| Title: |
Modeling Capelin Spawning Migration in the Barents Sea with Neural Networks and Environmental Data |
| Authors: |
Alrabeei, Salah; Rahman, Talal; Subbey, Sam |
| Publisher Information: |
Springer Science and Business Media LLC |
| Publication Year: |
2025 |
| Description: |
This paper presents a modeling framework for simulating capelin spawning migration in the Barents Sea. The framework is based on integrating artificial neural networks (ANNs) models, individual-based model (IBM), and environmental variables. The ANNs determine the direction of the fish's movement based on environmental variables such as temperature and ocean currents. The ANNs are trained by an evolutionary algorithm, whose fitness function is dynamically adapted based on the temperature and distance to the spawning route. The proposed model successfully reproduced the southeastward spawning in 2019, capturing the distribution of spawning capelin over the historical spawning sites along the eastern coast of northern Norway. The efficacy of the model is validated by comparing the spatial distributions of modeled and empirical data. Furthermore, the results show that the learning fitness function is crucial in developing such learning-based models. Additionally, three migration models based on three different movement mechanisms—passive swimmers, gradient detection, and restricted-area search—were compared with the proposed approach in terms of their effectiveness in replicating the spawning migration patterns of capelin. The results reveal that our approach outperforms the other models in mimicking the migration pattern. Most simulated spawning stocks managed to reach the spawning sites, unlike in the other models where water currents played a significant role in pushing the fish back from their migration direction towards the coast. The temperature gradient detection model and restricted-area search model were found to be inadequate for accurately simulating capelin spawning migration in the Barents Sea. |
| Document Type: |
other/unknown material |
| Language: |
unknown |
| DOI: |
10.21203/rs.3.rs-6153816/v1 |
| Availability: |
https://doi.org/10.21203/rs.3.rs-6153816/v1; https://www.researchsquare.com/article/rs-6153816/v1; https://www.researchsquare.com/article/rs-6153816/v1.html |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.A1D2FE8 |
| Database: |
BASE |