Humpback whale use of areas off eastern Canada is poorly understood, a knowledge gap that could impact future conservation efforts. We describe the acoustic occurrence of humpback whales in and around the Gully Marine Protected Area (MPA), an eastern Scotian Shelf submarine canyon. Near-continuous acoustic recordings sampling at 16 kHz were collected from the MPA and nearby slope areas from October 2012 to September 2014 using near-bottom recorders. In an offshore region where humpbacks were thought to be rare, we observed calls from October to June with a peak in song and nonsong calls in December and January. This suggests that some individuals occur in Canadian waters in winter and the Gully region may be a North Atlantic humpback whale migratory corridor. Calls were predominantly songs indicating potential mating activities. Song and nonsong calls occurred more at sunset and during hours of darkness than during daylight. This study improves our understanding of the seasonal occurrence of humpback whales on the Scotian Slope and, more specifically, their use of an offshore protected area.

1. Many marine industries may pose acute risks to marine wildlife. For example, tidal turbines have the potential to injure or kill marine mammals through collisions with turbine blades. However, the quantification of collision risk is currently limited by a lack of suitable technologies to collect long-term data on marine mammal behaviour around tidal turbines.2. Sonar provides a potential means of tracking marine mammals around tidal turbines. However, its effectiveness for long-term data collection is hindered by the large data volumes and the need for manual validation of detections. Therefore, the aim here was to develop and test automated classification algorithms for marine mammals in sonar data.3. Data on the movements of harbour seals were collected in a tidally energetic environment using a high-frequency multibeam sonar on a custom designed seabedmounted platform. The study area was monitored by observers to provide visual validation of seals and other targets detected by the sonar.4. Sixty-five confirmed seals and 96 other targets were detected by the sonar. Movement and shape parameters associated with each target were extracted and used to develop a series of classification algorithms. Kernel support vector machines were used to classify targets (seal vs. nonseal) and cross-validation analyses were carried out to quantify classifier efficiency.5. The best-fit kernel support vector machine correctly classified all the confirmed seals but misclassified a small percentage of non-seal targets (~8%) as seals. Shape and non-spectral movement parameters were considered to be the most important in achieving successful classification.6. Results indicate that sonar is an effective method for detecting and tracking seals in tidal environments, and the automated classification approach developed here provides a key tool that could be applied to collecting long-term behavioural data around anthropogenic activities such as tidal turbines.

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