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Frontiers in Animal Science ·

Volume 7 - 2026 | https://doi.org/10.3389/fanim.2026.1957894

Beyond minimizing suffering: a comparative review of positive welfare assessment in fish and terrestrial farmed animals

  • Kallirroi Papadaki1
  • Athanasios Samaras1
  • Fleur A. Hoorweg2
  • Michail Pavlidis1*corresponding author
  1. 1Fish Physiology Laboratory, Department of Biology, University of Crete, Heraklion, Greece
  2. 2Wageningen Livestock Research, Wageningen University & Research, Wageningen, Netherlands
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Abstract

Positive animal welfare has emerged as an increasingly important component of sustainable production systems, shifting the focus of welfare assessment beyond the reduction of suffering toward the promotion of positive affective states. This goal is supported by the Quality of Life (QoL) concept, which balances positive and negative animal experiences, an approach built on the definition of welfare as the animal’s physical and mental state. This scoping review examines current knowledge on positive welfare indicators across farmed animal taxa, with particular emphasis on comparing terrestrial livestock systems and aquaculture. Key differences between terrestrial and aquatic systems are discussed, including variability of production environments, environmental complexity, species diversity, domestication, human–animal interactions and the interpretation of affective states. The literature survey revealed a substantial increase in scientific interest in positive welfare indicators over the past 25 years for terrestrial and aquatic species, although research remains heavily biased toward terrestrial livestock, with fish studies limited and fragmented across numerous species. Across taxa, similar behavioral, cognitive and physiological indicators are commonly used as well as similar ways to enhance positive animal welfare, supporting the inclusion of sentient fish in this research. The review concludes that while terrestrial findings provide a foundation, species-specific, commercial-scale research is critically needed in aquaculture to develop robust welfare standards and ensure a positive animal welfare.

Keywords

1 Fish welfare: concepts and assessment

1.1 Evolutionary homology in fish and mammals

Despite their evolutionary divergence, fish and mammals share a substantial degree of homology in the anatomical, physiological and neurobiological systems that underpin the processing of internal and external stimuli (O’Connell and Hofmann, 2011; Sneddon, 2019). As vertebrates, both groups inherited from a common ancestor a suite of conserved neural, endocrine and molecular mechanisms involved in sensation, learning, memory, motivation and behavioral regulation (O’Connell and Hofmann, 2011). Major neurotransmitter systems, including dopaminergic, serotonergic, opioidergic and noradrenergic pathways, are highly conserved across vertebrate taxa, as are the neuroendocrine stress-response systems represented by the hypothalamic–pituitary–adrenal axis in mammals and the homologous hypothalamic–pituitary–interrenal axis in fish (Gorissen and Flik, 2016). Comparative studies further demonstrate evolutionary continuity in the neural circuits associated with nociception, reward processing, cognitive flexibility and affective responses (O’Connell and Hofmann, 2011; Sneddon, 2019). Although the organization of the fish brain differs from that of mammals, contemporary evidence indicates that many functions traditionally associated with mammalian forebrain structures are mediated by homologous or functionally analogous regions in teleosts. These conserved features reflect a shared evolutionary foundation for the perception, integration and behavioral expression of biologically significant experiences across vertebrate lineages.

1.2 Fish sentience

According to Cambridge University Press sentience is defined as the quality of being able to experience feelings (Cambridge University Press). Based on this general definition, Nussbaum (2023) describes sentience as marking a “dividing line in nature that isn’t just the ability to feel pain and pleasure, but also the ability to have subjective perceptual experiences of many types, emotional experiences and the cognitive awareness of good and bad”. Considerable debate has occurred regarding which species are capable of sentience with many scientists examining the cognitive, emotional and communicative capacities of a wide range of animals (Lambert et al., 2022). Fish, one of the latest major production groups to be considered sentient, are now frequently associated with terms such as ‘anxiety’, ‘fear’ and ‘pain’ (Lambert et al., 2022; Lambooij et al., 2002; Sneddon, 2019; Stevens et al., 2017) as well as ‘affective state’, ‘emotion’, ‘sentience’ (Lambert et al., 2022) and coping styles, behavioral syndromes, personality dimensions or personality (Benrejdal et al., 2025; Koolhaas et al., 1999; Luchiari and Maximino, 2023; Øverli et al., 2007; Réale et al., 2010).

This growing body of evidence supports the understanding that fish are sentient organisms capable of subjective experience. As Godfrey-Smith (2016) suggests, sentience develops through evolutionary processes in which organisms gain sensory abilities and the capacity to act within their environment. Recognizing fish as sentient has important ethical implications as their ability to experience emotions creates a responsibility to protect them from negative states and to promote positive welfare.

1.3 Concepts and frameworks of animal welfare

Given the evolutionary conservation of key biological systems involved in perception, cognition and affective processing, the principles underpinning animal welfare assessment in mammals and other vertebrates can provide a relevant foundation for critically evaluating welfare in fish. Animal welfare has received increasing attention in recent years. The concept has undergone several revisions and continues to evolve, leading to the most recent definition which according to the Terrestrial Code of the World Organization for Animal Health (WOAH), is “the physical and mental state of an animal in relation to the conditions in which it lives and dies” (WOAH, 2025). Consistent with this framework, WOAH has established international welfare standards for terrestrial livestock under the Terrestrial Animal Health Code (WOAH, 2025) and for farmed fish (excluding ornamental species) under the Aquatic Animal Health Code (WOAH, 2025). Across both contexts, these standards emphasize the provision of handling practices appropriate to the biological characteristics of the animals and environments that enable them to fulfil their physiological and behavioral needs. The above definition and conceptual framework of welfare incorporate the Five Domains Model, comprising three physical and functional domains fundamental to survival (i.e. nutrition, physical environment and health) together with a fourth situational domain addressing behavioral interactions and a fifth domain representing mental or affective states (Mellor et al., 2020). These mental states encompass both positive and negative emotions, moods and feelings and are conceptualized either as a distinct fifth domain or, more appropriately, as the cumulative outcome arising from the preceding four domains.

1.4 Positive welfare and affective states

Positive animal welfare (PAW) is becoming a very popular concept, not only for animal welfare scientists but also for the public who is now demanding sustainable production methods, with animal welfare being considered an important component of sustainability (Broom, 2021; Dwyer, 2020). Positive emotions arise when animals have the opportunity and the resources to perform behaviors that are satisfactory and they are motivated to perform. Such behaviors lead to a rewarding positive affective state (Rault et al., 2025). Positive emotions associated with welfare include comfort, satiation, sensory pleasures, social bonds, or the pleasures accompanying behaviors such as exploration or play (Mellor, 2016). Based on this theoretical background, providing resources that allow animals to engage in motivating behaviors will promote positive affective states and thus positive welfare (Dawkins, 2008; Mellor, 2016).

Nevertheless, the occurrence of negative experiences and emotions cannot be entirely avoided, as they serve important adaptive functions that are essential for survival and fitness (Mellor, 2012). Negative emotions such as fear, pain, hunger, or discomfort motivate animals to avoid danger, respond to harmful or challenging situations and engage in behaviors necessary for coping and survival (Arndt et al., 2022; Ohl and van der Staay, 2012; Rutherford, 2002). These emotions function as anticipatory mechanisms, enabling animals to take pre-emptive action to prevent or reduce potential harm before damage occurs (Arndt et al., 2022; Ohl and van der Staay, 2012). In addition, negative and positive emotional states are fundamental for reinforcement learning, allowing animals to evaluate the consequences of their actions, adapt their behavior to changing environments and develop flexible coping strategies beyond simple instinctive responses (Dawkins, 2000; Ohl and van der Staay, 2012). However, although temporary negative experiences are biologically necessary and can be adequately regulated under normal conditions (Moberg, 2000), persistent or intense negative states constitute suffering and therefore represent a major welfare concern (Dawkins, 2000). Consequently, for an animal to experience a “life worth living”, it is important not only to minimize prolonged negative experiences but also to provide opportunities for positive experiences and rewarding states (Five Domains Model; Mellor, 2016).

