Abstract
Norwegian spring spawning herring is a migratory pelagic fish stock that seasonally navigates between distant locations in the Norwegian Sea. The spawning migration takes place between late winter and early spring. In this article, we present an individual-based model that simulated the spawning migration, which was tuned and validated against observation data. Individuals were modelled on a continuous grid coupled to a physical oceanographic model. We explore the development of individual model states in relation to local environmental conditions and predict the distribution and abundance of individuals in the Norwegian Sea for selected years (2015–2020). Individuals moved position mainly according to the prevailing coastal current. A tuning procedure was used to minimize the deviations between model and survey estimates at specific time stamps. Furthermore, 4 separate scenarios were simulated to ascertain the sensitivity of the model to initial conditions. Subsequently, one scenario was evaluated and compared with catch data in 5 day periods within the model time frame. Agreement between model and catch data varies throughout the season and between years. Regardless, emergent properties of the migration are identifiable that match observations, particularly migration trajectories that run perpendicular to deep bathymetry and counter the prevailing current. The model developed is efficient to implement and can be extended to generate multiple realizations of the migration path. This model, in combination with various sources of fisheries-dependent data, can be applied to improve real-time estimates of fish distributions.
Highlights
Incomplete knowledge or inadequate access to time-sensitive spatial distributions can result in inefficient harvesting of fish stocks
This paper describes the development of an Individual-based models (IBMs) of the Norwegian spring spawning herring (NSSH) spawning migration, centred on an individuals response to environmental forcing
The IBM developed in this paper modelled individuals in a 2D environment where position was updated on a continuous horizontal plane in a Lagrangian approach (Figure 1)
Summary
Incomplete knowledge or inadequate access to time-sensitive spatial distributions can result in inefficient harvesting of fish stocks. This is especially true of migratory species that migrate vast distances for periods of their life cycle. Acoustic technology makes use of a multi-beam system that can resolve multiple targets at once (Chu, 2011). Such data is an untapped resource for understanding the development of stocks throughout a fishing season, as it provides good coverage of stocks in real-time, all-year round (Pennino et al, 2016).
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