Influences of diet quality and nursery-habitat complexity on brain development and cognitive performance of brown trout (Salmo trutta L.)

Access to omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA), and the physical and social complexity of habitat have been proposed to influence brain development and cognitive ability. We aimed to investigate how juvenile brown trout mitigate dietary n-3 LC-PUFA deprivation in complex habitats, typical of natal streams, by testing the effects of n-3 LC-PUFA deficiency in diet and habitat complexity on somatic growth, cognitive performance, encephalisation, and n-3 LC-PUFA biosynthesis and nutrient routing capacity. Brown trout were raised from egg for seven months post-hatch on either a high (8.91%) or low (1.79%) n-3 LC-PUFA diet; for the final three months, trout were further divided into complex (heavily ornamented tanks with small, changing, populations) or simple habitats (bare tanks with many, constant, inhabitants). Cognitive abilities including recognition, memory and inference were tested by comparing the times required to establish stable hierarchical relationships in agonistic dyadic trials featuring naïve trout and trials in which one of the trout had previously observed the other. Gas chromatography and compound-specific stable hydrogen isotope analysis revealed increased biosynthesis and routing of n-3 LC-PUFA to the brain among trout on n-3 LC-PUFA-deficient diets. Fed ad libitum, trout did not sacrifice somatic growth to fuel biosynthesis and routing of n-3 LC-PUFA, but dietary deficiency in n‑3 LC-PUFA did lead to smaller brains, and smaller brains were associated with lower cognitive performance. Complex habitats gave rise to trout showing better cognitive performance, and were associated with lower somatic growth, but habitat complexity played only minor roles in encephalisation and the n-3 LC-PUFA composition of brain lipids. We conclude that brown trout can only partially compensate for the paucity of dietary n-3 LC-PUFA, and we suggest that cognitive divergences may play a role in the diversification of life-history variants among brown trout in the wild. \received DD MMMM YYYY \acceptedDD MMMM YYYY INTRODUCTION Collectively, the processes of perception, learning and memory that permit decision making constitute cognition (Shettleworth, 2010a). Cognitive performance impacts fitness and welfare of animals by influencing their capacities to forage, find potential mates and avoid predators (Boogert et al., 2018; Fuss, 2021). To collect and process the multitude of sensory inputs from their surroundings, fish have evolved sophisticated brains that allow them to distinguish between predator and prey, assess competitors, discern their own movements through their environments, evaluate risk, and decide whether and where to hide (Fernö et al. 2020). Although not yet well studied, brain development has been found in some fish species to be reliant upon the quality of their diet during larval stages (Hou and Fuiman, 2020). In particular, omega-3 long-chain (≥ 20 C) polyunsaturated fatty acids (n‑3 LC‑PUFA), especially docosahexaenoic acid (DHA 22:6n‑3), are important for neural development, structure and function in vertebrates (Pilecky et al., 2021; Twining et al., 2021; Závorka et al., 2023). Because vertebrates have limited capacity to biosynthesise n‑3 LC‑PUFA, they must be acquired, at least in part, via the diet (Twining et al., 2016). Laboratory studies have shown a positive association between n‑3 LC-PUFA enrichment and brain growth in freshwater and marine fishes (Lund et al., 2016; Ishizaki et al., 2001). In many animals, including fishes, cognition has been linked to relative brain size (Kotrschal et al., 2013; Triki et al., 2021). Brain size is associated with fitness-influencing behaviours such as prey capture (Edmunds et al., 2016) and predator avoidance (Kondoh, 2010). However, fish brain morphology varies widely across species (Triki et al., 2021), and so relative brain size alone may not always be the best proxy for fish cognition (Marhounová et al., 2019; Závorka et al., 2022b). In addition, different cognitive skills are associated with the relative sizes of specific brain regions: for example, learning and cognitive flexibility in response to visual stimuli, which are often investigated in animal cognition studies, are regulated by the telencephalon and optic tectum in guppies ( Poecilia reticulata ) (Triki et al., 2022). Therefore, there is a need for experimental work integrating the impacts of diet quality on brain size, morphology and biochemical composition, and the cognitive and behavioural abilities they promote. For stream-dwelling fishes, the combination of prey from freshwater aquatic and terrestrial origins provides for a varied dietary intake of essential nutrients (Sánchez-Hernández et al., 2018; Syrjänen et al., 2011). Of the invertebrates typically consumed by juvenile salmonids, freshwater prey tends to be relatively rich in a n‑3 LC-PUFA, eicosapentaenoic acid (EPA 20:5n-3), which can be converted to DHA at relatively low metabolic cost, while prey of terrestrial origin tends to be richer in the short-chain precursor, α-linolenic acid (ALA 18:3n‑3), which can only be converted to DHA at high metabolic cost (Pilecky et al., 2021; Twining et al., 2021). The energetic costs of short- to long-chain conversion may constrain the rate of DHA biosynthesis and thus the ability to allocate DHA to the brain in consumers of diets low in LC-PUFA (Pilecky et al., 2021; Závorka et al., 2021). Furthermore, because n‑3 and n‑6 precursor fatty acids compete for the same enzymes, both synthesis pathways from α-linolenic acid (ALA 18:3n‑3) to docosahexaenoic acid (DHA 22:6n‑3) and linoleic acid (LIN 18:2n‑6) to arachidonic acid (ARA 20:4n-6) should be considered when investigating bioconversion of DHA precursors (Geiger et al., 1993): biosynthesis of ARA is, ipso facto, powerful evidence of even greater n‑3 LC-PUFA conversion than what may be deduced from DHA alone (Sprecher, 2000; Hastings et al., 2001). Apart from diet, greater environmental complexity or enrichment can increase brain size and cognitive ability in fish (Ebbesson and Braithwaite, 2012; Salena et al., 2021): evidence of plasticity-inducing effects of habitat complexity on relative brain size exists for related salmonids, chinook salmon Oncorhynchus tschawytscha (Kihslinger et al., 2006) and Atlantic salmon Salmo salar (Näslund et al., 2012), as well as brown trout themselves (Závorka et al. 2022b). Most of the conversion of short-chain to long-chain fatty acids occurs in the liver, from which DHA is distributed to the brain (Rapoport et al., 2007; Wang et al., 2017), where it may be incorporated into polar lipids (PL – mainly phospholipids) that increase neuronal membrane fluidity and accelerate the formation of synapses and synaptic vesicles as vehicles for neurotransmitters (Pilecky et al. 2021). DHA and its precursors that are not currently needed for a specific physiological function are stored in form of neutral lipids (NL, mainly triacylglycerols) or allocated to other tissues, such as gonads or gametes for the next generation (Rigaud et al., 2023; Závorka et al., 2023; Zhu et al., 2019). Uneven distribution of DHA, and its precursors, whether biosynthesised or dietary, may be caused by activation of storage lipids and rerouting of the required fatty acids to the brain (Lacombe et al., 2018; Pifferi et al., 2021). The combination of biosynthesis and priority routing of DHA and its precursors may represent a compensatory mechanism for those animals with diets depleted in such nutrients. Brown trout Salmo trutta are a widespread species throughout Europe, and introduced worldwide, that exhibit enormous genetic, morphological and life-history variation and sexual dimorphism (Klemetsen, 2013; Koene et al., 2020; Reyes-Gavilán et al., 1997). Most variants typically are born and spend their early years as juveniles in low-productivity streams (Ferguson et al., 2019), which offer both physical habitat complexity and instability (Guo et al. 2018) and the opportunity for social complexity in the form of dominance hierarchies in response to competition for preferred microhabitats (Sloman et al., 2000). Juvenile brown trout often lack morphological features that correspond to competitive ability that allows them to assess one another at the onset of each potential dyadic conflict ( sensu Kodric-Brown and Brown, 1984), beyond mere differences in size (Jacob et al., 2007). So, recognition of individual rivals, memory of previous confrontations, and inference of competitive ability become important cognitive competencies when a potential opponent’s fighting capabilities can otherwise be assessed only through escalated contests (Drew, 1993). To test the effect of dietary quality on memory and inference abilities that are crucial for juvenile brown trout during conflicts (Johnsson and Åkerman, 1998; Grosenick et al., 2007; Shettleworth, 2010b), we conducted an experiment in which juvenile brown trout raised in either complex or simple habitats and on either high or low n‑3 LC-PUFA diets were subjected to a series of agonistic dyadic trials. We hypothesised that larger brains, generally, larger optic tecta and telencephala, specifically, and greater DHA content of brain polar lipids are associated with greater cognitive performance; and that habitat complexity and dietary DHA deficiency stimulates increased compensatory DHA biosynthesis and routing to brain polar lipids, the energetic cost of which is reflected in lower somatic growth. We tested the predictions that: 1) trout deprived of n‑3 LC-PUFA and in a complex habitat would show lower somatic growth than those raised on a high n‑3 LC-PUFA diet and in a simple habitat; 2) trout raised on a high n‑3 LC-PUFA diet and in a complex habitat would show greater cognitive performance than those raised on a low n‑3 LC-PUFA diet and in a simple habitat; 3) trout raised on a high n‑3 LC-PUFA diet and in a complex habitat would have larger relative brain size, and larger optic tecta and telencephala, than those raised on a low n‑3 LC-PUFA diet and in a simple habitat; 4) that larger brains, and larger optic tecta and telencephala, would be associated with greater cognitive performance; and 5) trout deprived of n‑3 LC-PUFA and in a complex habitat would biosynthesise DHA, and route DHA to brain polar lipids, to a greater extent than those raised on a high n‑3 LC-PUFA diet and in a simple habitat. There has been a suggestion that sex can play a role in routing and synthesis of n‑3 LC-PUFA in fish, with female Eurasian perch Perca fluviatilis showing significantly higher proportions of n‑3 LC‑PUFA in muscle tissue (Scharnweber and Gårdmark, 2020), and rates of n‑3 LC-PUFA biosynthesis before spawning (Rigaud et al., 2023), than males. Although sex differences are normally unexpected in brown trout until they reach maturity (Reyes-Gavilán et al., 1997), we considered the potential impact of sex in all of our analyses.

