86 and 99 Hz. This study investigates the extent to which these HF components indicate distinct acoustic fin whale populations and their spatial distribution across the ASSO. By automatically analysing passive acoustic data from 2013, across 10 recording positions, our data show that while the 99-Hz component was detected at seven recording positions throughout the ASSO, the 86-Hz HF component is only present in its western area, centred around the Western Antarctic Peninsula. Additional 2019 data from the Western Antarctic Peninsula confirmed the consistent presence of the 86-Hz component, suggesting that these components are robust indicators of distinct acoustic populations. Knowledge on population-specific key habitats is key to strategic and effective conservation efforts.

IntroductionAcoustic monitoring and soundscape analysis provide valuable data for the conservation and restoration of underwater habitats. However, before these methods can be widely implemented for management purposes, it is crucial to validate the ecological relevance of different sampling methodologies and quantify potential biases.MethodsWe investigated how the distance and orientation of an acoustic sensor relative to a target habitat influence the received soundscape. Using a spatial array of hydrophones, we recorded sound at different distances (1 m, 2 m, 5 m) and orientations (vertical vs. horizontal) from a shallow coral reef.ResultsHydrophones oriented horizontally toward the reef exhibited the expected decrease in sound levels with increasing distance. In contrast, hydrophones oriented vertically showed an inverse trend, with lower sound pressure levels at closer distances and higher levels further away.DiscussionThese findings indicate that sensor directivity significantly influences the received soundscape, introducing a potential methodological bias within and across acoustic datasets. To improve the accuracy and comparability of acoustic sampling in coastal habitats, sensor beam patterns should be carefully considered in experimental design.

Emerging detection and classification algorithms based on deep learning models require manageable large-scale manual annotations of ground truth data. To date, the challenge of creating large and accurate annotated datasets of underwater sounds has been a major obstacle to the development of robust recognition algorithms. APLOSE (Annotation PLatform for Ocean Sound Explorers) is an open-source, web-based tool which facilitates collaborative annotation campaigns in underwater acoustics. The platform was used to carry out research projects on inter-annotator variability, to build training and testing data sets for detection algorithms and to perform bioacoustics analysis on noisy datasets. In the future, it will enable the creation of high-quality reference datasets to test and train the new detection and classification algorithms.

Passive acoustic monitoring has emerged as a useful technique for monitoring vocal species and contributing to biodiversity monitoring goals. However, finding target sounds for species without pre-existing recognisers still proves challenging. Here, we demonstrate how the embeddings from the large acoustic model BirdNET can be used to quickly and easily find new sound classes outside the original model’s training set. We outline the general workflow, and present three case studies covering a range of ecological use cases that we believe are common requirements in research and management: monitoring invasive species, generating species lists, and detecting threatened species. In all cases, a minimal amount of target class examples and validation effort was required to obtain results applicable to the desired application. The demonstrated success of this method across different datasets and different taxonomic groups suggests a wide applicability of BirdNET embeddings for finding novel sound classes. We anticipate this method will allow easy and rapid detection of sound classes for which no current recognisers exist, contributing to both monitoring and conservation goals.

Marine protected areas contribute to mitigating the effects of human activities on marine ecosystems. The waters of Southampton Island, Nunavut, Canada are under consideration to become a marine protected area, but baseline information of marine mammal presence and habitat use is lacking. This study represents the first passive acoustic monitoring of marine mammals in this area, with data collected in 2018 and 2019. Bioacoustics analyses and generalized linear models were used to investigate species seasonal and temporal vocalization trends; infer behavior and habitat use from vocal cues; and explore vocal activity correlation with sea ice. Over a diel period, bearded seals (Erignathus barbatus (Erxleben, 1777)) and walruses (Odobenus rosmarus (Linnaeus, 1758)) were found to increase their vocal activity at night, whereas belugas (Delphinapterus leucas (Pallas, 1776)) mainly vocalized during daytime. Seasonally, bearded seals were recorded only during their breeding season at sea ice break-up, while walruses vocalized consistently throughout the open-water period outside their breeding season. Beluga vocal trends suggest their use of the area as a migratory corridor during sea ice break-up and freeze-up. Finally, bowhead whales (Balaena mysticetus Linnaeus, 1758) were recorded later than expected, concurrently with the onset of their mating vocal displays during the fall migration at sea ice freeze-up.

