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Introduction: In production forests, management can have cascading effects on biodiversity and ecosystem services. Acoustic diversity reflects the diversity of vocalizing animals and has also considerable recreational value for human well-being, but the relationship between acoustic diversity and forest management remains largely unexplored Method: We recorded acoustic diversity on forest plots along a gradient of silvicultural management intensity (SMI) in three regions of Germany. We explored the diurnal and seasonal temporal dynamics in acoustic diversity index (ADI) from March to July using generalized additive mixed models (GAMMs). We further investigated the interrelation between acoustic diversity and silvicultural management intensity, forest structural diversity, as well as tree diversity, bird species richness and abundance using structural equation modeling (SEM). Results: Silvicultural management intensity had significant effects on the temporal dynamics of ADI in May and June from dawn till dusk, but variance explained by SMI was low. We confirmed our hypothesis that ADI was reduced by SMI due to its cascading effects on forest structural diversity and bird species richness and abundance. Discussion: Acoustic diversity indices can provide valuable insights into how forest management affects the acoustic activity of soniferous communities. We discuss how this can indicate both changes in species diversity as well as their vocal activity. We further address potential implications for forest management. [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]

Introduction: Forests managed for timber production can also be managed for biodiversity conservation by retaining structures typical of old-growth forests, which provide heterogenous structures for forest-dwelling species, including birds. Ecoacoustic monitoring of forest birds is now a well-studied field, however the extent to which acoustic indices can reflect bird assemblage responses to stands of lower or higher structural heterogeneity is disputed. Method: In this study, we acoustically surveyed 75 plots over two years in mature, previously managed forests and computed acoustic indices from the recordings. We first identified an acoustic index that significantly correlated with bird richness over more than one spring season. Next, we tested the response of bird richness to individual forest structural elements using linear regressions. We then repeated this analysis but combined the individual structural elements into one structural composition variable using an NMDS and gam smooth overlay, to compare the effect of individual forest structures versus overall forest heterogeneity on bird richness. We then repeated this analysis using our selected acoustic index, the Normalized Difference Soundscape Index (NDSI), to see if it followed the same patterns as bird richness in response to individual and collective forest structural elements. Results: Our results showed that plots with high bird richness were also associated with high NDSI values, and high values of both variables occurred in plots with high structural heterogeneity (tree species richness, tree size variability, and snag height variability) and low amounts of standing and lying deadwood. Discussion: Our findings suggest that once an acoustic index can be identified as a robust correlate of bird richness, it can serve as a proxy for the response of birds to differences in forest structural heterogeneity within a managed forest context. It is therefore possible for forest managers interested in conserving or increasing bird richness in their production forests to identify a robust acoustic index to track the response of bird richness to management decisions over time. [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]

Birds use sound for conspecific communication. According to the acoustic niche hypothesis, they avoid signal competition with other species by partitioning sound in frequency and time. Others have observed introduced species changing the vocal behavior of native species; however, communitylevel effects of multiple introduced species remain unknown. Hawai'i, with its legacy of bird extinctions followed by a surge of human introductions, offers a unique opportunity to study acoustic signal partitioning between native and introduced species. We predicted that communities with higher percentages of introduced birds would exhibit more acoustic overlap between members of different species due to shorter time frames to evolve signal partitioning. Using autonomous recording units, we recorded forest bird communities during the summer in montane primary and secondary forests as well as a low-elevation agricultural site. Random samples of recordings from each site were visualized as spectrograms, and all bird vocalizations >5 decibels above background noise were identified and labeled by species. Frequency range and the proportion of overlap with other species were compared between native and introduced species. We also used a null model which randomized the start time of each bird vocalization within a location over 500 iterations, then compared the amount of heterospecific signal overlap in the randomizations to that observed in the recordings. While native and introduced species generally used similar frequency ranges, native-native heterospecific species vocalization pairs had a significantly higher proportion of overlap than introduced-native and nativenative pairs. Additionally, the incidence of signal overlap in the original recordings tended to be lower than in the null model randomizations, but this difference was not significant, and was not influenced by the percentage of introduced species vocalizations at the site. The lack of significant difference between observed and null model signal overlap occurrence suggests that native and introduced forest birds were not partitioning acoustic space either spectrally or temporally, and that introduced birds are not strongly influencing signal partitioning in forest bird communities of Hawai'i. However, this may have been a consequence of recording during the non-breeding season, and future work should compare vocal activity in native and introduced birds throughout the year. [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]

Soundscape analysis has become integral to environmental monitoring, particularly in marine and terrestrial settings. Fish choruses within marine ecosystems provide essential descriptors for environmental characterization. This study employed a month-long sequence of continuous underwater recordings to generate 24-h spectrograms, utilizing Principal Component Analysis (PCA) specifically adapted to analyze fish choruses. The spectrograms were constructed using a frequency range from 0 to 5 kHz, represented by 1,025 spectral points (frequency binwidth 5 Hz) on a linear scale. A preliminary spectral subsampling reduced the frequency components to 205 spectral points. PCA was then applied to this subsampled data, selecting 7 principal components (PCs) that explained 95% of the variance. To enhance visualization and interpretation, we introduced "acoustic maps" portrayed as heatmaps. This methodology proved valuable in characterizing the structure of the observed environment and capturing pertinent diel patterns of fish choruses. Additionally, these PCA components can be analyzed using acoustic maps to reveal hidden dynamics within the marine acoustic environment. The dimensionality reduction achieved by PCA not only streamlined data handling but also enabled the extraction of spectral information pertinent to fish choruses and the temporal dynamics of the soundscape. In conclusion, our methodology presents a versatile framework extendable to diverse biological choruses and ecoacoustic studies. The straightforward, easily interpretable analysis leverages computations derived from 24-h spectrograms, offering novel insights into the daily dynamics of biological. Choruses and contributing to future advancements in ecoacoustic research. [ABSTRACT FROM AUTHOR]

