Dietary omega-3 long chain polyunsaturated fatty acids can enhance ecologically relevant cognitive traits in juvenile brown trout

Dietary omega-3 long chain polyunsaturated fatty acids can enhance ecologically relevant cognitive traits in juvenile brown trout | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecology and Evolution This is a preprint and has not been peer reviewed. Data may be preliminary. 20 February 2025 V1 Latest version Share on Dietary omega-3 long chain polyunsaturated fatty acids can enhance ecologically relevant cognitive traits in juvenile brown trout Authors : Stefano Mari 0009-0007-2964-7843 [email protected] , Stefan Auer , Benedikte Austad 0000-0002-1779-2396 , Pernilla Hansson , Simon Vitecek , Mourine Yegon , and Libor Závorka 0000-0002-0489-3681 Authors Info & Affiliations https://doi.org/10.22541/au.174006732.26493876/v1 Published Ecology and Evolution Version of record Peer review timeline 382 views 161 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA) dietary acquisition is beneficial for survival, reproduction, and brain development in many vertebrates including fishes, positively affecting their cognitive abilities. However, how n-3 LC-PUFA impact fish behaviour, and cognition in natural habitats remains unclear. Populations and individuals of the same species often vary in their capacities to synthesize n-3 LC-PUFA. This may affect their sensitivity to dietary intake of these nutrients and, in turn, their cognitive traits and ecological performance. Here, we tested how dietary n-3 LC-PUFA affects behavioural and cognitive traits of brown trout Salmo trutta from two lacustrine and three riverine populations. We combined laboratory behavioural tests with experiments in semi-natural stream mesocosms to see how trout can acquire resources in natural environment (i.e., prey size and taxonomic composition in their stomach contents). Trout raised on a high n-3 LC-PUFA diet showed less bold behavioural types and better cognitive performance in laboratory tests, and capacity to capture and consume larger prey in the stream mesocosm. Additionally, we observed inter-population differences in behaviour and cognition, although these differences were independent from whether fish were from lakes or river. Dietary omega-3 long chain polyunsaturated fatty acids can enhance ecologically relevant cognitive traits in juvenile brown trout Stefano Mari 1,2 , Stefan Auer 3 , Benedikte Austad 4 , Pernilla Hansson 2,4 , Simon Vitecek 5 , Mourine J. Yegon 2,3 , Libor Závorka 2 1 Department of Functional and Evolutionary Ecology, University of Vienna Djerassiplatz 1 1030 Vienna 2 WasserCluster Lunz—Biologische Station, Inter-University Center for Aquatic Ecosystem Research , Dr. Carl Kuperlwieser-Promenade 5 3 Department of Water, Atmosphere and Environment, Institute of Hydrobiology and Aquatic Ecosystem Management, University of Natural Resources and Life Sciences, Vienna, Austria 4 Department of Biological & Environmental Sciences, University of Gothenburg, Gothenburg, Sweden 5 Department of Ecology, University of Innsbruck, Austria Abstract Omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA) dietary acquisition is beneficial for survival, reproduction, and brain development in many vertebrates including fishes, positively affecting their cognitive abilities. However, how n-3 LC-PUFA impact fish behaviour, and cognition in natural habitats remains unclear. Populations and individuals of the same species often vary in their capacities to synthesize n-3 LC-PUFA. This may affect their sensitivity to dietary intake of these nutrients and, in turn, their cognitive traits and ecological performance. Here, we tested how dietary n-3 LC-PUFA affects behavioural and cognitive traits of brown trout Salmo trutta from two lacustrine and three riverine populations. We combined laboratory behavioural tests with experiments in semi-natural stream mesocosms to see how trout can acquire resources in natural environment (i.e., prey size and taxonomic composition in their stomach contents). Trout raised on a high n-3 LC-PUFA diet showed less bold behavioural types and better cognitive performance in laboratory tests, and capacity to capture and consume larger prey in the stream mesocosm. Additionally, we observed inter-population differences in behaviour and cognition, although these differences were independent from whether fish were from lakes or river. Keywords: Omega-3 fatty acids; Brown trout; Behavioural types; Cognition; Resource acquisition Corresponding authors: [email protected] ; [email protected] Introduction Animal individuals consistently differ in a suit of often-correlated behavioural traits such as boldness (i.e., tendency of an individual to express risky behaviour), activity, and aggressiveness (Réale et al., 2007). They can also differ in cognitive flexibility, which has been proposed to associate with these behavioural traits following the speed-accuracy trade-off, where bold, active and aggressive individuals should be able to explore the environment faster but less efficiently and have lower cognitive flexibility (Sih & Del Giudice, 2012). These behavioural traits are related to resource acquisition in the wild, which mediates relationship between behaviour and fitness (i.e., survival and reproduction, Haave-Audet et al., 2022). The theory predicts that individuals which forage on diet rich in higher omega-3 long chain polyunsaturated fatty acids (n-3 LC-PUFA) could have higher cognitive flexibility and show less bold (i.e., less prone to get engaged in risky situations), less active and less aggressive behavioural types, and are able to explore the environment more efficiently. n-3 LC-PUFA are a group of vital dietary biomolecules beneficial for the development of somatic and reproductive tissues (Bogevik et al., 2020; Yonar et al., 2020) and for improving neuronal functioning in vertebrates (Pilecky et al., 2021). They may affect behaviour, cognition, and fitness of the wild consumers (Závorka et al., 2023). For instance, in riparian insectivorous birds n-3 LC-PUFA (i.e. DHA - docosahexaenoic acid and EPA - eicosapentaenoic acid) increase breeding success (Twining et al., 2018, 2019), while in fishes the acquisition through maternal provisioning determines the antipredator response of offspring (Fuiman & Ojanguren, 2011; Perez & Fuiman, 2015). The availability of these biomolecules across food webs depends on differences in capacities of primary producers to synthetize them (Twining