Freshwater history exerts a larger effect than the feeding regime at sea on the occurrence of rainbow trout sexual maturation in seawater cages

Freshwater history exerts a larger effect than the feeding regime at sea on the occurrence of rainbow trout sexual maturation in seawater cages | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Freshwater history exerts a larger effect than the feeding regime at sea on the occurrence of rainbow trout sexual maturation in seawater cages Enrique Pino-Martinez, Cindy Pedrosa, Jonas Ismael Silva-Marrero, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9042484/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Sexual maturation of rainbow trout during grow-out at sea poses economic risks for aquaculture due to reduced growth, feed intake, and downgraded harvest quality. This study evaluated the effects of freshwater rearing systems and feed rationing at sea on maturation in a commercial production site in Western Norway. Two batches of trout reared in Recirculating Aquaculture Systems (RAS) or Flow-Through (FT) systems were transferred to sea cages in March 2024 (mean sizes 450 g and 179 g, respectively). All fish received full rations for the first three months at sea, after which half of the cages from each system were subjected to feed restriction (one fasting day every three days; ~70% ration) until harvest. Fish reared in RAS showed a higher incidence of sexual maturation than FT fish irrespective of the feed regime (12% vs. 0%). This difference was associated with more intensive freshwater conditions, particularly elevated temperatures during the last period of the freshwater phase, and larger size at sea transfer. Gonadotropic upregulation in RAS trout was evident by June, prior to visible maturation, suggesting activation of the reproductive axis triggered by increasing photoperiod and temperature following spring transfer. Maturation occurred in both sexes, unlike in Atlantic salmon where is male-predominant. Feed restriction did not reduce maturation rates but negatively affected growth, feed efficiency, and harvest quality. Thus, ration reduction is not an effective strategy against early maturation and incurs additional costs. These results highlight a trade-off in freshwater rearing strategies: while intensive RAS conditions promote faster growth, they may increase the risk of early maturation and associated welfare issues at sea. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Ocean sciences Biological sciences/Physiology Biological sciences/Zoology Recirculation Aquaculture Systems Flow-Through early puberty gonadotropins feed restriction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Nowadays, aquaculture of rainbow trout ( Oncorhynchus mykiss ) in Norway is carried out following similar production protocols as Atlantic salmon ( Salmo salar ). Juveniles are reared up to 150–300 g during an early freshwater stage that takes place in land-based recirculation (RAS) or flow-through (FT) facilities, being then transferred to sea pens or cages located in the fjords for the grow-out phase (Aas et al., 2022 ; Morro et al., 2019 ). This is possible due to the ability of rainbow trout to act as facultative anadromous and adapt to seawater (Ohms et al., 2014 ; Van Doornik et al., 2013 ). However, despite benefiting from the technical developments for salmon aquaculture, total production of rainbow trout in Norway has been stagnant for the last few years between 85–90 thousand tonnes (Aas et al., 2022 ; Norwegian Directorate of Fisheries, 2024 , 2023 ), and is significantly lagging behind salmon production the same years (~ 1.5 M tonnes, Norwegian Directorate of Fisheries, 2024 , 2023 ). One aspect that industry players consider a challenge for trout aquaculture is sexual maturation during the production cycle (E. Eide, Eide Fjordbruk AS, personal communication). Similarly to Atlantic salmon, sexual maturation in rainbow trout leads to appetite loss, growth reduction, and finally, degradation of the fillet properties due to lipid depletion and muscle atrophy during gonad maturation (Ahongo et al., 2021 ; Aussanasuwannakul et al., 2011 ; Cleveland et al., 2012 ). As a result, sexually mature individuals are downgraded at harvest (Norwegian Directorate of Fisheries, 2023 ), and the fish can only be discarded or sold as a byproduct at a significantly lower price, thus causing economic losses to the company. Based on this, the interest of trout producers in understanding what factors are contributing to the occurrence of sexual maturation during the production period has risen, in order to minimize the issue. Unfortunately, early maturation of rainbow trout has not been studied in the context of modern intensive aquaculture production as thoroughly as Atlantic salmon has been during the last decade (Fjelldal et al., 2018 , 2011 ; Fraser et al., 2023b , 2023a ; Imsland et al., 2014 ; Melo et al., 2014 ; Pino Martinez et al., 2023a , 2023b ). Certainly, the reproductive development of rainbow trout is well understood in both males and females, since this species has been widely used as a model organism (Billard, 1992 ; Bon et al., 1999 , 1997 ; Choi et al., 2010 ; Davies et al., 1999 ; Magri et al., 1985 ; Sambroni et al., 2009 ; Taranger et al., 2010 ). But, despite this good knowledge on the development and physiology of the process, the way in which intensive rearing conditions applied during modern trout production influence the occurrence of sexual maturation before harvest is not as clear as for Atlantic salmon. As in Atlantic salmon, initiation of sexual maturation in rainbow trout requires the activation of the Brain-Pituitary-Gonad axis (Choi et al., 2010 ; Davies et al., 1999 ; Taranger et al., 2010 ). Various environmental (water temperature, photoperiod, feed availability) and internal factors (age/body size, energy availability, genetic determination) determine the occurrence of this activation (Davies et al., 1999 ; Davies and Bromage, 2002 ; Taranger et al., 2010 ). When this starts, the hypothalamus sends a signal to the pituitary to initiate the production of the gonadotropins, first Follicle-Stimulating Hormone (Fsh) and then Luteinizing Hormone (Lh) (Schulz et al., 2010 ; Taranger et al., 2010 ). These hormones bind to their receptors in the gonads, inducing sex steroid production, and thus triggering both the processes of spermatogenesis in males and oogenesis in females (Choi et al., 2010 ; Planas et al., 2008 ; Schulz et al., 2010 ). Fsh is the main initiator of the whole process and is responsible for coordinating the proliferative or mitotic phases of spermatogenesis and oogenesis; Lh in turn is secreted later, coordinating the meiotic phases of both genders and the final gamete maturation (Crespo et al., 2019 ; Nóbrega et al., 2015 ; Schulz et al., 2019 ). As a result, both testes and ovaries experience a rapid mass increase, ending up representing a significant percentage of the body weight at maturation. In Atlantic salmon, high intensity of the rearing conditions (elevated temperature, continuous light, unlimited availability of high-energy feeds) has been consistently linked to the occurrence of early maturation as post-smolt or “jacking” (Fjelldal et al., 2011 ; Fraser et al., 2023b ; Imsland et al., 2014 ; Melo et al., 2014 ; Pino Martinez et al., 2023b , 2023a ). These rearing conditions (especially temperature) are generally considered more intense in modern RAS than in FT facilities, since the high percentage of water recirculation in RAS allows to maintain a more constant environment with a more efficient use of energy than a FT (Brown et al., 2025 ; Dalsgaard et al., 2013 ). As a result, early maturation in Atlantic salmon has sometimes been linked to RAS production where constant intensive conditions are used (Crouse et al., 2022 , 2021 ; Good and Davidson, 2016 ; Pino Martinez et al., 2021 ). In addition, it has been shown that the rearing system used during the freshwater phase of salmon aquaculture (RAS or FT) exerts an important influence on several other aspects later at sea, such as their osmoregulatory capacity, physiological robustness to seasonal changes and growth during the grow-out phase in sea cages (Lai et al., 2024 ). Thus, as with the mentioned aspects, it is likely that the conditions experienced during the freshwater phase which are linked to the rearing system, also influence later the incidence of sexual maturation at sea. This has been observed in Atlantic salmon post-smolts reared at constant high temperature (15ᵒC) for an early 2-month period in freshwater (between 50–300 g), and afterwards exposed to constant 8ᵒC in a subsequent phase of the experiment; such early period at 15ᵒC was stimulatory enough to induce maturation in individuals even when the temperature was later reduced drastically to 8ᵒC for the rest of the trial (Pino Martinez et al., 2025b ). This evidences the importance that the intensity of the conditions during the juvenile period of salmon can have for their decision to mature early. Given the various similarities between Atlantic salmon and rainbow trout (Taranger et al., 2010 ), we hypothesize that the intensity of the conditions during the freshwater stage is also likely to influence the decision of rainbow trout to mature during the grow-out phase at sea. Another relevant aspect for sexual maturation is energy availability, a factor that is intrinsically linked with access to feed. In this regard, previous theoretical studies on Atlantic salmon state that, to initiate maturation, salmon must first surpass a certain energy threshold concomitantly with the occurrence of key environmental cues (i.e. increasing daylength and temperature); if this energy threshold is not met, the process of maturation is inhibited (Thorpe, 2007 , 2004 , 1994a ; Thorpe et al., 1998 ). To test such hypothesis, some authors have studied the effects on sexual maturation of reducing the energy input by restricting access to feed (i.e. providing a reduced feed ration) or offering a low-lipid diet. Generally, the use of restricted feed rations has impacted the tendency to mature, resulting in a lower proportion of individuals initiating the process (Rowe et al., 1991 ; Rowe and Thorpe, 1990 ; Thorpe et al., 1990 ; Trombley et al., 2014 ). Similarly, providing a low-lipid diet has resulted in a lower proportion of fish maturing compared to salmon fed a high-lipid diet (Jonsson et al., 2013 ). However, other studies have shown that if a strong determination to mature is induced by exposing salmon to highly stimulatory conditions (i.e. constant temperature as high as 18ᵒC), a restricted regime providing a 67% ration is not sufficient to impact significantly on the number of maturing fish (Pino Martinez et al., 2023b ). As such, the influence of a feed restriction on sexual maturation seems dependent upon other variables that can exert a larger effect on the process, like temperature or photoperiod. Therefore, clarification of its effects on rainbow trout maturation requires further research. Furthermore, similar studies applying energy restrictions are rare in large-scale commercial settings at sea, due to the obvious reduction in growth that producers may not be able to assume. All in all, the influence that the various factors manipulated in modern aquaculture facilities exert on sexual maturation of rainbow trout requires more clarity. The present large-scale study was designed to assess how the early freshwater rearing conditions first, and then the feed regime received during the period at sea, influence the occurrence of sexual maturation during the commercial production of rainbow trout. For this, groups of rainbow trout juveniles reared in two different freshwater systems (RAS or flow-through) were subjected either to 100% or 70% feed rations after transferred to sea in early spring 2024. The study took place in a sea water site in Norway during the commercial production of rainbow trout. 2. Materials and methods 2.1 Ethic statement Authors confirm that ethical policies of the journal have been followed. The trial was ethically reviewed, approved and registered by the local representatives of Animal Welfare at the Department of Biological Sciences of University of Bergen (FOTS application 30812). The study follows the ARRIVE 2.0 guidelines for reporting animal research (du Sert et al., 2020). All samplings and methods were performed in accordance with the relevant guidelines by the Norwegian Animal Research Authority (NARA). 2.2 Fish stock, experimental setup and samplings The fish used in this study was a commercial batch of 331447 mixed-sex rainbow trout of Aquagen strain. Although rainbow trout is native to North America, this strain was originated in Norway in the 80’s, as a result of selective breeding programs carried out by norwegian aquaculture researchers using broodstock imported from Denmark in the early 70’s. Over these decades, the strain has undergone selection for faster growth, improved disease resistance, better fillet quality and reduced early sexual maturation. In the study, the Aquagen commercial batch was transferred to sea cages located at the Eikebærånæ marine site (Alver municipality, Vestland, Norway) owned by Eide Fjordbruk AS. The transfer was carried out between 23-26 March. This fish stock was produced at the Sørebø freshwater facility owned by Osland Stamfisk AS (Høyanger municipality, Vestland, Norway). At Sørebø, part of the batch (188074 trout, hatched in January 2023) was produced in a Recirculation Aquaculture System (RAS), while the rest of the batch (143373 trout, hatched in late April 2023) was produced in Flow-Through (FT). The water temperature profiles experienced by the RAS and FT groups in freshwater are displayed in Figure 1. Counting all period in the facility, mean temperatures (± standard deviation) for the two groups were similar (8.8 ± 2.6ᵒC for the RAS group versus 9.6 ± 2.6ᵒC for the FT group, p=0.075, t-test). However, from 15 December until transfer to sea cages on 23-26 March, the RAS fish experienced 11.5 ± 1.4ᵒC, compared to 7.1 ± 0.4ᵒC in the FT group (p<0.001, t-test). The whole batch was reared under constant light. Fish were fed commercial feeds adequate for their size and life stage (see Table 1). The feed was provided with automatic feeders until satiation. The trout remained at Sørebø until transfer to the sea water cages (23 to 26 March 2024). On this date, trout produced in RAS had reached a mean weight of 450 g while trout from FT had a mean weight of 179 g. Considering the hatching dates, the Specific Growth Rate (SGR, % body weight gained per day) in the facility was very similar in both groups, approximately 1.5%. Table 1. Feeds provided to the two rainbow trout stocks used in the present study, months during the freshwater phase and feed commercial suppliers. At seawater transfer on 23-26 March, the trout produced in RAS had a mean weight of 450 g and were equally distributed in cages 1, 2, 3 and 4; the trout from FT had a mean weight of 179 g and were deployed in cages 5 and 6 (see diagram in figure 2). All sea cages had square shape, with a side length of 25 m and a net of 40 m depth in the center. From seawater transfer until 22 June 2024, all cages received a full feed ration until satiation (Full). The fish received Skretting commercial feeds of different sizes according to the weight of the fish and were delivered to the fish using automatic feeders controlled by the farm system. On 22 June, the feeding ration was reduced in three cages (two from RAS and one from FT, see figure 2) down to approximately 70% ration (Restricted). This was achieved by introducing a fasting day every three days. The fish were kept under natural photoperiod and temperature throughout the trial. Figure 3 displays the daily water temperatures recorded at the sea site at 5, 15 and 25 m depth during the study period. No additional light was used in the cages at any time. Four samplings were carried out (7 June, 21 August, 25 September and 8 October/15 December). In the first three samplings, 10 fish per cage (irrespective of sex) were sampled, measuring morphometric data and collecting pituitary samples. The last sampling was carried out in two different and separate days, due to the different harvest dates projected for the RAS and FT cages as a result of their different growth rates. Thus, on 8 October, 20 fish per cage were collected from each of the four RAS cages, while on 15 December, 20 fish from each of the two FT cages were collected. During this last divided sampling, only morphometric data were collected, but not pituitaries. In all samplings, individuals were euthanized with an overdose of benzocaine (Benzoak vet.® 20%, ACD Pharma AS, Norway). Fork length and body weight were measured to the nearest 0.1 cm and g. Fish were then dissected, their sex registered and gonads extracted and weighed to the nearest 0.001 g in order to calculate gonadosomatic index. The pituitary gland was excised, placed in RNAlater®, incubated overnight at 4 ᵒC and stored at −80 ᵒC until analysis. However, pituitaries from the FT groups could only be collected in June and September (samplings 1 and 3) but not in August (sampling 2). In all samplings, fish welfare was assessed by scoring skin ulcers, eye damage, dorsal and caudal fin damage, spine deformities and presence of nephrocalcinosis in the kidney. Scoring of these welfare issues was done according to the system publisehd by Noble et al. (2020). Key Performance Indicators (KPI) for each cage during their period at sea, including mortality (%), Feed Conversion Ratio (FCR) and SGR (% body weight gained per day) were provided by the company. Fish in the group RAS-Full was harvested during October, fish in RAS-Restricted during November, and fish in both FT cages were harvested in late February. The harvest reports provided dates, mean weight, and proportion of fish included in three different quality categories used to grade farmed salmonids in Norway (Sommerset et al., 2023). These categories determine the purpose for which the product can be used and its market price. In the present case, the categories are: Superior: Top quality product, without any defect in skin, flesh or pigmentation, no wounds or deformities, normal shape, no sexual maturation Production: Product with minor or moderate defects, i.e. scale loss, shallow skin damage, or minor melanin deposits in flesh, no sexual maturation. Production B: Product with severe faults, such as significant scale loss or wounds, deformities, lack of firmness in flesh, large melanin deposits, sexual maturation. It can still be used for human consumption but requires a high degree of processing. Some fish will also be discarded for human consumption if they have massive wounds, necrosis, active infections, or very advanced sexual maturation. 2.3 Calculations Morphometric parameters like condition factor and GSI were calculated for each fish using values measured in samplings: Condition factor: K = 100 × Wbody / FL 3 , where Wbody is the body weight (g) and FL is the fork length (cm). Gonadosomatic index: GSI (%) = 100 × Wgonads / Wbody, where Wgonads is the weight of the gonads (g) and Wbody is the body weight (g). KPIs like FCR and SGR at sea were calculated by the company with the following formulas: FCR = F / Biomass gain, where F is the total amount of feed provided (kg) during a period to obtain the correponding biomass gain (kg). SGR (% body weight gained per day) = [(ln(W 2 ) – ln(W 1 )) × 100] / t, where W 2 is the final mean body weight (g), W 1 is the initial mean body weight (g), and t is the time between the weight measurements (in days). 2.4 Identification of early maturing individuals Early maturing individuals were identified based on visual inspection of gonads (Figures 4A – ovaries, and 4B - testes), external features (darker coloration, development of kype in males – Figure 4C) and increased GSI. A GSI threshold above which individuals are engaged in sexual maturation has been established for Atlantic salmon males at around 0.06%-0.09% (Ciani et al., 2021; Fraser et al., 2023b; Melo et al., 2014; Pino Martinez et al., 2023b; Rowe et al., 1991; Skjold et al., 2024), while for females it is estimated around 0.2% (Thorpe, 1994a). For rainbow trout such threshold is not clear. In this study, we decided to use a maturity threshold of GSI > 0.16% for both males and females. This choice was based on the clear bimodality observed in the GSI distribution (see results section) and on visual evidence of gonadal development such as increased size, thickness (both sexes) and granulosity (ovaries) (see Skjold et al., 2024, for a similar system for Atlantic salmon). 