1.5 Quality of life scale

The above line of reasoning led to the formation of the concept of Quality of Life (QoL; Edgar et al., 2013; Green and Mellor, 2011; McMillan, 2005; Yeates, 2011), which highlights that animals experience both positive and negative emotions and the goal is to achieve a balance between the two. This concept is based on a four-tier scaling system with two positive and two negative life conditions around a neutral point of balance. The four conditions defined in the Quality of Life (QoL) scale, based on the relative balance of positive and negative experiences are (1) “a good life” defined as “a strongly positive balance of salient positive and negative experiences”, (2) “a life worth living” defined as “a favorable, but less than a good life balance of salient positive and negative experiences”, (3) “a life worth avoiding” defined as “an unfavorable balance of salient positive and negative experiences, but this situation can be remedied rapidly” and lastly (4) “a life not worth living” defined as “a strongly negative balance of salient positive and negative experiences that cannot be remedied rapidly” (Green and Mellor, 2011). The most recent notion suggests that welfare standards for animals under human care should not only ensure a ‘life worth living’ but may also aim to provide a ‘good life’, a concept emphasizing the importance of positive experiences (Littlewood et al., 2025; Mellor, 2016; Wilkins et al., 2026).

1.6 Translating welfare concepts to fish

The Five Domains Model and the Quality-of-Life approach have been developed primarily within the context of terrestrial livestock systems and have been most extensively applied to mammals and birds, where behavioral repertoires, environmental interactions and affective expressions are comparatively well characterized. Their application to farmed fish has recently emerged and remains conceptually challenging. To evaluate the extent to which concepts and indicators developed for terrestrial livestock can be translated to farmed fish, it is necessary to examine the key biological, environmental and management differences between terrestrial and aquatic production systems.

This scoping review aims to identify current knowledge on positive welfare indicators across farmed species, with a focus on comparing terrestrial livestock production and aquaculture. The objective is to identify key differences or similarities between these systems, highlight knowledge gaps in aquaculture welfare research and outline directions for developing more robust, evidence-based welfare standards in aquaculture. The survey was performed in Scopus and Web of Science. Search terms used are listed in Supplementary Material S1.

2 Terrestrial and aquatic production systems

2.1 Variability of production systems

Although terrestrial livestock production encompasses a diversity of management systems, most animals are reared within a limited number of well-described production systems, ranging from extensive and free-ranging to intensive and fully housed operations. These systems often involve relatively standardized inputs and management practices. Aquaculture, on the other hand, includes a remarkably wide range of rearing environments, including water-based (inshore or offshore, cages and pens) and land-based enclosures (ponds, irrigated or flow-through systems, tanks and raceways, recycling systems ranging from more open pond based recirculation to Recirculating Aquaculture Systems - RAS), as well as integrated multi-trophic environments (combining terrestrial livestock and fish or agriculture and aquaculture) (FAO, 2001), also ranging from extensive, semi-intensive, intensive, to hyper-intensive systems. The diversity of production systems results in substantial variation in environmental conditions, resource availability, stocking densities and opportunities for behavioral expression. Consequently, the interpretation of welfare indicators and the implementation of strategies to promote positive animal welfare are highly context-dependent, underscoring the importance of clearly defining the production system under consideration (Fong et al., 2024).

2.2 Production system complexity

Physical complexity, such as dimensions and dynamics, as well as environmental complexity, like ambient conditions and biological diversity of the rearing environment represent key differences between terrestrial and aquatic production systems, with important implications for welfare assessment. While terrestrial systems can also present observational challenges, environmental heterogeneity and vertical complexity (Herlin et al., 2021; Neethirajan, 2025), terrestrial livestock are generally managed in environments where access to animals and resources is more direct and environmental conditions are comparatively easier to monitor and control. In addition, despite challenges in high-density systems such as commercial poultry flocks, individual animals can often be identified. On the other hand, farmed fish live in three-dimensional environments where movement takes place along both vertical and horizontal axes and where important environmental factors, like water flow, light, oxygen availability and water quality, are constantly changing and require specialized monitoring systems. In addition, the physical properties of water differ fundamentally from air, altering how gravity, oxygen diffusion and sensory cues are experienced by animals (Davis et al., 2021) and thereby shaping fish behavior and environmental interactions in ways that have no direct terrestrial equivalent. Moreover, inspection by human observers under water and animal identification is difficult (Franks et al., 2021; Sánchez-Suárez et al., 2020).

2.3 Species complexity

These challenges are further intensified by the exceptional species diversity in aquaculture. While terrestrial animal farming focuses on a small number of domesticated species with well-characterized biology, behavior and welfare requirements, global teleost fish aquaculture encompasses approximately 368 species (FAO, 2024). These species span diverse evolutionary lineages with markedly different environmental requirements, physiological adaptations, stress tolerance, behavioral repertoire, social structures and subsequently welfare needs, leading to the fact that welfare guidelines appropriate for one species may be irrelevant or even detrimental to another (Friedman, 2015; Near et al., 2012; Sánchez-Suárez et al., 2020). When crustaceans, mollusks, and other aquatic animals not discussed in this review are also considered, the need for species-specific welfare research becomes even more apparent.

2.4 Domestication and behavioral plasticity

Most terrestrial livestock species have been domesticated for thousands of years, resulting in substantial genetic, morphological and behavioral divergence from their wild ancestors (Saraiva et al., 2019). This long history of domestication and selective breeding has been accompanied by extensive research into their biology, behavior and husbandry requirements, providing a strong scientific basis for the development and refinement of welfare assessment frameworks. In contrast, the domestication of fish species is relatively recent and variable (Saraiva et al., 2019; Teletchea, 2019). While a few species, such as common carp, have been domesticated several centuries ago, most aquaculture species have only been intensively farmed over the last few decades, often spanning only a few generations (Saraiva et al., 2019; Teletchea, 2019).

Genetic selection in production species has evolved from simple mass selection to highly sophisticated genomic strategies aiming to maximize efficiency and output. In poultry, genetic selection has reached such extremes that modern specialized breeds far exceed the production levels of their ancestors. For example, due to the rapid growth of broiler chickens and the high egg numbers of laying hens, the generation interval has been reduced in half over the past decades, to just seven months (Wolc, 2015). Similar dramatic changes have also been recorded in dairy cattle (Mark and Sandøe, 2010; Oltenacu and Broom, 2010; Pryce et al., 2023), where milk yield has more than doubled in 40 years, with some cows producing over 20,000 kg per lactation. However, this intensive focus on production has led to significant maladaptive traits in terrestrial livestock, such as declining fertility, metabolic disorders, chronic leg diseases and a dangerous reduction in genetic diversity due to the global monopoly of a few breeding companies (Pryce et al., 2023).

As the aquaculture industry increasingly adopts quantitative genetic approaches and achieves substantial rates of genetic gain (Gjedrem and Baranski, 2009; Næve et al., 2022), it has the opportunity to build on the experience gained from terrestrial livestock breeding. Evidence from terrestrial breeding programs indicates that when selection is heavily weighted toward production traits, the inclusion of welfare traits may be insufficient to prevent unfavorable genetic trends. These findings highlight the importance of incorporating welfare considerations into breeding objectives alongside production goals.

2.5 Human–animal interactions

Human–animal interaction (HAI) is a broad construct encompassing the reciprocal and dynamic behavioral exchanges that occur between humans and animals during contact, including human actions toward animals and the animals’ behavioral and physiological responses. HAI occurs across individual and societal contexts and varies in valence from positive to negative, with direct effects on welfare (Acharya et al., 2022).

In terrestrial livestock systems HAIs mostly occur during routine management practices such as feeding, milking, handling, transport and veterinary care. Such interactions, particularly in mammalian species, are frequent, direct and often individualized. The farmed animals are frequently exposed to handling, visual contact and vocal or tactile interactions with caretakers, allowing welfare assessments to incorporate behavioral responses to humans (Hosey and Melfi, 2014). The human–animal relationship is critical for welfare, as negative interactions can induce fear and stress to the animals and ultimately compromise their health and productivity (Acharya et al., 2022). Empirical studies, particularly in dairy and pig production, have demonstrated links between stockperson attitudes and behavior, animal fear and stress and overall welfare outcomes (Hemsworth and Coleman, 2025). Contrary, positive human–animal interactions can enhance welfare and performance. It is possible for trust-based bonds to develop between livestock animals and caretakers; however, these relationships are mostly functional and management-oriented rather than emotionally analogous to those observed with companion animals.