et al., 2021). Aquatic food webs, whose primary producers are eukaryotic algae, have a high availability of n-3 LC-PUFA. In contrast, terrestrial food webs, whose primary producers are mostly vascular plants capable of synthesizing only short-chain n-3 PUFA molecular precursors are depleted in these vital biomolecules (Hixson et al., 2015; Twining et al., 2016). There is also an additional level of variation in n-3 LC-PUFA abundance and distribution within aquatic food webs, with lacustrine systems being more enriched in these molecules than the riverine ones, which rely on both aquatic autochthonous resources and terrestrial allochthones inputs (Brett et al., 2017). The uneven distribution of n-3 LC-PUFA across food webs can lead to adaptive intra-specific differences among individuals and populations of the same species in capacity to internally synthesize n-3 LC-PUFA from molecular precursors (e.g. ALA - α-Linolenic acid). For example, fish populations occupying different habitats show intraspecific endogenous differences in synthesis of n-3 LC-PUFA (Ishikawa et al., 2021; Mock et al., 2019). Lacustrine three-spined sticklebacks, which are zooplanktivourous, have a higher n-3 LC-PUFA dietary acquisition than river populations, which feed prevalently on macroinvertebrates (Hudson et al., 2022). On the other side, riverine sticklebacks have a higher copy number of the fads2 gene responsible for the desaturation of molecular precursors to counterbalance the dietary deficiency (Ishikawa et al., 2021). However, is not clear how this variation affects the sensitivity of individuals to n-3 LC-PUFA intake and their behavioural and cognitive traits, and eventually their ecological performance. Brown trout ( Salmo trutta ) show a wide variation in behaviour (Johnsson & Näslund, 2018), resource acquisition (Sánchez-Hernández & Cobo, 2018) and habitat use (Ferguson et al., 2019). Lake migratory populations of brown trout have a piscivorous diet, from which they acquired the most of the n-3 LC-PUFA, on the other hand, the stream resident populations are insectivorous and the amount of n-3 LC-PUFA is determined from both diet and internal synthesis (Murray et al., 2014; Syrjänen et al., 2011). These two ecotypes may differ in their sensitivity to dietary intake of n-3 LC-PUFA, which may generate differences in brain development (Závorka et al., 2022), and possibly also in behavioural and cognitive traits and ecological performance. However, the effects of dietary n-3 LC-PUFA on ecologically important behaviours across different ecotypes or populations of a plastic species such as brown trout have never been tested. Here, we report the results of a common garden feeding experiment that investigated the role of dietary n-3 LC-PUFA in the development of brown trout behavioural traits and cognition, and ecological performance in stream mesocosms simulating natural environment. We hypothesized that brown trout individuals receiving a high n-3-LC-PUFA diet would show higher cognitive performance, and additionally assumed that trade-offs in cognitive performance in function of ecotype persist in brown trout from activity and performance in inhibitory control detour tests (ICDT) to evaluate decision making of individuals fed on two diets differing in n-3 LC-PUFA content. In addition, we assessed in flume mesocosms whether individuals differed in their prey acquisition efficiency when exposed to natural habitat and prey availability. In line with our hypotheses, we predicted that higher dietary n-3-LC-PUFA availability would result in better performance in cognitive tests and that lacustrine brown trout are more sensitive to a n-3 LC-PUFA deprived diet than stream residents, because stream residents should a have greater capacity to synthesize n-3 LC-PUFA. Finally, we expected that enhanced cognitive skills of fish results in higher capacity to catch large size prey item and detect benthic cryptic invertebrates in a semi-natural stream mesocosm. Material and Methods Fish origin, laboratory conditions and dietary treatment In November 2022, we collected juveniles (age 0+) from five different populations in Austrian freshwater systems (Drau River, Kamp River, Ois River, Lake Attersee and Lake Weissensee, N = 70 individuals per population). Except from one riverine population, which was collected in a natural stream by electrofishing (Ois), all the remaining trout were hatchery fish from conservation programmes aiming to maintain the original wild populations. All fish from hatcheries were 10 months old at the beginning of the experiment. Age of the wild fish was estimated based on the length-frequency distribution of the population of origin and corresponded to age 0+ and a fork length of 85.27 ± 8.52 mm. Fish were kept in flow-through holding tanks (500 L) supplied with filtered spring water in a flow-through systems, with water temperature naturally fluctuating over time (depending on outdoor conditions) in a range suitable for growth and development of brown trout i.e., from 4 °C in January to 15 °C in July (Jonsson & Jonsson, 2011) and maximal daily amplitude of ~ 0.5 °C. Fish were fed daily ad libitum and the feed was supplied automatically between 9.00 and 16.00 by a feeding belt. The daily amount of pellet (GARANT AQUA, Austria) was distributed in 5 silicon cups placed and then released by the feeder. Fish were initially fed for two weeks by feed from their hatchery of origin. After this period, we split fish from each population to two holding tanks and fed them two different experimental diets. One group received rapeseed oil pellet whose n-3 LC-PUFA content was low while the other received fish oil pellet whose n-3 LC-PUFA content was instead higher. Feeds were both isonitrogenous and isocaloric as described in (Závorka et al., 2021) and differed only in n-3 LC-PUFA composition. Wild fish were fed with the experimental diets from the beginning of the experiment. Individuals were initially kept in the holding tanks by their population of origin (i.e, 2 tanks with ~ 35 individuals per population). In February 2023, when all individuals achieved the minimal size for tagging (i.e., 69 mm, Vollset et al., 2020), fish were anaesthetized (2-phenoxyethanol, 0.5 ml∙L -1 ), tagged with 12-mm (PIT tags), and measurements of their fork length (distance from the tip of the snout to the end of the central caudal fin ray) to the nearest millimetre and body mass to the nearest 0.1 g were taken (Tab. 1). Fish were then re-distributed across eight