2.5 Laboratory analyses Gene transcription of pituitary gonadotropins fshb and lhb Real-time Quantitative Polymerase Chain Reaction (RT-qPCR) was used to analyze relative transcription of the genes fshb and lhb in the pituitary, responsible for production of the beta subunits of the gonadotropins follicle-stimulating hormone (Fsh) and luteinizing hormone (Lh). These hormones together with sex steroids initiate and coordinate the progression of gonad maturation (Taranger et al., 2010). To carry out this analysis, pituitaries collected in RNAlater during samplings were first homogenized to extract total RNA with TRI Reagent (Sigma-Aldrich) according to the manufacturer’s instructions. Samples were then treated with TURBO DNase-free kit (Ambion Applied Biosystem, CA, USA) to remove any remaining genomic DNA. The total RNA concentration was quantified both before and after DNase treatment with the NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Termo Fisher Scientific), and purity was confirmed by 260/280 and 260/230 ratios above 1.8. Then, total RNA (1000 ng) was reverse-transcribed to cDNA using the Superscript III Reverse Transcriptase kit (Thermo Fisher) and Oligo(dT)20 Primer following the manufacturer’s instructions. Finally, gene transcription was analyzed in a Bio-Rad CFX96 Touch Real-Time PCR detection system using 6.25 μL iTaq Universal SYBR® Green Supermix (Bio-Rad, USA), 2.5 μL of 1:20 diluted cDNA and 0.25 μL of each forward and reverse primers (10 μM). The RT-qPCR protocol consisted of 3 min at 95 °C followed by 34 cycles at 95 °C for 15 s and 60 °C for 1 min. Samples were run in duplicate with the oligos listed in Table 2. Duplicates in fshb and lhb plates with a CV > 1.5 % were not considered for analysis. Before RT-qPCR, a standard curve was built for each pair of primers to estimate primer efficiency, using six 2-fold dilutions (starting from 1:5) obtained from a cDNA pool of 36 samples. The efficiency (E) was estimated with the formula E = 10( −1/slope ), and this slope was obtained from the plot of log (cDNA concentration of the pool) versus threshold cycle (Ct) values. A melting curve was used to confirm specificity of the amplicon. After RT-qPCR, relative transcription of the genes was calculated with the efficiency-corrected method using a geometric mean of ef1a and b-actin as reference genes (Olsvik et al., 2005). Table 2. Oligo sequences for RT-qPCR in rainbow trout pituitaries. Gene Primer sequence (5’ – 3’) Accession No. Reference ef1α CCCCTCCAGGATGTCTACAAA AF498320 Morro et al., 2019 CACACGGCCCACGGGTACT b-actin TCCTTCCTCGGTATGGAGTCTT AJ438158 Baron et al., 2005 ACAGCACCGTGTTGGCGTACAG fshb GCGAAACAACGGACCTGAACTAT AB050835.1 Baron et al., 2005 GGACCACTCCTTGAAGTTACACA lhb CTGCGTCACCAAGGAGCCG AB050836.1 Adapted from Baron et al., 2005 GACAGTCAGGTAGGCGGATCG 2.6 Statistical analyses Fisher’s Exact Test for count data was used to statistically compare the proportion of maturing and immature rainbow trout within different rearing systems, feed regimes, and sexes. This test was also performed for different rearing systems within samplings, and for treatments within males and females. The test was also used to assess differences in incidence of dorsal and caudal fin damage, and of nephrocalcinosis. Linear models (LM) were used to find out statistical differences in morphometrics, GSI and pituitary fshb transcription between treatments at given samplings and within treatments over time. The GSI and fshb transcription models were fitted considering both sexes together, and also for males and females separately. Before fitting these models, the distribution of the response variables was assessed graphically and with Shapiro-Wilk’s test, while the existence of outliers was explored with boxplots. The LMs were fitted between the response and the variables “Treatment”, “Time” and their two-way interaction. The model residuals were assessed graphically to determine if assumptions like normality, linearity and homogeneity of variance were met. When models did not meet parametric assumptions, the response was log-, square-root- or inverse-transformed, the model repeated, and residuals checked again. In all cases, homogeneity of variance was also confirmed with a Levene’s test. Tukey HSD post-hoc tests were applied to reveal significant differences in the responses between treatments at given times and within treatments over time. The distribution of pituitary lhb transcription did not allow the use of parametric models, and therefore Kruskal-Wallis tests were applied at each sampling to compare statistical differences between treatments. This was done for all sexes together, and for males and females separately. Plots of response variables over time display mean ± standard error. A significance level α = 0.05 was used. All statistical analyses were performed in R and RStudio using the packages “car” (Fox and Weisberg, 2019), “ggplot2” (Wickham, 2016), “ggpubr” (Kassambara, 2017), “Rmisc” (Hope, 2013) and “emmeans” (Lenth et al., 2018). 3. Results 3.1 Sexual maturation Individuals were classified as maturing when GSI > 0.16%. This threshold was applied to both males and females, and was chosen based on the clear bimodality observed in the GSI distribution (Figure 5) as well as on visual evidence of gonad development. The bimodality was very obvious in males (Figure 5); in females it was also clear, although few individuals classified as maturing had a GSI that although higher, was close to this threshold. These females were still classified as maturing since their ovaries displayed a clear increase in length, thickness and granularity that indicated initiation of gonad development. Sexual maturation was observed similarly in various males (n=11) and females (n=13) during the trial (Table 3-top). All maturing individuals (n=24 out of 202, ~12%) appeared only in the groups transferred from RAS, none in the FT groups (0 out of 60 fish). Thus, the tendency to mature was significantly higher in RAS than in FT groups (p<0.01, Table 3-middle). The feed restriction had no influence on sexual maturation, since a statistically similar number of individuals matured in each feed regime (Table 3-bottom). First signs of sexual development were visually detected in June (Sampling 1) in some females that displayed slightly elevated GSI and increased granularity and thickness in the ovaries, although not being classified as maturing (see Table 4) because their GSI was lower than 0.16%. Maturing individuals appeared in the three remaining samplings, but only in the RAS groups (p<0.01 in Sampling 3, Table 4). No difference in proportion of maturation was observed between the two RAS groups (Full and Restricted feeding, Table 5) for any sex. Table 3. Number of immature and maturing individuals of each sex (top), number of immature and maturing fish transferred to sea from FT or RAS (middle) and number of immature and maturing fish within each feeding regime (bottom). Asterisks (**) indicate significant differences after the Fisher’s Exact Test for count data (p<0.01). Table 4. Number of immature and maturing individuals from each FW system that were collected in each of the four samplings. Asterisks (**) indicate significant differences after the Fisher’s Exact Test for count data (p<0.01). Table 5. Number of immature and maturing males and females within each treatment. 3.2 Gonadosomatic index Considering both sexes together, GSI was significantly dependent only upon treatment (p < 0.05). Due to the high standard error of the GSI in samplings 2, 3 and 4 (Figure 6A) that was caused by the relatively low number of maturing fish (with high GSI) versus immature fish (with very low GSI), no significant differences between treatments at given samplings or within treatments were observed over time. However, the mean GSI trend over time switched from similar values in the RAS and FT groups in June before introducing the feeding regimes, towards an increase in the two RAS groups compared to the two FT groups as time passed. When considering females alone (Figure 6B left), GSI was dependent upon treatment and its interaction with time (both p < 0.01). Although no female was classified as maturing in sampling 1 in June, mean GSI was significantly higher in individuals from RAS than from FT (p < 0.01). Then in sampling 2, GSI in females from FT-Full was significantly lower than in the rest of groups (all p < 0.01). In sampling 3 and 4, however, despite the presence of maturing females with elevated GSI in the two RAS groups, the statistical analysis did not show any significant difference between treatments. Differences in GSI over time within treatments were not present either, due to the relatively low abundance of maturing females with elevated GSI. Finally, when considering males alone (Figure 6B right), GSI was significantly dependent only upon treatment (p < 0.01). As with both sexes together, no significant differences between treatments at given samplings or within treatments over time were observed; however, the increasing trend in the RAS treatments over time contrasted with the stable GSI levels in the FT treatments. Male and female individuals with GSI above the chosen threshold value of 0.16% (dashed horizontal red line in Figure 6A and B) were present in samplings 2, 3 and 4 only in the RAS treatments. 3.3 Transcription of gonadotropins fshb and lhb in the pituitary Pituitary fshb transcription was dependent upon treatment and time (both p < 0.001). In June, before any fish was classified as maturing according to their GSI, mean pituitary transcription of fshb was higher in individuals transferred from RAS than in those from FT (Figure 7A – both sexes together, and 7B – females on the left and males on the right). Afterwards, in August and September (samplings 2 and 3), more individuals from RAS origin (both males and females) displayed upregulations in pituitary transcription of fshb , indicative of progressing sexual maturation. This resulted in a significant increase in mean fshb transcription in both groups from RAS (both p < 0.05) irrespective of the feed regime. The same was observed when considering males alone (both p < 0.05) but not when analyzing females alone, although the trends over time in both sexes were similar. In contrast, pituitary fshb transcription remained very low in all individuals transferred from FT, independently of feed regime and sex. The patterns observed in pituitary lhb transcription were similar to those in fshb transcription. Pituitary lhb (Figure 8A) was significantly upregulated in June only in fish from RAS (p < 0.001), before any of them was classified as maturing by GSI. This was primarily caused by females from RAS (p < 0.01) but not by males (Figure 8B). In contrast, lhb remained very low in fish from FT. In subsequent samplings, pituitary lhb was upregulated in some male and female individuals from RAS that were maturing in both feeding regimes, but this did not result in significant differences between groups. In contrast, all individuals from FT in both feed regimes maintained very low levels of lhb transcription in September (sampling 3). 3.4 Morphometrics Body weight was significantly dependent on treatment, time and their interaction (all p < 0.001). At transfer to sea in late March 2023, the mean body weight was larger in the fish from RAS (450 g) than in the fish from FT (179 g). These differences were maintained and expanded throughout the trial, with RAS fish displaying larger body weight than FT fish in all samplings (Figure 9A). This resulted in RAS groups reaching harvest size much earlier (Oct-Nov 2024) than the fish from FT (harvested in Feb 2025). The feed restriction also tended to affect body weight, as the two feed-restricted groups displayed lower mean weight than their counterparts receiving a full feed ration, although this difference was not statistically significant. Condition factor was significantly dependent upon treatment, time and their interaction (all p < 0.001). Condition factor generally ranged between 1.4 - 2.2 and tended to be higher in the RAS groups than in the FT groups throughout the trial (Figure 9B). Within fish from the same origin, those receiving a restricted ration tended to have a lower condition factor than their counterparts fed a full ration, but these differences were not statistically significant. 3.5 Welfare indicators Throughout the trial, skin ulcers or eye damage were not observed. Generally, the fish evidenced very good skin health (see Figure 4C). In contrast, dorsal and caudal fin damage was common in all groups, although more prevalent in fish from RAS origin (Table 6). Thus, low-intermediate scores of dorsal fin damage (1 and 2), as well as intermediate-severe scores of caudal fin damage (2 and 3) were more prevalent in groups from RAS origin than in those from FT origin (all p<0.001, Table 6). Table 6. Number of fish from RAS and FT displaying each score of dorsal fin damage (top-left) and caudal fin damage (top-right). Number of fish within each experimental group displaying each score of dorsal fin damage (bottom-left) and caudal fin damage (bottom-right). Asterisks (***) indicate significant differences after the Fisher’s Exact Test for count data (p<0.001). Nephrocalcinosis was present in rainbow trout from both RAS and FT, in a similar proportion in the trial (23% and 20% of the fish respectively), and a similar proportion of individuals was found at each severity score (Table 7-top, p=0.27). Something similar occurred between feed regimes, (Table 7-bottom), although with a tendency towards less presence of nephrocalcinosis in restricted feeding groups (p=0.18). Table 7. Number of fish from RAS and FT displaying each score of nephrocalcinosis (top). Number of fish from Full and Restricted feeding regimes displaying each score of nephrocalcinosis (bottom). Caudal deformity was observed in a total of 21 individuals that were collected throughout the trial (Table 8). These individuals were all from RAS origin, while none from FT origin displayed this deformity. Table 8. Number and percentage of fish within each sampling displaying caudal deformity considering the group from RAS origin and from FT origin. 3.6 Key Performance Indicators Table 9 presents a summary of KPIs in each cage during their corresponding period at sea (between transfer and harvest). Mortality was roughly similar in RAS-Full and RAS-Restricted, slightly higher in the FT groups. Feeding efficiency was better in the Full ration groups (lowest FCR), and worse in the Restricted feeding groups (highest FCR). Finally, SGR was the best in the RAS-Full treatment, and similar and lower in RAS-Restricted and the two FT groups. Table 9. Summary of KPIs, including mortality, FCR, and SGR, in the six study cages during their corresponding period at sea (starting at seawater transfer and ending at harvest) . 3.7 Harvest results Table 10 presents a summary of the harvest results. The groups from RAS were the first harvested at an average weight of 4.2-4.3 kg, but there was almost a month delay between RAS-Full (210-212 days at sea, harvested in October 2024) and RAS-Restricted (228-244 days at sea, harvested in November 2024). In contrast, the two groups from FT had to wait three more months (until February 2025, 330-338 days at sea) to be harvested at a similar weight. The FT groups and RAS-Full had a high and roughly similar proportion of superior fish (87-89%), in contrast to RAS-Restricted that contained around 80% of superior fish. Unfortunately, the % of fish within Production B or discarded that was due to early maturation was not specified in the harvest reports; however, this could not be very high, since the sum of these two numbers together was never higher than 3.8% (cage 3, RAS-Restricted). Table 10. Summary of results after harvesting the rainbow trout groups used in the present study. Cage, treatment, days in the sea, harvest period, mean weight of the fish harvested, and proportion of fish within each of the quality categories are displayed. Days in the sea were calculated using the day of seawater transfer as initial date (23 rd March) and the middle day of the harvest period for each cage as the final date. 4. Discussion The present study aimed to assess the influence of both, the freshwater rearing system, and the feed regime provided during the grow-out phase, on the incidence of sexual maturation of rainbow trout in sea cages in Norway. Results showed that the freshwater rearing system was the determinant factor for the occurrence of sexual maturation at sea, irrespective of the feed regime. Apart from maturation, the freshwater rearing system also influenced growth, the incidence of some welfare issues, and mortality, while the feed regime exerted a modulatory effect on growth, condition factor, feeding efficiency or harvest results, but not on sexual maturation. However, stating that “the rearing system used during the freshwater phase” was the main factor influencing sexual maturation at sea is too unspecific. The concept “freshwater rearing system” is too wide and general. It comprises or is related to many different parameters including temperatures, photoperiod, feeds used, growth rate attained during that period, water quality parameters, or even size reached at transfer to sea as well as season of this transfer. As such, an adequate discussion on the effects of the freshwater rearing system on sexual maturation at sea requires taking all these aspects into consideration. 