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基于脉冲重复率的蛙类鸣声和合唱自动检测。

Automated detection of frog calls and choruses by pulse repetition rate.

机构信息

Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Department of Biology, University of Nevada, Reno, Reno, Nevada, USA.

出版信息

Conserv Biol. 2021 Oct;35(5):1659-1668. doi: 10.1111/cobi.13718. Epub 2021 May 7.

Abstract

Anurans (frogs and toads) are among the most globally threatened taxonomic groups. Successful conservation of anurans will rely on improved data on the status and changes in local populations, particularly for rare and threatened species. Automated sensors, such as acoustic recorders, have the potential to provide such data by massively increasing the spatial and temporal scale of population sampling efforts. Analyzing such data sets will require robust and efficient tools that can automatically identify the presence of a species in audio recordings. Like bats and birds, many anuran species produce distinct vocalizations that can be captured by autonomous acoustic recorders and represent excellent candidates for automated recognition. However, in contrast to birds and bats, effective automated acoustic recognition tools for anurans are not yet widely available. An effective automated call-recognition method for anurans must be robust to the challenges of real-world field data and should not require extensive labeled data sets. We devised a vocalization identification tool that classifies anuran vocalizations in audio recordings based on their periodic structure: the repeat interval-based bioacoustic identification tool (RIBBIT). We applied RIBBIT to field recordings to study the boreal chorus frog (Pseudacris maculata) of temperate North American grasslands and the critically endangered variable harlequin frog (Atelopus varius) of tropical Central American rainforests. The tool accurately identified boreal chorus frogs, even when they vocalized in heavily overlapping choruses and identified variable harlequin frog vocalizations at a field site where it had been very rarely encountered in visual surveys. Using a few simple parameters, RIBBIT can detect any vocalization with a periodic structure, including those of many anurans, insects, birds, and mammals. We provide open-source implementations of RIBBIT in Python and R to support its use for other taxa and communities.

摘要

两栖动物(青蛙和蟾蜍)是全球受威胁程度最高的分类群之一。成功保护两栖动物将依赖于改善有关当地种群现状和变化的信息,特别是对于稀有和受威胁物种。自动化传感器,如声学记录器,通过大规模增加种群抽样工作的空间和时间尺度,有可能提供此类数据。分析此类数据集将需要强大且高效的工具,这些工具可以自动识别音频记录中物种的存在。与蝙蝠和鸟类一样,许多两栖物种会发出独特的叫声,可以被自主声学记录器捕捉到,是自动识别的绝佳候选者。然而,与鸟类和蝙蝠不同,用于两栖动物的有效自动声学识别工具尚未广泛应用。有效的两栖动物自动叫声识别方法必须能够应对现实世界野外数据的挑战,并且不应该需要大量标记的数据集。我们设计了一种基于声音周期性结构的叫声识别工具,即重复间隔生物声学识别工具(RIBBIT),用于对音频记录中的两栖动物叫声进行分类。我们将 RIBBIT 应用于实地录音,以研究北美的温带草原的草原蛙(Pseudacris maculata)和中美洲热带雨林中极度濒危的多变角蟾(Atelopus varius)。该工具可以准确识别草原蛙,即使它们在高度重叠的合唱中鸣叫,并且在一个很少在视觉调查中遇到的实地地点识别出多变角蟾的叫声。使用几个简单的参数,RIBBIT 可以检测任何具有周期性结构的叫声,包括许多两栖动物、昆虫、鸟类和哺乳动物的叫声。我们以 Python 和 R 为基础提供了 RIBBIT 的开源实现,以支持其在其他分类群和群落中的使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3026/8518090/251d8875970e/COBI-35-1659-g003.jpg

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