In contrast, interactions between humans and fish in aquaculture are usually infrequent, indirect and largely mediated through group-level management procedures such as feeding, grading, or harvesting. Although direct physical contact is less frequent compared to terrestrial farming, fish rapidly learn to associate human presence with positive stimuli such as food or negative stimuli such as netting and handling. The limited possibility for individual recognition or habituation to humans reduces the applicability of welfare indicators based on direct human–animal relationships, leading to a greater reliance on group-level or environment-mediated measures. Despite these variations, research on how humans affect the welfare of farmed fish, beyond the indirect effects of husbandry practices, remains limited.

2.6 Affective states

The final major point of divergence between terrestrial livestock and farmed fish is the reflection of affective states through body posture and facial expression. In terrestrial animals, assessing affective states often draws on observable behavioral cues, body postures and facial expressions, which have been extensively studied and, in some species, systematically codified (Lezama-García et al., 2019; Mota-Rojas et al., 2020; Neethirajan, 2022; Neethirajan et al., 2021c). Farmed fish lack facial musculature capable of producing expressions analogous to those of mammals and their behavioral signals are often subtler, context-dependent and expressed at the group rather than individual level. Consequently, welfare assessment in fish relies more heavily on indirect indicators such as behavioral patterns, motivational tests, physiological correlates and environmental preferences, rather than on overt expressive cues.

3 Positive animal welfare

This literature review included studies specifically addressing the assessment of indicators of positive welfare. Based on the theoretical framework described above, this welfare approach is associated with positive affective states. Such states may be promoted by environmental and husbandry resources, social relationships and other conditions that enable animals to engage in motivated and rewarding behaviors. Accordingly, relevant welfare indicators are measurable animal-based variables that provide evidence of positive affective experiences.

The increasing scientific interest in this area was evident in the substantial rise in the number of publications addressing positive welfare indicators over the past 25 years in most examined farmed groups (cattle, pig, chicken), with the most profound increase observed in the last 10 years (Figure 1a). Among the retrieved papers, cattle and pigs were the main species of interest (35.1% and 32.0% of the studies, respectively), following by chicken (17.3%), fish (8.2%) and finally small ruminants (7.4%) (Figure 1b; Supplementary Material S2). The highest number of papers for cattle and pig may also reflect the fact that they historically received more research attention than chicken, as their sentience was established earlier in the scientific literature.

Distribution of scientific papers on positive welfare indicators identified through the literature review: **(a)** number of published papers by production group and publication period (2000 to 2025), and **(b)** percentage of papers represented by each production group over the entire period.
Figure 1. Distribution of scientific papers on positive welfare indicators identified through the literature review: (a) number of published papers by production group and publication period (2000 to 2025), and (b) percentage of papers represented by each production group over the entire period.

Furthermore, the distribution of studies assessing positive welfare varies considerably across production phases/animal groups. In cattle, dairy breeds, particularly dairy cows, represent the most extensively studied group, while in pigs, research has primarily focused on fattening animals rather than breeding stock (Figure 2). In contrast, research on chickens is more evenly divided between broilers and laying hens and the same in sheep between meat and dairy production systems. Finally, in goats, dairy breeds appear to be the most represented in the literature. Overall, these patterns suggest that research effort is unevenly distributed across production phases and animal groups, likely reflecting differences among species in production systems, industry priorities, research accessibility and the maturity of the respective research fields. This uneven distribution of research across animal groups and production phases may contribute to important gaps in our understanding of species-specific needs and positive affective states. Furthermore, extrapolating evidence across animal groups, from one production or life-cycle phase to another may overlook differences in biological characteristics, environmental and husbandry conditions, management practices and welfare requirements. Consequently, the current evidence base may disproportionately reflect knowledge derived from better-studied animal groups and production phases and may not adequately capture the conditions that promote positive welfare, underscoring the need for further research on underrepresented species and life stages.

Number of selected papers addressing positive welfare indicators at different production stages/phases within each farmed animal group.
Figure 2. Number of selected papers addressing positive welfare indicators at different production stages/phases within each farmed animal group.

Compared with terrestrial livestock, fish represent a distinct case, as the scientific literature is dispersed across numerous species, with relatively few studies addressing each species. In the selected literature, 137 fish species were mentioned and the selected papers were therefore organized by taxonomic family (Figure 3). Most of the published papers referred primarily on Salmonidae, particularly Atlantic salmon and rainbow trout, followed by Sparidae, mainly gilthead seabream, Cichlidae, with tilapias (Nile tilapia, Mozambique tilapia) and Moronidae, primarily European seabass. This concentration likely reflects the commercial importance or the ex-value of these species, especially in European and intensive aquaculture systems. Nevertheless, even within these prominent families, evidence was distributed across different species and production contexts. Research addressing broodstock was limited to a single study (Arechavala-Lopez et al., 2019). Figure 3 therefore illustrates the taxonomic complexity of fish-welfare research. Together with the significantly lower number of papers identified for fish, the broad distribution of studies across species reveals a highly fragmented, uneven and limited knowledge base.

Number of selected papers on positive welfare indicators in farmed fish, classified by taxonomic family.
Figure 3. Number of selected papers on positive welfare indicators in farmed fish, classified by taxonomic family.

3.1 Indicators in fish

Based exclusively on primary research papers (excluding review articles), the reviewed studies demonstrate that research on positive welfare indicators is highly fragmented across species, life stages and production settings (Table 1). Most work has been conducted under laboratory conditions, with common behavioral indicators including exploration, reduced aggression, increased affiliation, cognitive or judgment bias, shoaling or group cohesion, swimming activity, anticipation, together with physiological measures and productive characteristics like growth (Alnes et al., 2021; Alvarado et al., 2023; Batzina et al., 2014; Brunet et al., 2022; Cerqueira et al., 2017; Gauy et al., 2021; Millot et al., 2014; Nilsson et al., 2012; Rauw et al., 2017; Rogers et al., 2020; Tatemoto et al., 2021). A smaller proportion of studies has been carried out in production environments and systems such as recirculating aquaculture systems (RAS), broodstock units, hatcheries and weaning facilities, where indicators such as social interactions, swimming activity, group cohesion, anticipation, exploration, feeding behavior and responses to environmental enrichment have been assessed (Anderson et al., 2022; Arechavala-Lopez et al., 2019; Kleiber et al., 2022; Oliveira et al., 2024; Prentice et al., 2025; Shen et al., 2023; Villarroel et al., 2021).

Table 1. Positive welfare indicators identified per respective fish species.

SpeciesExperimental setupProduction environment/system#Positive welfare indicatorsReferences
Atlantic cod(Gadus morhua)Laboratory1anticipatory behavior, swimming speed and oxygen consumption(Nilsson et al., 2012)
Atlantic salmon(Salmo salar)Laboratory2exploration, swimming activity(Alnes et al., 2021)
ProductionRAS juvenile1group cohesion, exploration(Prentice et al., 2025)
Black rockfish(Sebastes melanops)ProductionHatchery1cognitive bias (T-maze experiment)(Shen et al., 2023)
European seabass (Dicentrarchus labrax)Laboratory1judgment bias test(Alvarado et al., 2023)
Gilthead seabream (Sparus aurata)ProductionRAS broodstock1distribution, swimming activity, social interactions, feeding behavior, exploration, anticipation(Oliveira et al., 2024)
Hatchery2reduced aggression, distribution(Arechavala-Lopez et al., 2019)
Laboratory3preference/avoidance test, affiliation, serotonin, dopamine, reduced aggression(Batzina et al., 2014; Cerqueira et al., 2017; Millot et al., 2014)
Murray cod (Maccullochella peelii)Laboratory1judgment bias test(Rogers et al., 2020)
Wild1judgment bias test(Freire and Nicol, 2024)
Nile tilapia (Oreochromis niloticus)Laboratory5exploration, cognitive bias(Gauy et al., 2021; Tatemoto et al., 2021)
Rainbow trout (Oncorhynchus mykiss)Laboratory3reduced aggression, shoal cohesion, exploration, cognitive bias, growth, swimming activity(Brunet et al., 2022; Rauw et al., 2017)
ProductionRAS fingerlings3reduced aggression, interaction with enrichment(Anderson et al., 2022; Kleiber et al., 2022; Villarroel et al., 2021)

