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Table of Content
    Volume 32 Issue 10
    20 October 2024
    Passive acoustic monitoring (PAM) has effectively expanded the potential for monitoring ecosystems and biodiversity in time and space, painting the most promising direction for solving fundamental questions in long-term ecology and exploring new research avenues. This issue focuses on application of passive acoustics for biodiversity monitoring and assessment. The cover image shows the key processes and research scales of passive acoustic monitoring. (Designed by Zhishu Xiao)
      
    Editorial
    Reviews
    Application of passive acoustic monitoring in Chiropteran research
    Yingying Liu, Lixin Gong, Hao Zeng, Jiang Feng, Yongjun Dong, Lei Wang, Tinglei Jiang
    Biodiv Sci. 2024, 32 (10):  24233.  doi: 10.17520/biods.2024233
    Abstract ( 282 )   HTML ( 4 )   PDF (489KB) ( 452 )   Save
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    Background: Passive acoustic monitoring (PAM) technology has become increasingly significant in wildlife research due to its non-invasive nature and capacity for continuous monitoring. A key taxonomic group for biodiversity and environmental monitoring using PAM are the order Chiroptera, commonly known as bats, with their unique echolocation abilities, nocturnality, and high sensitivity to environmental changes.

    Progress: This review aims to comprehensively explore the applications of PAM in Chiropteran research and note the scientific and ecological breakthroughs that this new tool facilitates. We analyze the advantages and limitations of PAM, and summarize methods for effectively collecting and processing acoustic data to estimate and monitor bat diversity, activity patterns, population dynamics, habitat selection, and distribution. The review concludes with case studies from the literature that compare the impact of different environmental factors on bat diversity and activity, and that discuss how these variables affect data collection.

    Prospects: This review concludes its assessment by noting the challenges that PAM faces in practical applications; by exploring the future prospects of the technology and its potential contributions to biodiversity conservation; and by proposing future research directions including technological innovation, citizen science involvement, and monitoring strategy optimization. These suggestions will help further advance the application of PAM technology in bat conservation and management by contributing to the protection of biodiversity.

    Applications of passive acoustic monitoring and evaluation in urban bird research
    Zezhou Hao, Chengyun Zhang, Le Li, Bingtao Gao, Wei Zeng, Chun Wang, Zixuan Wang, Wantao Huang, Yue Zhang, Nancai Pei, Zhishu Xiao
    Biodiv Sci. 2024, 32 (10):  24123.  doi: 10.17520/biods.2024123   cstr: 32101.14.biods.2024123
    Abstract ( 508 )   HTML ( 3 )   PDF (3862KB) ( 507 )   Save
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    Background & Aims: Rapid urbanization has proved the importance of effectively monitoring and evaluating urban bird diversity, making it a crucial area of technique inquiry within urban ecology and biodiversity conservation. Passive acoustic monitoring (PAM), a technique that utilizes the environment to assess biodiversity, provides long-term and continuous data on urban avian population dynamics. This approach offers valuable insights into the influence of human activities on natural habitats. Although PAM technology has been adopted globally for urban biodiversity monitoring and has resulted in extensive acoustic datasets, variations in monitoring and assessment methodologies show significant challenges in effectively evaluating urban avian diversity.

    Review Results: We synthesize representative cases of urban avian diversity research conducted using PAM technology, focusing on aspects such as spatio-temporal experimental design, recording device parameters, and quantification techniques of acoustic signals. The results indicate that current case studies exhibit general routines in experimental frameworks, parameter selection, and quantification methods. However, variability in monitoring and evaluation technologies, along with their effects on factors such as signal-to-noise ratio and representativeness of sound signals, remains a significant challenge that hinders the application of PAM in urban bird diversity research, yet this issue has not received adequate attention. Therefore, this paper advocates for a comprehensive examination of passive acoustic monitoring and evaluation techniques for urban bird sounds, which would facilitate the creation of eco-acoustic big data and address broader ecological questions.

