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

Human-dominated urban ecosystems have encroached on native habitats, positioning urban parks as crucial sanctuaries that support bird diversity conservation. Bird communities tend to act as significant indicators of habitat quality. Birds play a key role in generating soundscapes in urban environments, where a healthy acoustic environment is essential not only for human well-being but also serves as a core focus for environmental monitoring and landscape enhancement. Previous studies have pointed out that acoustic indices have great advantages and strong feasibility for long-term environmental monitoring. However, few studies on the comparative analysis of bird diversity across urban parks subject to varying degrees of urbanization have been published in this field. In this study we recorded the springtime soundscape across 8 urban parks in Nanjing (China) from March to April 2023 for 10 hours each day. Six acoustic indices of Acoustic Complexity Index, Acoustic Diversity Index, Normalized Difference Soundscape Index, Bioacoustic Index, Acoustic Entropy Index and Power Spectral Density were used to quantify the monitoring results. To investigate the influence of vegetation habitats on bird activity, we utilized K-means clustering analysis to categorize the parks based on their developmental gradient, while Principal Component Analysis (PCA) was employed to simplify the dimensions of 12 vegetation factors. The results revealed that the distribution of birds in urban parks is a complex interplay of variables including the gradient of urban development, the functions of park plots, and the characteristics of vegetation habitats, among others. Day-to-day, human and bird activities show similarities, yet birds' responses to urban environments vary by location. The daily movement of visitors exerts a discernible influence on park birds, even those adapted to urban life. In instances of heightened human activity, birds demonstrate a proclivity for seeking refuge in less disturbed areas, and their activity space tends to be concentrated on higher plants. Vegetation communities in areas of high human disturbance are found to provide substantial support to bird habitats. Specifically, vegetation communities featuring tall trees or ground cover that form buffer zones have a significant positive effect on weakening the impact of human activities on bird communities. In terms of monitoring bird diversity with acoustic indices, Bioacoustic Index and Acoustic Diversity Index show more consistent performance, rendering them more reliable than other acoustic indices.

Aim: The equilibrium theory of island biogeography predicts the positive species–area relationship and the negative species–isolation relationship, resulting in higher species richness on large and close islands. Unlike species richness, soundscape diversity integrates sound from various sources (e.g. biophony, geophony and anthrophony). However, how soundscape diversity varies with island area and isolation still needs to be tested. Here, we explored the island biogeography of bird soundscapes and the determinants of island attributes in shaping bird diversity and soundscape diversity. Location: Thousand Island Lake, Zhejiang, China. Taxon: Birds. Methods: We recorded avian soundscapes by audio recorders and censused bird diversity by line transects on 20 land‐bridge islands. We calculated four acoustic indices (acoustic complexity index, bioacoustic index, acoustic evenness index and acoustic entropy index) to assess acoustic richness, evenness and heterogeneity to explore the soundscape diversity of birds. We used multiple linear regressions, spatial autoregressions and piecewise structural equation models to examine the relationships between bird richness and acoustic diversity, and island attributes. Results: We found positive diversity–area relationships for avian soundscapes. Larger islands had more vocal species and higher habitat diversity, which led to an increment in the richness and unevenness of avian soundscapes on large islands. Acoustic evenness decreased with increasing isolation (distance to the mainland). Main Conclusions: Soundscapes on large islands are more diverse than those on small islands. Rich acoustic assemblages and heterogeneous habitats promote increased soundscape diversity on islands. Conversely, the lack of vocal contributors, resulting in a decrement in the communication of acoustic signals, can create a lower soundscape diversity on small and remote islands. Our study emphasizes the necessity of examining both species and habitat diversity in island biogeography for better understanding the underlying mechanisms determining biological soundscapes on islands. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring serves as a minimally invasive and effective method for biodiversity assessment, particularly in bird monitoring through the application of acoustic indices. However, the use of different recording devices and different types of environmental noise (e.g., rain, wind, stream, traffic noise) lead to signal distortions that affect the application of ecoacoustics indices. Currently, there are no established guidelines specifying the technical requirements for recording devices and the signal-to-noise ratio (SNR) threshold for accurate calculation of acoustic indices. To enhance the accuracy of acoustic indices in bird biodiversity assessments, this study investigated the impact of different recording devices and environmental noise (rain, wind, stream, traffic noise) on acoustic indices. In this study, we selected six acoustic indices: Acoustic Complexity Index, Acoustic Diversity Index, Acoustic Evenness Index, Bioacoustic Index, Acoustic Entropy Index, and Normalized Difference Soundscape Index, and we used four different recording devices to simultaneously record 104 h of bird-sound data at the same location. In addition, 44 h of noisy bird signals with different noise types and intensities were artificially synthesized for comparison. The sound data were then used to analyze the effects of the recording devices and environmental noise on the acoustic indices used for bird biodiversity assessment. Our results showed that (a) all six acoustic indices were affected by the recording device used; (b) each acoustic index had different sensitivities to different noise types; and (c) there was a SNR threshold above which the effect of noise on the acoustic index was negligible. This study provides recommendations for recording device selection and determines SNR thresholds for noisy bird signals, contributing to the refinement of protocols for acquiring and preprocessing signals in the application of acoustic indices for bird biodiversity assessment. These findings aim to establish standardized protocols for signal acquisition and preprocessing in future application of acoustic indices.

Effective conservation management relies on survey methods that accurately represent the biological communities being monitored. Here, we describe a novel approach using long-duration acoustic recordings to estimate abundance of a threatened wetland bird, the Australasian Bittern ( Botaurus poiciloptilus ). Whereas acoustic monitoring enables a large increase in effort compared to traditional on-site monitoring, e.g., triangulation surveys, it is difficult to estimate the number of individuals of a target species in acoustic recordings. We describe a semi-automated approach to estimate bittern abundance at four sites in the Barmah-Millewa Forest of southern Australia using single-channel, long-duration recordings. Our approach leveraged several known characteristics of bittern calling behavior. We obtained abundance estimates that are larger than those previously found using triangulation surveys at the same site. This is primarily attributed to our ability to find the peak calling hours in a long-duration recording, which does not require the training of a machine-learning call-recognizer. If the method we describe is performed in a consistent, standardized manner, it can identify population trends, which is an important outcome for a threatened species. Our method should be suitable for other furtive wetland species with a similar call structure or frequency range.

