Why do some fish grow faster than others?

11 All animals must acquire food to grow, but there is a vast diversity in how different species 12 and even different individuals approach and achieve this task. Individuals within a species 13 appear to fall along a bold-shy continuum, whereby some fish acquire food aggressively and 14 with seemingly high risk, while others appear more submissive and opportunistic. Greater food 15 consumption generally results in faster growth, but only if the energy acquired through food is 16 more than enough to compensate for heightened metabolism associated with a more active 17 lifestyle. Fast-growing phenotypes also tend to have elevated baseline metabolism – at least 18 when food is plentiful – which may be linked with gut morphology and digestive efficiency. 19 The net energy gained from a meal (as calculated from the specific dynamic action (SDA) 20 coefficient) is optimised with larger meal sizes, but the digestion of large meals can erode the 21 aerobic metabolic scope available for other critical activities such as predator avoidance, 22 perhaps at an interindividual level. Thus, complex interactions between an individual’s genes 23 and environment are likely to regulate the growth phenotype. This review compiles available 24 knowledge to shed light on the question: Why do some fish grow faster than others? We discuss 25 the elaborate interrelationships between behaviour, physiology and the gut microbiome with a 26 goal to better understand what drives intraspecific differences in growth performance. 27


Introduction
Growth is arguably the most important determinant for the survival of most organisms, perhaps especially aquatic ectotherms like fishes where growth is particularly plastic and early-life mortality can be extreme (Peters & Peters, 1986;Clark et al., 2016;Goatley & Bellwood, 2016).In a fish's early life stages, survival depends on the ability of an individual to avoid predation and compete for resources like space and food.Indeed, many species are known to cannibalise conspecific individuals as they outgrow them (Duk et al., 2017).Thus, an individual is much more likely to succeed and become established if it grows big and does so quickly (Stige et al., 2019).Despite this, wide discrepancies in growth and feed intake among closely related individuals are common in fish populations (Carter et al., 1992;Pfister & Stevens, 2003;Vincenzi et al., 2014).Even when genetic diversity is minimised (e.g., siblings) and individuals are reared in the same environment with surplus food, some fish grow faster and bigger (Table 1).
In addition to the importance of growth phenotypes in shaping ecological communities, the applied importance is widespread.For example, fisheries sectors often harvest based on fish size (Darimont et al., 2009;Sutter et al., 2012;Uusi-Heikkilä et al., 2015).In aquaculture, fastgrowing phenotypes are able to achieve target sizes sooner to reduce production costs and resource use (Asche, 2008;Kumar & Engle, 2016).In fish stocking programs, whether for conservation or recreational angling purposes, the release of fast-growing juvenile fish may minimise predation mortality to enhance survival and reduce the time taken to achieve target adult sizes for spawning or capture (Hutchison et al., 2012;Barrow et al., 2021).
Living in the current era of climate change, there is also much interest in understanding which genotypes and phenotypes may help to future-proof animal populations and associated industries (Somero, 2010;Seebacher, White & Franklin, 2015;Morgan et al., 2022).It is not known whether the fast-growing phenotypes in today's climate will be the fast-growing phenotypes of future climates, or whether interindividual rankings will reorder as environmental conditions change.
Filling these gaps requires an understanding of the drivers of phenotypic growth variation in fish, yet to our knowledge, there has been no previous attempt to compile the mechanisms underlying interindividual differences in fish growth.No doubt, the development of a beneficial growth phenotype will depend on complex interactions between a range of determining factors.This review presents a summary of current knowledge on interindividual growth differences within fish populations.We draw on examples from lab-and field-based studies to identify potential drivers of this phenotypic diversity, and provide future directions to help guide research in this field.

Parental influences
The growth phenotype of an individual will be influenced by its own environment and potentially the environment experienced by its parents (Monaghan, 2008).For instance, environmental factors that affect maternal fitness will influence maternal investment into individual offspring and the emergence and diversity of early life phenotypes (Burton & Metcalfe, 2014;Van Leeuwen et al., 2016;Feiner et al., 2016;Cortese et al., 2022).Parental temperature (Colson et al., 2019;Chang, Lee & Munch, 2021), oxygen (Ho & Burggren, 2012), stress (Eriksen et al., 2006(Eriksen et al., , 2007(Eriksen et al., , 2011)), social ranking or interactions with conspecifics (Burton et al., 2013) can all influence egg size and/or composition to modulate offspring growth, survival and even behaviour.Variation in egg size both between females and within the same clutch has been reported in salmonids (Beacham & Murray, 1987, 1993;Einum, 2003;Self et al., 2018).It is generally assumed that larger eggs give rise to an individual with a competitive size advantage (Einum & Fleming, 1999;Thorn & Morbey, 2018).In brown trout (Salmo trutta), individuals hatched from larger eggs had growth and survival advantages over individuals hatched from smaller eggs (Einum & Fleming, 1999).However, other studies on the same species have found higher rates of survival in individuals hatching from small eggs as opposed to large eggs (Régnier et al., 2013).Likewise, in steelhead trout (Oncorhynchus mykiss), smaller eggs hatched sooner and had higher growth rates than fish from larger eggs (Self et al., 2018).Both within-and between-clutch variation in offspring phenotypes, like egg size and larval growth, are known to increase in more variable or unpredictable environments (Crean & Marshall, 2009).This adaptive phenotypic response to environmental heterogeneity is an example of 'bet hedging' and allows mothers to adjust the phenotype of sibling offspring to increase variability and improve fitness and survival of at least some of the clutch (Mousseau & Fox, 1998;Crean & Marshall, 2009).In some salmonid species or populations, egg size remains consistent within a clutch, suggesting sibling survival or growth could be related to the distribution of phenotypes within an egg mass before spawning (Burton et al., 2013).The maternal endocrine system is closely associated with its progeny and will at least partly drive phenotypic differences between siblings (Eriksen et al., 2007;Sopinka et al., 2017).