This review identified several behavioral and physiological responses that could indicate positive welfare in farmed fish. These responses were generally assessed under comparatively favorable environmental or husbandry conditions, making them a good starting point for developing positive welfare assessment frameworks. However, research in this area is still in its early stages and the studies reviewed rarely aimed to identify or confirm clear indicators of positive welfare. Instead, they examined connections between better rearing conditions and the animals’ responses. As a result, many of the suggested indicators cannot yet be clearly interpreted as proof of positive feelings. Among the indicators identified, behavioral responses such as exploration and affiliative behavior appear relatively promising, particularly when their responses are consistent with favorable environmental or social conditions. Cognitive approaches, including judgement-bias and choice tests, are also promising because of their potential to provide more direct insight into affective state. However, the application of behavioral measures on farms can be challenging, as they may require controlled testing conditions, specialized equipment, sophisticated and costly monitoring systems, trained personnel and additional time that can be difficult to accommodate within routine farm management. Several indicators reflected reductions in anxiety-like states, such as decreased aggression (Cerqueira et al., 2017), whereas others were bidirectional or remain conceptually ambiguous, including group cohesion and anticipation behaviors (Anderson et al., 2020; Franks et al., 2018; Kleinhappel et al., 2023; Papageorgiou et al., 2023). These indicators require careful interpretation, as their expression may be context-dependent. In addition, some studies relied on production-related outcomes, such as growth, whose validity as evidence of positive welfare, as opposed to general physiological performance or acclimation, remains uncertain (Colditz et al., 2024). Such outcomes should therefore be regarded as indirect rather than direct evidence of positive welfare.

This ambiguity is partly attributable to the relatively recent emergence of positive welfare research, particularly in fish species, where multiple behavioral and physiological parameters are still being evaluated to determine their validity. Furthermore, although reductions in anxiety-like behaviors may indicate an improvement in welfare conditions, they do not necessarily constitute evidence of positive welfare. Considerable conceptual distance remains along the continuum from poor to good quality of life, progressing from the alleviation of negative states, through neutral conditions and ultimately toward genuinely positive experiences. Overall, although some behavioral and cognitive measures demonstrate comparatively stronger empirical support, the available evidence remains insufficient to establish any of these measures as validated indicators of positive welfare in fish. Consequently, it is apparent that substantially more research is needed in this field to clarify which behavioral and physiological measures can be reliably interpreted as indicators of genuinely positive welfare states in fish species.

3.2 Indicators across taxa

The comparison of positive welfare indicators across production groups (small ruminants, cattle, pigs, chicken, fish) reveals both shared approaches and notable taxon-specific differences (Table 2). Across all taxa the overarching categories of positive welfare indicators show strong commonality, despite the expression of species-specific behaviors. Behavioral measures (e.g., exploration, affiliative behavior, locomotion), productive characteristics and physiological markers are represented in every group, showing that fish align well with the indicator categories originally developed for terrestrial livestock.

Table 2. Indicators used to assess positive welfare per production group (SR; small ruminants, CA; cattle, PI; pigs, CH; chicken, FI; fish).