Passive acoustic monitoring has been an effective tool for bird sound analysis. However, bird sounds often include cicada noise, which is an obstacle for investigating bird sounds. For example, cicada noise can result in large deviations of acoustic index, which will lead to the mismonitoring of species richness trends. Therefore, there is a critical need to filter cicada noise for helping bird sound analysis. We develop a novel end-to-end deep learning model, named CicadaNet for filtering cicada chorus from recordings containing bird sound. CicadaNet utilizes a convolutional encoder-decoder network to encode and decode acoustic features and a conformer module for global and local sequence modeling. We build a clean bird sound dataset and collect a large amount of real cicada noise data for model evaluation. We compare CicadaNet with current state-of-the-art deep denoising models and traditional denoising algorithms. Experimental results show that CicadaNet achieves the best denoising performance (SegSNR is improved by 9.59 dB and SI-SNR is improved by 20.08 dB when the noisy SNR = 0 dB). Meanwhile, CicadaNet achieves good performance for the real-time denoising of cicada noise. Furthermore, CicadaNet achieves bird species-independent noise reduction. We evaluate the effectiveness of CicadaNet for bird diversity survey. CicadaNet achieves the best performance, which can effectively eliminate the deviation caused by cicada noise to the acoustic index. CicadaNet can be easily extended to the cancellation of other environmental noise, and we propose it for the acoustic denoising of other vocalizing animals.

Acoustic indicators serve as an effective means of assessing the quality of urban green space soundscape. The informative, easy accessibility and non-invasive nature of acoustic monitoring renders it an excellent tool for studying the interaction among the natural environment, wildlife, and human activities. Urban green space is essential in the urban ecosystem and constitutes the primary location for public outdoor recreation. However, the existing methods for monitoring public recreational behavior, such as on-site observation, drone observation, or questionnaire interviews, require significant labor or professional expertise. All of these methods have their limitations, so there is still much to be researched in the acoustic indices and recreational behavior. As a result, the potential for using acoustic characteristics to monitor public recreational behavior remains underexplored. To address this gap, this study investigates the potential of 5 widely used acoustic indices and acoustic intensity for monitoring public recreational behavior: Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Richness (AR), Normalized Difference Soundscape Index (NDSI), and Power Spectral Density (PSD). Data were collected from 35 monitoring points in urban green spaces during the opening hours (6:00–22:00) to analyze the relationship between these indices and public recreational behavior. The findings indicate that (1) ACI, ADI, and AR daily exhibited multi-peak daily variation characteristics similar to those of public recreational behavior, displaying a “W” shape, while NDSI exhibits opposite variation characteristics; (2) the spatial variation characteristics of ACI, ADI, and AR change in response to the green space, and these changes align with public recreational behavior; (3) the correlation analysis and generalized linear mixed model construction further demonstrate that acoustic indices are effective in capturing the dynamic activities of visitor behavior; and (4) PSD undergoes significant temporal dynamic changes along the frequency gradient, with different frequency intervals reflecting the activity information of different recreational behaviors. In conclusion, this research highlights the effectiveness of using acoustic indices to analyze both the spatial and temporal variation characteristics of public recreational behavior in urban green spaces. The results can provide valuable data support for the enhancement and renovation of urban green spaces.

Urban green space sound sources play an important role in providing people with high quality recreation experiences. However, the evaluation index for urban green space sound sources is still limited. The main purpose of this study is to propose indices for soundscape diversity in urban green spaces, compare soundscape diversity with acoustic diversity, and understand the role of soundscape diversity in urban green spaces. In our field investigation, the sound sources of 30 sample sites were recorded over a five-minute period, and the soundscape diversity index (SDI), soundscape richness index (SRI), soundscape dominance index (SDO), and soundscape evenness index (SEI) were calculated. The results showed that soundscape diversity indices are more suitable for the evaluation of urban green space sound diversity than acoustic diversity indices. There is a significant correlation between soundscape diversity and urban green space landscape patterns, and there is a varied relationship between soundscape diversity and sound perception in different urban green spaces. Soundscape diversity can be explained by existing objective acoustic indicators, which can explain 55% of SDI model variations, 71% of SRI model variations, 43% of SDO model variations, and 32% of SEI model variations. Soundscape diversity can be applied to future urban green space planning and acoustic environment design.

Passive acoustic monitoring (PAM) has emerged as a transformative tool for applied ecology, conservation and biodiversity monitoring, but its potential contribution to fundamental ecology is less often discussed, and fundamental PAM studies tend to be descriptive, rather than mechanistic.Here, we chart the most promising directions for ecologists wishing to use the suite of currently available acoustic methods to address long‐standing fundamental questions in ecology and explore new avenues of research. In both terrestrial and aquatic habitats, PAM provides an opportunity to ask questions across multiple spatial scales and at fine temporal resolution, and to capture phenomena or species that are difficult to observe. In combination with traditional approaches to data collection, PAM could release ecologists from myriad limitations that have, at times, precluded mechanistic understanding.We discuss several case studies to demonstrate the potential contribution of PAM to biodiversity estimation, population trend analysis, assessing climate change impacts on phenology and distribution, and understanding disturbance and recovery dynamics. We also highlight what is on the horizon for PAM, in terms of near‐future technological and methodological developments that have the potential to provide advances in coming years.Overall, we illustrate how ecologists can harness the power of PAM to address fundamental ecological questions in an era of ecology no longer characterised by data limitation. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

