Developing AI-Powered Acoustic Monitoring for Wildlife Population Estimation

Thesis Abstract

The decline of global biodiversity and the increasing need for effective wildlife monitoring have underscored the importance of accurate, scalable, and non-invasive population estimation methods. Traditional survey techniques, such as direct counts and visual sightings, are often labor-intensive, spatially limited, and subject to observer bias, thereby necessitating innovative solutions that leverage technological advancements. This study aims to develop and validate an AI-powered acoustic monitoring system tailored for wildlife population estimation, with a focus on terrestrial bird and mammal species within tropical forest ecosystems. Specifically, it seeks to (1) design a robust acoustic data collection framework using autonomous recording units (ARUs), (2) develop deep learning models for species identification and vocalization analysis, and (3) evaluate the efficacy of the system in estimating population densities relative to ground-truth data. The research adopts a mixed-methods approach, integrating quantitative model development and qualitative validation. The study population comprises sound recordings collected from 50 strategically placed ARUs within a protected tropical forest reserve, with recordings spanning a six-month period to account for seasonal variation. A stratified random sampling technique ensures representative coverage across habitat types and diurnal cycles. Data collection instruments include high-sensitivity autonomous recording devices capable of capturing a broad frequency range of wildlife vocalizations, coupled with field surveys documenting actual species presence for validation purposes. The AI models are trained using a labeled dataset of approximately 10,000 annotated sound clips, employing convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for feature extraction and sequence modeling. Model performance is assessed through metrics such as precision, recall, and F1-score, with cross-validation techniques ensuring robustness. Ecological inference is conducted via statistical analyses, including generalized linear models (GLMs) to correlate acoustic activity indices with known population parameters, and spatial analysis techniques to map population distribution patterns. The study also applies the theory of bioacoustic signal processing and machine learning classification, drawing on auditory ecology and computational learning frameworks. Expected findings include high-accuracy species identification, improved population estimates compared to traditional indices, and demonstrated scalability of the AI system for extensive field applications. This research contributes significant new knowledge by demonstrating that AI-driven acoustic monitoring can enhance the precision, efficiency, and cost-effectiveness of wildlife population assessment. It offers a replicable framework for deploying autonomous bioacoustic sensors, coupled with advanced machine learning algorithms, to facilitate large-scale conservation monitoring efforts, especially in remote or inaccessible habitats. The anticipated outcome is a validated protocol that integrates AI technologies into mainstream biodiversity assessments, thereby supporting adaptive management and conservation policies. The thesis concludes with recommendations for scaling the system across different ecological zones, integrating additional sensory data streams, and refining models with ongoing machine learning training. It underscores the importance of interdisciplinary approaches combining ecology, computer science, and conservation policy and calls for further research to extend the system’s applicability to diverse taxa and habitats. Overall, the study aims to bridge technological innovation with ecological necessity, advancing both methodological rigor and practical conservation outcomes in wildlife management.