  Narwhal is one of the most mysterious marine mammals, due to its isolated
habitat in the Arctic region. Tagging is a technology that has the potential to
explore the activities of this species, where behavioral information can be
collected from instrumented individuals. This includes accelerometer data,
diving and acoustic data as well as GPS positioning. An essential element in
understanding the ecological role of toothed whales is to characterize their
feeding behavior and estimate the amount of food consumption. Buzzes are sounds
emitted by toothed whales that are related directly to the foraging behaviors.
It is therefore of interest to measure or estimate the rate of buzzing to
estimate prey intake. The main goal of this paper is to find a way to detect
prey capture attempts directly from accelerometer data, and thus be able to
estimate food consumption without the need for the more demanding acoustic
data. We develop 3 automated buzz detection methods based on accelerometer and
depth data solely. We use a dataset from 5 narwhals instrumented in East
Greenland in 2018 to train, validate and test a logistic regression model and
the machine learning algorithms random forest and deep learning, using the
buzzes detected from acoustic data as the ground truth. The deep learning
algorithm performed best among the tested methods. We conclude that reliable
buzz detectors can be derived from high-frequency-sampling, back-mounted
accelerometer tags, thus providing an alternative tool for studies of foraging
ecology of marine mammals in their natural environments. We also compare buzz
detection with certain movement patterns, such as sudden changes in
acceleration (jerks), found in other marine mammal species for estimating prey
capture. We find that narwhals do not seem to make big jerks when foraging and
conclude that their hunting patterns in that respect differ from other marine
mammals.
