哥斯达黎加Sarapiquí地区红冠金丝雀(Ceratopipra mentalis)声学检测。

Roberto Vargas-Masís, David Segura-Sequeira, Emelin Mendoza-Garro, Dania Vargas-López
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引用次数: 1

摘要

哥斯达黎加加勒比海山麓的大部分生物多样性都没有得到充分的研究。被动声学监测对于收集大型野生动物数据集并将其应用于保护计划是有用的,但如果不应用自动检测算法来减少标记时间,处理这些大型数据集可能是不切实际的。模式机(PM)和随机森林模型(RFM)的使用有助于通过数据集识别单个物种,简化了工作。我们用10台Audiomoth录音机对哥斯达黎加加勒比海山麓的一个私人保护区进行了采样。我们使用PM和RFM来标记红帽侏儒鸟(Ceratopipra mentalis)在录音中基于其独特发声的一部分的存在。RFM检测记录中物种的准确度为0.86,精密度为0.82。三个地点的检测数量最高。鸟的声音活动在白天有三个不同的高峰。探测的高峰出现在3月份。在此基础上,我们提出了如何减少旅游对小径的影响以及如何利用鸟类及其种子传播者的活动来恢复景观的保护措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Acoustic detection of Red-capped Manakin (Ceratopipra mentalis) in Sarapiquí, Costa Rica.
Most of the vast biodiversity of the Caribbean foothills of Costa Rica is poorly studied. Passive acoustic monitoring is useful for collecting large datasets of wildlife and apply them for conservation initiatives, but handling these large datasets can be impractical if automated detection algorithms are not applied to reduce labeling time. The use of Pattern Machine (PM) and Random Forest Models (RFM) helps to identify single species though a dataset, simplifying the work. We sampled a private reserve in the Caribbean foothills of Costa Rica with 10 Audiomoth recorders. We used PM and RFM to label the presence of the Red-capped Manakin (Ceratopipra mentalis) in the recordings based on a portion of its distinctive vocalization. The RFM scored 0.86 of accuracy and 0.82 of precision to detect the species within the recordings. Three sites showed the highestnumber of detections. The acoustic activity of the bird got three different peaks during the day. The peak of detections took place in March. Based on the results, we recommended several conservation actions to the reserve on how to reduce the impact of tourism in the trails and how to rehabilitate the landscape with assistance of the bird and its activity as seed disperser.
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