Search Results (53)

(1) Pholidoptera griseoaptera (De Geer, 1773) (Orthoptera, Tettigoniidae) is a common and widespread inhabitant of forest edges in Europe and may therefore serve as a suitable model species for understanding past and future changes in forest wildlife. (2) We recorded the presence or absence of the species in 189 forest and forest-edge plots within the Kaluga Region using acoustic observations and pitfall trapping, and analysed the data using logistic regression. (3) Across the region, the main positive factor affecting species presence was the dominance of nemoral herbs in the herb layer. The main negative factors were habitat isolation caused by physical barriers and location within moraine plains formed during the late stage of the Moscow glaciation. The presence of coniferous tree species and spatial autocovariation were also significant factors, although their contributions were relatively small. The abundance of Ph. griseoaptera was higher in forests located within river valleys. Within Kaluga, the long-term persistence of tree vegetation and habitat isolation were the main significant factors affecting species occurrence. The smallest urban habitat occupied by the species covered approximately 13 ha, whereas the total area of unmown patches within this habitat was only about 0.2 ha. (4) Ph. griseoaptera may be used as an indicator of the long-term persistence of broadleaved deciduous (nemoral) forests. Under conditions of high urbanization, however, the species may become threatened. Full article

Uncrewed aerial systems (UASs) have diverse applications in natural environments, yet their deployment can inadvertently disturb wildlife. This PRISMA-guided systematic review synthesised 39 studies (2015–2025) encompassing birds, mammals, and marine taxa to identify UAS operational drivers of disturbance. We applied a Factors–Responses–Consequences (F–R–C) framework linking UAS operational characteristics, observed wildlife responses, and ecological consequences. Three patterns emerged: (i) Factors: Contextual and operational conditions such as flight altitude, noise, and approach direction interact with species-specific sensitivities to shape disturbance potential. (ii) Responses: Physiological measures (e.g., elevated heart rates) often reveal hidden stress not evident from behaviour alone. (iii) Consequences: Short-term effects may accumulate into long-term impacts on health, reproduction, and habitat use. These findings highlight the need for species- and context-specific flight envelopes rather than solely uniform altitude limits. By structuring existing evidence within the F–R–C framework, this synthesis provides a transferable foundation for UAS mission planning, drone development, operational decision-making, ethical practice, and environmental impact assessment in conservation and wildlife-management contexts. As all screening and data extraction were conducted by a single reviewer, the findings should be interpreted with appropriate caution pending independent validation. Full article

The natural and cultural components of the acoustic environment are a resource intrinsic to parks and protected areas and are critical to wildlife and the visitor experience. However, noise degrades the natural acoustic environment, and aircraft introduce spatially extensive noise into such environments. This study examined aircraft noise events at Great Smoky Mountains National Park, U.S., for different jet aircraft types categorized as “Light” (<20,000 pounds) and “Heavy” (>20,000 pounds). Detection distances were determined for these aircraft types by examining the active space of each aircraft’s noise events. The results of this study determined mean detection distances of 15.2 km for “Light” aircraft and 18.3 km for “Heavy” aircraft to the active space boundaries. Increased thrust or jet velocity from the higher mean altitude resulted in a larger active space. From a practical management perspective, to minimize noise impacts on the park’s natural and cultural resources, efforts should focus on “Heavy” aircraft because they produce greater thrust and frequently operate above GRSM. Using detection distances, managers could work with these aircraft operators or airports to reduce thrust and velocity when flying above protected areas and to discuss routing around noise-sensitive areas, especially with low-level overflights. Full article

Wind energy stands as one of the most technologically mature renewable sources, playing a pivotal role in the mitigation of greenhouse gas emissions. However, wind farms and associated infrastructures increase collision risk for flying organisms. Implementing higher cut-in speeds is a proven mitigation strategy to significantly decrease wildlife mortality rates, particularly for bat species, by preventing turbine operation during low-wind periods of high activity. The suggested, non-standard, increased cut-in speed for wind turbines is generally 5.0 m/s. To test the effectiveness of cut-in speed increase, bat activity was monitored at three wind farms in northern Portugal (Gevancas, Azinheira, and Lagoa de Dom João e Feirão), to characterize spatial and temporal activity patterns and assess the potential associated risk. Ultrasonic acoustic detection was carried out at fixed stations, at heights of 55 m above ground level from March to October. Wind speed data were recorded concurrently using anemometers mounted on meteorological towers. Contradicting recommendations, the results show that significant bat activity might occur at wind speeds above the current curtailment values. Since turbine operation coincides with peak bat activity, it is imperative to implement site-specific mitigation strategies, such as optimized cut-in speeds, to minimize mortality risk. Full article