Maternal hormones are accumulated and absorbed into the nutritive yolk sac of developing embryos during oogenesis (Hwang et al., 1992;Mylonas, Sullivan & Hinshaw, 1994;Schreck, Contreras-Sanchez & Fitzpatrick, 2001;Eriksen et al., 2007Eriksen et al., , 2011;;Sopinka et al., 2017).In fact, the developmental, reproductive and metabolic hormones present in the yolk sac of developing embryos occur in similar quantities to that of maternal blood plasma (Hwang et al., 1992;Mylonas et al., 1994;Schreck et al., 2001).An example of this relationship is seen in a consistency of hormone patterns between stressed mothers and their offspring (Eriksen et al., 2011).Given that growth suppression in teleost fish is a consequence of most forms of environmental stress (Pickering, 1990(Pickering, , 1993)), maternal stressor exposure activates the transmission of the stress response, binding circulating cortisol in target tissues and developing follicles in the female's ovaries (Sopinka et al., 2017).Some studies suggest that paternal effects will also influence the endocrine state of offspring (McGhee & Bell, 2014;Hellmann, Carlson & Bell, 2021).Paternal predation exposure of threespined sticklebacks (Gasterosteus aculeatus) reduced activity and elevated cortisol concentrations in offspring (Hellmann et al., 2021).Parental experiences of stress can therefore expose the developing embryos to elevated concentrations of glucocorticoids, which may impact subsequent growth at an individual level.
Experimentally manipulating the maternal endocrine state of female Atlantic salmon (Salmo salar) with cortisol led to offspring that grew less efficiently, had reduced survival and higher incidences of malformation compared with half-sib offspring from mothers with no cortisol treatment (Eriksen et al., 2006(Eriksen et al., , 2007(Eriksen et al., , 2011)).In some species, like the Atlantic halibut (Hippoglossus hippoglossus), differences in egg cortisol have no influence on offspring phenotypes like larval size (Skaalsvik et al., 2015).As well, differential impacts of egg cortisol exist between wild fish populations and populations reared in laboratory environments (Gingerich & Suski, 2011).Variation in total egg cortisol concentration also exists between individuals from the same clutch (i.e., between full sibs) (Sopinka et al., 2017).Previous research suggests that intra-female variation in egg cortisol of brown trout will depend on the position of eggs within the ovary (anterior, middle, and posterior) (Suter, 2002).Interestingly, other studies have reported that location in the egg mass affects social dominance, behavioural phenotypes and egg size in resulting juveniles of the same species (Burton et al., 2013).Taken together, independent of egg size differences, there may be a role of egg positioning within the clutch in determining the growth rates of early life stages.

Stress and the social environment
Stress hormones like catecholamines and cortisol function to mobilise energy reserves that help fish to escape, avoid or overcome an immediate threat (Bonga, 1997).Fish experiencing stress will divert resources like oxygen and energy away from investment activities (e.g., growth and reproduction) and toward activities like tissue repair (Bonga, 1997).As a result, the performance capacity of fish during stress can be compromised (Madison et al., 2015).
Maintenance of plasma cortisol at 116 ng/ml via implant micro-pumps in rainbow trout led to a 60% reduction in feed intake and up to 80% reduction in mass gain (Madison et al., 2015).