Type of indicatorsIndicatorProduction groupReferences
Human experienceQualitative Behavioural Assessment (QBA)SR, CA, PI, CHSmall ruminants: Battini et al., 2014Cattle: Boissy et al., 2007a; De Rosa et al., 2009; de Vries et al., 2014; Keeling et al., 2021; Lavrijsen-Kromwijk et al., 2024; Mandel et al., 2016; Mattiello et al., 2019; Napolitano et al., 2009; Popescu et al., 2013; Rowe and Mullan, 2022; Russell et al., 2023; Valente and Stilwell, 2022; Ventura et al., 2021; Wemelsfelder and Mullan, 2014Pigs: Camerlink et al., 2016; De Angelis et al., 2025; Ibach et al., 2024; Mendes et al., 2025; Murphy et al., 2014; Rowe and Mullan, 2022; Rutherford et al., 2012; Zapata Cardona et al., 2024, 2022Chicken: Rasmussen et al., 2024; Rayner et al., 2020; Ribeiro et al., 2024; Sans et al., 2021a, 2021b; Vas et al., 2023
BehaviorFeedingSR, CA, PI, CH, FISmall ruminants: Greiveldinger et al., 2007; Tamioso et al., 2020Cattle: Ebinghaus et al., 2022; Harris et al., 2024; Ishiwata et al., 2006; Kremer et al., 2021; Mattiello et al., 2019; Miranda et al., 2023; Occhiuto et al., 2025; Papageorgiou and Simitzis, 2022; Ramos et al., 2025; Stokes et al., 2022; Unsal et al., 2025; Vigors and Lawrence, 2019Pigs: Archer et al., 2022; Durand et al., 2023; Franchi et al., 2023; Kells, 2022; Mkwanazi et al., 2018; Reimert et al., 2017Chicken: Arnould et al., 2024; Davies et al., 2014; Evans et al., 2023; Herrera-Alcaíno et al., 2024; McGrath et al., 2016; Mocz et al., 2022; Moe et al., 2014; Moss et al., 2024; Nicol et al., 2009; Pap et al., 2024; Paul et al., 2023, 2022; Rana and Campbell, 2021; Šemrov et al., 2024Fish: Kittilsen, 2013; Oliveira et al., 2024; Spitzer et al., 2022; Zhang et al., 2022; 2023b
Lying/Resting timeSR, CA, PI, CHSmall ruminants: Bozakova et al., 2024; Hansen, 2015; Papageorgiou and Simitzis, 2022Cattle: Boissy et al., 2007a, 2007b; Bravo et al., 2019; De Rosa et al., 2009; Ebinghaus et al., 2022; Harris et al., 2024; Kimeli et al., 2021; Lavrijsen-Kromwijk et al., 2024; Logue and Mayne, 2014; Mattiello et al., 2024, 2019; Miranda et al., 2023; Napolitano et al., 2009; Papageorgiou and Simitzis, 2022; Sreekumar and Sejian, 2024; Stokes et al., 2022; Unsal et al., 2025; Vigors and Lawrence, 2019Pigs: Averós et al., 2010; Lidfors et al., 2021; Raoult et al., 2024; Reimert et al., 2017; Villain et al., 2020aChicken: Arnould et al., 2024; Baxter et al., 2018; Herrera-Alcaíno et al., 2024; Pap et al., 2024; Rana and Campbell, 2021; Rowe and Mullan, 2022
RuminationSR, CASmall ruminants: Papadaki et al., 2024Cattle: Boissy et al., 2007a, 2007b; Bravo et al., 2019; Harris et al., 2024; Mattiello et al., 2024; Sreekumar and Sejian, 2024
AggressionSR, CA, PI, FISmall ruminants: Bozakova et al., 2024; Hansen, 2015; Papadaki et al., 2024Cattle: Dunston-Clarke et al., 2025; Edwards-Callaway et al., 2021; Harris et al., 2024; Spitzer et al., 2022; Unsal et al., 2025Pigs: Anil et al., 2005; Archer et al., 2022; Kresnye et al., 2022; Lykhach et al., 2020; Moscovice et al., 2022; Zebunke et al., 2013Fish: Arechavala-Lopez et al., 2022, 2019; Brunet et al., 2022; Cerqueira et al., 2017; Kleiber et al., 2023, 2022; Rauw et al., 2017; Spiliopoulos et al., 2025; Spitzer et al., 2022; Zhang et al., 2023b
ExplorationSR, CA, PI, CH, FISmall ruminants: Oesterwind et al., 2016; Papageorgiou and Simitzis, 2022; Stephenson and Haskell, 2022Cattle: Bertenshaw and Rowlinson, 2008; Goma et al., 2023; Harris et al., 2024; Mattiello et al., 2019; Papageorgiou and Simitzis, 2022; Russell et al., 2024b; Schulze Westerath et al., 2009; Stokes et al., 2022; Unsal et al., 2025; Ventura et al., 2021; Wemelsfelder and Mullan, 2014Pigs: Averós et al., 2010; Fàbrega et al., 2019; Kells, 2022; Kim et al., 2023; Kresnye et al., 2022; Lidfors et al., 2021; McKenna et al., 2019; Mkwanazi et al., 2018; Reimert et al., 2017; Rørvang et al., 2024; Steybe et al., 2021; Stracke et al., 2017b; Zebunke et al., 2013; Zhang et al., 2023a; Zupan et al., 2016Chicken: Campbell et al., 2019b; Jacobs et al., 2023; Nicol et al., 2009; Rana and Campbell, 2021; Rowe and Mullan, 2022; Šemrov et al., 2024; Skånberg et al., 2024; Van Poucke et al., 2024; Vas et al., 2023Fish: Alnes et al., 2021; Arechavala-Lopez et al., 2022; Brunet et al., 2022; Kittilsen, 2013; Oliveira et al., 2024; Prentice et al., 2025; Spence-Jones et al., 2025; Tatemoto et al., 2021; Zhang et al., 2023b
Behavioral synchronicitySR, CA, FISmall ruminants: Hansen, 2015; Papageorgiou and Simitzis, 2022Cattle: Boissy et al., 2007a, 2007b; De Rosa et al., 2009; Lavrijsen-Kromwijk et al., 2024; Mattiello et al., 2024, 2019; Napolitano et al., 2009; Neethirajan, 2022; Papageorgiou and Simitzis, 2022; Špinka, 2012; Sreekumar and Sejian, 2024Fish: Brunet et al., 2022; Fife-Cook and Franks, 2019; Franks et al., 2018; Kleinhappel et al., 2023; Prentice et al., 2025; Spiliopoulos et al., 2025
AffiliationSR, CA, PI, CH, FISmall ruminants: Bozakova et al., 2024; Papadaki et al., 2024; Papageorgiou and Simitzis, 2022Cattle: Arndt et al., 2022; Boissy et al., 2007a, 2007b; De Rosa et al., 2009; Dunston-Clarke et al., 2025; Ede et al., 2019; Goma et al., 2023; Harris et al., 2024; Ishiwata et al., 2006; Keeling et al., 2021; Laister et al., 2011; Lavrijsen-Kromwijk et al., 2024; Logue and Mayne, 2014; Mandel et al., 2016; Mattiello et al., 2019; Mota-Rojas et al., 2024a; Napolitano et al., 2009; Nawroth and Rørvang, 2022; Neethirajan, 2021a; Papageorgiou and Simitzis, 2022; Rault, 2019; Rowe and Mullan, 2022; Salvin et al., 2020; Schnaider et al., 2022; Silva-Antunes et al., 2024; Špinka, 2012; Spitzer et al., 2022; Stokes et al., 2022; Sreekumar and Sejian, 2024; Unsal et al., 2025; Ventura et al., 2021; Vigors et al., 2021; Vigors and Lawrence, 2019; Wemelsfelder and Mullan, 2014Pigs: Franchi et al., 2023; Lidfors et al., 2021; Moscovice et al., 2022; Neethirajan, 2022; Rault et al., 2020; Reimert et al., 2017; Rowe and Mullan, 2022; Witt et al., 2023; Zhang et al., 2024Chicken: Bellezza Oddon et al., 2024; Jacobs et al., 2023; Pap et al., 2024Fish: Batzina et al., 2014; Cerqueira et al., 2017; Kittilsen, 2013; Oliveira et al., 2024; Spiliopoulos et al., 2025
PlaySR, CA, PI, CHSmall ruminants: Battini et al., 2014; Papageorgiou and Simitzis, 2022Cattle: Bailly-Caumette et al., 2023; Boissy et al., 2007a, 2007b; Dunston-Clarke et al., 2025; Ede et al., 2019; Harris et al., 2024; Heinsius et al., 2024; Keeling et al., 2021; Lavrijsen-Kromwijk et al., 2024; Lv et al., 2018; Mandel et al., 2016; Mattiello et al., 2019; Miranda et al., 2023; Miller-Cushon and Van Os, 2019; Napolitano et al., 2009; Nawroth and Rørvang, 2022; Neave, 2025; Neethirajan, 2021a; Occhiuto et al., 2025; Papageorgiou and Simitzis, 2022; Ramos et al., 2025; Rowe and Mullan, 2022; Russell et al., 2024b; Salvin et al., 2020; Schütz et al., 2012; Špinka, 2012; Stokes et al., 2022, 2022; Unsal et al., 2025; Ventura et al., 2021; Vigors et al., 2021; Vigors and Lawrence, 2019; Wemelsfelder and Mullan, 2014Pigs: Baciadonna et al., 2018; Franchi et al., 2023; Kells, 2022; Krugmann et al., 2020; Lidfors et al., 2021; Marcet Rius et al., 2018b; Moscovice et al., 2022; Murphy et al., 2014; Nian et al., 2023; Rault et al., 2020; Reimert et al., 2013, 2015, 2017; Rowe and Mullan, 2022; Steinerová et al., 2024, 2025; Steybe et al., 2021; Witt et al., 2023; Zhao et al., 2020; Zupan et al., 2016Chicken: Baxter et al., 2019; Bellezza Oddon et al., 2024; Evans et al., 2023; Jacobs et al., 2023; Rasmussen et al., 2024; Rayner et al., 2020; Rowe and Mullan, 2022; Van Poucke et al., 2024; Vas et al., 2023
GrazingSR, CASmall ruminants: Hansen, 2015; Papageorgiou and Simitzis, 2022Cattle: Harris et al., 2024; Logue and Mayne, 2014; Mattiello et al., 2019; Mota-Rojas et al., 2024a; Papageorgiou and Simitzis, 2022; Russell et al., 2024b; Ventura et al., 2021; Vigors and Lawrence, 2019
AnticipationSR, CA, PI, CH, FISmall ruminants: Boissy and Lee, 2014; Papageorgiou and Simitzis, 2022; Tamioso et al., 2018Cattle: Arndt et al., 2022; Heinsius et al., 2024; Neethirajan, 2021a; Papageorgiou and Simitzis, 2022Pigs: Lidfors et al., 2021; Murphy et al., 2014; Neethirajan, 2022; Raoult et al., 2024; Steinerová et al., 2024Chicken: Davies et al., 2014; McGrath et al., 2016; Moe et al., 2014; Zimmerman et al., 2011Fish: Kittilsen, 2013; Kleiber et al., 2023; Nilsson et al., 2012; Oliveira et al., 2024
LocomotionCA, PI, CH, FICattle: Boissy et al., 2007a, 2007b; Erasmus et al., 2023; Harris et al., 2024; Miranda et al., 2023; Occhiuto et al., 2025Pigs: Averós et al., 2010; Durand et al., 2023; Franchi et al., 2023; Imfeld-Mueller et al., 2011; Krugmann et al., 2020; Leliveld et al., 2016Chicken: Baxter et al., 2019; Jacobs et al., 2023; Kang et al., 2024; McGrath et al., 2016; Mocz et al., 2025, 2022; Paul et al., 2023; Rana and Campbell, 2021; Rasmussen et al., 2024Fish: Alnes et al., 2021; Arechavala-Lopez et al., 2019; Kleiber et al., 2022; Nilsson et al., 2012; Oliveira et al., 2024; Rauw et al., 2017; Spence-Jones et al., 2025; Zhang et al., 2022, 2023b
VocalizationsSR, CA, PI, CHSmall ruminants: Boissy et al., 2007a, 2007b; Papageorgiou and Simitzis, 2022; Veissier et al., 2009Cattle: Ede et al., 2019; Gavojdian et al., 2024; Green et al., 2019; Harris et al., 2024; Laurijs et al., 2021; Manteuffel et al., 2004; Miller-Cushon and Van Os, 2019; Neave, 2025; Neethirajan, 2020, 2021a, 2021c, 2022; Papageorgiou and Simitzis, 2022; Schnaider et al., 2022; Špinka, 2012; Wemelsfelder and Mullan, 2014Pigs: Baciadonna et al., 2018; Briefer, 2012; Friel et al., 2019; Gavojdian et al., 2024; Imfeld-Mueller et al., 2011; Laurijs et al., 2021; Leliveld et al., 2017, 2016; Linhart et al., 2015; Maigrot et al., 2018; Manteuffel et al., 2004; Mills, 2022; Murphy et al., 2014; Neethirajan, 2022, 2021c; Raoult et al., 2024; Reimert et al., 2013; Steinerová et al., 2024; Truong et al., 2024; Villain et al., 2020a, 2020bChicken: Collins et al., 2024; Golfidis et al., 2024; Herborn et al., 2020; Rowe and Mullan, 2022
Nesting/laying behaviorCA, PICattle: Dunston-Clarke et al., 2025; Keeling et al., 2021; Lavrijsen-Kromwijk et al., 2024; Mandel et al., 2016; Mattiello et al., 2019; Mota-Rojas et al., 2024a; Napolitano et al., 2009; Nawroth and Rørvang, 2022; Neave, 2025; Russell et al., 2024a; Stokes et al., 2022; Unsal et al., 2025; Wemelsfelder and Mullan, 2014Pigs: Kells, 2022Chicken: Alm et al., 2016; Marino, 2017; Spitzer et al., 2022
Maternal careSRSmall ruminants: Papageorgiou and Simitzis, 2022
PreeningCHChicken: Arnould et al., 2024; Bellezza Oddon et al., 2024; Evans et al., 2023; Marino, 2017; Nicol et al., 2009; Rana and Campbell, 2021; Šemrov et al., 2024; Spitzer et al., 2022
DustbathingCHChicken: Arnould et al., 2024; Baxter et al., 2018; Jacobs et al., 2023; Kang et al., 2024; McGrath et al., 2016; Rana and Campbell, 2021; Rowe and Mullan, 2022; Šemrov et al., 2024
Ground scratchingCHChicken: Jacobs et al., 2023; Rana and Campbell, 2021; Rayner et al., 2020; Rowe and Mullan, 2022; Šemrov et al., 2024
SparringCHChicken: Jacobs et al., 2023
Wing flappingCHChicken: Bellezza Oddon et al., 2024; Jacobs et al., 2023; Rowe and Mullan, 2022; Šemrov et al., 2024
Ground scratchingCHChicken: Vas et al., 2023
Vertical wing shakingCHChicken: Vas et al., 2023
Goal-directed behaviorFIFish: Fife-Cook and Franks, 2019
CognitionCognitive biasSR, CA, PI, CH, FISmall ruminants: Boissy et al., 2007a, 2007b; Bozakova et al., 2024; Destrez et al., 2013; Doyle et al., 2014; Oesterwind et al., 2016; Papadaki et al., 2024; Papageorgiou and Simitzis, 2022Cattle: Boissy et al., 2007a, 2007b; Crump et al., 2021; Dickson et al., 2024; Ede et al., 2019; Kremer et al., 2021, 2022; Mandel et al., 2016; Mattiello et al., 2019; McConnachie et al., 2018; Miller-Cushon and Van Os, 2019; Mota-Rojas et al., 2024a; Nawroth and Rørvang, 2022; Neave et al., 2024; Neave, 2025; Neethirajan, 2022; Papageorgiou and Simitzis, 2022; Salvin et al., 2020; Špinka, 2012; Stokes et al., 2022; Suchon et al., 2025; Windschnurer et al., 2009Pigs: Amann et al., 2025; Brajon et al., 2015; Douglas et al., 2012; Ede et al., 2024; Elmore et al., 2012; Ernst et al., 2005; Friel et al., 2019; Horback and Parsons, 2019; Imfeld-Mueller et al., 2011; Krugmann et al., 2019, 2020; Marsh et al., 2021; Murphy et al., 2014; Neethirajan, 2022; Puppe et al., 2007; Raoult et al., 2024; Scollo et al., 2014; Statham et al., 2020; Stracke et al., 2017b, 2017a; Villain et al., 2020b; Zebunke et al., 2013Chicken: Adler et al., 2020; Anderson et al., 2021; Baxter et al., 2019; Campbell et al., 2019b; Dixon et al., 2013; Ferreira et al., 2025; Garnham et al., 2022; Herrera-Alcaíno et al., 2024; Jacobs et al., 2023; Košťál et al., 2020; Lourenço-Silva et al., 2023; Neuhauser et al., 2023; Oyeniran et al., 2025; Paul et al., 2022, 2018; Prasad-Shreckengast et al., 2024; Ross et al., 2020, 2019; Skalná et al., 2023; Tahamtani et al., 2018; Tobias Krause et al., 2006; Van Poucke et al., 2024; Zidar et al., 2018Fish: Alvarado et al., 2023; Anderson et al., 2022; Brunet et al., 2022; DePasquale et al., 2020; Freire and Nicol, 2024; Gauy et al., 2021; Kittilsen, 2013; Kleiber et al., 2022; Maia et al., 2024; Millot et al., 2014; Rogers et al., 2020; Shen et al., 2023; Zhang et al., 2022, 2023b
Resilience (startle test)PIPigs: Lee et al., 2024
Contra freeloading/AutonomyCACattle: Contreras-Torres et al., 2024; Kutzer et al., 2015; Lemcke et al., 2021; Miller-Cushon and Van Os, 2019; Mota-Rojas et al., 2024a; Russell et al., 2024a; Stokes et al., 2022; Vigors et al., 2021; Vigors and Lawrence, 2019
ProductiveMilk yieldSR, CASmall ruminants: Bozakova et al., 2024; Hansen, 2015Cattle: Andreasen and Forkman, 2012; dos Santos Lemes Lechuga et al., 2023; Erasmus et al., 2023; Goma et al., 2023; Kutzer et al., 2015; Lemcke et al., 2021; Sreekumar and Sejian, 2024
Daily gain/GrowthCA, FICattle: Mandel et al., 2016Fish: Arechavala-Lopez et al., 2022; Brunet et al., 2022; Spence-Jones et al., 2025; Spitzer et al., 2022; Zhang et al., 2022
Body posture and facial expressionVisible eye whiteSR, CA, PISmall ruminants: Papageorgiou and Simitzis, 2022Cattle: Battini et al., 2019; Boissy et al., 2007a, 2007b; de Vere and Kuczaj, 2016; Ede et al., 2019; Lambert (Proctor) and Carder, 2017; Mattiello et al., 2019, 2024; Miller-Cushon and Van Os, 2019; Neave, 2025; Neethirajan, 2020, 2021b; Papageorgiou and Simitzis, 2022; Parra Cerezo et al., 2025; Proctor and Carder, 2015a; Salvin et al., 2020; Sandem et al., 2002, 2006; Wemelsfelder and Mullan, 2014Pigs: Neethirajan, 2021b, 2021c
Ear positionSR, CA, PISmall ruminants: Boissy et al., 2007a, 2007b; Boissy et al., 2011; Papageorgiou and Simitzis, 2022; Reefmann et al., 2009b; Tamioso et al., 2017, 2020Cattle: Battini et al., 2019; Boissy et al., 2007a, 2007b; de Vere and Kuczaj, 2016; Ede et al., 2019; Goma et al., 2023; Keeling et al., 2021; Lambert and Carder, 2019; Lange et al., 2020a, 2020b, 2021; Mattiello et al., 2019, 2024; Miller-Cushon and Van Os, 2019; Neave, 2025; Neethirajan, 2021a, 2021b, 2021c, 2022; Oliveira and Keeling, 2018; Papageorgiou and Simitzis, 2022; Proctor and Carder, 2014, 2015a; Rowe and Mullan, 2022; Wemelsfelder and Mullan, 2014Pigs: Krugmann et al., 2020, 2021; Marcet Rius et al., 2018b; Marcet Rius et al., 2019a, 2019b; Neethirajan, 2021b, 2021c; Rowe and Mullan, 2022; Steinerová et al., 2024
Tail postureSR, CA, PISmall ruminants: Tamioso et al., 2020, 2017Cattle: Mattiello et al., 2019; Neethirajan, 2021a; Oliveira and Keeling, 2018; Schnaider et al., 2022; Vigors and Lawrence, 2019; Wemelsfelder and Mullan, 2014Pigs: Camerlink and Ursinus, 2020; Goursot et al., 2019; Krugmann et al., 2021, 2020; Marcet Rius et al., 2018a, 2018b; Marcet Rius et al., 2019a, 2019b; Murphy et al., 2014; Nian et al., 2023; Reimert et al., 2015, 2013; Steinerová et al., 2024; Zhao et al., 2020
LateralitySR, CSmall ruminants: Papageorgiou and Simitzis, 2022Cattle: Goma et al., 2023; Kappel et al., 2017; Mattiello et al., 2019; Salvin et al., 2020
Facial changesSR, PI, CHSmall ruminants: Tamioso et al., 2020, 2017Pigs: Camerlink et al., 2018; Yunas et al., 2025Chicken: Arnould et al., 2024; Soulet et al., 2025
PhysiologyOxytocinSR, CA, PI, FISmall ruminants: Papageorgiou and Simitzis, 2022Cattle: Boissy et al., 2007a, 2007b; Chen and Sato, 2016; Lange et al., 2021; Lavrijsen-Kromwijk et al., 2024; Miranda et al., 2023; Mota-Rojas et al., 2024a; Napolitano et al., 2009; Neethirajan, 2020, 2021a, 2022; Skånberg et al., 2025Pigs: Franchi et al., 2024; López-Arjona et al., 2024; López-Arjona et al., 2020a, 2020b; Marcet Rius et al., 2018a; Moscovice et al., 2022; Reimert et al., 2015Fish: Batzina et al., 2014; Fife-Cook and Franks, 2019; Kittilsen, 2013
Plasma serotoninCA, PICattle: dos Santos Lemes Lechuga et al., 2023; Harris et al., 2024; Mota-Rojas et al., 2024aPigs: Lykhach et al., 2020
DopamineCA, FICattle: Ishiwata et al., 2006; Mota-Rojas et al., 2024aFish: Batzina et al., 2014; Fife-Cook and Franks, 2019; Kittilsen, 2013
Root mean square successive difference (RMSSD)SRSmall ruminants: Doyle et al., 2011; Reefmann et al., 2009a; Tamioso et al., 2020, 2018
HAIHuman animal relationshipCA, PI, CHCattle: Andreasen et al., 2020; Bertenshaw and Rowlinson, 2008; Crossley et al., 2022; De Rosa et al., 2009; Ebinghaus et al., 2022; Jardim et al., 2022; Kutzer et al., 2015; Lange et al., 2021, 2020a; Mandel et al., 2016; Marchesini et al., 2024; Mattiello et al., 2024, 2019; Miller-Cushon and Van Os, 2019; Mota-Rojas et al., 2024a, 2024a, 2024b; Napolitano et al., 2009; Nawroth and Rørvang, 2022; Neethirajan, 2022; Pacor et al., 2025; Proctor and Carder, 2015b; Schütz et al., 2012; Sharma and Phillips, 2019; Silva et al., 2017; Silva-Antunes et al., 2024; Stokes et al., 2022; Vigors et al., 2021; Vigors and Lawrence, 2019; Westerath et al., 2014; Windschnurer et al., 2009Pigs: Amann et al., 2025; Brajon et al., 2015; Calderón-Amor et al., 2024; Hemsworth and Coleman, 2025; Kells, 2022; Krugmann et al., 2019, 2020; Neethirajan, 2022; Truong et al., 2024; Villain et al., 2020b; Zupan et al., 2016Chicken: Calderón-Amor et al., 2025; Ferreira et al., 2025; Herrera-Alcaíno et al., 2024; Sans et al., 2021a; Sans et al., 2021b; Skalná et al., 2023