& METHODS Ethics statement: All licenced experimental work involving live specimens was carried out under UK Home Office Licence No. PPL 70/8794. Fish and rearing environment Approximately 1000 late-stage ‘eyed’ brown trout ( Salmo trutta, Linnæus 1758) eggs were obtained in early February 2021 from a stocking hatchery (AE Fishery, Moffat, UK). The eggs were the offspring of a mixture of riverine and lacustrine parents removed to captivity from the wild three to four generations ago. They were transported to the Scottish Centre for Ecology and the Natural Environment (SCENE) on Loch Lomond, UK and approximately evenly distributed between 12 cylindrical 30 L flow-through tanks held at ca. 4°C. Hatching was complete within two weeks of arrival at the centre. At first feeding ( ca. 10–14 days post hatch), to ensure that differences between individuals in aggression, swimming abilities, etc. did not affect group-level results in later behaviour trials, alevins-turned-fry were randomly split into two diet groups and moved to bare flow-through tanks as above, six replicates per diet group, but without temperature control. Ambient water temperature naturally warmed throughout the growth season from ca. 4–21°C. Two diet types of near-identical nutritional value were specially prepared by Garant-Tiernahrung GmbH (Pöchlarn, Austria): one diet was high in n-3 LC‑PUFA (EPA = 3.69%, DHA = 4.95%); the other diet was deficient in n-3 LC‑PUFA (EPA = 0.9%, DHA = 0.89%) (Appendix, Table A1). Fry were fed to satiation twice daily for ca. four months. For the final twelve weeks of rearing, trout from each diet treatment were further randomly divided between ‘simple’ and ‘complex’ habitats. Simple habitats consisted of bare flow-through cylindrical 90 L tanks with water supplied directly from Loch Lomond at ambient temperatures; 30–40 individuals per tank approximated conventional hatchery densities, which have been shown to be socially simpler than natural densities by inhibiting the development of competitive behaviours (Brockmark and Johnsson, 2010). Complex habitats used the same tank type but were heavily ornamented, with ornaments changing position every second day ( i.e. physical complexity); 6–7 fish per tank and weekly part-exchanges of fish between pairs of replicates fostered social complexity by allowing the continuous formation and reformation of dominance hierarchies (Sloman et al., 2000). All trout remained on their original diets and were, for the final twelve weeks, fed once daily to satiation, giving four distinct treatment groups, from which selections were made for further analysis: simple habitat, high n-3 LC-PUFA diet (four replicates); simple habitat, low n-3 LC-PUFA diet (four replicates); complex habitat, high n-3 LC-PUFA diet (eight replicates organised as four pairs part-exchanging inhabitants); and complex habitat, low n-3 LC-PUFA diet (eight replicates organised as four pairs part-exchanging inhabitants) (Table 1; Fig. 1). Table 1. Treatment groups and replicates at three stages of experimental rearing period of captive brown trout | Eggs & alevins | 2 | Common | 12 | ~80–85 | 0 | | Fry in diet groups | 16 | High* | 6 | ~50 | 2 | | only | Low* | 6 | ~50 | 2 | | | Fry in habitat and | 12 | High, simp. † | 4 | ~35 | 1 | | diet groups | Low, simp. † | 4 | ~35 | 1 | | | High, comp. † | 8 | 6–7 | 1 | || | Low, comp. † | 8 | 6–7 | 1 | \received DD MMMM YYYY \acceptedDD MMMM YYYY * High = high n-3 LC-PUFA diet; Low = low n-3 LC-PUFA diet; † simp. = simple habitat; comp. = complex habitat Figure 1. Schematic of treatment group replicates at final stage of rearing ( i.e. final 12 weeks), with number of fish per tank, and indicating paired replicates of complex habitat tanks, in which partial exchanges of inhabitants occurred. \received DD MMMM YYYY \acceptedDD MMMM YYYY Behavioural trials Following the final 12-week rearing period, all trout were anaesthetised with a solution of benzocaine, measured between the tip of the snout and the fork of the tail ( i.e. ‘fork length’: 71 ± 8 mm, mean ± s.d.), and marked with a pattern of variously coloured visible implant elastomer tags (Northwest Marine Technology Inc., Anacortes, WA, USA). Sixty groups of three trout each were established. Within each triad, trout were size-matched, in which the largest was larger by < 5% of the fork length of the smallest, to minimise possible effects of size on dominance (Huntingford et al., 1990; Johnsson and Åkerman, 1998 ). Additionally, individuals were unfamiliar with one another, having never shared a rearing tank. Each triad consisted of a dyad from different replicates of a treatment group, plus an ‘observer’ from another replicate. Across the 60 triads, all combinations of replicates and treatment groups were represented with the same approximate frequency. Behavioural tests were held as dyadic trials in successive stages between groups of three. Triad members were kept alone and separate from one another before and between behavioural trials in identical 30 L glass aquaria, adorned with an air stone and one plastic plant set in a corner, for 24 hours and fasted. For the initial behavioural trials ( naïve trials ), a dyad was placed simultaneously into a replica of the fasting aquaria. This trial tank was open at the top and illuminated by a 26 W, 1750 lm ceiling lamp. The sides were visually blocked, except the front, to allow observation by the researcher, and one side, to allow observation by the adjacent tank inhabitant. In an identical aquarium adjacent, the third trout of the size-matched triad was given the opportunity to observe the first two. This aquarium was shaded to prevent the dyad from observing the observer, and it was visually blocked on all sides except that facing the trial tank (Fig. 2). Figure 2. Aquarium set for behavioural trials of a trout triad: the naïve trial (upper left tank) shows a trout dyad establishing a dominant/subordinate relationship while a third trout observes unseen (upper right tank). The observer trial (lower tank) shows the former observer and one of the previous dyad establishing a new dominant/subordinate relationship. Visual blockages on all sides, except that facing the researcher and between the naïve trail tank and the observer trout, are not shown here. The dyad was observed by a researcher for five minutes every half hour until a dominant/subordinate relationship was seen to be clearly established, or until five hours had elapsed ( vide Appendix). Dominance and subordination were assessed by assigning points at each observation using a point-scoring system that evaluated levels of concealment, activity, and aggression (Table 2), based on a previous study of brown trout behaviour (Sloman et al., 2000). A stable dominant/subordinate relationship was suspected if one fish had a positive score that was at least two points higher than the other fish’s. At the next observation following suspicion of a stable relationship, food (two single bloodworms dropped simultaneously) was introduced at the centre of the forward-facing glass panel. If the suspected dominant fish took the food first and retained a two-point or greater score advantage, a dominant/subordinate relationship was declared . Otherwise, the relationship was considered unsustained. At the observation following declaration, the food drop was repeated, and if the same results were seen, the relationship was considered confirmed as stable, and the time of the declaration recorded. Without confirmation, a declaration of a stable relationship was voided. All trials were observed by the same researcher. After the naïve trials, trout were fed, then returned to their individual fasting tanks for 24 hours. Table 2. Scoring system used at repeated observations to detect the establishment of a dominant/subordinate relationship between pairs of brown trout in behavioural trials. | Concealment | Hiding | 0 | | Not hiding | 1 | | | Activity | Active avoidance | -1 | | Resting (inactive) | 0 | | | Swimming in water column | 1 | | | Aggression | Victim | -1 | | No aggression | 0 | | | Rubbing against, or quickly darting at, other | 1 | | | Extended chasing and/or nipping other | 2 | | | Feeding | No food taken | 0 | | Second to take food | 1 | | | First to take food | 2 | A second behavioural trial ( observer trial ) was conducted 24 h after the naïve trial. This tested the observer trout’s ability to establish a dominant/subordinate in less time than the original dyad, indicating greater cognitive performance. This was based on the premise that a trout that can recognise a con-specific it has previously observed, remember something of its attributes, and infer how its own attributes compare, will more quickly adopt a stable social position, either dominant or subordinate, than it would if presented with an unknown trout (Drew, 1993). The previous day’s observer trout was placed into a new trial tank with one from the original dyad (alternating initially dominant or subordinate with each trial replicate). Apart from the absence of a trout in an adjacent tank overlooking the dyad, the observer trial followed the same procedure as the naïve, with the former observer now as an active dyad participant. Sexing by genotype Upon completion of the observer trials, all trout were euthanised with an overdose of benzocaine solution. Genomic DNA was extracted from adipose fin clips using a NucleoSpin™ Tissue kit (Macherey-Nagel GmbH & Co. KG, Düren, Germany) following the manufacturer’s instructions; quality controlled with spectrophotometry (NanoDrop, ThermoFisher Scientific, Waltham, MA, USA); and quantified fluorometrically (Qubit 2.0, ThermoFisher Scientific, Waltham, MA, USA). Extracted DNA was diluted to 20 ng μL -1 . To determine the sex of each trout, following a modification of established protocols (Anglès d’Auriac et al., 2014; Lavender et al., 2024), duplex PCR amplified the male-specific Y-chromosome gene, sdY (forward primer: CCC AGC ACT GTT TTC TTG TCT CA; reverse primer: CTT AAA ACC ACT CCA CCC TCC AT), using the 18S gene as a positive amplification control (forward primer: GTY CGA AGA CGA TCA GAT ACC GT; reverse primer: CCG CAT AAC TAG TTA GCA TGC CG). PCR was performed using 3 μL of DNA with 0.3 μL of each sdY primer, 0.075 μL of each 18S primer, 7.5 μL of Qiagen Multiplex PCR mix containing 3 mM MgCl 2, HotStarTaq DNA polymerase and proprietary buffer (Qiagen N.V., Hilden, Germany), and 2.2 μL nuclease-free H 2 O. Thermal cycling consisted of initialisation for 15 minutes at 95 °C, followed by 35 amplification cycles of 30 seconds at 94 °C, 90 seconds at 63 °C and 90 seconds at 72 °C, with a final extension for 10 minutes at 72 °C. The resulting PCR products were visualised with 2 % agarose gel electrophoresis. Encephalisation and brain morphology To standardise dissection and measurement procedures, all were performed by a single researcher. Heads of trout from half the trial triads ( i.e. 90 individuals), representing all treatment groups, were removed and fixed for 24 h in 4% buffered (pH 6.9) paraformaldehyde solution. Brains were removed following the procedure described by Závorka et al. (2022a) and fixed for a further 24 h in the buffered paraformaldehyde solution. They were then weighed to the nearest 0.1 mg. Dorsal and right lateral views of brains were photographed with a Nikon D50 DSLR camera (Nikon Corporation, Tokyo, Japan) and Sigma 70 mm F2.8 DG Macro lens (Sigma Corporation, Tokyo, Japan). Measurements were made using Image J 1.48 (Schneider et al. 2021) of the length (L), width (W) and depth (D) of the whole brain and, independently, of cerebellum, optic tectum, telencephalon, and olfactory bulb (Fig. 3). Volumes (V) were calculated for whole brains with a corrected ellipsoid formula. To temper the systematic overestimation of volume due to deviations of brain shape from ellipsoid, a correction factor was introduced, following Pollen et al. (2007), using the slope of the linear regression of uncorrected brain volume on actual brain mass ( i.e. 1.022): V = (L × W × D) π / (6 × 1.022). Volumes of brain regions were calculated with the ellipsoid formula without the correction factor (Pollen et al., 2007). As measures of relative whole brain mass, residuals of a linear regression of actual whole brain mass on body mass were used. Similarly, volumes of each brain region were regressed on whole brain volume, and residuals were used as relative volumes of each brain region. On eight occasions during removal, brains were damaged (typically the delicate olfactory bulbs were severed); these samples were removed from subsequent analyses. Figure 3. Dorsal and lateral views of a brown trout brain, showing width, length and depth measurements of A. whole brain, B. cerebellum, C. optic tectum, D. telencephalon, and E. olfactory bulb. Gas chromatography and mass spectrometry Whole brains and samples of dorsal muscle tissue were removed immediately upon death from the observers of the remaining triads ( i.e. 30 trout), also representing all combinations of treatment group, flash frozen in liquid nitrogen, then freeze-dried and stored at −80 °C to limit lipolytic degradation. The immediacy of this method of preservation was not compatible with the brain-measuring procedure described above. Samples of food sources were included with tissue samples in subsequent fatty acid analyses. Lipid extraction and esterification from freeze-dried samples followed the protocol described by Pilecky et al. (2023). Briefly, whole brains ( ca. 10 mg) and ca. 30 mg of muscle and food source samples were weighed to the nearest 0.01 mg, homogenised and stored in chloroform (2 mL) under N 2 gas overnight at −80 °C. With the addition of 1 mL MeOH and 750 μL of 0.9 % NaCl solution, samples were repeatedly sonicated, vortexed and centrifuged to remove non-lipid materials. Solvent was fully evaporated from extracted lipids and 2 mL chloroform was added under N 2 . Gravimetry of aliquots was performed as a measure of total lipids for each sample. Neutral and polar lipids were separated using BondElut Ultra Inert GC columns (Agilent Technologies, Inc., Santa Clara, CA, USA). Column equilibration was conducted by passing hexane through each column; then, 10 mg of lipids-chloroform solution were loaded. Neutral lipids (NL) were isolated with 4 mL of 2:1 chloroform:isopropanol and evaporated fully. Then, to remove free fatty acids, 4 mL of 2 % acetic acid in diethyl ether was run through the columns and discarded. Polar lipids (PL) subsequently eluted with 4 mL of methanol and evaporated fully. Fatty acid methylated esters (FAME) of the extracted NL and PL were formed by incubation with 1 % H 2 SO 4 in MeOH for 16 h at 50 °C, followed by the addition of 2 mL of 2 % KHO 3 and 2 mL hexane, following Pilecky et al. (2023). Samples were mixed and centrifuged, and the upper organic layer of each sample was collected and concentrated under N 2 gas. Gas chromatography (TRACE GC 1310, Thermo, Waltham, MA, USA) of FAME followed the protocol established by Pilecky et al. (2023), using external standards for calibration; concentrations were reported as mg g -1 dry weight. Data involving candidate n‑3 and n‑6 FAME were carried forward for further investigation. For fatty-acid-specific stable isotope ratio mass spectrometry (DELTA V Advantage, ThermoFisher Scientific, Waltham, MA, USA), the gas chromatograph was coupled via CONFLO IV (Thermo, Waltham, MA, USA). Samples were run against certified Me-C20:0 stable isotope reference material (USGS70: δ 2 H = −183.9 ‰, USGS71: δ 2 H = −4.9 ‰ and USGS72: δ 2 H = +348.3 ‰) and corrected for methylation, as described elsewhere (Pilecky et al., 2023). Food sources were used further to correct the δ 2 H signature of each FAME in each sample: Δ δ 2 H FAME = sample δ 2 H FAME – mean(food source δ 2 H FAME ). Depletion of Δ δ 2 H in successive FAME in the biosynthesis pathways indicates bioconversion of shorter chain PUFA rather than a dietary source of LC-PUFA (Pilecky et al., 2022). \received DD MMMM YYYY \acceptedDD MMMM YYYY Statistical analyses All statistical analyses were conducted in R v.4.2.2 (R Core Team, 2022). To test for influences on somatic growth, the effects of diet, habitat (plus interaction), and sex on fork length for all trout ( n = 180) were modelled with a linear model. To test factors influencing the cognitive performance of observer trout, a linear model modelled the effects of diet, habitat (plus interaction), and sex on the difference in time for each triad between the observer trials and naïve trials ( i.e. observer trial time minus naïve trial time) until the dominant/subordinate relationship was clearly established. Triads that were unable clearly to establish dominance/subordination in either the naïve or observer trial were omitted from this and subsequent models. To ensure that triads were appropriately size-matched, linear mixed effects models using triad as a random effect confirmed that whether individuals would become dominant or subordinate was not affected by small differences in size after approximate size matching (naïve trail: F 1,104 = 0.013, p = 0.911; observer trial: F 1,104 = 0.06, p = 0.809). To test factors influencing encephalisation, the effects of diet, habitat (plus interaction) and sex on relative whole-brain mass for all trout whose brains were preserved in formalin were modelled with a linear model. To consider brain morphology, MANOVA tested the effects of diet, habitat (plus interaction), and sex on relative volumes of brain regions. To test the effects of encephalisation and brain morphology on cognitive performance, a linear regression modelled time differences between naïve and observer trials on relative brain mass and relative volumes of brain regions in addition to habitat and sex of those observer trout whose brains had been extracted intact ( n = 52); diet was omitted as it was colinear with, and causally linked to, encephalisation. To examine differences between treatment groups in how individual fatty acids were routed to specific tissue/lipid types ( i.e. brain and muscle tissue, polar and neutral lipids), mean percentages of total lipids composed of individual fatty acids across tissue/lipid types were tested with one-way ANOVAs followed by Tukey’s HSD post hoc . To determine whether fatty acid contents of various tissue/lipid types were influenced by treatment, the effects diet, habitat (plus interaction) and sex on the percentage of each fatty acid in the n-3 and n-6 bioconversion pathways were tested with MANOVA. Effects on each fatty acid percentage were then tested with ANOVA for individual tissue/lipid types. Differences between treatment groups in the depletion of Δ δ 2 H in fatty acids across tissue/lipid types were evaluated by modelling the effects of diet, habitat (plus interaction) and sex on the Δ δ 2 H of individual fatty acids and tested with MANOVA. For specific tissue/lipid types, effects on Δ δ 2 H were tested with ANOVA. Growth, encephalisation and brain morphology Across all trout in the study ( n = 180), habitat and sex had small, but significant, effects on fork length: trout raised in simple habitats were larger than those raised in complex habitats ( F 1,175 = 6.71, p = 0.01), and males were larger than