However, growth responses to stressors can vary.For example, in threespined stickleback (Gasterosteus aculeatus), early exposure to a predation risk increased juvenile somatic growth, but caused a decrease in size at adulthood (Bell et al., 2011).In fish and other vertebrates, the stress response is initiated and controlled by the activation of the hypothalamic-pituitaryinterrenal/adrenal (HPI or HPA) axis (Bonga, 1997;Bernier & Peter, 2001;Gilmour, Dibattista & Thomas, 2005).The HPI or HPA axis describes the communication that is present between the hypothalamus and the pituitary gland in the fish brain and the head kidney (Bonga, 1997;Bernier & Peter, 2001).When exposed to a stressor, the hypothalamus releases corticotropinreleasing factor/hormone (CRF or CRH), which stimulates the release of adrenocorticotropic hormone (ACTH) from the pituitary (Bernier & Peter, 2001).ACTH binds to receptors on the interrenal cells in the head kidney, initiating a biochemical cascade that results in the synthesis of cortisol (Bernier & Peter, 2001).Anthropogenic stressors have also been shown to disrupt the thyroid and alter levels of thyroid hormones (TH) in fishes (Deal & Volkoff, 2020;Besson et al., 2020).Thyroid hormones are critical to early fish development, behaviour (Besson et al., 2020) and the regulation of somatic growth and appetite (e.g., through the hypothalamicpituitary-somatotropic (HPS) axis) (Deal & Volkoff, 2020).Wild and captive fish can face a multitude of stressors that all have the potential to induce chronic stress (i.e., sustained, elevated plasma glucocorticoids), and inhibit growth through impacts on the metabolic, digestive and behavioural phenotype (Bonga, 1997;Mommsen, Vijayan & Moon, 1999;Barton, 2002;Deal & Volkoff, 2020).
Stressful social interactions (or complete lack of social stimuli) between conspecifics can lead to a stress response that controls behaviours like aggression, appetite, foraging and locomotion (Gilmour et al., 2005).These behaviours are often associated with descriptive terms for individuals like 'bold', 'shy', 'dominant' or 'subordinate' (Gilmour et al., 2005;Metcalfe, Van Leeuwen & Killen, 2016).These 'behavioural phenotypes' may shift through time and change with the social environment and with resources like food availability, shelter and habitat (Wieser, Krumschnabel & Ojwang-Okwor, 1992;Hofmann, Benson & Fernald, 1999;Höjesjö, Johnsson & Bohlin, 2004;Reid, Armstrong & Metcalfe, 2012).As a result, in some species, subordinate fish show suppression in appetite, feed intake, aggression, locomotion and growth (Gilmour et al., 2005), while dominant individuals have opposing behaviours that allow them to monopolise resources and gain a competitive growth advantage (Abbott & Dill, 1989;Metcalfe, Wright & Thorpe, 1992).In subordinate European eels (Anguilla anguilla), social dominance acted as a significant stressor causing reduced feed intake, growth, extensive intestinal lesions and a reduced stomach size (Peters, 1982).When dominant and subordinate salmonids were confined in pairs, an antagonistic interaction caused a rapid increase in plasma cortisol in both fish (Øverli, Harris & Winberg, 1999a), yet the blood cortisol concentration of the dominant individual returned to resting levels much quicker (within 3 h; Øverli et al., 1999a) than the subordinate individual (up to 7 days; Øverli et al., 1999a;Sloman et al., 2001).Thus, in salmonids, social subordination is viewed as a chronic stressor, which causes a chronic activation of the HPI axis, regulating subordinate traits like appetite to affect grow rates (Øverli et al., 1999b, 1999a;Gilmour et al., 2005).
In the African cichlid fish, Haplochromis burtoni, only territorial males (i.e., dominant individuals) are reproductively active (Hofmann et al., 1999).The territorial males will work to maintain territories and court females, while non-territorial males (i.e., subordinate individuals) are sexually regressed and school with females (Fernald & Hirata, 1977).Because of reduced energy expenditure, non-territorial males and animals ascending in social rank have higher rates of somatic growth (Hofmann et al., 1999).Social status is highly flexible in H. burtoni and as a result the growth rates of individuals change frequently within a population (Hofmann et al., 1999).Reversible phenotypic plasticity is a crucial life-history trait that is thought to enable this species to shift resources from reproduction to growth and vice versa (Hofmann et al., 1999;Trainor & Hofmann, 2007;Dijkstra et al., 2017).The shifts in social dominance and growth of H. burtoni are thought to be regulated by multiple endocrine pathways and involve gonadotropin-releasing hormone (GnRH), somatostatin and the melanocortin system (Hofmann et al., 1999;Trainor & Hofmann, 2007;Dijkstra et al., 2017).
In other cichlid species (Lamprologus callipterus), males within a population can adopt different reproductive strategies that lead to multiple growth patterns and the presence of both small 'dwarfed' and large 'nested' males of the same age within the same population (Wirtz-Ocaňa et al., 2013).The endocrine profiles of these species, and those that show clear sexspecific size dimorphism (Pietsch, 1976;Isakov, 2022) could provide useful insight into the drivers of interindividual growth differences (Malison et al., 1985(Malison et al., , 1988)).
In social species where social dominance determines appetite and access to food and resources, behavioural phenotypes and stress will play an important role in the development of multiple growth phenotypes within a population.However, in schooling, non-social or non-aggressive species, where social dominance is not considered to be a significant factor, interindividual differences in growth can still exist (Cui & Liu, 1990;Carter et al., 1992).Similarly, in labbased studies, where social interactions are removed (e.g., through isolation in individual tanks), obvious growth differences persist (Norin, Malte & Clark, 2016).In the above cases, grow rates are unlikely to be regulated by social stress, and therefore metabolic and digestive phenotypes may play a role.

