An acoustic detection dataset of birds (Aves) in montane forests using a deep learning approach.

Journal: Biodiversity data journal
Published Date: Feb 24, 2023

Abstract

BACKGROUND: Long-term monitoring is needed to understand the statuses and trends of wildlife communities in montane forests, such as those in Yushan National Park (YSNP), Taiwan. Integrating passive acoustic monitoring (PAM) with an automated sound identifier, a long-term biodiversity monitoring project containing six PAM stations, was launched in YSNP in January 2020 and is currently ongoing. SILIC, an automated wildlife sound identification model, was used to extract sounds and species information from the recordings collected. Animal vocal activity can reflect their breeding status, behaviour, population, movement and distribution, which may be affected by factors, such as habitat loss, climate change and human activity. This massive amount of wildlife vocalisation dataset can provide essential information for the National Park's headquarters on resource management and decision-making. It can also be valuable for those studying the effects of climate change on animal distribution and behaviour at a regional or global scale.

Authors

  • Shih-Hung Wu
    Department of Biological Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan Department of Biological Sciences, National Sun Yat-Sen University Kaohsiung Taiwan.
  • Jerome Chie-Jen Ko
    Endemic Species Research Institute, Nantou, Taiwan Endemic Species Research Institute Nantou Taiwan.
  • Ruey-Shing Lin
    Endemic Species Research Institute, Nantou, Taiwan Endemic Species Research Institute Nantou Taiwan.
  • Wen-Ling Tsai
    Yushan National Park Headquarters, Nantou, Taiwan Yushan National Park Headquarters Nantou Taiwan.
  • Hsueh-Wen Chang
    Department of Biological Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan Department of Biological Sciences, National Sun Yat-Sen University Kaohsiung Taiwan.

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