MyFishCheck: Assessing Fish Welfare In Aquaculture

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Despite a growing body of evidence that shows fishes can feel pain and experience emotions, fish welfare is often overlooked. Trillions of fishes are caught every year, and they often endure miserable lives before dying from stress and injury as part of a multi-billion-dollar industry. However, unlike farmed animals on land, fish welfare has been largely overlooked. 

Previous attempts have been made to measure fish welfare in aquaculture. However, these welfare trackers are often either species-specific, difficult to standardize, or challenging to apply on farms. Other measurement systems don’t include enough welfare indicators. To remedy the situation, the authors of this study have developed a software application that allows users to assess fish welfare in a standardized, comprehensive, and efficient way.

The model considers welfare conditions on farms to ensure that the animals can live in an environment where they can express their natural behavior (natural-based), while their physiological systems can work well (function-based), and that they are shielded from negative experiences and emotions such as pain or fear (feelings-based). In all, the authors identified fourteen welfare needs as part of this framework with over 200 detailed welfare parameters.  

After listing all potential welfare parameters, the researchers narrowed the list down to 80 parameters that were relevant, reliable, and easy to assess in different settings. They grouped these parameters into five dimensions of fish welfare:

1. Water quality
2. Farm management
3. Fish group behavior
4. Fish external appearance
5. Fish internal appearance

From there, using literature reviews and expert surveys, the authors were able to quantify and weight the parameters to develop a comprehensive model with a corresponding welfare score between 0 and 1. 

While a score between 0 and 0.25 indicates that fish welfare has been severely compromised, a score between 0.75 and 1 suggests that fishes are likely to experience good welfare under the given circumstances. The authors tested the model on six different farms with different fish species and aquaculture systems and found that the tool behaved as predicted — it showed a lower score when conditions on farms had negative effects on fish welfare, such as poor water quality or visible problems with the fishes’ gills.

The authors envision the application being customized by fish farmers to be used as a tool for quality control that also improves fish welfare standards. The model is a further step toward quantifying fish welfare in measurable terms that can be used in different settings, such as farms and scientific research locations. However, as the model relies on past models and available data from scientific literature, a thorough scientific verification of the model in its current form cannot be carried out. Furthermore, as in any such model, its robustness also relies heavily on the accuracy of the data that is entered by the user, which in turn can skew results. 

Applications such as this attempt to shift aquaculture away from treating fishes as commodities and instead as animals worthy of humane consideration. However, using this application to improve fish welfare in aquaculture can only happen if stakeholders commit to using and improving it. By promoting the use of such applications, activists can urge the aquaculture industry and government organizations to take fish welfare more seriously. This may also encourage more transparency in the aquaculture industry as advocates continue to raise awareness of the cruelties of eating fishes.