In recent years, passive acoustic monitoring (PAM) has become increasingly popular. Many acoustic indices (AIs) have been proposed for rapid biodiversity assessment (RBA), however, most acoustic indices have been reported to be susceptible to abiotic sounds such as wind or rain noise when biotic sound is masked, which greatly limits the application of these acoustic indices. In this work, in order to take an insight into the influence mechanism of signal-to-noise ratio (SNR) on acoustic indices, four most commonly used acoustic indices, i.e., the bioacoustic index (BIO), the acoustic diversity index (ADI), the acoustic evenness index (AEI), and the acoustic complexity index (ACI), were investigated using controlled computational experiments with field recordings collected in a suburban park in Xuzhou, China, in which bird vocalizations were employed as typical biotic sounds. In the experiments, different signal-to-noise ratio conditions were obtained by varying biotic sound intensities while keeping the background noise fixed. Experimental results showed that three indices (acoustic diversity index, acoustic complexity index, and bioacoustic index) decreased while the trend of acoustic evenness index was in the opposite direction as signal-to-noise ratio declined, which was owing to several factors summarized as follows. Firstly, as for acoustic diversity index and acoustic evenness index, the peak value in the spectrogram will no longer correspond to the biotic sounds of interest when signal-to-noise ratio decreases to a certain extent, leading to erroneous results of the proportion of sound occurring in each frequency band. Secondly, in bioacoustic index calculation, the accumulation of the difference between the sound level within each frequency band and the minimum sound level will drop dramatically with reduced biotic sound intensities. Finally, the acoustic complexity index calculation result relies on the ratio between total differences among all adjacent frames and the total sum of all frames within each temporal step and frequency bin in the spectrogram. With signal-to-noise ratio decreasing, the biotic components contribution in both the total differences and the total sum presents a complex impact on the final acoustic complexity index value. This work is helpful to more comprehensively interpret the values of the above acoustic indices in a real-world environment and promote the applications of passive acoustic monitoring in rapid biodiversity assessment.

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

Acoustic indices are valuable tools for measuring and tracking changes in biodiversity. However, the method used to collect acoustic index data can be made more effective by recent developments in electronics. The current process requires recording high-quality audio in the field and computing acoustic indices in the lab. This produces vast quantities of raw audio data, which limits the time that sensors can spend in the field and complicates data processing and analysis. Additionally, most field audio recorders are unable to log the full range of contextual environmental data that would help explain short-term variations. In this paper, we present the BioAcoustic Index Tool, a smart acoustic index and environmental sensor. The BioAcoustic Index Tool computes acoustic indices as audio is captured, storing only the index information, and logs temperature, humidity, and light levels. The sensor was able to operate completely autonomously for the entire five-month duration of the field study. In that time, it recorded over 4000 measurements of acoustic complexity and diversity all while producing the same amount of data that would be used to record 3 minutes of raw audio. These factors make the BioAcoustic Index Tool well-suited for large-scale, long-term acoustic biodiversity monitoring. [ABSTRACT FROM AUTHOR]

Passive acoustic sensor-based soundscape analysis has become an increasingly important ecological method for evaluation of ecosystem conditions using acoustic indices. Understanding the soundscape composition and correlations between acoustic indices and species richness of birds, the most important sound source in the ecosystem, are of great importance for measuring biodiversity and the level of anthropogenic disturbance. In this study, based on yearlong sound data obtained from five acoustic sensors deployed in Dalongtan, Shennongjia National Park, we analyzed the soundscape composition by comparing the distributions of the soundscape power in different frequency ranges, and examined the correlations between acoustic indices and bird species richness by means of the Spearman rank correlation coefficient method. The diurnal dynamic characteristics of acoustic indices in different seasons were also described. Results showed that the majority of sounds were in the frequency of 2–8 kHz, in which over 50% sounds were in 2–6 kHz, commonly considered the bioacoustic frequency range. The Acoustics Complexity Index, Bioacoustic Index, and Normalized Difference Soundscape Index were significantly correlated with bird species richness, suggesting that these indices can be used for evaluation of bird species richness; Apparent diurnal dynamic patterns of bird acoustic activities were observed in spring, summer, and autumn; however, the intensity and duration of bird acoustic activities in summer is larger/longer than in spring and autumn. [ABSTRACT FROM AUTHOR]

Context: Most data collection and analyses in soundscape ecology have focused on summer or breeding seasons in urban or protected landscapes, missing important acoustic dynamics in winter, non-breeding periods and in agricultural landscapes, a land-use that constitutes 39% of ice-free surface globally. Objectives: To address these gaps, we examined the variation of winter soundscapes across a rural agricultural landscape of Nebraska, USA. We compared high and low traffic sites, testing if traffic levels affected soundscape structure. Methods: We recorded sound over two winters at 19 sites located adjacent to major and minor roadways. We calculated eight unique soundscape indexes to quantify the soundscape over time as a function of traffic and land cover. We applied filters at 80, 1000, and 2000 Hz. Results: We found clear statistical differences between high and low traffic sites in 7 of 8 soundscape indexes. Soundscape varied throughout the day, but not throughout the season. There was a clear negative correlation between technophony (human-derived sounds) and biophony (ecologically derived sounds) across sites. We found that not all indices may be suitable for all ecosystems. Conclusions: We quantified the effects of noise pollution on the soundscape of understudied habitats during winter months. By using soundscape indexes as surrogates for biodiversity, acoustic sampling could be an effective method for monitoring biodiversity when traditional methods may be ineffective or too costly. However, caution needs to be taken when choosing indices. [ABSTRACT FROM AUTHOR]

Biodiversity assessment, in the aftermath of a natural disaster, remains a challenge. Sonic (acoustic) analysis is increasingly gaining acceptance as a means of rapid assessment of biodiversity. However, its utility in the immediate aftermath of natural disasters is unreported. We compare pre-flood, flood-period and post-flood acoustic indices of Salim Ali Bird Sanctuary, Kerala, India, to illustrate their utility in assessing avian biodiversity. The temporal dynamics of acoustic indices capture the spectre of floods on avian diversity and its resurgence with time in the sanctuary.