In addition, cognitive indicators represent one of the most recent developments in the assessment of animal affective states, relying on the use of cognitive bias tests. Methods such as judgment-bias tasks, social recognition tests, avoidance distance assessments and choice tests have become increasingly prominent because they provide a window into how animals perceive and interpret their environment (Destrez et al., 2013; Doyle et al., 2011). These tests are based on the theory that emotional states shape cognitive processes, including memory, attention, motivation and decision-making (Nematipour et al., 2022; Wilson et al., 2023). Consequently, animals in different affective states tend to respond differently to ambiguous cues, social partners and potentially stressful situations. This cognitive-based framework represents the newest frontier in the scientific assessment of animal emotions, moving beyond traditional behavioral and physiological indicators toward a more integrative understanding of affective experience. In fish, emerging evidence suggests that individuals are also capable of engaging with and responding to such cognitive test paradigms, indicating their potential applicability across taxa (Alvarado et al., 2023; Anderson et al., 2022; Brunet et al., 2022; DePasquale et al., 2020; Freire and Nicol, 2024; Gauy et al., 2021; Kittilsen, 2013; Kleiber et al., 2022; Maia et al., 2024; Millot et al., 2014; Rogers et al., 2020; Shen et al., 2023; Zhang et al., 2023b, 2022).