Abstract Restoration of vulnerable marine habitats is becoming increasingly popular to cope with widespread habitat loss and the resulting decline in biodiversity and ecosystem services. Lately, restoration strategies have been employed to enhance the recovery of degraded meadows of the Mediterranean endemic seagrass Posidonia oceanica. Typically, habitat restoration success is evaluated by the persistence of foundation species after transplantation (e.g., plant survival and growth) on the short and long-term, although successful plant responses do not necessarily reflect the recovery of ecosystem biodiversity and functions. Recently, soundscape (the spatial, temporal and frequency attribute of ambient sound and types of sound sources characterizing it) has been related to different habitat conditions and community structures. Thus, a successful restoration action should lead to acoustic restoration and soundscape ecology could represent an important component of restoration monitoring, leading to assess successful habitat and community restoration. Here, we evaluated acoustic community and metrics in a P. oceanica restored meadow and tested whether the plant transplant effectiveness after one year was accompanied by a restored soundscape. With this goal, acoustic recordings from degraded, transplanted and reference meadows were collected in Sardinia (Italy) using passive acoustic monitoring devices. Soundscape at each meadow type was examined using both spectral analysis and classification of fish calls based on a catalogue of fish sounds from the Mediterranean Sea. Seven different fish sounds were recorded: most of them were present in the reference and transplanted meadows and were associated to Sciaena umbra and Scorpaena spp. Sound Pressure Level (SPL, in dB re: 1 μPa-rms) and Acoustic Complexity Index (ACI) were influenced by the meadow type. Particularly higher values were associated to the transplanted meadow. SPL and ACI calculated in the 200–2000 Hz frequency band were also related to high abundance of fish sounds (chorus). These results showed that meadow restoration may lead to the recovery of soundscape and the associated community, suggesting that short term acoustic monitoring can provide complementary information to evaluate seagrass restoration success.

Abstract Knowledge on the occurrence and behaviour of baleen whales around sub-Antarctic regions is limited, and usually based on short, seasonal sighting research from shore or research vessels and whaling records, neither of which provide accurate and comprehensive year-round perspectives of these animals’ ecology. We investigated the seasonal acoustic occurrence and diel vocalizing pattern of baleen whales around the sub-Antarctic Prince Edward Islands (PEIs) using passive acoustic monitoring data from mid-2021 to mid-2023, detecting six distinct baleen whale songs from Antarctic blue whales, Madagascan pygmy blue whales, fin whales, Antarctic minke whales, humpback whales, and sei whales. Antarctic blue and fin whales were detected year-round whereas the other species’ songs were detected seasonally, including a new Antarctic minke whale bio-duck song sub-type described here for the first time. Antarctic minke and sei whales were more vocally active at night-time whereas the other species had no clear diel vocalizing patterns. Random forest models identified month and/or sea surface temperature as the most important predictors of all baleen whale acoustic occurrence. These novel results highlight the PEIs as a useful habitat for baleen whales given the number of species that inhabit or transit through this region.

Abstract Passive acoustic monitoring (PAM) is an effective remote sensing approach for sampling acoustically active animal species and is particularly useful for elusive, visually cryptic species inhabiting remote or inaccessible habitats. Key advantages of PAM are large spatial coverage and continuous, long‐term monitoring. In most cases, a signal detection algorithm is utilized to locate sounds of interest within long sequences of audio data. It is important to understand the demographic/contextual usage of call types when choosing a particular signal to use for detection. Sampling biases may result if sampling is restricted to subsets of the population, for example, when detectable vocalizations are produced only by a certain demographic class. Using the African forest elephant repertoire as a case study, we test for differences in call type usage among different age‐sex classes. We identified disproportionate usage by age‐sex class of four call types—roars, trumpets, rumbles, and combination calls. This differential usage of signals by demographic class has implications for the use of particular call types in PAM for this species. Our results highlight that forest elephant PAM studies that have used rumbles as target signals may have under‐sampled adult males. The addition of other call types to PAM frameworks may be useful to leverage additional population demographic information from these surveys. Our research exemplifies how an examination of a species' acoustic behavior can be used to better contextualize the data and results from PAM and to strengthen the resulting inference.

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.

