Introduction
Analyzing telemetry data and translating it into meaningful indicators of fish welfare remains a challenge , as it is necessary to distinguish between typical and atypical behavior .
Entropy approaches can be used to analyze telemetry data and detect changes in fish behavior , providing valuable insights into fish welfare . Telemetry is an important tool for studying fish behavior and allows for the real-time monitoring of fish movements . However , analyzing telemetry data and translating it into meaningful indicators of fish welfare is a challenge . Entropy-based methods , which use information theory to quantify the complexity and unpredictability of animal behavior , provide a more comprehensive understanding of the animal state .
By analyzing data probability density function with entropy approaches , it is possible to identify atypical behavior that may indicate compromised welfare . These methods can detect irregularities in fish behavior and provide insight into the animal’s state.
Results and Discusion
Typical behavior is not a single type of distribution , but rather a set of distributions . Entropy analysis is an effective method for identifying atypical behavior in fish welfare assessment , as it provides a more robust evaluation of telemetry datasets than classical statistical analysis . Entropy analysis allows for continuous monitoring of behavior and can identify when fish start behaving atypically . It can also determine which fish and which values are atypical or typical . By analyzing the variability of feeding behavior , social interactions , and behavior in response to different environmental conditions or stressors , entropy analysis can provide insights into the complexity and variability of fish behavior and promote more effective management practices . Entropy approaches can help to improve telemetry data analysis and provide objective indicators of fish welfare for management and regulatory purposes.
Acknowledgment
The study 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
Barta , A., Soucek , P., Bozhynov , V., Urbanova, P., Bekkozhayeova , D.: Trends in online biomonitoring. In: Bioinformatics and Biomedical Engineering: 6th International Work-Conference , IWBBIO 2018, Granada, Spain, 2018, Proceedings , Part I 6, Springer (2018) 3/14
Føre , M., Svendsen , E., Alfredsen , J.A. , Uglem , I., Bloecher , N., Sveier, H., Sunde, L.M., Frank, K.: Using acoustic telemetry to monitor the e_ects of crowding and delousing procedures on farmed atlantic salmon (salmo salar ). Aquaculture 495 (2018) 757-765
Urban, J.: Information entropy. Applications from Engineering with MATLAB
Concepts (2016) 43