4.1 The freshwater rearing system appeared determinant for trout maturation at sea Rainbow trout produced in RAS during the freshwater phase showed more tendency to mature in the sea (12% of fish maturing) than trout produced in FT (0% maturation). This is consistent with previous research in Atlantic salmon, which has linked production in RAS with occurrence of early maturation (Crouse et al., 2022 ; Fraser et al., 2023b ; Good and Davidson, 2016 ; Pino Martinez et al., 2021 ). In those studies, elevated constant water temperatures and higher growth rates in RAS were the main suspicious factors linked to the higher occurrence of sexual maturation in RAS-produced fish. The constant and elevated water temperatures are known to induce an early activation throughout all components of the BPG axis in Atlantic salmon, evident at pituitary (gonadotropin upregulation, see Pino Martinez et al., 2025b , 2023a , 2023b ) and gonad level (downregulation of inhibitory factors, early signs of mitotic proliferation, see Pino Martinez et al., 2024 , 2022 ). In addition, such higher and constant temperatures used in RAS induce higher growth rates (Handeland et al., 2008 ), and as a result the fish can attain sufficient size or energy resources for sexual maturation earlier (Thorpe, 2007 , 2004 ). All considered, fish reared at elevated constant temperatures are more likely to have further developed their reproductive capacity and acquired energy stores to sexually mature earlier compared to fish reared at lower temperature. However, paradoxically, in our study the rainbow trout from RAS experienced a similar, even slightly lower mean temperature than the fish from FT during their period in the land-based facility (8.8 ± 2.6ᵒC in RAS, versus 9.6 ± 2.6ᵒC in FT). In addition, the growth rates during the freshwater period were very similar in both groups (~ 1.5% of body weight gained per day). This appears to contradict our findings (maturation observed only in rainbow trout from RAS) and the previously provided knowledge about conditions in RAS and their relation to sexual maturation in salmonids. Because of this, we must then pay closer attention to the details. Looking at the figure of the two freshwater temperature profiles over time, it is evident that they were similar after hatching, displaying only a 3-month phase or delay between them. The major difference, however, was in the temperature profiles experienced during the last 3–4 months, until both groups were transferred to seawater in late March. From December 2023 to late March 2024, the RAS fish experienced a significantly higher mean temperature than the FT fish (11.5 ± 1.4ᵒC versus 7.1 ± 0.4ᵒC respectively), and this temperature in RAS followed an increasing trend over time. It is likely that this higher and increasing temperature during the last 3 to 4 months induced a higher determination in the RAS group to mature later at sea, due to both higher stimulation of the BPG axis and higher growth rate during this last period. As mentioned, it is well known that elevated temperatures increase growth rates and induce a tendency to mature earlier in Atlantic salmon and other salmonids (Atkinson, 1994 ; Fjelldal et al., 2011 ; Pino Martinez et al., 2023b ; Thorpe, 2004 , 1994b ). Specifically in rainbow trout, some studies have shown higher proportion of maturation in individuals experiencing higher temperatures. For example, Wilkinson et al. ( 2010 ) reported that elevated “winter-spring” temperatures increased the incidence of sexual maturation in female rainbow trout of up to 600 g, although with the photoperiod playing a more important role in triggering the process. Similarly, after a series of trials with temperature and photoperiod, Davies and Bromage ( 2002 ) found that generally, female trout that experienced elevated mean temperatures matured in higher proportion, although with minor differences and with photoperiod acting as the main regulatory cue. This considered, the period of 3 to 4 months at an elevated (and increasing) temperature that trout in the RAS facility experienced pre-transfer may have been sufficient to induce that a certain percentage of them matured later at sea. This is also consistent with findings in Atlantic salmon reported by Pino Martinez et al. ( 2025b ), who found that an early period of 2 months at constant high temperature (15ᵒC) was stimulating enough to later induce early maturation in post-smolts, even if they had been subsequently subjected to a severe drop in temperature (to 8ᵒC). However, while elevated temperature generally seems to stimulate sexual maturation in rainbow trout (Davies and Bromage, 2002 ; Wilkinson et al., 2010 ), this link does not seem as clear as in Atlantic salmon. This is illustrated by studies with rainbow trout that reported three times lower proportions of maturing females at elevated (20ᵒC) than at lower (12ᵒC) temperature (Zelennikov and Golod, 2019 ), or lower proportion of male maturation as well as detrimental effects on spermatozoa development also at elevated temperature (Butzge et al., 2021 ). As a result, more research is needed to understand how the constant high temperatures typical of land-based facilities impact on rainbow trout maturation. A projected fully controlled, small-scale experiment with smaller rainbow trout grown under the appropriate temperature conditions will help determine the specific effects that elevated temperatures have on the decision to mature in this species. Other factors during the freshwater phase of our study, like photoperiod or feeds, are unlikely to have exerted any important influence on the differences in maturation observed at sea between the two groups (RAS and FT), since the mentioned factors were very similar for both groups 4.2 Mechanisms behind sexual maturation of rainbow trout from RAS The inducing effect that the RAS system had on the reproductive axis of rainbow trout was noticeable already in the first sampling in early June, at pituitary and gonad level. At that time, although we did not classify any fish as sexually maturing based on GSI, we visually detected early signs of ovary development in some females from RAS; this, in turn, resulted in significantly elevated female mean GSI (although still under the established threshold value for maturity of 0.16%). But more importantly, on the same date, we observed a clear upregulation in pituitary transcription of the gonadotropins (primarily fshb but also lhb ) in individuals of both genders. Gonadotropins are key hormones for initiation and coordination of sexual maturation, regulating the process in both genders from the initial mitotic phase of gonad development to sex steroid production and gametogenesis (Choi et al., 2010 ; Schulz et al., 2010 ; Taranger et al., 2010 ). The gonadotropic upregulation observed only in the RAS groups suggests that only individuals from RAS were ready or “had made the decision” to mature. This is, there was a differential activation of gonadotropin production in the pituitary depending on the early conditions experienced. Considering the relatively low proportion of maturing fish in the full trial and in the last sampling, it is possible that not all individuals showing that early upregulation of fshb transcription would have completed sexual maturation. An increase in Fsh production can be seen in salmonids in response to inducing environmental cues like increasing photoperiod, but sometimes this does not result in sexual maturation; this is a phenomenon known as “dummy run” (Maugars and Schmitz, 2008 ; Okuzawa, 2002 ; Prat et al., 1996 ) and rather appears as if individuals are “testing” the capacity of their reproductive system. But in any case, the fact that such upregulation was not present in FT groups suggests that these fish did not even need to test their reproductive capacity in June, meaning that they had already discarded or inhibited the process of sexual development (Thorpe et al., 1998 ). In subsequent samplings, more individuals and with a more pronounced upregulation of both gonadotropins were found only in the RAS group, never in the FT groups. This was consistent with the increasing number of individuals of both genders displaying increased GSI, which is highly indicative of gonad development and sexual maturation. The trigger for this reproductive activation was most likely the increasing photoperiod that the fish experienced from March until June, as well as the steep rise in temperature during the same period (from 6ᵒC to 18ᵒC in surface water up to 5 m depth). Rainbow trout is very responsive to increasing photoperiod, maybe more than to temperature, and several studies have demonstrated that the increasing daylength typical of spring has a very strong effect in entraining sexual maturation in this species (Bon et al., 1999 ; Choi et al., 2010 ; Davies et al., 1999 ; Davies and Bromage, 2002 ; Taylor et al., 2005 ; Wilkinson et al., 2010 ). Similar findings have been consistently reported in Atlantic salmon (Fraser et al., 2023b ; Pino Martinez et al., 2023a ; Skjold et al., 2024 ). As such, the combination of a more advanced developmental status and a stimulated BPG axis in RAS-produced trout, together with a spring transfer to seawater (which entails increasing daylength and temperature) seemed to have caused that a number of individuals in this group, but not in the FT groups, ended up sexually maturing during the production cycle. 4.3 May the different size at transfer have confounded our results? Despite the consistent differences in the maturation responses between RAS and FT rainbow trout, we cannot overlook that other factor, which in our study was intrinsically linked to the freshwater rearing systems, might be confounding our results. This factor is age/fish size at transfer. Thus, trout reared in RAS hatched in early January 2023 and weighed 450 g at seawater transfer, while trout reared in FT hatched in late April 2023 and weighed 179 g. This size difference at transfer could be important for maturation, in addition to the different rearing conditions experienced. Size can be seen as a proxy for development and energy availability, and thus larger fish most likely have had the opportunity to further advance their reproductive development and accumulate more energy than smaller fish. As a result, larger fish are more likely to initiate sexual maturation than smaller fish in response to stimulating cues like increasing daylength and temperature. This is supported by findings in McMillan et al. ( 2012 ), who reported that the probability of male rainbow trout to mature early increased a 49% for every 5 mm increase in length, and 33% for each 0.5% increase in lipid content. Further support can be found in Taylor et al., ( 2008 ), who found that both size and growth rate in late spring-early summer was determinant for female rainbow trout to initiate sexual maturation. Finally, in a study with sibling Atlantic salmon, Pino Martinez et al., ( 2025a ) subjected groups of different sizes to “winter signal” photoperiods for 5 weeks (LD12:12, introduced at 70 g, 110 g, 180 g or no photoperiod changes), followed by daylength increases to constant light. The authors reported that the larger Atlantic salmon were more likely to become jacks in response to the photoperiod change, while maturation was not present in fish exposed to a winter signal at a smaller size. Other studies that have suggested a larger probability of maturation in larger than in smaller salmon in response to a photoperiod cue are Fraser et al. ( 2023b , 2019 ) or Melo et al. ( 2014 ). While in our study the conditions during the freshwater phase surely influenced the occurrence of maturation in RAS-produced trout and not in FT trout, the different age/size at transfer to sea water must also have contributed to this. To clarify the influence of size at transfer on maturation, a projected study will transfer to sea water two groups of rainbow trout that differ only in size, but not in early rearing conditions. 4.4 The season of seawater transfer must have influenced maturation to a high degree In addition, the fact that the fish were transferred to sea water in early spring, when daylength and temperature start to rise, surely influenced the occurrence of maturation in the RAS groups. Rainbow trout maturation is very responsive to photoperiod and increasing daylength, and therefore if the groups had been transferred in the fall instead of the spring, probably sexual maturation would not have been observed until the following spring (Bon et al., 1999 ; Choi et al., 2010 ; Davies et al., 1999 ; Davies and Bromage, 2002 ; Taylor et al., 2005 ; Wilkinson et al., 2010 ). The relevance of this combined effect of “environmental cues at transfer – size – freshwater origin” on maturation is also obvious in the fact that FT groups did not show any sign of sexual maturation at any time, even when they reached the same market size as the RAS groups (but in February 2024, this is, 3 to 4 months later). This suggests that size itself was not the driving factor for sexual maturation, and instead this was triggered by the combination of previous stimulatory conditions experienced, large size, and exposure to appropriate changes in environmental factors like photoperiod and temperature in spring. This highlights the potential importance of the season of seawater transfer to induce or skip sexual maturation during the production cycle. With high probability, if kept until summer 2025 in the cages instead of only February (experiencing increasing daylength and temperatures), individuals in the FT groups would also have initiated maturation; however, in that case, maturation in FT trout would be occurring one year later than in RAS trout, which aligns with the hypothesis of gonadal growth inhibition put forward for Atlantic salmon by Thorpe et al. ( 1998 ). 4.5 A restricted feed ration did not help reduce incidence of maturation As previously mentioned, providing a restricted feed ration during the grow-out phase did not influence the proportion of rainbow trout maturing in the RAS treatments, but it reduced fish growth and negatively impacted feed utilization and harvest results. Similar findings were reported by Pino Martinez et al. ( 2023b ) in a study with Atlantic salmon post-smolts reared under different temperatures and feed regimes. In that study, a 67% feed ration did not induce a significant reduction in the proportion of maturing males reared at constant high temperatures (18ᵒC, highly stimulatory for sexual maturation), and physiologically only modulated the process by slightly reducing pituitary fshb transcription and sex steroid production. In contrast, at lower temperature (12ᵒC, less stimulating for sexual maturation) the effect of the feed restriction was comparatively much larger, significantly affecting proportion of maturation and its physiological regulation, growth and body condition. But apart from these effects related with freshwater rearing conditions/temperature, the absence of effects of the feed restriction may be related to the date in which it was introduced (late June). Before that date, we had already carried out the first sampling (in early June) in which, as mentioned, signs of sexual activation had been observed. This means that when the feed restriction commenced, individuals from RAS appeared already sexually activated, and this is consistent with the time of the year and environmental conditions present then (with a peak in daylength and temperature). If the decision to mature had already taken place in some individuals and therefore the process was ongoing, it would be expected that a late introduction of a caloric restriction would not arrest it. This is because the process may be reversible only in the very early stages (Schulz et al., 2010 ) and once it is triggered, it tends to gain evolutionary priority as a life strategy (Thorpe, 1994a ; Thorpe et al., 1998 ), progressing even if the energy input is reduced as shown by Pino Martinez et al. ( 2023b ). Thus, a feed restriction should have been introduced earlier, possibly during the freshwater stage, in order to induce accumulative effects to influence maturation later at sea more clearly. All considered, we do not perceive a reduction in feeding intensity as a good solution to reduce the risk of early maturation in rainbow trout during the seawater phase. Our view is aligned with previous studies (Crespo et al., 2019 ; Pino Martinez et al., 2023b ) that reached similar conclusions; furthermore, our conclusion is also related to the negative effects on growth, feed utilization and harvest results that the feed restriction induced in this study and that are discussed in the last sub-section. 4.6 Female and male rainbow trout have a similar tendency to mature early Based on our results, the tendency to mature during the grow-out phase appears similar in male and female rainbow trout. This seems different to Atlantic salmon, whose early maturation during aquaculture production is primarily a male phenomenon (Fjelldal et al., 2011 ; Pino Martinez et al., 2023b ). The absence of early maturation in female Atlantic salmon has been linked to the higher energy investments required to produce eggs compared to sperm (Adams and Thorpe, 1989 ; Schulz et al., 2006 ) and thus it is rare to find maturing females before they reach harvest size (Taranger et al., 2010 ). A revision of the fish weight attained by Atlantic salmon in a number of small-scale studies on early maturation published in the last decade (Fjelldal et al., 2011 ; Fraser et al., 2023b , 2019 ; Imsland et al., 2014 ; Melo et al., 2014 ; Pino Martinez et al., 2025b , 2023a , 2023b ), may suggest that the size salmon reached (roughly 100–800 g) would be insufficient for females to mature. In contrast, in the present study, the female trout maturing in sampling 2 (late August) weighed 2.5 kg on average, and 3.1 kg in sampling 3 (late September), a much larger size. Similarly, a study with Atlantic salmon where females reached a size in that range also reported female sexual maturation, although in less numbers than males (Kadri et al., 1996 ). Thus, while there might be some specific differences between Atlantic salmon and rainbow trout females in their tendency to mature early, in our study the large size that trout achieved in summer probably contributed significantly to the occurrence of female maturation. Whether sexual maturation can also be consistently induced in female rainbow trout at smaller size (0.5-1 kg) as occurs with salmon males (“jacks”) will be discerned in future small-scale trials where fish will be subjected to stimulatory conditions for maturation at a smaller size. 4.7 Aquaculture implications of using a specific freshwater rearing system and feeding regime at sea Finally, the convenience of using a specific freshwater rearing system and a restricted feed regime during the grow-out phase of trout aquaculture must be discussed from a production perspective, taking into consideration risks associated to sexual maturation, fish performance and its implications. On the negative implications linked to RAS production, the RAS groups showed a certain tendency to sexually mature, and displayed a higher incidence of welfare issues (i.e. dorsal and caudal fin damage, spinal deformity). As stated, a high incidence of sexual maturation is detrimental for the producer’s economy since that fish is downgraded or discarded at harvest. Welfare issues are also negative for the fish, for public perception of the industry, and represent a risk of economic losses for the producer (i.e. downgrading of deformed fish). However, on the positive side, trout from RAS grew faster in the sea, displayed lower mortality, and was harvested 3 to 4 months earlier than trout from FT. As a result, by stocking RAS-produced rainbow trout, the company had a market-size product available earlier and shortened the production period, thus reducing potential risks linked to a long seawater residence such as delousing, disease outbreaks, etc. These positive and negative implications represent a trade-off that farmers must consider when choosing their ideal early rearing system. Producers need to analyze the benefits (i.e. fast growth, less mortality, shorter period at sea) obtained from rearing juveniles in intensive systems like RAS, and weigh them against the economic risks linked to the potential occurrence of early maturation or welfare issues at sea that may result in the downgrade of a percentage of fish at harvest. In relation to the choice of feed regime at sea, our results indicate that providing a full feed regime was more efficient than a restricted ration. Irrespective of the freshwater rearing system of origin, the groups that received a full regime displayed faster growth, higher feeding efficiency and better harvest results compared to their counterparts receiving a restricted ration. This implies that by providing a full ration, a market-size product was available earlier, less feed resources were wasted, and higher income was obtained due to harvesting a higher share of trout with superior quality. In addition, the restricted ration did not produce the results that were expected in terms of sexual maturation, this is, did not reduce its incidence, and had hardly any other beneficial effect. All considered, some discussion among producers may occur about the ideal freshwater system to produce rainbow trout juveniles, and the best choice will be dependent on the many aspects discussed and others (i.e. fish size at transfer to sea, season of transfer, temporal changes in trout market price, etc.). However, in regard to the choice of feed regime, very few reasons seem to justify providing a restricted ration for the whole duration of a production cycle. 5. Conclusion The intensity of the rearing conditions during the freshwater phase can have an important influence on the probability of sexual maturation of rainbow trout later at sea, affecting both males and females. Thus, fish produced in RAS under elevated temperatures may later mature at sea in higher proportion, due to their more advanced development and growth rate, that result in the activation of the reproductive axis. But maturation may also occur in FT-produced juveniles if similar conditions are applied. Other factors like body size at seawater transfer and the season of transfer will also contribute to initiating or inhibiting sexual maturation. Restricting the feed ration seems unlikely to exert a major effect on the process, maybe unless introduced much earlier than maturation is initiated. Producers must assess the benefits of using intensive conditions in freshwater (i.e. increased growth) against the economic risks linked to higher probability of early maturation. Declarations Author contribution SH and Eide Fjordbruk AS established the project, agreed on the funding and designed this study. EPM, JISM, CP and SH performed the samplings. CP carried out lab analyses. EPM performed data analysis, drafted and wrote the manuscript. SH provided assistance during the writing process. All authors critically reviewed the manuscript and approved the final version. Funding This study was funded through a research concession granted by the Norwegian Directorate of Fisheries (Fiskeridirektoratet) to Eide Fjordbruk AS and University of Bergen. Data availability statement The data supporting the findings of this study are available from the corresponding author, upon reasonable request. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could influence the work included in this paper. Acknowledgements The authors thank Erlend Eide, Nikolai Eide, Stein Inge Holstad and all the staff at Eide Fordbruk AS’s marine site Eikebærånæ, for their continuous support to this research. References Aas, T.S., Åsgård, T., Ytrestøyl, T., 2022. 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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-9042484","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":608289586,"identity":"0c51c23d-c080-48cf-b481-592dccc45283","order_by":0,"name":"Enrique Pino-Martinez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYJCCA2CSGYg/MEgkkKaFcQaxWuCAmYeBgbAWfunehwfettkwmLPzHnxs22aRxyDdfACvFsk5xw0Ozm1LY7Bs5ks2zm2TKGaQOYbfJoMbaQyHedsOMxgc5jGTBmpJbJDIMcCrxR6i5T9EiyUxWgwkwFoOQLQwEqNFAmjLwTnnknksm3mMDXvOSRSzSaTh9wv/jDTmD2/K7OTM+c8YPvhRVpfHL5F8AK8WMABGBw/cMWyE1UO0MOB3/ygYBaNgFIxoAAAV8DyD9nfOGwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Bergen","correspondingAuthor":true,"prefix":"","firstName":"Enrique","middleName":"","lastName":"Pino-Martinez","suffix":""},{"id":608289587,"identity":"1af5bd7d-1a21-442e-9692-197b42379369","order_by":1,"name":"Cindy Pedrosa","email":"","orcid":"","institution":"University of Bergen","correspondingAuthor":false,"prefix":"","firstName":"Cindy","middleName":"","lastName":"Pedrosa","suffix":""},{"id":608289588,"identity":"c9799aec-8cd6-4273-931d-fea855855caa","order_by":2,"name":"Jonas Ismael Silva-Marrero","email":"","orcid":"","institution":"University of Bergen","correspondingAuthor":false,"prefix":"","firstName":"Jonas","middleName":"Ismael","lastName":"Silva-Marrero","suffix":""},{"id":608289589,"identity":"a6edca2e-b96e-4939-a19c-b598540edcd1","order_by":3,"name":"Sigurd O. Handeland","email":"","orcid":"","institution":"University of Bergen","correspondingAuthor":false,"prefix":"","firstName":"Sigurd","middleName":"O.","lastName":"Handeland","suffix":""}],"badges":[],"createdAt":"2026-03-05 16:25:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9042484/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9042484/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105040487,"identity":"be997158-66b4-4b24-92cb-6cbc41c5c39c","added_by":"auto","created_at":"2026-03-20 07:49:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55765,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eWater temperatures experienced during the freshwater phase by the two groups of rainbow trout (FT and RAS) used in the present study and produced at Sørebø (Osland Stamfisk AS). The temperature profile in each group commences at their respective hatching dates and ends in late March 2023 when both were transferred to the marine site.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9042484/v1/b42e0b5d9ef216c0cf6b25a0.png"},{"id":105040546,"identity":"d37ac7cf-7693-47ba-b918-83682ddcbb87","added_by":"auto","created_at":"2026-03-20 07:50:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSchematic diagram of the trial run between March-December 2024 at the Eide Fjordbruk AS marine site Eikebærånæ. The yellow curve reprensents the photoperiod o duration of the day during the study. Between 23-26 March 2024, four cages (1-4) were stocked with rainbow trout raised in RAS, while two cages were stocked with trout raised in FT. Half of the cages were subjected to a feeding restriction from 22 June by introducing a fasting day every third day (fed 70% of the ration). Four fish samplings were carried out (S1-4, indicated with “X” on the photoperiod line). S4 was divided in two to sample RAS and FT groups on different and distant days due to the very different growth patterns and projected harvest dates of those groups. All fish were reared under natural photoperiod and temperature. Harvest on the group RAS-Full took place in October, of RAS-Restricted in November, and on both FT cages in February (indicated with doubled-arrows in red).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9042484/v1/f2932da3dbc95d2768024816.png"},{"id":105040485,"identity":"06b73045-79f3-4b42-ae56-38e6040b81d9","added_by":"auto","created_at":"2026-03-20 07:49:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eWater temperatures recorded at the study site Eikebærånæ at 5, 15 and 25 m depth for the period between seawater transfer (23 March 2024) until harvest of the last group (late February 2025).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9042484/v1/bbae7e316d285add836ae2aa.png"},{"id":105040545,"identity":"5f111d79-8102-49af-9cfa-46f764ac1678","added_by":"auto","created_at":"2026-03-20 07:50:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":840598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRainbow trout ovaries (A) and testes (B) collected during the trial, both ordered from mature (top) to immature (bottom). A scalpel was placed in both pictures at the bottom to provide a reference of size. Picture C displays three rainbow trout individuals: a maturing male with normal coloration but starting to develop a kype (top), an advanced maturing male with pointy snout, kype and darker coloration (middle), and an immature female (bottom).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9042484/v1/bb35efba19b031096513c9d5.png"},{"id":105040540,"identity":"a0ee0402-ad7a-4939-8b58-c74c96563787","added_by":"auto","created_at":"2026-03-20 07:50:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":33264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHistogram displaying the GSI distribution in females (left) and males (right). All individuals with GSI \u0026gt; 0.16% were classified as maturing (in light orange). This threshold value was based on the clear bimodality in the GSI distribution, as well as on the visual inspection of the gonads. The X axis is displayed on a logarithmic scale to optimize visualization.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9042484/v1/598feea1b82c873e9fa8c065.png"},{"id":105040613,"identity":"50d3fce8-e1ed-4001-b16c-0b3dcea51896","added_by":"auto","created_at":"2026-03-20 07:50:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":187889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMean (± Standard Error) GSI over time in each of the treatments. In graph A, the mean GSI is calculated including males and females, while in graph B they appear separated by sex (left females, right males). All individual observations are presented in the graphs as scattered points to display the variation range of the variable within each group and sampling. In the first sampling in June, only the full ration RAS and FT groups appear as the feeding regimes had not been established yet. The restricted feeding groups were first sampled in August. The green and black dashed lines represent the projected trajectories of the restricted feeding groups from the introduction of the feeding restriction on 22 June until their mean value in August. The horizontal red line indicates our chosen threshold for maturation at GSI \u0026gt; 016%. The Y axes in A and B are displayed on a logarithmic scale to improve visualization. Letters “a” and “b” are used to display significant differences (p \u0026lt; 0.05) between treatments at given samplings.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9042484/v1/fb7c88174640a979e33235e0.png"},{"id":105040681,"identity":"0521f9fc-61d1-4d7e-925f-a62c160aac95","added_by":"auto","created_at":"2026-03-20 07:50:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":151845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMean (± Standard Error) mRNA transcription of pituitary fshb over time in each of the treatments. In graph A, the mean fshb transcription is calculated including males and females, while in graph B they appear separated (left females, right males). All observations are included in the graphs as scattered points to display the variation range of the variable within each group and sampling. In the first sampling in June, only the full ration RAS and FT groups appear as the feeding regimes had not been established yet. The restricted feeding group from RAS origin was first sampled in August. The green dashed lines represent the projected trajectories of the restricted feeding groups from the introduction of the feeding restriction on 22 June until their mean value in August. Pituitaries from the FT groups could only be collected in June and September (samplings 1 and 3), and thus they do not appear in the graph in August (sampling 2). The Y axes in plot B are displayed on a logarithmic scale to provide a clearer overview of the variability among low values of relative transcription. Letters “a” and “b” are used to display significant differences (p \u0026lt; 0.05) between treatments at given samplings. Asterisks (“*”) are used to indicate significant differences (p \u0026lt; 0.05) within groups between consecutive samplings over time.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9042484/v1/8b52846ed2cb8a9205d64296.png"},{"id":105040548,"identity":"09f2289a-6828-457c-b862-62c60a7966cf","added_by":"auto","created_at":"2026-03-20 07:50:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":163870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMean (± Standard Error) mRNA transcription of pituitary lhb over time in each of the experimental groups. In graph A, the mean lhb transcription is calculated including males and females, while in graph B they appear separated (left females, right males). All observations are included in the graphs as scattered points to display the variation range of the variable within each group and sampling. In the first sampling in June, only the RAS and FT groups appear as the feeding regimes had not been established yet. The restricted feeding group from RAS origin was first sampled in August. The green dashed lines represent the projected trajectories of the restricted feeding groups from the introduction of the feeding restriction on 22 June until their mean value in August. Pituitaries from the FT groups could only be collected in June and September (samplings 1 and 3), and thus they do not appear in the graph in August (sampling 2). All Y axes appear in logarithmic scale to provide a clearer overview of the variability among low values of relative transcription. Letters “a” and “b” are used to display significant differences (p \u0026lt; 0.05) between treatments at given samplings.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9042484/v1/091ed3bde009e6d30760391a.png"},{"id":105040397,"identity":"d76181e7-a4df-45e8-845a-2500e261fd49","added_by":"auto","created_at":"2026-03-20 07:49:41","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":212070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMean (± Standard Error) body weight (A) and condition factor (B) over time in the four treatments. All observations are included in the graphs as scattered points to display the degree of variation of the variables within each group and sampling. In the first sampling in June, only the RAS and FT groups appear as the feeding regimes had not been established yet. The restricted feeding groups were first sampled in August. The green and black dashed lines represent the projected trajectories of the restricted feeding groups from the introduction of the feeding restriction on 22 June until their mean value in August. Letters “a” and “b” are used to display significant differences (p \u0026lt; 0.05) between treatments at given samplings.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-9042484/v1/2887034bd680ac26a41699b8.png"},{"id":105040706,"identity":"c1344712-ff18-4adb-8e4c-ddac1d298191","added_by":"auto","created_at":"2026-03-20 07:50:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3207312,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9042484/v1/f65225d5-07f7-4aea-96bd-be67932903ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Freshwater history exerts a larger effect than the feeding regime at sea on the occurrence of rainbow trout sexual maturation in seawater cages","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNowadays, aquaculture of rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e) in Norway is carried out following similar production protocols as Atlantic salmon (\u003cem\u003eSalmo salar\u003c/em\u003e). Juveniles are reared up to 150\u0026ndash;300 g during an early freshwater stage that takes place in land-based recirculation (RAS) or flow-through (FT) facilities, being then transferred to sea pens or cages located in the fjords for the grow-out phase (Aas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Morro et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is possible due to the ability of rainbow trout to act as facultative anadromous and adapt to seawater (Ohms et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Van Doornik et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, despite benefiting from the technical developments for salmon aquaculture, total production of rainbow trout in Norway has been stagnant for the last few years between 85\u0026ndash;90 thousand tonnes (Aas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Norwegian Directorate of Fisheries, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and is significantly lagging behind salmon production the same years (~\u0026thinsp;1.5 M tonnes, Norwegian Directorate of Fisheries, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne aspect that industry players consider a challenge for trout aquaculture is sexual maturation during the production cycle (E. Eide, Eide Fjordbruk AS, personal communication). Similarly to Atlantic salmon, sexual maturation in rainbow trout leads to appetite loss, growth reduction, and finally, degradation of the fillet properties due to lipid depletion and muscle atrophy during gonad maturation (Ahongo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Aussanasuwannakul et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cleveland et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). As a result, sexually mature individuals are downgraded at harvest (Norwegian Directorate of Fisheries, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the fish can only be discarded or sold as a byproduct at a significantly lower price, thus causing economic losses to the company. Based on this, the interest of trout producers in understanding what factors are contributing to the occurrence of sexual maturation during the production period has risen, in order to minimize the issue. Unfortunately, early maturation of rainbow trout has not been studied in the context of modern intensive aquaculture production as thoroughly as Atlantic salmon has been during the last decade (Fjelldal et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fraser et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Imsland et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Melo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pino Martinez et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Certainly, the reproductive development of rainbow trout is well understood in both males and females, since this species has been widely used as a model organism (Billard, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Bon et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Davies et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Magri et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Sambroni et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Taranger et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). But, despite this good knowledge on the development and physiology of the process, the way in which intensive rearing conditions applied during modern trout production influence the occurrence of sexual maturation before harvest is not as clear as for Atlantic salmon.\u003c/p\u003e \u003cp\u003eAs in Atlantic salmon, initiation of sexual maturation in rainbow trout requires the activation of the Brain-Pituitary-Gonad axis (Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Davies et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Taranger et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Various environmental (water temperature, photoperiod, feed availability) and internal factors (age/body size, energy availability, genetic determination) determine the occurrence of this activation (Davies et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Davies and Bromage, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Taranger et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). When this starts, the hypothalamus sends a signal to the pituitary to initiate the production of the gonadotropins, first Follicle-Stimulating Hormone (Fsh) and then Luteinizing Hormone (Lh) (Schulz et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Taranger et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These hormones bind to their receptors in the gonads, inducing sex steroid production, and thus triggering both the processes of spermatogenesis in males and oogenesis in females (Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Planas et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schulz et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Fsh is the main initiator of the whole process and is responsible for coordinating the proliferative or mitotic phases of spermatogenesis and oogenesis; Lh in turn is secreted later, coordinating the meiotic phases of both genders and the final gamete maturation (Crespo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; N\u0026oacute;brega et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Schulz et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As a result, both testes and ovaries experience a rapid mass increase, ending up representing a significant percentage of the body weight at maturation.\u003c/p\u003e \u003cp\u003eIn Atlantic salmon, high intensity of the rearing conditions (elevated temperature, continuous light, unlimited availability of high-energy feeds) has been consistently linked to the occurrence of early maturation as post-smolt or \u0026ldquo;jacking\u0026rdquo; (Fjelldal et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fraser et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Imsland et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Melo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pino Martinez et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). These rearing conditions (especially temperature) are generally considered more intense in modern RAS than in FT facilities, since the high percentage of water recirculation in RAS allows to maintain a more constant environment with a more efficient use of energy than a FT (Brown et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dalsgaard et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As a result, early maturation in Atlantic salmon has sometimes been linked to RAS production where constant intensive conditions are used (Crouse et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Good and Davidson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pino Martinez et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, it has been shown that the rearing system used during the freshwater phase of salmon aquaculture (RAS or FT) exerts an important influence on several other aspects later at sea, such as their osmoregulatory capacity, physiological robustness to seasonal changes and growth during the grow-out phase in sea cages (Lai et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, as with the mentioned aspects, it is likely that the conditions experienced during the freshwater phase which are linked to the rearing system, also influence later the incidence of sexual maturation at sea. This has been observed in Atlantic salmon post-smolts reared at constant high temperature (15ᵒC) for an early 2-month period in freshwater (between 50\u0026ndash;300 g), and afterwards exposed to constant 8ᵒC in a subsequent phase of the experiment; such early period at 15ᵒC was stimulatory enough to induce maturation in individuals even when the temperature was later reduced drastically to 8ᵒC for the rest of the trial (Pino Martinez et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). This evidences the importance that the intensity of the conditions during the juvenile period of salmon can have for their decision to mature early. Given the various similarities between Atlantic salmon and rainbow trout (Taranger et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), we hypothesize that the intensity of the conditions during the freshwater stage is also likely to influence the decision of rainbow trout to mature during the grow-out phase at sea.