experimental tanks, each of which held 32 individuals taken from all the five populations mixed together in each holding tank (N = 256). Mortality was low and consistent with the previous long-term feeding studies with salmonids from this system (Murray et al., 2014). Remaining fish from all populations were kept in four additional holding tanks also supplied with the two experimental diets. Dead individuals from the 8 experimental tanks were replaced by individuals from the additional tank with similar body size, and the same population of origin and dietary treatment. Behavioural scoring in laboratory conditions We individually tested all the fish in laboratory conditions for both behaviour and cognitive skills repeating six trials in total (N = 6) three of which were conducted in spring (late February- early April 2023) and three in summer (early May-mid August 2023). We scored fish behaviour in 16 rectangular plastic tanks (W = 56 cm, L = 77 cm, H = 30 cm, water depth ~ 15 cm), cleaned and filled with fresh freshwater from the same water source to account for potential interference by olfactory cues. In spring, fish were acclimated for 15 minutes inside the acclimation box (W = 56 cm, H = 30 cm, L = 20 cm, Fig.1) of the experimental tank before the trial starting. We then hoisted the guillotine door of the acclimation box and measured the time until emergence to assess boldness of individuals (Budaev, 1997). After 15 minutes, regardless of whether individual has emerged, we removed the acclimation box and recorded activity through the open field test (i.e., distance moved in cm, Bell., 2005) on individual in a bare tank for 20 minutes. Following these two tests, we remotely inserted a mirror (20 x 20 cm) along the wall of the scoring tank and recorded for 10 minutes how much time individual spent in front of their mirror image to measure their aggressiveness (Axling et al., 2023). Fish were taken back into their holding tank after the mirror image test. We tested in total 16 batch of fish per week (6 batches consisting of 16 individuals the first and second day and 4 batches the last one). Each behavioural measurement was repeated once per week. When all the fish were tested 3 times for behaviour, we started to test them for the inhibitory control. The inhibitory control detour test (ICDT) apparatus consisted in a transparent acrylic barrier with two openings as possible access to the reward, which was an artificial shelter (a PVC pipe [22 cm length, 4.5 cm diameter] attached to a stone). The opening closer to the reward was blocked with a transparent cup to prevent the shortest direct access to the reward, while the opening further from the reward was open (Fig.2a). Fish were left for 10 minutes in the starting box to recover from handling before the whole starting box was lifted and we recorded for 30 minutes if individuals found the opening in transparent barrier to reach the reward and how long it took them. All fish were moved back to their holding tank after the ICDT and other batches followed. As for the behavioural tests, fish were tested three times for the inhibitory control. From the second trail on of the ICDT, we changed the apparatus by adding a funnel to the open access (Fig.2b). This increased the difficulty of the test, since the experimental subject could not reach the opening physically interacting with the barrier (i.e., limiting the effect of thigmotaxis, “wall-hugging”, on the probability to pass the barrier). As for the behavioural tests, ICDT were repeated 3 times, once per week. Once all trials ended, we reorganized fish in the holding tanks before testing them again. Individuals from each population were held together to facilitate their observation. Each tank housed 32 fish from a single population, or at most, two populations when needed (16 from one and 16 from another). In summer, we tested fish again both for behaviour and for inhibitory control. The first steps of the protocol for behavioural and cognitive scoring were similar to the ones followed from spring, with the difference that both behavioural tests and ICDT were run all together. The inhibitory control detour test (ICDT) was the last test of the protocol following the mirror image test. Once the mirror image test ended, we placed the starting boxes back to the tank, gently guided fish back to them with the minimum disturbance and prepared the ICDT set up. Fish were acclimated for 10 minutes before the ICDT. Whilst in spring we tested fish for the easier version (no funnel) of the ICDT only for the 1 st trial and ran the difficult version (funnel) in the last two, in summer the difficult test was ran only as last trial. Since the additional trial made the duration of the testing battery longer, we tested only 3 batches of 16 fish per day. Each behavioural and cognitive measurement taken in summer was repeated once every three days. We tracked fish movements in all videos using a YOLOv3 model with the OpenCV package in Python (script available at https://github.com/P-Hansson/trout-tracking-and-behaviour). Videos showing issues during filming (i.e. fish jumping out of their tanks and mechanical failures with the starting boxes and mirrors) or that program failed to track some individuals from were excluded from the analysis. Semi-natural stream mesocosm experiment We used semi-natural flume mesocosms at the so-called HyTEC-facility in Lunz (HyTec Hydro morphological and Temperature Experimental Channels) (Auer et al., 2017). The HyTec Flumes simulated the conditions of a subalpine stream including natural flow conditions, substrate, and prey. It consisted in two artificial channel each split in four equivalent enclosures (N = 8, L = 7 m, W = 1.5 m). Each enclosure was separated by a 2 m buffer zone enriched with stones and wood in order to decrease the speed of the flow and delay the invertebrates washing out. Enclosure dividers had a wooden barrier that could be used to regulate the water level in each enclosure. An acclimation pond for fish was prepared at the end of the two channels. Prior to the experiment, flumes were inoculated with macroinvertebrates collected from the river Seebach (47°51’23.00” N, 15°02’12.44” E; 47°51’08.14” N, 15°03’52.46” E): We collected 48 Multi-Habitat samples (MHS), each made up of 20 subsamples of 25x25 cm 2 sediment. From the MHS macroinvertebrate samples, 6 MHS samples were supplied to each Flume enclosure. We estimated an initial density of ~7.500 macroinvertebrate individuals per square meter