Nile Tilapia and Gilthead Seabream Dietary Self-Selection of Alternative Feeds with Spirulina and Quinoa

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Conceição, Jorge Dias, Sofia Engrola, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3952045/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Classical assessments of new fish feeds are anthropocentric, focusing on growth. Although this methodology is accurate, it does not consider the fish’ perspective. This study aimed to investigate the behavioural responses and feed preferences of Nile tilapia - Oreochromis niloticus and gilthead seabream - Sparus aurata , in two self-selection trials (self-feeders and diet encapsulation). Using self-feeders, both species were offered three feeds: a control (PD) and two diets (ORG1 and ORG2) containing non-conventional ingredients, including spirulina ( Spirulina platensis ) and quinoa ( Chenopodium quinoa ). Three groups of tilapia with an average weight of 163.0 g ± 4.3 g (mean ± SD) and four groups of seabreams with 174.7 g ± 27.0 g were tested. To investigate the role of olfactory factors in dietary selection, three other diets were encapsulated and offered to tilapia: Diet A, a purified feed, Diet B that contained predominantly spirulina and Diet C which had a mixture of spirulina and quinoa. Seven individual tilapia of 331.9 g ± 31.4 g were used. Using self-feeders, tilapia exhibited a preference for ORG2 (46.5%), which was influenced by the sensory properties of feeds and post-ingestion signals, as their choice for ORG2 persisted during diet encapsulation using Diet C, which was also formulated with quinoa and spirulina. Seabream did not show a preference for any feed. These findings highlight the effectiveness of self-selection experiments in allowing fish to express their feeding behaviour and preferences. Therefore, this approach should be considered in the initial screening and design of new aquaculture feeds and ingredients. Animal Behaviour Fish Physiology Self-selection Alternative Feeds Nile tilapia Gilthead seabream Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction In the wild, since no single feed supplies all essential nutrients, most fish show dietary selection and pick up different items to create a complete and balanced diet according to their physiological needs to survive (Huntingford, 2020 ). Fish indeed are able to select and regulate the intake of macronutrients and energy (Luz et al., 2018 ). This selection and regulation occurs through a process known as “nutritional wisdom” (Raubenheimer and Simpson, 1997 ; Simpson and Raubenheimer, 2001 ). In order to restore the metabolic balance as a result of a nutritional challenge, “specific hungers” have the ability to sense and ingest particular nutrients and/or substances in diets (White et al., 2000 ). Therefore, fish select their feed based on a series of complex regulatory mechanisms, associated with physiological, learning, and behavioural processes, involving hormonal and neural activities in the brain, gastrointestinal tract and liver (Comesaña et al., 2020 ; Forbes, 2001 ; Fortes-Silva et al., 2016 ; Otero-Rodino et al., 2016 ; Richter, 1943 ; Simpson and Raubenheimer, 2001 ). Accordingly, fish feeding behaviour is a relevant characteristic that should be considered when farming species, especially to assess which feed fish would eat more or less. Aquaculture plays a major role in society by providing to the growing world population a vital source of animal protein. However, its development can be hampered by the environmental sustainability of the feeds used (FAO, 2022 ). Common ingredients (e.g., fishmeal and fish oil) used in feed formulation provide a valuable nutritional value to farmed organisms and most of them, in the European Union, are derived from well-regulated wild fish stocks and can even be certified (Hilmarsdottir et al., 2021 ). However, in some production areas, though inclusion rates of marine resources has dropped considerably over the past years, they remain widely used during the juvenile stages of carnivorous species, being sourced from non-sustainable stocks (Little et al., 2016 ). Therefore, the use of alternative ingredients, such as algae and plants may reduce the dependency of fish feed on traditional resources. Spirulina is a microalgae with a high protein content and a nutritionally reasonable fatty acid profile being produced by low-cost open pond technologies (Mosha, 2019). Quinoa is a plant with a high protein, fibre, and carbohydrate contents together with low fat (Pellegrini et al., 2018). Both ingredients possess bioactive compounds that regulate physiological processes and exhibit biological activities, which could improve weight gain, feed efficiency, and disease resistance in fish while reducing the environmental impacts of the sector (Balakrishnan and Schneider, 2022 ; Han et al., 2021 ). In order to achieve all these benefits, thus improving feed sustainability in aquaculture, it is necessary to address the inclusion of these functional ingredients in novel feed formulations and their effects on fish acceptability and intake, especially when knowledge about these alternatives is much lower compared with traditional ingredients (Carlberg et al., 2015 ; Pratiwy et al., 2017 ). The classic approach to investigate the effects of new diets occurs through growth experiments mainly based on physiological mechanisms (Roy et al., 2020 ). Although this methodology is accurate and have yielded considerable knowledge on animal production, it may not be suitable in the short-term, involves a large number of animals, it is time-consuming, expensive and more importantly it does not consider fish preferences, since they are unable to choose what feed to eat (Brännäs and Strand, 2015 ; Filho et al., 2018 ; Fortes-Silva et al., 2016 ). Conversely, self-selection methods allow fish to freely and voluntarily accept the given diet, while taking into account fish feeding behaviour and learning processes (Fortes-Silva et al., 2016 ). Furthermore, these methodologies allows fish to choose which feeds better suits their nutritional and energetic needs (Fortes-Silva et al., 2016 ). Additionally, it can also be used to investigate the detection and acceptance/rejection of feed additives, toxic substances and antinutritional factors (e.g., as phytic acid and phytate) present in plants and vegetables (Costa et al., 2022 ; Fortes-Silva et al., 2016 ). Therefore, to provide a more complete perspective on the potential of novel diets, growth experiments may be complemented with self-selection methods. Encapsulated diets and self-feeders are some of self-selection methodologies that can be used to investigate feed intake regulation and dietary preferences. Diet encapsulation consists on packaging experimental feeds inside coloured eatable gelatine capsules, avoiding the interference from the organoleptic characteristics of pelleted diets, with the colour of the capsules being the only external cue (Almaida-Págan et al., 2006 ; Rubio et al., 2003 ). Therefore, fish will select their diets based only on the nutritional content of the feeds, without the effect of chemosensitivity and olfactory factors, but rather mainly based on post ingestion and absorption metabolic signals (Almaida-Págan et al., 2006 ; Rubio et al., 2003 ; Ruohonen and Grove, 2001 ; Santos et al., 2019 ). Self-feeders consist of electronic devices that are placed on top of fish tanks, with a switch below the water level that when pulled and triggered by fish, allow different feeds, with distinct chemosensory properties, to fall from the dispenser into the tank (Jobling et al., 2001 ; Pratiwy et al., 2021 ; Sánchez-Vázquez et al., 1994 ). Therefore, contrary to capsules, the taste and texture of pelleted feeds that fall from the self-feeders is evaluated and sensed by the fish allowing them to make their selection based on post-ingestion and absorption signals coupled with orosensory feed properties (Filho et al., 2018 ; Fortes-Silva et al., 2016 ; Raubenheimer et al., 2012 ). Both methods have not only been used and validated for Nile tilapia ( Oreochromis niloticus ) and gilthead seabream ( Sparus aurata ), but also for several other fish species, such as perch ( Perca fluviatilis ), tambaqui ( Colossoma macropomum ), sharpsnout seabream ( Diplodus puntazzo ) and rainbow trout ( Oncorhynchus mykiss ) (Atienza et al., 2004 ; Brännäs and Strand, 2015 ; Costa et al., 2022 ; Filho et al., 2018 ; Fortes-Silva and Sánchez-Vázquez, 2012 ; Montoya et al., 2012 ; Puchol et al., 2022 ; Yamamoto et al., 2001 ). Although self-selection research in fish has been evaluated in several species, making them viable options to address behaviour, information about the topic remains scarce compared to what is known regarding terrestrial animals. Based on this, knowledge about fish behaviour and its controls have not yet been totally understood and should be further explored, especially when considering the initial screening and design of potential new aquaculture feeds (Fortes-Silva et al., 2012 ; Pratiwy and Kohbara, 2018 ; Puchol et al., 2022 ). The present work aims to investigate the acceptability, behavioural response, feed intake regulation and dietary preferences of two commercially important fish species - freshwater (Nile tilapia) and marine (gilthead seabream) – using the self-selection methods of self-feeders and diet encapsulation, to test the acceptance of non-conventional diets based on spirulina ( Spirulina platensis ) and quinoa ( Chenopodium quinoa ) as functional ingredients. 2. Materials and Methods 2.1. Formulation of self-feeder diets For the self-feeders, three experimental diets (PD, ORG1 and ORG2) for each species (Nile tilapia and gilthead seabream) were prepared by SPAROS Lda (Olhão, Portugal) with a pilot-scale twin-screw extruder (CLEXTRAL BC45, France) equipped with a screw diameter of 55.5 mm. A temperature range of 105–110°C was used for the extrusion process. All batches of extruded feeds were dried in a convection oven (OP 750-UF, LTE Scientifics, United Kingdom). Pellet size was 4mm. A control diet (PD) was formulated to mimic current commercial feeds for each species. The remaining two diets (ORG1 and ORG2) were formulated to include spirulina (3.5% and 7%, respectively) and quinoa (5% in both), as well as other organic alternatives (e.g., pea protein concentrate, corn meal) to address some of the current environmental concerns and/or ethical issues often associated with ingredients present in traditional commercial formulations. The ingredient selection (Tables 1 and 2 ) was chosen based within an organic framework, on market availability and nutritional composition. The inclusion levels were adjusted for each species, according to existing knowledge on tolerance to different ingredients as well as their nutritional and especially amino acid requirements, without compromising fish growth, development, and welfare. Tilapia feeds had similar protein, lipid, and energetic contents with around 33.8% WW crude protein, 7.6% WW crude lipid and 18.9 KJ gross energy/g WW (Table 1 ). Seabream diets also showed identical proximal compositions, having on average 41.7% WW crude protein, 16.8% WW crude lipid and 22.2 KJ gross energy/g WW (Table 2 ). Amino acid profiles of the experimental diets given to both species are presented in Tables 3 and 4 . Although in ORG1 the diets exhibited lower methionine levels, this amino acid requirement was fulfilled. Table 1 Diet formulation (inclusion levels %) and proximate composition (% as fed) of the experimental diets (PD, ORG1 and ORG2) in self-feeders for Nile tilapia ( Oreochromis niloticus ). Ingredients (inclusion levels %) PD ORG1 ORG2 Fishmeal 5.00 Poultry meal 5.00 Brewer's yeast 5.00 10.00 10.00 Spirulina 3.50 7.00 Pea protein concentrate 5.50 2.25 Wheat gluten 3.50 7.00 Corn gluten meal 11.60 Soybean meal 18.00 18.00 Rapeseed meal 6.50 6.50 13.00 Sunflower meal 3.25 7.50 15.00 Wheat meal 27.75 6.70 9.35 Rice bran full fat 10.00 10.00 10.00 Corn meal 6.95 6.95 Quinoa 5.00 5.00 Faba beans 9.00 7.00 Vitamin and mineral premix 1.00 1.00 1.00 Choline chloride 0.20 0.20 0.20 Antioxidant powder (Verdilox) 0.20 0.20 0.20 Mono-calcium phosphate 1.80 2.15 2.05 L-lysine 0.30 DL-methionine 0.10 Fish oil 0.90 1.00 1.00 Soybean oil 3.40 3.30 3.00 Proximate Composition (% as fed) Dry matter (DM) 92.10 91.74 90.58 Ash 6.27 6.33 6.22 Crude protein 33.50 34.06 33.75 Crude lipid 8.43 7.63 6.75 Gross energy (kJ/g − 1 ) 19.33 19.24 18.18 All values are reported as mean of duplicate analysis . Table 2 Diet formulation (inclusion levels %) and proximate composition (% as fed) of the experimental diets (PD, ORG1 and ORG2) in self-feeders for gilthead seabream ( Sparus aurata ). Ingredients (inclusion levels %) PD ORG1 ORG2 Fishmeal Super Prime 20.00 20.00 20.00 Poultry meal 10.00 Brewer's yeast 5.00 5.00 Spirulina 3.50 7.00 Pea protein concentrate 16.50 11.50 Wheat gluten 3.00 Corn gluten meal 8.00 Soybean meal 16.00 16.00 Rapeseed meal 3.30 3.30 Sunflower meal 6.00 10.00 Wheat meal 5.90 5.55 Wheat bran 5.00 5.00 Quinoa 5.00 5.00 Faba beans 7.00 7.00 7.00 Whole peas 7.00 7.00 Vitamin and mineral premix 1.00 1.00 1.00 Choline chloride 0.20 0.20 0.20 Antioxidant powder (Verdilox) 0.20 0.20 0.20 Mono-calcium phosphate 0.70 1.05 1.00 L-lysine 0.30 DL-methionine 0.10 Fish oil 4.60 4.60 4.60 Soybean oil 9.70 9.40 9.20 Proximate Composition (% as fed) Dry matter (DM) 94.22 95.11 91.50 Ash 7.07 6.36 6.33 Crude protein 40.91 43.03 41.01 Crude lipid 18.43 16.63 15.22 Gross energy (kJ/g − 1 ) 22.20 22.47 22.00 All values are reported as mean of duplicate analysis . Table 3 Amino acid profile (g/100g fed basis) of the experimental diets in self-feeders for Nile tilapia ( Oreochromis niloticus ). Amino acids (g/100g fed basis) PD ORG1 ORG2 Arginine 1.77 2.19 2.01 Histidine 0.74 0.77 0.76 Lysine 1.79 1.77 1.46 Threonine 1.24 1.25 1.27 Tryptophan 0.36 0.41 0.40 Isoleucine 1.32 1.36 1.30 Leucine 3.11 2.42 2.36 Valine 1.55 1.58 1.60 Methionine 0.75 0.52 0.59 Phenylalanine 1.62 1.55 1.53 Cysteine + Cystine 0.52 0.53 0.54 Tyrosine 1.14 1.16 1.12 Aspartic Acid 2.68 2.98 2.54 Glutamic Acid 6.46 6.32 6.95 Alanine 1.95 1.55 1.60 Glycine 1.63 1.48 1.54 Proline 2.22 1.93 2.04 Serine 1.62 1.61 1.60 Taurine 0.03 < 0.002 < 0.002 All values are reported as mean of duplicate analysis . Table 4 Amino acid profile (g/100g fed basis) of the experimental diets in self-feeders for gilthead seabream ( Sparus aurata ). Amino acids (g/100g fed basis) PD ORG1 ORG2 Arginine 2.80 2.75 2.69 Histidine 0.93 1.01 0.94 Lysine 2.69 2.84 2.55 Threonine 1.59 1.61 1.63 Tryptophan 0.47 0.49 0.50 Isoleucine 1.62 1.78 1.68 Leucine 3.36 3.15 2.96 Valine 1.97 2.07 2.08 Methionine 0.96 0.74 0.92 Phenylalanine 1.88 1.88 1.77 Cysteine + Cystine 0.52 0.51 0.49 Tyrosine 1.39 1.42 1.34 Aspartic Acid 3.69 4.31 3.74 Glutamic Acid 6.63 6.66 6.76 Alanine 2.41 2.17 2.15 Glycine 2.36 2.00 2.02 Proline 2.18 1.74 1.82 Serine 1.81 1.88 1.84 All values are reported as mean of duplicate analysis . 