[Display omitted] • Recording devices and environmental noise affects acoustic indices. • Further improve the reliability and accuracy of the acoustic index. • Microphone with omnidirectional and flat amplitude frequency response is necessary. • The signal-to-noise ratio of the signal should meet the requirement of threshold. Passive acoustic monitoring serves as a minimally invasive and effective method for biodiversity assessment, particularly in bird monitoring through the application of acoustic indices. However, the use of different recording devices and different types of environmental noise (e.g., rain, wind, stream, traffic noise) lead to signal distortions that affect the application of ecoacoustics indices. Currently, there are no established guidelines specifying the technical requirements for recording devices and the signal-to-noise ratio (SNR) threshold for accurate calculation of acoustic indices. To enhance the accuracy of acoustic indices in bird biodiversity assessments, this study investigated the impact of different recording devices and environmental noise (rain, wind, stream, traffic noise) on acoustic indices. In this study, we selected six acoustic indices: Acoustic Complexity Index, Acoustic Diversity Index, Acoustic Evenness Index, Bioacoustic Index, Acoustic Entropy Index, and Normalized Difference Soundscape Index, and we used four different recording devices to simultaneously record 104 h of bird-sound data at the same location. In addition, 44 h of noisy bird signals with different noise types and intensities were artificially synthesized for comparison. The sound data were then used to analyze the effects of the recording devices and environmental noise on the acoustic indices used for bird biodiversity assessment. Our results showed that (a) all six acoustic indices were affected by the recording device used; (b) each acoustic index had different sensitivities to different noise types; and (c) there was a SNR threshold above which the effect of noise on the acoustic index was negligible. This study provides recommendations for recording device selection and determines SNR thresholds for noisy bird signals, contributing to the refinement of protocols for acquiring and preprocessing signals in the application of acoustic indices for bird biodiversity assessment. These findings aim to establish standardized protocols for signal acquisition and preprocessing in future application of acoustic indices. [ABSTRACT FROM AUTHOR]

Passive acoustic sensor-based soundscape analysis has become an increasingly important ecological method for evaluation of ecosystem conditions using acoustic indices. Understanding the soundscape composition and correlations between acoustic indices and species richness of birds, the most important sound source in the ecosystem, are of great importance for measuring biodiversity and the level of anthropogenic disturbance. In this study, based on yearlong sound data obtained from five acoustic sensors deployed in Dalongtan, Shennongjia National Park, we analyzed the soundscape composition by comparing the distributions of the soundscape power in different frequency ranges, and examined the correlations between acoustic indices and bird species richness by means of the Spearman rank correlation coefficient method. The diurnal dynamic characteristics of acoustic indices in different seasons were also described. Results showed that the majority of sounds were in the frequency of 2–8 kHz, in which over 50% sounds were in 2–6 kHz, commonly considered the bioacoustic frequency range. The Acoustics Complexity Index, Bioacoustic Index, and Normalized Difference Soundscape Index were significantly correlated with bird species richness, suggesting that these indices can be used for evaluation of bird species richness; Apparent diurnal dynamic patterns of bird acoustic activities were observed in spring, summer, and autumn; however, the intensity and duration of bird acoustic activities in summer is larger/longer than in spring and autumn.

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

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

Tallgrass prairies are rapidly vanishing biodiversity hotspots for native species, yet little is known about the spatio-temporal variation of prairie soundscapes and how they relate to grassland biological communities. Using an ecoacoustic approach to examine the relationship between a southern tallgrass prairie soundscape and the spatial distributions of an ecologically important community of necrophilous invertebrates, we used pitfall traps and programmable passive acoustic recorders to conduct a Nicrophorus burying beetle field survey while simultaneously obtaining soundscape recordings across a 16,000 ha tallgrass prairie site. We quantified soundscape characteristics using six ecoacoustic indices and found that associations between Nicrophorus burying beetle abundances and acoustic index values were species-specific and unique to particular acoustic indices and times of day. Although Nicrophorine burying beetles share a dependence on vertebrate carcasses for reproduction, these distinctive relationships with the prairie soundscape likely reveal fine-scale temporal, spatial and carcass-size niche segregation that occurs between Nicrophorus species. Soundscape datasets analyzed using acoustic indices can produce useful time-series data that captures spatio-temporal trends associated with both biotic and abiotic ecosystem factors. Environmental acoustic data can be used to assess habitat quality for non-soniferous species of interest that depend on or interact closely with sound-producing species or acoustic communities. Broadly, acoustic indices may produce data that allows managers to rapidly assess critical habitats in the context of disturbance, restoration, or climate-mediated effects. [ABSTRACT FROM AUTHOR]

• A novel end-to-end deep learning model, named CicadaNet is proposed for filtering cicada chorus from recordings containing bird sound • Our proposed algorithm achieves the best performance over current state-of-the-art deep denoising models and traditional denoising algorithms • A clean bird sound dataset is built and a large amount of real cicada noise data is collected for model evaluation Passive acoustic monitoring has been an effective tool for bird sound analysis. However, bird sounds often include cicada noise, which is an obstacle for investigating bird sounds. For example, cicada noise can result in large deviations of acoustic index, which will lead to the mismonitoring of species richness trends. Therefore, there is a critical need to filter cicada noise for helping bird sound analysis. We develop a novel end-to-end deep learning model, named CicadaNet for filtering cicada chorus from recordings containing bird sound. CicadaNet utilizes a convolutional encoder-decoder network to encode and decode acoustic features and a conformer module for global and local sequence modeling. We build a clean bird sound dataset and collect a large amount of real cicada noise data for model evaluation. We compare CicadaNet with current state-of-the-art deep denoising models and traditional denoising algorithms. Experimental results show that CicadaNet achieves the best denoising performance (SegSNR is improved by 9.59 dB and SI-SNR is improved by 20.08 dB when the noisy SNR = 0 dB). Meanwhile, CicadaNet achieves good performance for the real-time denoising of cicada noise. Furthermore, CicadaNet achieves bird species-independent noise reduction. We evaluate the effectiveness of CicadaNet for bird diversity survey. CicadaNet achieves the best performance, which can effectively eliminate the deviation caused by cicada noise to the acoustic index. CicadaNet can be easily extended to the cancellation of other environmental noise, and we propose it for the acoustic denoising of other vocalizing animals. [ABSTRACT FROM AUTHOR]