Given that ecosystems are composed of sounds created by geophysical events (e.g., wind, rain), animal behaviors (e.g., dawn songbird chorus), and human activities (e.g., tourism) that depend on seasonal climate conditions, the phenological patterns of a soundscape could be coupled with long-term weather station data as a complimentary ecological indicator of climate change. We tested whether the seasonality of the soundscape coincided with common weather variables used to monitor climate. We recorded ambient sounds hourly for five minutes (01 January-30 June) over three years (2019-2021) near a weather-station in a subarctic ecosystem in south-central Alaska. We quantified sonic information using the Acoustic Complexity Index (ACItf), coupled with weather data, and used machine learning (TreeNet) to identify sonic-climate relationships. We grouped ACItf according to time periods of prominent seasonal events (e.g., days with temperatures >0°C, no snow cover, green up, dawn biophony, and road-based tourism) and identified distinct sonic phenophases (sonophases) for groups with non-overlapping 95% confidence intervals. In general, sonic activity increased dramatically as winter transitioned to spring and summer. We identified two winter sonophases, a spring sonophase, and a summer sonophase, each coinciding with hours of daylight, temperature, precipitation, snow cover, and the prevalence of animal and human activities. We discuss how sonophases and weather data combined serve as a multidimensional, systems-based approach to understanding the ecological effects of climate change in subarctic environments. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring (PAM) is an effective, non-intrusive method for studying ecosystems, but obtaining meaningful ecological information from its large number of audio files is challenging. In this study, we take advantage of the expected animal behavior at different times of the day (e.g., higher acoustic animal activity at dawn) and develop a novel approach to use these time-based patterns. We organize PAM data into 24-hour temporal blocks formed with sound features from a pretrained VGGish network. These features feed a 1D convolutional neural network with a class activation mapping technique that gives interpretability to its outcomes. As a result, these diel-cycle blocks offer more accurate and robust hour-by-hour information than using traditional ecological acoustic indices as features, effectively recognizing key ecosystem patterns. [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.

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]

Aquatic insects are a major indicator used to assess ecological condition in freshwater environments. However, current methods to collect and identify aquatic insects require advanced taxonomic expertise and rely on invasive techniques that lack spatio-temporal replication. Passive acoustic monitoring (PAM) is emerging as a non-invasive complementary sampling method allowing broad spatio-temporal and taxonomic coverage. The application of PAM in freshwater ecosystems has already proved useful, revealing unexpected acoustic diversity produced by fishes, amphibians, submerged aquatic plants, and aquatic insects. However, the identity of species producing sounds remains largely unknown. Among them, aquatic insects appear to be the major contributor to freshwater soundscapes. Here, we estimate the potential number of soniferous aquatic insects worldwide using data from the Global Biodiversity Information Facility. We found that four aquatic insect orders produce sounds totalling over 7000 species. This number is probably underestimated owing to poor knowledge of aquatic insects bioacoustics. We then assess the value of sound producing aquatic insects to evaluate ecological condition and find that they might be useful despite having similar responses in pristine and degraded environments in some cases. Both expert and automated identifications will be necessary to build international reference libraries and to conduct acoustic bioassessment in freshwaters. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

Insects play vital ecological roles; many provide essential ecosystem services while others are economically devastating pests and disease vectors. Concerns over insect population declines and expansion have generated a pressing need to effectively monitor insects across broad spatial and temporal scales. A promising approach is bioacoustics, which uses sound to study ecological communities. Despite recent increases in machine learning technologies, the status of emerging automated bioacoustics methods for monitoring insects is not well known, limiting potential applications.To address this gap, we systematically review the effectiveness of automated bioacoustics models over the past four decades, analysing 176 studies that met our inclusion criteria. We describe their strengths and limitations compared to traditional methods and propose productive avenues forward.We found automated bioacoustics models for 302 insect species distributed across nine Orders. Studies used intentional calls (e.g. grasshopper stridulation), by‐products of flight (e.g. bee wingbeats) and indirectly produced sounds (e.g. grain movement) for identification. Pests were the most common study focus, driven largely by weevils and borers moving in dried food and wood. All disease vector studies focused on mosquitoes. A quarter of the studies compared multiple insect families.Our review illustrates that machine learning, and deep learning in particular, are becoming the gold standard for bioacoustics automated modelling approaches. We identified models that could classify hundreds of insect species with over 90% accuracy. Bioacoustics models can be useful for reducing lethal sampling, monitoring phenological patterns within and across days and working in locations or conditions where traditional methods are less effective (e.g. shady, shrubby or remote areas). However, it is important to note that not all insect taxa emit easily detectable sounds, and that sound pollution may impede effective recordings in some environmental contexts.Synthesis and applications: Automated bioacoustics methods can be a useful tool for monitoring insects and addressing pressing ecological and societal questions. Successful applications include assessing insect biodiversity, distribution and behaviour, as well as evaluating the effectiveness of restoration and pest control efforts. We recommend collaborations among ecologists and machine learning experts to increase model use by researchers and practitioners. [ABSTRACT FROM AUTHOR]

Large‐scale biodiversity conservation and management necessitate rapid assessment tools and technologies. Indices derived from passive acoustic data offer a novel solution for rapid biodiversity monitoring. Although these indices quantify vocalizing biodiversity at a site, previous studies indicate inconsistencies in the indices' performance across different biomes. We tested the efficacy of seven acoustic indices—acoustic complexity index, acoustic diversity index, bioacoustic index (BI), acoustic entropy index, total entropy (H), normalized difference soundscape index, and number of peaks in an understudied biome, tropical dry forests, in central India. We measured the relationship between every index and a combination of these indices with a biodiversity metric, avian species richness. We found a weak correlation between individual indices and species richness (0.00 ≤ R ≤ 0.35), while a combination of indices was comparatively better at predicting species richness (R2 = 0.54). Although BI performed better than all other indices, our results indicate that acoustic indices do not accurately quantify avian species richness in this forest in central India. However, combining multiple indices increases their efficacy, limitedly. We recommend evaluating the efficiency of acoustic indices, especially in underrepresented habitats, before their application in avifauna‐based rapid acoustic surveys. [ABSTRACT FROM AUTHOR]

Passive acoustic recorders have emerged as powerful tools for ecological monitoring. However, effective monitoring is not simply an act of recording sounds. To have meaning for conservation and management, acoustic monitoring needs to be properly planned and analyzed to yield high quality information. Here, we provide a set of considerations for the design of an effective acoustic monitoring program. We argue that such a program, has the following attributes: (1) has established appropriate partnerships with landowners, Traditional Owners, researchers, or other relevant stakeholders, (2) is based on clear objectives and questions, (3) is explicit in its target sound signals, (4) has considered in‐field sensor placement for a range of factors, including experimental design, statistical power, background noise, and potential impacts on human privacy and animal disturbance, (5) has a justified recording schedule and periodicity, (6) has methods to process sound data in line with objectives, and (7) has protocols for permanent data storage and access. Acoustic monitoring is increasingly used in large‐scale programs and will be important in addressing global biodiversity targets and new biodiversity markets. It is critical that new monitoring programs are designed to effectively and efficiently capture data that address pertinent and emerging issues in conservation. [ABSTRACT FROM AUTHOR]