2.2. Formulation for diet encapsulation For the diet encapsulation three experimental diets (A, B and C) for Nile tilapia were prepared at the University of Murcia (Murcia, Spain) with distinct proximal composition (Table 5 ). Diet A had casein and dextrin (34% and 30%, respectively) as main ingredients and served as a proxy for a purified diet. Diet B was mainly composed of spirulina (58%), while Diet C had different doses of spirulina and quinoa (46% and 20%, respectively). Other ingredients present in the diets include fish oil, soy oil, cellulose, alginate, and vitamin premix. The spirulina and quinoa used in the supplemented diets B and C were provided by TILAMUR S.L (Murcia, Spain). Encapsulation occurred by filling eatable gelatine capsules (No. 4; FAGRON, S. A., Barcelona, Spain) with previous produced diets grounded, using a semi-automatic encapsulator - Tencyfarma, Miranda de Ebro, Barcelona, Spain). All capsules were weighed after manufacture (± 0.17g filled with feed) and stored (at 4ºC) in a common and single plastic bag for all the days of the experiment until use, to avoid any external contamination which might have allowed the fish to distinguish their content by their external chemical properties. Three colorations of capsules were used (red, green, and yellow), each corresponding to a specific diet, to help fish discriminate between them. Feed samples were grounded and analysed for dry matter (105°C for 24 h), crude protein calculated by the Kjeldahl method (automatic flash combustion; Leco FP-528, Leco, St. Joseph, USA) (N × 6.25%), lipid content by diethyl ether extraction (Soxtherm Multistat/SX PC (Gerhardt, Königswinter, Germany; 150°C) and ash content by heating in an oven at 450°C for 24 h. Diets proximal composition varied between 23.9% − 32.4% wet weight (WW) crude protein and 16.6% − 19.9% WW crude lipid (Table 5 ). Table 5 Diet formulation (inclusion levels %) and proximate composition (% as fed) for the experimental diets (A, B and C) in diet encapsulation for Nile tilapia ( Oreochromis niloticus ). Ingredients (% as is) A B C Casein 34.17 Dextrin 30.00 2.85 3.23 Gelatin 6.83 Fish oil 14.63 18.91 16.60 Soy oil 4.67 6.30 5.53 Cellulose 5.00 6.84 0.81 Alginate 4.60 6.55 7.43 Vitamin Premix 0.10 0.14 0.16 Quinoa 19.87 Spirulina 58.41 46.37 Proximate Composition (% as fed) Dry matter (DM) 93.50 90.80 90.30 Ash 6.92 6.90 9.84 Crude protein 32.44 30.15 23.93 Crude lipid 16.64 19.89 18.87 All values are reported as mean of duplicate analysis. 2.3. Fish and husbandry conditions Fish were reared and handled by trained scientists and following the Spanish legislation on Animal Welfare and Laboratory Practices, while the experimental protocol was approved by the National Committee of the University of Murcia on Ethics and Animal Welfare under the Guidelines of the European Union Council on the protection of animals used for experimental purposes (Directive 2010/63/EU). Nile tilapia was used for self-feeder and encapsulation experiments. The latter was used as a follow up experiment to discard the effects of oral factors on the selection of the feeds. Both experiments were conducted in the chronobiology laboratory at the University of Murcia. Individual fish and groups were weighed right before the start and at the end of each experiment to not compromise behaviour. Nile tilapia with an average initial individual weight of 163.0 g ± 4.3 g (mean ± S.D.; in groups for self-feeder studies) and 331.9 g ± 31.4 g (mean ± S.D.; individual for capsules studies) were provided by the University of Murcia, from a mono-sex male population (offspring tilapia, GMT®). The tanks with 300 L were maintained in an aquaculture recirculating system, with a protein skimmer, as well as mechanical, biological, UV filtered and aerated water. Fish were allowed to acclimate to laboratory conditions for at least 2 weeks, during which time they were fed a commercial diet (Skretting TI-3 (3.2mm); with % DM: 32.0% crude protein, 6.0% crude fat and 5.8% crude fibre), which was supplied by hand to visual satiety once a day. Abiotic parameters were measured, and mortality was recorded daily. The photoperiod was 12L:12D (lights on at 09:00). Values of 29.0 ± 1.0°C for water temperature, 7.2 ± 0.2 for pH, 6.9 ± 0.4 ppm for dissolved oxygen and 0.7 ± 1.0 mg/l ammoniac nitrogen were maintained. Groups of gilthead seabream were only used for the self-feeder experiment performed at the Aquaculture Laboratory located in Algameca (Cartagena, Spain). Fish had an average initial individual weight of 174.7 g ± 27.0 g (mean ± S.D) and were provided by IMIDA from San Pedro del Pinatar (Spain). The tanks with 150 L were maintained in a flow-through system, with a protein skimmer, as well as mechanical, biological, UV filtered and aerated water. Fish were allowed to acclimate to laboratory conditions for at least 2 weeks, during which time they were fed a commercial diet (Skretting L-4 Alterna 2P; with % DM: 46.5% crude protein, 20.0% crude fat and 3.4% crude fibre), which was supplied by hand to visual satiety once a day. Abiotic parameters were measured, and mortality was recorded daily. The animals were kept in a natural photoperiod with values of 27.0 ± 1.0°C for water temperature, 37 ± 1.0 ppm for salinity, 7.6 ± 1.0 for pH and 6.3 ± 0.5 ppm for dissolved oxygen. 2.4. Experimental setup 2.4.1. Self-feeders The self-feeder experiment was performed in accordance with Fortes-Silva et al. ( 2012 ). For Nile tilapia, it lasted 36 days and 30 fish were conditioned as groups in three plastic tanks (each with 11 fish), while for seabream it lasted 67 days and 32 fish were divided in four plastic tanks (each with 8 fish) (Figs. 1 and 2 ). Three self-feeders provided by the University of Murcia were equipped in each tank. The position of each diet (PD, ORG1 and ORG2) on the feeders also varied between tanks, to avoid a possible positional effect. The feeding systems were connected with an electric transformer (one for five self-feeders). Each of them was composed of a trigger (a switch with rubber tip), actuated by the fish, placed 2 cm above the water surface, connected to an electromagnet, and a feeder (EHEIM 3581, Deizisau, Germany) that delivered a predetermined amount of feed (1 pellet = 0.04 g) after each trigger actuation and electromagnet activation. To determine the daily intake, every day the feed remaining in the feeder was weighed at the same time (11:30) and subtracted with the total number of grams given the previous day, before refilling the feeder recipient for the next day. After percentages of the offered diets exhibited a statistically significant difference for one feed, diets were switched between feeders to provide a challenge for the fish, reduce the possible preference and influence for a particular string sensor or relative position of the self-feeders. Figure 1. Experimental setup for self-feeders trial using Nile tilapia ( Oreochromis niloticus ). Each of the three feeders inside each tank, contained a specific feed (PD, ORG1 and ORG2). 2.4.2. Encapsulated diets The experiment with encapsulated diets was performed according to Fortes-Silva et al. ( 2012 ). It lasted 35 days and 7 fish were housed in 4 plastic tanks, divided in half separated with a net, thus single individual fish were used (Fig. 3 ). Ten capsules of each colour (30 in total) were mixed and given to each fish daily at the same time (11:30h) and left in the water for 20 min. After this period, to determine the daily intake, the total number of uneaten capsules corresponding to each diet were removed from the tanks, counted, and subtracted with the total number of capsules given capsules. After a determined diet was preferred and exhibited a statistically significant difference over the others, feeds were switched between capsule’ colours to reduce the possible preference for a specific one. 2.5. Data analysis and statistics The statistical analysis was performed with the SPSS software, version 23.0. For the diet encapsulation the experimental unit was the number of fish ( n = 7), while for the self-feeders was the tank ( n = 3 or 4). The relative selection of each diet is expressed as a percentage of the total number of capsules or feed consumed, considering the total diets as 100%. The feed and capsules intakes were expressed as total grams of feed ingested/% of body weight and number of capsules/100g of body weight, respectively. Further, in the days where a specific diet was mostly selected, the percentages of capsules ingested and feed consumed were compared by one-way ANOVA, followed by a Tukey’s post-hoc test to examine significant pair-wise comparisons, before meeting criteria for normality and homogeneity using Shapiro – Wilk and Levene’s test, respectively. Arcsine transformations of capsules and feed intake percentages were performed to achieve homogeneity of variance. The statistical significance was considered at P < 0.05. 3. Results 3.1. Self-feeders Nile tilapia reached a final mean weight of 194.7 ± 3.9 g (mean ± S.D.), all fish survived and on average feed consumed daily represented 0.75% of average body weight/day. As reported by Fortes-Silva et al., 2012 , uneaten and wasted feed was negligible, only around 2% of the total given feed, thus the amount of feed demanded by the fish was almost entirely ingested. The dietary preference (Fig. 4 ) in self-feeders initially demonstrated an adaptation period to the feeders of around 5 days. During this time, fish preferred the position of specific feeders, rather than the inserted feed, but quickly changed their behaviour. All diets were chosen similarly for several days before an increase in preference for diet ORG2 was observed during three consecutive days (with an average of 46.5%; p < 0.05). Throughout the same period diets PD and ORG1 were preferred on average 28.9% and 24.7%, respectively. Diets were switched between feeders on day 22, another period of equal preference remained, while from day 30 until the end of the experiment, diet ORG2 was mainly chosen (between 40.7% and 56.0%; p < 0.05). Diets PD (0.24 grams/% BW; p < 0.05) and ORG1 (0.21 grams/% BW; p < 0.05) were consumed 38.5% and 46.2%, respectively, less than diet ORG2 (0.39 grams/% BW; p < 0.05) during days with statistically significant differences (Fig. 5 ). Gilthead seabream final weight was 264.4 ± 29.5 g (mean ± S.D.), no mortality was recorded and average feed consumption was 1.21% of the animal’s biomass. From the initial days of the study, fish exhibited a clear preference for diet ORG1 (between 65.50% and 83.45%; p < 0.05). However, after diets were switched between feeders, the preference for diet ORG1 fell, while for diet PD and ORG2 increased (Fig. 6 ). For several days, no statistically significance was achieved (except only for one day). Then, fish were fasted for 10 days as a challenge test to motivate them to choose a diet, according to Aranda et al. ( 2001 ). Nevertheless, even after this approach, a consistent preference was not achieved ( p > 0.05). 3.2. Encapsulated diets When fed capsules, tilapia exhibited a mean final weight of 362.6 ± 33.6 g (mean ± S.D.), and no mortality was observed. The average feed consumption was 0.51% of the average body weight/day. Not all fish started to ingest capsules from the first day, but progressively all of them successfully swallowed the whole capsules without breaking the covering. In terms of dietary selection (Fig. 7 ), after 10 days of ingesting similar diet preferences, fish progressively increased their intake and selection of capsules containing diet C, while simultaneously decreasing that of diets A and B. When the preference for diet C reached values (maximum of 44.0%) that showed statistically significant differences ( p < 0.05) for several days (day 11–16) over diets A and B (remained at an average value of 28.9% and 30.5%, respectively), the colour of the capsules associated with each diet rotated (day 17). Initially, this change decreased the ingestion of diet C, while conversely increasing diet A and B. Up to day 29, the preference of fish towards the diets remained at a steady state, until a clear consistency and preference for diet C was re-established (between 41.0% and 47.2%; p < 0.05) during the rest of the experimental period. During days with statistically significant differences between intake of feeds, diets A (1.11 capsules/100g BW) and B (1.12 capsules/100g BW), were ingested 32.7% and 32.1%, respectively, less than diet C (1.65 capsules/100g BW; p < 0.05) (Fig. 8). Figure 8. Average daily intake (number of capsules ingested per 100g of body weight) of three diets (A, B and C) by Nile tilapia during days with statistically significant differences. Bars represent the mean counts ± SD ( n = 7 individual fish). The star represents significant differences (one-way ANOVA, p < 0.05). 