Behaviour represents a reaction to the environment as fish perceive it and is therefore a key element of fish welfare. This review summarises the main findings on how behavioural changes have been used to assess welfare in farmed fish, using both functional and feeling-based approaches. Changes in foraging behaviour, ventilatory activity, aggression, individual and group swimming behaviour, stereotypic and abnormal behaviour have been linked with acute and chronic stressors in aquaculture and can therefore be regarded as likely indicators of poor welfare. On the contrary, measurements of exploratory behaviour, feed anticipatory activity and reward-related operant behaviour are beginning to be considered as indicators of positive emotions and welfare in fish. Despite the lack of scientific agreement about the existence of sentience in fish, the possibility that they are capable of both positive and negative emotions may contribute to the development of new strategies (e.g. environmental enrichment) to promote good welfare. Numerous studies that use behavioural indicators of welfare show that behavioural changes can be interpreted as either good or poor welfare depending on the fish species. It is therefore essential to understand the species-specific biology before drawing any conclusions in relation to welfare. In addition, different individuals within the same species may exhibit divergent coping strategies towards stressors, and what is tolerated by some individuals may be detrimental to others. Therefore, the assessment of welfare in a few individuals may not represent the average welfare of a group and vice versa. This underlines the need to develop on-farm, operational behavioural welfare indicators that can be easily used to assess not only the individual welfare but also the welfare of the whole group (e.g. spatial distribution). With the ongoing development of video technology and image processing, the on-farm surveillance of behaviour may in the near future represent a low-cost, noninvasive tool to assess the welfare of farmed fish.

Behaviour represents a reaction to the environment as fish perceive it and is therefore a key element of fish welfare. This review summarises the main findings on how behavioural changes have been used to assess welfare in farmed fish, using both functional and feeling-based approaches. Changes in foraging behaviour, ventilatory activity, aggression, individual and group swimming behaviour, stereotypic and abnormal behaviour have been linked with acute and chronic stressors in aquaculture and can therefore be regarded as likely indicators of poor welfare. On the contrary, measurements of exploratory behaviour, feed anticipatory activity and rewardrelated operant behaviour are beginning to be considered as indicators of positive emotions and welfare in

As aquaculture continues to grow and intensify, there is an increasing public concern over the welfare of farmed fish. Stress and production‐related pathologies and repressed growth are examples of the challenges facing aquaculture, and their impacts could be minimised by effective identification of the early signs of impaired welfare. Many welfare monitoring methods have been recommended; however, continuous and reliable welfare monitoring in aquaculture is not yet widespread and commonplace. The aim of this scoping review was to present an overview of the most recent developments in fish welfare assessments with a specific focus on practical translation to the aquaculture industry. A keyword‐based search was undertaken to identify peer‐reviewed papers published between 2014 and 2020 in which a novel method with the potential to be used for the assessment of fish welfare in aquaculture was introduced. The results were sorted into two categories: non‐invasive and invasive methods. All methods were assessed for their advantages and disadvantages, potential applicability to aquaculture. Invasive methods were also ranked on their degree of impact. It is concluded that increased interest into fish welfare, in combination with more intelligent modern technology, has resulted in the development of newer and more refined alternatives to traditional methods of welfare assessment such as behaviour monitoring by 2D cameras and plasma cortisol evaluation. Although, in many cases, more research is needed before these methods are suitable for widespread industry use, studies that focus on increasing the precision, automation and practical applicability of these methods are a promising avenue for future research.

Offshore aquaculture has gained momentum in recent years, and the production of an increasing number of marine fish species is being relocated offshore. Initially, predictions of the advantages that offshore aquaculture would present over nearshore farming were made without enough science‐based evidence. Now, with more scientific knowledge, this review revisits past predictions and expectations of offshore aquaculture. We analysed and explained the oceanographic features that define offshore and nearshore sites. Using Atlantic salmon (Salmo salar) as a case study, we focussed on sea lice, amoebic gill disease, and the risk of harmful algal blooms, as well as the direct effects of the oceanography on the health and physiology of fish. The operational and licencing challenges and advantages of offshore aquaculture are also considered. The lack of space in increasingly saturated sheltered areas will push new farms out to offshore locations and, if appropriate steps are followed, offshore aquaculture can be successful. Firstly, the physical capabilities of the farmed fish species and infrastructure must be fully understood. Secondly, the oceanography of potential sites must be carefully studied to confirm that they are compatible with the species‐specific capabilities. And, thirdly, an economic plan considering the operational costs and licencing limitations of the site must be developed. This review will serve as a guide and a compilation of information for researchers and stakeholders.

The farming of decapod crustaceans is a key economic driver in many countries, with production reaching around 9.4 million tonnes (USD 69.3 billion) in 2018. These efforts are currently dominated by the farming of Pacific whiteleg shrimp, Penaeus vannamei, which translates into approximately 167 billion farmed P. vannamei being harvested annually. Further production growth is expected in the future and hence the need for more research into its health and welfare is required. Herein, from an extensive survey of the available literature, we scrutinise farming practices and the challenges associated with the production of P. vannamei from an animal-centric welfare perspective (1), we propose potential welfare indicators (2) and we critically review current scientific evidence of sentience in penaeid shrimp among other commercially important decapods (3), since it is plausible that in the near future not only the largest, but in fact all decapod crustaceans will receive welfare protection. This review highlights that despite the wide knowledge on crustacean stress physiology and immunology as well as disease control, still little is known about some key parameters related to the five welfare dimensions. We recommend that further research should focus on developing a systematic integrated welfare assessment encompassing all the different aspects of the crustaceans farming and life cycle up to slaughter. Furthermore, direct and indirect species-specific operational welfare indicators should be developed for all decapod crustaceans currently farmed, similar to the ones suggested in this review for P. vannamei.

Aquaculture, or the farmed production of fish and shellfish, has grown rapidly, from supplying just 7% of fish for human consumption in 1974 to more than half in 2016. This rapid expansion has led to the growth of Precision Aquaculture concept that aims to exploit data-driven management of fish production, thereby improving the farmer's ability to monitor, control, and document biological processes in farms. Fundamental to this paradigm is monitoring of environmental and animal processes within a cage, and processing those data toward farm insight using models and analytics. This paper presents an analysis of environmental and fish behaviour datasets collected at three salmon farms in Norway, Scotland, and Canada. Information on fish behaviour were collected using hydroacoustic sensors that sampled the vertical distribution of fish in a cage at high spatial and temporal resolution, while a network of environmental sensors characterised local site conditions. We present an analysis of the hydroacoustic datasets using AutoML (or automatic machine learning) tools that enables developers with limited data science expertise to train high-quality models specific to the data at hand. We demonstrate how AutoML pipelines can be readily applied to aquaculture datasets to interrogate the data and quantify the primary features that explains data variance. Results demonstrate that variables such as temperature, wind conditions, and hour-of-day were important drivers of fish motion at all sites. Further, there were distinct differences in factors that influenced in-cage variations driven by local variables such as water depth and ambient environmental conditions (particularly dissolved oxygen). The framework offers a transferable approach to interrogate fish behaviour within farm systems, and quantify differences between sites.

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