in the source stream, resulting in an estimated density of 5000-6000 prey items per square meter (or an estimate total of 55.000 prey items per enclosure) which corresponded to densities found in nature (Brown & Brussock, 1991; Leitner et al., 2015) (see supplementary material for more taxonomic details on the macroinvertebrate prey items). We periodically checked that the density of the prey in the HyTec Flume remained at natural levels and repeated the inoculations when density decreased. Fish were acclimated to the facility 7 days in the acclimation pond. In total six focal brown trout of the same size and population of origin were released into each of the eight enclosures (N = 48) for 10 consecutive days. Individuals were distributed among the enclosure according to their feeding treatment and population of origin, so each enclosure contained individuals from the same population where three individuals received n-3 LC-PUFA rich diet (N = 3) and three n-3 LC-PUFA poor diet (N = 3) respectively. Water level of each channel was changed during the experiment in the following way: high level (day 1-4), low level (day 4-7), high level (day 7-10), to simulate the water level fluctuation in a natural stream. One fish per enclosure were removed after the seventh day of the rearing to be used in another study (Austad et al. unpublished). At the 10 th day (end of the experiment) fish were overdosed with 2-phenoxyehtanol (1 ml∙L -1 ) measured it for fork length and body mass and flushed their stomach by injecting water into their oesophagus through a syringe (Kamler & Pope, 2010). Fish were then euthanized by cutting their spinal cord. Stomach contents were stored in 15 ml vials containing 90% ethanol in a freezer at -20°C until further processing. Stomach content analysis To measure dry biomass of the stomach content we first oven dried 47-mm diameter filter papers at 50°C for at least 24 hours and measured individually their weight to the nearest mg. Then we offloaded the vial with stomach content and ethanol on the filter paper using a vacuum filtration and kept the samples inside Petri dishes. Samples were then freeze dried (Genesis Freeze dryer, Virtis, NYC) for 48 hours and weighted. Stomach content dry biomass was calculated by subtracting the mass of the filter paper with sample from the mass of them empty filter paper. After this, we counted the number of prey items on each filter and determine them to the lowest possible taxonomic level (supplementary material Tab.S1). For our analysis, we then calculated average size of the prey item by dividing dry biomass by number of prey items and we calculated the ration between number of items of terrestrial prey and aquatic benthic prey. Statistical analysis We ran statistical models for behavioural and cognitive measures in laboratory conditions and stomach content measures collected in the HyTec Flume. All statistical analyses were done in R version 4.4.1 (R Core Team, Vienna, Austria). The effect of the n-3 LC-PUFA dietary treatment on the latency time before fish solved the ICDT was tested with a mixed linear model (LMM) using the dietary treatment, the population of origin of fish, the numerical order of the test (1 st , 2 nd and 3 rd trial) and the season when they were tested (spring or summer, see supplementary material) as fixed factors and Fish ID as a random effect. Another model was used to test the effects on the success rate (binary measure on whether fish passed or not the barrier), but since it did not converge even after scaling and centring model variables, it could not be interpreted. We included only measurements from the simple version of the ICDT in our data analysis, as the complex version of the test produced a zero-inflated dataset (supplementary material Fig.S2). We tested the effect of the n-3 LC-PUFA dietary treatment on fish boldness, activity and aggressiveness using their respective measures as the response of the mixed linear model (latency time before emergence from the acclimation area, distance moved inside the tank and time spent in front of the mirror) using as independent variables, the dietary treatment, population and test order, and Fish ID as random effect. For the emergence test, the mixed linear model included only the fish that emerged the acclimation box, so we also ran a generalized linear mixed model (GLMM) that included both emerged and not-emerged fish. The response of the model was the binary measure of whether the fish emerged or did not emerge during the trial, while independent variables and random effects were the same of the previously mentioned mixed linear models. Repeatability of an individual’s performance in both behavioural trials and ICDT was tested as the random intercept of an individual’s ID adjusted to the fixed factors used in the main models described above (Stoffel et al., 2017). We used 1000 bootstraps to calculate the 95% confidence interval (CI) for repeatability estimates to see the consistency of performances across trials (Nakagawa & Schielzeth, 2010). For the measures taken from the HyTec Flume experiment, we used GLMM to see the effect of dietary treatment on the proportion of benthic prey versus drifting prey and LMM for average prey size, and prey biomass. For both of the two responses the fixed factors were the interaction dietary treatment and population, the sex of the fish and scaled values of their fork length. We included fork length of the fish in these models, but not for the behavioural and cognitive measures from laboratory, because the final size of individuals was measured only after the HyTec Flume experiment, and thus it did not reflect the size of the fish during the laboratory scoring several months or weeks earlier. Similarly, we included sex as fixed factor only in the model from HyTec flumes, because this information was available only for the subset of individuals that were tested in the mesocosms and not for all the fish tested for behaviour in the laboratory. We used as random effect the combination of the round of the HyTec flume experiment fish took part (from 1 st to 10 th round) and the enclosure of the HyTec flume where fish were placed (from 1 st to 8 th enclosure of the flume) (Tab. 4). The significance of the response variables of the fitted models was evaluated through an ANOVA (Type II sum of squares or type III for models without and with interactions respectively) using the car package from R. Differences among trials on the ICDT and personality tests were