A key distinction, however, lies in the distribution of these indicators across species. While terrestrial livestock represent a small number of domesticated species, the category “fish” encompasses a very large and diverse set of species (FAO, 2024), as also shown in Figure 3. Consequently, although fish appear to match terrestrial animals in most types of indicators reported, the evidence is spread thinly across numerous species with significant differences in sensory, cognitive and environmental characteristics, revealing a substantial knowledge gap and highlighting the need for far more species-specific research in aquatic taxa.

Moreover, the identification of similar indicator categories across taxa does not imply an equivalent level of empirical support. Behavioral and cognitive measures may provide comparatively stronger evidence of affective state, whereas production-related outcomes and some physiological measures are more indirect and therefore require cautious interpretation. Accordingly, even when broad categories are shared across taxa, the validity of individual measures depends on the species and context in which they are applied, as well as the strength of the supporting empirical evidence.

3.3 Promoting positive welfare

The welfare indicators discussed above were identified under environmental and husbandry conditions assumed to promote positive welfare across production animal taxa. The corresponding positive welfare situations and enrichment strategies are summarized in Supplementary Material S3. The recurrence of similar enrichment strategies across species suggests that knowledge gained in one production system may help guide the development of positive welfare interventions in others.

Small ruminants, cattle, pigs and chicken were considered to benefit from access to environments that include indoor-outdoor areas, loose or organic housing, hiding space, appropriate shade or lighting and comfortable bedding (Fleming et al., 2016; Hendriks et al., 2025; May et al., 2022; Nowland et al., 2019; Scaillierez et al., 2024). Structural enrichment, such as manipulable materials, climbing or perching structures, or varied substrates, was also extensively studied and perceived as a management that improves animal welfare (Supplementary Material S3) (Barreto et al., 2022; Biesek et al., 2023; Campbell et al., 2019a; Marcet Rius et al., 2018c).

Beyond the physical environment, cognitive opportunities (novel object/arenas, learning tasks, mirrors, laser light, change in feed locations), appetitive events (unexpected/signaled feeding events, dietary supplementation, live feed) and social enrichments (mother-offspring interaction, familiarity, affiliation, behavioral synchrony) were considered to lead to positive welfare states (MaChado and Silva, 2020; Meyer et al., 2010; Neave et al., 2023; Ninomiya et al., 2025; Proctor and Carder, 2016; Zebunke et al., 2011). Additional sensory inputs, including auditory (music, maternal calls), olfactory (added aroma) and visual stimulation (stimulatory color and patterns, video images), were also considered to enhance welfare (Supplementary Material S3) (Campbell et al., 2019a; Palermo Mendes et al., 2023; Papoutsoglou et al., 2008; Zapata Cardona et al., 2023).

Automation is increasingly recognized as an important component of animal-friendly production systems (Kearton et al., 2020; Rose and Lewton, 2025). Technologies such as automated feeders and milking stations can reduce the need for direct handling and allow animals greater choice and control, such as the opportunity to determine when they access specific resources (Mandel et al., 2019). When appropriately designed and managed, such systems may reduce stress and facilitate positive welfare experiences.