The metabolic phenotype
The metabolic phenotype shapes an animal's energy budget and will dictate the energy spent by an animal at rest, during digestion and during routine or maximum activity (Clark, Sandblom & Jutfelt, 2013).Large individual variations in the standard metabolic rate (SMR), routine metabolic rate (RMR, metabolic rate at regular activity levels), maximum metabolic rate (MMR), specific dynamic action (SDA, energy cost of digestion) and aerobic scope (aka 'scope for activity') are common in fish populations (Metcalfe et al., 2016).Between individuals of the same species there can be a 2-3-fold variation in SMR and MMR (Rice, 1990).Such differences in energy allocation and use between individuals will influence the capacity to convert food energy into tissues for subsequent growth.
Under ad-libitum feeding conditions we expect faster growers to have a higher SMR than their slow-growing conspecifics (Norin & Malte, 2012;Norin & Clark, 2017) (Fig. 1).Previous research on barramundi (Lates calcarifer) has shown that SMR is positively correlated with specific growth rate (SGR) (Norin et al., 2016).Norin et al (2016) found that individuals with a high SMR ate more food and grew quicker than conspecifics with a low SMR.That is, high SMR individuals consumed a surplus of food to more than compensate for their higher baseline metabolic requirements (Norin et al., 2016).In social species, high SMR individuals tend to display a dominant behavioural phenotype that drives behaviours allowing them to monopolise resources, consume more food and grow bigger (Reid et al., 2012;Hoogenboom et al., 2013;Metcalfe et al., 2016).This competitive growth advantage among high SMR fishes is thought to be modulated by environmental conditions like food supply (Burton et al., 2011), feeding conditions (Killen, Marras & McKenzie, 2011;Metcalfe et al., 2016) and habitat complexity (Robertsen et al., 2014).When food is restricted, the relative growth rate of high SMR individuals may be less than their low SMR conspecifics (O'Connor, Taylor & Metcalfe, 2000;Norin & Malte, 2011).While there is evidence of a link between high SMR individuals and growth when food is abundant, this relationship does not persist across all species or life stages.
In larval Atlantic herring (Clupea harengus; 7 days post-hatch), interindividual differences in SMR were not associated with growth (Moyano et al., 2017).There is some evidence that observed links between metabolism and growth may be related to interindividual variation in the efficiency with which substrates are converted into ATP at the mitochondria (e.g., via 'proton leak'; Salin et al., 2019).Additionally, the metabolic traits of individuals respond differently to environmental challenges (Norin et al., 2016), suggesting that the relative ranking of slow-and fast-growing individuals may change across days, seasons, and with climate change.
In the context of the metabolic phenotype, we might expect that individuals with large relative organ masses would exhibit proportionally greater metabolic rate with potential implications for growth (Ferrell, 1988;Piersma & Lindström, 1997).However, in brown trout (Salmo trutta), no relationship between SMR, MMR and the residual size (mass) of metabolically active internal organs (stomach, intestine, liver, heart, spleen) was found (Norin & Malte, 2012).Instead, this study found that the SMR, MMR and aerobic scope were significantly correlated with liver activity of the aerobic mitochondrial enzyme, cytochrome c-oxidase.The study concluded that intraspecific variation in the metabolic rate of fish can be found at a lower organisational level than organ size alone (Norin & Malte, 2012).Thus, while it appears that there is no clear link between relative organ size, metabolism and growth, more research is required to understand the relationships between these parameters.
Variation in the energy cost of digestion, SDA, is also thought to be correlated with SMR (Secor, 2009).The SDA accounts for the energy expended on every physiological, mechanical and biochemical process that facilitates the breakdown of food, and the absorption, transport, and assimilation of its nutrients (Secor, 2017).Fish with a higher SMR can exhibit a higher SDA peak (i.e., peak in oxygen consumption is higher during digestion), but shorter SDA duration (i.e., digestion finishes sooner), meaning high SMR individuals can have faster digestion rates and potentially faster growth (Metcalfe et al., 2016).Juvenile Atlantic salmon (Salmo salar) with a high SMR had a greater (more energetically expensive) SDA, but a shorter SDA duration than those with a low SMR phenotype (Millidine, Armstrong & Metcalfe, 2009).
Thus, despite having a greater baseline energy expenditure, salmon with a high SMR have a shorter SDA duration and can therefore feed more frequently to facilitate faster growth (Millidine et al., 2009).