analysed using Tukey’s HSD post hoc test. Responses variables latency to solve the ICDT and prey size were log transformed for the fit of the models that was evaluated by visually inspecting the normality distribution of model’s residual. All models reported in the results has satisfyingly met the evaluated criteria for their robustness. n-3 LC-PUFA dietary treatment and fish scoring in laboratory conditions The measure of fish ICDT performance (time of latency before the experimental subject passed the barrier) was affected by the n-3 LC-PUFA dietary treatment (F = 8.95, p = 0.003). Fish that received a high n-3 LC-PUFA dietary treatment took less time to pass the barrier than the low n-3 LC-PUFA treatment group (Fig. 3). An effect was found also for the population of origin with fish from River Ois having performed better than fish from River Drau and Lake Weissensee (F = 4.29, p = 0.002, Fig. 3. Post-hoc River Ois vs River Drau and Lake Weissensee p < 0.05 the rest of the p = NS). Additionally, season (spring or summer) was associated to ICDT performance, with fish performing better in spring than summer (F = 3.94, P = 0.047). Repeatability for the performance in ICDT was very low at 0.0899 (CI 95%: 1.47e -16 - 0.189). The emergence test (time to emerge from the shelter), was not affected by any of the considered variables during spring but showed differences for all of them in summer (Tab. 2). Repeatability of emergence test was 0.130 (CI 95%: 0.014-0.233) during spring and 0.280 (CI 95%: 0.164-0.384) during summer. Both of these values indicated a low but significant consistency for individual boldness. In summer, fish that received a high n-3 LC-PUFA dietary intake had higher latency time to emerge than the ones that fed on a low n-3 LC-PUFA diet instead. Latencies times were not different between the first and the second trial but they were between the first and the third (Post-hoc first emergence test trial vs third p < 0.05). Fish from Lake Weissensee were faster than the ones from River Kamp in leaving the shelter (Post-hoc Lake Weissensee vs River Kamp p < 0.05). The success rate in emergence test was significantly affected by population of origin ( χ 2 = 20.34, p < 0.001) in spring with fish from River Ois performed the emergence test worse than fish from Lake Weissensee and River Drau (post-hoc Ois vs Drau and Weissensee p < 0.05 and the rest p = NS, Tab. 3). In summer there was a tendency for the numerical order of the trial (post-hoc 1 st trial vs 2 nd and 3 rd trials p < 0.05, χ 2 = 5.822, p = 0.054) on probability of leaving the shelter rate in emergence test. No effects on probability of leaving the shelter in the emergence test were found from dietary treatment and population (Tab. 3). Individual repeatability of probability of leaving the shelter in the emergence test was 0.323 (CI 95%: 0.064-0.154) in spring and 0.617 (CI 95%: 0.149-0.452) in summer. Activity of fish in open field test was significantly affected by population (χ 2 = 69.77, p < 0.001) and test order (χ 2 = 21.52, p < 0.001) in spring, while there are no differences between the two dietary treatment groups. Riverine fish from Drau showed similar level of activity as lacustrine from Attersee and Weissensee, while fish from River Kamp and River Ois were less active than the rest of the populations (Fig. 4b). All fish were less active in the first trial compared to the second and the third one (post-hoc 1 st trial vs 2 nd and 3 rd trials p < 0.01, Fig. 4b). The effect of population on fish activity was significant in summer model ( χ 2 = 23.69, p < 0.001, Fig. 4e), whilst there is no effect of the dietary treatment ( χ 2 = 0.249, p = 0.617) nor of test order ( χ 2 = 3.07, p = 0.216). Fish from River Drau remained most active compared to all the other population (post-hoc Drau vs all the rest p < 0.05) while fish from lake Weissensee were more active than the ones from River Kamp (post-hoc Weissensee vs Kamp p < 0.05), activity among the rest of the populations did not differ. Repeatability for activity during the open field test was 0.606 (CI 95%: 0.537-0.665) in spring and 0.615 (CI 95%: 0.542-0.678) in summer indicating a strong consistency for activity across time for both the two seasons. Time spent by fish in front of the mirror was affected by the population both in spring (χ 2 = 77.72 p < 0.001) and in summer (χ 2 = 52.09, p < 0.001). Particularly, fish from Lake Attersee spent more time in front of their mirror image compared to the other populations (post-hoc Attersee vs other population p < 0.01) (Fig. 4c and Fig. 4f). Individual repeatability of aggressiveness was 0.289 (CI 95%: 0.201-0.369) in spring, and 0.500 (CI 95%: 0.416-0.579) in summer. n-3 LC PUFA dietary treatment and resource acquisition We found an effect of population of origin on the proportion of benthic prey versus the drifting prey in trout stomachs ( χ 2 = 10.06, p = 0.039). Fish from Ois fed more on benthic prey than fish from Lake Attersee and River Drau (post-hoc Ois vs Attersee and Drau p < 0.05, Fig. 5a). There were no effects of dietary treatment, sex, and their fork length on the proportion of benthic prey. We found an effect of the dietary n-3 LC-PUFA treatment on the prey size that fish have been selecting in the stream mesocosms ( χ 2 = 5.35, p = 0.021). Fish that received a high dietary n-3 LC-PUFA treatment fed on larger prey than the low dietary n-3 LC-PUFA treatment conspecifics (Fig. 5b). We also found a tendency for the interaction between population of origin and dietary n-3 LC-PUFA treatment ( χ 2 = 9.46, p = 0.051). Fish coming from lacustrine population (Attersee and Weissensee) and fish from River Drau conditioned to low n-3 LC-PUFA diet fed on larger prey compared to the ones under the high n-3 LC-PUFA dietary treatment (Fig. 5b). We found no effects of any variable considered on total prey biomass (Tab. 4). Discussion Our results showed that n-3 LC-PUFA dietary intake increased fish performance in the ICDT. Differences in this performance between the five populations suggest that each one had its own sensitivity for n-3 LC-PUFA for the development of cognition potentially due to differences in metabolism. However, fish cognitive performance was not affected by whether fish came from lake or river. The dietary treatment had no effect on aggressiveness and activity, but it affected one of the two boldness measures (i.e, latency time) with high n-3 LC-PUFA dietary group taking more time to leave the shelter. Additionally, we found that fish