4. Discussion The main purpose of this research was to assess the feeding behaviour and ability of Nile tilapia and gilthead seabream to self-select their preferred diets. Fish choose feeds and regulate their feeding behaviour based on homeostatic and hedonic mechanisms. The homeostatic pathway maintains normal energy balance homeostasis and it takes over in response to nutritional demands and metabolic needs. The hedonic control is related to the brain reward system, where fish mainly sense the orosensory, pellet quality and palatable aspects of feed, being independent on nutritional requirements (Kulczykowska and Sánchez Vázquez, 2010 ; Volkoff, 2019 ). Thus, fish can choose which feed items to ingest mainly based on size, palatability and nutritional properties (Raubenheimer et al., 2012 ). All feeds offered had the same size, thus fish choice was only based on the other two parameters. For this reason, fish tested with self-feeders and diet encapsulation, which enabled the isolation of palatability and nutritional parameters, allowed for a better understanding of the effects of “nutritional wisdom”. Fish feeding is modified by both Pavlovian (to find the feed) and operant learning (to catch and manipulate the feed) behaviour (Millot et al., 2014 ). Operant learning is the process that is associated between a behavioural action and its outcomes. If fish are rewarded enough times, fish will learn the relationship and will increase the probability and frequency of repeating the same action. In a population, there may be only one dominant fish that is curious enough to pull the trigger, but if rewarded, this information may be socially transmitted and become more common in all individuals (Millot et al., 2014 ). On the initial days of the self-feeding experiment tilapia showed a preference for one feeder, which changed after some time after assessing the content of the other feeders, demonstrating their exploratory and learning behaviour, as shown by Figueiredo et al. ( 2023 ). A similar situation was recorded using European seabass, that when fish were fed with the standard diet they exhibited a preference for one of the self-feeders (Aranda et al., 2000 ). Although tilapia kept in groups developed strong social hierarchies that could affect feeding behaviour (Toguyeni et al., 1997 ), this was not observed as fish did not show an aggressive behaviour and still choose a feed on both types of experiment and regardless being kept alone or in groups. Similarly, Fortes-Silva et al. ( 2012 ) showed that tilapia were not aggressive and a exhibited a similar pattern of diet selection whether maintained isolated or in groups, when offered diets with a balanced or unbalanced composition of essential amino acids. Indeed, in our self-feeders experiment, tilapia were not isolated as in diet encapsulation, meaning that social learning may not play a significant role in the feeding and behavioural mechanism as suggested by Vivas et al. ( 2006 ). Using self-feeders, Nile tilapia chose diet ORG2 with an intake 0.75% of fish weight/day. Similarly to other studies, it was possible to observe that tilapia consumed most diet pellets and that the feed waste (less than 2% of the total given feed) that remained in the tanks was residual. For example, Fortes-Silva et al. ( 2012 ) reported a negligible food waste of 1% with tilapia. To compare the intake, Pratiwy et al. ( 2017 ) tested the growth performance of Nile tilapia reared under self-feeding systems and showed feed intake values of around 1.85%/body weight. The choice for diet ORG2 was presumably based on tilapia nutritional needs (post-ingestive) coupled with feed organoleptic characteristics. Likewise, other studies with European seabass ( Dicentrarchus labrax ) and tilapia reported a similar behaviour (Fortes-Silva et al., 2016 , 2012 ; Rubio et al., 2006 ). It is important to note since fish required almost three weeks to exhibit a preference and no feed was predominantly chosen from the beginning of the study, this can reflect the less clear taste differences between feeds. In a study by Carlberg et al. ( 2015 ), Arctic charr ( Salvelinus alpinus ) took 9 days, while Fortes et al. (2010) noted that tilapia clearly preferred since the beginning of the experiment diets containing phytase with an intake and that after switching feeds, the pattern was re-established only after 3 days. In the present study, after feed was switched between feeders, tilapia also resumed, re-established and sustained the previous pattern of selection of diet ORG2, while maintaining a constant consumption of other diets, meaning that the fish established levels of consumption for each feeds. However, once again they took some time (9 days), pointing out the effect of the minor differences between the diets. These findings are in accordance with Fortes et al. (2010), who reported that a diet with 1500 IU kg − 1 phytase was preferred throughout the trial, even after switching feeders. The same author showed that tilapia retained and maintained the intake of specific levels of protein, fat and carbohydrate when feeds were switched over (Fortes-Silva and Sánchez-Vázquez, 2012 ). Fish are able to identify and evaluate distinct amino acid profiles between diets (Fortes-Silva et al., 2012 ). All feeds were formulated to contain the minimum requirements of every essential amino acid (EAA). However, diet ORG1 presented the lowest levels of methionine. Although methionine was near the lowest requirement in ORG1, it still was enough to fulfil the species physiological state for normal growth (NRC, 2012). Overall, the self-feeder experiment with tilapia resulted in a preference for diet ORG2, which was influenced by a combination of several factors including learning-reward behaviour, nutritional requirements, as well as the orosensory properties of the diets. To discard the effects of olfactory factors on the selection of the feeds, Nile tilapia were fed encapsulated diets in a second experiment. Tilapia preferred diet C (with spirulina and quinoa), after 11 days and after colour rotation, and presented an intake of 0.51% of their body weight. While individual differences between fish of the same species can exist, in our case the preference was unanimous for all fish. The same species with a final body weight of around 53g required 15 days to distinguish between two vegetable oil blends at 30ºC and after switching capsules’ colours only 3 days, with an average intake of 1.12% between all treatments (de Almeida et al., 2021 ). In another study, the feed intake of tilapia with around 80g was 1.36% of the animal’s biomass and required 7, 11 and 23 days to differentiate diets with distinct protein levels (0, 25 and 42%) and 6, 10 and 4 days after inverting the content of the capsules (Costa et al., 2022 ). Capsule colour used in this study did not affect our results. In other studies, using tilapia, by Fortes-Silva et al. ( 2011 ) and de Almeida et al. ( 2021 ) coloration also did not affect fish preference and only served as a “reference” for the fish to identify which diet was associated with each colour. Therefore, in the present study it was clear that by randomizing capsules’ colours for each individual and after changing the colour paired to each diet, tilapia continued to choose capsules with diet C, meaning that the content, rather than the capsule colour, was the main factor considered for their preference. Tilapia is able to address the content based in a wide range of physiological processes. All capsules presented the same chemosensory properties at oropharyngeal level, meaning that palatability, texture, flavour and odour associated with traditional pelleted diets were negligible for the fish (Rubio et al., 2006 ). Moreover, it is important to note that neither the capsules or pellets leached nutrients into the water, which could act as attractants to fish (Busti et al., 2022 ). Therefore, fish had to evaluate the quality and nutritional composition of the content of the capsules and learn to associate this information with the capsules' colour (Almaida-Págan et al., 2006 ; Rubio et al., 2003 ). Accordingly, tilapia opted for the diet C, which after evaluation, was probably more able to satisfy their physiological state, based on post-ingestive and/or post-absorption processes as suggested by Fortes-Silva et al. ( 2016 ) and Rubio et al. ( 2003 ). However, it is important to note that in the present study, besides the constituting ingredients, the formulations of the three experimental diets, mainly protein and lipid, were different, thus they could have affected fish feeding behaviour. For the experimental size (around 300g) of Nile tilapia used, the recommend percentage of these macronutrients on the feeds is between 32–36% for proteins and 8–12% for lipids (NRC, 2012). Our results were in accordance with Pereira-da-Silva et al. ( 2004 ) that registered a crude protein feed intake of 24% by Nile tilapia, when given the possibility to self-select between distinct protein dietary levels. Although diet A (mainly with casein and dextrin) and B (rich in spirulina) had more similar nutritional profiles to these values than diet C (mixture of spirulina and quinoa), the latter was still preferred. Since fish had at their disposal other feeds, they also consumed them in a different proportion, possibly to create a balanced diet and compensate the lack of some essential nutrients from diet C (Simpson and Raubenheimer, 2001 ). A similar situation was found by Costa et al. ( 2022 ), who reported that when tilapia of 80g were offered two rations, with different crude protein and amino acids levels, it showed a significant preference for the consumption of one of the feeds, while also eating at lower levels the other one. In the present study, the intake of diet C, which had the lowest protein content (23.93%) increased, could mean that this defect was mild for the fish, perhaps due to their size, so they increased its intake, and possibly also consumed other feeds available, to reach their level of requirement, especially in the case of protein (Henry, 1985 ). This scenario was also confirmed by Fortes et al., (2011), who noted that when protein was restricted, tilapia increased its intake by consuming more capsules with this nutrient to maintain their energy intake (Fortes-Silva et al., 2011 ). Similarly, Almaida-Págan et al. ( 2006 ) and García-Meilán et al. ( 2013 ) showed that when given a diet with lower protein to sharpsnout and gilthead seabream, respectively, the fish increased its intake. Indeed, studies have shown that fish have the ability to regulate its consumption and defend a given nutritional intake target (Almaida-Págan et al., 2006 ; Brännäs and Strand, 2015 ; Fortes-Silva et al., 2016 ; Rubio et al., 2003 ). Nile tilapia exhibited a consistent preference for diet C driven by post-ingestive and post-absorption processes, even in the absence of olfactory factors present in the self-feeder experiment, reflecting that they are able to identify and select a feed with spirulina and quinoa as functional ingredients. Conversely to tilapia, gilthead seabream did not show a consistent preference for a particular feed using self-feeders. The experiment was performed with rapidly growing juveniles at high water temperature in summer (since it was a flow-through aquaculture system), a scenario which could have made that the homeostatic system, associated with high energetic demands, override the hedonic regulation of feeding behaviour, not allowing seabream to discriminate diets efficiently (Puchol et al., 2022 ). Another possible explanation for the lack of diet discrimination is because all three experimental diets were nutritionally similar to the previously fed commercial feed, meaning that fish were familiar with it and did not notice enough differences (Pulido-Rodriguez et al., 2021 ). In our study, gilthead seabream wasted and rejected more feed than tilapia, while the intake was at an average of 1.21%/body weight. In addition, there was a much higher variation on the daily intake of feeds compared with tilapia, which could be related with the more curious and aggressive behaviour of seabream towards feed, especially when limited and defendable as in self-feeders (Puchol et al., 2022 ). There are few studies available regarding seabream using self-feeders with similar methodology to compare results. Nevertheless, in a previous investigation with seabream, it was shown that fish with 254g could select a diet with distinct oxidation levels of dietary lipids after 10 days with a preference of 82% and 7 days after switching feeders with an average intake of 1.57%/body weight (Montoya et al., 2011 ). Similarly, to what occurred with tilapia, it is possible that on the initial days seabream were preferring a specific feeder on each tank that, by coincident, contained diet ORG1. A specific feeder was also selected on the initial experimental days with European seabass and gilthead seabream by Aranda et al. ( 2000 ) and Montoya et al. ( 2011 ), respectively. The preference for a particular feeder can then indirectly affect fish’ choice based on pre-ingestive parameters (Montoya et al., 2011 ). Therefore, it was necessary to change the positions of the feeders to assert dietary preferences and avoid any preference for a specific position as it was noted by Puchol et al. ( 2022 ). Indeed, after changing the position of the feeders, seabream decreased their intake for ORG1 and never achieved a clear preference for any of the given feeds, suggesting that both pre- and post-ingestive signals were involved in diet selection (Montoya et al., 2011 ). Montoya et. al. ( 2011 ) observed two selection patterns after changing the position of the feeders: some fish groups resumed their selection for a specific diet, while the other groups did not show a clear preference for any diet until they were subjected to a 3-week fasting period, after which they shortly resumed their dietary preferences. In the present study, seabream were also fasted, for a duration of 10 days in order to present them with a challenge, aiming to define a feeding pattern, as the physiological state of fish caused by oxidative stress due to fasting would reinforce their selection behaviour (Montoya et al., 2011 ). However, seabream were not observed to demand more feed and define a pattern, as the compensatory bite activity increase was not enough to attain a sufficient feed intake level to reach a preference. Conversely, European seabass fasted for 6 and 15 days, increased demand for diets to recover metabolic status with hyperphagia, mainly for protein used as energy source (Aranda et al., 2001 ; Vivas et al., 2003 ). Since seabream were unable to exhibit a preference for any feed, it was not necessary to conduct a capsule experiment. The feed intake and growth rates obtained in all our studies were in general lower compared to other performance experiments, as it was expected. It should be noted that fish sizes differ among experiments, which in turn directly affects their intake requirements and growth rates. Moreover, the diets were not formulated with the goal of optimizing fish growth but rather to study fish behaviour. Indeed, experiments on dietary selection do not necessarily correlate the most selected diet with optimal performance (Fortes-Silva et al., 2012 ). Even in growth experiments, although a diet is formulated to provide maximum growth, when given the opportunity, fish might not prefer that formula (de la Higuera, 2001 ). When using capsules, tilapia “feel” that it is a “stranger” method of feeding, which can lead to a decrease in the intake. It is important to note that although tilapia were isolated in the diet encapsulation experiment, it should not represent a factor of stress that would have affected feed intake (Fortes-Silva et al., 2012 ). Despite it has been seen that animals in a self-feeding scheme can perform well (in terms of weight gain, dietary intake, etc), in some species, these method can impair growth and decrease feeding efficiency (Gélineau et al., 1998 ; Montoya et al., 2011 ; Santos et al., 2019 ; Tidwell et al., 1991 ). These differences are related to the adaptation of self-feeders by fish, where some animals in the same group assimilate the self-feeding system more than others (Ferrari et al., 2014 ; Tidwell et al., 1991 ). Nevertheless, the lower performance indicators obtained in our experiments were not a concern, especially as no mortality occurred and fish still gained weight. 5. Conclusions In one hand, tilapia was able to show a preference and selected one of the given feeds (with spirulina and quinoa) by sensing the orosensory properties, in the case of self-feeders, but also based only on post-ingestion and absorption signals, for diet encapsulation, confirming their ability to choose a specific feed. On the other hand, gilthead seabream did not show a consistent preference for any diet. Accordingly, self-selection studies based on fish “nutritional wisdom”, allow fish to exhibit their behaviour, thus it may be considered in the initial screening and design of potential new aquaculture feeds and ingredients, before they are commercially available. Declarations Acknowledgments The authors would like to thank José Oliver for the participation in the trials. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956129 “EasyTRAIN”. Also, received funding from the Portuguese national funds from FCT – Foundation for Science and Technology through projects UIDB/04326/2020, UIDP/04326/2020 and LA/P/0101/2020. Animal Welfare Statement The authors confirm that the ethical policies of the journal, as noted on the journal’s author guidelines page, have been adhered to and the appropriate ethical review committee approval has been received. The authors confirm that they have followed EU standards for the protection of animals used for scientific purposes. Fish were reared and handled by trained scientists and following the Spanish legislation on Animal Welfare and Laboratory Practices, while the experimental protocol was approved by the National Committee of the University of Murcia on Ethics and Animal Welfare under the Guidelines of the European Union Council on the protection of animals used for experimental purposes (Directive 2010/63/EU). Statements and Declarations Funding This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956129 “EasyTRAIN”. Also, received funding from the Portuguese national funds from FCT – Foundation for Science and Technology through projects UIDB/04326/2020, UIDP/04326/2020 and LA/P/0101/2020. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions R.M: Conceptualization, Methodology, Investigation, Resources, Data Curation, Formal Analysis, Writing - original draft, Writing - review & editing. L.C: Conceptualization, Writing-review & editing, Supervision, Funding acquisition. J.D: Conceptualization, Methodology, Resources. S.E: Writing-review & editing, Supervision, Funding acquisition. F.V: Conceptualization, Methodology, Resources, Writing - review & editing, Supervision, Funding acquisition. The first draft of the manuscript was written by Rodrigo Mendes and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated during and/or analysed during the current study are not publicly available due to privacy concerns but are available from the corresponding author on reasonable request. 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Design and performance of an accurate demand feeder for the study of feeding behaviour in sea bass, Dicentrarchus labrax L. Physiology & Behavior 56, 789–794. https://doi.org/10.1016/0031-9384(94)90244-5 Santos, F.A.C., Fortes-Silva, R., Costa, L.S., Luz, R.K., Guilherme, H.O., Gamarano, P.G., Oliveira, C.G., Santos, W.M., Ribeiro, P.A.P., 2019. Regulation of voluntary protein/energy intake based practical diet composition for the carnivorous neotropical catfish Lophiosilurus alexandri . Aquaculture 510, 198–205. https://doi.org/10.1016/j.aquaculture.2019.05.038 Simpson, S.J., Raubenheimer, D., 2001. A framework for the study of macronutrient intake in fish: A framework for nutrient regulation. Aquaculture Research 32, 421–432. https://doi.org/10.1046/j.1365-2109.2001.00593.x Tidwell, J., Webster, C., Knaub, R., 1991. Seasonal production of rainbow trout, Oncorhynchus mykiss (Walbaum), in ponds using different feeding practices. Aquaculture Research 22, 335–342. Toguyeni, A., Fauconneau, B., Boujard, T., Fostier, A., Kuhn, E.R., Mol, K.A., Baroiller, J.-F., 1997. Feeding Behaviour and Food Utilisation in Tilapia, Oreochromis Niloticus : Effect of Sex Ratio and Relationship With the Endocrine Status. Physiology & Behavior 62, 273–279. https://doi.org/10.1016/S0031-9384(97)00114-5 Vivas, M., Rubio, V.C., Sánchez-Vázquez, F.J., Mena, C., García García, B., Madrid, J.A., 2006. Dietary self-selection in sharpsnout seabream ( Diplodus puntazzo ) fed paired macronutrient feeds and challenged with protein dilution. Aquaculture 251, 430–437. https://doi.org/10.1016/j.aquaculture.2005.06.013 Vivas, M., Sánchez-Vázquez, F.J., García García, B., Madrid, J.A., 2003. Macronutrient self-selection in European sea bass in response to dietary protein or fat restriction: Protein or fat restriction in macronutrient self-selection in sea bass. Aquaculture Research 34, 271–280. https://doi.org/10.1046/j.1365-2109.2003.00799.x Volkoff, H., 2019. Fish as models for understanding the vertebrate endocrine regulation of feeding and weight. Molecular and Cellular Endocrinology 497, 110437. https://doi.org/10.1016/j.mce.2019.04.017 White, B.D., Porter, M.H., Martin, R.J., 2000. Protein selection, food intake, and body composition in response to the amount of dietary protein. Physiology & Behavior 69, 383–389. https://doi.org/10.1016/S0031-9384(99)00232-2 Yamamoto, T., Shima, T., Furuita, H., Suzuki, N., Sánchez-Vázquez, F.J., Tabata, M., 2001. Self-selection and feed consumption of diets with a complete amino acid composition and a composition deficient in either methionine or lysine by rainbow trout, Oncorhynchus mykiss (Walbaum): Self-feeding of amino acid-deficient diets by trout. Aquaculture Research 32, 83–91. https://doi.org/10.1046/j.1355-557x.2001.00007.x Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 May, 2024 Reviews received at journal 06 May, 2024 Reviewers agreed at journal 16 Apr, 2024 Reviews received at journal 06 Mar, 2024 Reviewers agreed at journal 21 Feb, 2024 Reviewers agreed at journal 20 Feb, 2024 Reviewers invited by journal 19 Feb, 2024 Editor assigned by journal 19 Feb, 2024 Submission checks completed at journal 19 Feb, 2024 First submitted to journal 12 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3952045","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273819237,"identity":"fb73f97f-6ca7-48e3-98d8-3ebdd0bae1b9","order_by":0,"name":"Rodrigo Mendes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYHACA4YEGIOhgnQtZ5BE8WqBMxjbiNBi3t687cODXzbR/NLNGx/+nFeXxz/78OEPjDv+4NQic+ZY8YzEvrTcmXOOFRvzbjtcLHEuLU2C8QxuWyQkcowZEnsO5264kWMmzbjtQGLDGR4zoAuJ0LL/Ro75z59z6hLnn+Ex/kBQS8IPoC0SOWYMvA3MiRvO8BhI4NXCc6yYIbEhLXfGjbRiaZ5jh4sNz7ClSSSeMcathb15M+OPPza5/TOSN378UVOXJ3eG+fCHjzvkcGoBA6TogEZrYgN+HQwMSJEA0cJIUMsoGAWjYBSMIAAAxolYK7+lbLAAAAAASUVORK5CYII=","orcid":"","institution":"Universidad de Murcia","correspondingAuthor":true,"prefix":"","firstName":"Rodrigo","middleName":"","lastName":"Mendes","suffix":""},{"id":273819238,"identity":"0a55e84f-2fde-46c9-b643-c6d98236bcca","order_by":1,"name":"Luís E.C. Conceição","email":"","orcid":"","institution":"Sparos (Portugal)","correspondingAuthor":false,"prefix":"","firstName":"Luís","middleName":"E.C.","lastName":"Conceição","suffix":""},{"id":273819239,"identity":"faf87bc4-b0b1-4514-8404-bf02f457ca6e","order_by":2,"name":"Jorge Dias","email":"","orcid":"","institution":"Sparos (Portugal)","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"","lastName":"Dias","suffix":""},{"id":273819240,"identity":"8ad7ce52-5a4f-4c76-a81e-fdb7c248fbcd","order_by":3,"name":"Sofia Engrola","email":"","orcid":"","institution":"Centre of Marine Sciences (CCMAR/CIMAR LA)","correspondingAuthor":false,"prefix":"","firstName":"Sofia","middleName":"","lastName":"Engrola","suffix":""},{"id":273819241,"identity":"b03c7530-6ca4-463b-b6c9-00c103d0a022","order_by":4,"name":"Francisco J. Sánchez Vázquez","email":"","orcid":"","institution":"Universidad de Murcia","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"J. Sánchez","lastName":"Vázquez","suffix":""}],"badges":[],"createdAt":"2024-02-12 22:05:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3952045/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3952045/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51445529,"identity":"34cae62a-c437-4272-b1dd-d2f504fee2a8","added_by":"auto","created_at":"2024-02-21 18:10:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":186725,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental setup for self-feeders trial using Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e). Each of the three feeders inside each tank, contained a specific feed (PD, ORG1 and ORG2).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3952045/v1/78c839ff50270ebf9a2787cf.png"},{"id":51445530,"identity":"6ae88f2f-ea5d-4c82-ac57-2dae490f7a8d","added_by":"auto","created_at":"2024-02-21 18:10:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196033,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental setup for self-feeders trial using gilthead seabream (\u003cem\u003eSparus aurata\u003c/em\u003e). Each of the three feeders inside each tank, contained a specific feed (PD, ORG1 and ORG2).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3952045/v1/bf2068770b2a5f97db647c97.png"},{"id":51445532,"identity":"194a1b03-2299-4284-b80e-b44928b7bcd1","added_by":"auto","created_at":"2024-02-21 18:10:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":256124,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental setup for diet encapsulation trial using Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e). Each capsule colour represent a specific feed (A, B and C).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3952045/v1/35e901a65408afde816b4732.png"},{"id":51445531,"identity":"61a86e26-e219-44ad-8115-650115e6a530","added_by":"auto","created_at":"2024-02-21 18:10:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":124439,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of average daily intake (% total grams of feed ingested) of three diets (PD, ORG1 and ORG2) by Nile tilapia over 36 days. Diets were changed between feeders on day 22. Lines represent the mean counts ± SD (\u003cem\u003en\u003c/em\u003e=3 tanks). Stars represent significant differences (one-way ANOVA, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3952045/v1/0380dc66b3fb698bee31014f.png"},{"id":51445533,"identity":"13225a73-db00-4e58-b264-8bc45f8ac281","added_by":"auto","created_at":"2024-02-21 18:10:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":13015,"visible":true,"origin":"","legend":"\u003cp\u003eAverage daily intake (total grams of feed ingested per percentage of body weight) of three diets (PD, ORG1 and ORG2) by Nile tilapia only during days with statistically significant differences. Bars represent the mean counts ± SD (\u003cem\u003en\u003c/em\u003e=3 tanks). The star represents significant differences (one-way ANOVA, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3952045/v1/2f9921d18cc11b4396fdee50.png"},{"id":51445534,"identity":"ad9b94b6-837b-46f9-a5d8-4209d3bfe968","added_by":"auto","created_at":"2024-02-21 18:10:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":408246,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of average daily intake (% total grams of feed ingested) of three diets (PD, ORG1 and ORG2) by gilthead seabream over 67 days. Diets were changed between feeders on day 22 and fish were fasted on day 50. Lines represent the mean counts ± SD (\u003cem\u003en\u003c/em\u003e=4 tanks). Stars represent significant differences (one-way ANOVA, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-3952045/v1/7ea6be14091273d804fdb735.png"},{"id":51446902,"identity":"2a523bfd-28d9-4006-99f3-f5b98b17f427","added_by":"auto","created_at":"2024-02-21 18:18:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":109635,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of average daily intake (% total number of capsules ingested) of three diets (A, B and C) by Nile tilapia over 35 days. The content of the capsules changed on day 17. Lines represent the mean counts ± SD (\u003cem\u003en\u003c/em\u003e=7 individual fish). Stars represent significant differences (one-way ANOVA, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-3952045/v1/1dd0e0279716e4f406bd5558.png"},{"id":51445537,"identity":"e9e7df75-88c8-41bd-a897-c19e7cbaaa3e","added_by":"auto","created_at":"2024-02-21 18:10:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":31564,"visible":true,"origin":"","legend":"\u003cp\u003eAverage daily intake (number of capsules ingested per 100g of body weight) of three diets (A, B and C) by Nile tilapia during days with statistically significant differences. Bars represent the mean counts ± SD (\u003cem\u003en\u003c/em\u003e=7 individual fish). The star represents significant differences (one-way ANOVA, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-3952045/v1/342217dcd6f344f4856e978f.png"},{"id":51447549,"identity":"17012aa8-0e6f-4d4a-bc48-edeafa95f84e","added_by":"auto","created_at":"2024-02-21 18:26:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1585348,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3952045/v1/4952ad59-7389-473a-8de8-d6eae4fe0fb0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nile Tilapia and Gilthead Seabream Dietary Self-Selection of Alternative Feeds with Spirulina and Quinoa","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the wild, since no single feed supplies all essential nutrients, most fish show dietary selection and pick up different items to create a complete and balanced diet according to their physiological needs to survive (Huntingford, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Fish indeed are able to select and regulate the intake of macronutrients and energy (Luz et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This selection and regulation occurs through a process known as \u0026ldquo;nutritional wisdom\u0026rdquo; (Raubenheimer and Simpson, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Simpson and Raubenheimer, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In order to restore the metabolic balance as a result of a nutritional challenge, \u0026ldquo;specific hungers\u0026rdquo; have the ability to sense and ingest particular nutrients and/or substances in diets (White et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Therefore, fish select their feed based on a series of complex regulatory mechanisms, associated with physiological, learning, and behavioural processes, involving hormonal and neural activities in the brain, gastrointestinal tract and liver (Comesa\u0026ntilde;a et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Forbes, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Fortes-Silva et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Otero-Rodino et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Richter, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1943\u003c/span\u003e; Simpson and Raubenheimer, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Accordingly, fish feeding behaviour is a relevant characteristic that should be considered when farming species, especially to assess which feed fish would eat more or less.