Premises for digital twins reporting on Atlantic salmon wellbeing

Area of Science:

• Animal Welfare Science
• Aquaculture Technology
• Computational Biology

Background:

• Current animal welfare principles often overlook the cognitive and affective states of sentient species like fish.
• Wellbeing issues in captive Atlantic salmon lead to significant production losses, including slow growth and high mortality.
• Existing welfare assessments lack a comprehensive understanding of the cognitive-affective component crucial for vertebrate decision-making.

Purpose of the Study:

• To propose a novel approach for monitoring and enhancing the welfare of captive vertebrates, specifically Atlantic salmon.
• To introduce the concept of using digital twins for simulating and predicting animal wellbeing.
• To identify key components for developing agent-based digital twin models for salmon welfare.

Main Methods:

• Developing agent-based digital twin models simulating salmon's bodily mechanisms and subjective wellbeing.
• Incorporating computational evolutionary models to predict factors influencing behavior, stress, and wellbeing.
• Modeling salmon's near-future expectations and allostasis (bodily preparation for future demands).

Main Results:

• Digital twin models can capture predictions related to boredom, stress, and overall wellbeing in salmon.
• These models can identify building blocks for early warning systems regarding escalating welfare issues in aquaculture.
• The approach offers insights for optimizing production and reducing reliance on animal experiments.

Conclusions:

• Digital twins provide a powerful tool for understanding and improving the welfare of captive Atlantic salmon and other vertebrates.
• This technology can support the implementation of the 3Rs (replacement, reduction, refinement) in animal research and welfare.
• Actionable information from digital twin populations can guide fish farmers, regulators, and consumers towards better welfare practices.