    Perspectives: Given the increasing prevalence of PAM applications, there is an urgent need for the development of technical standards for passive acoustic monitoring and evaluation of urban birdsong. Establishing these standards would promote the standardization of sound data collection and analysis, leading to the creation of a comprehensive urban bird sound database. Such advancements would enable the utilization of big data to elucidate the impacts of urbanization on birdsong diversity and response mechanisms, thereby enriching urban avian studies and supporting biodiversity conservation efforts in urban environments. This paper summarizes current monitoring schemes and technological applications, providing a foundation for future theoretical frameworks. Methodological approaches and technological implementations are proposed for future passive acoustic monitoring and evaluation of urban bird diversity in China.

    Advances in bioacoustic monitoring and animal welfare assessment in zoos
    Xiaoyuan Li, Wenli Zhang, Shuliao Tian, Zhenlong Wang, Zhishu Xiao
    Biodiv Sci. 2024, 32 (10):  24297.  doi: 10.17520/biods.2024297
    Abstract ( 205 )   HTML ( 2 )   PDF (904KB) ( 343 )   Save
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    Background & AimsZoos are increasingly vital for education, conservation, and scientific research which focuses on animal welfare. The acoustic environment significantly affects the well-being of captive animals and associated people. However, the ability to monitor and manage the acoustic environment in zoos remains poorly studied. We review the advances of acoustic monitoring and animal welfare assessment in zoos and identify future research directions.

    ProgressThe application of passive and active acoustic monitoring technologies in zoos provides insight into animal behavior and welfare. Additionally, the use of machine learning for the analysis of acoustic data offers a non-invasive method to assess animal welfare.

    Perspective There are opportunities for developing acoustic monitoring devices and intelligent recognition and analysis technologies for acoustic data. Doing so will help establish a comprehensive evaluation system and standards for acoustic environment quality while enhancing educational efforts related to acoustic welfare in zoos. The integration of disciplinary theories and technical methods enables researchers and management to provide systematic solutions for captive animal reproduction, health, conservation, and acoustic environment management in zoos.

    The progress and prospects of terrestrial bioacoustics data acquisition equipment
    Zixin Zhang, Chengyun Zhang, Zezhou Hao, Kaiying He, Yongqiao Huang, Zhishu Xiao
    Biodiv Sci. 2024, 32 (10):  24265.  doi: 10.17520/biods.2024265
    Abstract ( 194 )   HTML ( 7 )   PDF (2524KB) ( 158 )   Supplementary Material   Save
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    Aims: Terrestrial bioacoustics data acquisition equipment plays a pivotal role in the study and conservation of terrestrial biodiversity. These devices enable research into the acoustic characteristics of various biological populations and provide insight into species distribution, behavior, and interactions. Our contribution to the literature is bridging the gap in research between the development and application of terrestrial bioacoustics data acquisition equipment in China. We outline the current development status of these devices and identify the critical factors that influence their selection by conducting a comprehensive review of existing literature and resources. Providing a guide for researchers in the selection process and encouraging the broader application of these devices is an important objective for future biodiversity research and conservation initiatives.

    Method: We employ a systematic literature review to assess the current landscape of terrestrial bioacoustics data acquisition equipment. The review includes scholarly articles, technical reports, and relevant websites to synthesize information on the development of these devices. We identify the key factors considered when selecting bioacoustics equipment such as sensitivity, frequency range, durability, and cost. Additionally, we explore various passive acoustic monitoring experimental approaches being used in the field. Based on these analyses, several equipment selection recommendations are made tailored to meet the diverse needs and performance criteria of various research projects.

    Results: Our findings indicate a growing interest in the application of terrestrial bioacoustics data acquisition equipment in biodiversity studies. However, there is a need for more comprehensive documentation and understanding of the development and application of these devices. We identify several prominent passive acoustic monitoring experiment schemes effectively used in capturing bioacoustics data. Furthermore, we provide examples of device options well-suited for different research contexts, taking into account specific requirements and constraints of each project. These insights are instrumental in facilitating the adoption and integration of terrestrial bioacoustics data acquisition equipment into biodiversity research and conservation efforts.