females ( F 1,175 = 5.06, p = 0.026). However, there were no significant diet or diet:habitat interaction effects (Table 3). Among those subjects whose brains had been preserved for morphological analyses ( n = 78), neither sex nor rearing habitat had a significant effect on relative brain mass, but those trout raised on the high n-3 LC-PUFA diet had larger brains than those raised on the low n-3 LC-PUFA diet ( F 1,77 = 9.62, p = 0.003) (Table 4; Fig. 4). Although sex had no significant effect on the relative volume of any brain region, an interaction between diet and habitat did affect olfactory bulb size ( F 1,77 = 5.96, p = 0.017): in simple habitats, trout raised on low n-3 LC-PUFA had larger olfactory bulbs, while in complex habitats, it was those raised on high n-3 LC-PUFA that had the larger olfactory bulbs. No other brain region was specifically affected by diet or habitat. Table 3. Effects of diet, habitat and sex on fork length (FL) of 180 experimental brown trout, tested with ANOVA. | Diet | High LC-PUFA | 71.3 | 8.2 | 3.35 | 0.553 | | Low LC-PUFA | 70.7 | 8.4 | ||| | Habitat | Complex | 69.4 | 9.4 | 6.71 | 0.010 | | Simple | 72.5 | 6.8 | ||| | Sex | Female | 69.5 | 7.8 | 5.06 | 0.026 | | Male | 72.2 | 8.5 | ||| | Diet : Habitat | (interaction) | 1.36 | 0.245 | Table 4. Effects of diet, habitat and sex on the relative brain mass ( i.e. residuals of linear regression of actual brain mass on body mass) of 78 experimental brown trout, tested with ANOVA. | Diet | High LC-PUFA | 1.241 | 3.669 | 9.616 | 0.003 | | Low LC-PUFA | -1.509 | 4.273 | ||| | Habitat | Complex | 0.386 | 5.076 | 0.801 | 0.374 | | Simple | -0.350 | 3.135 | ||| | Sex | Female | -0.113 | 4.115 | 0.001 | 0.970 | | Male | 0.084 | 4.239 | ||| | Diet : Habitat | (interaction) | 0.455 | 0.502 | Figure 4. Effect of high and low n-3 LC-PUFA diets on relative brain mass (i.e. residuals of a linear regression of actual brain mass on body mass) of 78 experimental brown trout. Cognitive performance In the observer trials, 55 of 60 pairs established clear dominant/subordinate relationships within the five-hour period. Considering time differences between observer and naïve trials ( i.e. observer trial time minus naïve trial time), trout raised in complex habitats showed greater improvement in time needed to establish a stable hierarchy in the observer trial than did those from simple habitats ( F 1,98 = 13.09, p < 0.001). Females generally showed less improvement than did males in the observer trials over the naïve trials ( F 1,98 = 4.57, p = 0.034), especially those from the simple habitat; but, females from the complex habitat showed greater improvement than did males (sex/habitat interaction: F 1,98 = 11.41, p = 0.001) (Fig. 5A). Greater brain mass was also associated with quicker times in the observer trial ( F 1,42 = 6.93, p = 0.011) (Fig. 5B), but no specific brain region exerted a significant effect on time differences between trials (telencephalon: F 1,42 = 1.87, p = 0.179; optic tectum: F 1,42 = 0.24, p = 0.627; cerebellum: F 1,42 = 0.05, p = 0.828; olfactory bulb: F 1,42 = 0.88, p = 0.353). Figure 5. Effects of A. habitat and sex and B. relative brain mass on the difference between observer and naïve trials in time ( i.e. observer trial time minus naïve trial time) required by trout triads to establish stable dominance/subordination. Fatty acid analyses There were significant differences in DHA routing to various tissue/lipid types ( F 3,114 = 239.6, R 2 adj = 0.86, p < 0.001), with brain PL receiving the highest percentage ( post hoc : brain PL compared to each other type, all p < 0.001) and muscle PL having a higher percentage than NL in either brain or muscle ( post hoc : both p < 0.001) (Fig. 6A). For individual tissue/lipid types, however, there were differences in DHA content between diet groups (Pillai = 0.97, F 1,25 = 153.3, p < 0.001) and habitats (Pillai = 0.38, F 1,25 = 3.34, p = 0.027). Trout on the low n-3 LC-PUFA diet had lower DHA percentages than trout on the high n-3 LC-PUFA diet in muscle PL ( F 1,25 = 10.0, p = 0.004) and muscle NL ( F 1,25 = 460.0, p < 0.001), although there was no significant difference in brain lipids. Trout raised in complex habitats had higher DHA percentage in muscle NL ( F 1,25 = 13.3, p = 0.001), habitat had no significant effect on routing DHA to other tissue/lipid types (Fig. 6A). Depletion of ∆ δ 2 H DHA indicated there was considerable biosynthesis amongst trout fed the low n-3 LC-PUFA diet (Pillai = 0.95, F 1,21 = 80.6, p < 0.001), despite lower DHA content. The effect of diet on ∆ δ 2 H DHA depletion was highly significant (all p < 0.001) for all tissue/lipid types (Fig. 6B). Neither habitat nor sex exerted a significant effect on ∆ δ 2 H DHA depletion. Because content and depletion of ∆ δ 2 H in the n‑6 LC-PUFA, ARA 20:4n‑6, are related to n‑3 LC-PUFA biosynthetic activity, it is important to consider these when investigating effects on DHA (Sprecher, 2000). ARA content across tissue/lipid types was affected by diet (Pillai = 0.73, F 1,23 = 13.52, p < 0.001), but not habitat or sex. Trout raised on the low n-3 LC-PUFA diet had significantly higher percentages of ARA than the trout raised on high n-3 LC-PUFA in each tissue/lipid type (from brain NL, F 1,23 = 6.06, p = 0.023 to brain PL, F 1,23 = 36.08, p < 0.001) (Fig. 6C). This was reflected in greater biosynthesis among the low n-3 LC-PUFA-fed trout than the high n-3 LC-PUFA-fed trout, seen in the greater depletion of Δ δ 2 H ARA in brain PL ( F 1,18 = 5.49, p = 0.031) and muscle NL ( F 1,25 = 23.45, p < 0.001). Trout from simple habitats also showed greater biosynthesis of ARA than trout from complex habitats in muscle NL ( F 1,25 = 6.02, p = 0.021) (Fig. 6D). Figure 6. Effects of diet and habitat (and sex), for each of four tissue/lipid sample types, on A. the percentage of total lipids composed of DHA 22:6n-3, B. Δ δ 2 H DHA, C. percentage ARA 20:4n-6, and D. Δ δ 2 H ARA . Significant effects are noted. PL = polar lipids; NL = neutral lipids

Using the new method of fatty acid-specific stable hydrogen isotope analysis, a clear compensatory mechanism based on n‑3 LC-PUFA biosynthesis and routing was demonstrated, potentially offering protection against neural impairment under a diet deprived of n‑3 LC‑PUFA (Lund et al., 2012). However, our results also clearly show that an n‑3 LC‑PUFA-deprived diet is suboptimal for brain development, for which there are three main indicators: 1) n‑3 LC‑PUFA-deprived trout needed to expend energy to biosynthesise DHA, shown by Δ δ 2 H depletion of DHA and high ARA concentration in most tissue/lipid types; 2) n‑3 LC-PUFA-deprived trout needed to route DHA to the brain at the expense of muscles, shown by DHA content differing by dietary treatment in muscle tissue but not brain; and 3) n‑3 LC‑PUFA-deprived trout had smaller brains. In short, even when the biochemical composition of brain PL was maintained through increased biosynthesis and allocation of DHA from muscle tissue, poor diets resulted in smaller brains, and trout with smaller brains performed less well in the behavioural trials. Growth rates did not differ significantly between diet groups, which suggests that, as intended, there was no important difference in overall nutritional or energetic value between the two pellet formulae. However, it also shows, against our hypothesis, that groups raised on the low n‑3 LC-PUFA diet did not sacrifice somatic growth to fuel the energetic demand of fatty acid biosynthesis. Our study did not consider caloric intake, and it may be that ad libitum feeding allowed sufficient energy from the diet to make a sacrifice of somatic growth redundant. Trout raised in complex habitats had smaller bodies than the trout raised in simple habitats, which appears to contradict previous findings of reduced competition via visual isolation associated with increased habitat complexity ( cf. Sundbaum and Näslund, 1997; Koljonen et al., 2012). However, the size discrepancy may be explained by a decrease in aggressive, dominant strategies in the complex habitats, as territory size and resource monopolisation by dominants may be reduced (Höjesjö et al., 2004). The complex habitat tanks in this experiment were designed so that there were at least two hiding spots for each trout, minimising the effectiveness of dominant strategies; and the weekly partial exchange of tank inhabitants occasioned the regular collapse and re-establishment of dominance hierarchies. The lower density of trout in the complex habitats may also explain their smaller size: low stocking densities have been found in juvenile rainbow trout Oncorhynchus mykiss to induce chronic stress and lower feeding efficiency (Roy et al., 2021). Sex also played a small role in size differentiation ( i.e. somatic growth), which was surprising. Sex differences are normally unexpected until brown trout near maturity (Reyes-Gavilán et al., 1997), but the juveniles of the present experiment were much younger than that. Perhaps the unlimited food resources promoted growth which accentuated sex differences that ordinarily would not be apparent until later in ontology ( cf. Riguad et al., 2023). In any case, the effect of sex upon any other aspect of the experiment proved minimal. On cognitive performance, diet quality exerted its effect only indirectly. Its direct effects were on brain size and stimulation of n‑3 LC-PUFA biosynthesis and routing to brain polar lipids, which themselves played important roles in influencing cognitive performance. In contrast, the habitats in which trout were reared resulted in direct significant differences in cognitive performance