Urban biodiversity conservation has become a central topic and a major area of action in modern conservation biology, yet species trait data collected in cities remain scarce. Anuran calls are key behavioral signals in breeding communication and population evolution. Our study collected anuran calls in the main urban area of Guiyang from February 2025 to June 2026 using a combination of active recording and passive acoustic monitoring. The dataset includes advertisement calls from 14 anuran species in Guiyang, including 13 native species belonging to seven families and 11 genera, and one invasive alien species. We provide oscillograms and spectrograms for all 14 species to show the temporal and spectral characteristics of their calls, including call duration, note duration, inter-note interval, and dominant frequency. Our dataset provides basic data for studies on amphibian distribution, breeding patterns, and activity in Guiyang and other mountainous cities in Southwest China. It also provides local data and methods to support updates to regional biodiversity records, the monitoring and control of alien species, and long-term ecological monitoring.

Vocalizations are the primary communication signals of anurans, and quantifying acoustic parameters provides essential data for bioacoustic research. However, a systematic acoustic characteristics dataset for anurans in East China is currently lacking. By comprehensively reviewing public audio platforms, published acoustic literature, and online amphibian databases, we constructed an acoustic characteristics dataset covering 94 species of 30 genera and 8 families of anurans in East China. The dataset records taxonomic information, provincial distribution, vocal sac type, and call structure for each species, documents the recording locality of each audio file corresponding to each species, and extracts quantitative parameters for each call unit, including duration, dominant frequency, bandwidth, spectral centroid, spectral skewness, spectral kurtosis, and harmonic-to-noise ratio. In addition, representative sound figures (oscillograms and spectrograms) are provided for 94 species. This dataset will provide fundamental data support for comparative studies of species acoustic characteristics, adaptive evolution analysis of call traits, and regional passive acoustic monitoring.

Aims: Traffic noise is one of the major sources of urban noise that seriously harms residents’ physical and mental health. Roadside green belts can effectively reduce traffic noise. Studies have shown that green belts with more complex vertical structures or mixed conifer–broadleaf plantings tend to achieve greater noise reduction. However, whether tree species diversity itself makes an independent contribution remains unclear. This study aimed to investigate the independent effect of tree species diversity on traffic noise attenuation and its pathways. 

Methods: We selected 120 plots (60 pure and 60 mixed forest plots with varying richness) along five arterial roads in Harbin, China, for in-situ acoustic measurements. Single-tree morphological attributes were extracted using laser point cloud scanning. Covariance analysis, hierarchical regression, and Bootstrap robustness tests were combined to examine the independent effect and pathways of tree species diversity. 

Results: (1) Pure and mixed forests plots exhibited significant inter-group differences in physical volume and spatial arrangement. After correcting for these differences, mixed forests plots still achieved significantly greater noise reduction than pure forests plots. (2) Tree species richness demonstrated an independent and positive contribution to noise attenuation. This contribution was not mediated by morphological diversity, nor did species evenness exhibit an independent effect. (3) The noise reduction gain from increased diversity showed a diminishing marginal effect. 

Conclusions: This study provides preliminary scientific evidence for tree species selection and configuration in noise-abatement-oriented urban roadside green belt design. The primary gain in noise reduction was found to arise from the transition from pure to mixed forests, suggesting that introducing even one or two companion tree species may be an effective strategy.

Aims: Identifying target calls from large volumes of passive acoustic monitoring data commonly requires manual spectrogram review with selective audio playback and is particularly time-consuming in montane forests dominated by wind, stream and cicada noise. Hoolock tianxing has a small and highly fragmented population and urgently requires long-term, non-invasive monitoring methods. This study aimed to develop an automated call-detection framework, evaluate its cross-site generalisation and cross-species transferability, and characterise its diurnal calling rhythm. 

Methods: Two eight-channel PAM systems were deployed in Sudian Township, Yingjiang County, Yunnan. We developed CBSkyNet, which combines PCEN features, a CNN backbone, a convolutional block attention module and a bidirectional long short-term memory network. The dataset was split by spatial source to evaluate cross-site generalisation. 

Results: Across three random-seed runs, PCEN achieved the highest mean recall and F1-score for all four evaluated backbone networks and showed the smallest cross-architecture ranges for both metrics. In the fixed-seed ablation experiment, CBSkyNet used only 6.62 M parameters and achieved 99.30% recall and a 99.60% F1-score on the cross-site test set. At the bout level, all 20 independent calling events in the test set were detected. For approximately 2,008 h of continuous recordings, model inference and manual verification of candidate segments required about 16.1 h in total, making the complete workflow approximately 4-6 times faster than manual spectrogram screening. The model also recovered three low-signal-to-noise calling days missed during initial manual screening. Cross-species transfer showed that CBSkyNet was transferable to a closely related gibbon species, with a clearer benefit from pretraining when target-domain annotations were limited. Calling activity was concentrated in the early morning after sunrise, whereas the between-site difference in first detected calls requires further validation using independent family groups. 