Most of the studies have used destructive techniques to evaluate the storage parameters while various authors have explored acoustics and machine vision to determine the firmness and defects in apples respectively. However, in all the reported studies destructive and non-destructive techniques have been employed separately to study the storage behaviour of apples. In the present study both destructive and non-destructive techniques were used and were correlated to predict the storage behaviour of three exotic apple varieties; Gala Redlum , Red Velox and Super Chief grown under temperate conditions Himalayan conditions of Kashmir for the first time. Acoustic coefficient, firmness, pectin, juice yield, acidity, non-reducing sugars and overall acceptability decreased, whereas, cumulative physiological loss in weight (CPLW) and rot percentage increased from H1 to H3 irrespective of variety and storage conditions. Total soluble solids, total sugar, reducing sugar and anthocyanin content showed slight increase followed by a progressive decline under both the storage conditions. Early harvested apples (H1) showed higher spoilage and color change as compared to mid (H2) and late (H3) harvested apples irrespective of variety and storage conditions. Overall apples harvested at H1 had a shelf life of 60 days under ambient conditions and 130 days under cold storage conditions with a post-cold storage life of 10 days. Apples harvested at H2 had a shelf life of 75 days under ambient conditions and 170 days under cold storage conditions with a post-cold storage life of 15 days, while apples harvested at H3 had a shelf life of 60 days under ambient conditions and 110 days under cold conditions with a post-cold storage life of 10 days. Correlation analysis between destructive and non-destructive parameters was also performed. Acoustic coefficient showed significant positive correlation with firmness measured using texture analyzer (r = 0.71; 0.92, p ≤ 0.01) and negative correlations with starch content (r = - 0.76, p ≤ 0.01) and CPLW (r = - 0.72, p ≤ 0.01). Inference drawn from the study was that in future acoustics can emerge as a quick and reliablenon-destructive method to predict the storage behaviour of apples. • Acoustic and machine vision were used during storage and post-cold storage studies of three exotic apple varieties. • Correlation of acoustic coefficient with all other parameters was done for both the storage conditions. • Post cold storage studies showed apples harvested at medium maturity showed acceptable firmness upto 15 days. • The study provides optimum harvest date for long term storage of all the tested varieties. [ABSTRACT FROM AUTHOR]

Ecoacoustics, the study of environmental sound, is a growing field with great potential for biodiversity monitoring. Audio recordings could provide a rapid, cost‐effective monitoring tool offering novel insights into ecosystem dynamics. More than 60 acoustic indices have been developed to date, which reflect distinct attributes of the soundscape, (i.e. the total acoustic energy at a given location, including noise produced by animals, machinery, wind and rain). However, reported patterns in acoustic indices have been contradictory, possibly because there is no accepted best practice for the collection and analysis of audio recordings.Here, we propose: (a) guidelines for designing studies using audio recordings for the rapid assessment of multiple sites; and (b) a workflow for comparing recordings with seven of the most commonly used indices, permitting discrimination among habitat‐specific soundscapes. We collected and analysed over 26,000 hr of recordings from 117 sites across a range of habitats in a human‐modified tropical landscape in central Panama; an order of magnitude more recordings than used in previously published studies.We demonstrate that: (a) Standard error variance of indices stabilizes within 120 hr of recordings from a single location. (b) Continuous recording should be used rather than subsample recording on a schedule; sub sampling is a common practice but delays capture of site variability and maximizing total duration of recording should be prioritized. (c) Use of multiple indices to describe soundscape patterns reveals distinct diel and seasonal soundscape patterns among habitats.We advocate collecting at least 120 hr of continuous recordings per site, and using a range of acoustic indices to categorize the soundscape, including the Acoustic Complexity Index, Acoustic Evenness Index, Acoustic Entropy Index and the Normalized Difference Soundscape Index. Differences among habitat types can be captured if multiple indices are used, and magnitude of variance is often more important than mean values. The workflow we provide will enable successful use of ecoacoustic techniques for environmental monitoring. [ABSTRACT FROM AUTHOR]

• Acoustic indices were used to estimate wolf pack size. • Indices positively correlated with numbers of howling wolves in artificial choruses. • Numbers of wolves predicted in real choruses was often overestimated. • Three potential biases found in field recordings were investigated. • Acoustic indices are promising tool for wolf pack monitoring. Acoustic indices were recently developed for biodiversity evaluation by measuring the acoustic heterogeneity generated by animals in natural environments. Some of these indices focus on the species diversity in a community by studying frequency and temporal variations in acoustic signals. We explored the possibility of using acoustic indices to estimate the population size of a specific species. More precisely, the objective was to estimate the size of grey wolf packs by testing six acoustic indices: H , Hf , Ht , M , AR , and ACI. The relationship between the averaged values of the indices and the number of howling wolves was studied based on artificial solos and choruses created from howls extracted from wolf choruses recorded in captivity. Then, 16 real choruses were used to test the size predictions based on index values calculated previously and considered as references. Finally, we explored three biases that might influence the acoustic index values and thus the chorus size estimates. All of the acoustic indices were positively correlated with chorus size, although large standard deviations were observed. Moreover, H , Hf , and Ht reached a plateau at 7–8 wolves. The size predictions based on real choruses were overestimated or underestimated. However, ACI was the most accurate with chorus size predictions close to the actual value. M and AR also had good predictive power, especially for choruses made by a relatively small number of howling wolves. The overestimates may be explained by several sources of bias related to the natural composition of real choruses. Indeed, the acoustic indices were influenced by the audio file duration, signal-to-noise ratio (SNR), and temporal overlap of the wolf howls, but not in the same manner for each index. In particular, H , Ht , and M were significantly influenced by the audio file duration and their values decreased as the duration increased. Excluding AR , all of the indices were affected by adding background noise. The H and Hf values decreased as the SNR decreased, but the opposite trend occurred for ACI. Only Hf and AR were not influenced by the temporal overlap of howls and the values of the four other indices decreased to a greater extent when more wolf howls overlapped. The most promising indices were ACI , AR , and Hf , and they may provide an innovative census tool for estimating wolf pack size. Our results are encouraging although further research is needed to obtain a more effective and accurate tool. Several recommendations and directions for further studies are discussed. [ABSTRACT FROM AUTHOR]