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As global wildfire activity increases, wildlife are facing greater exposure to hazardous smoke pollution – with unknown consequences for biodiversity. Research on the effects of smoke on wild animals is extremely limited, in part due to the inherent logistical challenges of observing how animals respond to smoke in real time. Passive acoustic monitoring may be a powerful tool to safely and effectively monitor biodiversity before, during, and after major smoke events. In this study, we used data collected from a large-scale network of bioacoustic recorders at 92 sites in central and eastern Washington state during August–September, 2019–2020 to investigate the effect of wildfire smoke on dawn soundscapes and, by extension, acoustically active wildlife. We used acoustic indices to document and characterize changes in soundscapes related to smoke exposure, including the Acoustic Complexity Index (ACI), Bioacoustic Index (BI), and Normalized Difference Soundscape Index (NDSI). Higher values of these indices likely indicate higher levels of biodiversity in our study area. We hypothesized that wildfire smoke would reduce bird vocalizations, leading to declines in ACI, BI, and NDSI at dawn, when birds are most active. We used linear and quantile regression models to test for an effect of daily exposure to fine particulate matter (PM2.5), a marker of wildfire smoke, on the mean daily values and the upper 90th percentile of each index at dawn. We also conducted a before-during-after analysis of a particularly hazardous smoke event that impacted our study area on September 12–14, 2020. We did not observe linear effects of daily PM2.5 on average or peak daily values of acoustic indices; however, we did observe a significant reduction in ACI and BI during the three-day smoke event in 2020 and in the two weeks following this air pollution episode. Our results indicate that, on average, ACI and BI were reduced by 2.7 % and 15.9 % during and 1.5 % and 11.0 % afterward, respectively. These findings add further evidence that wildfire smoke alters soundscapes, likely due to changes in the presence, abundance, or behavior of acoustically active animals. Furthermore, our study demonstrates that wildfire smoke may have delayed and/or cumulative effects on acoustically active wildlife. Our study highlights the potential for passive acoustic monitoring to document wildlife responses to smoke pollution and identify potentially relevant exposure periods.

IntroductionIn production forests, management can have cascading effects on biodiversity and ecosystem services. Acoustic diversity reflects the diversity of vocalizing animals and has also considerable recreational value for human well-being, but the relationship between acoustic diversity and forest management remains largely unexploredMethodWe recorded acoustic diversity on forest plots along a gradient of silvicultural management intensity (SMI) in three regions of Germany. We explored the diurnal and seasonal temporal dynamics in acoustic diversity index (ADI) from March to July using generalized additive mixed models (GAMMs). We further investigated the interrelation between acoustic diversity and silvicultural management intensity, forest structural diversity, as well as tree diversity, bird species richness and abundance using structural equation modeling (SEM).ResultsSilvicultural management intensity had significant effects on the temporal dynamics of ADI in May and June from dawn till dusk, but variance explained by SMI was low. We confirmed our hypothesis that ADI was reduced by SMI due to its cascading effects on forest structural diversity and bird species richness and abundance.DiscussionAcoustic diversity indices can provide valuable insights into how forest management affects the acoustic activity of soniferous communities. We discuss how this can indicate both changes in species diversity as well as their vocal activity. We further address potential implications for forest management.

The association of urban greenspace and human health and well-being is widely recognised, but the underlying mechanisms are incompletely understood. The acoustic environment (AE) is frequently proposed as a mediator between greenspace and human health. While it is commonly viewed as a negative health factor (e.g. noise pollution), there is growing evidence that it also has positive effects on human health. However, a general problem is the lack of information on the AE for greenspaces in high spatial resolution. To provide evidence-based support for research on this issue, we identify and assess acoustic properties of health-related urban greenspace by estimating the association between urban green area and selected acoustic indices.The analysis is based on 5-minute audio recordings collected systematically from 730 locations, four times a year (n = 2,746) in Bochum, Germany. To quantify the acoustic properties, we use an indicator-set of 30 sound pressure levels, 11 eco-, 78 psychoacoustic indices and 3 complex network measures. Greenspace is defined, in alignment with various health studies, as the percentage of green area within three buffer sizes (50, 100, 300 m) around each recording location.Through cluster analysis, we identify eleven acoustic dimensions in our indicator-set, for which representative indices are derived using principal component analysis. Descriptive statistics and multi-level regression models are used to analyse the associations between representative indices and greenspace. We find the highest effect sizes for Link Density (representing “acoustic dominance”), Acoustic Richness (representing “persistent sound volume”) and the Articulation Index (representing “sound volume”). Thus, in addition to “sound volume”, the AE of urban greenspace is characterised to a similar extent by at least two additional acoustic dimensions. If properties of the AE act as mediators between urban greenspace and human health, these three acoustic dimensions hold the potential to reflect them. Studies on pathways between greenspace and human health could incorporate measures of these dimensions to extend the understanding of the AE’s relevance for Public Health.

The soundscape encompasses all biological, geophysical, and anthropogenic sounds in an environment and serves as an important communication medium for biota. While macro-level observations have suggested a correlation between the acoustic and physical environments, detailed and synthesized empirical evidence establishing nuanced relationships remains limited. Establishing empirical relationships between the soundscape and the environment is essential to ensure monitoring programs are grounded in robust ecological principles. However, empirical evidence about the relationship between the soundscape, environmental attributes, and biotic communities is scarce and scattered. Here, we synthesize evidence about correlations between the soundscape and at least one environmental variable or indicator. We analyse where (location) and when (year) the relationships between the soundscape and the environment have been investigated, what study designs and methods have been used to quantify these relationships, and what relationships were found between the soundscape and environmental or biological attributes. Our key findings include (1) the relationship between the soundscape and the environment is underexplored in aquatic ecosystems – particularly freshwater; (2) riparian and underwater soundscapes are seldom monitored simultaneously; and (3) the relationships found are not tested across various climate regions, elevations, temporal and spatial scales, or study designs. Since most studies use acoustic indices to analyse their soundscape, future research needs to assess whether the relationships between the soundscape and the environment change under different audio processing methods. We also show there is potential to extend the likes of acoustic monitoring far beyond monitoring soniferous biota populations, to include monitoring ecological health, ecosystem processes, and non-soniferous biota. Finally, we present a list of key questions to increase our knowledge of the connection between the soundscape and the environment. These questions will promote a solid conceptual base for future acoustic monitoring programs that enhance the interpretation of acoustic data, develop new technologies, and consider a broader cross-system (land to water) context.

