Geolocation of North Sea cod (Gadus morhua) using Hidden Markov Models and behavioural switching

When geolocating fish based on archival tag data, a realistic assessment of uncertainty is essential. Here, we describe an application of a novel Fokker–Planck-based method to geolocate Atlantic cod (Gadus morhua) in the North Sea area. In this study, the geolocation relies mainly on matching tidal patterns in depth measurements when a fish spends a prolonged period of time at the seabed with a tidal database.

Each day, the method provides a nonparametric probability distribution of the position of a tagged fish and therefore avoids enforcing a particular distribution, such as a Gaussian distribution. In addition to the tidal component of the geolocation, the model incoporates two behavioural states, either high or low activity, estimated directly from the depth data, that affect the diffusivity parameter of the model and improves the precision and realism of the geolocation significantly.

The new method provides access to the probability distribution of the position of the fish that in turn provides a range of useful descriptive statistics, such as the path of the most probable movement. We compare the method with existing alternatives and discuss its potential in making population inference from archival tag data.