between treatment groups. Although trout raised in complex habitats showed significantly better cognitive performance than those raised in simple habitats, as predicted, this was not because their brains were larger; nor did they show significantly different percentages of either EPA or DHA (except in muscle NL). Habitat complexity did not appear to stimulate biosynthesis of n‑3 LC‑PUFA, or their preferential allocation to the brain. We suggest that constant exposure to habitat complexity during ontogeny may continually reinforce interactions between existing neurons without requiring n‑3 LC‑PUFA for the formation of new neurons ( vide Dorman et al., 2018). Although nursery habitat played no role in encephalisation, counter to our prediction, the effect of diet followed the predicted pattern previously observed among wild brown trout: those trout with access to greater proportions of n‑3 LC‑PUFA in their diet had significantly larger brains than their lower dietary n‑3 LC‑PUFA counterparts ( cf. Závorka et al., 2022b). However, save one exception, there was no difference in the relative size of any specific brain region, including optic tectum or telencephalon, between treatment groups, contrary to our prediction. We suspect that n‑3 LC‑PUFA routed to the brain was distributed proportionately to brain regions, but our study design, which analysed lipids in the whole brain, was unable to determine fatty acid contents of individual regions. The exception was the olfactory bulb, which showed an interaction effect of diet and habitat. Presumably there is an advantage in complex habitats to having heightened processing abilities of olfactory cues, although this may less important than other brain functions and, so, may be sacrificed when trout are subjected to n‑3 LC-PUFA scarcity. However, when raised in a simple habitat, it remains a mystery why trout fed a low n‑3 LC-PUFA diet should have larger olfactory bulbs than those fed the n‑3 LC-PUFA-enriched diet. As predicted, trout with larger brains showed significantly better cognitive performance than smaller-brained trout in a task requiring ecologically important competences of recognition, memory and inference to de-escalate conflicts (Drew, 1993), in line with our prediction. However, no individual brain region had an effect on cognitive performance. Brain regions in teleost fishes are each involved in a variety of specific cognitive functions from learning and engagement in complex social tasks (telencephalon), through processing primary visual input (optic tectum), to spatial orientation and proprireception (cerebellum) (Kotrschal & Kotrschal, 2020). We suggest that all these functions may be needed together to confer the cognitive abilities required by the environments presented in this study. Therefore, total brain size was a better predictor of cognitive performance than any particular brain region. This study found evidence that deprivation of dietary n‑3 LC‑PUFA stimulated trout to biosynthesise EPA and DHA from precursor fatty acids (such as ALA; vide Appendix Fig. A2). Converting short-chain to long-chain PUFA as a likely compensatory mechanism has been previously established in experimental rats (Rapoport and Igarashi, 2009; Rapoport et al., 2010) and humans (Barceló-Coblijn and Murphy, 2009; Domenichiello et al., 2015), reminiscent of patterns seen in the present study. Significant depletion of δ 2 H in EPA and DHA in the trout raised on the low n-3 LC-PUFA diet compared to those raised on high n-3 LC-PUFA, without significant differences in the percentage of total brain polar lipids composed of those n‑3 LC‑PUFA, suggests compensation for deficiency in the diet. The increased percentage of the n-6 LC-PUFA, ARA, across all tissue/lipid types amongst the trout fed low n-3 LC-PUFA may appear to be an overcompensation. Omega‑6 PUFA, and ARA in particular, are important for wound healing, inflammation, coagulation and osmoregulation (Castro et al., 2016), although they can also have negative effects by increasing risk of hyperinflammation (Layé, 2010). The abundance of ARA in n‑3 LC-PUFA-deprived trout is more likely to be merely a consequence of bioconversion. Neither ALA 18:3n‑3 nor LIN 18:2n‑6 can be synthesised de novo by vertebrates and must be obtained from food sources (Blondeau et al., 2015; Malcicka et al., 2018). However, they compete for the same elongases and desaturases to perform endogenous conversion to respective n‑3 and n‑6 LC‑PUFA (Sprecher, 2000). Although n‑3 fatty acids have been observed to be the preferred substrates for desaturase activity (Jeromson et al., 2025; Nakamura and Nara, 2004), this is not absolute; the conversion of n‑6 fatty acids has been seen, at least in zebrafish ( Danio rerio ), to occur in a ratio to n‑3 of ca. 1:2.5 (Hastings et al., 2001). Therefore, the relative abundance of biosynthetic ARA found in trout raised on the LC-PUFA deprived diet provides additional evidence of compensatory biosynthesis of DHA (and EPA). Furthermore, the distribution of fatty acids amongst various tissue/lipid samples, particularly the increasing percentages of longer chain PUFA in brain PL (n‑3) or muscle PL (n‑6), suggests routing priorities (Lacombe et al., 2018; vide Appendix Fig. A1). Faced with deficiency in dietary n‑3 LC-PUFA, trout routed available DHA (and EPA) away from muscle tissue, where it might promote hyperplasia and muscle fibre development (Wang et al., 2020), to brain polar lipids, where it might help maintain neural function, and hence cognition (Pilecky et al., 2021; Zavorka et al., 2023). Trout raised on a low n-3 LC-PUFA diet appear to have used a combination of biosynthesis and priority routing of LC-PUFA for active use in membranes (PL) at the expense of triacylglycerol storage (NL) to compensate for dietary lack. The compensation was, however, not complete: despite similar fatty acid composition in brain PL between the two diet treatment groups, the low n-3 LC-PUFA diet still resulted in smaller brains. A limitation of the study was that all fish were fed ad libitum . With no curtailment of the amount of energy or precursor short-chain PUFA available, the compensatory effects of LC‑PUFA biosynthesis and routing under a suboptimal LC‑PUFA deprived diet in the present study are likely to be exaggerated. In nature, where ad libitum feeding is not observed, we expect differences in fatty acid profiles between natural diet groups to be more pronounced with attendant ramifications for brain morphology and cognitive ability (Závorka et al., 2022b). The alternative diets upon which trout were raised proved to be the most important differentiator of treatment groups in this study. Diet exercised clear effects on the brain development, cognitive abilities, and LC‑PUFA biosynthesis and routing of brown trout, and effected a divergence in the fatty acid profiles of muscle tissue. The potential decrease in the production of n‑3 LC‑PUFA by primary producers due to climate change appears, based on the results of this study, to presage profound changes to the behavioural ecology of stream-dwelling fishes such as brown trout. Although the extent of these changes has yet to be determined, this study makes clear that a diet bereft of adequate n‑3 LC‑PUFA is suboptimal. In wild settings, where fish do not feed ad libitum, the effects of n‑3 LC‑PUFA deprivation are likely to be more severe. Furthermore, the complexity of nursery habitat also plays an essential role, independently of diet quality, in the development of cognitive skills. Therefore, further studies are needed, that integrate consideration of life history and diet in wild animals. Whether the divergence observed in this study is substantial enough to play a role in the development of the morphological and life-history variants observed in wild populations of brown trout deserves study (Závorka et al., 2022b). DATA AVAILABILITY STATEMENT All data and code are available to reviewers via the private link, https://figshare.com/s/ae27893dcc166089cdad, and will be made publicly available upon acceptance for publication. COMPETING INTERESTS No competing interests declared. AUTHOR CONTRIBUTIONS JPK: conceptualisation, data curation, formal analysis, investigation, methodology, project administration, writing – original draft, writing – review & editing.LZ: conceptualisation, methodology, writing – review & editing.MP: methodology, formal analysis, writing – review & editing.KRE: conceptualisation, resources, writing – review & editing.CEA: conceptualisation, resources, supervision, writing – review & editing. \received DD MMMM YYYY \acceptedDD MMMM YYYY ACKNOWLEDGEMENTS The authors sincerely thank Neil Metcalfe for advice on the design of the trials; Rowan Smith and Phoebe Kaiser-Wilks for assistance with animal husbandry at the Scottish Centre for Ecology and the Natural Environment (SCENE), University of Glasgow; Neil Evans for assistance with sample processing; Samuel-Karl Kämmer and Maria Capstick for laboratory assistance; Martin Kainz for hosting the lipid analyses at WasserCluster Lunz – Biologische Station GmbH; and Garant-Tiernahrung GmbH (Pöchlarn, Austria) for producing and donating fish feed. This work was supported by the Fisheries Society of the British Isles [PhD Studentship to JPK] and the Austrian Science Fund [Stand Alone Project P35515-B to LZ]. LITERATURE CITED Anglès d’Auriac, M.B., Urke, H.A., and Kristensen, T. (2014). A rapid qPCR method for genetic sex identification of Salmo salar and Salmo trutta including simultaneous elucidation of interspecies hybrid paternity by high-resolution melt analysis. Journal of Fish Biology 84 (6), 1971–1977. https://doi.org/10.1111/jfb.12401 Barceló-Coblijn, G., and Murphy, E.J. (2009). Alpha-linolenic acid and its conversion to longer chain n -3 fatty acids: Benefits for human health and a role in maintaining tissue n -3 fatty acid levels. Progress in Lipid Research 48, 355–374. https://doi.org/10.1016/j.plipres. 