Natural resource managers use barriers to deter the movement of aquatic invasive species. Research and development of new invasive species barriers is often evaluated in pond and field scales using high‐resolution telemetry data. Telemetry data sets can be a rich source of data about fish movement and behavior but can be difficult to analyze due to the size of these data sets as well as their irregular sampling intervals. Due to the challenges, most barrier studies only use summary endpoints, such as barrier passage counts or average (e.g., mean or median) fish distance from the barrier, to describe the data. To examine more fine‐scale fish movement patterns, we developed a first‐order Markov chain. We then used this model to help understand the impacts of a barrier on fish behavior. For our study system, we used data from a previous study examining how bighead and silver carp (two invasive fish species in the United States) responded to a carbon dioxide (CO₂) barrier in a pond.

中文翻译：

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使用马尔可夫链定量评估在室外池塘中受二氧化碳屏障影响的侵入性鱼类的运动方式

自然资源管理者利用障碍来阻止水生入侵物种的移动。通常使用高分辨率遥测数据在池塘和田间尺度上评估新的入侵物种壁垒的研究和开发。遥测数据集可能是有关鱼类运动和行为的丰富数据来源，但由于这些数据集的大小及其不规则的采样间隔而可能难以分析。由于挑战，大多数屏障研究仅使用汇总终点（例如屏障通过计数或鱼类与屏障的平均距离（例如，平均值或中位数））来描述数据。为了研究更精细的鱼类运动模式，我们开发了一阶马尔可夫链。然后，我们使用此模型来帮助了解障碍对鱼类行为的影响。对于我们的学习系统₂）在池塘的屏障。