Finally, predictability represents a distinct but complementary feature of the animals’ experience of their environment (Bassett and Buchanan-Smith, 2007). Consistent management routines and informative cues, such as light or acoustic signals, allow animals to anticipate upcoming husbandry events. Predictability may be further enhanced through associative or operant learning (Hintze et al., 2025; Högberg et al., 2025; Ryan et al., 2021). By reducing uncertainty and increasing perceived control, production animals may experience reduced anxiety, improved coping ability and enhanced positive welfare.

Despite their distinct biology, fish respond positively to many of the same enrichment themes as terrestrial animal. Several fish species have been shown to benefit from cognitive challenges, appetitive rewards, structural complexity (e.g., plants, ropes, varied water flow, automated feeders, self-feeders) and sensory stimulation through colors, lighting, or video imagery (Fife-Cook and Franks, 2019; Gauy et al., 2021; Poirier et al., 2019; Rauw et al., 2017). Tactile, social and hiding enrichments of the aquatic environments further support positive welfare. Together, these findings illustrate that although the specific implementations vary, similar enrichment principles promoting choice, stimulation, comfort and predictability are relevant across terrestrial and aquatic species.

3.4 Context-dependent interpretation

Importantly, the interpretation of positive welfare indicators cannot be separated from the husbandry and experimental context in which they are measured. Accordingly, studies assessing positive welfare should not only be based on the behavioral, physiological or cognitive responses observed, but they should also critically describe and evaluate the environmental and management conditions under which these responses were elicited. Without this contextual information, it is difficult to determine whether a given response genuinely reflects positive welfare or is influenced by characteristics of the production system or experimental design.

For instance, increased physical complexity of the environment is generally regarded as beneficial for farmed animals; however, concerns have been raised regarding potential trade-offs, including reduced biosecurity, harder health and welfare inspection due to limited visibility of animals and an increased risk of injuries (Godyń et al., 2019; Taylor et al., 2023). Similarly, cognitive opportunities and appetitive events, while often associated with positive engagement, may also introduce frustration, competition, or heightened arousal if expectations are not met or if access to resources is limited (Kleiber et al., 2023; Taylor et al., 2023).

Social enrichment, although intended to reflect natural affiliative conditions, can also lead to increased competition, social instability, or aggression depending on group composition and stocking density (Lee et al., 2022). Sensory enrichment is additionally complex, as the extent to which auditory, olfactory, or visual stimuli are perceived as positive by animals may not always align with human assumptions about their valence (Mota-Rojas et al., 2024a; Wells, 2009).

Finally, automation and increased environmental predictability, while generally associated with reduced handling stress and improved consistency of management, may also have unintended consequences. In particular, predictability may facilitate resource monopolization by dominant individuals, thereby increasing social inequalities within groups and potentially exacerbating competition for access to key resources (Bassett and Buchanan-Smith, 2007). At the same time, excessive predictability may reduce environmental variability and behavioral opportunity, limiting adaptive engagement (Bassett and Buchanan-Smith, 2007).

Overall, these examples illustrate that the same intervention or welfare indicator may have different implications depending on the production, husbandry and experimental context. This is particularly relevant for aquaculture, where the diversity of production systems makes context an essential component of welfare assessment. Accordingly, studies of positive welfare should interpret welfare indicators alongside a clear description and evaluation of the conditions under which they were measured.

3.5 Enrichment in aquaculture systems

As noted, the term enrichment includes a broad spectrum of stimuli, including physical, cognitive, social, sensory and management-related inputs. Identifying environmental enrichment that is compatible with production systems is essential for promoting positive welfare while maintaining operational feasibility.

In aquaculture, as in terrestrial systems, the application of enrichment requires particular consideration of system hygiene and handling constraints. Structural enrichment has been associated with improved welfare, but it has also been frequently linked to practical challenges, i.e. increased difficulty in cleaning and maintaining biosecurity, which can limit its incorporation in commercial systems (Arechavala-Lopez et al., 2019). Consequently, other forms of enrichment may be more readily applicable. These include cognitive and appetitive enrichment, such as variable or signaled feeding regimes and the use of live or novel feed items, as well as sensory enrichment through manipulation of light intensity, spectral composition, background color, or visual stimuli.

Providing animals with choice, i.e. self-feeders, access to areas with different light conditions (spectrum and/or intensity) or background characteristics, can support positive welfare and may be relatively feasible in controlled aquaculture on land systems such as tanks. In sea cages, however, environmental conditions already vary naturally across depth, space and time, requiring fish to trade-off between different preferences (Oppedal et al., 2011). While this makes it more challenging for farmers to actively provide optimal choices, adding environmental complexity may still enhance opportunities for behavioral choice and control. In addition, predictability achieved through consistent routines, informative cues and automated feeding systems can enhance perceived control and reduce stress. Together, these enrichment strategies can offer practical and effective means for promoting positive welfare in farmed fish without compromising system manageability.

Notably, the search for effective enrichment strategies in aquaculture is very recent, with many potentially relevant factors yet to be explored. Future work is needed to understand how productive features and external stimuli may shape welfare, as their effects may be either beneficial or detrimental based on species and system. Aquatic production systems, such as earthen ponds for carp or tilapia, or open marine sea cages, expose animals to a wider range of natural stimuli, including interactions with wild fish, invertebrates, predators or other organisms aggregating around cages. Those systems create a biologically complex and dynamic environment. The presence of these organisms can function as environmental enrichment, but its welfare impact is context dependent. Potential positive effects include enhanced cognitive stimulation, promotion of natural foraging and inspection behavior, enhanced spatial learning or attention and reduced behavioral monotony, while potentially negative consequences include increased perceived predation risk, elevated vigilance and stress responses, transmission of pathogens or parasites, or creation of competitive or aggressive encounters, yet the actual welfare implications of such exposure have not been systematically investigated.

4 Conclusion

This review summarizes positive welfare indicators identified in production animals. Our findings indicate that knowledge gained from terrestrial livestock is applicable to teleost fish, which appear to experience their environments in similar ways, despite the challenges associated with evaluating underwater conditions. Comparable enrichments and environmental conditions lead to the expression of analogous positive welfare indicators in fish. Thus, both the biological basis of sentience and the observed response of fish to positive stimulation underline their capacity to experience emotions and the need to further include them in the scientific research of positive welfare assessment.

Although aquaculture has grown rapidly, research on positive welfare in farmed fish remains comparatively limited, particularly given the large number of species currently cultured. Nevertheless, the principles, indicators and approaches developed in terrestrial livestock research provide valuable opportunities for advancing positive welfare in aquaculture. At the same time, the considerable biological diversity of farmed fish and the wide range of production systems require that welfare indicators and management strategies be evaluated and interpreted within their species-specific and production-specific contexts. Rather than directly transferring terrestrial approaches, future research should build on this knowledge to develop and validate welfare assessment frameworks that are appropriate for different fish species and farming systems. Overall, this review highlights that positive welfare in fish shares many conceptual foundations with terrestrial livestock, creating important opportunities for cross-learning while emphasizing the need for context-specific research to support robust welfare assessment and management in aquaculture.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fanim.2026.1957894/full#supplementary-material

Supplementary material Supplementary material at the source

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Footnotes

Edited by: Leonie Jacobs, Virginia Tech, United States

Reviewed by: Milena Milojević, Academy of Applied Studies Šabac, Serbia

Snežana Paskaš, University of Novi Sad, Serbia

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the European Union – NextGeneration EU through the National Recovery and Resilience Plan Greece 2.0, under the project “Development of innovative tools and services for the assessment of aquaculture fish welfare (CARFISH-TRN)” (MIS 5180519), within the framework of the action Clusters of Research Excellence (CREs).

Author contributions

KP: Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing. AS: Methodology, Writing – review & editing, Conceptualization. FH: Writing – review & editing. MP: Conceptualization, Methodology, Project administration, Visualization, Writing – review & editing.

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Citation

Papadaki, K., et al. (2026). Beyond minimizing suffering: a comparative review of positive welfare assessment in fish and terrestrial farmed animals. Frontiers in Animal Science, Volume 7. https://doi.org/
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Copyright © 2026 Papadaki, Samaras, Hoorweg and Pavlidis.

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*Correspondence: Michail Pavlidis

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