Global efforts to mitigate climate change and reduce reliance on fossil fuels have intensified interest in sustainable, urban-compatible wind energy technologies. Conventional wind turbines, however, remain limited in densely populated environments due to acoustic emissions, mechanical complexity, cost, and risks to avian wildlife. This study proposes and numerically evaluates a bladeless wind turbine concept based on vortex-induced vibrations (VIVs) as a simplified alternative to conventional bladed systems. The proposed design replaces rotating blades with a vertical mast that undergoes wind-induced oscillations, which are passively converted into unidirectional rotational motion using a cylindrical cam (CCAM) mechanism. The aerodynamic behavior and structural response of the system are investigated using computational fluid dynamics (CFD) and finite element analysis (FEA) under low-wind-speed conditions representative of urban environments. The numerical results indicate well-defined flow separation and wake formation conducive to VIV, along with low stress and displacement levels in the mast, supporting reliable mechanical engagement with the CCAM mechanism. These findings demonstrate the feasibility of mechanically rectified VIV-based bladeless wind turbines and highlight their potential as low-noise, low-impact solutions for decentralized and urban wind energy applications. Full article

Monitoring wildlife has become increasingly important for understanding the evolution of species and ecosystem health. Acoustic monitoring offers several advantages over video-based approaches, enabling continuous 24/7 observation and robust detection under challenging environmental conditions. Deep learning models have demonstrated strong performance in audio classification. However, their computational complexity poses significant challenges for deployment on low-power embedded platforms. This paper presents a low-power embedded system for real-time bird audio detection. A hybrid CNN–RNN architecture is adopted, redesigned, and quantized to significantly reduce model complexity while preserving classification accuracy. To support efficient execution, a custom hardware accelerator was developed and integrated into a Zynq UltraScale+ ZU3CG FPGA. The proposed system achieves an accuracy of 87.4%, processes up to 5 audio samples per second, and operates at only 1.4 W, demonstrating its suitability for autonomous, energy-efficient wildlife monitoring applications. Full article

Red deer (Cervus elaphus), as a highly vocal species, provide versatile ecosystem functions beyond grazing. Their flexible use of different habitats allows them to occupy a variety of ecosystems. As global efforts to conserve biodiversity increase, there is a growing need for new approaches to continuous wildlife monitoring. Bioacoustics is a rapidly developing field that provides valuable data, especially in environments that are difficult to access. The spatial occupancy of red deer in Kopački Rit Nature Park was investigated using passive acoustic devices during the rutting season (September–October) in 2023 and 2024. A total of 332,302 recordings were collected with AudioMoth devices configured to record for 1 min every 5 min over a 10-day period. A recognition model was trained on the Arbimon platform, and a random forest model was applied to the detection data. The occupancy model revealed differences in spatial occupancy between the two years. Although none of the tested covariates showed statistically significant effects, the observed differences likely reflect unmeasured ecological dynamics, such as hydrological variability and resource availability. These findings highlight the potential of passive acoustic monitoring as a reliable, non-invasive approach for large mammal studies. Full article

Sound source localization represents one of the major challenges for soundscapes due to the dynamicity of a large variety of signals. Many applications are found related to ecosystems to study the migration process of birds and animals other than other terrestrial environments to survey wildlife. Other applications on sound recording are supported by sensors to detect animal movement. This paper deals with the immersive 3D soundscape by using a multi-channel spherical microphone probe, in combination with a 360° camera. The soundscape has been carried out in three Italian squares across the city of Parma. The acoustic maps obtained from the data processing detect the directivity of dynamic sound sources as typical of an urban environment. The analysis of the objective environmental parameters (like loudness, roughness, sharpness, and prominence) was conducted alongside the investigations on the historical importance of Italian squares as places for social inclusivity. A dedicated listening playback is provided by the AGORA project with a portable listening room characterized by modular unit of soundbars. Full article