\u003c/p\u003e \u003cp\u003eAquaculture plays a major role in society by providing to the growing world population a vital source of animal protein. However, its development can be hampered by the environmental sustainability of the feeds used (FAO, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Common ingredients (e.g., fishmeal and fish oil) used in feed formulation provide a valuable nutritional value to farmed organisms and most of them, in the European Union, are derived from well-regulated wild fish stocks and can even be certified (Hilmarsdottir et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, in some production areas, though inclusion rates of marine resources has dropped considerably over the past years, they remain widely used during the juvenile stages of carnivorous species, being sourced from non-sustainable stocks (Little et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, the use of alternative ingredients, such as algae and plants may reduce the dependency of fish feed on traditional resources. Spirulina is a microalgae with a high protein content and a nutritionally reasonable fatty acid profile being produced by low-cost open pond technologies (Mosha, 2019). Quinoa is a plant with a high protein, fibre, and carbohydrate contents together with low fat (Pellegrini et al., 2018). Both ingredients possess bioactive compounds that regulate physiological processes and exhibit biological activities, which could improve weight gain, feed efficiency, and disease resistance in fish while reducing the environmental impacts of the sector (Balakrishnan and Schneider, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Han et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In order to achieve all these benefits, thus improving feed sustainability in aquaculture, it is necessary to address the inclusion of these functional ingredients in novel feed formulations and their effects on fish acceptability and intake, especially when knowledge about these alternatives is much lower compared with traditional ingredients (Carlberg et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pratiwy et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe classic approach to investigate the effects of new diets occurs through growth experiments mainly based on physiological mechanisms (Roy et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although this methodology is accurate and have yielded considerable knowledge on animal production, it may not be suitable in the short-term, involves a large number of animals, it is time-consuming, expensive and more importantly it does not consider fish preferences, since they are unable to choose what feed to eat (Br\u0026auml;nn\u0026auml;s and Strand, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Filho et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fortes-Silva et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Conversely, self-selection methods allow fish to freely and voluntarily accept the given diet, while taking into account fish feeding behaviour and learning processes (Fortes-Silva et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, these methodologies allows fish to choose which feeds better suits their nutritional and energetic needs (Fortes-Silva et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, it can also be used to investigate the detection and acceptance/rejection of feed additives, toxic substances and antinutritional factors (e.g., as phytic acid and phytate) present in plants and vegetables (Costa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fortes-Silva et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, to provide a more complete perspective on the potential of novel diets, growth experiments may be complemented with self-selection methods.\u003c/p\u003e \u003cp\u003eEncapsulated diets and self-feeders are some of self-selection methodologies that can be used to investigate feed intake regulation and dietary preferences. Diet encapsulation consists on packaging experimental feeds inside coloured eatable gelatine capsules, avoiding the interference from the organoleptic characteristics of pelleted diets, with the colour of the capsules being the only external cue (Almaida-P\u0026aacute;gan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rubio et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Therefore, fish will select their diets based only on the nutritional content of the feeds, without the effect of chemosensitivity and olfactory factors, but rather mainly based on post ingestion and absorption metabolic signals (Almaida-P\u0026aacute;gan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rubio et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ruohonen and Grove, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Santos et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Self-feeders consist of electronic devices that are placed on top of fish tanks, with a switch below the water level that when pulled and triggered by fish, allow different feeds, with distinct chemosensory properties, to fall from the dispenser into the tank (Jobling et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Pratiwy et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; S\u0026aacute;nchez-V\u0026aacute;zquez et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Therefore, contrary to capsules, the taste and texture of pelleted feeds that fall from the self-feeders is evaluated and sensed by the fish allowing them to make their selection based on post-ingestion and absorption signals coupled with orosensory feed properties (Filho et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fortes-Silva et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Raubenheimer et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Both methods have not only been used and validated for Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e) and gilthead seabream (\u003cem\u003eSparus aurata\u003c/em\u003e), but also for several other fish species, such as perch (\u003cem\u003ePerca fluviatilis\u003c/em\u003e), tambaqui (\u003cem\u003eColossoma macropomum\u003c/em\u003e), sharpsnout seabream (\u003cem\u003eDiplodus puntazzo\u003c/em\u003e) and rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e) (Atienza et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Br\u0026auml;nn\u0026auml;s and Strand, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Costa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Filho et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fortes-Silva and S\u0026aacute;nchez-V\u0026aacute;zquez, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Montoya et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Puchol et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yamamoto et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Although self-selection research in fish has been evaluated in several species, making them viable options to address behaviour, information about the topic remains scarce compared to what is known regarding terrestrial animals. Based on this, knowledge about fish behaviour and its controls have not yet been totally understood and should be further explored, especially when considering the initial screening and design of potential new aquaculture feeds (Fortes-Silva et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pratiwy and Kohbara, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Puchol et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present work aims to investigate the acceptability, behavioural response, feed intake regulation and dietary preferences of two commercially important fish species - freshwater (Nile tilapia) and marine (gilthead seabream) \u0026ndash; using the self-selection methods of self-feeders and diet encapsulation, to test the acceptance of non-conventional diets based on spirulina (\u003cem\u003eSpirulina platensis\u003c/em\u003e) and quinoa (\u003cem\u003eChenopodium quinoa\u003c/em\u003e) as functional ingredients.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Formulation of self-feeder diets\u003c/h2\u003e \u003cp\u003eFor the self-feeders, three experimental diets (PD, ORG1 and ORG2) for each species (Nile tilapia and gilthead seabream) were prepared by SPAROS Lda (Olh\u0026atilde;o, Portugal) with a pilot-scale twin-screw extruder (CLEXTRAL BC45, France) equipped with a screw diameter of 55.5 mm. A temperature range of 105\u0026ndash;110\u0026deg;C was used for the extrusion process. All batches of extruded feeds were dried in a convection oven (OP 750-UF, LTE Scientifics, United Kingdom). Pellet size was 4mm. A control diet (PD) was formulated to mimic current commercial feeds for each species. The remaining two diets (ORG1 and ORG2) were formulated to include spirulina (3.5% and 7%, respectively) and quinoa (5% in both), as well as other organic alternatives (e.g., pea protein concentrate, corn meal) to address some of the current environmental concerns and/or ethical issues often associated with ingredients present in traditional commercial formulations. The ingredient selection (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was chosen based within an organic framework, on market availability and nutritional composition. The inclusion levels were adjusted for each species, according to existing knowledge on tolerance to different ingredients as well as their nutritional and especially amino acid requirements, without compromising fish growth, development, and welfare.\u003c/p\u003e \u003cp\u003eTilapia feeds had similar protein, lipid, and energetic contents with around 33.8% WW crude protein, 7.6% WW crude lipid and 18.9 KJ gross energy/g WW (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Seabream diets also showed identical proximal compositions, having on average 41.7% WW crude protein, 16.8% WW crude lipid and 22.2 KJ gross energy/g WW (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Amino acid profiles of the experimental diets given to both species are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Although in ORG1 the diets exhibited lower methionine levels, this amino acid requirement was fulfilled.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiet formulation (inclusion levels %) and proximate composition (% as fed) of the experimental diets (PD, ORG1 and ORG2) in self-feeders for Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIngredients (inclusion levels %)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eORG1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eORG2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFishmeal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoultry meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrewer's yeast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpirulina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea protein concentrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat gluten\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorn gluten meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRapeseed meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSunflower meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice bran full fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorn meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuinoa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaba beans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin and mineral premix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholine chloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntioxidant powder (Verdilox)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMono-calcium phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-lysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL-methionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProximate Composition (% as fed)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry matter (DM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude lipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross energy (kJ/g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csub\u003eAll values are reported as mean of duplicate analysis\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiet formulation (inclusion levels %) and proximate composition (% as fed) of the experimental diets (PD, ORG1 and ORG2) in self-feeders for gilthead seabream (\u003cem\u003eSparus aurata\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIngredients (inclusion levels %)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eORG1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eORG2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFishmeal Super Prime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoultry meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrewer's yeast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpirulina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea protein concentrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat gluten\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorn gluten meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRapeseed meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSunflower meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat bran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuinoa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaba beans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole peas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin and mineral premix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholine chloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntioxidant powder (Verdilox)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMono-calcium phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-lysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL-methionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProximate Composition (% as fed)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry matter (DM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude lipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross energy (kJ/g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003csub\u003eAll values are reported as mean of duplicate analysis\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAmino acid profile (g/100g fed basis) of the experimental diets in self-feeders for Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAmino acids (g/100g fed basis)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eORG1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eORG2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreonine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTryptophan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCysteine\u0026thinsp;+\u0026thinsp;Cystine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyrosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaurine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csub\u003eAll values are reported as mean of duplicate analysis\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAmino acid profile (g/100g fed basis) of the experimental diets in self-feeders for gilthead seabream (\u003cem\u003eSparus aurata\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAmino acids (g/100g fed basis)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eORG1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eORG2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreonine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTryptophan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCysteine\u0026thinsp;+\u0026thinsp;Cystine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyrosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003csub\u003eAll