\u003c/p\u003e \u003cp\u003eAnother relevant aspect for sexual maturation is energy availability, a factor that is intrinsically linked with access to feed. In this regard, previous theoretical studies on Atlantic salmon state that, to initiate maturation, salmon must first surpass a certain energy threshold concomitantly with the occurrence of key environmental cues (i.e. increasing daylength and temperature); if this energy threshold is not met, the process of maturation is inhibited (Thorpe, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1994a\u003c/span\u003e; Thorpe et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). To test such hypothesis, some authors have studied the effects on sexual maturation of reducing the energy input by restricting access to feed (i.e. providing a reduced feed ration) or offering a low-lipid diet. Generally, the use of restricted feed rations has impacted the tendency to mature, resulting in a lower proportion of individuals initiating the process (Rowe et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Rowe and Thorpe, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Thorpe et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Trombley et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly, providing a low-lipid diet has resulted in a lower proportion of fish maturing compared to salmon fed a high-lipid diet (Jonsson et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, other studies have shown that if a strong determination to mature is induced by exposing salmon to highly stimulatory conditions (i.e. constant temperature as high as 18ᵒC), a restricted regime providing a 67% ration is not sufficient to impact significantly on the number of maturing fish (Pino Martinez et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). As such, the influence of a feed restriction on sexual maturation seems dependent upon other variables that can exert a larger effect on the process, like temperature or photoperiod. Therefore, clarification of its effects on rainbow trout maturation requires further research. Furthermore, similar studies applying energy restrictions are rare in large-scale commercial settings at sea, due to the obvious reduction in growth that producers may not be able to assume.\u003c/p\u003e \u003cp\u003eAll in all, the influence that the various factors manipulated in modern aquaculture facilities exert on sexual maturation of rainbow trout requires more clarity. The present large-scale study was designed to assess how the early freshwater rearing conditions first, and then the feed regime received during the period at sea, influence the occurrence of sexual maturation during the commercial production of rainbow trout. For this, groups of rainbow trout juveniles reared in two different freshwater systems (RAS or flow-through) were subjected either to 100% or 70% feed rations after transferred to sea in early spring 2024. The study took place in a sea water site in Norway during the commercial production of rainbow trout.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Ethic statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors confirm that ethical policies of the journal have been followed. The trial was ethically reviewed, approved and registered by the local representatives of Animal Welfare at the Department of Biological Sciences of University of Bergen (FOTS application 30812). The study follows the ARRIVE 2.0 guidelines for reporting animal research (du Sert et al., 2020). All samplings and methods were performed in accordance with the relevant guidelines by the Norwegian Animal Research Authority (NARA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Fish stock, experimental setup and samplings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe fish used in this study was a commercial batch of 331447 mixed-sex rainbow trout of Aquagen strain. Although rainbow trout is native to North America, this strain was originated in Norway in the 80\u0026rsquo;s, as a result of selective breeding programs carried out by norwegian aquaculture researchers using broodstock imported from Denmark in the early 70\u0026rsquo;s. Over these decades, the strain has undergone selection for faster growth, improved disease resistance, better fillet quality and reduced early sexual maturation. In the study, the Aquagen commercial batch was transferred to sea cages located at the Eikeb\u0026aelig;r\u0026aring;n\u0026aelig; marine site (Alver municipality, Vestland, Norway) owned by Eide Fjordbruk AS. The transfer was carried out between 23-26 March. This fish stock was produced at the S\u0026oslash;reb\u0026oslash; freshwater facility owned by Osland Stamfisk AS (H\u0026oslash;yanger municipality, Vestland, Norway). At S\u0026oslash;reb\u0026oslash;, part of the batch (188074 trout, hatched in January 2023) was produced in a Recirculation Aquaculture System (RAS), while the rest of the batch (143373 trout, hatched in late April 2023) was produced in Flow-Through (FT). The water temperature profiles experienced by the RAS and FT groups in freshwater are displayed in Figure 1. Counting all period in the facility, mean temperatures (\u0026plusmn; standard deviation) for the two groups were similar (8.8 \u0026plusmn; 2.6ᵒC for the RAS group versus 9.6 \u0026plusmn; 2.6ᵒC for the FT group, p=0.075, t-test). However, from 15 December until transfer to sea cages on 23-26 March, the RAS fish experienced 11.5 \u0026plusmn; 1.4ᵒC, compared to 7.1 \u0026plusmn; 0.4ᵒC in the FT group (p\u0026lt;0.001, t-test). The whole batch was reared under constant light. Fish were fed commercial feeds adequate for their size and life stage (see Table 1). The feed was provided with automatic feeders until satiation. The trout remained at S\u0026oslash;reb\u0026oslash; until transfer to the sea water cages (23 to 26 March 2024). On this date, trout produced in RAS had reached a mean weight of 450 g while trout from FT had a mean weight of 179 g. Considering the hatching dates, the Specific Growth Rate (SGR, % body weight gained per day) in the facility was very similar in both groups, approximately 1.5%.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1. Feeds provided to the two rainbow trout stocks used in the present study, months during the freshwater phase and feed commercial suppliers.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;At seawater transfer on 23-26 March, the trout produced in RAS had a mean weight of 450 g and were equally distributed in cages 1, 2, 3 and 4; the trout from FT had a mean weight of 179 g and were deployed in cages 5 and 6 (see diagram in figure 2). All sea cages had square shape, with a side length of 25 m and a net of 40 m depth in the center. From seawater transfer until 22 June 2024, all cages received a full feed ration until satiation (Full). The fish received Skretting commercial feeds of different sizes according to the weight of the fish and were delivered to the fish using automatic feeders controlled by the farm system. On 22 June, the feeding ration was reduced in three cages (two from RAS and one from FT, see figure 2) down to approximately 70% ration (Restricted). This was achieved by introducing a fasting day every three days. The fish were kept under natural photoperiod and temperature throughout the trial. Figure 3 displays the daily water temperatures recorded at the sea site at 5, 15 and 25 m depth during the study period. No additional light was used in the cages at any time.\u003c/p\u003e\n\u003cp\u003eFour samplings were carried out (7 June, 21 August, 25 September and 8 October/15 December). In the first three samplings, 10 fish per cage (irrespective of sex) were sampled, measuring morphometric data and collecting pituitary samples. The last sampling was carried out in two different and separate days, due to the different harvest dates projected for the RAS and FT cages as a result of their different growth rates. Thus, on 8 October, 20 fish per cage were collected from each of the four RAS cages, while on 15 December, 20 fish from each of the two FT cages were collected. During this last divided sampling, only morphometric data were collected, but not pituitaries. In all samplings, individuals were euthanized with an overdose of benzocaine (Benzoak vet.\u0026reg; 20%, ACD Pharma AS, Norway). Fork length and body weight were measured to the nearest 0.1 cm and g. Fish were then dissected, their sex registered and gonads extracted and weighed to the nearest 0.001 g in order to calculate gonadosomatic index. The pituitary gland was excised, placed in RNAlater\u0026reg;, incubated overnight at 4 ᵒC and stored at \u0026minus;80 ᵒC until analysis.\u003cem\u003e\u0026nbsp;\u003c/em\u003eHowever, pituitaries from the FT groups could only be collected in June and September (samplings 1 and 3) but not in August (sampling 2). In all samplings, fish welfare was assessed by scoring skin ulcers, eye damage, dorsal and caudal fin damage, spine deformities and presence of nephrocalcinosis in the kidney. Scoring of these welfare issues was done according to the system publisehd by Noble et al. (2020).\u003c/p\u003e\n\u003cp\u003eKey Performance Indicators (KPI) for each cage during their period at sea, including mortality (%), Feed Conversion Ratio (FCR) and SGR (% body weight gained per day) were provided by the company.\u003c/p\u003e\n\u003cp\u003eFish in the group RAS-Full was harvested during October, fish in RAS-Restricted during November, and fish in both FT cages were harvested in late February. The harvest reports provided dates, mean weight, and proportion of fish included in three different quality categories used to grade farmed salmonids in Norway (Sommerset et al., 2023). These categories determine the purpose for which the product can be used and its market price. In the present case, the categories are:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eSuperior: Top quality product, without any defect in skin, flesh or pigmentation, no wounds or deformities, normal shape, no sexual maturation\u003c/li\u003e\n \u003cli\u003eProduction: Product with minor or moderate defects, i.e. scale loss, shallow skin damage, or minor melanin deposits in flesh, no sexual maturation.\u003c/li\u003e\n \u003cli\u003eProduction B: Product with severe faults, such as significant scale loss or wounds, deformities, lack of firmness in flesh, large melanin deposits, sexual maturation. It can still be used for human consumption but requires a high degree of processing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSome fish will also be discarded for human consumption if they have massive wounds, necrosis, active infections, or very advanced sexual maturation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Calculations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMorphometric parameters like condition factor and GSI were calculated for each fish using values measured in samplings:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCondition factor: K = 100 \u0026times; Wbody / FL\u003csup\u003e3\u003c/sup\u003e, where Wbody is the body weight (g) and FL is the fork length (cm).\u003c/li\u003e\n \u003cli\u003eGonadosomatic index: GSI (%) = 100 \u0026times; Wgonads / Wbody, where Wgonads is the weight of the gonads (g) and Wbody is the body weight (g).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eKPIs like FCR and SGR at sea were calculated by the company with the following formulas:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFCR = F / Biomass gain, where F is the total amount of feed provided (kg) during a period to obtain the correponding biomass gain (kg).\u003c/li\u003e\n \u003cli\u003eSGR (% body weight gained per day) = [(ln(W\u003csub\u003e2\u003c/sub\u003e) \u0026ndash; ln(W\u003csub\u003e1\u003c/sub\u003e)) \u0026times; 100] / t, where W\u003csub\u003e2\u003c/sub\u003e is the final mean body weight (g), W\u003csub\u003e1\u003c/sub\u003e is the initial mean body weight (g), and t is the time between the weight measurements (in days).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Identification of early maturing individuals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarly maturing individuals were identified based on visual inspection of gonads (Figures 4A \u0026ndash; ovaries, and 4B - testes), external features (darker coloration, development of kype in males \u0026ndash; Figure 4C) and increased GSI. A GSI threshold above which individuals are engaged in sexual maturation has been established for Atlantic salmon males at around 0.06%-0.09% (Ciani et al., 2021; Fraser et al., 2023b; Melo et al., 2014; Pino Martinez et al., 2023b; Rowe et al., 1991; Skjold et al., 2024), while for females it is estimated around 0.2% (Thorpe, 1994a). For rainbow trout such threshold is not clear. In this study, we decided to use a maturity threshold of GSI \u0026gt; 0.16% for both males and females. This choice was based on the clear bimodality observed in the GSI distribution (see results section) and on visual evidence of gonadal development such as increased size, thickness (both sexes) and granulosity (ovaries) (see Skjold et al., 2024, for a similar system for Atlantic salmon).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Laboratory analyses\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGene transcription of pituitary gonadotropins fshb and lhb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReal-time Quantitative Polymerase Chain Reaction (RT-qPCR) was used to analyze relative transcription of the genes \u003cem\u003efshb\u003c/em\u003e and \u003cem\u003elhb\u003c/em\u003e in the pituitary, responsible for production of the beta subunits of the gonadotropins follicle-stimulating hormone (Fsh) and luteinizing hormone (Lh). These hormones together with sex steroids initiate and coordinate the progression of gonad maturation (Taranger et al., 2010). To carry out this analysis, pituitaries collected in RNAlater during samplings were first homogenized to extract total RNA with TRI Reagent (Sigma-Aldrich) according to the manufacturer\u0026rsquo;s instructions. Samples were then treated with TURBO DNase-free kit (Ambion Applied Biosystem, CA, USA) to remove any remaining genomic DNA. The total RNA concentration was quantified both before and after DNase treatment with the NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Termo Fisher Scientific), and purity was confirmed by 260/280 and 260/230 ratios above 1.8. Then, total RNA (1000 ng) was reverse-transcribed to cDNA using the Superscript III Reverse Transcriptase kit (Thermo Fisher) and Oligo(dT)20 Primer following the manufacturer\u0026rsquo;s instructions. Finally, gene transcription was analyzed in a Bio-Rad CFX96 Touch Real-Time PCR detection system using 6.25 \u0026mu;L iTaq Universal SYBR\u0026reg; Green Supermix (Bio-Rad, USA), 2.5 \u0026mu;L of 1:20 diluted cDNA and 0.25 \u0026mu;L of each forward and reverse primers (10 \u0026mu;M). The RT-qPCR protocol consisted of 3 min at 95 \u0026deg;C followed by 34 cycles at 95 \u0026deg;C for 15 s and 60 \u0026deg;C for 1 min. Samples were run in duplicate with the oligos listed in Table 2. Duplicates in \u003cem\u003efshb\u003c/em\u003e and \u003cem\u003elhb\u003c/em\u003e plates with a CV \u0026gt; 1.5 % were not considered for analysis. Before RT-qPCR, a standard curve was built for each pair of primers to estimate primer efficiency, using six 2-fold dilutions (starting from 1:5) obtained from a cDNA pool of 36 samples. The efficiency (E) was estimated with the formula E = 10(\u003csup\u003e\u0026minus;1/slope\u003c/sup\u003e), and this slope was obtained from the plot of log (cDNA concentration of the pool) versus threshold cycle (Ct) values. A melting curve was used to confirm specificity of the amplicon. After RT-qPCR, relative transcription of the genes was calculated with the efficiency-corrected method using a geometric mean of \u003cem\u003eef1a\u003c/em\u003e and \u003cem\u003eb-actin\u003c/em\u003e as reference genes (Olsvik et al., 2005).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2. Oligo sequences for RT-qPCR in rainbow trout pituitaries.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer sequence (5\u0026rsquo; \u0026ndash; 3\u0026rsquo;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccession No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eef1\u0026alpha;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eCCCCTCCAGGATGTCTACAAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAF498320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 138px;\"\u003e\n \u003cp\u003eMorro et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eCACACGGCCCACGGGTACT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eb-actin\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eTCCTTCCTCGGTATGGAGTCTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAJ438158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 138px;\"\u003e\n \u003cp\u003eBaron et al., 2005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eACAGCACCGTGTTGGCGTACAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003efshb\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eGCGAAACAACGGACCTGAACTAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAB050835.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 138px;\"\u003e\n \u003cp\u003eBaron et al., 2005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eGGACCACTCCTTGAAGTTACACA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003elhb\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eCTGCGTCACCAAGGAGCCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAB050836.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eAdapted from Baron et al., 2005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eGACAGTCAGGTAGGCGGATCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical analyses\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFisher\u0026rsquo;s Exact Test for count data was used to statistically compare the proportion of maturing and immature rainbow trout within different rearing systems, feed regimes, and sexes. This test was also performed for different rearing systems within samplings, and for treatments within males and females. The test was also used to assess differences in incidence of dorsal and caudal fin damage, and of nephrocalcinosis.\u003c/p\u003e\n\u003cp\u003eLinear models (LM) were used to find out statistical differences in morphometrics, GSI and pituitary \u003cem\u003efshb\u003c/em\u003e transcription between treatments at given samplings and within treatments over time. The GSI and \u003cem\u003efshb\u003c/em\u003e transcription models were fitted considering both sexes together, and also for males and females separately. Before fitting these models, the distribution of the response variables was assessed graphically and with Shapiro-Wilk\u0026rsquo;s test, while the existence of outliers was explored with boxplots. The LMs were fitted between the response and the variables \u0026ldquo;Treatment\u0026rdquo;, \u0026ldquo;Time\u0026rdquo; and their two-way interaction. The model residuals were assessed graphically to determine if assumptions like normality, linearity and homogeneity of variance were met. When models did not meet parametric assumptions, the response was log-, square-root- or inverse-transformed, the model repeated, and residuals checked again. In all cases, homogeneity of variance was also confirmed with a Levene\u0026rsquo;s test. Tukey HSD post-hoc tests were applied to reveal significant differences in the responses between treatments at given times and within treatments over time.\u003c/p\u003e\n\u003cp\u003eThe distribution of pituitary \u003cem\u003elhb\u003c/em\u003e transcription did not allow the use of parametric models, and therefore Kruskal-Wallis tests were applied at each sampling to compare statistical differences between treatments. This was done for all sexes together, and for males and females separately.\u003c/p\u003e\n\u003cp\u003ePlots of response variables over time display mean \u0026plusmn; standard error. A significance level \u0026alpha; = 0.05 was used. All statistical analyses were performed in R and RStudio using the packages \u0026ldquo;car\u0026rdquo; (Fox and Weisberg, 2019), \u0026ldquo;ggplot2\u0026rdquo; (Wickham, 2016), \u0026ldquo;ggpubr\u0026rdquo; (Kassambara, 2017), \u0026ldquo;Rmisc\u0026rdquo; (Hope, 2013) and \u0026ldquo;emmeans\u0026rdquo; (Lenth et al., 2018).