A scalable transfer learning workflow for extracting biological and behavioural insights from forest elephant vocalizations

Remote Sensing in Ecology and Conservation, Год журнала: 2025, Номер unknown

Опубликована: Апрель 25, 2025

Abstract Animal vocalizations encode rich biological information—such as age, sex, behavioural context and emotional state—making bioacoustic analysis a promising non‐invasive method for assessing welfare population demography. However, traditional approaches, which rely on manually defined acoustic features, are time‐consuming, require specialized expertise may introduce subjective bias. These constraints reduce the feasibility of analysing increasingly large datasets generated by passive monitoring (PAM). Transfer learning with Convolutional Neural Networks (CNNs) offers scalable alternative enabling automatic feature extraction without predefined criteria. Here, we applied four pre‐trained CNNs—two general purpose models (VGGish YAMNet) two avian (Perch BirdNET)—to African forest elephant ( Loxodonta cyclotis ) recordings. We used dimensionality reduction algorithm (UMAP) to represent extracted features in dimensions evaluated these representations across three key tasks: (1) call‐type classification (rumble, roar trumpet), (2) rumble sub‐type identification (3) demographic analysis. A Random Forest classifier trained achieved near‐perfect accuracy rumbles, Perch attaining highest average (0.85) all call types. Clustering reduced identified biologically meaningful sub‐types—such adult female calls linked logistics—and provided clearer groupings than manual classification. Statistical analyses showed that factors including age significantly influenced variation P < 0.001), additional comparisons revealing clear differences among contexts (e.g. nursing, competition, separation), sexes multiple classes. BirdNET consistently outperformed when dealing complex or ambiguous calls. findings demonstrate transfer enables scalable, reproducible workflows capable detecting variation. Integrating this approach into PAM pipelines can enhance assessment dynamics, behaviour acoustically active species.

Язык: Английский

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AI-Powered Solutions for Sustainable Tourism Practices Through Wildlife Conservation Initiatives

Advances in hospitality, tourism and the services industry (AHTSI) book series, Год журнала: 2024, Номер unknown, С. 113 - 148

Опубликована: Окт. 15, 2024

As global tourism rises, wildlife habitats deteriorate, leading to human-animal competition and a surge in illegal activities threatening conservation. This necessitates new conservation approaches. Effective is crucial for sustainable leaving legacy. chapter explores AI's role enhancing through as well maps the research landscape. Literature review science mapping using Scopus VOSviewer revealed growing interest this field post-2010. Limited empirical addresses intersection of AI, tourism, comprehensively. Future should focus on AI solutions conservation, continues revolutionise various disciplines systems.

Язык: Английский

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Short-Term Entropy of Signal Energy Used for Effective Detecting of Weak Gunshots in Noisy Environments

Sensors, Год журнала: 2024, Номер 24(15), С. 4933 - 4933

Опубликована: Июль 30, 2024

Conventional gunshot detection systems can quickly and reliably detect gunshots in the area where acoustic sensors are placed. This paper presents of weak hunting using short-term entropy signal energy computed from signals an open natural environment. Our research this field was primarily aimed at detecting fired close range with usual intensity to protect wild elephants poachers. The extend existing more distant gunshots. developed algorithm optimized for two categories surrounding sounds, short impulsive events continuous noise, tested scenes power ratios between louder surroundings 0 dB -14 dB. overall accuracy evaluated terms recall precision. Depending on or noise binary successful down -8 -6 dB; then, efficiency decreases, but some very still be detected -13 Experiments show that proposed method has potential improve reliability systems.

Язык: Английский

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AI in Nature Reserves

Advances in hospitality, tourism and the services industry (AHTSI) book series, Год журнала: 2024, Номер unknown, С. 211 - 232

Опубликована: Ноя. 1, 2024

This chapter examines the multifaceted impacts of AI adoption in natural reserves, exploring both benefits and challenges. On positive side, AI-powered tools can assist real-time monitoring wildlife populations, habitat conditions, visitor numbers, enabling timely data-driven decision-making for conservation. AI-based personalization experiences also improve engagement education. However, relying on algorithms raises issues related to data privacy, algorithmic bias, potential disruption ecosystems through excessive interventions. Through a review academic literature, this provides balanced analysis advantages disadvantages integration reserves. The findings aim guide policymakers, conservation practitioners, technology researchers navigating complex landscape AI-enabled nature

Язык: Английский

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Potential Breakthroughs in Environmental Monitoring and Management

Advances in geospatial technologies book series, Год журнала: 2024, Номер unknown, С. 239 - 282

Опубликована: Дек. 6, 2024

Environmental monitoring and management are critical for sustainable development the preservation of natural resources. Recent advancements in artificial intelligence (AI) integrated with geospatial technologies promise to revolutionize these fields. Delving into potential breakthroughs, AI offers precise, real-time environmental parameters through machine learning (ML) algorithms, remote sensing data, geographic information systems (GIS). Enhanced data analysis techniques facilitate early detection anomalies, predictive modeling ecological trends, efficient resource management. Successful implementations tracking climate change impacts, managing disasters, biodiversity presented various case studies. Challenges such as privacy, algorithm transparency, need interdisciplinary collaboration also addressed. Future research directions explore AI's foster more resilient adaptive practices. Synthesizing technology underscore a transformative approach safeguarding our environment.

Язык: Английский

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A Review of the Endemic Plants of Asteraceae Family in Morocco: Use the Artificial Intelligence for the Conservation

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 8 - 13

Опубликована: Янв. 1, 2024

Язык: Английский

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Computational Methods for Detecting Insect Vibrational Signals in Field Vibroscape Recordings

Опубликована: Янв. 1, 2024

Язык: Английский

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GajGamini: Mitigating Man-Animal Conflict by Detecting Moving Elephants Using Ground Vibration-Based Seismic Sensor

IEEE Sensors Letters, Год журнала: 2024, Номер 8(9), С. 1 - 4

Опубликована: Авг. 13, 2024

Язык: Английский

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Estimating an Elephant Population Size Through Local Ecological Knowledge

Biology, Год журнала: 2024, Номер 13(12), С. 971 - 971

Опубликована: Ноя. 25, 2024

In planning and monitoring measures to protect wildlife in an area, it is important have a reliable baseline estimate of population size trends. There has been minimal published information on small elephants, keystone endangered species, large area west Etosha National Park Namibia, known locally as the Northern Highlands. It highly remote, mountainous which difficult count elephants. semi-desert, where protection at increasing risk from climate change events, research elephant priority. We interviewed 34 community game guards Highlands, focusing number elephants distinguishing features groups. Based collated knowledge, analysis reduce double counting groups, we that there are between 78 212 with best 128. The wide range indication current uncertainties method. However, conclude this low-cost method, if adapted based lessons pilot study, would be applicable for longer-term ecological areas low density.

Язык: Английский

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The Role of Technology in Environmental Governance

Advances in environmental engineering and green technologies book series, Год журнала: 2024, Номер unknown, С. 287 - 322

Опубликована: Ноя. 30, 2024

Technology's integration into environmental governance signifies a fundamental change in how societies safeguard and manage their natural resources. This chapter will present the significance of technology advancing frameworks variety ways, with focus on data collection, policy enforcement, monitoring. Modern technologies allow more accurate tracking changes efficient management techniques. Examples these include big analytics, geographic information systems (GIS), remote sensing. Furthermore, blockchain digital platforms increase accountability transparency, which encourages increased public participation compliance. The convergence addresses complicated ecological concerns world that is changing rapidly, while also improving decision-making processes enabling robust adaptive governance.

Язык: Английский

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