Passive acoustic monitoring of soundscapes and biodiversity produces vast amounts of audio recordings, but the management and analyses of these raw data present technical challenges. A multitude of software solutions exist, but none can fulfil all purposes required for the management, processing, navigation, and analysis of acoustic data. The field of ecoacoustics needs a software tool that is free, evolving, and accessible. We take a step in that direction and present ecoSound-web: an open-source, online platform for ecoacoustics designed and built by ecologists and software engineers. ecoSound-web can be used for storing, re-sampling, organising, analysing, and sharing soundscape recording or metadata projects. Specifically, it allows manual annotation of soniferous animals and soundscape components, automatic annotation with deep-learning models for all birds and for UK bat species, peer-reviewing annotations, analysing audio in time and frequency dimensions, computing alpha acoustic indices, and providing reference sound libraries for different taxa. We present ecoSound-web’s structure and features, and describe its operation for typical use cases such as sampling bird and bat communities, using a primate call library, and the analysis of soundscape components and acoustic indices. ecoSound-web is available from: https://github.com/ecomontec/ecoSound-web

Knowledge on the occurrence and behaviour of baleen whales around sub-Antarctic regions is limited, and usually based on short, seasonal sighting research from shore or research vessels and whaling records, neither of which provide accurate and comprehensive year-round perspectives of these animals’ ecology. We investigated the seasonal acoustic occurrence and diel vocalizing pattern of baleen whales around the sub-Antarctic Prince Edward Islands (PEIs) using passive acoustic monitoring data from mid-2021 to mid-2023, detecting six distinct baleen whale songs from Antarctic blue whales, Madagascan pygmy blue whales, fin whales, Antarctic minke whales, humpback whales, and sei whales. Antarctic blue and fin whales were detected year-round whereas the other species’ songs were detected seasonally, including a new Antarctic minke whale bio-duck song sub-type described here for the first time. Antarctic minke and sei whales were more vocally active at night-time whereas the other species had no clear diel vocalizing patterns. Random forest models identified month and/or sea surface temperature as the most important predictors of all baleen whale acoustic occurrence. These novel results highlight the PEIs as a useful habitat for baleen whales given the number of species that inhabit or transit through this region. [ABSTRACT FROM AUTHOR]

Restoration of vulnerable marine habitats is becoming increasingly popular to cope with widespread habitat loss and the resulting decline in biodiversity and ecosystem services. Lately, restoration strategies have been employed to enhance the recovery of degraded meadows of the Mediterranean endemic seagrass Posidonia oceanica. Typically, habitat restoration success is evaluated by the persistence of foundation species after transplantation (e.g., plant survival and growth) on the short and long-term, although successful plant responses do not necessarily reflect the recovery of ecosystem biodiversity and functions. Recently, soundscape (the spatial, temporal and frequency attribute of ambient sound and types of sound sources characterizing it) has been related to different habitat conditions and community structures. Thus, a successful restoration action should lead to acoustic restoration and soundscape ecology could represent an important component of restoration monitoring, leading to assess successful habitat and community restoration. Here, we evaluated acoustic community and metrics in a P. oceanica restored meadow and tested whether the plant transplant effectiveness after one year was accompanied by a restored soundscape. With this goal, acoustic recordings from degraded, transplanted and reference meadows were collected in Sardinia (Italy) using passive acoustic monitoring devices. Soundscape at each meadow type was examined using both spectral analysis and classification of fish calls based on a catalogue of fish sounds from the Mediterranean Sea. Seven different fish sounds were recorded: most of them were present in the reference and transplanted meadows and were associated to Sciaena umbra and Scorpaena spp. Sound Pressure Level (SPL, in dB re: 1 μPa-rms) and Acoustic Complexity Index (ACI) were influenced by the meadow type. Particularly higher values were associated to the transplanted meadow. SPL and ACI calculated in the 200–2000 Hz frequency band were also related to high abundance of fish sounds (chorus). These results showed that meadow restoration may lead to the recovery of soundscape and the associated community, suggesting that short term acoustic monitoring can provide complementary information to evaluate seagrass restoration success. [ABSTRACT FROM AUTHOR]

