通过新型记录器技术和自动物种识别推进鸟类调查工作

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Matthew Toenies, L. Rich
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引用次数: 7

摘要

声学记录技术和自动物种识别的最新进展为鸟类监测工作带来了巨大的希望。评估这些创新与现有的记录模型和传统的物种识别技术相比如何,对于了解它们对研究人员和管理人员的效用至关重要。作者在美国加利福尼亚州蒙特雷县进行了实地试验,比较了四种声学记录器模型(AudioMoth、Swift recorder和野生动物声学SM3BAT和SM Mini)对鸟类的检测和并发点计数,并评估了人工神经网络BirdNET从AudioMoth记录中正确识别鸟类种类的能力。我们发现,成本最低的单元(AudioMoth)的表现与成本较高的单元相当,并且平均而言,五种记录器模型中的三种(范围9.8至14.0)的物种检测高于点数计数(12.8)。在我们对BirdNET的评估中,我们开发了一个子集过程,使我们能够实现高的正确识别物种率(96%)。使用单个记录器模型的较长时间记录,BirdNET在5天的时间内平均每次记录确定了8.5个经过验证的物种,平均每个地点确定了16.4个经过验证的物种(比在类似栖息地进行的点计数多)。我们证明,结合低成本记录仪的长记录和BirdNET的子集自动识别的保守方法,提供了一种低误认率和有限的人工审查的鸟类群落组成采样过程。这些低成本的自动化工具可以极大地改善鸟类群落及其生态系统的调查工作,从而保护受威胁的本土生物多样性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing bird survey efforts through novel recorder technology and automated species identification
Recent advances in acoustic recorder technology and automated species identification hold great promise for avian monitoring efforts. Assessing how these innovations compare to existing recorder models and traditional species identification techniques is vital to understanding their utility to researchers and managers. We carried out field trials in Monterey County, California, to compare bird detection among four acoustic recorder models (AudioMoth, Swift Recorder, and Wildlife Acoustics SM3BAT and SM Mini) and concurrent point counts, and to assess the ability of the artificial neural network BirdNET to correctly identify bird species from AudioMoth recordings. We found that the lowest-cost unit (AudioMoth) performed comparably to higher-cost units and that on average, species detections were higher for three of the five recorder models (range 9.8 to 14.0) than for point counts (12.8). In our assessment of BirdNET, we developed a subsetting process that enabled us to achieve a high rate of correctly identified species (96%). Using longer recordings from a single recorder model, BirdNET identified a mean of 8.5 verified species per recording and a mean of 16.4 verified species per location over a 5-day period (more than point counts conducted in similar habitats). We demonstrate that a combination of long recordings from low-cost recorders and a conservative method for subsetting automated identifications from BirdNET presents a process for sampling avian community composition with low misidentification rates and limited need for human vetting. These low-cost and automated tools may greatly improve efforts to survey bird communities and their ecosystems, and consequently, efforts to conserve threatened indigenous biodiversity.
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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