Tracking capelin spawning migration -- Integrating environmental data and Individual-based modeling (2311.00424v1)

Abstract: This paper presents a modeling framework for tracking the spawning migration of the capelin, which is a fish species in the Barents Sea. The framework combines an individual-based model (IBM) with artificial neural networks (ANNs). The ANNs determine the direction of the fish's movement based on local environmental information, while a genetic algorithm and fitness function assess the suitability of the proposed directions. The framework's efficacy is demonstrated by comparing the spatial distributions of modeled and empirical potential spawners. The proposed model successfully replicates the southeastward movement of capelin during their spawning migration, accurately capturing the distribution of spawning fish over historical spawning sites along the eastern coast of northern Norway. Furthermore, the paper compares three migration models: passive swimmers, taxis movement based on temperature gradients, and restricted-area search, along with our proposed approach. The results reveal that our approach outperforms the other models in mimicking the migration pattern. Most spawning stocks managed to reach the spawning sites, unlike the other models where water currents played a significant role in pushing the fish away from the coast. The temperature gradient detection model and restricted-area search model are found to be inadequate for accurately simulating capelin spawning migration in the Barents Sea due to complex oceanographic conditions.