Passive acoustic monitoring (PAM) using Cetacean Porpoise Detectors (C-PODs) is a frequently applied method for studying the presence of harbor porpoises. In quiet environments, the KERNO classifier, an algorithm supplied by the manufacturer, can easily detect narrow-band high-frequency click trains emitted by echolocating harbor porpoises. However, precision is low in noisy habitats, as found in a monitoring data set (0.632; Ems, Elbe, and Wismar Bay, Germany, 2018–2023). We validated and labeled a subsample of 235 529 click trains (Elbe and Ems estuaries, 2023–2024) identified by the KERNO classifier and exported their physical characteristics to train a machine learning (ML) model. Extreme gradient boosting performed very well on the testing data (accuracy: 0.985) and the monitoring data set (0.849). The results show that the model could generalize well, beyond the training data. Moreover, this ML tool can reduce the risk of missing out low-precision estimates from random manual validation. The ML tool can complement the validation process, especially if intervals with only one click train are manually validated because false model predictions occurred particularly in these intervals. Hence, this validation tool may significantly improve the workflow in PAM studies using C-PODs, especially in noisy habitats.
Gauger et al. (Thu,) studied this question.