In recent years, several automated and noninvasive methods for wildlife monitoring, such as passive acoustic monitoring (PAM), have emerged. PAM consists of the use of acoustic sensors followed by sound interpretation to obtain ecological information about certain species. One challenge associated with PAM is the generation of a significant amount of data, which often requires the use of machine learning tools for automated recognition. Here, we couple PAM with BirdNET, a free-to-use sound algorithm to assess, for the first time, the precision of BirdNET in predicting three tropical songbirds and to describe their patterns of vocal activity over a year in the Brazilian Pantanal. The precision of the BirdNET method was high for all three species (ranging from 72 to 84%). We were able to describe the vocal activity patterns of two of the species, the Buff-breasted Wren (Cantorchilus leucotis) and Thrush-like Wren (Campylorhynchus turdinus). Both species presented very similar vocal activity patterns during the day, with a maximum around sunrise, and throughout the year, with peak vocal activity occurring between April and June, when food availability for insectivorous species may be high. Further research should improve our knowledge regarding the ability of coupling PAM with BirdNET for monitoring a wider range of tropical species. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring (PAM) is a reliable method for studying the behaviour and ecology of vocally active animals, providing insights into species' activity patterns. Here, we utilised a year‐long recording programme conducted in six areas of the Afromontane tropical forest of Cameroon to assess the utility of PAM, describe the annual and circadian vocal activity patterns, and assess the relationship between vocal activity and climate conditions for two African turaco species: the endemic and Endangered Bannerman's turaco (Tauraco bannermani) and the common, but locally at risk, Great blue turaco (Corythaeola cristata). The automated software detected 8670 vocalisations of Bannerman's turaco and 374 of the Great blue turaco, proving the ability of passive acoustics, coupled with automated software, for monitoring these species. Our findings suggest that both turacos are sedentary and seasonal breeders, exhibiting larger peaks of vocal activity during the dry season, although both species showed smaller activity peaks during the wet season. Similar circadian patterns were observed for both species, with activity peaks occurring at dawn and dusk. Long‐term monitoring programmes of both species should be implemented in the study areas, especially regarding the Great blue turaco, which may soon disappear from Bamenda Highlands. [ABSTRACT FROM AUTHOR]

This paper describes the Santos Basin Underwater Soundscape Monitoring Project (PMPAS-BS), a Brazilian ocean soundscape monitoring initiative. The main objective of the project is to quantify and assess hydroacoustic noise of anthropogenic origin in a large sedimentary basin extending from 23° S to 28° S on the southeastern Brazilian continental margin of the South Atlantic Ocean. Noise associated with oil and gas (O&G) exploration and production activities is the primary target, but this oceanic region also has busy shipping lanes for commercial, military, and fishing vessels. The two main hubs of Brazil’s export and import of goods by sea are located in this region: Santos and Rio de Janeiro ports. The project has three measurement components: mobile monitoring based on gliders and drifting acoustic profilers, fixed shallow-water monitoring based on acoustic measurements at coastal stations near shipping lanes associated with exploration and production activities in the Santos Basin, and fixed oceanic monitoring based on deep-water mooring lines equipped with passive autonomous acoustic recorders near production units, shipping lanes, and areas with lower intensity of O&G activities (pristine or reference sites). Numerical modeling of anthropogenic underwater acoustic noise has also been included as a fourth project component. The PMPAS-BS covers an area of more than 251,000 km² and uses several instruments with different methods and sensors for acoustic measurements. Its results provide current sound levels over a very large region of the western South Atlantic, both in areas more and less affected by anthropogenic activities. [ABSTRACT FROM AUTHOR]

Acoustic methods are widely used to study bird communities. They are particularly helpful in remote and hard reachable regions. In this study, we evaluated four acoustic indices (Acoustic Complexity Index, Acoustic Diversity Index, Acoustic Evenness Index and Bioacoustic Index) for biodiversity assessment in mountain beech (Fagus sylvatica) forests inside Belasitsa Nature Park, Bulgaria. We used twelve autonomous recording units and deployed four each in three forest types: old-growth forests in Kongura Reserve, mature managed forests (80-130 years of age) and premature managed forests (50-60 years of age). First, we verified that there were correlations between the acoustic indices and bird species richness detected by manual spectrogram scanning and listening of series of 3-min sound samples. Then, we compared the values of acoustic indices among the three forest types, finding higher bird species richness and abundance in old-growth forests and mature managed forests. We found no significant difference in the bird species composition in the three beech forest types, but more hole-nesting birds and species of conservation concern were identified in the old forests. We demonstrated that acoustic indices could be used for avian diversity monitoring and conservation management in mountain beech forests. [ABSTRACT FROM AUTHOR]