fed an enriched n-3 LC-PUFA diet caught larger prey compared to individuals fed the low n-3 LC-PUFA diet, when exposed to natural prey in HyTec Flume mesocosms. Finally, we found that n-3 LC-PUFA dietary treatment had no effect on the acquisition on more benthic prey compared to the drifting ones. Inhibitory control is an ecologically relevant cognitive process improving foraging skills in birds (Coomes et al., 2022) and guaranteeing survival in mammals (Rochais et al., 2023). Fishes’ ICDT performances are comparable to the ones found in birds and mammals (Lucon-Xiccato et al., 2017). However, the ecological relevance for this group had never been empirically tested (Lucon-Xiccato, 2024; Lucon-Xiccato et al., 2017). Brown trout inhibitory control may favor a better evaluation of the environment and selection of higher quality prey. Our study suggests that ICDT measures have an ecological relevance improving fish foraging efficiency (i.e. high n-3 LC-PUFA dietary treatment group fed on larger prey). The effect of n-3 LC-PUFA on prey size tend to differ in different populations with fish from lacustrine population (Lake Weissensee and Lake Attersee) feeding on smaller prey when they were fed with a high n-3 LC-PUFA diet, while fish from streams (Kamp, Drau, Ois Rivers) fed on larger prey in the same dietary treatment conditions (i.e. high n-3 LC-PUFA diet). In the Atlantic blue fin tuna larvae ( Thunnus thynnus ), DHA the most important n-3 LC-PUFA promoted the synthesis of opsin and improved catching on rotifers prey (Koven et al., 2018). In the retina, the presence of DHA in phospholipids integrated in cells’ membranes facilitates conformational changes of photopigments and their bound with G-Proteins improving the elaboration of visual stimuli (Mitchell et al., 2003). According to these mechanisms, we propose that an enrichment in dietary n-3 LC PUFA (i.e. DHA) may have positively affected the visual acuity of trout from lake ecotypes improving their sight and making them capable to better detect and catch smaller prey. Our analysis revealed that n-3 LC-PUFA dietary treatment did not influence the proportion of benthic versus drifting prey in trout diets. However, several factors must be considered when interpreting these results. The HyTec Flume facility, being an open system, was subject to environmental variables that could influence prey community composition and availability. Contribution of benthic prey in trout diet increased with time (supplementary material Fig. S3). Possibly, the availability of the drifting prey, which included prey with an aerial imago stage were more available in the early weeks of the experiment and decreased through time when they flew away. Repeatability for ICDT performance was very low. We suggest that this value may indicate a high level of cognitive flexibility rather than low consistency or reliability of the measures (Nakagawa & Schielzeth, 2010) since, with repeated trials for the same test, fish may had learned how to solve the task in a reduced amount of time, increasing the variation in cognitive performance measures (Cauchoix et al., 2018). The latency time to leave the shelter in the emergence test (boldness) performed in spring did not differ between the dietary treatment groups in any variables, but the measures collected in summer differed between all the variables, dietary treatment, population and test order. Fishes’ behavioral types can emerge later across their lifetime during the passage from juvenile to subadult and after their sexual maturation (Polverino et al., 2016). This is because within-individual behavioural variation (i.e. behavioural plasticity) is age-dependent and tends to decrease the more the time passes, increasing in turn the consistency of behavioural types (Fischer et al., 2014). In trout we tested, differences in boldness for dietary treatment, population and test order may have come out later in their development due to the age-dependence of behavioural plasticity. In another salmonid species—the arctic charr ( Salvelinus alpinus )— the level of boldness relied more on between population variation than whether fish is a surface or a benthic feeder (i.e, feeding modalities) in determining behavioural types (Dellinger et al., 2023). However, the authors of this study provided the same feed to both benthic and surface feeding group, not considering the effect of diet biochemical composition of the different prey that benthic and surface feeders find in nature. Fish from high n-3 LC-PUFA dietary group showed to be less bold than the low n-3 LC-PUFA group. Since they are also the ones that performed better in ICDT, these results confirm our hypothesis on speed-accuracy trade-off, with less bold individuals having higher cognitive performance than low n-3 LC-PUFA bolder group. Variation in behavioural types in brown trout are often heritable (Kortet et al., 2014). We suggest that population genetic difference of brown trout in our study could play a role in determining behavioural traits of boldness (time to leave the shelter and success rate from our experiment), activity and aggressiveness than feeding modalities or diet quality. Activity and aggressiveness did not differ between the dietary treatment groups. Prior studies in different animal taxa (i.e., invertebrates) showed that several behavioural types are affected by a surplus or a decrease of carbohydrates and/or proteins in their diet (Han & Dingemanse, 2015) . n-3 LC-PUFA are component of phospholipids (polar lipids) involved in determining biological structures such as biological membranes and their features (Quinn et al., 1989). On the other side carbohydrates function in energy storage. The same function is also performed by neutral lipids (i.e. triacylglycerol). Additionally, in southern field cricket it was found that high protein diet made individuals more aggressive (Han & Dingemanse, 2017) . Thus, we suggest that the number of macronutrients implied in energy provisioning such as carbohydrates and/or neutral lipids for fish activity, while proteins for aggressiveness are the determinant rather than n-3 LC-PUFA. Our experiment could not test this effect as our dietary treatment consisted in giving to fish an isocaloric diet, which differed only in n-3 LC-PUFA content . Repeatability of behavioural measures we took from our experiments range from moderate to high both in spring and summer. These values confirmed the consistency of these behavioural types across time and contexts. Fish