values are reported as mean of duplicate analysis\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Formulation for diet encapsulation\u003c/h2\u003e \u003cp\u003eFor the diet encapsulation three experimental diets (A, B and C) for Nile tilapia were prepared at the University of Murcia (Murcia, Spain) with distinct proximal composition (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Diet A had casein and dextrin (34% and 30%, respectively) as main ingredients and served as a proxy for a purified diet. Diet B was mainly composed of spirulina (58%), while Diet C had different doses of spirulina and quinoa (46% and 20%, respectively). Other ingredients present in the diets include fish oil, soy oil, cellulose, alginate, and vitamin premix. The spirulina and quinoa used in the supplemented diets B and C were provided by TILAMUR S.L (Murcia, Spain). Encapsulation occurred by filling eatable gelatine capsules (No. 4; FAGRON, S. A., Barcelona, Spain) with previous produced diets grounded, using a semi-automatic encapsulator - Tencyfarma, Miranda de Ebro, Barcelona, Spain). All capsules were weighed after manufacture (\u0026plusmn;\u0026thinsp;0.17g filled with feed) and stored (at 4\u0026ordm;C) in a common and single plastic bag for all the days of the experiment until use, to avoid any external contamination which might have allowed the fish to distinguish their content by their external chemical properties. Three colorations of capsules were used (red, green, and yellow), each corresponding to a specific diet, to help fish discriminate between them. Feed samples were grounded and analysed for dry matter (105\u0026deg;C for 24 h), crude protein calculated by the Kjeldahl method (automatic flash combustion; Leco FP-528, Leco, St. Joseph, USA) (N \u0026times; 6.25%), lipid content by diethyl ether extraction (Soxtherm Multistat/SX PC (Gerhardt, K\u0026ouml;nigswinter, Germany; 150\u0026deg;C) and ash content by heating in an oven at 450\u0026deg;C for 24 h. Diets proximal composition varied between 23.9% \u0026minus;\u0026thinsp;32.4% wet weight (WW) crude protein and 16.6% \u0026minus;\u0026thinsp;19.9% WW crude lipid (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiet formulation (inclusion levels %) and proximate composition (% as fed) for the experimental diets (A, B and C) in diet encapsulation for Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIngredients (% as is)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCasein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDextrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGelatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoy oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlginate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin Premix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuinoa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpirulina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProximate Composition (% as fed)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry matter (DM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude lipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll values are reported as mean of duplicate analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Fish and husbandry conditions\u003c/h2\u003e \u003cp\u003eFish were reared and handled by trained scientists and following the Spanish legislation on Animal Welfare and Laboratory Practices, while the experimental protocol was approved by the National Committee of the University of Murcia on Ethics and Animal Welfare under the Guidelines of the European Union Council on the protection of animals used for experimental purposes (Directive 2010/63/EU).\u003c/p\u003e \u003cp\u003eNile tilapia was used for self-feeder and encapsulation experiments. The latter was used as a follow up experiment to discard the effects of oral factors on the selection of the feeds. Both experiments were conducted in the chronobiology laboratory at the University of Murcia. Individual fish and groups were weighed right before the start and at the end of each experiment to not compromise behaviour. Nile tilapia with an average initial individual weight of 163.0 g\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 g (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.; in groups for self-feeder studies) and 331.9 g\u0026thinsp;\u0026plusmn;\u0026thinsp;31.4 g (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.; individual for capsules studies) were provided by the University of Murcia, from a mono-sex male population (offspring tilapia, GMT\u0026reg;). The tanks with 300 L were maintained in an aquaculture recirculating system, with a protein skimmer, as well as mechanical, biological, UV filtered and aerated water. Fish were allowed to acclimate to laboratory conditions for at least 2 weeks, during which time they were fed a commercial diet (Skretting TI-3 (3.2mm); with % DM: 32.0% crude protein, 6.0% crude fat and 5.8% crude fibre), which was supplied by hand to visual satiety once a day. Abiotic parameters were measured, and mortality was recorded daily. The photoperiod was 12L:12D (lights on at 09:00). Values of 29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u0026deg;C for water temperature, 7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 for pH, 6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 ppm for dissolved oxygen and 0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 mg/l ammoniac nitrogen were maintained.\u003c/p\u003e \u003cp\u003eGroups of gilthead seabream were only used for the self-feeder experiment performed at the Aquaculture Laboratory located in Algameca (Cartagena, Spain). Fish had an average initial individual weight of 174.7 g\u0026thinsp;\u0026plusmn;\u0026thinsp;27.0 g (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D) and were provided by IMIDA from San Pedro del Pinatar (Spain). The tanks with 150 L were maintained in a flow-through system, with a protein skimmer, as well as mechanical, biological, UV filtered and aerated water. Fish were allowed to acclimate to laboratory conditions for at least 2 weeks, during which time they were fed a commercial diet (Skretting L-4 Alterna 2P; with % DM: 46.5% crude protein, 20.0% crude fat and 3.4% crude fibre), which was supplied by hand to visual satiety once a day. Abiotic parameters were measured, and mortality was recorded daily. The animals were kept in a natural photoperiod with values of 27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u0026deg;C for water temperature, 37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 ppm for salinity, 7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 for pH and 6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 ppm for dissolved oxygen.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Experimental setup\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Self-feeders\u003c/h2\u003e \u003cp\u003eThe self-feeder experiment was performed in accordance with Fortes-Silva et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For Nile tilapia, it lasted 36 days and 30 fish were conditioned as groups in three plastic tanks (each with 11 fish), while for seabream it lasted 67 days and 32 fish were divided in four plastic tanks (each with 8 fish) (Figs.\u0026nbsp;1 and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Three self-feeders provided by the University of Murcia were equipped in each tank. The position of each diet (PD, ORG1 and ORG2) on the feeders also varied between tanks, to avoid a possible positional effect. The feeding systems were connected with an electric transformer (one for five self-feeders). Each of them was composed of a trigger (a switch with rubber tip), actuated by the fish, placed 2 cm above the water surface, connected to an electromagnet, and a feeder (EHEIM 3581, Deizisau, Germany) that delivered a predetermined amount of feed (1 pellet\u0026thinsp;=\u0026thinsp;0.04 g) after each trigger actuation and electromagnet activation. To determine the daily intake, every day the feed remaining in the feeder was weighed at the same time (11:30) and subtracted with the total number of grams given the previous day, before refilling the feeder recipient for the next day. After percentages of the offered diets exhibited a statistically significant difference for one feed, diets were switched between feeders to provide a challenge for the fish, reduce the possible preference and influence for a particular string sensor or relative position of the self-feeders.\u003c/p\u003e \u003cp\u003eFigure 1. Experimental setup for self-feeders trial using Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e). Each of the three feeders inside each tank, contained a specific feed (PD, ORG1 and ORG2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Encapsulated diets\u003c/h2\u003e \u003cp\u003eThe experiment with encapsulated diets was performed according to Fortes-Silva et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It lasted 35 days and 7 fish were housed in 4 plastic tanks, divided in half separated with a net, thus single individual fish were used (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Ten capsules of each colour (30 in total) were mixed and given to each fish daily at the same time (11:30h) and left in the water for 20 min. After this period, to determine the daily intake, the total number of uneaten capsules corresponding to each diet were removed from the tanks, counted, and subtracted with the total number of capsules given capsules. After a determined diet was preferred and exhibited a statistically significant difference over the others, feeds were switched between capsule\u0026rsquo; colours to reduce the possible preference for a specific one.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data analysis and statistics\u003c/h2\u003e \u003cp\u003eThe statistical analysis was performed with the SPSS software, version 23.0. For the diet encapsulation the experimental unit was the number of fish (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7), while for the self-feeders was the tank (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 or 4). The relative selection of each diet is expressed as a percentage of the total number of capsules or feed consumed, considering the total diets as 100%. The feed and capsules intakes were expressed as total grams of feed ingested/% of body weight and number of capsules/100g of body weight, respectively. Further, in the days where a specific diet was mostly selected, the percentages of capsules ingested and feed consumed were compared by one-way ANOVA, followed by a Tukey\u0026rsquo;s post-hoc test to examine significant pair-wise comparisons, before meeting criteria for normality and homogeneity using Shapiro \u0026ndash; Wilk and Levene\u0026rsquo;s test, respectively. \u003cem\u003eArcsine\u003c/em\u003e transformations of capsules and feed intake percentages were performed to achieve homogeneity of variance. The statistical significance was considered at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Self-feeders\u003c/h2\u003e \u003cp\u003eNile tilapia reached a final mean weight of 194.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 g (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.), all fish survived and on average feed consumed daily represented 0.75% of average body weight/day. As reported by Fortes-Silva et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, uneaten and wasted feed was negligible, only around 2% of the total given feed, thus the amount of feed demanded by the fish was almost entirely ingested. The dietary preference (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) in self-feeders initially demonstrated an adaptation period to the feeders of around 5 days. During this time, fish preferred the position of specific feeders, rather than the inserted feed, but quickly changed their behaviour. All diets were chosen similarly for several days before an increase in preference for diet ORG2 was observed during three consecutive days (with an average of 46.5%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Throughout the same period diets PD and ORG1 were preferred on average 28.9% and 24.7%, respectively. Diets were switched between feeders on day 22, another period of equal preference remained, while from day 30 until the end of the experiment, diet ORG2 was mainly chosen (between 40.7% and 56.0%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eDiets PD (0.24 grams/% BW; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and ORG1 (0.21 grams/% BW; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were consumed 38.5% and 46.2%, respectively, less than diet ORG2 (0.39 grams/% BW; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) during days with statistically significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGilthead seabream final weight was 264.4\u0026thinsp;\u0026plusmn;\u0026thinsp;29.5 g (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.), no mortality was recorded and average feed consumption was 1.21% of the animal\u0026rsquo;s biomass. From the initial days of the study, fish exhibited a clear preference for diet ORG1 (between 65.50% and 83.45%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, after diets were switched between feeders, the preference for diet ORG1 fell, while for diet PD and ORG2 increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For several days, no statistically significance was achieved (except only for one day). Then, fish were fasted for 10 days as a challenge test to motivate them to choose a diet, according to Aranda et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Nevertheless, even after this approach, a consistent preference was not achieved (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Encapsulated diets\u003c/h2\u003e \u003cp\u003eWhen fed capsules, tilapia exhibited a mean final weight of 362.6\u0026thinsp;\u0026plusmn;\u0026thinsp;33.6 g (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.), and no mortality was observed. The average feed consumption was 0.51% of the average body weight/day. Not all fish started to ingest capsules from the first day, but progressively all of them successfully swallowed the whole capsules without breaking the covering. In terms of dietary selection (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e), after 10 days of ingesting similar diet preferences, fish progressively increased their intake and selection of capsules containing diet C, while simultaneously decreasing that of diets A and B. When the preference for diet C reached values (maximum of 44.0%) that showed statistically significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for several days (day 11\u0026ndash;16) over diets A and B (remained at an average value of 28.9% and 30.5%, respectively), the colour of the capsules associated with each diet rotated (day 17). Initially, this change decreased the ingestion of diet C, while conversely increasing diet A and B. Up to day 29, the preference of fish towards the diets remained at a steady state, until a clear consistency and preference for diet C was re-established (between 41.0% and 47.2%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) during the rest of the experimental period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring days with statistically significant differences between intake of feeds, diets A (1.11 capsules/100g BW) and B (1.12 capsules/100g BW), were ingested 32.7% and 32.1%, respectively, less than diet C (1.65 capsules/100g BW; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;8).