In the current biodiversity crisis, ecologists grapple with the formidable task of monitoring spatial and temporal trends in biodiversity. Ecoacoustics, or soundscape studies, leverage sound energy properties to characterize environmental aspects over time and space and are promising tools in the search for cost-effective methods for biodiversity monitoring. However, the potential of ecoacoustics can only be realized if ecoacoustic indices (EIs) summarizing the sound properties of ecosystems demonstrably and reliably reflect the biodiversity observed on the ground. In this study we assessed the correlation between seven widely used EIs and three survey-based indices of community diversity: species richness, Shannon’s diversity index and community composition. Utilizing standard field methods (censusing, trapping, etc.) for species richness and abundance, we surveyed local diversity of vertebrates at eight sites in a heterogeneous subtropical landscape while recording soundscapes at each site. We calculated community diversity indices from samples of bird, amphibian, reptile, and mammal guilds, and assessed how well different temporal aggregation of each EI correlated with known community diversity. We calculated seven EIs in addition to an index summarizing them and aggregated their values at five time frames (morning, evening, winter, spring, summer, and all recordings). Out of the 15 combinations arising from the exploration of the three community diversity dimensions for the four taxa and overall vertebrate diversity, 13 combinations presented significant correlations with at least one EI. Three indices, “Bioacoustics index”, “Acoustic Complexity’ and “Median of the amplitude envelope” correlated with aspects of biodiversity in all the analyzed groups, highlighting their value. Importantly, the time frame used for aggregating the indices influenced relationships across guilds and biodiversity aspects. We demonstrate that EIs aggregated at the appropriate time scale can accurately track various aspects of the diversity of local communities of vertebrates, supporting the use of EIs in biodiversity monitoring. However, we caution researchers and conservationists that detailed empirical assessments of the relation between diversity and soundscapes are still required across temporal and spatial scales to continue developing reliable acoustic indices that will accurately reflect the state of biodiversity.

IntroductionForests managed for timber production can also be managed for biodiversity conservation by retaining structures typical of old-growth forests, which provide heterogenous structures for forest-dwelling species, including birds. Ecoacoustic monitoring of forest birds is now a well-studied field, however the extent to which acoustic indices can reflect bird assemblage responses to stands of lower or higher structural heterogeneity is disputed. MethodIn this study, we acoustically surveyed 75 plots over two years in mature, previously managed forests and computed acoustic indices from the recordings. We first identified an acoustic index that significantly correlated with bird richness over more than one spring season. Next, we tested the response of bird richness to individual forest structural elements using linear regressions. We then repeated this analysis but combined the individual structural elements into one structural composition variable using an NMDS and gam smooth overlay, to compare the effect of individual forest structures versus overall forest heterogeneity on bird richness. We then repeated this analysis using our selected acoustic index, the Normalized Difference Soundscape Index (NDSI), to see if it followed the same patterns as bird richness in response to individual and collective forest structural elements.ResultsOur results showed that plots with high bird richness were also associated with high NDSI values, and high values of both variables occurred in plots with high structural heterogeneity (tree species richness, tree size variability, and snag height variability) and low amounts of standing and lying deadwood. DiscussionOur findings suggest that once an acoustic index can be identified as a robust correlate of bird richness, it can serve as a proxy for the response of birds to differences in forest structural heterogeneity within a managed forest context. It is therefore possible for forest managers interested in conserving or increasing bird richness in their production forests to identify a robust acoustic index to track the response of bird richness to management decisions over time.

Birds use sound for conspecific communication. According to the acoustic niche hypothesis, they avoid signal competition with other species by partitioning sound in frequency and time. Others have observed introduced species changing the vocal behavior of native species; however, community-level effects of multiple introduced species remain unknown. Hawai‘i, with its legacy of bird extinctions followed by a surge of human introductions, offers a unique opportunity to study acoustic signal partitioning between native and introduced species. We predicted that communities with higher percentages of introduced birds would exhibit more acoustic overlap between members of different species due to shorter time frames to evolve signal partitioning. Using autonomous recording units, we recorded forest bird communities during the summer in montane primary and secondary forests as well as a low-elevation agricultural site. Random samples of recordings from each site were visualized as spectrograms, and all bird vocalizations ≥5 decibels above background noise were identified and labeled by species. Frequency range and the proportion of overlap with other species were compared between native and introduced species. We also used a null model which randomized the start time of each bird vocalization within a location over 500 iterations, then compared the amount of heterospecific signal overlap in the randomizations to that observed in the recordings. While native and introduced species generally used similar frequency ranges, native-native heterospecific species vocalization pairs had a significantly higher proportion of overlap than introduced-native and native-native pairs. Additionally, the incidence of signal overlap in the original recordings tended to be lower than in the null model randomizations, but this difference was not significant, and was not influenced by the percentage of introduced species vocalizations at the site. The lack of significant difference between observed and null model signal overlap occurrence suggests that native and introduced forest birds were not partitioning acoustic space either spectrally or temporally, and that introduced birds are not strongly influencing signal partitioning in forest bird communities of Hawai‘i. However, this may have been a consequence of recording during the non-breeding season, and future work should compare vocal activity in native and introduced birds throughout the year.

Passive acoustic monitoring (PAM) through acoustic recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, acoustic diversity, community interactions, and human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due to the large volumes of ARU data. In this work, we propose a non-supervised framework using autoencoders to extract soundscape features. We applied this framework to a dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based on autoencoder features and represents cluster information with prototype spectrograms using centroid features and the decoder part of the neural network. Our analysis provides valuable insights into the distribution and temporal patterns of various sound compositions within the study area. By utilizing autoencoders, we identify significant soundscape patterns characterized by recurring and intense sound types across multiple frequency ranges. This comprehensive understanding of the study area's soundscape allows us to pinpoint crucial sound sources and gain deeper insights into its acoustic environment. Our results encourage further exploration of unsupervised algorithms in soundscape analysis as a promising alternative path for understanding and monitoring environmental changes. [ABSTRACT FROM AUTHOR]

Sound classification plays a crucial role in enhancing the interpretation, analysis, and use of acoustic data, leading to a wide range of practical applications, of which environmental sound analysis is one of the most important. In this paper, we explore the representation of audio data as graphs in the context of sound classification. We propose a methodology that leverages pre-trained audio models to extract deep features from audio files, which are then employed as node information to build graphs. Subsequently, we train various graph neural networks (GNNs), specifically graph convolutional networks (GCNs), GraphSAGE, and graph attention networks (GATs), to solve multi-class audio classification problems. Our findings underscore the effectiveness of employing graphs to represent audio data. Moreover, they highlight the competitive performance of GNNs in sound classification endeavors, with the GAT model emerging as the top performer, achieving a mean accuracy of 83% in classifying environmental sounds and 91% in identifying the land cover of a site based on its audio recording. In conclusion, this study provides novel insights into the potential of graph representation learning techniques for analyzing audio data. [ABSTRACT FROM AUTHOR]