activity in the open field test tended to increase on the second trial done in spring and stayed on similar levels on the third one. Trout may result less active in the first trial of the experiment because of the lack of habituation of the new experimental conditions. This habituation effect on fish may explain also differences in open field test between spring and summer, with 3 out of 5 populations (River Mur, and lakes Attersee and Weissensee) showed to be the most active in spring, and only one of these 3 resulted to be consistently active in summer (River Mur) (Stamps & Groothuis, 2010) . Finally, the habituation could be also the reason for the loss of significant differences between spring and summer emergence success rate measures in the emergence test and the decrease of latency time in the third trial of the same test. In summary, our study demonstrates that dietary n-3 LC-PUFA could play an important role in development of fish cognitive skills and foraging efficiency (i.e. catching larger prey) in natural habitat. Our findings showed the positive influence of n-3 LC-PUFA in cognitive skills and the negative effect on fish boldness in non-model species and confirmed the ecological relevance of laboratory ICDT for the evaluation of cognition and the speed-accuracy trade-offs. Future studies could dive deeper and highlight the inner, physiological mechanisms of improved by n-3 LC-PUFA dietary intake cognitive performance and the inter population differences in sensitivities for these molecules to highlight both proximal and distal causes of the development and evolution of fish cognition. Acknowledgements The research complied with Federal Act on the Protection of Animals, Austria (http://www.ris.bka.gv.at) and EU Directive 2010/63. The host institute possessed ethical permission to carry out all the tasks necessary for this project (BMBWF - v/3b (Tierversuchwesen und Gentechnik), license number: 2023-0.053.856). We want to express our gratitude to the all the hatchery owners who allowed us to use their fish to pursuit our studies and all additional collaborators (students and interns) who helped us in data collection. This research was funded in whole or in part by the Austrian Science Fund (FWF) [10.55776/P35515]. 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Ecology and Evolution , 12 (3), e8684. https://doi.org/10.1002/ECE3.8684 Figures legend Fig. 1 — Representation of all apparatus in the behavioural scoring in laboratory condition. a) Experimental set up of the emergence test. Fish were tested in plastic scoring boxes (W = 56 cm, L = 77 cm, H = 30 cm). Before the test they waited into an (W = 56 cm, L = 20 cm, H = 30 cm) acclimation box. The dashed line highlights the opening of the guillotine door to allow the emergence from the acclimation box. b) Experimental set up of the open field test. Dash line indicates the complete acclimation box removal. Fish was test to see the space it could cover through swimming expressed in cm. c) Experimental set up of the mirror image test. A guillotine mirror (20 x 20 cm 2 ) was inserted after the open field test duration trial ended. Area selected valuable for interaction between experimental subject and mirror image (blue-sky outline square) was 19.75 X 20 cm 2 . Fig. 2—Representation of Inhibitory control detour test (ICDT) apparatus performed in laboratory conditions. a) Experimental set up of the simple version of the ICDT. Transparent barrier separated the experimental subject from the pipe/shelter. Fish could swim through two different circular holes (5 cm of diameter). The one closer to the pipe was filled with a transparent cup (grey) while the other was left open as the only possible access to reach the reward (dash-line). b) Experimental set up of the complex version of the ICDT. Transparent barrier separated the experimental subject from the pipe/shelter. Fish could swim through two different circular holes (5 cm of diameter). The one closer to the pipe was filled with a transparent cup (grey) while in the other an additional funnel was insert (grey rounded dots) to avoid that fish could pass the barrier through thigmotaxis. Fig. 3 — Effect of n-3 LC-PUFA dietary treatment on inhibitory control detour test . The box plot displays the time (in seconds, logarithmic scale) required to pass the transparent barrier for the fish populations Kamp, Attersee, Ois, Drau, and Weissensee. Two colours represent the n-3 LC-PUFA dietary treatment conditions: red for high n-3 LC-PUFA content and blue for low n-3 LC-PUFA content. The dots represent individual data points for each treatment group, highlighting the distribution of latencies within each population and treatment group. Each population has two box plots representing the high and low n-3 LC-PUFA dietary treatment, showing the median, quartiles, and potential outliers. Fig. 4 —Influences of variables on behavioural measures in spring (a,b,c,d) and summer (e,f,g,h). a-e) Effect of n-3 LC-PUFA on boldness (latency time). The boxplots compare the time passed before fish left the shelter (y-axis) in the five populations (y-axis) under the two dietary treatments. The dots represent individual data points for each group, highlighting the distribution of individuals in populations and n-3 LC-PUFA diet. Each population has 6 box plots (2 dietary treatment x 3 behavioural trial) representing the high and low n-3 LC-PUFA treatments, showing the median, quartiles, and potential outliers . The black line across the box plots represents the averaged measures from the two dietary groups . b-f) Effect of n-3 LC-PUFA on boldness (binary measure on whether fish emerged or not from the shelter). The scatter plot with error bars compares the emergence success (y-axis) for fish populations (Kamp, Attersee, Ois, Drau, Weissensee) under high (red dots) and low (blue dots) n-3 LC-PUFA dietary treatments. Each population has two corresponding points representing the high and low n-3 LC-PUFA treatments, showing variability within the groups. c-g) Differences between populations in fish open field test performance (activity). The boxplot shows the distance in centimetres swam in open arena (y-axis) by different fish populations (x-axis) under the two ecotypes (lake or river). The dots represent individual data points for each group, highlighting the distribution of individuals in populations and ecotypes. Each population has one box plots representing the population of origin, showing the median, quartiles, and potential