The evolutionary success of a species is linked to its ability to communicate. Auditory, optic and olfactory systems are biological communication channels. Compared to the latter two, auditory systems are less impeded by physical obstructions. Successful species have effectively articulated this to their advantage. Decoding the acoustic dynamics of a landscape can ingeniously be crafted as a rapid tool to assess biological diversity. Here, we present results of the acoustic analysis carried out in three contrasting soundscapes in Kerala, India. Representative sound samples were recorded at Ernakulam, Kerala, India using Marantz PMD 661 III sonic recorder from 6.00 a.m. to 6.00 p.m. (IST) from an urban park [Hill Palace Museum (L1)], a sacred grove [Iringole Kavu (L2)], and a legally protected area Salim Ali Bird Sanctuary (L3). Acoustic characteristics of these sites expressed as Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bioacoustic Index (BI) and Normalized Difference Soundscape Index (NDSI) were related to corresponding avian diversity. The objective finding reveals the distinctiveness of sonic characteristics and the status of diversity in each soundscape. Rapid assessment of biodiversity using acoustic indices is a prospective option that can be adopted as a means to generate biodiversity indicators of Sustainable Develeopment Goals (SDGs). [ABSTRACT FROM AUTHOR]

[Display omitted] • Soundscape diversity suitable for describing sound diversity in urban green spaces. • Soundscape diversity correlated with landscape patterns in urban green spaces. • Soundscape diversity correlated with sound perception in urban green spaces. • Soundscape diversity could be explained by existing acoustic indicators. Urban green space sound sources play an important role in providing people with high quality recreation experiences. However, the evaluation index for urban green space sound sources is still limited. The main purpose of this study is to propose indices for soundscape diversity in urban green spaces, compare soundscape diversity with acoustic diversity, and understand the role of soundscape diversity in urban green spaces. In our field investigation, the sound sources of 30 sample sites were recorded over a five-minute period, and the soundscape diversity index (SDI), soundscape richness index (SRI), soundscape dominance index (SDO), and soundscape evenness index (SEI) were calculated. The results showed that soundscape diversity indices are more suitable for the evaluation of urban green space sound diversity than acoustic diversity indices. There is a significant correlation between soundscape diversity and urban green space landscape patterns, and there is a varied relationship between soundscape diversity and sound perception in different urban green spaces. Soundscape diversity can be explained by existing objective acoustic indicators, which can explain 55% of SDI model variations, 71% of SRI model variations, 43% of SDO model variations, and 32% of SEI model variations. Soundscape diversity can be applied to future urban green space planning and acoustic environment design. [ABSTRACT FROM AUTHOR]

Measuring, monitoring, and managing biodiversity across agricultural regions depends on methods that can combine high-resolution mapping of landscape patterns with local biodiversity observations. This study explores the potential to monitor biodiversity in agricultural landscapes by linking high-resolution remote sensing with passive acoustic monitoring. Land cover maps produced using a small unmanned aerial system (UAS) and PlanetScope (PS) satellite imagery were used to investigate relationships between landscape patterns and an acoustically derived biodiversity index (vocalizing bird species richness) across 12 agricultural sample locations equipped with acoustic recorders in Iowa, USA during the 2018 growing season. Statistical assessment revealed a significant direct association between vocalizing bird richness and percent noncrop vegetation cover. High spatial resolution (1 m) UAS mapping produced stronger statistical associations than PS-based maps (3 m) for landscape composition metrics. Landscape configuration metrics (Shannon’s diversity index, contagion, perimeter-area-ratio, and circumscribing circle index) were either cross-correlated with composition metrics or unusable owing to complete landscape homogeneity in some agricultural landscape samples. This study shows that high resolution mapping of noncrop vegetation cover can be linked with acoustic monitoring of unique bird vocalizations to provide a useful indicator of biodiversity in agricultural landscapes.

Dry forests are important sources of biodiversity where lianas are highly abundant given their ability to grow during times of drought and as a result of secondary growth processes. Lianas provide food and shelter for fauna such as birds, but there are no studies assessing the influence of liana abundance on birds in dry forests. Here we evaluate the influence of liana abundance on the avian acoustic community in the dry forests of Costa Rica at Santa Rosa National Park. We selected forest sites with different levels of liana abundance and set up automated sound recorders for data collection, analysis and estimation of the avian acoustic community. When the number of lianas increases, the avian acoustic community becomes more complex. Lianas could provide important direct and indirect resources for birds such as structure for shelter, protection, nesting and roosting, and food. The positive relationship that lianas have with birds is particularly important in dry forests where lianas are becoming highly abundant due to the level of forest disturbance and climate change, especially for some bird species that are restricted to this ecosystem. By validating the number of bird species detected in the recordings with the acoustic complexity index, we found that a higher acoustic complexity means higher species richness. [ABSTRACT FROM AUTHOR]