    Conclusion: Terrestrial bioacoustics data acquisition equipment is crucial in advancing our knowledge of biodiversity and conservation efforts. Our findings contribute to a more informed selection process for researchers and highlight the potential of these devices in enhancing biodiversity research and conservation initiatives. This systematic overview of the current development status and key considerations for selecting bioacoustics equipment, serves as a valuable resource for the scientific community. Further research and development in the field of terrestrial bioacoustics is necessary to ensure these devices remain at the forefront of biodiversity conservation efforts. Terrestrial bioacoustics data acquisition equipment will play an increasingly significant role in the global endeavor to protect and preserve our diverse ecosystems.

    Bioacoustics data archives management standards and management technology progress
    Kaiying He, Xinhui Xu, Chengyun Zhang, Zezhou Hao, Zhishu Xiao, Yingying Guo
    Biodiv Sci. 2024, 32 (10):  24266.  doi: 10.17520/biods.2024266
    Abstract ( 167 )   HTML ( 1 )   PDF (1642KB) ( 166 )   Save
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    Background & Aims: In recent years, bioacoustics monitoring has emerged as a crucial tool for biodiversity conservation, enabling a continuous, real-time, and non-invasive means to gather data on various species and their habitats. This method provides superior efficiency and coverage compared to traditional field surveys and observations. However, the vast amount of diverse bioacoustics data generated presents significant challenges in terms of storage, management, and analysis. This review aims to systematically outline the characteristics and management standards of bioacoustics data archives. It highlights the latest advancements in recording and storage technologies and explores the value and challenges of bioacoustics data archiving in practical applications. Additionally, this review also provides an overview of the current state of bioacoustics databases and data-sharing platforms, both domestically and internationally.

    Progress: Bioacoustics data archives are systematic repositories dedicated to the preservation and management of bioacoustics data. These archives include raw audio recordings, metadata detailing recording times, locations, and equipment specifications, and processed data derived from species identification and classification tools. These archives are essential for biodiversity conservation efforts, as they store crucial data on species’ vocalizations, behaviors, and interactions. Standardized management protocols are essential for ensuring data integrity, accessibility, and usability. Recent technological advances have facilitated better data collection, processing, and storage methods, making bioacoustics monitoring more scalable and sophisticated.

    Conclusion: The development and implementation of advanced bioacoustics data management and archiving systems are pivotal for effective biodiversity monitoring and conservation. By leveraging the latest technological advancements, bioacoustics data archives can significantly enhance automated data annotation, storage efficiency, intelligent retrieval, and real-time sharing capabilities. These improvements will help meet the increasing demands of bioacoustics monitoring, evaluation, and historical baseline establishment, thereby significantly supporting biodiversity conservation efforts.

    Advances in bird sound annotation methods for passive acoustic monitoring
    Qianrong Guo, Shufei Duan, Jie Xie, Xueyan Dong, Zhishu Xiao
    Biodiv Sci. 2024, 32 (10):  24313.  doi: 10.17520/biods.2024313
    Abstract ( 207 )   HTML ( 1 )   PDF (1546KB) ( 137 )   Supplementary Material   Save
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    Background & Aim Bird sound annotation is essential for marking bird-related information in audio data, such as species identification and sound structure. It serves as a crucial foundation for passive acoustic monitoring, birds acoustic data analysis, as well as automatic species identification and classification. The purpose of this review is to help bird sound dataset creators and annotators better understand the existing labeling technologies and their potential development trends. It also provides technical support for improving the efficiency of automatic species identification in large-scale avian acoustic monitoring data.

    Summary This paper compares the advantages of various common methods such as manual annotation, automatic annotation, and semi-automatic annotation. It highlights the challenges each method faces in terms of data quality, annotation consistency and annotation efficiency. The review also discusses recent applications of these methods in passive acoustic monitoring annotation models, establishing cross-regional datasets, and enhancing semi-automatic annotation systems.