Conclusion: CBSkyNet provides an efficient and reusable framework for screening large-scale recordings of H. tianxing and may facilitate long-term acoustic monitoring of this species and related endangered gibbons. The code and representative data are publicly available at https://github.com/mf0302/CBSkyNet.

Aims: The estrous behavior of polygynous ruminants is strictly seasonal, and males exhibit complex display behaviors during the rut, with vocal activity serving as an important display. The Milu (Elaphurus davidianus) is a typical large polygynous ruminant. During the rut, stags attract females and repel rivals through frequent roaring. This species has a large home range and a prolonged estrous period, making long term monitoring of its reproductive status challenging. Passive acoustic monitoring (PAM) technology has become an effective method for tracking vocalization behavior and population dynamics of wildlife due to its convenience, covertness, and capacity for long term operation. In this study, we employed PAM technology to conduct long term tracking of vocal activity in Milu across the rutting season. 

Methods: We employed PAM technology to record the group vocal activity of a Milu herd throughout the rut. Based on 92 days of acoustic data, we analyzed the seasonal and diurnal rhythms of vocal activity, examined the temporal coordination between mother - calf call interactions and the vocal activity of stags, and explored the effects of meteorological factors on the number of rutting roars produced by stags. 

Results: Vocal activity increased progressively as the rut advanced, peaking in mid July. The diurnal rhythm exhibited a distinct multimodal distribution, dominated by two primary peaks at dawn and dusk, with smaller peaks also occurring in the morning, afternoon, and at night. Calls of females and calves were highly synchronized, and mother - calf call interactions promoted vocal activity in rutting stags. The vocal intensity of rutting stags fluctuated with meteorological conditions and was significantly correlated with temperature, precipitation, and solar shortwave radiation, but not with wind speed, humidity, or atmospheric pressure. 

Conclusion: The vocal activity of Milu exhibited pronounced rhythmicity. Mother - calf call interactions promote the vocal activity of rutting stags, whose vocal intensity also fluctuates with meteorological conditions. This study provides valuable data for ecological monitoring of acoustic activity patterns in large ungulates during the rut and validates the feasibility of PAM technology for long term monitoring of large wildlife.

Global biodiversity is undergoing rapid change and sustained loss, creating an urgent need for ecological monitoring technologies capable of continuous operation over large spatial scales and long time series. Passive acoustic monitoring (PAM) records environmental sounds in an unattended manner, enabling the simultaneous capture of biotic, geophysical, and anthropogenic sounds. It thus provides a vital tool for identifying changes in biological community activity, soundscape structure, and ecosystem dynamics under anthropogenic disturbance. In recent years, PAM has been widely applied to biodiversity monitoring in the context of disturbances such as traffic, construction, and recreational noise, land-use change, and habitat fragmentation. However, changes in the soundscape cannot be directly equated with changes in biodiversity. Human-generated noise may affect recording results through acoustic masking and reduced detectability, while animals may compensate behaviorally by adjusting call frequency, singing duration, or activity rhythms; habitat disturbance, in turn, may trigger community restructuring and changes in the spatiotemporal patterns of soundscapes by altering vegetation structure, resource availability, landscape connectivity, and sound propagation conditions. Based on this, this paper proposes an analytical framework of disturbance type—mechanism of action—acoustic response—ecological implications—validation pathway. Focusing on four pathways—direct noise disturbance, changes in land use and management, alterations in landscape pattern, and the superposition of multiple disturbances—it systematizes typical responses of the bioacoustic landscape and their ecological interpretations. The paper further discusses the key challenges facing PAM in the interpretation of acoustic indices, the development of monitoring standards, and its application in ecological early warning systems. It proposes recommendations to enhance the reliability and operability of soundscape monitoring in biodiversity assessment, including strengthening multi-source data validation, refining data collection and analysis standards, establishing long-term baselines, and improving model interpretability.

Birds are key indicator taxa for biodiversity conservation due to their sensitive responses to habitat changes. The development of bird vocalization recognition models in China is constrained by incomplete datasets for model training, insufficient integration of geographical distribution information, and a lack of field validation through comparative studies. To address these gaps, this study constructed an acoustic dataset comprising vocalizations of 1,368 bird species in China. A bird vocalization recognition model was developed using a two-stage transfer learning approach. Concurrently, a species spatial distribution probability model at the municipal scale, covering 1,428 species, was established based on citizen science bird observation data. Finally, a joint bird vocalization recognition model integrating acoustic and spatial distribution information via a Sigmoid function was developed and applied. Model performance was validated using a dataset of vocalizations from 66 common nationwide bird species. The results indicate that as the confidence threshold increases, the joint model demonstrates more stable performance than the standalone acoustic recognition model, with a maximum accuracy improvement of 16.8%. For practical application, a comparative analysis is conducted using four years of data from site-based monitoring and manual synchronous observation. In terms of species richness, the machine identifies 68 species, accounting for 60.2% of the species recorded manually. The machine-based method reflects the activity patterns of bird species accurately and responds more rapidly to changes in bird activity rhythms.