Aggregated time‐series features boost species‐specific differentiation of true and false positives in passive acoustic monitoring of bird assemblages,Remote Sensing in Ecology and Conservation

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Aggregated time‐series features boost species‐specific differentiation of true and false positives in passive acoustic monitoring of bird assemblages
Remote Sensing in Ecology and Conservation ( IF 3.9 ) Pub Date : 2024-02-25 , DOI: 10.1002/rse2.385
David Singer ₁ , Jonas Hagge _{1,

2} , Johannes Kamp ₃ , Hermann Hondong ₃ , Andreas Schuldt ₁

Affiliation

Passive acoustic monitoring (PAM) has gained increasing popularity to study behaviour, habitat preferences, distribution and community assembly of birds and other animals. Automated species classification algorithms like ‘BirdNET’ are capable of detecting and classifying avian vocalizations within extensive audio data, covering entire species assemblages. PAM reveals substantial potential for biodiversity monitoring that informs evidence‐based conservation. Nevertheless, fully realizing this potential remains challenging, especially due to the issue of false‐positive species detections. Here, we introduce an optimized thresholding framework, which incorporates contextual information extracted from the time‐series of automated species detections (i.e. covariates on quality and quantity of species' detections measured at varying time intervals) to improve the differentiation of true and false positives. We verified a sample of BirdNET detections per species and modelled species‐specific thresholds using conditional inference trees. These thresholds were designed to minimize false‐positive detections while maximizing the preservation of true positives in the dataset. We tested this framework for a large dataset of BirdNET detections (5760 h of audio data, 60 sites) recorded over an entire breeding season. Our results revealed considerable interspecific variability of precision (percentage of true positives) within raw BirdNET data. Our optimized thresholding approach achieved high precision (≥0.9) for 70% of the 61 detected species, while species‐specific thresholds solely relying on the BirdNET confidence scores achieved high precision for only 31% of the species. Conservative universal thresholds (not species‐specific) reached high precision for 48% of the species. Our thresholding approach outperformed previous thresholding approaches and enhanced interspecific comparability for bird community analyses. By incorporating contextual information from the time‐series of species detections, the differentiation of true and false positives was substantially improved. Our approach may enhance a straightforward application of PAM in biodiversity research, landscape planning and evidence‐based conservation.

中文翻译：

聚合的时间序列特征促进了鸟类组合被动声学监测中真假阳性的物种特异性区分

被动声学监测 (PAM) 在研究鸟类和其他动物的行为、栖息地偏好、分布和群落聚集方面越来越受欢迎。像“BirdNET”这样的自动物种分类算法能够在覆盖整个物种组合的广泛音频数据中检测和分类鸟类发声。PAM 揭示了生物多样性监测的巨大潜力，可为循证保护提供信息。然而，充分实现这一潜力仍然具有挑战性，特别是由于假阳性物种检测的问题。在这里，我们引入了一个优化的阈值框架，它结合了从自动物种检测的时间序列中提取的上下文信息（即在不同时间间隔测量的物种检测的质量和数量的协变量），以提高真阳性和假阳性的区分。我们验证了每个物种的 BirdNET 检测样本，并使用条件推理树对特定物种的阈值进行了建模。这些阈值旨在最大限度地减少假阳性检测，同时最大限度地保留数据集中的真阳性。我们针对整个繁殖季节记录的 BirdNET 检测大型数据集（5760 小时的音频数据，60 个站点）测试了该框架。我们的结果揭示了原始 BirdNET 数据中精确度（真阳性百分比）的显着种间差异。我们的优化阈值方法对 61 个检测到的物种中的 70% 实现了高精度（≥0.9），而仅依赖 BirdNET 置信度得分的物种特定阈值仅对 31% 的物种实现了高精度。保守的通用阈值（非物种特异性）对 48% 的物种达到了高精度。我们的阈值方法优于以前的阈值方法，并增强了鸟类群落分析的种间可比性。通过结合物种检测时间序列的上下文信息，真假阳性的区分得到了显着改善。我们的方法可以增强 PAM 在生物多样性研究、景观规划和循证保护中的直接应用。

更新日期：2024-02-25

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