    Perspectives Despite significant progress in automatic annotation methods, challenges such as cold start remain. The field urgently needs larger-scale cross-regional datasets and efficient semi-automatic annotation systems to ensure quality control to meet the increasing demands for both annotation volume and accuracy.

    Technology and Methodologies
    A comparison of bird sound recognition performance among acoustic recorders
    Wantao Huang, Zezhou Hao, Zixin Zhang, Zhishu Xiao, Chengyun Zhang
    Biodiv Sci. 2024, 32 (10):  24273.  doi: 10.17520/biods.2024273
    Abstract ( 195 )   HTML ( 2 )   PDF (1859KB) ( 175 )   Save
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    Aims: Passive acoustic monitoring technology has been widely used for monitoring bird species, enabling non-invasive and long-term effective monitoring. Extensive data collection requires automated identification technologies for effective analysis. However, differences in recording device performance can affect the accuracy of automated software in identifying bird species.

    Methods: Six separate recording devices from various manufacturers are tested by recording bird call playback across four frequency bands. We use BirdNET as the automatic bird sound identifier under two types of vegetation environment, five categories of distance between the recording devices and sound source, and three sound source directions. Our goal is evaluating the impact of these variables on bird species identification performance. We assess the monitoring performance of different recording devices by comparing the basic parameters and configurations of the devices and constructing a generalized linear model (GLM) to statistically analyze the identification results.

    Results: Our analysis suggests the type of recording device significantly affects the ability for BirdNET to correctly identify bird species. As distance increases, the effectiveness of the devices in monitoring decreases, with the identification accuracy of BirdNET significantly higher for distances within 50 meters than beyond. Further, the direction of sound impacts identification performance, with accuracy significantly decreasing when the sound source is in opposite direction of the recording device in identifying the four types of bird sound signals with different frequency bandwidth ranges. Additionally, the vegetation type significantly affects the attenuation of bird call signals, with overall identification accuracy in grassland vegetation 40.1% higher than forest vegetation.

    Conclusions: Our findings suggest the effectiveness of field recording monitoring should be assessed before selecting and deploying long-term recording monitoring equipment, in addition to evaluating equipment costs and parameters. Based on our evaluation, monitoring distance and direction settings should be optimized to enhance the effectiveness of monitoring strategies.

    Cross-regional bird species recognition method integrating audio and ecological niche information
    Jiangjian Xie, Chen Shen, Feiyu Zhang, Zhishu Xiao
    Biodiv Sci. 2024, 32 (10):  24259.  doi: 10.17520/biods.2024259   cstr: 32101.14.biods.2024259
    Abstract ( 251 )   HTML ( 1 )   PDF (9784KB) ( 205 )   Supplementary Material   Save
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    Aim: Passive acoustic monitoring plays a pivotal role in studying avian populations, community dynamics, and behaviors. For extensive passive acoustic monitoring, employing deep learning techniques to automatically identify bird species from their vocalizations is essential. However, closely related species often produce highly similar calls, leading to confusion and false positives, which can compromise the effectiveness of deep learning models. This paper presents a novel method that integrates audio data with ecological niche information to enhance species recognition accuracy. Here, ‘ecological niche’ encompasses a species’ role in its environment, including its habitat, diet, and behavior.

    Methods: The approach begins with the development of an audio recognition model using the ResNet18, a prominent deep learning framework known for its capability to extract high-level features from audio signals. Subsequently, a maximum entropy model is employed to estimate the distribution of bird species and derive ecological suitability indices for various locations. These indices provide the necessary ecological niche information. An integrated model, NicheNet, is then constructed to combine audio features with ecological niche data for improved species recognition.