The urgent need for effective wildlife monitoring solutions in the face of global biodiversity loss has resulted in the emergence of conservation technologies such as passive acoustic monitoring (PAM). While PAM has been extensively used for marine mammals, birds, and bats, its application to primates is limited. Black‐and‐white ruffed lemurs (Varecia variegata) are a promising species to test PAM with due to their distinctive and loud roar‐shrieks. Furthermore, these lemurs are challenging to monitor via traditional methods due to their fragmented and often unpredictable distribution in Madagascar's dense eastern rainforests. Our goal in this study was to develop a machine learning pipeline for automated call detection from PAM data, compare the effectiveness of PAM versus in‐person observations, and investigate diel patterns in lemur vocal behavior. We did this study at Mangevo, Ranomafana National Park by concurrently conducting focal follows and deploying autonomous recorders in May–July 2019. We used transfer learning to build a convolutional neural network (optimized for recall) that automated the detection of lemur calls (57‐h runtime; recall = 0.94, F1 = 0.70). We found that PAM outperformed in‐person observations, saving time, money, and labor while also providing re‐analyzable data. Using PAM yielded novel insights into V. variegata diel vocal patterns; we present the first published evidence of nocturnal calling. We developed a graphic user interface and open‐sourced data and code, to serve as a resource for primatologists interested in implementing PAM and machine learning. By leveraging the potential of this pipeline, we can address the urgent need for effective primate population surveys to inform conservation strategies. Highlights: We conducted a passive acoustic monitoring concurrently with behavioral observations to assess each method's effectiveness in detecting black‐and‐white ruffed lemurs in Ranomafana National Park.We developed a deep learning model that enabled automated and accurate analysis of 2300 h of audio data, surpassing manual processing limitations, saving time and money, and yielding re‐analyzable data for lemur conservation efforts.We found that ruffed lemurs have consistent calling activity throughout the day (no dawn chorus), and also present the first published evidence of nocturnal calling, which peaked during the mating period. [ABSTRACT FROM AUTHOR]

According to the Sonotope Hypothesis, the heterogenous nature of the acoustically sensed, but not yet interpreted, environmental sounds (i.e., sonoscape) is created by the spatial and temporal conformation of sonic patches (sonotopes) as recently been described in a Mediterranean rural landscape. We investigated the Sonotope Hypothesis in a mountain beech forest of the Northern Apennines, Italy that is notoriously poor in soniferous species. Our aim was to test whether sonotopes were temporally distinct over seasonal and astronomical timeframes and spatially configured in relation to vegetation variables. We used the Acoustic Complexity Index (ACItf) to analyze the heterogeneity of sonic information gathered from an array of 11 sound recorders deployed within a lattice of eleven 4-ha hexagonal sample sites distributed throughout a 48-ha managed beech forest. We visualized and described the temporal patterns of ACItf between seasons (May-June and July-August 2021), across six astronomical periods (Night I, Morning Twilight, Morning, Afternoon, Evening Twilight, and Night II), and according to two aggregated frequency classes (<2000 and >2000 Hz). We introduced Spectral Sonic Signature (SSS) calculated from the sequence of ACItf values along frequency bins as a descriptor of the dynamic production of sounds across spatial and temporal scales. We calculated Mean Spectral Dissimilarity to compare SSS values across temporal periods and between sample sites. We identified sonotopes by grouping similar SSS for each sample site generated from cluster analyses and visualized their spatial arrangements. Frequencies <2000 Hz (mainly geophonies from wind and rain) were more prevalent than frequencies >2000 Hz (mainly biophonies from songbirds). Despite there being no strong relationship to vegetation variables and minimal biophony and anthropophony, distinct sonotopes still emerged for every astronomical and seasonal period. This suggests that the sonoscape expresses distinct spatial and temporal sonotope configurations associated with the temporal and spatial patterns of geophysical events that generate geophonies with minimal animal or anthropogenic occurrences. A new strategy based on the reintroduction of indigenous trees and shrubs in managed clearings should be considered for enhancing local biodiversity conservation along with ecoacoustic monitoring based on the Sonotope Hypothesis. [ABSTRACT FROM AUTHOR]

Freshwater fishes exhibit a wide range of auditory adaptations and capabilities, which are assumed to help them navigate their environment, avoid predators, and find potential mates. Yet, we know very little about how freshwater environments sound to fish, or how fish with different auditory adaptations respond to different soundscapes. We first compiled data on fish hearing acuity and adaptations and provided a portrait of how anthropogenic sounds compare to natural sounds in different freshwater soundscapes. We then conducted a sound‐enrichment field experiment at Lake Saint Pierre, a large fluvial lake in Canada, to evaluate the effect of motorboat sound exposure on the fish community by looking at the extent to which changes in species abundances were linked to auditory adaptations. Data compilation showed that the hearing acuity of most species overlaps with a wide range of ambient and anthropogenic underwater sounds while the field experiment showed that species with more specialized auditory structures were captured less often in sound‐enriched traps, indicating avoidance behavior. Our findings highlight the importance of considering species' sensorial adaptations when evaluating the community‐scale effects of anthropogenic sounds on the fish community, especially at low levels of anthropogenic activity. [ABSTRACT FROM AUTHOR]

Organisms, such as fishes and invertebrates and including their larval stages, listen to underwater soundscapes to detect information about nearby habitats. Such soundscapes may be influenced by habitat degradation or enhancement, which can lead to acoustically mediated feedback loops affecting the overall ecosystem.Despite the importance of underwater sounds on ecological functioning, there have been limited studies documenting soundscapes of intertidal ecosystems and few, if any, of living shoreline soundscapes. Living shorelines would especially benefit from acoustically mediated effects for objectives like encouraging fish and invertebrate settlement.This case study used a Before‐After‐Control‐Impact design to sample soundscapes and nekton (i.e., fishes and mobile macroinvertebrates) at a living shoreline construction and a nearby hardened shoreline in Cedar Key, FL (USA). Diel soundscape patterns and acoustic attenuation at the two sites were also described a year following the living shoreline construction.In the acoustic sampling, the high frequency bands of both shorelines were dominated by invertebrate sounds that were influenced by season, site and time of day, while the low frequency band of the living shoreline was often dominated by a loud anthropogenic sound. About a year after the living shoreline installation—despite similar measured acoustic attenuation at both sites—the living shoreline featured louder sound pressure levels compared to the hardened shoreline, which may be particularly beneficial for promoting foundational species and other organism settlement.These results demonstrate that Gulf of Mexico intertidal habitats may have soundscape differences even within close proximity and that living shorelines may enhance acoustic characteristics in ways beneficial to continued shoreline development. This represents an important step in better understanding the relationships between habitat structures, nekton communities, and their associated soundscapes as well as the application of passive acoustic monitoring to improve coastal management and conservation. [ABSTRACT FROM AUTHOR]

