Looking beyond the Shoal: Fish Welfare as an Individual Attribute

Fish Welfare-Related Issues and Their Relevance in Land-Based Olive Flounder (Paralichthys olivaceus) Farms in Korea

Korean aquaculture has expanded considerably in recent decades; however, this growth has often prioritized quantity over fish welfare. Therefore, we analyzed the aquaculture practices of olive flounder, the predominant species in Korean consumption and production, within the framework of fish welfare. We conducted extensive interviews and surveys across olive flounder farms in Jeju-do and Wando to examine prevalent issues impacting fish welfare in aquaculture. These issues include stressors, mass mortality events, and disease outbreaks, all of which strain the welfare of farmed fish. Moreover, our survey revealed farmers' varying perceptions of fish welfare, highlighting the necessity for a cohesive approach. Accordingly, we propose recommendations to enhance fish welfare and establish a more sustainable aquaculture model in Korea. Ensuring fish welfare in aquaculture operations requires a comprehensive approach that considers the physiological and behavioral needs of fish throughout the farming lifecycle. By prioritizing fish welfare, Korean aquaculture can strengthen its growth while maintaining ethical standards and ensuring the well-being of farmed fish. This welfare-centric approach is crucial for the long-term sustainability and resilience of the Korean aquaculture industry. By addressing welfare concerns and promoting responsible practices, Korean aquaculture can foster an ethically sound and sustainable future.

Trait sensitivity to stress and cognitive bias processes in fish: A brief overview

Like other animals, fish have unique personalities that can affect their cognition and responses to environmental stressors. These individual personality differences are often referred to as "behavioural syndromes" or "stress coping styles" and can include personality traits such as boldness, shyness, aggression, exploration, locomotor activity, and sociability. For example, bolder or proactive fish may be more likely to take risks and present lower hypothalamo-pituitary-adrenal/interrenal axis reactivity as compared to shy or reactive individuals. Likewise, learning and memory differ between fish personalities. Reactive or shy individuals tend to have faster learning and better association recall with aversive stimuli, while proactive or bold individuals tend to learn more quickly when presented with appetitive incentives. However, the influence of personality on cognitive processes other than cognitive achievement in fish has been scarcely explored. Cognitive bias tests have been employed to investigate the interplay between emotion and cognition in both humans and animals. Fish present cognitive bias processes (CBP) in which fish's interpretation of stimuli could be influenced by its current emotional state and open to environmental modulation. However, no study in fish has explored whether CBP, like in other species, can be interpreted as long-lasting traits and whether other individual characteristics may explain its variation. We hold the perspective that CBP could serve as a vulnerability factor for the onset, persistence, and recurrence of stress-related disorders. Therefore, studying fish's CBP as a state or trait and its interactions with individual variations may be valuable in future efforts to enhance our understanding of anxiety and stress neurobiology in animal models and humans.

Ethics in aquaculture: animal welfare and environmental sustainability

In recent decades, also driven by the European Union, aquaculture has undergone significant development to meet the increasing demand for seafood products. However, the concentration of efforts and resources in the fishing industry raises complex ethical issues that have yet to be fully explored, concerning animal welfare, environmental impact, and social justice. Balancing economic interests with environmental and ethical concerns is a challenging yet crucial task to ensuring a sustainable future for aquaculture. The adoption of ethical values in the fishing industry not only promotes economic, environmental, and social responsibility but also fosters consumer trust in responsible food sourcing. Interventions such as developing animal welfare standards, implementing sustainable farming techniques, adopting environmental management policies, and promoting ethically responsible business practices are pivotal. A multidimensional approach is essential to ensure an ethical and sustainable future for aquaculture, critical for global food security and marine environmental well-being. This holistic approach requires collaborative efforts from various stakeholders, including policymakers, researchers, industry players, and consumers, to address the multifaceted challenges faced by the aquaculture sector. Additionally, raising awareness among consumers about the impact of their choices on the environment and animal welfare can further drive the demand for ethically produced seafood and encourage responsible practices within the industry.

Fish and chips: Using machine learning to estimate the effects of basal cortisol on fish foraging behavior

Foraging is an essential behavior for animal survival and requires both learning and decision-making skills. However, despite its relevance and ubiquity, there is still no effective mathematical framework to adequately estimate foraging performance that also takes interindividual variability into account. In this work, foraging performance is evaluated in the context of multi-armed bandit (MAB) problems by means of a biological model and a machine learning algorithm. Siamese fighting fish (Betta splendens) were used as a biological model and their ability to forage was assessed in a four-arm cross-maze over 21 trials. It was observed that fish performance varies according to their basal cortisol levels, i.e., a reduced average reward is associated with low and high levels of basal cortisol, while the optimal level maximizes foraging performance. In addition, we suggest the adoption of the epsilon-greedy algorithm to deal with the exploration-exploitation tradeoff and simulate foraging decisions. The algorithm provided results closely related to the biological model and allowed the normalized basal cortisol levels to be correlated with a corresponding tuning parameter. The obtained results indicate that machine learning, by helping to shed light on the intrinsic relationships between physiological parameters and animal behavior, can be a powerful tool for studying animal cognition and behavioral sciences.