    Results & Conclusion: The integration of audio and ecological niche information through NicheNet demonstrates substantial improvements in recognition accuracy. Specifically, NicheNet enhances Top-1 recognition accuracy by 12.9% and Top-5 recognition accuracy by 10.6% compared to the ResNet18 model. Additionally, NicheNet reduces the near species error rate by 3.1%, the near genus error rate by 1.8%, and the near family error rate by 8.0%. Analysis of recognition outcomes for congeneric species with similar vocalizations reveals that NicheNet significantly refines classification by leveraging ecological niche information, thereby improving the discrimination of vocally similar but ecologically distinct species. This method effectively addresses the challenge of misidentification among closely related and vocally similar bird species that differ in their ecological niches, thereby advancing the accuracy of cross-regional bird species recognition based on vocalizations.

    Ensemble learning strategy for birdsong recognition under data imbalance
    Xiaohu Shen, Guanyu Li, Hongfei Shi, Chuanzhi Wang
    Biodiv Sci. 2024, 32 (10):  24215.  doi: 10.17520/biods.2024215
    Abstract ( 189 )   HTML ( 1 )   PDF (2738KB) ( 122 )   Save
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    Aim & Summary: The dynamics and distribution changes of bird populations are essential components of ecosystems and critical for maintaining ecological balance. Recently, the rapid development of acoustic monitoring technologies has enabled passive acoustic bird recognition to become an efficient and non-invasive method for bird monitoring. However, the collection and annotation of bird sound data face numerous challenges for practical application, particularly issues of data imbalance and sample scarcity, which severely limit the improvement of recognition accuracy. We focus on the application of ensemble learning methods in bird recognition to solve the issue of rare bird species identification under data imbalance conditions while enhancing the generalization ability and training efficiency of the model. Our study designs a cost-sensitive ensemble learning strategy to overcome the limitations posed by imbalanced and scarce bird sound data. Thus, we improve the recognition accuracy of rare bird species. We construct an efficient and accurate passive acoustic bird recognition system that provides strong support for the precise conservation of avian environments by integrating techniques such as transfer learning, self-attention mechanisms, and sensitive regularization terms.

    Methods: To achieve the aforementioned objectives, we propose an improved cost-sensitive stacking ensemble learning strategy (cost-sensitive stacking ensemble for bird sound recognition, CSE-BSR). The specific methods include: (1) preprocessing collected bird sound data, including noise reduction, feature extraction, and spectrogram analysis, to improve model performance and reduce training time; (2) selecting deep learning models pre-trained on large bird sound datasets as base learners and fine-tuning them through transfer learning to better adapt to new recognition tasks; (3) designing a feature fusion method based on self-attention mechanisms to effectively integrate homogeneous yet heterogeneous features output by base learners, enhancing feature representation and model generalization; (4) recognition classification by incorporating sensitive regularization terms into the loss function of the ensemble model and dynamically adjusting weights according to the rarity coefficients of bird species to ensure the model obtains a global optimal solution during inference.

    Results: We construct a proprietary dataset using samples from ten bird species in Laoshan Forest Park, Nanjing to verify the effectiveness of our proposed method. Additionally, experiments were conducted on the publicly available BirdCLEF 2023 dataset. Experimental results show that the proposed method achieved overall classification accuracies of 95.29% and 90.17% on the imbalanced proprietary dataset and the BirdCLEF 2023 dataset, respectively, significantly outperforming mainstream ensemble learning methods. Specifically, the proposed method exhibited higher sensitivity and generalization capability in recognizing rare bird species.

    Conclusion: We address the issues of data imbalance and sample scarcity in bird sound recognition by proposing a cost-sensitive ensemble learning strategy. The recognition accuracy and generalization ability of rare bird species is enhanced through techniques such as transfer learning, self-attention mechanisms, and sensitive regularization terms. The proposed approach demonstrates superior performance and scalability in practical applications compared to mainstream ensemble learning methods. However, the training and inference processes remain time-consuming and resource-intensive despite achieving significant recognition effects. Future research plans include how to optimize model structures, reduce computational costs, and enhance model interpretability to better serve the precise conservation of avian environments.

