Autonomous recording units (ARUs) provide a non‐invasive and efficient method for acoustic detection of elusive species across large temporal and spatial scales. However, species with indistinct vocalization structures can be a considerable challenge for automated signal recognizers. We investigated the performance of ARUs and signal recognizers in identifying the broadband, short‐syllable, pulsed calls of a secretive, threatened marsh bird, the King Rail Rallus elegans. Other sympatric species in the same habitat also have repetitive calls within the same frequency range that can be difficult to distinguish. Following serial ARU deployments at specified sites in known breeding habitat, we conducted standardized callback surveys and nest searches to provide an independent measure of breeder density. To analyse recordings, we developed a signal recognizer based on user‐input training files to detect two common call types, kek and grunt. Detections that remained following manual review of recognizer output revealed a previously undescribed seasonal decline and crepuscular diel pattern in calling rate. The rate of the grunt call also predicted density. These patterns emerged despite the recognizer's low precision and high false‐positive rate, which were largely due to misclassification of other species' calls, although ambient noise and effective detection radius also limited the detectability of King Rail calls. We demonstrate that with informed ARU scheduling, improved ability to manipulate user‐specified parameters within signal detection software, and attention to quality control, even the simplest call structures can be located consistently in a diverse acoustic landscape. Our behavioural findings will inform improvements to auditory surveys and to management of King Rails across their range.

中文翻译：

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自动检测罕见的，隐秘的沼泽鸟，发声不频繁且听不清

自主记录单元（ARU）提供了一种非侵入性的有效方法，可以在较大的时空范围内对可疑物种进行声学检测。但是，对于自动信号识别器而言，发声结构不清楚的物种可能是一个巨大的挑战。我们研究了ARU和信号识别器在识别秘密，受威胁的沼泽鸟King Rail Rallus elegans的宽带，短音节，脉冲呼叫中的性能。。同一栖息地中的其他同胞物种在相同的频率范围内也有重复的呼唤，这可能很难区分。在已知育种栖息地的指定地点进行连续ARU部署后，我们进行了标准化的回调调查和巢搜索，以提供独立的育种者密度度量。为了分析录音，我们基于用户输入的训练文件开发了一种信号识别器，以检测两种常见的呼叫类型kek和grunt。手动查看识别器输出后仍保留的检测结果表明，以前未描述的季节性下降和呼叫率的暗淡迪尔模式。该速率咕噜也称为预测密度。尽管识别器的精度低和假阳性率高，但还是出现了这些模式，这主要是由于其他物种的呼叫分类不正确，尽管环境噪声和有效检测半径也限制了King Rail呼叫的可检测性。我们证明，通过明智的ARU调度，提高的在信号检测软件中操作用户指定参数的能力以及对质量控制的关注，即使最简单的呼叫结构也可以始终位于不同的声学环境中。我们的行为调查结果将为改进听觉调查和整个范围内的King Rails管理提供信息。