Similarly, the SDA is also associated with and governed by the available aerobic scope and postprandial residual aerobic scope (PRAS) of an individual (Jutfelt et al., 2021) (Fig. 1).The aerobic scope describes the scope for activity and is calculated as the difference between MMR and SMR (Clark et al., 2013).PRAS describes the scope for activity on top of digestion and is calculated as the difference between the peak of the SDA and MMR (Jutfelt et al., 2021).In less athletic species that prioritise feeding over movement, the scope for activity can be defined as the difference between the active metabolic rate (AMR) and SMR (Steell et al., 2019).The SDA can take up a significant proportion of the aerobic scope during digestion in fish (e.g., up to 77% in barramundi (Lates calcarifer); Norin & Clark, 2017).Moreover, in the lionfish (Pterois spp.), the maximum metabolic rate during digestion (SDA peak) can exceed the metabolic rate reached following exhaustive exercise (Steell et al., 2019).In some species, environmental conditions like elevated temperatures can temporally compress the SDA, further constraining aerobic scope and PRAS and driving a reduction in feed intake (Jordan & Steffensen, 2007;Oliver et al., 2017;Wade et al., 2019;Jutfelt et al., 2021).Since the SDA increases with meal size to occupy more of the available aerobic scope (Fu, Xie & Cao, 2005;Jordan & Steffensen, 2007;Secor, 2009), modulating feed intake during warming is hypothesised to 'protect' PRAS and maximise the energy available for activities outside of digestion, like swimming and avoiding predation (Jutfelt et al., 2021).This hypothesis would suggest that in benign environments, individuals with a greater MMR (and therefore greater PRAS) may be able to maximise energy gains and growth by consuming more food relative to low MMR/PRAS individuals (Fig. 1).Conversely, recent work in sham-fed Chinook salmon (Oncorhynchus tshawytscha) showed that elevated temperature had no effect on PRAS during the digestion of a 2% meal ration (Lo et al., 2022).Contrary to the hypothesis presented by Jutfelt et al (2021), some species may not mediate food intake based on the occupation of the SDA in their scope for activity and in turn feed intake and growth may not be limited by phenotypic differences in AMR, SMR or MMR for those species.
The SDA coefficient (% of meal energy used in the SDA) typically ranges 5-20% in fish (Beamish, 1974;Fu et al., 2005;Secor, 2009), but can reach up to 50% in some fish species (Secor, 2017).It is generally assumed that a larger SDA coefficient for a given meal size is indicative of inefficient digestion and less absorbed energy available for growth.Therefore, if environmental and nutritional requirements remain constant, individuals with a smaller SDA coefficient should grow more efficiently than individuals with a larger SDA coefficient (Jobling, 1994;Secor, 2009).Recently tested in a study on juvenile barramundi (Lates calcarifer), Goodrich et al., (2021) showed that reducing the SDA coefficient through dietary acidification can lead to acute improvements in fish growth efficiency, but these improvements declined over time.
In contrast, Carter and Brafield (1992) reported a positive relationship between the SDA and the specific growth rate of grass carp (Ctenopharyngodon idella).These findings contradict the original theory presented by Jobling (1994) and Secor (2009) and suggest that the SDA coefficient may also be indicative of digestive capacity and not just energy expenditure.For example, a larger SDA coefficient may indicate greater capacity for energetically expensive processes like protein synthesis.Protein synthesis uses four ATPs to bind one amino acid to the next, and for this reason is known to be a primary contributor to the SDA (Lusk, 1922;Jobling, 1985;Brown & Cameron, 1991a, 1991b).The total energetic cost to synthesise 1 gram of protein has been estimated to equal ~50 mmol of ATP equivalents (Reeds, Fuller & Nicholson, 1985).Infusion of an amino acid mixture directly into the blood stream of fasted channel catfish (Ictalurus punctatus) was able to elicit an SDA response and significantly increase oxygen consumption above resting levels (Brown & Cameron, 1991a).In cod (Gadus morhua), protein synthesis is thought to contribute between 20 to 40% of the SDA (Lyndon, Houlihan & Hall, 1992;Smith & Houlihan, 1995).Therefore, while a larger SDA may indicate greater energetic costs, it may also indicate greater capacity to assimilate nutrients from food for subsequent growth.In these instances, individuals with a beneficial SDA phenotype (e.g., high SDA coefficient) may have a competitive growth advantage over conspecifics with a reduced SDA phenotype (e.g., low SDA coefficient), at least when food is abundant (Fig. 1).