    Exploring the application of frequency-dependent acoustic diversity index in human-dominated areas
    Lei Chen, Zhiyong Xu, Pukun Su, Xiaotian Lai, Zhao Zhao
    Biodiv Sci. 2024, 32 (10):  24286.  doi: 10.17520/biods.2024286
    Abstract ( 130 )   HTML ( 2 )   PDF (6730KB) ( 116 )   Supplementary Material   Save
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    Background & Aims: As a popular tool for rapid biodiversity assessment using passive acoustic monitoring (PAM), acoustic indices have attracted increasing attention in the field of soundscape ecology in recent years, which can help quantify the level of activity or diversity of biological sounds. However, the impact of complicated anthropogenic noise on the numerical results of acoustic indices and the methods for suppressing it have not been well understood. This deficit in understanding poses a challenge for wider applications of acoustic indices in human-dominated areas such as urban green infrastructure.

    Methods: In this paper, we investigated the frequency-dependent acoustic diversity index (FADI), a recently proposed acoustic index that is robust to noise, in relation to its application conditions and performance in human-dominated areas. Specifically, three controlled computational experiments were conducted focusing on three aspects including the lower limit of signal-to-noise ratio (SNR), the spatial coverage for bird vocalization monitoring, and the limitations imposed by different types of interferences.

    Results: The results of the controlled simulation experiments show that, (1) FADI was significantly robust to noise within the SNR range from −5 dB to 40 dB. (2) The monitoring area of FADI can be expanded by more than 6 times compared to conventional acoustic diversity index (ADI). (3) FADI can effectively suppress the effects of temporally stationary interference such as sounds from lawn mower, rain, and flowing water, but its performance shows a certain degree of degradation in environments with highly time-varying noise.

    Conclusions: FADI has a great potential to serve as a stable and reliable tool for rapid biodiversity assessment in human-dominated areas. Furthermore, the numerical robustness of FADI can be improved by use of the microphone array technology that can provide spatial signal processing capability in addition to current time-frequency processing.

    Wetland soundscape recording scheme and feature selection for soundscape classification
    Wanjun Hu, Zezhou Hao, Canwei Xia, Jiangjian Xie
    Biodiv Sci. 2024, 32 (10):  24121.  doi: 10.17520/biods.2024121   cstr: 32101.14.biods.2024121
    Abstract ( 335 )   HTML ( 1 )   PDF (6181KB) ( 186 )   Save
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    Aims: Soundscape describes the spatial and temporal patterns of biodiversity, human activities, and other sounds, reflecting important anthropogenic and ecological processes. Soundscape classification not only helps improve the accuracy of calculating and analyzing different soundscape components, but also helps researchers gain a deeper understanding of the characteristics and distribution of different sounds, thus providing a basis for protecting and improving the ecological environment by offering a deeper understanding of species composition in an ecosystem. However, the large number of recordings collected by passive acoustic devices poses difficulties in analyzing soundscape data. This study aims to explore an efficient recording scheme that balances the amount of sampling data with the sampling cost to achieve the most productive outcome for soundscape classification research.

    Methods: This study takes the recording data of Yeyahu Wetland Park of Beijing as the research object, compares the performance of seven acoustic indices (acoustic complexity index (ACI), acoustic diversity index (ADI), acoustic evenness index (AEI), bioacoustic index (BIO), acoustic entropy index (H), median of the amplitude envelope (M), normalized difference sound index (NDSI)) and BYOL-A (bootstrap your own latent for audio) features by different recording schemes, and explores appropriate recording schemes and acoustic features for soundscape classification (biophony, geophony, anthrophony).

    Results: (1) Uniformly collecting 10 1-min sub-samples per hour could effectively capture soundscape information and balances data volume and cost (Spearman correlation coefficient ρ > 0.9). (2) Among the multiple acoustic indices, ACI and H were the most stable indices. (3) BYOL-A features were more effective in completing soundscape classification than acoustic indices.