2009.07.002 Blondeau, N., Lipsky, R.H., Bourourou, M., Duncan, M.W., Gorelick, P.B., and Marini, A.M. (2015). Alpha-linolenic acid: an omega-3 fatty acid with neuroprotective properties – ready for use in the stroke clinic? BioMed Research International 2015, 519830. https://doi.org/10.1155/ 2015/519830 Boogert, N.J., Madden, J.R., Morand-Ferron, J., and Thornton, A. (2018). Measuring and understanding differences in cognition. Philosophical Transactions of the Royal Society B 373, 20170280. https://dx.doi.org/10.1098/rstb.2017.0280 Brockmark, S., and Johnsson, J.J. (2010). Reduced hatchery rearing density increases social dominance, postrelease growth, and survival in brown trout ( Salmo trutta ). Canadian Journal of Fisheries and Aquatic Sciences 67, 288–295. https://doi.org/10.1139/F09-185 Castro, L.F.C., Tocher, D.R., and Monroig, O. (2016). Long-chain polyunsaturated fatty acid biosynthesis in chordates: Insights into the evolution of Fads and Elovl gene repertoire. Progress in Lipid Research 62, 25–40. https://doi.org/10.1016/j.plipres.2016.01.001 Domenichiello, A.F., Kitson, A.P., and Bazinet, R.P. (2015). Is docosahexaenoic acid synthesis from α-linolenic acid sufficient to supply the adult brain? Progress in Lipid Research 59, 54–66. https://doi.org/10.1016/j.plipres.2015.04.002 Dorman, D., Jędrzejewska-Szmek, J., and Blackwell, K.T. (2018). Inhibition enhances spatially-specific calcium encoding of synaptic input patterns in a biologically constrained model. eLife 7, e38588. https://doi.org/10.7554/eLife.38588 Drew, C. (1993). The concept and definition of dominance in animal behaviour. Behaviour 125, 283–313. https://doi.org/10.1163/156853993X00290 Ebbesson, L.O.E., and Braithwaite, V.A. (2012). Environmental effects of fish neural plasticity and cognition. Journal of Fish Biology 81 (7), 2151–2174. https://doi.org/10.1111/j.1095-8649.2012.03486.x Edmunds, N.B., Laberge, F., and McCann, K.S. (2016). A role for brain size and cognition in food webs. Ecology Letters 19, 948–955. https://doi.org/10.1111/ele.12633 Ferguson, A., Reed, T.E., Cross, T.F., McGinnity, P., and Prodöhl, P. (2019). Anadromy, potamodromy and residency in brown trout Salmo trutta: the role of genes and the environment. Journal of Fish Biology 95, 692–718. https://doi.org/10.1111/jfb.14005 Fernö, A., Folkedal, O., Nilsson, J., and Kristiansen, T.S. (2020). Inside the fish brain: cognition, learning and consciousness. In: Kristiansen, T., Fernö, A. Pavlidid, M. and van de Vis, H. (eds), The Welfare of Fish . Animal Welfare, vol. 20. Springer, Cham. https://doi.org/10.1007/978-3-030-41675-7_7 Fuss, T. (2021). Mate choice, sex roles and sexual cognition in vertebrates: mate choice turns cognition or cognition turns mate choice? Frontiers in Ecology and Evolution 9, 749495. https://doi.org/10.3389/fevo.2021.749495 Geiger, M., Mohammed, B.S., Sankarappa, S., and Sprecher, H. (1993). Studies to determine if rat liver contains chain-length-specific acyl-CoA 6-desaturases. Biochimica et Biophysica Acta 1170, 137–142. https://doi.org/10.1016/0005-2760(93)90063-F Grosenick, L., Clement, T.S., and Fernald, R.D. (2007). Fish can infer social rank by observation alone. Nature 445, 429–432. https://doi.org/10.1038/nature05511 Hastings, N., Agaba, M., Tocher, D.R., Leaver, M.J., Dick, J.R., Sargent, J.R., and Teale, A.J. (2001). A vertebrate fatty acid desaturase with Δ5 and Δ6 activities. Proceedings of the National Academy of Sciences of the United States of America 8 (25), 14304–14309. https://doi.org/10.1073/pnas.251516598 Höjesjö, J., Johnsson, J., and Bohlin, T. (2004). Habitat complexity reduces the growth of aggressive and dominant brown trout ( Salmo trutta ) relative to subordinates. Behavioral Ecology and Sociobiology 56, 286–289. https://doi.org/10.1007/s00265-004-0784-7 Hou, Z., and Fuiman, L.A. (2020). Nutritional programming in fishes: insights from mammalian studies. Reviews in Fish Biology and Fisheries 30, 67–92. https://doi.org10.1007/s11160-019-09590-y Huntingford, F., Metcalfe, N.B., Thorpe, J.E., Graham, W.D., and Adams, C.E. (1990). Social dominance and body size in Atlantic salmon parr, Salmo salar L. Journal of Fish Biology 36, 877–881. https://doi.org/10.1111/j.1095-8649.1990.tb05635.x Ishizaki, Y., Masuda, R., Uematsu, K., Shimizu, K., Arimoto, M., and Takeuchi, T. (2001). The effect of dietary docosahexaenoic acid on schooling behaviour and brain development in larval yellowtail. Journal of Fish Biology 58, 1691–1703. https://doi.org/10.1111/j.1095-8649.2001.tb02323.x Jacob, A., Nusslé, S., Britschgi, A., Evanno, G., Müller, R., and Wedekind, C. (2007). Male dominance lined to size and age, but not to ‘good genes’ in brown trout ( Salmo trutta ). BMC Ecology and Evolution 7, 207. https://doi.org/10.1186/1471-2148-7-207 Jeromson, S., Gallagher, I.J., Galloway, S.D.R., and Hamilton, D.L. (2015). Omega-3 fatty acids and skeletal muscle health. Marine Drugs 13 (11), 6977–7004. https://doi.org/10.3390/ md13116977 Johnsson, J., and Åkerman, A. (1998). Watch and learn: preview of fighting ability of opponents alters contest behaviour in rainbow trout. Animal Behaviour 56, 771–776. https://doi.org/ 10.1006/anbe.1998.0824 Kihslinger, R.L., Lema, S.C., and Nevitt, G.A. (2006). Environmental rearing conditions produce forebrain differences in wild Chinook salmon Oncorhynchus tschawytscha . Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 145 (2), 145–151. https://doi.org/10.1016/j.cbpa.2006.06.041 Klemetsen, A. (2013). The most variable vertebrate on Earth. Journal of Ichthyology 53 (10), 781–791. https://doi.org/10.1134/S0032945213100044 Kodric-Brown, A., and Brown, J.H. (1984). Truth in advertising: the kinds of traits favored by sexual selection. The American Naturalist 124 (3), 309–325. https://doi.org/10.1086/284275 Koene, J.P., Elmer, K.R., and Adams, C.E. (2020). Intraspecific variation and structuring of phenotype in a lake-dwelling species are driven by lake size and elevation. Biological Journal of the Linnean Society 131 (3), 585–599. https://doi.org/10.1093/biolinnean/blaa137 Koljonen, J., Huusko, A., Mäki-Petäys, A., Mykra, H., and Muotka, T. (2012). Body mass and growth of overwintering brown trout in relation to stream habitat complexity. River Research and Applications 28, 62–70. https://doi.org/10.1002/rra.1435 Kondoh, M. (2010). Linking learning adaptation to trophic interactions: a brain size-based approach. Functional Ecology 24, 35–43. https://doi.org/10.1111/j.1365-2435.2009.01631.x Kotrschal, A. and Kotrschal, K. (2020). Fish brains: anatomy, functionality, and evolutionary relationships. In: Kristiansen, T., Fernö, A., Pavlidis, M. van de Vis, H. (eds), The Welfare of Fish. Animal Welfare, vol. 20 . Springer, Cham. https://doi.org/10.1007/978-3-030-41675-5_6 Kotrschal, A., Rogell, B., Bundsen, A., Svensson, B., Zajitschek, S., Brännström, I., Immler, S., Maklakov, A.A., and Kolm, N. (2013). Artificial selection on relative brain size in the guppy reveals cost and benefits of evolving a larger brain. Current Biology 23, 168–171. https://doi.org/10.1016/j.cub.2012.11.058 Lacombe, R.J.S., Chouinard-Watkins, R., and Bazinet, R.P. (2018). Brain docosahexaenoic acid uptake and metabolism. Molecular Aspects of Medicine 64, 109–134. https://doi.org/ 10.1016/j.mam.2017.12.004 Lavender, E., Hunziker, Y., McLennan, D., Dermond, P., Stalder, D., Selz, O., and Brodersen, J. (2024). Sex- and length-dependent variation in migratory propensity in brown trout. Ecology of Freshwater Fish 33 (1), e12745. https://doi.org/10.1111/eff.12745 Layé, S. (2010). Polyunsaturated fatty acids, neuroinflammation and well being. Prostaglandins Leukotrienes and Essential Fatty Acids 82 (4–6), 295–303. https://doi.org/10.1016/ j.plefa.2010.02.006 Lund, I., Skov, P.V., and Hansen, B.W. (2012). Dietary supplementation of essential fatty acids in larval pikeperch ( Sander lucioperca ); short and long term effects on stress tolerance and metabolic physiology. Comparative Biochemistry and Physiology A 162, 340–348. https://doi.org/10.1016/j.cbpa.2012.04.004 Malcicka, M., Visser, B., and Ellers, J. (2018). An evolutionary perspective on linoleic acid synthesis in animals. Evolutionary Biology 45, 15–26. https://doi.org/10.1007/s11692-017-9436-5 Marhounová, L., Kotrschal, A., Kverková, K., Kolm, N., and Němec, P. (2019). Artificial selection on brain size leads to matching changes in overall number of neurons. Evolution 73 (9), 2003-2012. https://doi.org/10.1111/evo.13805 Nakamura, M.T., and Nara, T.Y. (2004). Structure, function, and dietary regulation of Δ6, Δ5, and Δ9 desaturases. Annual Review of Nutrition 24, 345–376. https://doi.org/10.1146/ annurev.nutr.24.121803.063211 Näslund, J., Aarestrup, K., Thomassen, S.T., and Johnsson, J.I. (2012). Early enrichment effects on brain development in hatchery-reared Atlantic salmon ( Salmo salar ): no evidence for a critical period. Canadian Journal of Fisheries and Aquatic Sciences 69, 1481–1490. https://doi.org/10.1139/f2012-074 Pifferi, F., Laurent, B., and Plourde, M. (2021). Lipid transport and metabolism at the blood-brain interface: implications in health and disease. Frontiers in Physiology 12, 645646. https://doi.org/10.3389/fphys.2021.645646 Pilecky, M., Kämmer, S.K., Mathieu‐Resuge, M., Wassenaar, L.I., Taipale, S.J., Martin‐Creuzburg, D., and