Passive acoustic telemetry data are used to study animal movement in aquatic environments, but tools to process the data are limited. In areas that are too large to be fully covered by the limited detection ranges of receivers or acoustic listening stations, researchers generally assume that animals are residing in an area when they are detected frequently at specific receivers. There is, however, no consensus on how this area and frequency should be spatially and temporally defined respectively, thereby introducing some unaccounted uncertainty of this residency-at-receivers. In longitudinal aquatic systems such as rivers or estuaries, strategically placed receivers are often used as gates or curtains through which tagged animals have to pass. Rather than being a proxy for the time spent near receivers, the detections can serve as boundary conditions to delineate the durations that animals spent between receivers (i.e. residency-between-receivers). As such, providing a spatial and temporal context to the detections and enabling the quantification of epistemic uncertainty. To assess the usefulness of this approach, we analyzed telemetry data for migrating eel in a longitudinal estuarine acoustic tracking network of 21 gates. Results revealed a logarithmic relationship between epistemic uncertainty and gate network resolution, which indicates that transferring information from the spatial to the temporal level has a positive effect on the epistemic uncertainty. The poor correlation between the at-receivers and the between-receivers approach indicates that the latter, although less precise, may be more accurate. It should be noted that the suggested approach assumes that the gates of receivers are perfect detectors. If tagged animals are able to pass these gates undetected the uncertainty will actually be higher than assumed. Our approach is therefore less suited for large open areas such as lagoons or seas where gates with high detection probabilities are logistically challenging. This approach allows the quantification and reduction of epistemic uncertainty by providing a spatiotemporal context to the detections. Since establishing and maintaining passive telemetry networks is generally expensive and the results these networks generate are often used in decision making, an assessment of the network quality and data uncertainty is vital.

无源声遥测数据用于研究水生环境中的动物运动，但是处理数据的工具有限。在太大而无法被接收器或声音监听站的有限检测范围完全覆盖的区域中，研究人员通常会假设在特定接收器上频繁发现动物时，它们就位于该区域。但是，关于如何分别在空间和时间上定义该面积和频率尚无共识，从而给接收者居住带来一些不确定的不确定性。在河流或河口之类的纵向水生系统中，策略性放置的接收器通常用作带有标签的动物必须经过的门或窗帘。与其代替接收者附近花费的时间，这些检测可以作为边界条件来描述动物在接收者之间（即，接收者之间的驻留时间）所花费的时间。这样，为检测提供了空间和时间背景，并能够量化认知不确定性。为了评估这种方法的有效性，我们分析了遥测数据，用于在21个闸口的纵向河口声波跟踪网络中迁移鳗鱼。结果揭示了认知不确定性与门网络分辨率之间的对数关系，这表明将信息从空间水平转移到时间水平对认知不确定性具有积极影响。接收者之间的方法与接收者之间的方法之间的相关性较差，表明后者虽然不那么精确，但可能更为准确。应该注意的是，建议的方法假设接收器的门是完美的检测器。如果贴有标签的动物能够通过这些门而未被发现，那么不确定性实际上将高于假设。因此，我们的方法不太适合大型露天区域，例如泻湖或海洋，在这种区域中具有高检测概率的闸门在后勤方面具有挑战性。通过为检测提供时空背景，该方法可以量化和减少认知不确定性。由于建立和维护无源遥测网络通常很昂贵，而且这些网络产生的结果通常用于决策，因此评估网络质量和数据不确定性至关重要。如果贴有标签的动物能够通过这些门而未被发现，则不确定性实际上会高于假设。因此，我们的方法不太适合大型露天区域，例如泻湖或海洋，在这种区域中具有高检测概率的闸门在后勤方面具有挑战性。通过为检测提供时空背景，该方法可以量化和减少认知不确定性。由于建立和维护无源遥测网络通常很昂贵，并且这些网络产生的结果通常用于决策，因此评估网络质量和数据不确定性至关重要。如果贴有标签的动物能够通过这些门而未被发现，则不确定性实际上会高于假设。因此，我们的方法不太适合大型露天区域，例如泻湖或海洋，在这种区域中具有高检测概率的闸门在后勤方面具有挑战性。通过为检测提供时空背景，该方法可以量化和减少认知不确定性。由于建立和维护无源遥测网络通常很昂贵，并且这些网络产生的结果通常用于决策，因此评估网络质量和数据不确定性至关重要。通过为检测提供时空背景，该方法可以量化和减少认知不确定性。由于建立和维护无源遥测网络通常很昂贵，并且这些网络产生的结果通常用于决策，因此评估网络质量和数据不确定性至关重要。通过为检测提供时空背景，该方法可以量化和减少认知不确定性。由于建立和维护无源遥测网络通常很昂贵，并且这些网络产生的结果通常用于决策，因此评估网络质量和数据不确定性至关重要。