Acoustic signals that emanate from ecosystems are an important ecological variable which can provide evidence of current ecological condition as well as ecological change over time. The Terrestrial Ecosystem Research Network (TERN) established protocols to record sounds in ten SuperSites distributed throughout Australia with the objective of characterizing the soundscape in a representative landscape in different regions of Australia. This acoustic monitoring system enables a comparison of the soundscapes within and between Australian regions to determine similarities and differences in these landscapes and regions. This research quantifies the soundscape patterns in one of these SuperSites, Samford Ecological Research Facility (TERN-SERF), which is part of the South-East Queensland Peri-Urban SuperSite. An analysis and visualization of patterns in the soundscape was conducted using a continuous acoustic recording collected at TERN-SERF. The recording was made using a Song Meter (SM2) in a representative wooded habitat at TERN-SERF from 1 August to 30 September 2013. The recording was made in 16-bit stereo at 44 kHz and stored in wav file format. The recording was split into 1-minute-long recordings comprising 86,196 records and then sub-sampled at a 30-minute interval, providing 2878 one-minute-long recordings every 1/2 h. Soundscape metrics were computed for each of the two recording intervals. Soundscape power values were computed for each of ten frequency intervals (1–11 kHz) for both the 1-minute and the 30-minute interval recordings. In addition, six acoustic indices were computed from each recording. The acoustics metrics derived from the two sets of recordings (1-minute and 30-minute recording intervals) were examined to determine if they revealed different patterns. Several soundscape metrics were calculated for each recording including ten soundscape power values at 1 kHz frequency intervals and six acoustics indices. The soundscape shows a dynamic but consistent pattern over time of day during the monitoring period, depending on the metric examined. The metrics revealed different soundscape patterns. All soundscape power values at 1 kHz frequency intervals defined the dawn and dusk chorus, some more distinctly than others. Three of six acoustic indices also changed abruptly at the dawn chorus. No significant difference was found when soundscape metrics were compared between the 1-minute (high resolution) and 30-minute (lower resolution) recording intervals. A t -test was used to compare the mean values of ten soundscape power frequency intervals (p = 0.44) and the mean values of six acoustics indices (p = 0.41). Sounds were identified in 180 recordings made at 0530 h, 0600 h and 0630 h in the 1-minute long 30-minute interval recordings each day during the recording period (August and September). Sixty-seven species of birds were identified. Soundscape metrics were correlated with avian species counts and calls by all species using a correlation threshold of r > 0.7. This analysis revealed that soundscape power at the frequency interval 3–4 kHz was correlated with both the number of species (r = − 0.927) and total calls (r = − 0.996) over the three time periods. Three indices, the ADI (r = 0.953, r = 0.709), the AEI (r = 0.978, r = 0.774) and (H) (r = 0.795, r = 0.985) were similarly correlated as was an index derived soundscape power, the Shannon-Weaver Index (r = − 0.997, r = − 0.849). Other indices were correlated (r > 0.7) with only the number of avian species or only the number of calls. This methodology establishes an analysis protocol for analyzing large acoustic data sets, and demonstrates the effectiveness of using acoustic metrics for summarizing and interpreting long-term recordings. [ABSTRACT FROM AUTHOR]

Soundscape assessment has been proposed as a remote ecological monitoring tool for measuring biodiversity, but few studies have examined how soundscape patterns vary with landscape configuration and condition. The goal of our study was to examine a suite of published acoustic indices to determine whether they provide comparable results relative to varying levels of landscape fragmentation and ecological condition in nineteen forest sites in eastern Australia. Our comparison of six acoustic indices according to time of day revealed that two indices, the acoustic complexity and the bioacoustic index, presented a similar pattern that was linked to avian song intensity, but was not related to landscape and biodiversity attributes. The diversity indices, acoustic entropy and acoustic diversity, and the normalized difference soundscape index revealed high nighttime sound, as well as a dawn and dusk chorus. These indices appear to be sensitive to nocturnal biodiversity which is abundant at night in warm, subtropical environments. We argue that there is need to better understand temporal partitioning of the soundscape by specific taxonomic groups, and this should involve integrated research on amphibians, insects and birds during a 24 h cycle. The three indices that best connected the soundscape with landscape characteristics, ecological condition and bird species richness were acoustic entropy, acoustic evenness and the normalized difference soundscape index. This study has demonstrated that remote soundscape assessment can be implemented as an ecological monitoring tool in fragmented Australian forest landscapes. However, further investigation should be dedicated to refining and/or combining existing acoustic indices and also to determine if these indices are appropriate in other landscapes and for other survey purposes. [ABSTRACT FROM AUTHOR]

Dry forests are important sources of biodiversity where lianas are highly abundant given their ability to grow during times of drought and as a result of secondary growth processes. Lianas provide food and shelter for fauna such as birds, but there are no studies assessing the influence of liana abundance on birds in dry forests. Here we evaluate the influence of liana abundance on the avian acoustic community in the dry forests of Costa Rica at Santa Rosa National Park. We selected forest sites with different levels of liana abundance and set up automated sound recorders for data collection, analysis and estimation of the avian acoustic community. When the number of lianas increases, the avian acoustic community becomes more complex. Lianas could provide important direct and indirect resources for birds such as structure for shelter, protection, nesting and roosting, and food. The positive relationship that lianas have with birds is particularly important in dry forests where lianas are becoming highly abundant due to the level of forest disturbance and climate change, especially for some bird species that are restricted to this ecosystem. By validating the number of bird species detected in the recordings with the acoustic complexity index, we found that a higher acoustic complexity means higher species richness.

• Acoustic indices reflect biodiversity in tropical forests in Borneo. • For indices to reflect patterns of biodiversity requires sufficient spatial sampling. • Acoustic indices in tropical forests also change in association with changes in forest structure. • Relationships between species richness and acoustic indices vary by habitat context. • Acoustic indices exhibit no relationship with species richness in commercial oil palm estates. Approaches to characterise and monitor biodiversity based on the sound signals of ecosystems have become popular in landscape ecology and biodiversity conservation. However, to date, validation studies of how well acoustic indices reflect observed biodiversity patterns have often relied on low levels of either spatial or temporal replication, while focussing on habitats with similar underlying anthropological and geophysical sound characteristics. For acoustic indices to be broadly applicable to biodiversity monitoring, their capacity to measure the ecological facets of soundscapes must be robust to these potential sources of bias. Using two contrasting recording approaches, we examined the efficacy of four commonly used acoustic indices to reflect patterns of observed bird species richness across a tropical forest degradation gradient in Northeast Borneo. The gradient comprised intact and logged forests, riparian forests, remnants, and oil palm plantations, thus providing a highly variable anthrophonic and geophonic soundscape. We compared the degree to which acoustic indices derived from automated versus point count recording methods detected variation in inter-habitat species richness, as well as their capacity to capture changes in species diversity as a consequence of forest degradation quantified by high-resolution LiDAR derived forest canopy heights. We found Acoustic Diversity Index was associated with forest canopy height as measured by both automated recorders and recordings from point counts, whereas the association between canopy height and Acoustic Complexity Index was only detected using point count recordings. For both types of recordings, Acoustic Complexity Index exhibited the strongest relationship with observed bird richness in old growth and logged forest, whereas Acoustic Diversity was not linked, suggesting avian richness does not drive its association with canopy height. No acoustic indices were associated with observed bird richness in oil palm riparian areas. Our findings underscore the potential utility of soundscape approaches to characterise biodiversity patterns in degraded tropical landscapes, and may be used as a proxy for human inventories of bird communities. However, we also show that for acoustic indices to be effective on landscape-wide studies of environmental gradients, adequate spatial replication is required, and care must be taken to control for non-target elements of soundscapes in different habitats. [ABSTRACT FROM AUTHOR]