The digestive phenotype
Phenotypic flexibility is well documented in the digestive systems of reptiles (Secor, Stein & Diamond, 1994;Secor & Diamond, 2000), birds (McWilliams & Karasov, 2001), mammals (Naya et al., 2007), and fishes (Armstrong & Bond, 2013;Blier et al., 2007;Htun-Han, 1978;Jobling et al., 1998;Piersma & Gils, 2011;Piersma & Lindström, 1997).Digestive tract adjustments, like changing organ size or length (Bergot, Blanc & Escaffre, 1981) and rates of protein synthesis, retention and degradation (Carter & Houlihan, 2001), are often associated with the amount of nutrients and energy that fish consume and assimilate.A multitude of studies have shown that the response of the digestive tract will vary with the intensity of the energetic demand imposed on the animal (Naya et al., 2007), the frequency of feeding in nature (Secor & Diamond, 2000;Secor, 2005aSecor, , 2005b)), the time to and type of first feed consumed by fish larvae (Kolkovski, 2001;Ching et al., 2016), the environmental conditions experienced by different populations of the same species (Kristan & Hammond, 2003;Bacigalupe et al., 2004;Tracy & Diamond, 2005), and the level of environmental variability under which different species have evolved (Naya, Bozinovic & Karasov, 2008).When fed ad libitum and reared in the same environmental conditions, phenotypic changes that result in an increase in the functional capacity of the digestive system are likely to lead to better performance and interindividual differences in fish growth.
Proteins from ingested food are central to animal growth and tissue maintenance.Proteins are incorporated into new tissue for growth through processes like protein cycling (Smith & Houlihan, 1995;Carter & Houlihan, 2001).Growth rates in fish will be controlled by the balance between rates of protein synthesis, retention and degradation (Houlihan et al., 1988;Houlihan, Hall & Gray, 1989;Carter et al., 1993a).In grass carp (Ctenopharyngodon idella), faster growing individuals had a lower RNA to protein ratio (capacity for protein synthesis), variable rates of protein synthesis, yet higher retention of synthesized protein, higher RNA activity and lower rates of protein degradation (Carter et al., 1993a).In Atlantic salmon (Salmo salar), individual variation in growth efficiency was related to differences in protein retention efficiency but no difference in the capacity for protein synthesis (Carter et al., 1993b).
Similarly, more efficient, faster growing rainbow trout (Oncorhynchus mykiss) had reduced rates of protein degradation in comparison to their slower growing conspecifics (McCarthy, Houlihan & Carter, 1994).
In the wild, some fish species adaptively regulate digestive capacity to match ambient levels of demand (Kent, Prosser & Graham, 1992;Jobling et al., 1998;Armstrong & Bond, 2013;Furey et al., 2016).In their natural streams, Dolly Varden trout (Salvelinus malma) take advantage of annual resource pulses that occur as a result of the spawning migration of Pacific salmon.During a small 5-week period where Pacific salmon spawn, Dolly Varden maximise energy gain by significantly increasing gut size to gorge on the eggs of Pacific salmon (Armstrong & Bond, 2013).Similarly, binge-feeding (hyperphagia) in bull trout (Salvelinus confluentus) during a prey pulse of out-migrating juvenile sockeye salmon (Oncorhynchus nerka) was facilitated by an increase in gut volume (Furey et al., 2016).Outside of resource pulses, fishes adopt a significantly smaller, and less energetically expensive gut (Armstrong & Bond, 2013).Alternating periods of feast and famine could generate trade-offs between phenotypes that maximize energy gain during resource abundance, and those that conserve energy during resource scarcity (Gans, 1979;Diamond, 2002;Piersma & Gils, 2011;Armstrong & Schindler, 2011;Armstrong & Bond, 2013).When reared in the same environment and fed in a food surplus, we would therefore expect that individuals with a larger and more expensive gut would maximise the energy gained from ingested food.The greater energetic cost of a large gut, provide some explanation for why some individuals have proportionally higher SDA and higher growth rates.
Despite the above possibilities, few studies have assessed the relationship between interindividual differences in gut size/anatomy, and variation in fish growth or appetite.Some evidence suggests that full siblings with a greater number of pyloric caeca in the digestive tract grow larger and are bigger than individuals of the same age (Bergot et al., 1981).The pyloric caeca are an important digestive organ responsible for the uptake of nutrients from food in some fish species (Buddington & Diamond, 1986).Possessing a larger number of caeca would be advantageous in a benign environment where all individuals have unlimited access to resources.Indeed, research on the cichlid fish (Simochromis pleurospilus) found that plasticity in digestive efficiency and growth was facilitated by possessing heavier digestive organs, yet dependent on early-life food availability (Kotrschal, Szidat & Taborsky, 2014).S. pleurospilus that were kept at a constant higher ration grew considerably faster than conspecifics offered lower food rations.However, S. pleurospilus fed a lower food ration were able to buffer the negative growth impacts by developing significantly heavier digestive organs, which made them more efficient at digesting food as adults.This suggests that digestive efficiency is influenced by food availability, growth and feed intake during a narrow 'plasticity window' that occurs in a fish's juvenile stages (Kotrschal et al., 2014).Individuals reared in the same food-limited environment may therefore adjust their gut for either immediate or delayed growth benefits (e.g., reducing organ size to maintain energy efficiency in a low-food juvenile environment, or increasing organ size to maximise energy gain in a future high-food adult environment) leading to differential juvenile and adult growth phenotypes.
Similarly, other early developmental characteristics like the time to first feed can influence the functional capacity of the digestive system in fish larvae to affect early grow rates and survival.