outliers. Specifically for the spring plot (c), there are also indicated the repeated trials along the experiment. Each population, thus have three boxplots for each trial. d-h) Differences between populations in fish mirror image test performance (aggressiveness). The boxplots compare the time spent in front of the mirror (in seconds) by fish from various populations (Kamp, Attersee, Ois, Drau, Weissensee) under two ecotype conditions: lake (blue) and river (red). Each population has one boxplot showing the median, quartiles, and potential outliers. Fig. 5 — Effect of n-3 LC-PUFA dietary treatment on dietary intake in semi-natural stream mesocosm conditions. a) Effect of dietary n-3 LC-PUFA on acquisition of benthic prey versus drifting prey. The scatter plot with error bars shows the proportion of benthic versus drifting prey (y-axis) in the diet of fish from five populations (Kamp, Attersee, Ois, Drau, Weissensee) under high and low n-3 LC-PUFA dietary treatments (x-axis). Each population has two points and a vertical line, indicating confidence intervals. b) The boxplot compares prey size (logarithmic scale) on y-axis for five fish populations (Kamp, Attersee, Ois, Drau, Weissensee) under high and low n-3 LC-PUFA dietary treatments (x-axis). Each population has two box plots representing the high and low n-3 LC-PUFA treatments, showing the median, quartiles, and potential outliers. The black line across the box plots represents the averaged measures from the two dietary groups. c) Effect of n-3 LC-PUFA dietary treatment on the acquisition of biomass. The box plot compares the dry stomach content biomass on the y-axis (logarithmic scale) for fish populations (Kamp, Attersee, Ois, Drau, Weissensee) under high and low n-3 LC-PUFA dietary treatments (x-axis). Each population has two box plots representing the high and low n-3 LC-PUFA treatments, showing the median, quartiles, and potential outliers. The black line across the box plots represents the averaged measures from the two dietary groups. Tables Table 1 Summary table of fish body weight and fork length for all the populations Kamp River 96.07 ± 13.29 105.34 ± 12.19 105.16 ± 12.98 10.41 ± 4.36 12.60 ± 5.19 15.23 ± 17.00 71 33 33 Drau River 124.80 ± 16.72 137.16 ± 19.27 137.23 ± 18.82 25.37 ± 10.05 28.95 ± 11.77 28.30 ± 11.23 66 43 43 Ois River 85.27 ± 8.52 112.83 ± 13.45 113.12 ± 13.83 6.93 ± 2.49 16.58 ± 7.13 16.87 ± 6.81 60 26 26 Attersee Lake 126.66 ± 8.58 137.60 ± 12.77 138.40 ± 12.74 26.32 ± 6.52 28.90 ± 8.35 31.14 ± 8.79 68 43 43 Weissensee Lake 90.20 ± 15.43 123.71 ± 24.52 127.29 ± 23.48 9.95 ± 6.71 25.38 ± 17.91 25.61 ± 17.48 97 19 19 Table 2 Summary model of Inhibitory control detour test (ICDT) Latency time before passing the transparent barrier (log transformed) Population 4.29 4 221.47 0.0023 (1|fish_ID) Dietary n-3 LC-PUFA 8.95 1 221.56 0.003 R SE CI 0.089 0.049 1.47e -16 - 0.189 Test order 0.495 2 470.72 0.61 Season 3.945 1 563.99 0.047 Table 3 Summary models of behavioural scoring from spring and summer experiments Spring Boldness Latency to emerge from the shelter (square root transformation) Dietary n-3 LC-PUFA 2.46 1 0.117 (1|fish_ID) Population 1.25 4 0.869 R SE CI 0.13 0.55 0.014- 0.233 Test order 0.42 2 0.808 Emerged/not emerged Dietary n-3 LC-PUFA 0.46 1 0.497 (1|fish_ID) Population 20.34 4 <0.001 R SE CI 0.323 0.064 0.154-0.406 Test order 0.474 2 0.789 Activity Distance (cm) Dietary n-3 LC-PUFA 0.497 1 0.487 (1|fish_ID) Population 69.77 4 <0.001 R SE CI 0.606 0.033 0.537- 0.665 Test order 21.52 2 <0.001 Aggressiveness Time in front of the mirror (s) Dietary n-3 LC-PUFA 0.66 1 0.418 (1|fish_ID) Population 77.72 4 <0.001 R SE CI 0.289 0.042 0.201-0.369 Test order 2.54 2 0.281 Summer Boldness Latency to emerge from the shelter (square root transformation) Dietary n-3 LC-PUFA 4.964 1 0.026 (1|fish_ID) Population 12.66 4 0.013 R SE CI 0.280 0.057 0.164-0.384 Test order 7.252 2 0.027 Emerged/not emerge Dietary n-3 LC-PUFA 0.197 1 0.657 (1|fish_ID) Population 2.84 4 0.585 R SE CI 0.617 0.149 0.452-0.936 Test order 5.82 2 0.054 Activity Distance (cm) Dietary n-3 LC-PUFA 0.249 1 0.617 (1|fish_ID) Population 23.69 4 <0.001 R SE CI 0.615 0.035 0.542-0.678 Test order 3.07 2 0.216 Aggressiveness Time in front of the mirror (s) Dietary n-3 LC-PUFA 0.35 1 0.554 (1|fish_ID) Population 52.09 4 <0.001 R SE CI 0.5 0.043 0.416- 0.579 Test order 3.86 2 0.145 Table 4 Summary models of stomach content measures Proportion benthic prey versus drifting prey Dietary n-3 LC-PUFA (1|enclosure:round) 2.17 1 0.141 Population of origin 10.06 4 0.039 Sex 0.05 1 0.831 Scaled fish fork length 1.42 1 0.233 Prey size (log transformed) Dietary n-3 LC-PUFA (1|enclosure:round) 5.35 1 0.021 Population of origin 5.66 4 0.226 Sex 0.01 1 0.939 Scaled fish fork length 2.43 1 0.112 Population : Dietary treatment 9.46 4 0.051 Stomach content dry biomass (log transformed) Dietary n-3 LC-PUFA (1|enclosure:round) 0.228 1 0.633 Population of origin 3.24 4 0.519 Sex 0.454 1 0.500 Scaled fish fork length 1.438 1 0.230 Supplementary Material File (figures ms mari.docx) Download 1.43 MB Information & Authors Information Version history V1 Version 1 20 February 2025 Peer review timeline Published Ecology and Evolution Version of Record 16 Oct 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Ecology and Evolution Keywords behavioral ecology ecological experiment freshwater laboratory vertebrate Authors Affiliations Stefano Mari 0009-0007-2964-7843 [email protected] WasserCluster Lunz - Biologische Station GmbH View all articles by this author Stefan Auer BOKU View all articles by this author Benedikte Austad 0000-0002-1779-2396 University of Gothenburg View all articles by this author Pernilla Hansson University of Gothenburg View all articles by this author Simon Vitecek University of Innsbruck View all articles by this author Mourine Yegon WasserCluster Lunz - Biologische Station GmbH View all articles by this author Libor Závorka 0000-0002-0489-3681 WasserCluster Lunz - Biologische Station GmbH View all articles by this author Metrics & Citations Metrics Article Usage 382 views 161 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Stefano Mari, Stefan Auer, Benedikte Austad, et al. Dietary omega-3 long chain polyunsaturated fatty acids can enhance ecologically relevant cognitive traits in juvenile brown trout. Authorea . 20 February 2025. DOI: https://doi.org/10.22541/au.174006732.26493876/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 . 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