The concept of a soundscape is found in both ecology and music studies. Nature soundscapes and soundscape compositions are analyzed by both disciplines, respectively, to understand their biological diversity and ecosystem functioning and to interpret their compositional structure. A major challenge for both disciplines is visualizing the information embedded in a large variety of soundscapes and to share it with different audiences, from non-professionals to experts. To analyze soundscapes, both disciplines have independently developed similarity visualizations. However, no attempt has been made yet to combine these two fields of research to improve our ecological and musical perception of environmental sounds through shared similarity analysis methods. In this paper, we introduce a new visualization tool, the soundscape chord diagram (SCD), a circular similarity representation method that can be applied to any type of soundscape, either in ecoacoustics or electroacoustic studies. Our approach consists of visualizing spectral similarities between predefined sound segments based on the computation of a b-diversity acoustic index and on automatic clustering. SCDs were tested on two ecoacoustic forest databases and two electroacoustic soundscape compositions. SCDs were performant for the identification of specific acoustic events and highlighted known diel periods for nature soundscapes and written parts for soundscape compositions. This new visualization tool allows us to easily decipher the structure of musical and ecological acoustic data. SCDs could be applied to a large variety of soundscapes and promote their knowledge and preservation. This study opens a new way of investigating soundscapes at the interface between ecology and music, bringing together science and the arts. [ABSTRACT FROM AUTHOR]

Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suited for characterizing marine acoustic environments. Here, we describe the application of multiple machine‐learning techniques to the analysis of two PAM datasets. We combine pre‐trained acoustic classification models (VGGish, NOAA and Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine‐learned acoustic features capture different aspects of the marine acoustic environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labeled sounds in the 8 kHz range; however, low‐ and high‐frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow establishing a link between ecologically relevant information and PAM recordings at multiple scales, ranging from large‐scale changes in the environment (i.e., changes in wind speed) to the identification of marine mammal species. [ABSTRACT FROM AUTHOR]

Given that ecosystems are composed of sounds created by geophysical events (e.g., wind, rain), animal behaviors (e.g., dawn songbird chorus), and human activities (e.g., tourism) that depend on seasonal climate conditions, the phenological patterns of a soundscape could be coupled with long-term weather station data as a complimentary ecological indicator of climate change. We tested whether the seasonality of the soundscape coincided with common weather variables used to monitor climate. We recorded ambient sounds hourly for five minutes (01 January–30 June) over three years (2019–2021) near a weather station in a subarctic ecosystem in south-central Alaska. We quantified sonic information using the Acoustic Complexity Index (ACItf), coupled with weather data, and used machine learning (TreeNet) to identify sonic-climate relationships. We grouped ACItf according to time periods of prominent seasonal events (e.g., days with temperatures >0°C, no snow cover, green up, dawn biophony, and road-based tourism) and identified distinct sonic phenophases (sonophases) for groups with non-overlapping 95% confidence intervals. In general, sonic activity increased dramatically as winter transitioned to spring and summer. We identified two winter sonophases, a spring sonophase, and a summer sonophase, each coinciding with hours of daylight, temperature, precipitation, snow cover, and the prevalence of animal and human activities. We discuss how sonophases and weather data combined serve as a multi-dimensional, systems-based approach to understanding the ecological effects of climate change in subarctic environments.

Abstract Passive acoustic recorders have emerged as powerful tools for ecological monitoring. However, effective monitoring is not simply an act of recording sounds. To have meaning for conservation and management, acoustic monitoring needs to be properly planned and analyzed to yield high quality information. Here, we provide a set of considerations for the design of an effective acoustic monitoring program. We argue that such a program, has the following attributes: (1) has established appropriate partnerships with landowners, Traditional Owners, researchers, or other relevant stakeholders, (2) is based on clear objectives and questions, (3) is explicit in its target sound signals, (4) has considered in‐field sensor placement for a range of factors, including experimental design, statistical power, background noise, and potential impacts on human privacy and animal disturbance, (5) has a justified recording schedule and periodicity, (6) has methods to process sound data in line with objectives, and (7) has protocols for permanent data storage and access. Acoustic monitoring is increasingly used in large‐scale programs and will be important in addressing global biodiversity targets and new biodiversity markets. It is critical that new monitoring programs are designed to effectively and efficiently capture data that address pertinent and emerging issues in conservation.

Abstract Large‐scale biodiversity conservation and management necessitate rapid assessment tools and technologies. Indices derived from passive acoustic data offer a novel solution for rapid biodiversity monitoring. Although these indices quantify vocalizing biodiversity at a site, previous studies indicate inconsistencies in the indices' performance across different biomes. We tested the efficacy of seven acoustic indices—acoustic complexity index, acoustic diversity index, bioacoustic index (BI), acoustic entropy index, total entropy (H), normalized difference soundscape index, and number of peaks in an understudied biome, tropical dry forests, in central India. We measured the relationship between every index and a combination of these indices with a biodiversity metric, avian species richness. We found a weak correlation between individual indices and species richness (0.00 ≤ R ≤ 0.35), while a combination of indices was comparatively better at predicting species richness (R2 = 0.54). Although BI performed better than all other indices, our results indicate that acoustic indices do not accurately quantify avian species richness in this forest in central India. However, combining multiple indices increases their efficacy, limitedly. We recommend evaluating the efficiency of acoustic indices, especially in underrepresented habitats, before their application in avifauna‐based rapid acoustic surveys.