In larval tiger grouper (Epinephelus fuscoguttatus), delaying first feeding to 6 h after mouth opening resulted in an almost 50 % reduction in the height of the gut epithelium, causing delays in fish development and reduced growth (Ching et al., 2016).The type of food a larval fish first eats can also play a significant role in the capacity of their gastrointestinal tract.Most larval fish lack fully functioning digestive systems for the first weeks after hatching (Dabrowski, 1984).It has been proposed that larvae utilise the digestive enzymes present in their prey to facilitate the process of digestion until the larval alimentary system is fully developed (Dabrowski, 1984;Lauff & Hofer, 1984;Kolkovski et al., 1993;Kolkovski, 2001).
Support for this theory is mixed, with some studies reporting as much as 40 -80% of larval enzymatic activity is 'donated' by live food organisms (Dabrowski & Glogowski, 1977a, 1977b), and others suggesting live food contribution to direct digestive enzymes is negligible (Zambonino-Infante et al., 1996;Cahu & Zambonino-Infante, 1997).However, live feeds also contain gut neuropeptides and other nutritional growth factors that are known to enhance digestive capacity (Kolkovski, 2001).This may at least partly explain the improved grow rates observed in marine fish larvae reared on live foods as opposed to formulated micro diets (Kolkovski, 2001(Kolkovski, , 2013;;Giebichenstein et al., 2022).Variation in early developmental characteristics like the time to and type of first feed consumed by individual fish larvae could therefore contribute to differences in digestive efficiency, early growth phenotypes and interindividual fish growth within a population.

The gut microbiome
The community of microbes that colonise the gut of living animals (the gut microbiome) play an important functional role in almost every aspect of an animal's physiology (Tarnecki et al., 2017).Previous research has found that the gut microbiome can affect host metabolism, nutrient absorption, behaviour, satiety, reproduction, development, the immune response and growth (Avella et al., 2012;Carnevali, Avella & Gioacchini, 2013;Mayer, Tillisch & Gupta, 2015;Ghanbari, Kneifel & Domig, 2015;Wang et al., 2018;Johnson & Foster, 2018;Perry et al., 2020).In wild fish, microorganisms from food and the surrounding water adhere to and colonise the gut (Ghanbari et al., 2015).The function of the gut microbiota and the physiological response of the host will depend on the composition of the microbes present in the intestines of the individual (Tarnecki et al., 2017;Talwar et al., 2018).Factors like age, species, diet, social status, developmental stage, geographical location, sex and environmental conditions like temperature, salinity and pH can all influence the type, diversity and abundance of gut microbes in fishes (Ringø et al., 1997(Ringø et al., , 2016;;Nayak, 2010;Bevins & Salzman, 2011;Li et al., 2012Li et al., , 2014;;Ni et al., 2014;Borrelli et al., 2016).
Targeted manipulation of the fish microbiome is reported to alter gut morphology (Elsabagh et al., 2018), improve digestion and lipid metabolism (Falcinelli et al., 2015), influence satiety and appetite (Falcinelli et al., 2016;Gioacchini et al., 2018), improve fish memory and even influence shoaling behaviours in zebrafish (Borrelli et al., 2016;Zang et al., 2019).Zebrafish fed the probiotic Lactobacillus rhamnosus for 8 days expressed a significant downregulation of appetite-stimulating (orexigenic) genes and a simultaneous upregulation of appetitesuppressing (anorexigenic) genes (Falcinelli et al., 2016).These changes in gene expression were associated with differences in appetite and body glucose level between probiotic-fed fish and controls (Falcinelli et al., 2016).Similarly, Malaysian mahseer (Tor tambroides), fed Alcaligenes sp. and Bacillus sp., were able to enhance growth by upregulating the growthrelated genes, growth hormone (GH) and hepatic insulin-like growth factor IGF-1 (Asaduzzaman et al., 2018).These results indicate that gut microbiota can regulate metabolic pathways that modulate the physiological state of hunger and satiety to influence feed intake and/or growth and also provide evidence of a gut-brain interaction previously only described in higher vertebrates (Mayer et al., 2015;Butt & Volkoff, 2019).
The gut microbiota-brain axis describes the bi-directional communication that occurs between the gastrointestinal tract and the brain to influence host physiology and homeostasis (Mayer et al., 2015;Butt & Volkoff, 2019).It is thought that gut microbiota release metabolites that act either directly on the brain or indirectly through the enteroendocrine cells of the gastrointestinal tract (Butt & Volkoff, 2019).Here, metabolites function to alter neuropeptide release to modulate the feeding behaviours and energy homeostasis of the host (Butt & Volkoff, 2019).
For example, germ-free zebrafish treated with the bacterium Lactobacillus plantarum are able to attenuate stress-related behaviours (Davis et al., 2016), and decrease the stress response by lowering the expression of corticotrophin-releasing hormone (CRH) (Forsatkar et al., 2017).
