Dolphin and porpoise detections by the F-POD are not independent: Implications for sympatric species monitoring.

The F-POD is designed for passive acoustic monitoring of odontocetes. The offline classifiers can identify and separate porpoise-like sounds from dolphin-like sounds. We show that these two classifiers are not working independently. Run together, virtually no detections of both species were reported within the same minute, whereas 10% of the detection positive minutes were reported positive for both species when the two classifiers were run sequentially. This has important implications for interpretation of data in areas containing both species groups, and we call for reporting all analysis details in such studies and for further description and analysis of the classifiers.

Rerouting of a major shipping lane through important harbour porpoise habitat caused no detectable change in annual occurrence or foraging patterns.

Shipping is one of the largest industries globally, with well-known negative impacts on the marine environment. Despite the known negative short-term (minutes to hours) impact of shipping on individual animal behavioural responses, very little is understood about the long-term (months to years) impact on marine species presence and area use. This study took advantage of a planned rerouting of a major shipping lane leading into the Baltic Sea, to investigate the impact on the presence and foraging behaviour of a marine species known to be sensitive to underwater noise, the harbour porpoise (Phocoena phocoena). Passive acoustic monitoring data were collected from 15 stations over two years. Against predictions, no clear change occurred in monthly presence or foraging behaviour of the porpoises, despite the observed changes in noise and vessel traffic. However, long-term heightened noise levels may still impact communication, echolocation, or stress levels of individuals, and needs further investigation.

Porpoise click classifier (PorCC): A high-accuracy classifier to study harbour porpoises (Phocoena phocoena) in the wild.

Harbour porpoises are well-suited for passive acoustic monitoring (PAM) as they produce highly stereotyped narrow-band high-frequency (NBHF) echolocation clicks. PAM systems must be coupled with a classification algorithm to identify the signals of interest. Here, the authors present a harbour porpoise click classifier (PorCC) developed in matlab, which uses the coefficients of two logistic regression models in a decision-making pathway to assign candidate signals to one of three categories: high-quality clicks (HQ), low-quality clicks (LQ), or high-frequency noise. The receiver operating characteristics of PorCC was compared to that of PAMGuard's Porpoise Click Detector/Classifier Module. PorCC outperformed PAMGuard's classifier achieving higher hit rates (correctly classified clicks) and lower false alarm levels (noise classified as HQ or LQ clicks). Additionally, the detectability index (d') for HQ clicks for PAMGuard was 2.2 (overall d' = 2.0) versus 4.1 for PorCC (overall d' = 3.4). PorCC classification algorithm is a rapid and highly accurate method to classify NBHF clicks, which could be applied for real time monitoring, as well as to study harbour porpoises, and potentially other NBHF species, throughout their distribution range from data collected using towed hydrophones or static recorders. Moreover, PorCC is suitable for studies of acoustic communication of porpoises.