The analysis of audio recordings through acoustic indices has been proposed as an efficient way to measure and monitor biodiversity, given the assumption that higher levels of biodiversity produce more rapidly-changing and complex sound. However, in previous work in south China, we have found only moderate correlations between the acoustic indices and bird species richness, when analyzing recordings that were made at the same time as conducting point counts of birds. Here, we extended that work in three study regions in Guangxi Province, making observations both inside relatively undisturbed forest reserves and in the surrounding agricultural lands. We found that of 42 correlations between bird species richness and the acoustic indices (seven commonly used acoustic indices [ACI, ADI, AEI, AR, BIO, H, NDSI] each calculated in six habitat/region combinations) only seven had even a moderate relationship (i.e., |r| ≥ 0.20). To understand this paucity of relationships, we listened to a subsample of the recordings, scoring them for various kinds of biophony (incidence of bird and insect sounds), anthropophony (incidence of observer-produced sounds, sounds made by other humans, and sounds made by machines) and geophony (primarily wind, occasionally water). These analyses found that insects were positively related to the acoustic indices, especially in forests. Moreover, anthropophony and geophony, were, in general, negatively correlated to the indices. The incidence of bird vocalizations also had consistent and strong correlations to the residuals of the models relating the acoustic indices to bird species richness, demonstrating that the indices are clearly sensitive to the amount of bird sounds, but that is not necessarily strongly correlated to bird diversity. Multivariate models confirmed that the amounts of different sound categories were more influential on the acoustic indices than bird species richness. Our results demonstrate how competing noises, in both relatively undisturbed environments and heavily modified ones, can confound relationships between the acoustic indices and biodiversity. We discuss possible remedial steps for using acoustic indices in noisy soundscapes, including tailoring the selection of indices and frequency bands for the objectives of the project, or, alternatively, using a big-data approach that combines multiple indices.

Vocalization is the main form of communication in many animals, including frogs, which commonly emit advertisement calls to attract females and maintain spacing. In noisy environments such as streams, mechanisms to maximize signaling efficiency may include vocal plasticity and/or movement of individuals to quieter sections, but which strategy is used is still uncertain. We investigated the influence of stream geophony on the advertisement call of the torrent frog Hylodes perere in the Atlantic Rainforest, in southeastern Brazil. In a mark-recapture study, we tested if males remain in their territories and thus adjust their advertisement calls to maximize their communication. We ran linear mixed models to verify the relationship of call parameters and stream geophony, body size, and environmental temperature. We found that males remained in the same location across time, increased call intensity in noisier environments, but did not reduce call effort. Males also increased the dominant frequency in these situations, suggesting a modulation in this parameter. Our results indicate that territoriality is an important factor for males to increase call intensity to surpass stream noise instead of repositioning along the stream. However, because call effort was maintained, we suggest that sexual selection is crucial in this system, favoring males that better detect others and adjust their call efficiency. This is the first study to evaluate simultaneously frog movements and adaptations to geophony, which contributes to the investigation of the concomitant environmental and sexual selective pressures in species that communicate in noisy environments. [ABSTRACT FROM AUTHOR]

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The increasing conversion of natural areas for anthropic land use has been a major cause of habitat loss, destabilizing ecosystems and leading to a biodiversity crisis. Passive acoustic sensors open the possibility of remotely sensing fauna on large spatial and temporal scales, improving our understanding of the current state of biodiversity and the effects of human influences. Acoustic indices have been widely used and tested in recent years, with an aim towards understanding the relationship between indices and the acoustic activity of several taxa in different types of environments. However, studies have shown divergent relationships between acoustic indices and the vocal activity of most soniferous taxa. A combination of indices has, in turn, been reported as a promising tool for representing biodiversity in different contexts. We used uni- and bivariate models to test different combinations of 8 common indices in relation to bird assemblage metrics. We recorded twenty-two study sites in Brazil's Atlantic Forest and three different types of environments in each site (forest, pasture, and swamp). Our results showed that 1) the best acoustic indices for explaining bird richness, abundance, and diversity were Bioacoustic and Acoustic Complexity; 2) the type of environment (forest, pasture, and swamp) influenced the performance of acoustic indices in explaining bird biodiversity, with the highest score model (biggest R² value) being a combination between Acoustic Diversity and Bioacoustic indices. Our results do support the use of acoustic indices in monitoring the acoustic activity of birds, but combining indices is encouraged since it provided the best results. However, given the divergence we found across environments, we recommend that sets of indices are tested to determine which of them best describe the biodiversity pattern models for a specific habitat. Based on our results, we propose that biodiversity patterns can be predicted through acoustic patterns. However, the level of confidence will depend on the acoustic index used and on focal taxa of interest (i.e., birds, amphibians, insects, and mammals). [ABSTRACT FROM AUTHOR]

In a fast-changing environment, it is important to keep track of biodiversity. In Mexico, new herpetofauna is described every year. Vocalisations can portray ecological and evolutionary characteristics for better understanding of species. Here, we lay out the current state of knowledge on advertisement calls of Mexican anurans, as well as the publication trends and the use of metadata through an exhaustive literature review and bibliometric analysis. We obtained 174 documents with advertisement call descriptions of 58.4% of Mexican anuran species published between 1940–2021. We identified that most of the species with call descriptions are distributed in the Neotropics and belong to endemic and endangered categories. Anuran call descriptions in publications are frequently accompanied by associated data such as relative humidity, call frequency, or recording format. The mean number of recorded characteristics per description was 12.568 (of 38). Although almost half of the Mexican species have associated advertisement call descriptions, we identified several knowledge gaps, particularly in the degree of information that each description contains. Furthermore, most of the identified studies were performed outside of Mexico by foreign researchers, despite the high level of anuran endemism in the country. We highlight the need for more detailed descriptions published by Mexican researchers. [ABSTRACT FROM AUTHOR]

Knowledge of the ecology, spatial distribution and conservation status of fish populations is achieved by fishery‐dependent techniques, and by more recently developed non‐invasive fishery‐independent techniques. Passive acoustic monitoring (PAM) is a fishery‐independent method that provides remote sensing of soniferous species, populations, communities and ecosystems by recording soundscapes and their components.A case study is presented to demonstrate how PAM can contribute to a dynamic understanding of fish distribution, ecological preferences and conservation status. This case study refers to the cusk‐eel Ophidion rochei (Ophidiiformes), a nocturnal, behaviourally cryptic, soniferous fish species, described as uncommon and rare in the scientific literature, and listed as Data Deficient in the IUCN Red List.A systematized literature review was carried out using Ophidion+rochei as the search term, and by grouping records into two main categories: (i) traditional techniques (including all fishery‐dependent techniques and underwater visual census); and (ii) PAM.This review highlights how PAM has provided new sightings of O. rochei at a rate three times higher than all other monitoring techniques combined. In contrast with the knowledge achieved to date by fishery‐dependent techniques, the reported acoustic mass phenomena indicate that this species can be very abundant. Ophidion rochei was found to inhabit a wide range of depths and ecosystems, at least throughout the Mediterranean basin.This paper supports the urgency and the importance of relying on the integration of different fishery‐independent techniques for multidisciplinary monitoring, in line with the Goal 14 requirements of the UN Decade of Ocean Science for Sustainable Development. [ABSTRACT FROM AUTHOR]

