Department of Biology, Dalhousie University, 1355 Oxford Street, Halifax, NS, B4H 4R2, Canada.
Laboratory for Freshwater Ecology and Inland Fisheries, NORCE Norwegian Research Centre, Bergen, Norway.
Oecologia. 2022 Mar;198(3):605-618. doi: 10.1007/s00442-022-05138-3. Epub 2022 Mar 4.
Mortality and predation of tagged fishes present a serious challenge to interpreting results of acoustic telemetry studies. There is a need for standardized methods to identify predated individuals and reduce the impacts of "predation bias" on results and conclusions. Here, we use emerging approaches in machine learning and acoustic tag technology to classify out-migrating Atlantic salmon (Salmo salar) smolts into different fate categories. We compared three methods of fate classification: predation tag pH sensors and detection data, unsupervised k-means clustering, and supervised random forest combined with tag pH sensor data. Random forest models increased predation estimates by 9-32% compared to relying solely on pH sensor data, while clustering reduced estimates by 3.5-30%. The greatest changes in fate class estimates were seen in years with large class imbalance (one or more fate classes underrepresented compared to the others) or low model accuracy. Both supervised and unsupervised approaches were able to classify smolt fate; however, in-sample model accuracy improved when using tag sensor data to train models, emphasizing the value of incorporating such sensors when studying small fish. Sensor data may not be sufficient to identify predation in isolation due to Type I and Type II error in predation sensor triggering. Combining sensor data with machine learning approaches should be standard practice to more accurately classify fate of tagged fish.
标记鱼类的死亡率和被捕食率对解释声学遥测研究结果提出了严峻挑战。需要标准化的方法来识别被捕食的个体，并减少“捕食偏差”对结果和结论的影响。在这里，我们使用机器学习和声学标签技术的新兴方法，将洄游的大西洋鲑（Salmo salar）幼鱼分为不同的命运类别。我们比较了三种命运分类方法：捕食标签 pH 传感器和检测数据、无监督 k-均值聚类和结合标签 pH 传感器数据的监督随机森林。与仅依赖 pH 传感器数据相比，随机森林模型将捕食估计值提高了 9-32%，而聚类则将估计值降低了 3.5-30%。在类不平衡较大（与其他类相比，一个或多个类代表性不足）或模型精度较低的年份，命运类别估计值的变化最大。监督和无监督方法都能够对幼鱼的命运进行分类；然而，当使用标签传感器数据来训练模型时，样本内模型准确性会提高，这强调了在研究小鱼时结合使用此类传感器的重要性。由于捕食传感器触发中的 Type I 和 Type II 错误，传感器数据可能不足以单独识别捕食。将传感器数据与机器学习方法相结合应该成为标准做法，以更准确地分类标记鱼类的命运。