As discussed above, the stress response is a key factor that affects the feeding responses of fishes (Bonga, 1997).Therefore, interindividual differences in the gut microbiome of fish may interact with the stress response and other phenotypic traits to alter feeding, appetite and ultimately growth.Understanding which environments, microbes and/or diets promote a beneficial microbiome will be important to future studies assessing interindividual differences in fish growth.

Conclusions and future directions
The phenotype that promotes or drives better growth in some fish will be a consequence of complex interactions between a large number of genetic and non-genetic factors.The development of a beneficial growth phenotype depends on the interplay of the organism's own genetic make-up, the environmental experience of its parents and the environmental/social experiences during its own development (Fig. 2).External influences on phenotypic development are likely mediated in part by endocrine systems and resultant physiological processes.Based on the current gaps in knowledge, we suggest a number of research questions which will drive understanding of interindividual differences in fish growth: 1. How do parental influences impact offspring growth phenotypes?
2. What are the relative contributions of genetic vs. non-genetic influences on interindividual growth differences?
3. What are the relationships between organ size, digestive efficiency and growth?
4. What are the interindividual relationships between SMR, MMR, aerobic scope, PRAS, SDA, feed intake and growth? 5. How do interindividual differences in the SDA coefficient translate to differences in growth?
6. Are interindividual differences in predictive traits for growth maintained through time?
7. How does the gut microbiome interact with metabolism, behaviour and growth of individuals?
8. How are interindividual growth differences modulated by environmental parameters, and can we select genotypes/phenotypes with optimal performance in future environments?
9. Can gene knock-out experiments help to answer the above questions, and which target genes might prove most fruitful (e.g., digestive processes, protein synthesis)?
While the influence of genetic traits has played a role in the selection of fast-growing fish in aquaculture, there has been relatively little research attention given to other, non-genetic factors that play a role in determining interindividual growth phenotypes.We hope that this paper sparks further interest in this topic and paves the way for new insights into the question of why some fish grow faster than others.

Acknowledgements
Some of the ideas in this paper were developed for a presentation by TDC at the Society for Experimental Biology's Main Meeting in Montpellier, France in July 2022.TDC is supported by Deakin University and an Australian Research Council Future Fellowship (FT180100154) funded by the Australian Government.HRG is supported by the Institute for Marine and Antarctic Studies (IMAS) and the University of Tasmania.We thank Natalie Sopinka and Graham Raby for constructive feedback on an earlier version of this manuscript.e.g., specific dynamic action (SDA), standard metabolic rate (SMR)), energy uptake (the digestive phenotype; e.g., organ size and efficiency) and behaviour (the behavioural phenotype; bold, shy, dominant, subordinate).The presence of multiple metabolic, digestive and/or behavioural phenotypes will drive the development of interindividual fish growth within a closely related population.

References
Table 1: Summary of studies that have either directly or indirectly assessed drivers of interindividual differences in fish growth.Where available, positive (+), negative (-) and non-significant (n.s.) relationships between the driver, growth trait measured and/or interindividual fish growth are shown.Fish relatedness was left blank when information on parents was unavailable.NA (Bell et al., 2011)

Figure 1 :
Figure1: Conceptual diagram showing some of the traits of fish that may characterise a highgrowth phenotype (blue) compared with a slow-growth phenotype (orange).Time could be equivalent to ~5 days.Symbols + andindicate higher and lower levels, respectively.Highgrowth individuals may have a higher standard metabolic rate (SMR), maximum metabolic rate (MMR) and aerobic scope.They may exhibit elevated boldness/aggression/activity and thus have higher metabolic requirements.When encountering prey in a competitive environment, high-growth individuals may consume lots of food quickly (resulting in a high specific dynamic action [SDA]), while slow-growth individuals may be submissive/hesitant and ultimately consume less food (lower SDA).When both high-and low-growth phenotypes consume the same sized ration, high-growth individuals may exhibit a greater SDA coefficient (SDA %) due to greater protein synthesis and anabolism.Despite the greater SDA coefficient, high-growth individuals may maintain a higher postprandial residual aerobic scope (PRAS) because of their elevated MMR.

Figure 2 :
Figure 2: Schematic showing interactions between drivers of interindividual differences in fish growth.Individual variation in factors like stress, maternal investment/endocrine state, social interactions, and early development characteristics like time to hatch and/or first feed will all act on the fish brain and endocrine system (e.g., release of growth hormone (GH) or cortisol) to drive the development of phenotypes with differential energy use (the metabolic phenotype; Specific growth rate (SGR), routine metabolic rate (RMR), feed conversion ratio (FCR), specific dynamic action (SDA), residual standard metabolic rate (rSMR), standard metabolic rate (SMR), days post-fertilisation (dpf), degree days (dd), hepatosomatic index (HSI), growth hormone (GH), aerobic scope (AS), NA (not applicable).