Observatories are designed to collect data for a range of uses. The Australian Acoustic Observatory (A2O) was established to collect environmental sound, including audible species calls, from 344 recorders at 86 sites around Australia. We examine the potential of the A2O to monitor near threatened, threatened, endangered and critically endangered species, based on their vocal behaviour, geographic distributions in relation to the sites of the A2O and on some knowledge of habitat use. Using IUCN and EPBC lists of threatened and endangered species, we extracted species that vocalized in the audible range, and using conservative estimates of their geographic ranges, determined whether there was a possibility of hearing them at these sites. We found that it may be possible to detect up to 171 threatened species at sites established for the A2O, and that individual sites have the potential to detect up to 40 threatened species. All 86 sites occurred in locations where threatened species could possibly be detected, and the list of detectable species included birds, amphibians, and mammals. We have incidentally detected one mammal and four bird species in the data during other work. Threatening processes to which potentially detectable species were exposed included all but two IUCN threat categories. We concluded that with applications of technology to search the audio data from the A2O, it could serve as an important tool for monitoring threatened species. [ABSTRACT FROM AUTHOR]

Abstract Freshwater fishes exhibit a wide range of auditory adaptations and capabilities, which are assumed to help them navigate their environment, avoid predators, and find potential mates. Yet, we know very little about how freshwater environments sound to fish, or how fish with different auditory adaptations respond to different soundscapes. We first compiled data on fish hearing acuity and adaptations and provided a portrait of how anthropogenic sounds compare to natural sounds in different freshwater soundscapes. We then conducted a sound‐enrichment field experiment at Lake Saint Pierre, a large fluvial lake in Canada, to evaluate the effect of motorboat sound exposure on the fish community by looking at the extent to which changes in species abundances were linked to auditory adaptations. Data compilation showed that the hearing acuity of most species overlaps with a wide range of ambient and anthropogenic underwater sounds while the field experiment showed that species with more specialized auditory structures were captured less often in sound‐enriched traps, indicating avoidance behavior. Our findings highlight the importance of considering species' sensorial adaptations when evaluating the community‐scale effects of anthropogenic sounds on the fish community, especially at low levels of anthropogenic activity.

According to the Sonotope Hypothesis, the heterogenous nature of the acoustically sensed, but not yet interpreted, environmental sounds (i.e., sonoscape) is created by the spatial and temporal conformation of sonic patches (sonotopes) as recently been described in a Mediterranean rural landscape. We investigated the Sonotope Hypothesis in a mountain beech forest of the Northern Apennines, Italy that is notoriously poor in soniferous species. Our aim was to test whether sonotopes were temporally distinct over seasonal and astronomical timeframes and spatially configured in relation to vegetation variables. We used the Acoustic Complexity Index (ACItf) to analyze the heterogeneity of sonic information gathered from an array of 11 sound recorders deployed within a lattice of eleven 4-ha hexagonal sample sites distributed throughout a 48-ha managed beech forest. We visualized and described the temporal patterns of ACItf between seasons (May–June and July–August 2021), across six astronomical periods (Night I, Morning Twilight, Morning, Afternoon, Evening Twilight, and Night II), and according to two aggregated frequency classes (≤2000 and >2000 Hz). We introduced Spectral Sonic Signature (SSS) calculated from the sequence of ACItf values along frequency bins as a descriptor of the dynamic production of sounds across spatial and temporal scales. We calculated Mean Spectral Dissimilarity to compare SSS values across temporal periods and between sample sites. We identified sonotopes by grouping similar SSS for each sample site generated from cluster analyses and visualized their spatial arrangements. Frequencies ≤2000 Hz (mainly geophonies from wind and rain) were more prevalent than frequencies >2000 Hz (mainly biophonies from songbirds). Despite there being no strong relationship to vegetation variables and minimal biophony and anthropophony, distinct sonotopes still emerged for every astronomical and seasonal period. This suggests that the sonoscape expresses distinct spatial and temporal sonotope configurations associated with the temporal and spatial patterns of geophysical events that generate geophonies with minimal animal or anthropogenic occurrences. A new strategy based on the reintroduction of indigenous trees and shrubs in managed clearings should be considered for enhancing local biodiversity conservation along with ecoacoustic monitoring based on the Sonotope Hypothesis.

Abstract Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suited for characterizing marine acoustic environments. Here, we describe the application of multiple machine‐learning techniques to the analysis of two PAM datasets. We combine pre‐trained acoustic classification models (VGGish, NOAA and Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine‐learned acoustic features capture different aspects of the marine acoustic environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labeled sounds in the 8 kHz range; however, low‐ and high‐frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow establishing a link between ecologically relevant information and PAM recordings at multiple scales, ranging from large‐scale changes in the environment (i.e., changes in wind speed) to the identification of marine mammal species.

The increasing biodiversity loss worldwide has resulted in a growing need for cost-effective, efficient tools to monitor biodiversity over large spatial and temporal scales. The idea of using acoustic indices to monitor soniferous animal communities is becoming increasingly popular. Dozens of indices have been proposed over the last 15 years to measure acoustic complexity as a proxy of biodiversity. However, we still lack sufficient evaluation of the acoustic indices’ power to predict biodiversity, and the factors modulating their efficacy. Here, we extend a recent meta-analysis on the acoustic indices conducted by Alcocer et al. (2022; Biological Reviews) by increasing the dataset of studies 1.5 times and adding an important modulating variable: latitude. Latitude is strongly connected to species diversity, and it has previously been postulated that acoustic indices may be unable to fully reflect the high species diversity of the tropics, due to limitations related to phylogenetic inertia (i.e., closely related species sounding similar) and interference between species, with masking by insects being particularly common. Using a total of 524 effect sizes from 49 studies, we found a moderate positive correlation between acoustic indices and biodiversity (r = 0.32, 95 % CI [0.20, 0.43]), similar to the finding of Alcocer et al. (2022). Of five moderator variables, latitude was the second most important after the type of acoustic index, with higher latitude studies showing acoustic indices to have greater predictive power. When testing the indices separately with latitude as the only moderator, four of the seven acoustic indices (ACI, AR, BIO, NDSI) were found to be significantly influenced by latitude. Future work should investigate the mechanisms by which latitude influences the acoustic indices’ efficacy. For now, we can conclude that whatever mechanisms are driving acoustic indices to underestimate diversity in tropical forests, the influence is evident even when measuring acoustic complexity in different ways using different indices.