Abstract:

Background: Biodiversity loss driven by human activities and climate change has intensified the demand for efficient, scalable, and non-invasive monitoring approaches. Passive acoustic monitoring (PAM), based on the concept of soundscapes, captures biophony, geophony, and anthrophony over large spatial and temporal scales, providing a promising framework for biodiversity assessment. Because changes in species composition and habitat conditions are often reflected in the structure of soundscapes, soundscape-based analysis has become a key component of ecoacoustics.

Progress: Current soundscape-based biodiversity assessments mainly rely on two automated approaches: automatic recognition methods and acoustic index methods. Automatic recognition enables species- or sound-source-level identification using machine learning, but its performance is limited by the scarcity of annotated data, its sensitivity to noise and overlapping sounds, and poor cross-regional generalization. Acoustic index methods provide computational efficiency by summarizing soundscape characteristics into numerical metrics, but their ecological interpretability and robustness vary across environmental conditions and parameter setting, often resulting in inconsistent or contradictory outcomes.

Prospects: Future progress requires standardized data acquisition and processing protocols, the development of open soundscape feature databases, and methodological integration of automatic recognition and acoustic index approaches. Integrating these methods with multi-source environmental data is expected to enhance robustness, ecological interpretability, and comparability, supporting more reliable soundscape-based biodiversity monitoring and conservation decision-making.

Key words: soundscape assessment, biodiversity, automatic recognition, acoustic indices, passive acoustic monitoring