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure 8. Average daily intake (number of capsules ingested per 100g of body weight) of three diets (A, B and C) by Nile tilapia during days with statistically significant differences. Bars represent the mean counts\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7 individual fish). The star represents significant differences (one-way ANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe main purpose of this research was to assess the feeding behaviour and ability of Nile tilapia and gilthead seabream to self-select their preferred diets. Fish choose feeds and regulate their feeding behaviour based on homeostatic and hedonic mechanisms. The homeostatic pathway maintains normal energy balance homeostasis and it takes over in response to nutritional demands and metabolic needs. The hedonic control is related to the brain reward system, where fish mainly sense the orosensory, pellet quality and palatable aspects of feed, being independent on nutritional requirements (Kulczykowska and S\u0026aacute;nchez V\u0026aacute;zquez, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Volkoff, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, fish can choose which feed items to ingest mainly based on size, palatability and nutritional properties (Raubenheimer et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). All feeds offered had the same size, thus fish choice was only based on the other two parameters. For this reason, fish tested with self-feeders and diet encapsulation, which enabled the isolation of palatability and nutritional parameters, allowed for a better understanding of the effects of \u0026ldquo;nutritional wisdom\u0026rdquo;.\u003c/p\u003e \u003cp\u003eFish feeding is modified by both Pavlovian (to find the feed) and operant learning (to catch and manipulate the feed) behaviour (Millot et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Operant learning is the process that is associated between a behavioural action and its outcomes. If fish are rewarded enough times, fish will learn the relationship and will increase the probability and frequency of repeating the same action. In a population, there may be only one dominant fish that is curious enough to pull the trigger, but if rewarded, this information may be socially transmitted and become more common in all individuals (Millot et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). On the initial days of the self-feeding experiment tilapia showed a preference for one feeder, which changed after some time after assessing the content of the other feeders, demonstrating their exploratory and learning behaviour, as shown by Figueiredo et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A similar situation was recorded using European seabass, that when fish were fed with the standard diet they exhibited a preference for one of the self-feeders (Aranda et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Although tilapia kept in groups developed strong social hierarchies that could affect feeding behaviour (Toguyeni et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), this was not observed as fish did not show an aggressive behaviour and still choose a feed on both types of experiment and regardless being kept alone or in groups. Similarly, Fortes-Silva et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) showed that tilapia were not aggressive and a exhibited a similar pattern of diet selection whether maintained isolated or in groups, when offered diets with a balanced or unbalanced composition of essential amino acids. Indeed, in our self-feeders experiment, tilapia were not isolated as in diet encapsulation, meaning that social learning may not play a significant role in the feeding and behavioural mechanism as suggested by Vivas et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Using self-feeders, Nile tilapia chose diet ORG2 with an intake 0.75% of fish weight/day. Similarly to other studies, it was possible to observe that tilapia consumed most diet pellets and that the feed waste (less than 2% of the total given feed) that remained in the tanks was residual. For example, Fortes-Silva et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported a negligible food waste of 1% with tilapia. To compare the intake, Pratiwy et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) tested the growth performance of Nile tilapia reared under self-feeding systems and showed feed intake values of around 1.85%/body weight. The choice for diet ORG2 was presumably based on tilapia nutritional needs (post-ingestive) coupled with feed organoleptic characteristics. Likewise, other studies with European seabass (\u003cem\u003eDicentrarchus labrax\u003c/em\u003e) and tilapia reported a similar behaviour (Fortes-Silva et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rubio et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). It is important to note since fish required almost three weeks to exhibit a preference and no feed was predominantly chosen from the beginning of the study, this can reflect the less clear taste differences between feeds. In a study by Carlberg et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Arctic charr (\u003cem\u003eSalvelinus alpinus\u003c/em\u003e) took 9 days, while Fortes et al. (2010) noted that tilapia clearly preferred since the beginning of the experiment diets containing phytase with an intake and that after switching feeds, the pattern was re-established only after 3 days. In the present study, after feed was switched between feeders, tilapia also resumed, re-established and sustained the previous pattern of selection of diet ORG2, while maintaining a constant consumption of other diets, meaning that the fish established levels of consumption for each feeds. However, once again they took some time (9 days), pointing out the effect of the minor differences between the diets. These findings are in accordance with Fortes et al. (2010), who reported that a diet with 1500 IU kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e phytase was preferred throughout the trial, even after switching feeders. The same author showed that tilapia retained and maintained the intake of specific levels of protein, fat and carbohydrate when feeds were switched over (Fortes-Silva and S\u0026aacute;nchez-V\u0026aacute;zquez, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Fish are able to identify and evaluate distinct amino acid profiles between diets (Fortes-Silva et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). All feeds were formulated to contain the minimum requirements of every essential amino acid (EAA). However, diet ORG1 presented the lowest levels of methionine. Although methionine was near the lowest requirement in ORG1, it still was enough to fulfil the species physiological state for normal growth (NRC, 2012). Overall, the self-feeder experiment with tilapia resulted in a preference for diet ORG2, which was influenced by a combination of several factors including learning-reward behaviour, nutritional requirements, as well as the orosensory properties of the diets.\u003c/p\u003e \u003cp\u003eTo discard the effects of olfactory factors on the selection of the feeds, Nile tilapia were fed encapsulated diets in a second experiment. Tilapia preferred diet C (with spirulina and quinoa), after 11 days and after colour rotation, and presented an intake of 0.51% of their body weight. While individual differences between fish of the same species can exist, in our case the preference was unanimous for all fish. The same species with a final body weight of around 53g required 15 days to distinguish between two vegetable oil blends at 30\u0026ordm;C and after switching capsules\u0026rsquo; colours only 3 days, with an average intake of 1.12% between all treatments (de Almeida et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In another study, the feed intake of tilapia with around 80g was 1.36% of the animal\u0026rsquo;s biomass and required 7, 11 and 23 days to differentiate diets with distinct protein levels (0, 25 and 42%) and 6, 10 and 4 days after inverting the content of the capsules (Costa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Capsule colour used in this study did not affect our results. In other studies, using tilapia, by Fortes-Silva et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and de Almeida et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) coloration also did not affect fish preference and only served as a \u0026ldquo;reference\u0026rdquo; for the fish to identify which diet was associated with each colour. Therefore, in the present study it was clear that by randomizing capsules\u0026rsquo; colours for each individual and after changing the colour paired to each diet, tilapia continued to choose capsules with diet C, meaning that the content, rather than the capsule colour, was the main factor considered for their preference. Tilapia is able to address the content based in a wide range of physiological processes. All capsules presented the same chemosensory properties at oropharyngeal level, meaning that palatability, texture, flavour and odour associated with traditional pelleted diets were negligible for the fish (Rubio et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Moreover, it is important to note that neither the capsules or pellets leached nutrients into the water, which could act as attractants to fish (Busti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, fish had to evaluate the quality and nutritional composition of the content of the capsules and learn to associate this information with the capsules' colour (Almaida-P\u0026aacute;gan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rubio et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Accordingly, tilapia opted for the diet C, which after evaluation, was probably more able to satisfy their physiological state, based on post-ingestive and/or post-absorption processes as suggested by Fortes-Silva et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Rubio et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, it is important to note that in the present study, besides the constituting ingredients, the formulations of the three experimental diets, mainly protein and lipid, were different, thus they could have affected fish feeding behaviour. For the experimental size (around 300g) of Nile tilapia used, the recommend percentage of these macronutrients on the feeds is between 32\u0026ndash;36% for proteins and 8\u0026ndash;12% for lipids (NRC, 2012). Our results were in accordance with Pereira-da-Silva et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) that registered a crude protein feed intake of 24% by Nile tilapia, when given the possibility to self-select between distinct protein dietary levels. Although diet A (mainly with casein and dextrin) and B (rich in spirulina) had more similar nutritional profiles to these values than diet C (mixture of spirulina and quinoa), the latter was still preferred. Since fish had at their disposal other feeds, they also consumed them in a different proportion, possibly to create a balanced diet and compensate the lack of some essential nutrients from diet C (Simpson and Raubenheimer, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). A similar situation was found by Costa et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who reported that when tilapia of 80g were offered two rations, with different crude protein and amino acids levels, it showed a significant preference for the consumption of one of the feeds, while also eating at lower levels the other one. In the present study, the intake of diet C, which had the lowest protein content (23.93%) increased, could mean that this defect was mild for the fish, perhaps due to their size, so they increased its intake, and possibly also consumed other feeds available, to reach their level of requirement, especially in the case of protein (Henry, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). This scenario was also confirmed by Fortes et al., (2011), who noted that when protein was restricted, tilapia increased its intake by consuming more capsules with this nutrient to maintain their energy intake (Fortes-Silva et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Similarly, Almaida-P\u0026aacute;gan et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Garc\u0026iacute;a-Meil\u0026aacute;n et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) showed that when given a diet with lower protein to sharpsnout and gilthead seabream, respectively, the fish increased its intake. Indeed, studies have shown that fish have the ability to regulate its consumption and defend a given nutritional intake target (Almaida-P\u0026aacute;gan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Br\u0026auml;nn\u0026auml;s and Strand, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fortes-Silva et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rubio et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Nile tilapia exhibited a consistent preference for diet C driven by post-ingestive and post-absorption processes, even in the absence of olfactory factors present in the self-feeder experiment, reflecting that they are able to identify and select a feed with spirulina and quinoa as functional ingredients.