Background: São Tomé and Príncipe oceanic islands, in Central West Africa, are characterised by exceptional levels of endemism. Since human colonisation in the mid-15th century, São Tomé and Príncipe have lost 74% and 67% of their native habitat, respectively. Today, these islands are mainly covered by remaining old-growth forests, secondary regrowth forests, shaded plantations (mostly of cocoa), oil palm plantations (in the case of São Tomé), small-scale agricultural areas and urban areas. Yet, little is known about how species on these islands are coping with land-use changes. Island ecosystems are particularly important for bats, with about 25% of the world's bat species being entirely restricted to island systems. São Tomé and Príncipe Islands comprise six and four native insectivorous bats, respectively. Two species, Chaerephon tomensis and Macronycteris thomensis, are island-endemics in São Tomé; Pseudoromicia principis is an island-endemic in Príncipe; and Miniopterus newtoni is endemic from both São Tomé and Príncipe. Here, we present a dataset comprising a comprehensive compilation of occurrence records derived from acoustic sampling of insectivorous bats across the predominant land-use types of both the São Tomé and Príncipe Islands. In each sampling site, standardised surveys consisted of deploying one Audio Moth device that recorded for 1 minute every 5 minutes over a 48-hour period. We identified a total of 19,437 bat-passes across the 115 sites surveyed in São Tomé Island and 17,837 bat-passes across the 50 sites surveyed in Príncipe Island. New information: Based on a sampling effort of 1,584 hours of recordings manually processed to identify all the contained bat passes, this dataset, publicly available on GBIF, provides comprehensive information on the activity of insectivorous bats across two endemic-rich oceanic islands in the Gulf of Guinea. For each bat pass identified, we report the identified species, geographic coordinates, land-use type, altitude, date and time. This is the first public dataset providing detailed information on species-level habitat use for insectivorous bats on oceanic islands in Africa. [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]

Distillation columns are vital for substance separation and purification in various industries, where malfunctions can lead to equipment damage, compromised product quality, production interruptions, and environmental harm. Early fault detection using AI-driven methods like deep learning can mitigate downtime and safety risks. This study employed a lab-scale distillation column to collect passive acoustic signals under normal conditions and three potential faults: flooding, dry tray, and leakage. Signal processing techniques were used to extract acoustic features from low signal-to-noise ratios and weak time-domain characteristics. A deep learning-based passive acoustic feature recognition method was then applied, achieving an average accuracy of 99.03% on Mel-frequency cepstral coefficient (MFCC) spectrogram datasets. This method demonstrated robust performance across different fault types and limited data scenarios, effectively predicting and detecting potential faults in distillation columns. [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]

Passive acoustic monitoring (PAM) is an effective remote sensing approach for sampling acoustically active animal species and is particularly useful for elusive, visually cryptic species inhabiting remote or inaccessible habitats. Key advantages of PAM are large spatial coverage and continuous, long‐term monitoring. In most cases, a signal detection algorithm is utilized to locate sounds of interest within long sequences of audio data. It is important to understand the demographic/contextual usage of call types when choosing a particular signal to use for detection. Sampling biases may result if sampling is restricted to subsets of the population, for example, when detectable vocalizations are produced only by a certain demographic class. Using the African forest elephant repertoire as a case study, we test for differences in call type usage among different age‐sex classes. We identified disproportionate usage by age‐sex class of four call types—roars, trumpets, rumbles, and combination calls. This differential usage of signals by demographic class has implications for the use of particular call types in PAM for this species. Our results highlight that forest elephant PAM studies that have used rumbles as target signals may have under‐sampled adult males. The addition of other call types to PAM frameworks may be useful to leverage additional population demographic information from these surveys. Our research exemplifies how an examination of a species' acoustic behavior can be used to better contextualize the data and results from PAM and to strengthen the resulting inference. [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]

The underwater soundscape was recorded in Seaview Bay off Inexpressible Island, Ross Sea region Marine protected area, for 3 days in December 2021. Leopard seal Hydrurga leptonyx vocalizations were a prominent sound source that led to variations in ambient sound pressure levels in a frequency range of approximately 150–4500 Hz. Among the 14 call types previously identified, except ultrasound vocalizations, six types of broadcast calls were classified, and their acoustic characteristics were analyzed. We focused on the acoustic characteristics of four low‐frequency calls, clustered in a relatively narrow bandwidth, which have been relatively less studied. We identified a new call type of a triple ascending trill consisting of three trill parts, expanding upon the findings of previous studies. The audio data extracted from leopard seal vocalization videos, recorded by a monitoring camera on sea ice, enhanced the reliability of identifications of the underwater triple ascending trill. We present the unique results of underwater passive acoustic monitoring conducted at Seaview Bay, designated as Antarctic Specially Protected Area No 178. Our results could contribute to the development of detection and localization algorithms for leopard seal vocalizations and can be used as fundamental data for studies related to the vocalization and behavior of this species. [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]