Introduction
Fish welfare is a central concern in aquaculture, particularly regarding the physical well-being of fish and their behavioral patterns. This study explores fish telemetry data to analyze behavior and transition dynamics. The telemetry data, collected using various tags and transmitters, provides insights into fish movement, feeding habits, and habitat use. Through statistical analysis and modeling, this study aims to understand the underlying behavior patterns and their implications for fish welfare.
Dataset and Methods
The dataset comprises time-series data on fish acceleration levels, covering a period of 148 days with measurements taken approximately every 2 minutes. Analysis of the telemetry data involves constructing a stochastic causal system, with states defined by acceleration levels and transitions governed by conditional probabilities. Markov modeling is employed to analyze state transitions and their probabilities, offering a framework for understanding fish behavior dynamics.
Visualization techniques, including state trajectories and transition matrices, provide insights into the patterns and trends in fish behavior. The Perron-Frobenius theorem is applied to analyze the stability and convergence properties of the system. By evaluating the eigenvalues of the transition matrix, the long-term behavior and stability of the system are assessed. The steady-state distribution of the system is determined, offering insights into the long-term probabilities of being in each state.
Results and Discussion
The analysis reveals notable patterns in fish behavior, including frequent transitions within certain acceleration ranges and stability in others. The transition matrix highlights the conditional probabilities of transitioning between states, with certain states exhibiting higher probabilities of transition. Visualization of the transition dynamics through cobweb diagrams aids in understanding the causal relationships between states.
Overall, by understanding the behavior dynamics of fish populations, more informed decisions can be made to promote their well-being in captive environments.
Acknowledgment
The study was _supported by the Ministry of Education, Youth and Sports of the Czech Republic - project CENAKVA (LM2018099), the CENAKVA Centre Development (No:CZ:1:05/2:1:00/19: 0380) This project also received funding from the European Union’ s Horizon 2020 research and innovation programme under grant agreement N° 871108 (AQUAEXCEL3.0). This output reflects only the author’s view and the European Union cannot be held responsible for any use that may be made of the information contained therein
References
Urban, J. , Lastovka, D. 2024 . Fish acoustic telemetry as causal stochastic system and Perron-Frobenius analysis of its Markov model. In International Work-Conference on Bioinformatics and Biomedical Engineering. Springer, Cham.