    Conclusion: Appropriate recording scheme and high-performance deep learning features such as BYOL-A features can quickly capture soundscape information and help improve the accuracy of soundscape classification. This study is expected to provide a guideline for soundscape data collection and acoustic feature selection in future research.

    Original Papers
    Correlation analysis of urban green landscape patterns and bird diversity based on passive acoustic monitoring technology
    Le Li, Chengyun Zhang, Nancai Pei, Bingtao Gao, Na Wang, Jiarui Li, Ruichen Wu, Zezhou Hao
    Biodiv Sci. 2024, 32 (10):  24296.  doi: 10.17520/biods.2024296
    Abstract ( 252 )   HTML ( 2 )   PDF (2242KB) ( 261 )   Supplementary Material   Save
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    Aim: Urbanization-induced habitat fragmentation and habitat quality degradation have negatively impacts on biodiversity. Birds, as essential components of global biodiversity, act as critical indicators of ecological changes. Urban green spaces play a pivotal role in bird diversity conservation. Understanding the relationship between green space landscape characteristics and bird diversity is vital for sustainable urban landscape management and biodiversity conservation. This study aims to explore the response of bird diversity to urban green landscape pattern in different feeding guilds.

    Methods: The synchronous continuous recordings were conducted by passive acoustic monitoring technology at 30 urban parks across Guangzhou for 6 months. Deep learning models were used to identify species information. Redundancy analysis, random forest regression, and classification and regression tree models were used to quantify the relationship between bird species and green space landscape characteristics.

    Results: The analysis showed that higher green space ratios and patch areas positively affected bird species richness, while reduced habitat connectivity negatively affected species numbers. Birds with different feeding habits exhibited varying responses to landscape characteristics. Omnivorous birds were more adaptable to fragmented environment, carnivorous birds were highly sensitive to habitat connectivity, and insectivorous birds relied on larger green space patches. In addition, bird species richness showed a negative correlation with artificial nighttime light, with insectivorous birds being most sensitive to this disturbance. Nonlinear correlations were observed between bird species richness and green space landscape characteristics, with different response processes and thresholds. For example, exclusive feeding guild species richness increased when the average green space patch area exceeded 0.01-0.02 ha within a 2 km circular buffer zone or when isolated island area proportion were below 0.92%-10.40%.

    Conclusion: It is suggested to reduce the negative impact of artificial lighting on birds in order to enhance overall urban bird diversity. It is also necessary to protect and restore residual habitats dominated by native species, establish corridors and new complementary habitats and enhance habitat connectivity in landscape management.

    Application of passive acoustic technology in monitoring bird diversity in urban park green space: A case study of Chunshen Park in Minhang District, Shanghai
    Juan Tan, Dandan Zhu, Qing Wang, Min Wang
    Biodiv Sci. 2024, 32 (10):  24262.  doi: 10.17520/biods.2024262
    Abstract ( 222 )   HTML ( 1 )   PDF (2747KB) ( 164 )   Supplementary Material   Save
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    Aims: Urban green spaces are critical habitats for maintaining biodiversity in cities. As indicators of urban biodiversity, birds have attracted significant attention in urban ecology research due to their biodiversity patterns and conservation. Passive acoustic monitoring has emerged as an important tool in bird diversity studies. This study aims to explore the effectiveness between passive acoustic monitoring and traditional transect survey for bird monitoring in urban park green space.

    Methods: From July 2023 to April 2024, acoustic monitoring equipment and line transect surveys were utilized to monitor bird diversity in Chunshen Park in Minhang District within Shanghai’s outer-loop forest belt. One set of acoustic devices was deployed in each of the three habitat types, collecting animal sound data for the first 15 minutes of every hour, 24 hours a day. The data were processed using unsupervised syllable clustering analysis. An in-depth comparison and analysis of the monitoring results and line transect surveys were conducted to assess the effectiveness of both monitoring methods.