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Sexual maturation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividuals were classified as maturing when GSI \u0026gt; 0.16%. This threshold was applied to both males and females, and was chosen based on the clear bimodality observed in the GSI distribution (Figure 5) as well as on visual evidence of gonad development. The bimodality was very obvious in males (Figure 5); in females it was also clear, although few individuals classified as maturing had a GSI that although higher, was close to this threshold. These females were still classified as maturing since their ovaries displayed a clear increase in length, thickness and granularity that indicated initiation of gonad development.\u003c/p\u003e\n\u003cp\u003eSexual maturation was observed similarly in various males (n=11) and females (n=13) during the trial (Table 3-top). All maturing individuals (n=24 out of 202, ~12%) appeared only in the groups transferred from RAS, none in the FT groups (0 out of 60 fish). Thus, the tendency to mature was significantly higher in RAS than in FT groups (p\u0026lt;0.01, Table 3-middle). The feed restriction had no influence on sexual maturation, since a statistically similar number of individuals matured in each feed regime (Table 3-bottom). First signs of sexual development were visually detected in June (Sampling 1) in some females that displayed slightly elevated GSI and increased granularity and thickness in the ovaries, although not being classified as maturing (see Table 4) because their GSI was lower than 0.16%. Maturing individuals appeared in the three remaining samplings, but only in the RAS groups (p\u0026lt;0.01 in Sampling 3, Table 4). No difference in proportion of maturation was observed between the two RAS groups (Full and Restricted feeding, Table 5) for any sex.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3. Number of immature and maturing individuals of each sex (top), number of immature and maturing fish transferred to sea from FT or RAS (middle) and number of immature and maturing fish within each feeding regime (bottom). Asterisks (**) indicate significant differences after the Fisher\u0026rsquo;s Exact Test for count data (p\u0026lt;0.01).\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTable 4. Number of immature and maturing individuals from each FW system that were collected in each of the four samplings. Asterisks (**) indicate significant differences after the Fisher\u0026rsquo;s Exact Test for count data (p\u0026lt;0.01).\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eTable 5. Number of immature and maturing males and females within each treatment.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2\u0026nbsp;Gonadosomatic index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering both sexes together, GSI was significantly dependent only upon treatment (p \u0026lt; 0.05). Due to the high standard error of the GSI in samplings 2, 3 and 4 (Figure 6A) that was caused by the relatively low number of maturing fish (with high GSI) versus immature fish (with very low GSI), no significant differences between treatments at given samplings or within treatments were observed over time. However, the mean GSI trend over time switched from similar values in the RAS and FT groups in June before introducing the feeding regimes, towards an increase in the two RAS groups compared to the two FT groups as time passed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen considering females alone (Figure 6B left), GSI was dependent upon treatment and its interaction with time (both p \u0026lt; 0.01). Although no female was classified as maturing in sampling 1 in June, mean GSI was significantly higher in individuals from RAS than from FT (p \u0026lt; 0.01). Then in sampling 2, GSI in females from FT-Full was significantly lower than in the rest of groups (all p \u0026lt; 0.01). In sampling 3 and 4, however, despite the presence of maturing females with elevated GSI in the two RAS groups, the statistical analysis did not show any significant difference between treatments. Differences in GSI over time within treatments were not present either, due to the relatively low abundance of maturing females with elevated GSI.\u003c/p\u003e\n\u003cp\u003eFinally, when considering males alone (Figure 6B right), GSI was significantly dependent only upon treatment (p \u0026lt; 0.01). As with both sexes together, no significant differences between treatments at given samplings or within treatments over time were observed; however, the increasing trend in the RAS treatments over time contrasted with the stable GSI levels in the FT treatments.\u003c/p\u003e\n\u003cp\u003eMale and female individuals with GSI above the chosen threshold value of 0.16% (dashed horizontal red line in Figure 6A and B) were present in samplings 2, 3 and 4 only in the RAS treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u0026nbsp;Transcription of gonadotropins \u003cem\u003efshb\u003c/em\u003e and \u003cem\u003elhb\u003c/em\u003e in the pituitary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePituitary \u003cem\u003efshb\u003c/em\u003e transcription was dependent upon treatment and time (both p \u0026lt; 0.001). In June, before any fish was classified as maturing according to their GSI, mean pituitary transcription of \u003cem\u003efshb\u003c/em\u003e was higher in individuals transferred from RAS than in those from FT (Figure 7A \u0026ndash; both sexes together, and 7B \u0026ndash; females on the left and males on the right). Afterwards, in August and September (samplings 2 and 3), more individuals from RAS origin (both males and females) displayed upregulations in pituitary transcription of \u003cem\u003efshb\u003c/em\u003e, indicative of progressing sexual maturation. This resulted in a significant increase in mean \u003cem\u003efshb\u003c/em\u003e transcription in both groups from RAS (both p \u0026lt; 0.05) irrespective of the feed regime. The same was observed when considering males alone (both p \u0026lt; 0.05) but not when analyzing females alone, although the trends over time in both sexes were similar. In contrast, pituitary \u003cem\u003efshb\u003c/em\u003e transcription remained very low in all individuals transferred from FT, independently of feed regime and sex.\u003c/p\u003e\n\u003cp\u003eThe patterns observed in pituitary \u003cem\u003elhb\u003c/em\u003e transcription were similar to those in \u003cem\u003efshb\u003c/em\u003e transcription. Pituitary \u003cem\u003elhb\u003c/em\u003e (Figure 8A) was significantly upregulated in June only in fish from RAS (p \u0026lt; 0.001), before any of them was classified as maturing by GSI. This was primarily caused by females from RAS (p \u0026lt; 0.01) but not by males (Figure 8B). In contrast, \u003cem\u003elhb\u003c/em\u003e remained very low in fish from FT. In subsequent samplings, pituitary \u003cem\u003elhb\u003c/em\u003e was upregulated in some male and female individuals from RAS that were maturing in both feeding regimes, but this did not result in significant differences between groups. In contrast, all individuals from FT in both feed regimes maintained very low levels of \u003cem\u003elhb\u003c/em\u003e transcription in September (sampling 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4\u0026nbsp;Morphometrics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBody weight was significantly dependent on treatment, time and their interaction (all p \u0026lt; 0.001). At transfer to sea in late March 2023, the mean body weight was larger in the fish from RAS (450 g) than in the fish from FT (179 g). These differences were maintained and expanded throughout the trial, with RAS fish displaying larger body weight than FT fish in all samplings (Figure 9A). This resulted in RAS groups reaching harvest size much earlier (Oct-Nov 2024) than the fish from FT (harvested in Feb 2025). The feed restriction also tended to affect body weight, as the two feed-restricted groups displayed lower mean weight than their counterparts receiving a full feed ration, although this difference was not statistically significant.\u003c/p\u003e\n\u003cp\u003eCondition factor was significantly dependent upon treatment, time and their interaction (all p \u0026lt; 0.001). Condition factor generally ranged between 1.4 - 2.2 and tended to be higher in the RAS groups than in the FT groups throughout the trial (Figure 9B). Within fish from the same origin, those receiving a restricted ration tended to have a lower condition factor than their counterparts fed a full ration, but these differences were not statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5\u0026nbsp;Welfare indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThroughout the trial, skin ulcers or eye damage were not observed. Generally, the fish evidenced very good skin health (see Figure 4C).\u003c/p\u003e\n\u003cp\u003eIn contrast, dorsal and caudal fin damage was common in all groups, although more prevalent in fish from RAS origin (Table 6). Thus, low-intermediate scores of dorsal fin damage (1 and 2), as well as intermediate-severe scores of caudal fin damage (2 and 3) were more prevalent in groups from RAS origin than in those from FT origin (all p\u0026lt;0.001, Table 6).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 6. Number of fish from RAS and FT displaying each score of dorsal fin damage (top-left) and caudal fin damage (top-right). Number of fish within each experimental group displaying each score of dorsal fin damage (bottom-left) and caudal fin damage (bottom-right).\u003c/em\u003e\u003cem\u003e\u0026nbsp;Asterisks (***) indicate significant differences after the Fisher\u0026rsquo;s Exact Test for count data (p\u0026lt;0.001).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cimg 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\"\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cbr\u003e\u003c/em\u003e\u003c/p\u003e\u003cbr\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eNephrocalcinosis was present in rainbow trout from both RAS and FT, in a similar proportion in the trial (23% and 20% of the fish respectively), and a similar proportion of individuals was found at each severity score (Table 7-top, p=0.27). Something similar occurred between feed regimes, (Table 7-bottom), although with a tendency towards less presence of nephrocalcinosis in restricted feeding groups (p=0.18).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 7. Number of fish from RAS and FT displaying each score of nephrocalcinosis (top). Number of fish from Full and Restricted feeding regimes displaying each score of nephrocalcinosis (bottom).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cimg 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\"\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cbr\u003e\u003c/em\u003e\u003c/p\u003eCaudal deformity was observed in a total of 21 individuals that were collected throughout the trial (Table 8). These individuals were all from RAS origin, while none from FT origin displayed this deformity.\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 8. Number\u0026nbsp;\u003c/em\u003e\u003cem\u003eand percentage of fish within each sampling displaying caudal deformity considering the group from RAS origin and from FT origin.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cimg 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\"\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e3.6\u0026nbsp;Key Performance Indicators\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 9 presents a summary of KPIs in each cage during their corresponding period at sea (between transfer and harvest). Mortality was roughly similar in RAS-Full and RAS-Restricted, slightly higher in the FT groups. Feeding efficiency was better in the Full ration groups (lowest FCR), and worse in the Restricted feeding groups (highest FCR). Finally, SGR was the best in the RAS-Full treatment, and similar and lower in RAS-Restricted and the two FT groups.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cem\u003eTable 9. Summary of KPIs, including mortality, FCR, and SGR, in the six study cages during their corresponding period at sea (starting at seawater transfer and ending at harvest)\u003c/em\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cimg 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\"\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e3.7\u0026nbsp;Harvest results\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 10 presents a summary of the harvest results. The groups from RAS were the first harvested at an average weight of 4.2-4.3 kg, but there was almost a month delay between RAS-Full (210-212 days at sea, harvested in October 2024) and RAS-Restricted (228-244 days at sea, harvested in November 2024). In contrast, the two groups from FT had to wait three more months (until February 2025, 330-338 days at sea) to be harvested at a similar weight. The FT groups and RAS-Full had a high and roughly similar proportion of superior fish (87-89%), in contrast to RAS-Restricted that contained around 80% of superior fish. Unfortunately, the % of fish within Production B or discarded that was due to early maturation was not specified in the harvest reports; however, this could not be very high, since the sum of these two numbers together was never higher than 3.8% (cage 3, RAS-Restricted).\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cem\u003eTable 10. Summary of results after harvesting the rainbow trout groups used in the present study. Cage, treatment, days in the sea, harvest\u0026nbsp;\u003c/em\u003e\u003cem\u003eperiod, mean weight of the fish harvested, and proportion of fish within each of the quality categories are displayed. Days in the sea were calculated using the day of seawater transfer as initial date (23\u003csup\u003erd\u003c/sup\u003e March) and the middle day of the harvest period for each cage as the final date.\u003c/em\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cem\u003e\u003cimg 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f/HcC+iLL754WGGFFYJvNZP/ctv5OCOQEah3BHL/axmBTOpr+enkvg1xBITc+OtviD1ystpqq4X99tsves8/+uijwNP4ww8/BN5KLxHeeuut4eqrr45fChHOwPO+0korhdNPPz0ceeSRYZFFFmnWZ0ZDt27dAq/neeedF0N8/AVMnlCe+2aV80lGoB0QWHPNNYPdp2uuuSbYbaq8pZe2GaDfffddOOWUU4J6L730UrCLtddeewUhawzTDz/8ML7kXb5+/PHHDwsttFA5Kx9nBDICGYGMQDshkEl9OwGdb9N/BIZGjYcffjgg7WKNxx577PDqq6+G5ZZbLoYaCKdB6hFwfeOdH3fcccPhhx8evv322xiaIOSAtxLRf/TRR8OKK64Yibv6Kcn/97//HWaYYYYw2mijBZ8Q/OWXX1Jx/s0ItCsCDM9JJpkkHHvssfGLUJU3F2YmxEb+MsssE41V4Tfk97bbbguMAnJsfvhUpno5ZQQyAhmBjMDQR6BdSD1SxCMqieNEauQ1+tasGG1jrpZ4hxt9/ENfhiALYAAAEABJREFUvP/pgbhfmMshf56N+GDed4R+yy23jHHCQgs8LwQfuVEf6feLyAjHGX744cOkk04a45CV8Vx6lmLrhR8IX1BfQt7POOOM+DLuhBNOGD2bysk/g2HkkUcOPPdXXXVVDG9wTXsmfTHechJz7TOIKc+cNY727Fd738v4jJNsDMi9PW8yBCPE1rMckOuGRh19JftkVX99xnWBBRYISyyxRJR54yaD+mZMRVEE75p4r4RcktsnnngivP3228GuE489j7z6Enkxv8iS5F7wdF9l6rRxGqzm7ETAQdLfNBcHq9GBuBhWZMa9B+Kywa6anr9xlxMHxeA27nmTI+3Cd3DbS9endpN8pvwh9et+xlCZYDS4c5xuSXq1LecFOdJuW2Fkh65y/M4H9bnSA/SKNiqTsvKzNC/ULecNyWP3T30yPwbkXnBO15CXAbmmPesMcVJvG9cLgV27dg0bb7xx8LWPCy64IBx88MGR4LTnYNv7XvPNN19YddVV4ycTp5lmmviSpdhTIR7rrLNODOFoyz717t27LZvrb1t9+vTpb51aqXDfffcFccIrr7xyjIk/4ogjQo8ePWJ8sJcGZ5tttthV37IXVuPlVi/HLrXUUjG+Phb2/adbt24xPj7V56n3bI8//vj4WUyhCkJ5+laN/yv3icBZZpkliEs+5phjYiiPFxSRifPOOy9+ecQLuOONN168pj3/+eCDD6LndaqpporyucUWW8RxdO/ePUw22WTx2+XmLVLXnv0q34uytWiV89r6WKy4dxp69uw5QE1T6jvttFPEzKci23MhGqAOlirBjlf+qaeeCptuuml80VXIGD1UFEX81KtQMHNDmJj3STbZZJM4PyyyrhFmZl7svffegby//PLLYbHFFguu8WnY5557Lu5EzTHHHIERyyBmOCgrdWWoH3ovYPvttw/0sHFddNFFgTGOkLZX5+AjRMna2F73dB8y7pnTNXPNNVfcpWG8+dwv2UAO1RuU5Hq7OnYy6cJBaSNdY24l0nvFFVdEuWqvv51gHD5xzJAlyxtttFHU99YBumFwMPLp2FVWWSXqDC+Wp/EOyi99w9Bw7dNPPx3svNlFcz64SSip3Txygr/4my504wYbbDBInIX+2X333eM7adr0vo027fyde+65ocxb3Nc7ZtbGwR3HX9f3+1+yRhfql/V5QO5r7V500UXjc7zpppv6fYOhUDpESf0LL7wQkAOTgaAIUfC1DxYvL+ngTJChgNVA33LOOecMXkSj5CyEGjjttNNiXteuXZ22WfIHjHiR26zB/jREkfjyS3+q1UyxF1ot4GSwnLwAW+6kl2SV+2M9nptjX7Tx0h8FJJQG8U/X8Npvs802wXNVd/75509F8VdMPaWhTLIj4BdRTjH8zhl5KeQhXthO/1gMjjvuuDD55JPHO1pE9c2Lv7yxxof0IGixQjv/Q1d4WdPLyEPy1gircTJkBuQ+yItdnQGpO7TrWLC8J0LOJGEza6yxRjTm9E1eOS2//PKyA/Kb8tWXidTLYxQzkh2TFwu+Y0l4ml9ps802c1nNJHPa+wSXX355fDeG8dFWZGhAB8nZs/POO4f2NuKFTXGm6Sciz+BCZOi0/ffff5AIm7YkY0J84el8cBLHx8cffxyb8C6TecnpEDOG8D/GQfd718Qabf32xai33norIKaD6q3W7emmmy4I1XQ8OInBc9111zWRYbrI2u9DDoPTbrpWH8mKc3MZBtYwc2ZQSCz906NHj8Cxpc0999wzXHnllcG66UtZvpKFvyhzb+/t8KA7H9IJdmR/YO7jOa677roDc0m71h2ipN5kRN55c0wWBAeRAQirnhezXUfbzjejnKopbp5cHgAhHm3RJYqGcm4vb0bv3r2DScrL2xb9r/U2yKmFb4cddggMVQtNrfe5Ufpnq5MRxcM6JMdkQURAp5566iF5m9z2UEbADoP3Zu69997YEx77ZLDEjHb4hydS2F+1taEdbt/sFr7mJYPnF1l0PDQTHevLSqkPdkTtGrUXqU/3Lf/SCQxhf9+hFhyRSDaDmm7UT44IZHhIYYSzcf64lxA8v4ObhJ2ae6OOOmq45557mv5uBl5kbow00kiDe4sOe/0QI/WvvfZa/MSfh2OrD6FPKPP+2fpkJb3//vvRgmMZs/J5MEwcsUpilV2fynxa0HeRWXW2hTp37hxDBJDa1HYt/fZLaes7b4QxEWyeL95CmCDLFJvJqg1foICJWEw4iu2G4UEHHRS/dy6Eg3fBhDP5/PVIfzW1KIrAulZfu0JKbLe6Vh1bTTyhPFUUl3oXX3xx9NjYgvQ8eF947niRzzrrrABrxhqL3V+f9HlHceS1hHtb9wXmPHwWPt5ZJL+t71Er7QlDkMRFV3pLbN/DQqw1DHz+Ux2yRz7mnnvuYH5aBGyj8v4XRRE/++mrQXar7GSQZV5C3zQX9nHhhRfGl4fPPvvsYC6YGwx/csXL49OLdg5s2ZZx4m0siiLqAPezUCDmdId+IW9kXX8Zoe4rDEXffXaRISyshC7RL2OzU+FaISj6q39ImE+QmitCgbxIba7oU7k/+bj2EbCrYMtdGJ3wOmuJMCp6jQwVRRFfductJCf0aJ8+fYLtdvKBYCJ4ZDztVJIru3jesyG/5ok5pP2iKIJf3kjtIWSupTeFtImzplusA9Y5epn8MjrcuyiKQM8K1xHGp6wtUKb76TOeUnPA+HzhSP99dpdH31phjhkfWSfz5qY55nr9EHZlXMauz+aIfJ/7FcJSFEX8Wxw+FAA/OCm3lplHsDTHrHFCv0466aQgvIGX3PxUZu3ybod+mJfwM6fpCOFhnEzIf1EU4eSTT47vieiPkEv3GtRkjDASLsxTz7lAXugZ9xd9YIwcAr6URg/qL4wQVjLg3gxJa7tryIY86ZFHHokhHHDdd999o5zAyAcZlLu/9YZceEZ2VMmc3V5OpsUWWyzcfvvtUWcWRRG9364TQqRfsKWDxcjrP0y1L3pCaJH1Xeipa1pLQnzoUXJLHrfffvvWqg50vr4Zl3vgJkKS6Gv3wY3InGef8DQOmOgDzNTFb8gcuSEXsLLDnMZlh74oihgmCFfX4C6PPfZYgAGMRI+UO2/doSc8LzqBbrAm2Hm0Humfr3+Vr6ml4yFG6k0GCqt/g6UUAAdki7wF3vao31NPPTVQrh6CSUoxEkwLNGOAsFE8rFQPu3/3qrVyytPWHg+J8AKfRBSfS9nbmqLged8REd+VphDWXnvtgCBRiMqLooifkIOL9hIBWnrppeNwhY0QfIuBLTXGAmWAkIl1gzXlSOAJLYXBIKPA7ChQBgQcsWE8mIR2XvwFSvHXyk28eLP8T90jwFMimWNeiCwPiAwKTxIOYyvVImaRQDaQDjHCFhQyvf3228fvnFs4KFakQZ6vDZE9HjlbxuY247Rnz54xhIn8aZ9MItDkVR/IusXVcUru45jhyxhFjhieFls7KmI3hTVZ3M0vpMO88p124WNkmX5B8MQaO9ce+bfAkHOLuMXHe0HwEIpF7zCEta9+TvWDgGdNjzFAyQPddf7558cvWSG45JUckBtEzMg4Vzg5OJgQCE4Xcmqe+Kxnr169AmcUeWSsMhJ4dckQY/eZZ56Jhqd1yjyhT7UrIWnkH7FA0Dh3zAs6mk4viiIgEAxa5+ab6wY3mRP0t7EiRBw1nDdIPKPHumB8PPn6hWB7X8jcEMZorMbuIwPqPP744wGZQn70TVuceY6tFUguMuzcWu1+1g47cIwExMmchzHSRB8IdxHW5RqJXvD8YE3P6It1y/xEVNXxlSbPwzpIXw0IB3FdtURX6duzzz4b/04J4q1vPXr0CJwenh2e4oVz92WUeF8Cp6F/rNu86QxBGIhaQCLTveabb76Qxgc7zyNh5DqYC+s0bni7J0yFwjAkyC+nG56U2mRIiIKgmzyfovjLqcLY8AxGHHHEwAiCLb7FkZKurfZLNulI/YM1I6pavUHN44RM11oTEj6eHwPOs4alr8oxHulhBh79q1/K4MIIYPxyKjlOa4X1QPueJccN/kTmzTPGAhm0HqiTknWKbDE0tEUvuI/nC2/rHKM81W+737ZpaYiReqElHoJJBZjWuksACS8lYwGlFCzmfilQSrYoilAURaAMTH6kV11xoh6KF1D6dY/W7l0r+YTM5EboeTJYioQaEUGceIqMEaGi/HlUKAj9ZzyxKB1XS+LXUr6Jgainc78sfhPVBLLYwZiBpUwi8GLIHEsWFZPBcU6Nh4CFWxI6RpGXRyi+0oLOq2YxJjflcopOzLWX4RFiC7stYYufhdYiZl7zXJmv8i1avFzkndFu8bJ4kjNkqtx+a8cWAosDA1/7FjuKN5ETbSJZiHxqY9ZZZw1eWnMvi13K96s/lDhHgnPybk5YJHms3E+ZNpTnVD8IcFzQo+QYCbC+IC6I94CMgnGKeJJNnkPeVgSTY8YuD5KHWJG/1N5ss80W/8quOYXcpXy/HDXaM9d4GYuiCLzc1kDlEiPAzjaCY02VN7gJeWeAmOvaL7eHAMIIuTE/7r///vjCvP5ZP/QNiUf2re/CERnWiCY8y21VO7Z2W3eQSvUZMQwhc7goimqXxDxYI7t2lWHG62o+WgNjhb7/IOF9f9rkf0YUhxiMOAOs06lh95dnx48Hnf6i0+hAz4gnmUOOzmBceL44EQMotdGvX2PibeaMgy9i6VnAqF/XeS6udT8E3vNC4hka6Tp9Vp7O+/VL56c5w3OOVPer/sCU8dDT0Z4j+Slfy9hmWLunXSPjIGvmmPr+MCT+pxz/8eu9r0Ti7QThMqlNxif+g+O4FodktHumOE6q59f8s74h/ua49QR+cHQPuFrX1K3FNMRIPaXHoqQAgELplQGwaCozEQgZAm8i+PVAbImb6LxvCK+/hMhStNgD1UIrFpInmnU8oEJa7kOtHPPmEC79KYoiGJ9j3iQeIYSC5Y0M8fTwFCEV6pgYfgc18QxQlLaiTFjKljU7qO3l62oPgbbqEYXI20GhWaAq57T5W76XrXzeJ2FhPFnmaFEUIcm63R+eb6SBAYBIULZ0hwXTNny5vf4dk+WiKIIF1z0QBrtX7oGA8ASmNlJ5Oi//WjzKC4KyVN8YLbSVY1cnp9pHAEnhhRNahSgj1RxFHCnIbFG0TirLoyMP9LawGfqavPCuc6JY79JOqWvMA7/VUqVhrI41TvuOJXLtt72SeVO+fzVZT2PXJ0aw38okVKEoWuKZ2je/hdRUXtfaOb6AH1SWewaVeUP6HLGEgfsYA87iuJzoofTsyRgZKZc7phP9ViZtw0k+nPwOSKr2rIriH507IG1U1mGk4FvyzRe/bZHMQwaetkVrlNv0TO2+MOTgZqfW7lFR/OXcFamR6itnWCH/SLd8OhqvcSwZg+TY/DJnGT+cQPLKyb3Jbrdu3QLHqt1kBq1nYre2XLcWjzsNqU4B0LYe5Wa7BCEAIsvMF0cs8iaGLUphID9rbuIAABAASURBVEBzjUnrIZkEvMcE2kJviwmR8DB8VYZnhRXFomMJa3tIjWVw2iU0iDeioB3HBM6xMuNMY/ZbFEWYfPLJA+JEmBwTJBjw1Nj6l0cwtWESO07YwQweSaBhBE91KR719UXSD54DQswSTYTF1pYy/dMOZaq+pC33o3Ace37azqk+EbAg2b70awTmEZkkN54/mXRMoZq3jHEGIPlI8kBGHEtkQjsp8ZyZ80iC+VsURfxEncWM954su7+dOl5Q3jvkHrF3D3ImIQ50SGq3/Kt9Bq9QHQupUBremAkmmCB+4UQe0kbG9c9Y9VW/tePcvRybHzyBds0sHPDgbRPupq/a5omFg+1Z17g+teW81hLyyklCj7TWNyFL5n9RFIHToFq9hFG1MnhyCPB6MaJgXhRFNOCEcbiGU4aeousZWK31h67ncdOm69oyeb5CWXjSPVteds+VTFtb6DbPkp71nM0BMpP6QE558YyVfPHmcji51hY/TypZEzJB37rOOLSZjrWX2hVC4J7a5Ak0TzizzLt0jfno3PWDk/TH+qMNzxIWjlP69ddf49/M0Dfl6Z6caq71vMwhhMd8MF95SWHJcDcfjNW15gRjpCiK+Dc6rOPWEQmJs2vHu42I2YnmcTfP4Oj+1jFrPKz0Q5v0Ah1CF5FReXb56QfHxkFXwcuxPrvW8YAmmNNHrtNmpdGlb9pXntrHY+x82KnQN0SVbAl3NVbhOcYqqoADQ1/cB1bmnLbgQhblS4wB18GDg0N7djQ8P/eDr51OcupabcIKERUiBB/XwJYjhoPQeNxLv11jLPLcz/UpkQt1nMNTv+1sad9ugfyBSe6jHeN1nfbhgagrE9YinNM9nUv6bW7gJnaMjFk+HUIOhTYZH9kkN4x1mNE77uFeknuVzx2rh0MKq+JUxq3kuz98yLbICO3TDXb0YGoXxku97u8dK9ek9h3XSuo0pDvi00vitpB4D8AiTyl4UBS8xcb2EiJJ0bPYKEfbSATWVndKlKdJ5gVaZFIoCu+y623PDOmxDEr7+msy2nY14WwTWRi0xZhxTCEQInXl2ypieSI2MOOph4vFyAtHFhTfeNUeoqNt2FGUBBVehFO5XQyK2HVICkPLomq7SziCZyFmkxXKaLC9ZYcFaWH1ekbi07x4YxHzHOHtOVIctrT0Oaf6RMAztM1IaZEXW/EUOGIlvER4nHAbpBrRZaQr99zJnfc7LNjkyDmPeBkJcuVdEYoSYVFGrrRvwSLfFKVwHTGVzi3UYpgtkuYCXWGumAOur0yUrx09i6J5Yxy2VsVKu6/4UTqFIqdnzCuLqX5ry6KAiBkbz4z5oc8WZ4SUgWCr1zUMGzuEYvgZEu6lvbQwaK/WEkPJnEUeW+sbwqROa+WIDIzplco6SIFP1FnwxbQKM6Gf1fNuDv3veiTHlrrQQWEqrelsfaHj6Kpq99PuoCZypa9eqCRriKhnjozbBRX/rG1yjLALnSHj8iTEIpEs7SAbCJM5gbiYP2SB155uJ3+IHdl0PT1Prhg/9K8YaTqa44shAyvyRf+aL+YZz7+2XT84CQlkvJFZa41jxCe1qd9IkR0090+ERV+FyVlvzQMkzBdKPD+GufnBkPNrHtMpwt3sMlvfhW8go56r9UO7wkmE25lD5MYcsr7AgBySKc/evOR4QpbT/cgPY4AsuS+S5X7GZV4y7h1b95DWNL4B+SUP1mv3RPKQyPJ1nh1jw3rqN7VPdugeMgEjciCsz5yzRtsZ0meEUt/gjhgyuK3/HHGMCTvyZIyhZD2Gk2vIKiOKDqInYc3AQTrpNG3qu3km7AcBdw35IVvIK6JMxyHpZJ5+R+7hVB4jGRTiq02ElpGJB5iP+Fa57oAc093CiPESbQr78n6J56lPeI12GHJ0PD3NQ2+doEe8f2Es5JCskBvyK49u1y49TVbcyz3MSzsBuEv53H3MR2GinhmMrR/qeB5kU7imEFLP0j3UsZ4I07PGkTnGhmuEZmmzltIQJ/UW9bXXXjtQKBIB6969exMGiIJ8C7CH59iDotBMHAtESiavhZgwpLomMcXY1GCNHRA4Y0pJjB4LXDcRoZRPYAibfMliQPiVE0BeD8reOUJkkjhGyNRPixUBRPh98kq5+1EyjiVGhV+JULrW5HEuITNIDcPJOcFmFTuW9KtczpDQRk71iQAy47mmZH4iNkhKyjM3kWuyJo9smZOOyZCFw7FkUSojwYtGgbu+nO86BqRrKGpl8pxLSAPd4XpEGgnkKVGvMjFIkA7X6Xsqt2jJk9zDIiiO3rlkgVOXoqbI5Rm/OWsOIT3y3N9cVdcibY7IT3iYY/SY8lpMFkYv96UxDGwfESy4CHVEtCqvtzDDSqgVYlZZbtHkieWoYRggcJV1yueek0XagoyolMsG95guJfMMVc9QEnaT2kVc5JEn93dMLlN5165dm9YyJCPll+UK8bHoI3GuR+o4pNT1fpI8CWEj314kdy4hweoxJswzefQvMid/cJI1AsHWpgQLZC21yaNL3ytD5JDJVFae4+U5hlyqL5lD2ocdQ9IcMje0ac0iA+p5vjBnHDinX9zbvYThyYMNfF3vnFfXdeX70St0hF2/9NzU5RTwK3kO2h3QZD1zXTmVr8U9Uhmdgqekcs8x6TRfreG0UCYE0Bhdl3iLZ8tpoVy+5DnDynt1dJeypGuUW6flwcU5Z4f7O5ZcjyeYg+l+5qVr5PFmq4dXIOuOJSRanZQ8e/mViaEC71RvQH/pncRXym2aF3Z6UjvmPPmha5BqBiKZcc6AS/WS3GiLPGgDf3IOd790FeOUgVY+T23APumANE/VI2PaK/Mk64vr8FHYkUl6TH1JWS2lIU7qB3Wwwmp46i3mKYlX9KAGtc18XUZgYBCgNG3LS4xH1/KA8NTJo3Dl8RowROVJvDtt4VnTdkdLAzNeyl19XngLWvKayWukhBCRK4l3169kMbIwIpo85MZsMVTGm8VrZXF3jnDbbVHHrp68lJC9VKbcwqrMQumcd9VCz3uIAPAMyk9JSIE5wBuZ8sq/5gMvGKJWdlyok+aS+6WkrmN1zUH1hmayw+nlOZ5b5DJhPTT7lO+dEcgIZASqIVCzpN7iIZTEtmVKtp1YotUGkvMyAm2JgC1vHgKfVuOV1rZtUZ+0Ipc8Q7aKWe62Yf0iWUKXWO9CmlyT05BDgBeH59i2Nd1gy3XI3W3otcx7KVzOLgEPnp1OMmjr3RY/L7FdB+EtvKSIvvhshN6OB2+ssA9GD28iI4j82lJmJNiV4+1MI+R5tw2OwIrd5Wm09c/RwnPuXqmuX1vlPFxlz658SciG+eBZCZuQl5L7CoNhgPACcuLYFfFOg2dpB0VsunmXrhkav8YvXAEWcK3lXZmhgc9Qume+bUYgI1AFgZol9VX6mrMyAu2GgBABhqVtS/GPyLwFXQwpcuTFMJ47xAnhQYSQEOFh6gtZaLfO5hs1NALkCnHnwUbCkVzhGT55x2tM/sij+GThAeJhhbyIC68ERmwtki92WpiRci9p+k1JLHQ6JvNlwp/yy7/CIsvn5WMx9bay1WEklO/lRVR1bXcLk0DkjVFeOZV3Ecr5+TgjkBHICGQEmiOQSX1zPELI5xmBvgiI3xY3530Esac8m0IM+hYFZASJF78n1s/LU8p5I4U8CNHxwo26OWUE2gIB4SjItdhUYS5ikH2EgBHpyxGIvl0LBNiLil5wTi+gle9PnsW4iq/1YpqQGHHE5ToDe9y/eG/E3rsWdq+8GGhnxT30z7sWvPXi171rlL3gkMkpI5ARyAgMGgKZ1A8abvmqBkcAWfIVBS8eezFMCJjwA1vxRVEEL814C17oDXLkZUShEV4Qa3Bo8vD+RqA9f3wRhBfei3i+rCI8RjiInST9sLPE4+1YWI1wFsal83JirNqB8jKn90S8SIbkl+v065i8+xpXuQ6jwjsk1e6nnvaFsHkfypdkErFnpAjb4cn3fgpy7yVW10iMZfPNvHOeU0YgI5ARyAj0G4FM6vuNTy7toAggKEiFl/W8kInYC4NA9pEpscm+AuSLD4iVeoiUuGdJjH051KCDwpiH3UYICE0RM++FV6EzSD4i76VXt1DuhVVec9/S99d1JZ/ZI7M+EYlQ+2IHbz9iL6nvKz52mXw20Hl6L8SxL1AwJvyBQCFlCDhy7p4pMTLsXgmxkeflVrHxrmc4eJHXH60R+iNPyJBPBnr5Vr5+SN4b8AUMuw7CfrRnfg3KFzf0I6eMQI0gkLuREWg3BDKpbzeo843qCQFedwRGqACy42VF/fcyoLhf+ZI8pMUf03AuDMcv7z0jQHlOGYG2QMCXbXi8teVzgT4R6bicyJ4kPMynfhFs55K/LcEYFbpDniVk3SfafLIQiVbPS7iuT8fa9wUb5V56dV5OPg+39tprBy+WCztjSLg2JS+b884j6inPPXwDWliRfki+1+1zft4bYAz4A3zpE4/l++XjjEBGICOQEaiOwCCRen+kQlhCTjuHoYrBzvn+Gf8sA1kGBlwGvKzK2BSXL/mCjhh8u1GDiiPvv90AX93xnsmAtuMvRvLO64ckNKgoiiAUh3FsR0yY0IC2l+sNuBxkrDJWWQaGnAwIk6xOuYd87iCRenHDtoJzWjNkDDIGWQayDNS6DKT+2UFClNM5D7xvsPtMZsoblF+hNNrW3oBe78tR+++/f5MOFb4mTAjZ0CefvBzQtnK9PAezDGQZqBUZqAxRHPJU/p87DBKp93KTrd2c5goZg4xBloEsA1kGsgxkGcgy0AAykDnNXIMvx3ZA/6HZ7Xs0SKS+fbuY75YRyAhkBDICGYGMQEYgI5ARyAj0C4FM6vuFTi5rOwRySxmBjEBGICOQEcgIZAQyAkMMgUzqhxi0ueGMQEYgI5ARGFgEcv2MQEYgI5ARGDQEMqkfNNzyVRmBjEBGICOQEcgIZAQyAkMHgXzXKghkUl8FlJyVEcgIZAQyAhmBjEBGICOQEagnBDKpr6enlfvaPgjku2QEMgIZgYxARiAjkBGoMwQyqa+zB5a7mxHICGQEMgK1gUDuRUYgI5ARqCUEaorU+wuCP/30U/jzzz9rCaOmvvz222/hxx9/bJb0V7+bKpUOjEO531J2PJSnTHu//PJL0Ia8WFj6548//mh2P/UlfSlVa7NDfUj9abNG+zakzSHV577Nt/n/cE/P59dff23WvrF4BsrLBa6RL1VeU65Xi8fkz3gGtO/GB4fKschvrY3W8CFzP//8c5OcO69sd0ifk019H9L3qYf2PVfPqlpfyYnnK7VWp9p19ZxXHrNxmyfyjAkG8qSUJ78ykS11pMqyej83NimNw/wlQ8aaknP5qU76Ne/UgSksU35H/IUFHPo1dhjCS6IznferfiOXmW9wgFlrOMBInZTUHUqYtNtta4bU9+7dOxx66KFhsskmC6+//nq7ATAwN7r88svD4osvHiaaaKKwzDLLhFVWWSXMMsss4fjjjw9p7J2SAAAQAElEQVTffPNNi6aeeeaZMOGEEwZ/JbFcSBhvu+22sNRSS8U2dtlll+AvK37wwQflavEYFksssUSYeOKJ4z1XXnnl+Mchdthhh/Ddd9/FOm35zw8//BAOOOCA8MQTT7RJsybba6+9FpZddtlw7LHHtkmbQ7oRE//mm28O6623XujWrVt8Ru+99168bZ8+fcLee+8d82acccbw4IMPxnyL2oUXXhgWXHDBeM12220X3nnnnVhW6/945ldddVX43//+F8jXlltuGb766quq3fY8X3311bDOOuuEtdZaq1mdt956K2y77bZx/P4a6Lvvvtus/Nlnn43tzzHHHLHOhx9+GMvVg9ess84a1lhjjfDll1/G/Pb6x/302zxsr3vW6n08kxVXXDE8/PDDLbr47bffhjPOOCPKvj+yc/TRR9esA6ZF5wcj4+qrr45jpu+lhRZaKNx9991R/9L9yy23XFwXTjjhhFCNmJov5KtbX11C9qthOxjdG6qXPvDAA2HVVVcNW2yxRaBHdOaLL74I++23XxNm//nPf6Iu/fTTTxU3pY8//jjQEzBdaaWVwsUXXxydW00VOsgBmXnjjTfCRhttFGaeeeZ+jvr+++9vwtVfcU6Y9/OiBiyks0888cSw+uqrxwQXvKo81O+//z7KJfmScLdFF120XKUhj2uG1H/22WeBMqhllBGZY445Jkw//fTh7LPPDrfcckskwKecckp4pi+Br+w7EjvNNNOE66+/vlnRRx99FK/fc889Yxv+tDryzlJvVrHvybTTThssFoyHdE+L6Q033BAeeeSRvjUG7n/3QK7gXe3KUUYZJRx00EFh3nnnrVYc83hdHnvssaqGTKxQ8Y8JKFVk1+zp559/HkYcccRw7rnnhmuvvTYuVpdddllccBho/py9Z5/+JDWyg8Afcsgh4dJLLw033XRTGHXUUVsYc0NkwG3QKOXHmGaUGC9i99xzz7XaMvllUA477LBNdSwujJ1///vfcfyTTDJJuOSSS5rKHcw+++yBsbTzzjsH8+Wkk06SHSaddNIAO0YUuR5rrLFifnv9Y/yffPJJYLC01z1r9T7nnXde8Hyr9e/FF18MU045ZdRZFtSTTz45vP3229WqNlQeeTTfUyLHs802W7jnnnsiuSezjGKOGmS/cvBXXnllYLCS/QsuuCAgYwzgynr1eG7dsh6U+96pU6donCe8jjrqqMCYGXPMMcvVon4dbrjhojwdfvjhgf5hADWr1AFO6B0GDj1UFEWrI6Zz77vvvogXbOlP63WrFzRoAfJujcUXr7jiiugMprc43MpD5ohjXMJK2n333cMGG2xQrtKQxzVD6nl+1l577boDmeWH0Jhw5c6//PLLgcLabLPNIvkmYKmcEkQEk2eekcAAmGCCCVKVVn+LogiTTz553AGgCFqt2EqBHYUePXoMljf0hRdeiAq5cszVblkURZhpppliqlZei3njjjtuWHjhhQOFOfzww4euXbsGnmsGkV0Thpp+d+vreZNPGfO2UM489p57586dw8gjj6xazaexxx47zD333GGkkUYKRVGEqaeeOpK3ah0viiKQeZiUST2FyuO+wgorRLm3Y9GrV69qTQT4MmQZEIygqpXaMdMu2AILLBCGGWaYdrxr7d3KTuSaa64ZDdJqvZthhhkCr6syTgb6qqzX5DdiIhtpXIxdOuFf//pXuOOOOwKvPQPejiyD9qyzzkpVm34RMTrefDHP6O9Bccg0NTiIB0PiMiRpkUUWadb06KOPHh1fKfPJJ5+MHmh6MeVZO3j5YSbPrid5gqnzjpToHfrSzr3j1sbOaGT42E19//33W6vW8Pl20nEQuxqcb/T3GGOMESoNQo4qDqMECPys3+m8UX9rhtTXE8AInFgtwoX8zT///JEEpjEgd5TTJptsEv773/+Gr7/+OnomU3mXLl2ChWKrrbYK448/ftAOomOxSHUqf7XpnkjkqaeeGhfeJKDKbr/99ujxRES32WabuA2MhI4wwghxIbbDIKTG4sOzZEI8//zzgdd56aWXjkTOVigvHQWTCBnloQ1p++23j7spyOyZZ54ZpphiisBjj9DZgkYKLXaIrT7xao/Y1+MNI9uLlWOq1XPPQb9T/yxSiC4MeO1SPo/UdNNNFzxD5TDwXHkF5CG+qW4t/xqHxYSxKaxr/fXXDxbYgekzhQobGLnOb2vP3P2QQ7KHFPDauyYlsvPUU08FCz2Zmm+++YLwPAaAcyRKGIgdBfchX3ZKLrroomiY8AjyIvPomBfkeZ999gkUv7A5MptIFk9juq/6nqE2t99++7hDo0wb7jtiX1l2D/XsmsFJO3POOWd46aWXVK3bRBcURRHoptYGYR6kMs8QSUVqU16j/paNc3JnK99YxenyzJNX5+pxmjguJzqVnKV65IjOL9dppGO6xBwyJmPmVa3c+TWH6JsUvpiuQfZdl1NLBBhP9Ax9J/Xs2bNlpQ6Qw4mGl4wzzjjRCUUXkRue+/LwzbOi+Gvn48033wzjjTdecE25TiMetwGpb0RY+j2mV155JcZqERCxhGIuRxtttKaLeCyFm/DIICBICW+NRSBVsi2EGCMuCOBee+0VCGsqr/y1WLiXHQ0Cfeutt0YhVQ8p4vW0zWl7V8zmNddcE7elTj/99IDQI/hIuL7xFlnEJcaHviPgtkBtDeu7dt2H8nj66acDy1hYCsIurIJHzxYy44CRgcDqA4Jjd+Khhx4KwiwQfqErRVFosu6SOFDGFOOs3HnP0jOAd8rff//9o4fqiCOOCLzfZc9UqlPLv2LKycTyyy8fvZBDuq877rhj4NkXw4/4pPuRKyFtQhrMCe9jmC+MWAay2Ehx3wwPz0A8pZAyc4wMHnjggXEn6cYbbwz77rtvsGvGmEXmEXThbOraGXSc7suTc+SRRwYY8KTeeeedgSd6jz32iGFYdtO6d+8eyLNtX4Tu3nvvDTyRvNipnXr7tdPIKGL8DEjfOTVgyQC0oA7INY1QB3GgW5N3mSwgC6eddlogO3SeHbrKsQorY+hbJ9TzjhEDoLJeI56TE6E3DP7y+BjYcDH/YCKZd2XDsVw/H4fAYcb44bSjqznnrEMZm/4jwMEkdJDTs/+167tGJvWD8Pws4Oecc06MjRQnaUEsN4NE2FpEEBA9AnXXXXdFL7d6vO0Uu5c8EA/khsJXx8Ih1j4ldV1DCbonsoN082TKlxBn5IM3SLs8SQg8gq0dJFwIDPKufkpIDc+l+NCkdMXK8nSqIxaUEWG8PNG2/li7ylJCePVFn5B/3gTbiLwIjtX3YrEJla6pp19x9cgkkl7uNyIJF55a+YiRF70RSjswyD5SqKxekpf+GIMMNWEG+k2GyZBkgZZXLZGZsvfRcQviUuVCHnVECGFmuKpCnnny7F4hjXaGeOURf4sa8mnXSf30bgjDliySQaFt5gDj9bDDDosefy/1ej9Fe3YItM0od7+UeOk9U3OHMatN85P8atdc9L4JkmLu2G1gkKfr6/WXvjr//PODZ+E52xVBVBlX1cbkWdA3HcFLXx6/GHq7mimPjHCakAvOkV69ekVnTypPv+SNDNPPdCjD1I5eKm/kX3Hf1phqY/RivvfDzC0GvHlr7ahWN+f9g4AdMmuS9Zye/KekYxxxlhm7nR4j5mSQ0u6QvHKyLuBgdL5ry2WNeJxJ/WA81V133TXGHiNzPIqaYjnz5PNC+CoAjyJPpK0gHj11kBGk3zWIj7AXnmDlBJOnNCXb/q5JiZeId5NXnudcPqKDXPqCCK+ihHiY+IwF1qmFBRFSf0CTPlvA+1W/KIq4Y4DkuK/kCyaUzaDE/PfrXu1d5vkih16MK9/78ccfjx5cHuSUz8vEKEP0fMnB4uQ8ldfLL+PFLgylqc9kKMmiGEV51ZL3DMhjek/EDgePerW65TzEmvxb2JNxbDfIC5gMJXUtYjx4CDlZZ4QiWDxV6R5Cphix5C8lZNz1A5vIrj7YaTP/vPSb2vSylbk2sG3Wcn0GO697es5kAGH3bKr1m3OBHuI5rFbeqHmIO31QHt+SSy4ZyAaDka6vRtYZrdYD9RjLsC7H+pbba6Rj4RB20BjXrY1rww03DELdzFXhbMn501r9nP8XAoiq3Ue68a+cjvMvzmSX1hpBVyP3nJ6cMdVQYCwyvDuKbNUMqfdwLKRi8PyyvKo9oKGZl/qobwSJcAlZEboiZpfgIM62YZFcsfYSEt61a9dAmbvOGG3fi8s1OYXEiJXzAiJL0jUpISuuSfdEbHyFR1+06/qpppoqvozLU+ScR5UnWd9cJwymW7du8RNsCT91KFztpKTMvfRPHkPDIiS8JrWbvuSjjkklnIE33gtivPW8UHYGvEhnS1U/eP5YyryA6Xr3qtUEs/333z++EIrYGTvPpa12nuHjjjsuvitRFEXwzH0CFAZIqNAM2CG1rq3VMZb7ZbzGKNkpEueKtKnDoEmyaIzyjE9K18mjZHnDecZ51L10ybhTVk7mtkTOXF8Uf714S6bTAoUgmQfIoz717t07Go6JCDEgGMR2gxBQ7fPe22kQ2+8aYTiel3u5j/Az91TXcco3DueMBTtOrjUvGCg8sbyJQnwYEMoY7Oaua1yrHW3Wc2IwpWfsl0FnZ08+7ODm15gZq4xZJAwedEQ9j31A+54MTrtY6RrPHwZ0hR0uxikZphvJBbzUdUym7HqSJ0RWfiMkYzPOJB/lMTG8GTPWsJSvLnmCkWP4IfPmqk86wi/V7Ui/ZAkecEn4GL9jGDv2Cy9rDoeROcrxpqwjJY5PziaONHoaz7AzzJEJPxiRx4SJOuYvnpTyGvm3Zki9BVosMhJhS87i2fbAD16LyKrtd4udGF+eaF4YhN7n3pAY5SadsA3C5Y48u7a4CRWyjxgKN0BIEHPhGhQbUq9+OQmn6dGjR/zEIo+GWF8eNHF1tv7tBghXEPaBBGnP935ZpTwkSLQ890Z8GCIwRmB48RkUdgjg77680xZ1YUW8lIyU6667LsY+e1GRNwpZ5aVD9oUQ6VdRFIGHd9111w3Ku/U1IjxPePilgODnevep5cRIY5iJ94SdREkgMsin52ec8i1a3bt3j2MWcuO5y0dKLVK1PM7UN+9LIOT6TT4ZX4h1Ki//wgFhJi88ceQvlXsfg3wlWSZnqSz9kmUGErlAwFO+nSRk0Tn58R6IhV6fKG3XkUvlDAjvopAx5xLPMiPW98BdY76RbbrEey1ecPROCs8+OVRX+0JMPDe7XLyuriXXvKo8P0gGg1VoijLhSGRZjDTjBaFz/0ZK9Aa9Zkxw9MK/X1jKIx+wkDxzeY2efPJU6A3jL43VC6BkkDODc4PcKuMZtJNKrswXO7n0KrlH/NVplORTtHadvX9ljiGmaWxCtMr6QT5H1tZbbx1gZE5ZD4RpegE9hYCq19GS9dY6azfUfKNbYGB+0TEcRnSgOUe/0k3Wc3U6YrIjRm4YyAwccwwOnGnWMuuT72nIdgAAEABJREFUcwk/ws0cd4RUM6TeYossUI7IqdjWWnsAiJz+Sbx1SDXPuXALeeVkYrIojcGb/2lsYgeFCdjCRwbTNcgUAqF+OSFXqY5fwqy8a9euASkX82uxSefq8FjampOPUMsTt49wCcVBWizQPMzKEH/4a9dCJE85o8PWvHOJIaKOT0g5pojL5+rwaKY+GpM88bpiT1dbbTXVaz7xVup3OaUQqnKeY+NNHmTPVZ5kEUcma36wfTtoV4WnUb8RFQtL3+yq/5N3C7W6EvkrV+Spl4+kM2zKZY4pX+WSOSBPQtjJi2OJR1T8sXqMK/GQ8iXzilFb6SUu9wvxNCdcLzlHKBzTL76pr+8MBguDdr2srpxhNsMMM8iKydyQL2mDbjL3nDNOYqUG+odBxWgyJL/mr1+E1JjLabHFFlOt4RMvO3kj/2mwCKldHDt3Kc8vpw+jkFyp72VtmGlDeSMlzi1jk+wIl9cwzp5k6KQx+4gC8g8jhrT5h+Sn8o76i6zDUDLfOC5g4R03OsbaffDBBwfl1mv1lXfkhKhbr+Al/AYWnI3WsrTTLE9IbPlcXiOnmiH1jQxyHltGICOQEcgIZAQyAo2HQB5RRqCWEMikvpaeRu5LRiAjkBHICGQEMgIZgYxARmAQEMikfhBAa59L8l0yAhmBjEBGICOQEcgIZAQyAgOGQCb1A4ZTrpURyAhkBGoTgdyrjEBGICOQEcgI9EUgk/q+IOT/MwIZgYxARiAjkBHICDQyAnlsjY9AJvWN/4zzCDMCGYGMQEYgI5ARyAhkBBocgUzqG/wBt8/w8l0yAhmBjEBGICOQEcgIZASGJgKZ1A9N9PO9MwIZgYxAR0IgjzUjkBHICGQEhhgCmdQPMWhzwxmBjEBGICOQEcgIZAQyAgOLQK4/aAjUDKn/888/w++//x6T40EbTm1flcZX+dtar+Hwxx9/VC1WltpxLFWr6PpUr/wrv1r9cp76qV2/A3JN+fp6PjZe45eqjVueOpVjVF+qVlZZt9bP+zUO45P6NQYYacNvuZ7r5Evl/GrHrlVPclytzqDmaU9fBvX6Rr2uX7jAy7OQHDcqBpXjgokxS9XGLU+ZepXXpjLlKcmrrFev58YslftvfP0ba7lO/+qW227UYxjCAS6tjVG5pG5rdTpKPpxgIfVvzOp2FMxqgtR///334cILLwzLL798WGihhYI/H/3FF1/07znVVfnXX38d1l9//TDJJJOELl26BH+6eJFFFgn+rPGLL75YdSyHHXZYmG222cIPP/zQrPzbb78N/jz3kksuGfzpcn/W/bzzzgvffPNNs3pOjjzyyODP2ruvP5Xsvv60uz9jrk/qVKZff/01/jlq1zz22GPhq6++CrvvvntYaqmlKqvW4Xn/u/zzzz+Hm2++Oayyyiph8cUXDyuvvHL48MMP44UUw/333x/zPZ+Y+fc/zz33XFhmmWUiTscee2wg138X1dWPft94441hyimnDFdddVWLvv/4449RHshWi8K+GZTs448/HtZee+2wxBJLBHL+7LPPBthp+4gjjojybx5cdtll4Zdfful7Vcv/n3zyybDhhhvGuuQWtj179my1fssWWs959913g/lz9NFHt16pA5Z8/PHHUac89NBDLUb/5ZdfhqOOOio+j3nnnbeqbLS4qAEy+vTpEzbeeOOw6KKLhoUXXjhcfvnlgY5MQ3vttdfCrrvuGsuOO+64lN30aw6R35TmmmuucMcddzSV1/OB9WHVVVcNm266adM69dNPP4VLL700rLTSSoGcbL311uHTTz9tMcx77rknLLfcclGeYDPppJOGa665pkW9Rs/47bffwr333hvWWWediMVpp50WrPGV4yZn3bp1C9ZvuD344INRp1bW6wjnuMsJJ5wQll122cgb6SvrTrWxW3duuOGGuJbgMtXqNFJep1oYzAcffBAmnnjicN1110Vy/8QTT4S77767FrrWZn0YY4wxwkUXXRRJH5JofLfeemuYeeaZA4L93XffNbsXTF5++eXQu3fv8MADDzQre+SRR8ILL7wQLr744kBYxx577CbS2axi35Odd945bLPNNvG+iJr73nbbbWGjjTYKI444Yt8aLf+3YLkv5ayUsfDee+/FXRTnjZ4+++yzMNxww0V8b7nllkCBXHLJJXH8FMRbb73VwoBC+mF9/PHHh2uvvTa8//774a677qpLqBiRxsi4qRwAj8frr78eLOatkXF4ffTRR+Gkk06KGMw666zh1FNPjUYOoj7PPPPE+c0YZeharCrvY8FC6JdeeulYl9zutNNOYfPNN4+kqrJ++RyBeP7554PFspxfPvZ8Pv/883JWPu6LwHnnnRfonr6HLf6nAxZccMH4POisq6++OpCHFhUbJePvcdABwwwzTKA3HZ988smBjlCMTOywww7RWaNsxx13lN0sde7cOWJGhqW55547cNY0q1SnJ08//XQLAoo4jTLKKHGeWtPVqUbW6di0JiUnynzzzVenSAx6txnSjKBddtklyskzzzwT4FJuEcm37jMoyeEcc8wRzjrrrJDW6HLdRj9G3m+66aY4B8kXJxEs3nnnnapDh93tt98e1/GqFRossyZIPY/wAgssEIYffvgwwQQTRKKLSDYY1i2GM/roo0fvDhJVServvPPOJi9npULkKe3T13uEfFOMa621Vthkk03CyCOP3OIelRmI2lNPPRWmmWaaMMIII1QWx3Pt8LCOOeaY8ZwHxWLu+cSMBv9nnHHGid5lC5Mx2+HgpUQShx122LDaaqu1WJRhSnann376MNpoo8Xy++67ry6RGnfcccO6664bxhtvvBb9p1At0sbZovDvjFFHHTXuZMARGbLDYz4zAlzH06TqCiusEElhJbm2S3f66afHubHmmmuqGhNvHjnnyWJ0xsyKf5BMBMGc8bwqiptOkYdpp5226TwfhHDllVfGXSnyWw0PeNHTytTp2rWrw4ZP5IhcSZ07dw4cNPQAuWV4kmOeamXVwEiYKeOMMSeqzS3l9ZY222yzQD+W+23e2+EcaaSRorOOEQOrch3Hdizg6PjNN9+M+sa1zjtS4iRBzu2MGjdHRq9evZoRdmu1HRGYWp9hZ/3nZHJNR0qw4rThEOWYxB/NyVdffbUqDHbFpptuurguV63QBpm11ERNkHoPRgJMURQBieS5d96IyeIgIR/CNIQpjD/++E1DRfB5EpEfSlNYxxtvvNFUzsvDOz/ZZJNFbyjiiYglBdlUsXTgfhKDgMJQtHlfrydlcfDBB0ePKlIqHEhZR04woTwTBp37LuQUrvyUV/krvKS8UFO85WdWWb9ez4877rhgQWGQtjYG8mj8qdw5xYvsk9NyfqdOnQKymPL88vTzClPazsvJtrPQmaTADznkkKANidfU7hWi1aNHj7hN7Vo7XeaLOq43D+RLCBvSoeyKK66QFdM+++wT2zWnbI3LtFOwxRZbRKLC2LAjIL8RErLJYJt66qlbHY7nqBD+dnKEVxRFIauhEweHkDs7E3Y9yRw5Fj6CjCIY5F0oJKJVCQbnQMo744wzYlhfOm/EX3PGXE9jQ9Qr57gymBXFX/Lz6KOPhsknnzzukCrrSEmoIlIqGbe1x04QHuBcMvfKeMF4pplm6pB4CeG0PpiDRVFEPQ2raoajHWU7R/3Sa6HB/qsJUl/G1IKBPIldLOc3yjHLmhdx9tlnjzGHJi8SUh6f7TcLwVhjjRXfMSiKIghJSmSE0cOrJgZPiIPJbUuu2oKS2lXunQUxeTyh8nk8u3fv7jDuCpxzzjnxOP/zDwIUBUMIdv/kttdRbd3HbgRDcsYZZxzgjiHNr7zySiBn5nX5wvPPPz+G25UNWuU8+pS0434l264WRHNKbD6SZU6IkxcKhYwzjtUT9qOesCDE3I6Vts8999yAzAtj23777cMnn3wSY8WFoAiVEEMupELIhXA5IRYMOPezqGij3hP5tvPnfaYBGQvjBqbCCO0yDsg19VzHrqYQm8MPPzwwZpPxjoh674ZOffvttwMchd+1NlZkRFgZI6G1Oo2Wj3yZc3BqbWxwMR95UxnXrdXL+X8hQDfSTzz6lTr1rxr5XwhYux9++OEYxeC8o6SaIvU8dBbOFVdccYBCSerxIVFaFByvjxAApLw8DiRI7JzwGy8iSmLChHJQfuqa0KxPMfo8kwyEAw44IIjNQ2R4KSUvaKkveclQHJpEecrLqf8IIH22knmb+lVbOTKa6iA7PCvpvN5/ERa7Sl7qJlu830g0stu7d+/gpWH5Bx54YCTGabxiRaeYYoog7Cbl+RXCQ56FMjkvJ7jxrJPncr5jJN2unrAzcwh5l7/66quHvffe22GzdP311wdhZJP39QIq4CnlveE8cC6cp0uXLjFciLFsTDyvnqU5Ryd5/4ROMocYd/oWGug/7+7QI+JSPUOxqWeffXZAVKsNkxHkHQkY0kXV6jRSnp1ScsAJYneGnJFNpMH7Ikg6Y9eL1wzB1sZONwsha618sPJr8GJYkRVGcb+6x5CGZyPvzvdr/IxEISV0jnr0Is88Hee8MtmRtKvuAxiVZR3h3C6FnaCEF2eNVGngeA9OaBOdhitxxlinzOVGxqmmSL2XryyeHWGrRDjHmWeeGXi8eP+SkCHwXhzccsstQ9euXePb8EgFgbSIqIdY8dw7nnPOOcNee+0VCDoLHkFxnVRt0iNMts1dm1O/EYArEgfjftcM8T2QFBKiLu+U9xAcN0IiX75KRa4kBJiseelVeBIvr3xfCLHLZMy82RYmMcfOU2IEkF9EPOWVfy1yQnwYoOV8x7z7s8wyS/BMGMh2uuS3lihwxNPcUEcsuOuMx3lKjGkLA4NXn4W++cKU5OsdjUw4GF1iwj0/SRiAF/EYQwmfyl+GLh024YQTVhY13LmXFskG2eZkIUt2U3nw6eU0YEQD2Urnlb9iezl0KvMb8RwxveCCC4K5T076NUZGpDnJMdKveo1aJpyW0yTtoFvnGYvkqXLMwsDsqjEgEf/K8o5wbn0xz6wjQgaRezto1qPy+H0Zh9OIToOnHWHrFKdQuV6jHfeP1LfbeH0yzLauB0Np8gSx9NutA0PhRuIMeceQdvH1usAb5DNgBJIwSkIKxLqfeOKJqsRP+u22225BPKcMHjULscQj6RqpGqmvVJ4mBQKqHZ+IgnlrZEudjpK8Z2ART6EmXtxMeFfDYP75548vevMei6VnmNkerVa3HvMQXUSbXEmJ1ItHL5N6n6+kdK+77rr4hSCx58YrtEsYjhAxxizlahGnkHk+1UnJYiXcAdFmVKV8eb7+YB6QdQSJ0vblIXWEyvhNifeZThHygGDI92Ui3lLzxHnPnj39BM9NGJsF1g5aet5C3uyc2caNFRvwH8/PM00Jtnb/5Lc2XASV4SP+t7U6jZLPyBRqQ0/Cxi9SwQlFzn3lys6pkEhfMKk2bmEodC2DtFp5I+UJpbGDR4ammmqqYD3nsacXqwtEBvgAABAASURBVI0TtsJtvUBcrbzR8+zy0IUMRWMV5kWvVeJBj9l9t7to3tmFtPvjmo6UeOQ5FRjUZMv8szPO0VPGgTFpDafXvJ9lHlt3XF+u12jHQ53UU3QmPMKE2FsoLOSUp5jyRgGcxxA594IZYeTxIYiEUz5CYuwWSwSDoBo7YoPwWzTEBfPgE1DkZt99940vAyIvBx10UPxykGvKSbvac1/KoFzm2CLkJTn3hrudEqSLRxRxE07BY9qrV69AWe+///4ua+hEecL/0EMPjfjCxth5JZFSxg88PSvPIIFByQqn8jKdeGMLWiqrp1+ebeFcPLWMTIS8sv+8uxajf/L/ObJjYU4LvYGdJE8MOiJk+5gcy2e8Cl/45+q/jsx9Mfw85+pJnoGEUKkl9MZ2qhfNlfOcFkURv5pjJ4AhwkAQRoNwqfPVV18FLzq63hzi4ZH/0ksvxV0z+QiGNukjX/awcCJl5ol65od6jZqQMbgYn1AkWPhl6MBKYuiQ86L460VHdRs1MUwRBl8ZM3Y6NZFzDhUOALufZJu8+tITvUlOePjhwvBkTFbuECmr5+SFcnrQ+s3Ytl4JQRJGCwN4WVfsqNGHp5xySthggw2iwZ/GrQy+6byj/eI6W221VXy3Z7HFFgvCRhBSOPiYBQeeMBIhcULB1JHsINkpVa+jJeScnmb8eB9KxAMMhHF5Yd367TwlIZPmZqPNvzS+8u9QJ/WsUZ5hC2c5WUiUlTtbz8cmLvKdxohMC4Xh1bRIpHy/wnES0SGEvuUtX6IUkW1KMLXn1/ZSNXwoBNdJSExlHbsFyiREi+dJX5E6eQi9SeJa58IRKttotHNhM8ZaTttuu23gHUEUd9999/jHQpTDqTx+eRY08dfl/Ho6Zrz4yo2xWKArX+Q2FuEaPnvpuDKRKQuT61PifaJYK2Wd8uVJqWwjnTN+UxtkP+WnX/G6qdxWv3w7CvLWW289p/FrPc4lhlrM/Puf1E+LAm+ZbJ4coUbqS8iaxcO3jsXcVzOOXdcoidHKcWA8fmHk15e44CGRe0afOo2e6EPk1bglujeNmYEpT7IzVxRFfJlWfXJiZ1RdO1ocJulcXiMkxN3YJWuN9Yqh7rycrC3G6/fcc8+NGDmXzL1kRDrviEnIMecJzOzUJww4VejOcrk6kl1+/CHV7Wi/jGwGJeeROWr8vPE4S+JP8iTGz5577hmqhTQpb6Q01El9I4GZx5IRyAhkBDICGYGMQEYgI5ARGBoINBKpHxr45XtmBDICGYGMQEYgI5ARyAhkBIY6ApnUD/VHkDuQEcgItC8C+W4ZgYxARiAjkBFoPAQyqW+8Z5pHlBHICGQEMgIZgYzA4CKQr88I1BkCmdTX2QPL3c0IZAQyAhmBjEBGICOQEcgIVCKQSX0lIu1znu+SEcgIZAQyAhmBjEBGICOQEWgzBDKpbzMoc0MZgYxARqCtEcjtZQQyAhmBjEBGYMAQyKR+wHDKtTICGYGMQEYgI5ARyAjUJgK5VxmBvghkUt8XhPx/RiAjkBHICGQEMgIZgYxARqCeEcikvp6fXvv0Pd8lI5ARyAhkBDICGYGMQEagxhGoGVL/wQcfhIcffjimTz75pCZhe+edd8IDDzzQLOnzF198UbW/P//8c3j00UfDjz/+2KL8p59+Co8//nhs69VXXw2ffvpp+PXXX1vU+/bbb2Odyvt++OGHLeq2RcZvv/0WXnzxxdDamAblHr///nt47bXXwnvvvTcolw+Va77++uvw2GOPhQcffDC88cYbVfvw8ssvh88++6xF2VdffRXeeuutAMsWhTWWoa/PPPNM+OGHH5p69vHHHzeTOXKqXlOF0oH8JJuff/55qSSEchmZ+uWXX5rKv//++/Dkk0/G+zz77LPBXFH4559/hpdeeinma9c8kd+e6ZtvvgnmZLm/7Xn/oX0vY4dBtX68//778dk88sgjgW5qXqcxz+gvOoA8SmS3PFLzAx50Rjm/2vFTTz0V50W1snrNo+vg88cffzQbgjz6E2Zvv/12s7J0Yt1XLrXlmpPar5df6zkMpC+//LLVbr/yyitx/pG51uZoqxc3YIE1Gi/r19DoM7iSNetLv+o2QllNkHpg33DDDeGWW24J55xzTjj00ENrUvFRUmeffXZYa621wqWXXhpuu+22sO2224ZDDjkkIEKVAkHRL7XUUpEYlsuQhUsuuSQcccQRsQ3Hu+66a/joo4/K1eKxheLcc88N//vf/+I9YXTggQeGzTffvCqhjBcNxj+IKIXRVoaVSaS9NddcM/Z/MLrWbpciK3fccUe46aabwnXXXRe6d+8eEM9yB3r37h3WX3/90KtXr3J2QADOO++8cNhhh9U86WGQ7rjjjmGDDTYIffr0iePQ/7vuuiuQbenmm28O+++/f7CYxAqlf+B02mmnxbrGu8suuzSbt/DThvTEE080EXdNuBZ2rv/vf/8b7rnnHtmBvJg35oY5Vs0gjhWH0D+IifHutttuoa3mwBDq6hBplkG16aabhueff75F+2ServI8PW96yfNqUbHBMl544YXQo0ePcNVVV4XLLrssHHfccU2OmtNPPz2uWfJPPfXUOP9bGz4nwHLLLRedOa3Vqbf8s846K65N5KFsgDPwb7zxxrimk5dq+uPdd98NW265ZbjiiivCxRdfHE488cRgbaw3DAa3v4yZiy66KOpRa/u+++4bqnEBzpctttgi1rvggguCuVi3eA0maAwfGCyxxBJx/rXWXM+ePcNJJ50Urr/++nDkkUfWlWOxtTH1L79T/yq0R3mnTp3CkksuGcnD3nvvHZB8HqH2uPfA3OPf//532GyzzcKkk04akKGDDjooHHPMMVHZU/yVbSH7888/f7j22mubFRkbhbfTTjsFbZionTt3joSmWcW+JxNPPHFUfF26dIn3PPjgg4NJ//rrr4eHHnqob42B+99uAEHXh2pXjjjiiJHETjfddNWKYx7lzbiguGNGf/4Ze+yxwzjjjNOfWrVTzAMCd7JIyTpGbmGnl8aP9JBT5+UE12f6er7VKefX4vEEE0wQFlhggRZdm3rqqaNcks399tsvyvvMM8/coh4Mll566Vj3qKOOivJoZ0pF3rtRRx01lmmHATTaaKMpimn88ceP8rzxxhuHiSaaKBq4PC50AaN5lVVWCTvvvHMYc8wxY/32+qcoimD8ww47bHvdsqbug1y1Zkgx+FdbbbX4TDfv61RgFNZU54dQZ+6+++4w1lhjhcMPPzwSA+d033PPPReOPfbYQCdL5kMyTiu78t1338WdqWo7e5V16+l8pplmCtNPP32LLjNgFllkkSgr5v8yyyzToo51iDMM2WJI8+qnHbsWlRs4wy4GcgonRhIZ4girHPLll18esVYPD6mXdaZyHG1xjqeQu0kmmaTV5uwU41jrrLNOdBRb7zjcWr2gQQpqgtRb4KeccspgQR9++OGDBzXDDDPUBcRTTTVV9C5UKiOeyRlnnDHwepl8b775ZtN4hhlmmEDJp0XR+I8++ug47qZKrRwURRGQ5DHGGGOQPMEEfa+99moWbhEG8j9hE7yolWOu1kxRFGHCCScMJlS18jbOa5Pm9He++eYLZBG5m3XWWSNevLhuIJRo5JFHDv/617+cNksnnHBCWGmllZrl1erJ5JNPHhid5f6Rzbnnnrspi9ecvI0yyihNeenAtbPNNls8ZQR27muYMohkXHPNNdGDN/roozttNZnzSLx6DINWK7ZTQVEUYZZZZgnDDTdcO92xdm5Ddg844IAwwggjVO0UXTd5X5lRaC6svPLKoSgKpw2dzHV6k7Ez0kgjBXNB3n333RcWW2yxSPjl0RnXXXddVSzo+tlnnz3QJ1Ur1GmmMZOLcvd5Uc8888ygjGOrXFY+hpkdUPIm0adF0fjyVMbA8RxzzBHIhmPcZ4oppojrjfNyghcDQF7Ci/503tGSeWidwoNaG7vdIXpq3nnnjfNuoYUWijvvrdVvlPyaIPXA5AVFFtdbb72w4oorNtuqV15LSeiA2Eie8lVXXTWSl0UXXbSpi8YitEDIDG+FLWpj86sST//qq68etxsRJt5NW3BF0bpC06Z78m5YfCkCXlLtWWwsJs55XjfZZJNAsYphR1B4SVn3FKjtXx6lNdZYI9x///2hW7duQYiQEAjbn9qfZ555msJKhKHw0iK22uFp2nPPPcPtt98en5MdCu1utNFGYcEFFwzTTDNNjCfnqbYlPddcc4V11103xijraz2koiiayIpnRoHMOeeckewgrZ4DPIqiaBoOLyYvQPfu3ZuubSqss4Oi+GdcwuF4zasNoSj+qSfO2G4MxakuY5ZMMyDJpJ0dClZZZWJE8crbpkcGyuXwJ19wJV+Mf1upYin1ixFiN8FzIdPmGy+hnZXll18+7kSsvfbawQ7K1VdfHZU7OV944YWDRUGsJaOiS9+dMKEU6d5IHIKr75I2lamvH+Ra6J37atdOI71l/ORD3XpK9BMM4Nm/fnuW9IQdlv7Vrd3yAe8ZvWoXiVd+jz32iN55nnuY0eWppaIoou5L5+m3d+/eQRhluW4qa8RfO3TmGC88rzPdaWejcqyMQvPIXKQ7zSfXVtZr9POi+EePcpQhqrPNNluLYVtHvXtnrbVbRP91RLxaANNKBp1NRxXFX/gWRRHMxVaqN0x2zZB6sWEElcfOlnwtL4wI87333htWWGGFgDiI/2VFJ6kQn4xos7rlIwG8OohPqoN422pjxNia3HrrrUM1xZfqw8c9hYSItxP2wyBQjvRQihSkbTwGxa233hrEvNout7WJbPMAIDt2QWzlIaIWK4LP877NNttEok+xpHbFFouNtpDbeeBpstX8n//8J8apaZMC5zFgMFBI++yzT/AstSk+22JYDr3Qdr0kzxKxXHzxxWOX4YAMIrAx4+9/bMV73raj/86q+x+LMs9QtQWmcnCMP4tN2pExj+10eFfEVjEDsiz/lddb2BH7rbbaKiCMqVyM/ymnnBIYwbbnkX6yLx+ZRhiUud/2228fzDMGsLlFXpdddtlg10z85dNPPx08