Bias introduced by detection errors is a well-documented issue for abundance and occupancy estimates of wildlife. Detection errors bias estimates of detection and abundance or occupancy in positive and negative directions, which can produce misleading results. There have been considerable design- and model-based methods to address false-negative errors, or missed detections. However, false-positive errors, or detections of individuals that are absent but counted as present because of misidentifications or double counts, are often assumed to not occur in ecological studies. The dependent double-observer survey method is a design-based approach speculated to reduce false positives because observations have the ability to be confirmed by two observers. However, whether this method reduces false positives compared to single-observer methods has not been empirically tested. We used prairie songbirds as a model system to test if a dependent double-observer method reduced false positives compared to a single-observer method. We used vocalizations of ten species to create auditory simulations and used naive and expert observers to survey these simulations using single-observer and dependent double-observer methods. False-positive rates were significantly lower using the dependent double-observer survey method in both observer groups. Expert observers reported a 3.2% false-positive rate in dependent double-observer surveys and a 9.5% false-positive rate in single-observer surveys, while naive observers reported a 39.1% false-positive rate in dependent double-observer surveys and a 49.1% false-positive rate in single-observer surveys. Misidentification errors arose in all survey scenarios and almost all species combinations. However, expert observers using the dependent double-observer method performed significantly better than other survey scenarios. Given the use of double-observer methods and the accumulating evidence that false positives occur in many survey methods across different taxa, this study is an important step forward in acknowledging and addressing false positives.

中文翻译：

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相依双重观察者方法减少了听觉鸟类调查数据中的假阳性错误。

检测错误引入的偏差是野生动植物丰度和占用率估算的有据可查的问题。检测错误会在正向和负向上偏向于检测和丰度或占有率的估计，这可能会产生误导性的结果。已经有相当多的基于设计和模型的方法来解决假阴性错误或错过的检测。但是，在生态学研究中通常认为不会出现假阳性错误或由于错误识别或重复计数而检测到的缺失但被视为存在的个体。相依的双重观察者调查方法是一种基于设计的方法，被认为可以减少误报，因为观察结果能够被两名观察员确认。然而，与单观察者方法相比，该方法是否减少误报尚未经过经验检验。我们使用草原鸣禽作为模型系统，以测试相依双观察者方法与单观察者方法相比是否减少了误报。我们使用十种物种的发声来创建听觉模拟，并使用幼稚和专业的观察者使用单观察者和从属双观察者方法来调查这些模拟。在两个观察者组中，使用相关的双重观察者调查方法，假阳性率显着降低。专家观察员报告说，从属双重观察者调查中的假阳性率为3.2％，在单观察者调查中为9.5％，而幼稚的观察者报告在从属双重观察者调查中为39.1％的假阳性率和49 。在单观察者调查中，假阳性率为1％。在所有调查方案和几乎所有物种组合中都出现了误识别错误。但是，使用从属双观察员方法的专家观察员的表现明显优于其他调查方案。鉴于使用了双重观察者方法，并且越来越多的证据表明在不同分类单元中的许多调查方法中会出现误报，因此，这项研究是在承认和解决误报方面迈出的重要一步。