Kainz, M.J. (2022). Hydrogen isotopes ( δ 2 H) of polyunsaturated fatty acids track bioconversion by zooplankton. Functional Ecology 36 (3), 538–549. https://doi.org/10.1111/1365-2435.13981 Pilecky, M., Wassenaar, L.I., Taipale, S., and Kainz, M.J. (2023). Protocols for sample preparation and compound-specific stable-isotope analyses ( δ 2 H, δ 13 C) of fatty acids in biological and environmental samples. MethodsX 11, 102283. https://doi.org/10.1016/ j.mex.2023.102283 Pilecky, M., Závorka, L., Arts, M.T., and Kainz, M.J. (2021). Omega-3 PUFA profoundly affect neural, physiological, and behavioural competences – implications for systemic changes in trophic interactions. Biological Reviews 96, 2127–2145. https://doi.org/10.1111/brv.12747 Pollen, A.A., Dobberfuhl, A.P., Scace, J., Igulu, M.M., Renn, S.C., Shumway, C.A., and Hofmann, H.A. (2007). Environmental complexity and social organization sculpt the brain in Lake Tanganyikan cichlid fish. Brain Behavior and Evolution 70, 21–39. https://doi.org/10.1159/000101067 \received DD MMMM YYYY \acceptedDD MMMM YYYY R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ Rapoport, S.I., and Igarashi, M. (2009). Can the rat liver maintain normal brain DHA metabolism in the absence of dietary DHA? Prostaglandins Leukotrienes and Essential Fatty Acids 81, 119–123. https://doi.org/10.1016/j.plefa.2009.05.021 Rapoport, S.I., Igarashi, M., and Gao, F. (2010). Quantitative contributions of diet and liver synthesis to docosahexaenoic acid homeostasis. Prostaglandins Leukotrienes and Essential Fatty Acids 82, 273–276. https://doi.org/10.1016/j.plefa.2010.02.015 Rapoport, S.I., Rao, J.S., and Igarashi, M. (2007). Brain metabolism of nutritionally essential polyunsaturated fatty acids depends on both the diet and the liver. Prostaglandins Leukotrienes and Essential Fatty Acids 77, 251–261. https://doi.org/10.1016/j.plefa. 2007.10.023 Reyes-Gavilán, F.G., Ojanguren, A.F., and Braña, F. (1997). The ontogenetic development of body segments and sexual dimorphism in brown trout ( Salmo trutta L.). Canadian Journal of Fisheries and Aquatic Sciences 75, 651–655. https://doi.org/10.1139/z97-083 Rigaud, C., Kahilainen, K.K., Calderini, M.L., Pilecky, M., Kainz, M.J., Tiirola, M., and Taipale, S.J. (2023). Preparing for the future offspring: European perch (Perca fluviatilis) biosynthesis of physiologically required fatty acids for the gonads happens already in the autumn. Oecologia 203, 477–489. https://doi.org/10.1007/s00442-023-05480-0 Roy, J., Terrier, F., Marchand, M., Herman, A., Heraud, C., Surget, A., Lanuque, A., Sandres, F., and Marandel, L. (2021). Effects of low stocking densities on zootechnical parameters and physiological responses of rainbow trout ( Oncorhynchus mykiss ) juveniles. Biology 10, 1040. https://doi.org/10.3390/biology10101040 Salena, M.G., Turka, A.J., Singh, A., Pathak, A., Hughes, E., Brown, C., and Balshine, S. (2021). Understanding fish cognition: a review and appraisal of current practices. Animal Cognition 24, 395–406. https://doi.org/10.1007/s10071-021-01488-2 Sánchez-Hernández, J., and Cobo, F. (2018). Examining the link between dietary specialization and foraging modes of stream-dwelling brown trout Salmo trutta . Journal of Fish Biology 93, 143–146. https://doi.org/10.1111/jfb.13672 Scharnweber, K., and Gårdmark, A. (2020). Feeding specialists on fatty acid-rich prey have higher gonad weights: pay-off in Baltic perch? Ecosphere 11 (8), e03234. https://doi.org/10.1002/ecs2.3234 Schneider, C.A., Rasband, W.S., and Eliceiri, K.W. (2021). NIH Image to ImageJ: 25 years of image analysis. Nature Methods 9, 671–675. https://doi.org/10.1038/nmeth.2089 Shettleworth, S.J. (2010a). Cognition, Evolution and Behaviour, 2 nd edn. Oxford University Press, Oxford. Shettleworth, S.J. (2010b). Clever animals and killjoy explanations in comparative psychology. Trends in Cognitive Sciences 14 (11), 477–481. https://doi.org/10.1016/j.tics.2010.07.002 Sloman, K.A., Gilmour, K.M., Taylor, A.C., and Metcalfe, N.B. (2000). Physiological effects of dominance hierarchies within groups of brown trout, Salmo trutta, held under simulated natural conditions. Fish Physiology and Biochemistry 22, 11–20. https://doi.org/10.1023/A: 1007837400713 Sprecher, H. (2000). Metabolism of highly unsaturated n -3 and n -6 fatty acids. Biochimica et Biophysica Acta 1486 (2–3), 219–231. https://doi.org/10.1016/S1388-1981(00)00077-9 Sundbaum, K., and Näslund, I. (1997). Effects of woody debris on the growth and behaviour of brown trout in experimental stream channels. Canadian Journal of Zoology 76, 56–61. https://doi.org/10.1139/z97-174 Syrjänen, J., Korsu, K., Louhi, P., Paavola, R., and Muotka, T. (2011). Stream salmonids as opportunistic foragers: the importance of terrestrial invertebrates along a stream-size gradient. Canadian Journal of Fisheries and Aquatic Sciences 68, 2164–2156. https://doi.org/10.1139/f2011-118 Triki, Z., Aellen, M., van Schaik, C.P., and Bshary, R. (2021). Relative brain size and cognitive equivalence in fishes. Brain Behavior and Evolution 96, 124–136. https://doi.org/10.1159/ 000520741 Triki, Z., Granell-Ruiz, M., Fong, S., Amcoff, M., and Kolm, N. (2022). Brain morphology correlates of learning and cognitive flexibility in a fish species ( Poecilia reticulata ). Proceedings of the Royal Society B 289, 20220844. https://doi.org/10.1098/rspb.2022.0844 Twining, C.W., Bernhardt, J.R., Derry, A.M., Hudson, C.M., Ishikawa, A., Kabeya, N., Kainz, M.J., Kitano. J., Kowarik. C., Nemiah Ladd, S., Leal, M.C., Scharnweber, K., Shipley, J.R., and Matthews, B. (2021). The evolutionary ecology of fatty-acid variation: implications for consumer adaptation and diversification. Ecology Letters 24, 1709–1731. https://doi.org/10.1111/ele.13771 Twining, C.W., Brenna, J.T., Hairston Jr, N.G., and Flecker, A.S. (2016). Highly unsaturated fatty acids in nature: what we know and what we need to learn. Oikos 125 (6), 749-760. https://doi.org/10.1111/oik.02910 Wang, C-c., Liu, W-b., Huang, Y-y., Wang, X., Li, X-f., Zhang, D-d., and Jiang, G-z. (2020). Dietary DHA affects muscle fiber development by activating AMPK/Sirt1 pathway in blunt snout bream ( Magalobrama amblycephala ). Aquaculture 518, 734835. https://doi.org/10.1016/j.aquaculture.2019.734835 Wang, S-h., Pan, Y., Li, J., Chen, H-q., Zhang, H., Chen, W., Gu, Z-n., and Che, Y.Q. (2017). Endogenous omega-3 long-chain fatty acid biosynthesis from alpha-linolenic acid is affected by substrate levels, gene expression, and product inhibition. RSC Advances 7, 40946–40951. https://doi.org/10.1039/c7ra06728c Závorka, L., Blanco, A., Chaguaceda, F., Cucherousset, J., Killen, S.S., Liénart. C., Mathieu-Resuge, M., Nĕmec, P., Pilecky, M., Scharnweber, K., Kainz, M.J. (2023). The role of vital dietary biomolecules in eco-evo-devo dynamics. Trends in Ecology and Evolution 38, 7–84. https://doi.org/10.1016/j.tree.2022.08.010 Závorka, L., Crespel, A., Dawson, N.J., Papatheodoulou, M., Killen, S.S., and Kainz, M.J. (2021). Climate change-induced deprivation of dietary essential fatty acids can reduce growth and mitochondrial efficiency of wild juvenile salmon. Functional Ecology 35, 1960–1971. https://doi.org/10.1111/1365-2435.13860 Závorka, L., Koene, J.P., Armstrong, T.A., Fehlinger, L., and Adams, C.E. (2022a). Differences in brain morphology of brown trout across stream, lake, and hatchery environments. Ecology and Evolution 12, e8684. https:// doi.org/0.1002/ece3.8684 Závorka, L., Lovén Wallerius, M., Kainz, M.J., and Höjesjö, J. (2022b). Linking omega-3 polyunsaturated fatty acids in natural diet with brain size of wild consumers. Oecologia 199, 797–807. https://doi.org/10.1007/s00442-022-05229-1 Zhu, Y., Tan, Q., Zhang, L., Yao, J., Zhou, H., Hu, P., Liang, X., and Liu, H. (2019). The migration of docosahexenoic acid (DHA). To the developing ovary of female zebrafish ( Danoi rerio ). Comparative biochemistry and Physiology Part A: Molecular & Integrative Physiology 233, 97–105. https://doi.org/10.1016/j.cbpa.2019.04.005 \received DD MMMM YYYY \acceptedDD MMMM YYYY APPENDIX Establishment of trial duration To determine an appropriate duration for behavioural trials, in which dyads would consistently form stable dominant/subordinate relationships, a preliminary test, using the same protocol as the observer trials, had established five hours to be a reasonable period in which to assess the development of dominant/subordinate relationships: of 27 preliminary size-matched dyads, 26 formed a clear dominant/subordinate relationship after 5 h (χ 2 = 23.15, df = 1, p < 0.001, showing a goodness-of-fit departure from a 50:50 ratio of clear to unclear hierarchies), with a mean time ± s.d. to detection by the observing researcher of 3 h 21 min ± 54 min. The dyad was left in the test tank for a further 24 h after the end of the trial. The stability of the dyad relationship was then re-tested by 5 min observation of behaviour followed by confirmatory bloodworm feeding. The re-test showed that most (24 of 26) dominant/subordinate relationships remained stable (χ 2 = 18.62, df = 1, p < 0.001, showing a goodness-of-fit departure from a 50:50 ratio of hierarchies that remained the same after 24 h to those that became unclear or changed). Specimens used in the preliminary trials were not used again in the main behavioural trials. Routing of ARA 20:4n-6 Distribution of ARA differed by tissue/lipid type ( F 3,114 = 80.1, R 2 adj = 0.67, p < 0.001), with a higher percentage used in polar lipds ( post hoc : muscle PL vs. all other types p < 0.001; brain PL vs. other types p < 0.001, but lower than in muscle PL). Muscle NL had, comparatively, the