• Each of the eight habitats studied had unique and predictable soundscapes. • Statistical indices better distinguished habitat types than complexity indices. • The acoustic evenness index was strongly connected to changes in canopy cover. • Soundscapes can track changes in biodiversity across habitats in the Amazon. The rapidly developing field of ecoacoustics offers methods that can advance multi-taxa animal surveys at policy-relevant extents. While the field is promising, there remain foundational assumptions that need to be tested across different biomes before the methods can be applied widely. Here we test two of these assumptions in the Amazon: 1) that acoustic indices can be used to predict soundscapes of different habitat types, and 2) that acoustic indices are related to vegetation structure. We recorded soundscapes and collected vegetation data in 143 sites spanning six natural and two human-modified habitats in Viruá National Park, Roraima, Brazil. We grouped the eight habitats into three categories based on vegetative characteristics and flooding regime: open habitats, flooded-forests, and non-flooded forests. Thirteen acoustic indices were calculated from 92,283 one-minute recordings to describe the soundscapes of the habitats. We found that each habitat type had unique and predictable soundscapes. Random forest models were 74% accurate at predicting the eight habitats types and 87% accurate at predicting the three broader habitats categories. The most important acoustic indices to distinguish habitats were the third quartile and centroid. Canopy cover significantly affected 11 of 13 acoustic indices, and while other vegetation variables (e.g., shrub cover and number of trees) appeared in top models for some indices, their effects were not significant. The best indices linking soundscapes to vegetation structure were the acoustic evenness index and skewness, with canopy cover explaining 81% and 52% of the variance in these indices, respectively. These results expand our knowledge regarding which acoustic indices best connect changes in habitats to changes in soundscapes. These findings are particularly important for diverse ecosystems, like the Amazon, which are known to have complex soundscapes with sound-producing animals that are difficult to detect with traditional survey methods (e.g., visual transects). Ultimately, our results suggest that soundscapes are able to track changes in biodiversity levels across major habitat types of the Amazon. [ABSTRACT FROM AUTHOR]

• Seven acoustic indices are tested using three controlled computational experiments. • Three acoustic dimensions of bird vocalizations affect acoustic indices' performance. • H, ADI, and AEI show moderate correlations with avian species richness. • ADI is relatively more robust regarding bird species richness surveys. Passive acoustic monitoring of biotic sounds is increasingly popular in conservation biology. Among vocalizing animals, birds are most frequently studied and multiple acoustic indices have been proposed for rapid acoustic assessment of avian diversity. Preliminary results suggest that several indices can be used as proxies for bird species richness, however it is still unclear to what extent different conditions of bird vocalizations affect the relationship between indices and bird species richness – should it matter if the bird vocalizations contain different sound unit shapes, or if the frequency of vocalization occurrence differs, or if the vocalizations are made with various intensities? In this work, seven commonly used acoustic indices were tested using three controlled computational experiments with real-world recordings to provide an objective measure of each index's performance for answering the aforementioned questions. In the experiments, different options of sound unit shape and frequency of vocalization occurrence were precisely controlled and intensity variations were expressed as different signal-to-noise ratios (SNR) of the vocalizations. The first experiment showed that three indices (the acoustic entropy index (H), acoustic diversity index (ADI), and acoustic evenness index (AEI)) performed better than the other four, showing moderate correlations with avian species richness. The second experiment revealed that ADI for each sound unit shape tended towards a constant value with increasing frequency of vocalization occurrence while the influence from frequency of vocalization occurrence on H and AEI varied with different sound unit shape options. Our third experiment showed that the vocalization intensity affected the values of these three indices while the performance disparity among different sound unit shapes for only ADI explicitly appeared a decreasing tendency with increasing vocalization intensities. We conclude that ADI, among the tested indices, is relatively more robust with regard to bird species richness surveys when sound unit shape, frequency of vocalization occurrence, and vocalization intensity are considered. Meanwhile, since multiple indices are usually applied together to provide a comprehensive observation, the above acoustic dimensions should be taken into account especially in comparative research of different bird communities. [ABSTRACT FROM AUTHOR]

Sound is an important way of communication among organisms. The monitoring and analy-sis of biological sound is an emerging method to describe and evaluate biodiversity. This method does not invade or damage the natural environment. By recording ecological information through sound, it can effectively reflect the relevant characteristics of biodiversity. The sound-based exploration of biodiversity change has broadened the interdisciplinary approach and has been increasingly applied to ecological research. Here, we expounded on the main theoretical foundations and research methods of using acoustic monitoring to assess biodiversity. We introduced related research fields from two aspects, namely the biodiversity of vocal animals and the temporal and spatial diversity of soundscape. We presented examples of the application of acoustic monitoring to assess the impact of land-use change, climate change and urbanization on biodiversity. Finally, we proposed the future direction of development, and hope that the potential of sound surveys could be further explored to provide an effective reference for biodiversity monitoring and assessment.