    Results: The acoustic devices recorded 49 species from 11 orders and 28 families, while the transect surveys recorded 32 species from 5 orders and 19 families, both predominantly including species from the Passeriformes. A total of 24 common species were recorded by the two methods, with consistent characteristics in residency type, predominantly featuring resident birds. Acoustic devices recorded more dominant species compared to transect surveys, with the common dominant species recorded by the two methods being Pycnonotus sinensis, Spilopelia chinensis, Turdus mandarinusand Cyanopica cyanus. Across different habitats, both methods consistently identified the highest species richness in mixed forest wetlands, while the levels were similar in pure forest wetlands and mixed forests. Generally, spring and autumn exhibited higher species richness and abundance across habitat types, though significant seasonal variations were observed in community metrics such as dominance, diversity, and evenness indices between the two monitoring methods.

    Conclusion: Intelligent bird monitoring based on passive acoustic techniques provides a new technical path for urban biodiversity conservation research, which can be combined with traditional survey techniques and methods to improve the accuracy and comprehensiveness of monitoring data.

    Data Paper
    A dataset of call characteristics of anuran from the Chebaling National Nature Reserve, Guangdong Province
    Ruirui Mao, Tuo Shen, Hui Li, Linchu Tian, Hairong Tan, Lirong Lu, Xiaogang Wu, Zongji Fan, Guoyi Wu, Jie Li, Yong Wu, Bicheng Zhu, Zhishu Xiao
    Biodiv Sci. 2024, 32 (10):  24356.  doi: 10.17520/biods.2024356
    Abstract ( 266 )   HTML ( 4 )   PDF (1009KB) ( 163 )   PDF(mobile) (692KB) ( 46 )   Save
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    Calls are a critical mode of interspecific and intraspecific interactions in anurans. Analyzing their call characteristics is fundamental for bioacoustics research, species behavior studies, and biodiversity monitoring. It is vital for understanding species identification, reproduction, distribution and evolutionary processes. In June, 2024, we collected call data from 11 anuran species (belonging to 6 families and 8 genera) in the Chebaling National Nature Reserve and nearby areas in Guangdong Province. The dataset includes call characteristics for these species, featuring oscillograms and spectrograms data from 11 advertisement calls and 1 fighting call. The dataset encompasses both temporal and spectral parameters, including simple calls, complex calls, call duration, note duration, note interval, number of pulses, dominant frequency, and fundamental frequency. This resource provides essential data for bioacoustic research and species monitoring of anurans in South China.

    Database/Dataset Profile

    Title A dataset of call characteristics of anuran from the Chebaling National Nature Reserve, Guangdong Province
    Data author(s) Ruirui Mao, Tuo Shen, Hui Li, Linchu Tian, Hairong Tan, Lirong Lu, Xiaogang Wu, Zongji Fan, Guoyi Wu, Jie Li, Yong Wu, Bicheng Zhu, Zhishu Xiao
    Data corresponding author Zhishu Xiao (xiaozs@ioz.ac.cn)
    Time range 2024.5.31~2024.6.14
    Geographical scope 114°09°-114°16° E, 24°40°-24°46° N
    File size Recording data file: 59.8 MB; Worksheet file: 13 KB; Spectrum file: 12.5 MB
    Data volume Anuran audio records: 11
    Data format *.zip, *.xlsx, *.docx
    Data link https://www.scidb.cn/en/anonymous/UkZOWmZx
    https://doi.org/10.57760/sciencedb.14051
    https://www.biodiversity-science.net/fileup/1005-0094/DATA/2024356.zip
    Database/Dataset composition The dataset comprises three data files: (1) “Chebaling anuran calls data.zip” contains audio data of the vocalizations of 11 species; (2) “Chebaling anuran calls sampling information.xlsx” records detailed information on the sampling of each species; (3) “Chebaling anuran calls spectrograms” consists of spectrograms and waveforms of the vocalizations for each species.

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