Animal-borne acoustic sensors provide valuable insights into wildlife behavior and environments but face significant power and storage constraints that limit deployment duration. We present a novel adaptive acoustic monitoring system designed for long-term, real-time observation of wildlife. Our approach combines low-power hardware, configurable firmware, and an unsupervised machine learning algorithm that intelligently filters acoustic data to prioritize novel or rare sounds while reducing redundant storage. The system employs a variational autoencoder to project audio features into a low-dimensional space, followed by adaptive clustering to identify events of interest. Simulation results demonstrate the system’s ability to normalize the collection of acoustic events across varying abundance levels, with rare events retained at rates of 80–85% while frequent sounds are reduced to 3–10% retention. Initial field deployments on caribou, African elephants, and bighorn sheep show promising application across diverse species and ecological contexts. Power consumption analysis indicates the need for additional optimization to achieve multi-month deployments. This technology enables the creation of novel wilderness datasets while addressing the limitations of traditional static acoustic monitoring approaches, offering new possibilities for wildlife research, ecosystem monitoring, and conservation efforts. Full article

Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework aimed at enhancing energy efficiency and sustainability in applications of marine wildlife monitoring in underwater sensor networks, according to the vision of implementing an underwater acoustic sensor network. The framework integrates intelligent computing directly into underwater sensor nodes, employing lightweight AI models to locally classify marine species. Transmitting only classification results, instead of raw data, significantly reduces data volume, thus conserving energy. Additionally, a software-defined radio methodology dynamically adapts transmission parameters such as modulation schemes, packet length, and transmission power to further minimize energy consumption and environmental disruption. GNU Radio simulations evaluate the framework effectiveness using metrics like energy consumption, bit error rate, throughput, and delay. Adaptive transmission strategies implicitly ensure reduced energy usage as compared to non-adaptive transmission solutions employing fixed communication parameters. The results illustrate the framework ability to effectively balance energy efficiency, performance, and ecological impact. This research contributes directly to ongoing development in sustainable and energy-efficient underwater wireless sensor network design and deployment. Full article

While open-set recognition algorithms have been extensively explored in computer vision, their application to environmental sound analysis remains understudied. To address this gap, this study investigates how to effectively recognize unknown sound categories in real-world environments by proposing a novel Kernel Density Estimation-based Generative Adversarial Network (KDE-GAN) for data augmentation combined with Attractor–Reciprocal Point Learning for open-set classification. Specifically, our approach addresses three key challenges: (1) How to generate boundary-aware synthetic samples for robust open-set training: A closed-set classifier’s pre-logit layer outputs are fed into the KDE-GAN, which synthesizes samples mapped to the logit layer using the classifier’s original weights. Kernel Density Estimation then enforces Density Loss and Offset Loss to ensure these samples align with class boundaries. (2) How to optimize feature space organization: The closed-set classifier is constrained by an Attractor–Reciprocal Point joint loss, maintaining intra-class compactness while pushing unknown samples toward low-density regions. (3) How to evaluate performance in highly open scenarios: We validate the method using UrbanSound8K, AudioEventDataset, and TUT Acoustic Scenes 2017 as closed sets, with ESC-50 categories as open-set samples, achieving AUROC/OSCR scores of 0.9251/0.8743, 0.7921/0.7135, and 0.8209/0.6262, respectively. The findings demonstrate the potential of this framework to enhance environmental sound monitoring systems, particularly in applications requiring adaptability to unseen acoustic events (e.g., urban noise surveillance or wildlife monitoring). Full article

Forest soundscapes contain rich ecological information that reflects the composition, structure, and dynamics of biodiversity within forest ecosystems. The effective monitoring of these soundscapes is essential for forest conservation and wildlife management. However, traditional manual annotation methods are time-consuming and limited in scalability, while commonly used acoustic indices such as the Normalized Difference Soundscape Index (NDSI) lack the capacity to resolve overlapping or complex sound sources often encountered in dense forest environments. To overcome these limitations, this study applied a deep learning-based multi-label classification approach to long-term field recordings collected from Shennongjia National Park, a typical subtropical forest ecosystem in China. The model automatically classifies sound sources into biophony, geophony, and anthrophony. Compared to the NDSI, the model demonstrated higher precision and robustness, especially under low-signal-to-noise-ratio conditions. While the NDSI provides an efficient overview of soundscape disturbances, it demonstrates limitations in differentiating geophonic components and detecting subtle variations. This study supports a complementary “macro–micro” analytical framework that enables capturing broad, time-averaged soundscape trends through the NDSI, while achieving fine-grained, label-specific detection of biophony, geophony, and anthrophony through the multi-label classification model. This integration enhances analytical resolution, enabling the scalable, automated monitoring of complex forest soundscapes. This study contributes a novel and adaptable approach for real-time biodiversity assessment and long-term forest conservation. Full article