\u003c/p\u003e \u003cp\u003eConversely to tilapia, gilthead seabream did not show a consistent preference for a particular feed using self-feeders. The experiment was performed with rapidly growing juveniles at high water temperature in summer (since it was a flow-through aquaculture system), a scenario which could have made that the homeostatic system, associated with high energetic demands, override the hedonic regulation of feeding behaviour, not allowing seabream to discriminate diets efficiently (Puchol et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another possible explanation for the lack of diet discrimination is because all three experimental diets were nutritionally similar to the previously fed commercial feed, meaning that fish were familiar with it and did not notice enough differences (Pulido-Rodriguez et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, gilthead seabream wasted and rejected more feed than tilapia, while the intake was at an average of 1.21%/body weight. In addition, there was a much higher variation on the daily intake of feeds compared with tilapia, which could be related with the more curious and aggressive behaviour of seabream towards feed, especially when limited and defendable as in self-feeders (Puchol et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There are few studies available regarding seabream using self-feeders with similar methodology to compare results. Nevertheless, in a previous investigation with seabream, it was shown that fish with 254g could select a diet with distinct oxidation levels of dietary lipids after 10 days with a preference of 82% and 7 days after switching feeders with an average intake of 1.57%/body weight (Montoya et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Similarly, to what occurred with tilapia, it is possible that on the initial days seabream were preferring a specific feeder on each tank that, by coincident, contained diet ORG1. A specific feeder was also selected on the initial experimental days with European seabass and gilthead seabream by Aranda et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Montoya et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), respectively. The preference for a particular feeder can then indirectly affect fish\u0026rsquo; choice based on pre-ingestive parameters (Montoya et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, it was necessary to change the positions of the feeders to assert dietary preferences and avoid any preference for a specific position as it was noted by Puchol et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Indeed, after changing the position of the feeders, seabream decreased their intake for ORG1 and never achieved a clear preference for any of the given feeds, suggesting that both pre- and post-ingestive signals were involved in diet selection (Montoya et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Montoya et. al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) observed two selection patterns after changing the position of the feeders: some fish groups resumed their selection for a specific diet, while the other groups did not show a clear preference for any diet until they were subjected to a 3-week fasting period, after which they shortly resumed their dietary preferences. In the present study, seabream were also fasted, for a duration of 10 days in order to present them with a challenge, aiming to define a feeding pattern, as the physiological state of fish caused by oxidative stress due to fasting would reinforce their selection behaviour (Montoya et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, seabream were not observed to demand more feed and define a pattern, as the compensatory bite activity increase was not enough to attain a sufficient feed intake level to reach a preference. Conversely, European seabass fasted for 6 and 15 days, increased demand for diets to recover metabolic status with hyperphagia, mainly for protein used as energy source (Aranda et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Vivas et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Since seabream were unable to exhibit a preference for any feed, it was not necessary to conduct a capsule experiment.\u003c/p\u003e \u003cp\u003eThe feed intake and growth rates obtained in all our studies were in general lower compared to other performance experiments, as it was expected. It should be noted that fish sizes differ among experiments, which in turn directly affects their intake requirements and growth rates. Moreover, the diets were not formulated with the goal of optimizing fish growth but rather to study fish behaviour. Indeed, experiments on dietary selection do not necessarily correlate the most selected diet with optimal performance (Fortes-Silva et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Even in growth experiments, although a diet is formulated to provide maximum growth, when given the opportunity, fish might not prefer that formula (de la Higuera, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). When using capsules, tilapia \u0026ldquo;feel\u0026rdquo; that it is a \u0026ldquo;stranger\u0026rdquo; method of feeding, which can lead to a decrease in the intake. It is important to note that although tilapia were isolated in the diet encapsulation experiment, it should not represent a factor of stress that would have affected feed intake (Fortes-Silva et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Despite it has been seen that animals in a self-feeding scheme can perform well (in terms of weight gain, dietary intake, etc), in some species, these method can impair growth and decrease feeding efficiency (G\u0026eacute;lineau et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Montoya et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Santos et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tidwell et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). These differences are related to the adaptation of self-feeders by fish, where some animals in the same group assimilate the self-feeding system more than others (Ferrari et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tidwell et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Nevertheless, the lower performance indicators obtained in our experiments were not a concern, especially as no mortality occurred and fish still gained weight.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn one hand, tilapia was able to show a preference and selected one of the given feeds (with spirulina and quinoa) by sensing the orosensory properties, in the case of self-feeders, but also based only on post-ingestion and absorption signals, for diet encapsulation, confirming their ability to choose a specific feed. On the other hand, gilthead seabream did not show a consistent preference for any diet. Accordingly, self-selection studies based on fish \u0026ldquo;nutritional wisdom\u0026rdquo;, allow fish to exhibit their behaviour, thus it may be considered in the initial screening and design of potential new aquaculture feeds and ingredients, before they are commercially available.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Jos\u0026eacute; Oliver for the participation in the trials.\u003c/p\u003e\n\u003cp\u003e\u003cimg style=\"width: 181px;\" 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alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work has received funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956129 \u0026ldquo;EasyTRAIN\u0026rdquo;. Also, received funding from the Portuguese national funds from FCT \u0026ndash; Foundation for Science and Technology through projects UIDB/04326/2020, UIDP/04326/2020 and LA/P/0101/2020. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal Welfare Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the ethical policies of the journal, as noted on the journal\u0026rsquo;s author guidelines page, have been adhered to and the appropriate ethical review committee approval has been received. The authors confirm that they have followed EU standards for the protection of animals used for scientific purposes.\u003c/p\u003e\n\u003cp\u003eFish were reared and handled by trained scientists and following the Spanish legislation on Animal Welfare and Laboratory Practices, while the experimental protocol was approved by the National Committee of the University of Murcia on Ethics and Animal Welfare under the Guidelines of the European Union Council on the protection of animals used for experimental purposes (Directive 2010/63/EU).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatements and Declarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis work has received funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956129 \u0026ldquo;EasyTRAIN\u0026rdquo;. Also, received funding from the Portuguese national funds from FCT \u0026ndash; Foundation for Science and Technology through projects UIDB/04326/2020, UIDP/04326/2020 and LA/P/0101/2020.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg style=\"width: 343px;\" 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\" alt=\"\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting Interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthor Contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eR.M: Conceptualization, Methodology, Investigation, Resources, Data Curation, Formal Analysis, Writing - original draft, Writing - review \u0026amp; editing. L.C: Conceptualization, Writing-review \u0026amp; editing, Supervision, Funding acquisition. J.D: Conceptualization, Methodology, Resources. S.E: Writing-review \u0026amp; editing, Supervision, Funding acquisition. F.V: Conceptualization, Methodology, Resources, Writing - review \u0026amp; editing, Supervision, Funding acquisition. The first draft of the manuscript was written by Rodrigo Mendes and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eData Availability\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe datasets generated during and/or analysed during the current study are not publicly available due to privacy concerns but are available from the corresponding author on reasonable request.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmaida-P\u0026aacute;gan, P., Rubio, V., Mendiola, P., Decosta, J., Madrid, J., 2006. Macronutrient selection through post-ingestive signals in sharpsnout seabream fed gelatine capsules and challenged with protein dilution. 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Protein selection, food intake, and body composition in response to the amount of dietary protein. Physiology \u0026amp; Behavior 69, 383\u0026ndash;389. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0031-9384(99)00232-2\u003c/span\u003e\u003cspan address=\"10.1016/S0031-9384(99)00232-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamamoto, T., Shima, T., Furuita, H., Suzuki, N., S\u0026aacute;nchez-V\u0026aacute;zquez, F.J., Tabata, M., 2001. Self-selection and feed consumption of diets with a complete amino acid composition and a composition deficient in either methionine or lysine by rainbow trout, \u003cem\u003eOncorhynchus mykiss\u003c/em\u003e (Walbaum): Self-feeding of amino acid-deficient diets by trout. Aquaculture Research 32, 83\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1355-557x.2001.00007.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1355-557x.2001.00007.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"fish-physiology-and-biochemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fish","sideBox":"Learn more about [Fish Physiology and Biochemistry](https://www.springer.com/journal/10695)","snPcode":"10695","submissionUrl":"https://submission.nature.com/new-submission/10695/3","title":"Fish Physiology and Biochemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Animal Behaviour, Fish Physiology, Self-selection, Alternative Feeds, Nile tilapia, Gilthead seabream","lastPublishedDoi":"10.21203/rs.3.rs-3952045/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3952045/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClassical assessments of new fish feeds are anthropocentric, focusing on growth. Although this methodology is accurate, it does not consider the fish\u0026rsquo; perspective. This study aimed to investigate the behavioural responses and feed preferences of Nile tilapia - \u003cem\u003eOreochromis niloticus\u003c/em\u003e and gilthead seabream - \u003cem\u003eSparus aurata\u003c/em\u003e, in two self-selection trials (self-feeders and diet encapsulation). Using self-feeders, both species were offered three feeds: a control (PD) and two diets (ORG1 and ORG2) containing non-conventional ingredients, including spirulina (\u003cem\u003eSpirulina platensis\u003c/em\u003e) and quinoa (\u003cem\u003eChenopodium quinoa\u003c/em\u003e). Three groups of tilapia with an average weight of 163.0 g\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 g (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) and four groups of seabreams with 174.7 g\u0026thinsp;\u0026plusmn;\u0026thinsp;27.0 g were tested. To investigate the role of olfactory factors in dietary selection, three other diets were encapsulated and offered to tilapia: Diet A, a purified feed, Diet B that contained predominantly spirulina and Diet C which had a mixture of spirulina and quinoa. Seven individual tilapia of 331.9 g\u0026thinsp;\u0026plusmn;\u0026thinsp;31.4 g were used. Using self-feeders, tilapia exhibited a preference for ORG2 (46.5%), which was influenced by the sensory properties of feeds and post-ingestion signals, as their choice for ORG2 persisted during diet encapsulation using Diet C, which was also formulated with quinoa and spirulina. Seabream did not show a preference for any feed. These findings highlight the effectiveness of self-selection experiments in allowing fish to express their feeding behaviour and preferences. Therefore, this approach should be considered in the initial screening and design of new aquaculture feeds and ingredients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Nile Tilapia and Gilthead Seabream Dietary Self-Selection of Alternative Feeds with Spirulina and Quinoa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 18:09:59","doi":"10.21203/rs.3.rs-3952045/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-06T18:27:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-06T12:25:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ef36fccb-3056-4daf-9a15-ed7c6562b633","date":"2024-04-16T07:34:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-06T14:38:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6ce1e839-1c53-4e60-8825-344449febd2f","date":"2024-02-21T13:47:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"088fa9d3-9ea9-41fe-8223-62fec706bc12","date":"2024-02-20T07:22:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-19T13:29:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-19T13:25:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-19T08:34:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Fish Physiology and Biochemistry","date":"2024-02-12T22:02:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"fish-physiology-and-biochemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fish","sideBox":"Learn more about [Fish Physiology and Biochemistry](https://www.springer.com/journal/10695)","snPcode":"10695","submissionUrl":"https://submission.nature.com/new-submission/10695/3","title":"Fish Physiology and Biochemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"372617a1-b932-4fed-b894-0a714d78d92a","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-22T20:41:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-21 18:09:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3952045","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3952045","identity":"rs-3952045","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}