关键词: 鸟类多样性, 样线法, 被动声学监测, AI鸟鸣识别模型

Abstract Aims: This study aims to explore the effectiveness of AI recognition-based passive acoustic monitoring of bird species in urban wetland parks, as well as to compare its results with traditional manual transect survey. A three- month concurrent monitoring was carried out from March to May in 2023 at Wanzuitou Wetland Park in Guangzhou City, China.
Methods: The line transect method involved a twice-monthly survey, while acoustic monitoring method utilized two acoustic monitoring devices operating in triggered recording mode throughout the day. Audio files were transmitted via a 4G mobile network to a server, processed using an AI model based on the Pearl River Delta bird list, filtered by confidence scores and manually reviewed.
Results: The line transect method recorded 2,200 individuals, whereas the acoustic monitoring collected 96,848 audio files, and obtained 34,117 valid records after screening and validation. Two methods identified a total of 70 bird species: 48 species by the transect survey and 49 species by the acoustic monitoring, with 27 species common to both survey methods.
Conclusions: The proportion of species overlapping between the two survey methods was less than half of the total species, suggesting that neither method can fully replace the other in this type of wetland park habitat. Transect survey is more accurate and make it easier to estimate the population density, but it requires a higher level of bird ID skills and involve more workloads. The acoustic monitoring can be automated and unmanned, making it easy to expand the scale of monitoring. However, processing the data from audio files is more challenging, and AI species identification results still need manual correction. Acoustic monitoring technology is developing rapidly and is expected to achieve higher accuracy and broader application in the future. As a non-invasive monitoring method, passive acoustic monitoring of birds has developed rapidly in recent years. The combination with machine learning-based AI recognition technology has also significantly improved the data processing efficiency. The combination of traditional transect survey methods and passive acoustic monitoring will provide higher accuracy and broader application prospects in the future.

Key words: bird diversity, transect method, passive acoustic monitoring, bird AI recognition model