Ecoacoustics examines the interactions between soundscapes, ecological processes, and anthropogenic disturbance. Acoustic communication is crucial for wildlife, making noise pollution a key factor in shaping biodiversity, though its effects are also modulated by habitat characteristics. In this work, we assess the influence of highway noise and vegetation structure on the soundscape and avian distribution of the Moriano oxbow lake (Bereguardo, PV, Italy), a Site of Community Importance in the Ticino Valley Regional Park. A two-week monitoring campaign (April 2022) used eight recorders arranged in a grid to analyze soundscape dynamics through eight ecoacoustic indices (ACI, ADI, AEI, BI, NDSI, H, DSC, ZCR). Vegetation surveys quantified tree diversity and structural parameters such as basal area, height, stem density, biomass, and leaf cover. Correlation analyses revealed that Quercus robur abundance and tree diversity significantly influenced the acoustic environment, while bird richness correlated positively with vegetation biomass and Quercus robur presence. Highway proximity was a key structuring factor, with indices (ADI, H, NDSI, ACI) increasing with distance. These findings underscore the dual role of noise and vegetation in shaping soundscapes and highlight the importance of incorporating habitat features into ecoacoustic assessments to better understand biodiversity patterns in anthropized landscapes. Full article

Compared with traditional field techniques, automated and noninvasive bird monitoring techniques, such as passive acoustic monitoring, offer significant advantages. However, the extensive data collected through passive acoustic monitoring can be challenging to analyze and may require the use of machine learning algorithms for efficient processing. BirdNET is a user-friendly and ready-to-use machine learning tool that can recognize more than 6500 wildlife species, including several tropical species. However, the performance of BirdNET in tropical ecosystems has rarely been assessed. Here, we evaluate the effectiveness of BirdNET for monitoring the vocal activity of three Neotropical wetland species from recordings collected over a year in the Brazilian Pantanal: Green Ibis (Mesembrinibis cayennensis), Limpkin (Aramus guarauna), and Sunbittern (Eurypyga helias). BirdNET was able to detect the presence of the three species in 82–92% of the recordings with known presence. Similarly, BirdNET’s ability to correctly identify vocalizations was consistently greater than 77% (range 77–98%), confirming its effectiveness for monitoring these three tropical bird species. The peak vocal activity for the three species occurred during crepuscular periods, at the end of the rainy season, and during the receding season, a period when the risk of nest damage from flood pulses is low and food availability is high owing to the large presence of small water bodies. The use of machine learning algorithms such as BirdNET may improve bird monitoring in tropical areas but also facilitate research that improves our knowledge of birds’ natural history, which remains unknown for many tropical species. Full article

(1) Background: Human–wildlife conflict can lead to adverse consequences for both parties, particularly in areas with a high concentration of wild ungulates. Ungulates cause frequent, severe plant damage by stripping the bark or browsing on the youngest plants. In the latter case, they damage vegetative sprouts and leaves, which can cause a delay in growth or the plant’s death. Tuscany is notable for its significant population of wild boar, which cause substantial damage to vineyards and cereal crops, costing farmers millions annually. In Tuscany, given the highly cultivated landscape of olive trees, damage has also been recorded in these plants. Balancing human and wildlife needs is crucial for minimizing damage and ensuring coexistence. (2) Methods: This study tested innovative electronic playback devices using long-range radio technology (LoRa) to deter wild ungulates and prevent crop damage. These devices use sounds and lights to induce wild animals to be afraid and thus run away from the cultivated plot to be protected. The experiment was conducted on a farm in Chianti, Tuscany, involving four plots of land planted with olive trees: in two test areas, four playback devices and four camera traps were installed, and in the two control areas, only camera traps were installed. Playback devices aimed to deter wild ungulates and camera traps aimed to test their effectiveness. Data from the camera traps were analyzed statistically and behaviorally. (3) Results: Playback devices significantly reduced wild animal activity in the equipped areas. Statistical analysis revealed that the use of acoustic–luminous deterrent devices (PDs) significantly reduced wildlife visits to the olive groves. (4) Conclusion: The study’s findings, supported by heatmaps and frequency analyses, provide insights into wildlife activity patterns and guide the development of targeted, effective wildlife management strategies. Full article