lowest amount of ARA ( post hoc : vs. all other tissue/lipid types p < 0.001) (Fig. 6C). Table A1. Ingredients of contrasting experimental diets produced by GARANT™, Austria, with nutritional information and mean percentages of total lipids composed of individual n-3 and n-6 fatty acids as indicated by the producer. | Ingredients | Fish meal, Super prime, 67% XP | 20 | — | | Sunflower protein concentrate, 45% XP | 10 | 10 | | | Blood meal SD | 7 | 7 | | | Haemoglobin powder | 3 | 3 | | | Poultry meal | 14.8 | 20 | | | Wheat gluten, 80% XP | 2 | 8.5 | | | Soy protein concentrate | — | 2.9 | | | Soybean meal XP | — | 5 | | | Wheat, feed quality | 13.4 | 10.9 | | | Wheat feed flower | 10 | 10 | | | Fish oil | 7.9 | — | | | Rapeseed oil | 10.9 | 14.8 | | | Lin oil | — | 5 | | | Monocalciumphosphate | — | 1.1 | | | Lysine-HCl | 0.06 | 0.69 | | | Methionine | 0.22 | 0.4 | | | Threonine | 0.09 | 0.24 | | | Premix | 0.6 | 0.6 | | | Nutrition | Digestible energy, MJ | 20.3 | 20.3 | | Crude protein, % | 42 | 42 | | | Crude fat, % | 23 | 22 | | | Crude fibre, % | 1.4 | 1.5 | | | P, % | 1.06 | 1.05 | | | Lysine, % | 2.65 | 2.65 | | | Methionine, % | 1 | 1 | | | Meth + Cys, % | 1.45 | 1.5 | | | Threonine, % | 1.6 | 1.6 | | | n-3 fatty acids | α-Linolenic acid (ALA) 18:3n-3, % | 4.81 | 5.25 | | Stearidonic acid (SDA) 18:4n-3, % | — | — | | | Eicosatetraenoic acid (ETA) 20:4n-3, % | — | — | | | Eicosapentaenoic acid (EPA) 20:5n-3, % | 3.69 | 0.9 | | | Docosapentaenoic acid (DPA) 22:5n-3, % | — | — | | | Docosahexaenoic acid (DHA) 22:6n-3, % | 4.95 | 0.89 | | | n- 6 fatty acids | Linoleic acid (LIN) 18:2n-6, % | 15.77 | 18.38 | | γ-Linolenic acid (GLA) 18:3n-6, % | — | — | | | Dihomo-γ-linolenic acid (DGLA) 20:3n-6, % | 0.04 | 0.17 | | | Arachidonic acid (ARA) 20:4n-6, % | 0.38 | 0.14 | \received DD MMMM YYYY \acceptedDD MMMM YYYY Table A2. Results from MANOVA testing effects of diet, habitat and sex on the percentage of total lipids composed of individual n-3 and n-6 FAME from four tissue/lipid types. (Data shown in Fig. 6 and Fig. A1.) | ALA 18:3n-3 | Diet | 0.148 | 0.96 | 0.451 | | Habitat | 0.246 | 1.79 | 0.166 | | | Sex | 0.088 | 0.53 | 0.716 | | | Diet:Habitat | 0.176 | 1.18 | 0.348 | | | SDA 18:4n-3 | Diet | 0.747 | 16.20 | < 0.001 | | Habitat | 0.232 | 1.66 | 0.194 | | | Sex | 0.205 | 1.42 | 0.261 | | | Diet:Habitat | 0.061 | 0.36 | 0.834 | | | ETA 20:4n-3 | Diet | 0.849 | 31.00 | < 0.001 | | Habitat | 0.070 | 0.41 | 0.797 | | | Sex | 0.128 | 0.81 | 0.532 | | | Diet:Habitat | 0.233 | 1.67 | 0.193 | | | EPA 20:5n-3 | Diet | 0.942 | 89.78 | < 0.001 | | Habitat | 0.145 | 0.93 | 0.465 | | | Sex | 0.141 | 0.91 | 0.478 | | | Diet:Habitat | 0.069 | 0.41 | 0.802 | | | DPA 22:5n-3 | Diet | 0.911 | 55.98 | < 0.001 | | Habitat | 0.215 | 1.50 | 0.236 | | | Sex | 0.020 | 0.11 | 0.978 | | | Diet:Habitat | 0.222 | 1.57 | 0.218 | | | DHA 22:6n-3 | Diet | 0.965 | 153.34 | < 0.001 | | Habitat | 0.380 | 3.34 | 0.027 | | | Sex | 0.041 | 0.23 | 0.917 | | | Diet:Habitat | 0.122 | 0.76 | 0.560 | | | LIN 18:2n-6 | Diet | 0.859 | 33.42 | < 0.001 | | Habitat | 0.506 | 5.64 | 0.003 | | | Sex | 0.062 | 0.37 | 0.830 | | | Diet:Habitat | 0.219 | 1.54 | 0.226 | | | GLA 18:3n-6 | Diet | 0.950 | 103.53 | < 0.001 | | Habitat | 0.207 | 1.43 | 0.256 | | | Sex | 0.212 | 1.48 | 0.242 | | | Diet:Habitat | 0.319 | 2.58 | 0.066 | | | DGLA 20:3n-6 | Diet | 0.909 | 54.89 | < 0.001 | | Habitat | 0.339 | 2.83 | 0.049 | | | Sex | 0.017 | 0.10 | 0.983 | | | Diet:Habitat | 0.316 | 2.54 | 0.069 | | | ARA 20:4n-6 | Diet | 0.730 | 13.52 | < 0.001 | | Habitat | 0.166 | 0.99 | 0.435 | | | Sex | 0.335 | 2.52 | 0.073 | | | Diet:Habitat | 0.091 | 0.50 | 0.735 | \received DD MMMM YYYY \acceptedDD MMMM YYYY * degrees of freedom for ARA 20:4n-6 are 1 and 23 for each variable Table A3. One-way ANOVA testing differences in means of the percentage of total lipids composed of individual n-3 and n-6 FAME across four tissue/lipid types; where significant, Tukey’s HSD was used to test pairwise differences between tissue/lipid types. (Data shown in Fig. 6 and Fig. A1.) | Sum Sq. | Mean Sq. | F 3,114 | Pr(< F ) | Difference | Lower | Upper | p adj | || | ALA 18:2n-3 | 143.50 | 47.83 | 543.90 | < 0.001 | Brain PL : Brain NL | −0.924 | −1.127 | −0.721 | < 0.001 | | Residuals | 10.03 | 0.09 | Muscle NL : Brain NL | 2.102 | 1.901 | 2.303 | < 0.001 | || | Muscle PL : Brain NL | 0.186 | −0.015 | 0.388 | 0.080 | ||||| | Muscle NL : Brain PL | 3.026 | 2.825 | 3.227 | < 0.001 | ||||| | Muscle PL : Brain PL | 1.110 | 0.909 | 1.312 | < 0.001 | ||||| | Muscle PL : Muscle NL | −1.916 | −2.115 | −1.716 | < 0.001 | ||||| | SDA 18:3n-3 | 29.75 | 9.92 | 453.30 | < 0.001 | Brain PL : Brain NL | −0.001 | −0.102 | 0.100 | 1.000 | | Residuals | 2.49 | 0.02 | Muscle NL : Brain NL | 1.215 | 1.114 | 1.315 | < 0.001 | || | Muscle PL : Brain NL | 0.258 | 0.158 | 0.358 | < 0.001 | ||||| | Muscle NL : Brain PL | 1.216 | 1.115 | 1.316 | < 0.001 | ||||| | Muscle PL : Brain PL | 0.259 | 0.158 | 0.359 | < 0.001 | ||||| | Muscle PL : Muscle NL | −0.957 | −1.056 | −0.857 | < 0.001 | ||||| | ETA 20:4n-3 | 5.15 | 1.72 | 61.83 | < 0.001 | Brain PL : Brain NL | 0.248 | 0.134 | 0.362 | < 0.001 | | Residuals | 3.16 | 0.03 | Muscle NL : Brain NL | 0.429 | 0.316 | 0.542 | < 0.001 | || | Muscle PL : Brain NL | 0.557 | 0.443 | 0.670 | < 0.001 | ||||| | Muscle NL : Brain PL | 0.181 | 0.068 | 0.294 | < 0.001 | ||||| | Muscle PL : Brain PL | 0.301 | 0.196 | 0.422 | < 0.001 | ||||| | Muscle PL : Muscle NL | 0.128 | 0.015 | 0.240 | 0.019 | ||||| | EPA 20:5n-3 | 67.18 | 22.40 | 132.70 | < 0.001 | Brain PL : Brain NL | 0.094 | −0.188 | 0.375 | 0.822 | | Residuals | 19.23 | 0.17 | Muscle NL : Brain NL | −1.784 | −2.063 | −1.505 | < 0.001 | || | Muscle PL : Brain NL | −0.767 | −1.046 | −0.488 | < 0.001 | ||||| | Muscle NL : Brain PL | −1.877 | −2.156 | −1.598 | < 0.001 | ||||| | Muscle PL : Brain PL | −0.960 | −1.139 | −0.581 | < 0.001 | ||||| | Muscle PL : Muscle NL | 1.017 | 0.740 | 1.293 | < 0.001 | ||||| | DPA 22:5n-3 | 30.58 | 10.19 | 29.97 | < 0.001 | Brain PL : Brain NL | 0.881 | 0.482 | 1.281 | < 0.001 | | Residuals | 38.78 | 0.34 | Muscle NL : Brain NL | −0.508 | −0.904 | −0.112 | 0.006 | || | Muscle PL : Brain NL | 0.379 | −0.017 | 0.775 | 0.066 | ||||| | Muscle NL : Brain PL | −1.389 | −1.785 | −0.993 | < 0.001 | ||||| | Muscle PL : Brain PL | −0.502 | −0.898 | −0.106 | 0.007 | ||||| | Muscle PL : Muscle NL | 0.887 | 0.494 | 1.280 | < 0.001 | ||||| | DHA 22:6n-3 | 16127 | 5376 | 239.6 | < 0.001 | Brain PL : Brain NL | 20.354 | 17.111 | 23.597 | < 0.001 | | Residuals | 2558 | 22 | Muscle NL : Brain NL | −9.592 | −12.808 | −6.376 | < 0.001 | || | Muscle PL : Brain NL | 13.778 | 10.562 | 16.995 | < 0.001 | ||||| | Muscle NL : Brain PL | −29.946 | −33.162 | −26.730 | < 0.001 | ||||| | Muscle PL : Brain PL | −6.575 | −9.792 | −3.359 | < 0.001 | ||||| | Muscle PL : Muscle NL | 23.370 | 20.182 | 26.559 | < 0.001 | ||||| | LIN 18:2n-6 | 3099.9 | 1033.3 | 541.7 | < 0.001 | Brain PL : Brain NL | −5.731 | −6.677 | −4.785 | < 0.001 | | Residuals | 217.5 | 1.9 | Muscle NL : Brain NL | 8.471 | 7.533 | 9.408 | < 0.001 | || | Muscle PL : Brain NL | −0.924 | −1.862 | 0.013 | 0.055 | ||||| | Muscle NL : Brain PL | 14.201 | 13.264 | 15.139 | < 0.001 | ||||| | Muscle PL : Brain PL | 4.806 | 3.869 | 5.744 | < 0.001 | ||||| | Muscle PL : Muscle NL | 9.395 | −10.325 | −8.465 | < 0.001 | ||||| | GLA 18:3n-6 | 0.53 | 0.18 | 0.39 | 0.758 | not applicable | |||| | Residuals | 51.31 | 0.45 | ||||||| | DGLA 20:3n-6 | 66.95 | 22.32 | 80.11 | < 0.001 | Brain PL : Brain NL | −1.864 | −2.288 | −1.441 | < 0.001 | | Residuals | 43.63 | 0.38 | Muscle NL : Brain NL | −1.745 | −2.165 | −1.325 | < 0.001 | || | Muscle PL : Brain NL | −0.815 | −1.235 | −0.395 | < 0.001 | ||||| | Muscle NL : Brain PL | 0.119 | −0.301 | 0.539 | 0.882 | ||||| | Muscle PL : Brain PL | 1.049 | 0.629 | 1.469 | < 0.001 | ||||| | Muscle PL : Muscle NL | 0.930 | 0.514 | 1.347 | < 0.001 | ||||| | ARA 20:4n-6 | 114.14 | 38.05 | 80.11 | < 0.001 | Brain PL : Brain NL | 0.216 | −0.256 | 0.688 | 0.633 | | Residuals | 54.14 | 0.47 | Muscle NL : Brain NL | −1.009 | −1.477 | −0.541 | < 0.001 | || | Muscle PL : Brain NL | 1.719 | 1.251 | 2.187 | < 0.001 | ||||| | Muscle NL : Brain PL | −1.225 | −1.693 | −0.757 | < 0.001 | ||||| | Muscle PL : Brain PL | 1.503 | 1.036 | 1.971 | < 0.001 | ||||| | Muscle PL : Muscle NL | 2.728 | 2.264 | 3.192 | < 0.001 | Figure A1. Effects of diet, habitat and sex, for each of four tissue/lipid sample types, on the percentage of total lipids composed of A. ALA 18:3n-3, B. SDA 18:4n-3, C. ETA 20:4n-3, D. EPA 20:5n-3, E. DPA 22:5n-3, F. LIN 18:2n-6, G. GLA 18:3n-6, and H. DGLA 20:3n-6. Significant effects are noted. PL = polar lipids; NL = neutral lipids Figure A2. Effects of diet, habitat and sex, for each of four tissue/lipid sample types, A. δ 2 H of ALA 18:3n-3, B. Δδ 2 H of EPA 20:5n-3, and C. δ 2 H of LIN 18:2n-6. Significant effects are noted. PL = polar lipids; NL = neutral lipids Information & Authors Information Version history Peer review timeline Published Ecology and Evolution Version of Record25 Aug 2025Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection

Authors Metrics & Citations Metrics Article Usage 398views 229downloads Citations Download citation J. Peter Koene, Libor Zavorska, Matthias Pilecky, et al. Influences of diet quality and nursery-habitat complexity on brain development and cognitive performance of brown trout (Salmo trutta L.). Authorea. 28 January 2025. DOI: https://doi.org/10.22541/au.173809429.92898125/v1 DOI: https://doi.org/10.22541/au.173809429.92898125/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.