S+XieN2DbB533HFBP7UvT7J4mjtIvPGL/Rdvzngn50Lxrrzyyhgz7NkLxbLVqz6DWxv1khhfdIqdlwHps/FyRNDTrh2Qa+q5DqOT4efZmg8cFgMzHuSfnNarDhyYsaprl8t8ZPhYB+hLc1VZZbKeWSPpVbi2ZvhXXteo5+LkOdmmnXbaFkOcbLLJ4i6w9Znu4a1vUSlntD0CddZizZB6E9vCapFFTnnWahVLHhcLuHABk9BiX+6rL3148WXNNdeMxB8pRnrSAugFPEQD2RenyUMopAPhZjCY1Cmpq234uKeXPrSDvMiXEAoEn0eRp1AdJAnxFtaDVPE8Ilnqp2TrmMLl/UwLDlKVrH99Fl9MmSD+no8Qi3S9X8aGPiK9nhvCr5+MMqRKfQTYwqh+vSVYIncWKbsUu+++e4wF93y8OG3OMkhLAAAQAElEQVQHxDNkJPHsyuc5ZszAvd7GW+4vuTX2lJfGZ4wMOYarMrJr94pB6byciqKIIT5CzMhKuazy2E6Qd1W8M2LrVDnyjoxP/nfYB088eTLHlJM1ceDub+7IQ8jtrpgDdp08Q1585J/xiaAxPiyK8scbb7ygPoPV9RK5F7OpPhKP9DKW7WiRc2M9//zzY4iBcCs7YvUq454fQ1zsqWdr922HHXZo8XI4XCRzwPN3TBf5beRkR5aBe8YZZ0RZ9kK4d0bICFlLY4cFWUrnfsmNnU3vIMDWHCDfSJnyjpDMa7qzcqzkzPrJULdW0i+9++5qVNbrKOfkjA4q69zy2DnqOEaOP/74wLlArui9cp18/A8CnGxlR6ldVWvHPzUa86hmSH2Cl1JEdhPJTPm1+EtZWeSFGKTJZdJZJC+88MLoxfNCrMQQSJ5AHgkvwqiLaAvh4VH06SUk2JeAUhp33HGbDZ3XUGiDLwRZaBR6aYSHHKFH5CUCjdCoo5++MlA2BFzXv8RQ6J+SRXr0mWfBfSWeSh5R4T/9u0ctl9ve9BKXF2X1Ex6eUXo2dinsZCCWjBlGkDLeJwZTIj6urZJqPssiQr5TR4UeGJ/k5W5EmNfbV0GMt7U5a24g0AzT1Fa1X3XsKtEBvXr981UhMlyWJfdJhqfwMaRfqBdirV3tmA9iKcmj5FkpG9hEjhkLjFpzytzWXkrJ2BjYdmupPj0h5ttzlezkMVSF3bXWT1gzjITstFanUfI5W8g654fdTE4OhNTOhjmSxtmnT58YapjO/ZJlOxtwleh7BpTQE+UdISH0doorx+q9G3PVvOrevXsgS3ZDKut1hHMOInqMXFjPq435vPPOC/SNdUioEh5BH1Wrm/NCoL/wl7R2+GyoEKZGx6YmSD1vh0VF6tmzZwC+habWwCcclDmSIi6d1wWp89IrkqPvQk7uvPPO+BKlbUhKyySksHh4kHkeTr9CBIxZjLD4LzG5RVHE2G3XSYi/e7EyTXreIAYAkuLXNjjiT3iFGGjPVqcXHIUFWUjcW7gPUlUURQxBsFBZkBAmuyIUL7wtQMbnc2OIC1KDzGmXEka2eBMoFPGSYpuRAJ9zEx4kzALB4lHVb2PktUaGTz755KZYffeq1QRjnjVjsKNi7EI0YOKZpMSg8YzhIaV8eWKTLeC1Okb9Mu+EbvlGvedDLuVLPOGILDlzLhlXGqPxkjleb0aduQEnStO80J5zhqyFmte8crFyvftYmMige2ifxy7d130s+LykYu61qX3hL+rrkxfyGKxkVR7yZb4IP1PfHLDr4D7qmGv6R859RUo/EDJ42CFgNIiB1m/eMzLPy8/A8YKk3QTteq/ELoFreIS05f71lsgx3FNyTnb9woje8Qsj45YQDJioU2/jHdj+WouMmQzRt3QynceQ9YKjd5zgwZDkQRXWSBaENrpXwtWvczJLPzgesFS7tcwj47RbKzTJ/KJPkt6Em3WD48oozDU6B0b0qzlkflojyJjdZvU6UqIDyRBHhfWXc4g80UHWUPoKHsJ0rUMwtfYyhuwWKetoiTNJhATuZe2xcwYDoUm4FT7DoLZrLloh8R0OUfUaOXWqhcF5KLzZkrAN27vi8Wqhb+U+UGCEx+JuIlLuCMD+++8fP/tnMiq3PU/ghA64HnHm7RU/j5hYCHv06BF43Y0ZMRZTL+5S/XISNoOM2LqFje3czp07Bx4jRgBhVV97PIrao2BNdiExFIM84QI86BSHF3iREyTG4uSrA56BdvTF+ChmISdewC2KImhDfYuWrx1oDxHzK7ZW24wZypkXr2vXrvFzn/DQf+SP0eF696nlBGPy57kZt4QoCiEp93uDDTYIdijKeY5hD8NKEquslpJnzki1I+E5kedy/8hJ+bzy2LU8cNqBkSTkiGIlR84Zjgi4UKxq1zOU3BcJSuU8LIgzkoT8iJX3siLijgQI0SOz6ptL3mvh4Up5PMh2Syh+fUA27JQhH0LILJQUv7Aacu68KIro2UHa3N/Wtjks1tdcYzi4n/t7rtpFdr2Eq31z3q869Z48d/rKOBgr5jRDF7kwbgkxFa5TFH+9hKZuoyZywoBh4Bk7okAHGq/wEfqCbJETRiijUxw9vNQpJ44dTpZyXj0fm0c+O+tdIuMVE4+cW+dgJTGu0xitm5w/MLJGcPRwQMmz40nPprod5ZehY13GL+BFznxNjP6j8+gkWHBuCI9Vh86iI63Ryjpa4lQkN9ZZep+hCAOOKWuONck555wwSs4luxsNH37Td9A1Qep5O3z6SuIV5lHu27ea+x9J1kdJyA3ih1TwLsgrJ1a1hc8gTFALgXJbr0gfksETLk9abLHFAgKjfjlZXJWnhIQr9/UVsZm8muLiEWltq2fXwO4AooFwy0PckBHJ4sNiZWQoYxAwMLSL9MhTTmHw1jqXEDZ1TAyTxS5F+VwdBk7qozHJs5Btt912gWdG/VpP+q/f5YQ48l6W+84Qs5iV8xxTIkiALXvntZrK884C4bmmviLh5CqdV/ulUMsYORYCxtDlsXfunQMkuX/XW+BTHfOGkWh+yUMakhxrjwdGfkrqimtPnlD5ngFirg9kz/wwxnSOuDoWNuXZmkvOhaq53jjMLeWulSfBSD2J86EoikBnORfipk69J/NciIlx2O43f50zrIxTQugREXUaPXGAiPk2bikR+jRueeQnkfVUnyOmKJobPWSq8vrUTj3+pnkEA954a5j5yaiWJ5XHxUBipMNIvnN1JPqmKJrjpU6jpzIGcLAeJycCEo/MJwycq4NPVHMopXqN/muN2XDDDeMf/oQH/W/MHJ7mYuIz8nAZdYR142vyGjnVBKlvZIDz2DICDYBAHkJGICOQEcgIZAQyAjWOQCb1Nf6AcvcyAhmBjEBGICNQHwjkXmYEMgJDE4FM6ocm+vneGYGMQEYgI5ARyAhkBDICGYE2QKBuSH0bjDU3kRHICGQEMgIZgYxARiAjkBFoSAQyqW/Ix5oHlRHosAjkgWcEMgIZgYxARqBDIpBJfYd87HnQGYGMQEYgI5AR6MgI5LFnBBoPgUzqG++Z5hFlBDICGYGMQEYgI5ARyAh0MAQyqR8CDzw3mRHICGQEMgIZgYxARiAjkBFoTwQyqW9PtPO9MgIZgYzAPwjko4xARiAjkBHICLQZAjVH6n/55Zfw2muvhV9//bXNBlkLDf3222/hueeeC48//niz9Mwzz7TavU8//TS8/PLLVcs/+eST8NRTT8X02Wefha+++ir8/vvvLeq+/fbbze6X7i+/Wv3UwM8//xz7+/3338cs9/Nc4kkH+Oebb74Jzz77bHjiiSdCnz59mo3Ys3zzzTfDhx9+2Czfc4Dvk08+GRw3K6zjk48++qhJhr777rtWR/LDDz801XvvvffCH3/80VTXOWzg+eOPPzblVx78+eef4fXXX29qx3FlncE5f/7558MXX3wxOE00/LVku7XnDDvPUTJHGh6MvgOkJ3v37t0kk+S8b3Zco8gzLFJST1k5uZ4OSXXS9eU69Xz8zjvvBOMuz3fj+fLLLwNdaE13Xi1ZZ+Dy4osvhn7phWrXNlLeTz/9FNd6WHz99ddVhvZXFizVkWBGtv4q6Xj/WmPJF15lTa6GAP4Eq5TUrVavkfJqjtRfddVVYdttt224hZfyuuWWW8JGG20Utthii3DllVeGs88+OyyyyCLhpptuqipTRx55ZOjWrVsLLD7++ONw0EEHhXPOOSdcfvnl4aSTTgrnnntuSAS83Nijjz4a9thjj3jfiy66KN73/PPPD5dddllobeGmXC+++OKw2GKLhRdeeCEgZLvsskvYfPPNy0037DFc7rjjjogRzNZdd92IgwFTHpdeemlYZZVVwnnnnScrJkbP1VdfHfH13HbaaaeGIPYMyz333DOcdtppUd723nvvqnJmUSLT0oknnhi23nrr8MorrwT/MSAPOeSQcMEFF4Rjjz02nHLKKeHbb79V1CLB7tBDD404amu77bYL8DZ/WlQeyIwbb7wxLL744uGuu+4ayCs7TnXPjI5i0FaO2iJK53gudAq5YIRV1mu085deeinss88+UceeeeaZgXyTdxjBIiWY0BeV43e9eUNHu/6EE04Irq+sV4/n1pJ11lknrkflMTGed9555zDffPMFOqTa2KxX5jqZgou1n36tVreR8+i2G264IcCALB199NHBGl9tzDiEOpI1qtGcn9XGXC3v/fffj3hZG0499dRg7WXwlOtyQFx44YVNa8nxxx8frMvlOo14XFOk/o033oie0UYEepRRRgm77757mHvuucNCCy0UjjjiiEhwll122UhyeDXK44YFi/2DDz4IvXr1KhdFjxFF2aNHj4AsLbjggmHYYYdtViedrLbaatEwcN8DDzww3veYY44JG264YdCnVK/8O8www4R//etfYfjhh4/ZI488chh77LFDp041JS6xb0PiH7hPOOGEAb6HHXZYGG+88QJCSIEWRREmm2yyiE/53pTMAgssEPHdd999g+dn0S/Xqcfjhx56KO5UUIgI+XXXXRfIZOVY+vTdzZh66qnj+I866qgojxYd3rs777wzTDfddMFiRfYefvjh8Mgjj1Q2EWDNQN1tt91iO+bIXnvtFSjt22+/vUX9cgbjFXkq51UeTzTRRE0yXVmWz/9CgDGPbP111vzfV199NSy11FLx2SBjnitvYfNajXfGCOzcuXPU18cdd1y47bbbosGOgMKBnEozzzxz+N///tcCgLvvvjuMMcYYTdeTZQZSi4p1mNGlS5cwxRRTtOg5vKaffvp+rhnkx24zvbLjjjtGYkaPtGiswTPshJKpjTfeOM4t5PSaa65pMWq78eYgWZN22GGHMOKII7ao1+gZ5p312Npy8MEHB8ajOWqHsTx2HGn77bePmMJrxRVXDN27dy9XacjjTrU0qksuuSQAvpb6NCT7MtJIIwVEyCRmrZfvhRD95z//CTz1PBjlsqIoYtiN7W8EfIklloje/9FGG61creoxLzxliqi2Zggg8xQyMq+RscYaK0w11VSRqIUO8B9Cz8M0wggjxDHPMccc0TtNicB7rrnmCpNMMkkzJGadddYw00wzxTzYTjzxxKERttnJAJlh6FhAnI866qhxnOV/yPH8888fsxiE5IU33vYwwj3++ONHQj3OOONEWXr66adj3fSPHSE7H4zUaaedNmVHT592r7jiivD555835ZcPLHann3563L4u51cee476X5mfz/9C4OSTTw6MKM/5r5xm/8ZngbjK9XwZ+rxhzhs50dPk3zwgP+Rf3jzzzNNEWhn1dDjdUYkFPOnqdD1niusr69XjuZ1mBntl3+nHMj6V5c4ZN9Y3+Kg/7rjjBk4EZR0pIeqI6qSTThqHDVM6s9K4vvbaawMj0jpj9zNW7oD/mEecZnCwRltvRh999OhIK8NhTVKW8kREwDadN+pvTZB6Am0bZdVVV40kqlHBTuOyiFOxKAAAEABJREFUtSYWk/ezZ8+eMdwI6UnlvDgWEWRmgw02CCxQpCeVzznnnKFr165hhhlmCLvuumugFJCnoihSlRa/2kSkHnvssejpVwGBsgixdikRRLY8CdTpiKkoiqbFWniBBXj22WcPFEho5T+7GEXxF/48+hR0tcWulctrNnvRRReNuxJ2H+z0CNuaYIIJWvS3cvywsqgXRRFjZcUywsWFZJWB5Dgl4UuMoEqvn3bJOSWOGCFPPFp2nhibPHvCemxf8/DzXgmfItvzzjtv3BnbZJNNmkLYPE87X3bIzDkeH33Q7pprrhnrL7bYYuHee++NOxR2X1ZeeeUAB9vj6jZi4jFFrBCs1sbHoE1lQi2QMLuOKa9Rf+1O9O7dOwib2b3vbqvdJgYN2UxjFttLJqs5SpZeeulQvp5n2vXp2o74a33zvozdM+MviiLqXPM5dLD/7OjayWEsGjq54hBBXp2nxOHJiLbm40p0Hn2Wymvnd8j2BC52NxD5dCdrg5TO/cLRrySs0HoMZ+eNnGqC1FN4PHhTTjllI2Mdx2YSeuFUqIGY1JtvvjmssMIKsSz9Y5Kb4EgL76JjZDwRIWTEwrLffvsFL9paNMRyEvbURuXvW2+9FXj8xZ5ZkJW7//LLL+8wIEDaiyf5nyYE3n333fjCp92Qpsx+HHi+FAhCK0ynH1Xromi44YYLm266aSA/4mQTMe9X5y3WiD+jFBEUkkBOvQdCbr20VKlc7VYl+e5X25tvvnmYbbbZomEqFAIZXW+99QKSbk5pH1En4/fcc08Q6uOF5rPOOiu4h7Y9I7uC6iLsnpe+IamIvb7x/AubcG3nzp2D7XHGgesbLQn9M07Pa0DGxgnDCbDZZptFIjYg19RzHcRT3Dh58n6R8ZfHQ654TpGGMpFIdThO+nV9qpd/MwL9QgAfIF9kifOCQ7Bfa36/2upoZTgXR5tdskYf+1An9UiCBXP//fcP4r952yzUXixilTbaAyiKIiy88MIxTvjf//53fIm1PEYLxAMPPBBfAll99dWDCYx03H///U0vF/JaMoS8mMV7KE7M1jlvp5hYRoLkpcPUNpLJI5+271J+Pf0Ojb7C1UuxiN6A3N8WvF0Y3l5hTANyTS3X8WWL66+/PsrrkksuGV+4tmsk5hMu5MxL1HaCjMN8Ft61zDLLBJ6Uoijiy6kMRmFLDFRhNN4DUT8lhJqXmKynPL8IOCIlpMkCZsveLpUyZH6llVZy2JQYBvQHo4ICZ1SoY6eAB78oiuB6RB2h1z6DmYd1lllmCdNMM00co0Vz7bXXDogaI8JuTdNNGuzAgnfAAQfEcXue3k2ghxlx1YZKJujmjuClN37vf9CtZ5xxRiAL5J3+VSaJ3SWb448/vtMWqdr1jIAWFTtQhjAmc5RBmYbtuCPuYEw++eQxVJMjAhZ2MejC1tYPOs3c69zX2UDfuaYjJbiMOeaYcQfYuNPOb2s62ty0A8S4hp1rGjkNdVLPE8gDduuttwaE1OJvceVF9uAaFXwkxhvsQge8oZ3GaduNd5ihAw/hDryMFlhkSD2LMJLimDcYqWfFi8GzLec6aZtttlGlWUJ0eCObZeaTqgisscYa8asOMK5aoUomQ83Xm+w8VSmuuyxfV0LUkVtfRRFiwMD873//G8TAkzMGeVqMGZpIcxkzoTiusyuElHtJO8VmJ0CEN3kvQUhayvNLYXsJyvsl+iAvzQPH1dJXX30VkCYGlnI6BolA3J2nhEQURRFmnHHG+DUSYW36KNm5srCmuo38y9iCsWcpeaeBHhb2VG3cXmaGEaOtWnkN5LVpF8TikgUGH2OP554+TjdB6hmMDM+UV/61G2sOtHZ9uW5HOTYnhXs++OCDcchCJ8xHOjdmdKB/6EK6KjlG4ACbfr0jR8eRQzh2IKjiUHEYBjTnpvWBI9T6Y37FChX/mJvw6tKlS0VJY54OdVIPVgqPAEtp8fVbFH/FKKtT78mWrS9FICS8PrxdJiQvqAVUvCYPY48ePeJLmBYRePBsIkiOlYmfZ6ny0CNIrvFSLWtfPHwZS224DyPBfXng4FgmNwwnC5R2eKIoFJ99EmNM0Zg4lI0YNooXWdJGIyeekq222irGUfvOLWyQTdY+zy5MYeIrMMJSYMGbjMwyRnmyXSP2UVk9J7Hk3ukQtiWExrFdHzJIJiVzlefWFyzIIEVr/D5HCS842SpGzClWxKgoms9t28o+m0k2GaOulxgPwvJ45dUR228eiItXbu7wvugP2bSzwvgwr/RZrDO5t6uAhHoBXFiNa30Rwe4VMm9h1bZ8oSWu9UvmhaaYF/X8HPvVd/h5jik590z9WhDpLb+803ZYEA46BVZCq/rVdiOUkQ9hXHaghEaSiWRgGp93lcgPzJzT9bAi9+Q/XU9PV7veNfWa6D/jtL6QCeM1FiTKfHRuzeKskk9P0qkw4lyyK82BxbHlc7NkUL2OlHxUgSGd1nOOPLuLRVEEO5fWYDrVrzln54f+E5prve9IWBkr3rTccsvFkFAvVpMdRN+Lswi+OWY9Ulcic/Q3juS8eWq8s5og9WVYbZHYFk8KslxWz8cIMqKBoNh2NDF5QL2IZ6sbYeQlQ05sw1kUjNf2GmVpAvPuU4B2MhAghMU1CCYyWc07jJhQoO5rUdJmOSGvLH7tbLDBBjEcyFY8Zc1rghBRJiaSmFte2vL1jXgMbwYRAgsXSTye50a5WsR9/cOzuu+++yIElK3F2wuW6kt2TWJhHf/Dwy7cgPI0Ji/5GWflkMiI7U8vgasnMQgs6vAS586j7/rKa9M5+RYK42VZ10tC0OxUpcWenCOSXhJT3r1796CMzmB4+roB0uk+5ocX8IW7ednRfXxBgo5xrS3sZHh5+RapkK8OT5AdMW0xNNJ81EajJ/MeRsbJC+0zpn55VeFBp8BJYsip18iJYYlk0b3GTI59WSONmdySsXRO1yOr5J78IyCMSqQtXd+aVzG1US+/yCU9aM4ar/VK3zk+6E/rCyzoBfnmJF2AfPGuki3rkhCJ9ddfP1hn1OtIiePO2ouIkg9rO6IPAxEM1hRcwa9yPMLumt1NdTpiEgYnVNAuGr7ks5ZwEK3gS2llfQ07a4byjpBqjtTz5Hkxz0LdSA+AJYmQ+Fa3hKwgjpIXzuSlJO4diTd+3jIezlTG+8kbalFI7fmlVNWvTLwh6Vp/QKWy3BcvUrlJgtDrK7IknyfTQu5a5zymlW002jmL31jLyaJu8ULw1lprrfjNdeVwMn7YOU/JL4+2snpOvONIjfFIQmSqjUe4CllRJyVE2/VIjTxGYbVry3nknSdffYnXvVyuPXnKJPpCuRdlnVP25XN57i9Pcj3vvnxES57EiZDu64VbHml/dEw9X/3pSAuoPxyWcPXrM5d+vd8Dj3JKegqGjZrowyQLxk73lsdqfiQjSL76SAS5I2/p3LVS5fWuqdeEjBqTZK0xf42F48laIt8OGCeIfF9wYUibb87tQquDlHGayOuICYnnPIFFeReIThKejCf4VY4f0E8dEafymIW6Hn744fEzvOaYMpEH1qGyXrIOl8/Va+RUc6S+kcHOY8sIZAQyAhmBjEAJgXyYEcgIZATaDIFM6tsMytxQRiAjkBHICGQEMgIZgYxARqCtERiw9jKpHzCccq2MQEYgI5ARyAhkBDICGYGMQM0ikEl9zT6a3LGMQPsgkO+SEcgIZAQyAhmBjED9I5BJff0/wzyCjEBGICOQEcgIDGkEcvsZgYxAjSOQSX2NP6DcvYxARiAjkBHICGQEMgIZgYxA/xCoDVLfv17m8oxARiAjkBHICGQEMgIZgYxARqBVBDKpbxWaXJARyAjUGgK5PxmBjEBGICOQEcgIVEcgk/rquOTcjEBGICOQEcgIZATqE4Hc64xAh0Qgk/oO+djzoDMCGYGMQEYgI5ARyAhkBBoJgZoi9XfddVc44YQTYnryySfDH3/8UXtYV/To+eefj/1N/U6/V199dfjhhx+qlqmTysvN9e7du0X9k08+OTzxxBPlam16fOqpp4a33nqrzdr0zB5//PFw//33h19//bXN2m3vht54441w5plnhpNOOik8+uijTbf/7bffwg033BCf05VXXtmU7+DTTz+N+Z5vr1696nL8L7/8cjCu7777zpBi+vzzz8Oll14ax3bTTTfFvNb+eeWVV2I9GHz00UdN1X766aemNrT1xRdfNJU5+Oabb8LFF1/cdK3rpRNPPDE8/PDDqlRNX375Zbj88stDub9VK+bMAUbgtttuCx988EGr9V977bVw7bXXhm+//bbVOo1U8Msvv4R77723STY/++yzpuF9+OGHTflPP/10+PPPP5vK0kHl9Sm/0X7ffPPNYL06/fTTwwsvvNDP4f38889RhspY9vOCBi/86quvwhVXXBFlqU+fPq2O1pyjFyV6lGy1WrkDFJxzzjnN1ufKIX/88ccRU3g988wzdcEpK8cwsOc1Q+opR4Rg4oknDtJoo40WiqIY2PG0e/1RRx01IC8XXnhh0Gd9l4wFSafknCNG++67bxzb6KOPHu64446AkJQ7POKII4YxxhgjINpIjutefPHFsP322wdGTrluWx1PNNFEYaSRRmqr5sJTTz0VunfvHq655ppQrwqHLD7wwAPBcxpzzDGD52bBAhJC/8knn8TneMQRR4REcinbXXfdNbz33nth3HHHDZdccskQe2b6MSTS2WefHdZdd91wzDHHBIuMe/z+++/hzjvvjAbKyCOPHDbffPNIzpVVJvJ+2WWXRWzI63rrrddUBR6wI9MII2X8448/NpUPO+ywYbzxxgvnnntuuO+++2Ib6k4wwQThwQcfbKpXPmAYrLHGGuHoo49uMZfK9YbWcT3el9zvvffe4e23367a/euuuy7KCL1Wqb+qXtAAmYx6RJWuH3744cMee+wRjUjjN1/IIT1x+OGHh/fff7/FiDmrELZ//etfAfF3fYtKdZ7BCXLYYYeFccYZJ67bdAjjr7VhPfbYY4G+pAtaq9NR8jn/Lrjggig7dN4BBxwQ0npTiQHHhzoSfVkUtc+RKsfQFuc411577RW22mqrcOutt1Zt8uuvvw6cQrDicMOj+iWTVRupw8yaIfU8m6usskpYeeWVY5pmmmmicqh1TLt06RIWWGCBgHwsu+yyse/GsPPOO4dRRhkl7L///jFvwQUXDBYEZcjOCiusEJD48vjGH3/8sNxyy4Wxxx47LLLIIvG6/fbbLyD4SFK57oAeI6QEurX6+qHvrZUj5rxyA+rNn2GGGUK3bt1aa64u8hlnnsPqq68en8Gkk04a3nnnndj3mWaaKay//vox/7///W/o2bNnzIcPGT7ooIPCmmuuGUmpvFhYJ//o95577tmstzyPyMg666wTNtxww9C9e/dosDSr9PeJhWiXXXaJ2FC47777brB4MxDgRC7IP9lGhCxmf18aGAxLLLFEJPbTTjttbEPdVVddNWy99alnmVAAABAASURBVNapWrPfzp07hwMPPLBZXj4ZPATs0owwwgitNrLUUkuFffbZp9XyRiyw60gHkP1NN9008KRy0vBGM0DJ+v/+978AN3OgEoN77rknyjMDlMOGnnjooYcqq9X1OcN7+umnD9Zwhg7HBocHp0Co+I8+ePXVVwNDvqKoQ55+8MEHwe72SiutFOVkuummi7uPlWBYxzlC6EXp3//+dxhuuOEqq3WIc/LFKJx55plbHS8Z22677SKmO+ywQ2CUm6+tXtAgBZ1qYRy24IBtwUhKUxhHLfRtYPuAqFBmthcnm2yySPAq2+jUqVMTea8sqzy3eFB+ZW/6999/H+wECP1hsboG+SbEL730UvQisVKRcZYq65QnmSfVdpTreKP1UX191gaFoUxK5bxUDAthGUIoPBftqFMmra7Vp9dffz0MM8wwmqvbZPJTGkVRBDhMMcUUYe65547jYWxSpPA15h133DHm81Dx1gmr8nws8IhALKyTfxihdifK3SV7Fg/PlNwi3IzTcp10vPjii0dD1jk8GK3q+uVVeuSRRwJs4DbJJJO0MGpdV05kD/lxvXzyR+4kcl8URUDslSW5JuvOkQneQwuh+uTcrhfPILlXRl7VNXd4ptVD1Dxb+fQSWVdXWUp2Y5STf/X1y3k9J2MQanbccccFz7m1sdBDlTLSWt1GyTePPWvPndzR6+SOM4QDwK4rmZGnbuW47X7IK4oiyjzym2RPfr0n88m8SyFwZISeZOCYh5XjgyUdynlVWdYRz+kQepaONH5Elb6yZjtPiXHIUz/XXHMFuz90aSrraL/WI1ENcGtt7PPMM0/cNVc+1lhjxbWC3Dlv5FQTpJ5itF333HPPBZ5BnjkLaT0Bb9tV+IJtaZ6Ywem7BUL4CkJ+6KGHBl6gpZdeOjZJgfKmnXXWWTHmm/cUIREicdpppwXp2GOPjaEft99+e7BYI5q2f3mWt9hii3idGFGL+Pzzzx+3hN3TMxDKcPDBB4fddtsteqRsCwo3Ebdsu/nuu+8OxinefOONN46hEZS5PP31O6i7CnGANfSPUBL4IujJ8NE9i9JOO+0UQz5gI08Y0+677x54BmDIo58MAeWNksxLHsf+jYcxOttss4XZ+iaknPeOXPDyMvx4pRgRle2Y/94hIUunnHJKU7EFjtyTO/iSTyRLBc9A+JuyRRddNL7PcXtf2WeM8OTLJ8cbbLBBMJ94Csm+Prje/TxPc1d4kXlgTm2//fbR+CbT2nCt8dx8880uC0ceeWSwVW5exIw6/kdYCLJZFB1zO79fj84OEhLBuSFsrEePHjFMcqqppgqHHHJIsCsrZne11VYLk08+eYumkA/vhdDdCulLv42SYMMJYk1J71EZq/ePKseI6DPuYVpZ1lHP6UOOJMZQwoDjA4bp3K9IADuf9BAdSB9WM5rUzak5AviNSAgRE81LGu+sJkg90mThBy/lKITDQuu8XhKPJEtbMp7B6TclyYshBplnkTeY0tQmpWgh4ZXnRaZIkQyT3b0RjYUWWiiwUu16IE4IEPKuXwQbxsIaHGtTYvlTLogUw2CjjTYKE044YSRBFipkjGdqyy23DDxP7s17zwBgjCEFYnHdv5q3yj3qLfE2L7/88jGECklM/SerMBbLx0hK+Ui80JJnn302GjsMqlTWCL+eM6+2kK1+jcdiREY22WSTpmp2O3igLELisnnWmworDixwZK1z585NJQwCZJvcIfGwdw8VeF+ESpFrv2SQt1k+Ei5fvK9npr55IkzAHHPOICO7//nPfwLln3YHzB0LrRhobQidmHXWWZu8Pww3efqqnXpNMBUONe+889brEIZov5FyBiK5pmvp4HTDrl27BnJgV0kIBaM0laVf+ttONNlE/O0YlXVvqlevv7ylxmW+CNNz7B0Ea5Z5WB4XR8naa69dzsrHA4gA5yc87YhyfNo5bLQ1ZgChGOhq5qcdDuvQQF9cZxfUBKkvY2YRRUgpinJ+rR9TYDzhFFoiD/3rM/LnOkkcXe/eveMlSA2Po/AZC4Utt1jQ9x/xnBYSLxQiFLyVvMlCl84444wYy62+NvpWb/qfoeQ+fmUyQtzDsWShQU7ENiNTyirbYGBY/HkI3JtS8QIkUqAN1zJIvJjivN4TbxLlue222wahGGk8jB1x9XC2TSqft3attdYK5513Xjj++OMDjzavnrJGSMaH0HhHg1FjTGSOTEk81kiPfJiYx2WSiJAvvPDCwcuEyyyzTPRwC2VQv5yKogjmAqMTxqnM3BDzT+543d0jedqFPSVDksGhr7b/O3fuHIQLpTZa+2WoeBfANrgXr9QzF7TBAEjhJsgdg5qXX11lyTBwTb0msopoeT/Is7QAeqckGU31Oq626rddUO+H2B0l//QBfckwJdPmPM/pVVddFaTK+9Ij8u342HFFfpdccsnKanV9zjBG2K1BDOSpp546GGt5Heehh5OyWWaZJb5vY52hL+t68IPZeWuy9+bSbrC1xlqa9Gxl84g9ZwXdU82IrKzfxud11xx+wgnqPYSyPNbdQAawwzVH6hED2+MW1QEcQ01VQ2Yos0QE+tU5Xj8xv5IYSx7xcn2k+rrrrovhMhZYXk6E0gIDJ55khMaxrWBtINy89whnagtp6Z9FL8ZZyAHl4jpbxOVrbAdSIhYkC5p7S+rwiJo4rhVzL9ZP+IVzbdVzYqTABu7lcRRFEb92xACVDxPbzcav/oorrhjDl5TVexL6IvyEUiTfZEOye0F2JWTHThCCiPwjhbDr06dP4Fnn5UeyLVQ84shj/+RjjjnmiC+hw48+QOTJGrkzN8wHZTCHvWM7fDwy7uW8MpkL8oyJnFL2SBlyYTfKgqm8tYSMkQXkZPbZZ2+tWl3lI6Z2KDxHiTHmGQ6oc6KuBjsInUVGOUEkBumcc84ZX5xn/NCHiBVDlJFpx7TyFuaBeWNd8+EE7y2Q48p69X5uTpqHPomKuJuH5THNN998gVOIjNEHdKdQVSGL5Xod7VjYmzFzmvm1DtMz8HRemeg7dTgyyGRleT7/BwGGt7XLegwrTtB/ShvzqCZIPaDPP//8+I1boR9FUTS9mFjrsPPYWQB5EnnPkYXKPgtrufHGGwNiTMDUrazjHAHi3UWQkXnk3cK62GKLBVu4iARLkweRZ1QMMLx8RlIMvsWCsiTADATECenmTdeuL7gILUCO5LsHkuPLBRSwhUZ8qHb10/2RHORJTL5z8aRCbtSRtIHc8GLyxqb+6IfYc+MaoFRDlcgjr5zxGY84Ubsjuih2W750/fXXx7hq+RQzIy3FZgtH4slWVi9Jn4V8UYQIO1m2CHtXgNwI0TJuqRyCkMZnMUdsyBZvunq82gg2LGzJy2N48k7xdKdr3Yunj4wKtenZs2ezT6IKbUKMEr48guRY2+aDsDFtI/VCxxjByAP59L6L+yBeZF09+XYK3NOLjwiaEBtziZzzqsozJ43b9ZL5YDfAworQyWvkRBdddNFF8YVx42S02UE0R7yrg9DKb+Qk9IY+E9ZFdjhSeJrJJDn2xSj59LqdWvLv5VnGAHzMC1/For/pVaGQjYaXUExzUqgnJ5N1hAFvnHQJRxPC7zyn5ghwWJCJpDOtm+Ln1aIrfXjDOu2XnNmtpKcYSYiqeh0t2dWwVtHhohmsFzCwjli7vbgNI+9RqYN/wQ53Ua+RU02QeoLJC+zTeVNOOWUQl5cUQq2Dj2hQ+rx8+s4rU9lnYxFrjVgj2sZbWce5/C5dugTxvya5c0lsvIVDGRKz++67Bx4jeAl18fIHMoVYUhA8qrwgPMYMDYuPuE9eImEN+lwUReDdp0gQem1btHgItMtbxxMgjk/IgTg+W6xeeBSqoI4kTtR9LFrGqD8+8cZI04Zx1Vti3PAGG18acyKgSU6V+TSpbVDjkw8DXmhlFneYKauXZMuX11aIDHlAYBmRDBpz0rgkzxWZqRwXw47HnnGjnsQLR4aFd3gJVR4j0Mvf6qc23MsXCsgoUk7u5KXyVMY40AZ51gfPxYtj5F0+I0zIzfjjjx+/X19e+Lzg62VH9cgxY9e5+WKO8cD79XKt5y/cp0ePHvHvFaR+FEURZpxxxuBdC3M55TfSL4cBDI3JXCDbfp3bjfEJXxjxxspr79Te9zMn6Dz6kuw4phfoWoaqcvmbbbZZfJeJ3JLtZPQ5ZgSQNfLb3v1vj/tZ48wHc5cOIDPpvnatzeeiKFJWMJ/JEIO6KbODHliP6SYOPHJEn9E/4ICTNb8oivhytnJ6Cuknj+p0xGSOwcVa1b1797hrDgf8C3bk0fzzMQ9OUbhJKbxS3UZNNUHqgZ3i0f1aOOoFcEQegUsJMarsO9Kbyrt16xZfQK2s49wiker5RVhSvuvkpcUknfMaEmB1EQ11KIe0CDtndCDcjhEYhA3GwoTkKbfVhwg5lyzc6mgH0TcxKJ90ro5E6egjHBgW2rfIIXYIobJ6S7BM+BqTRSmNgTI1bgmOKd9vGT8kE5mVXy8JkTMuiUxQisbuvJy6du1a9fvIDNFyPceMPOPXFkIjj9wluVEmKWcQKZeEOFTKj+eiTEr9cy1iIE9CJsgo/J17IRbZUA+5kieRUTIt3yJgd0u+kB+/zskyI1hb6kk8r7ySsLKAyGu0BBeYGJdfDgS/zsu6jK4uin+ImvJGTPQgQ5ZcSOQwjZN8y5O8CF4URfx7JOqn8zQv6Id0XaP90oV0pXWmcmzGz3FUni/WEnJmTlbW74jndn+S/ivLCcdQWs/pLHIG545M6MkHzkPW4CFZL+TjX9ZuDslpp502fqNeeUqcQeo1curUyIPLY8sIZAQyAm2BgJhzu1KImvAKv23Rbm4jI5ARyAhkBDICbYVAJvVthWRup+MgkEfa4RDgUfReiXdohA51OADygDMCGYGMQEag5hHIpL7mH1HuYEYgI5ARyAjUIwK5zxmBjEBGoD0RyKS+PdHO98oIZAQyAhmBjEBGICOQEcgI/INAmx1lUt9mUOaGMgIZgYxARiAjkBHICGQEMgJDB4FM6ocO7vmuGYH2QSDfJSOQEcgIZAQyAhmBDoFAJvUd4jHnQWYEMgIZgYxARqB1BHJJRiAjUP8IZFJf/88wjyAjkBHICGQEMgIZgYxARqCDI9AOpL6DI5yHnxHICGQEMgIZgYxARiAjkBEYwghkUj+EAc7NZwQyAgOIQK6WEcgIZAQyAhmBjMAgI1BzpP7VV18Na6+9djjuuOPC999/P8gDq5cLt9122zheYy6na6+9Ntx1111Vy9RT/ttvvzUb5mGHHVa1/hFHHBF+/PHHZnXLJx988EHYb7/9wnvvvRezH3300XDMMceEb7/9Np53xH9gsNlmmwV/aOjqq69uguCXX34Jhx9+eMQZZk0Ffx9st912YYMNNgg9e/b8O6c+f+69996w0UYbxfHfcccdrQ7ijz/+CJdddlnEg1yWK3755ZdN+SeddFILeerTp09TuWtT2nTTTcPDDz89AMlcAAAQAElEQVRcbqrp+M8//wz33XdfOOuss/op000X5IOBRuDEE08M9HBrF5599tlNz+2hhx5qrVrD5NOd5TG/8847TWN77bXXmrB49tlnm/LLB59++mnYd999Y71DDz20XNQwx9aQPffcM44xzeNLLrkk/P777y3GeMEFFzTVg22LCgOQ0WhVPvroo7DPPvtEXJ555plWh3f88cfHOtYemLdascELvvrqq3DIIYdELKy5/eIq1h4yeeuttwbrVYNDE2qK1B955JHhv//9b1hnnXXC+uuvH0YaaaRGxz/ssssuYeSRRw6TTDJJQMqPPvrosMUWW4QRRhgh3H///WHJJZcM8oYbbrjgr1o6Xm211UKfvoTo119/bYYPMjT77LOHTz75JPTo0SNed9BBB4Wxxx47VNZNF1KqBx54YLz3119/HT7//POwzDLLxHv//PPPqVqH+n377bfD888/H3baaae4GFuI04KNTM4yyywR25tvvjmceeaZTdh07949zDbbbIGSufjii/tJjJouqsEDhO6NN94IFulNNtkk7L///uH111+v2lPGD3kjl+RniimmaKpnHk8zzTQRK0TommuuaSpzMOGEE0a5+/DDD8Oiiy4a62ln3XXXDdpSpzK9+eabYcUVVwyPP/54qzJdeU0+H3AErr/++nDyySeHzz77rNWLvvjii6ZnRd+0WrFBCu6+++5gru+2225xbpvnSIV5Qm7pcLJurlTTmZwCSyyxRMSMjnfeINA0DQPBXHnlleMYzeG55porzs+iKJrqOIAjYrr33nuH1VdfPSy99NKyO3T65ptvAkM6rSvk7LnnnmuByXF9HZ0vvvhixHj00UcP1m3rd4uKDZ6By9x+++1h5plnjlg88cQTYeedd67qBLZ2M5isYeeee2546qmnGhydUBuknjV/6qmnRoLUq1evsNxyy4UxxxwzdOrUqZ0fQPvfbtxxxw2jjDJKGG200cIEE0wQxhtvvGChXHbZZaPQ8vrKV0dy3LVr17DwwguHoiiadXisscaKxJ9BMP7448f25PG4UgLNKv99wnDafvvtw5xzzhlzGACnn356PO6o/8BujTXWCAgpY2u++eYL3333XYQDoWT0eA6OEVIFjz32WNzp8LwQ+2mnnTZcd911iuouTTbZZGHNNdcMU045ZTD2Ll26hB9++KHqOJBvu03wsINhcbfwIPESHJVNN910LZQuQxXW5LVz585RXv3OO++8obXFXp+QgqqdyZmDhYCdlWeeeSbQSa01hHAssMACYcQRR4zPi/5orW6j5CNY5A6JIJvDDz98QMRuvPHGQHfS19YsOvTggw9uMeyVVlopLLLIIhEv5J/nvkWlOs+gJ6eeeuo4RnOYo2rGGWdssYbbZaMfZ5hhhqhbOE/qfOiD3f133303OukWXHDBiF+3bt3CFVdc0cyrzJDmaeadpk8ZUOSyX8b3YHesRhuwW05+ll9++YgXTDh7KrF4//33o4Niq622CmTTen3KKafU6Kjarls1wZp79+4dCRBP80033RR49Ahx2w2zflri9bzllltih1ddddX4W/kPg8cCYmGtLCufC1867bTTYtYDDzwQzjjjjIBwsVxtjd5www2xLP/THAFEhZEllyfaoj3HHHM4DRNNNFH85c1HSikMGRYni5hjCRF+6aWXHNZdIldp/HfeeWdYaKGFwqyzzlp1HGXyTS6LoogGOeNw+umnjx4o5F7YDE9U1UZKmRau8q4AI4HcSjAvVQ3k105JCnVieNhJQRzUt1ui/IUXXoiL5kUXXRQXS22YA5deemmcE5dffnn4+OOPZQfzRB+EFGlDOuecc0J6ls6Fvn33t5EXL2qAfxD6K6+8MtjWb2048GXE8eQj9zxkrdVtpHzzGoGSHnzwwcCoId90KeKQxqqeHaR0nn45ahyrz8nCYHbeSMkY7SQbk7nEcEm6Ul5K//nPf4LQvqeffjo8+eSTIenPVN4Rf+0C07lkyvitHW+99VazcEU76OYefqCOuvB2rfOOlDg3GdhpzIwc+HEOpTy/5uvkk08eib9zu8iiHxw3cqoJUm+rHwFlgQ077LBB/CJL1TZLI4NfHpsFdb311ouhN/2KDytf09rxM329bRtvvHGMh7ZYq0fgLcYU6jDDDBMIfEewWo19UJPwk8033zzwwiePvLYsRp4V8pcWcQsZjJWnZIs+Hdfbr52JPfbYIxi/MIMyeWltLIiv0DFhNRTvAQccEMRcI4AW+LnnnrvqpeTd9jMvpu3SVIkxcOyxx4ZRRx017oLsuOOOqSjcc889MUSEMhfu5P0TW/regxBC4hp9IPPq0ivmhdhmekZcr76pw8DlTBAasOGGGwbe1p9++inelxftqKOOCq5xcwaEBbbRdhGRg/4ZXUVRBE4CesNOlOfaEWJUF1tsscBAJWfmu50pcsPjzAFFTsmG+c54dVyZGKDdu3cPnASMzMryRjqnK+FTbcdn8cUXD8JHN9hggwAH87GRxl5tLP3Lo1s5ksrrhzCuMv+xo4nI02faqyyX11ETL72dsnHGGacZBBw3do3KmR3BWVwTpN62iUVCTKLJ7uVEHkJbnOUH0sjHSy21VEBottlmm8EeplAH8XZ77bVXU1sIFYtWBmXL+2wb2XlO1RGwtY5s8tTedtttTZV45ISLIfJktqmggQ5sn++www4xZtH4zcd+DQ+hEY6AEKd6PPcIPi/ecccdF9/1SGXlXwQAofdCt9CmVIace8lp1113DfDWfvIOC4NgWLlusb6kC6n3MhSyzRPq2DZ2knlGBZnXth0Wu4Lim+2wiJm2AIiPnm+++cI888wTxPVrg2Fn6xaRd+2//vWv4H0f+DhvhGQHhFNFHHS/xoN4KO/cuXOgpyyiSK68Rk5kCqknF+SbkWi8ZIjeplsnnnjiQH7JpbLKREbt8thF8r5TZXkjnTNceFIZ0tXGJVSna9eu8WV3hnW1OjmvOQJ2Tn284qqrrgpkjQHO65x0WvPaHeuMo4EzpjV561hohNqIqac0eY953YqiCBZOQtya16MRH5KYd8Rj/vnnD7aM+j/GEHgmxX1LyAfvquvgiMxYpClXeTkNPALk8N///nc0tspfGoLvTDPNFOyupBeVnJe9ALZJJ5100oG/aY1cYU4avxAwHtk0frKWUvoqDq+2l/94MhPx40laYYUVYngL8kwOxT5WG557CUsg/+Iek7HJA+r9Dl9lsp2vD2RaGwyBpMSRBG3wZBVF0d8X7HmXeea9lyK0SL+1mbz7PGb0kTzP2vstPK0MB+EDjAJljZJgy4ATp+rZ2oliGCFnrY3RMxKSlnBqrV4j5Hs3xo6NDzl4Qc8OFIzIul1lxr2QLDJOD1cbs/UMAUPEzI1qdRohj3eZXmDoVBvPeeedF+zMM/J5ncVFCzWpVrej5JlHQmuSE5M80WWVjgOOT2U+krH//vuHrbfeOn4Eo6PgVDlOeNkZ5vyhsyvLzUdriHVDmTWZw9NxI6ea8NQjRAipye2lWdt34p8qhbqRH0Qam4XCFmU679evL9v4pJqEMCEllfXtepTz4EzICTvlwEtZLs/HzRFAAOVMNdVUfpolGFqUZNqKhyevJ2WjDKlVVs/J+C3UvNXGQdZSQnDNVyEIjEgv2DrnTUfEyZrwL8TYi4LkThv9St4VSfey2F144YWBPnCNbWqLmmNt65f+iVXmRa2m2DkGkuH18ssvx6/q8HYxQrzYi7CXjTFtl1NRFMF85KkXk69+ubwujvvTSdilZ+rXM/BOQWteZ80huYwBdZ03crIuMVy9U0AmGT9IWBqz+PDzzz8/8Nz3z+BDaHmp07WN9uudA4ah3ZxqY7OzI9yOYcOotttjp75a3Y6SBy9Gcu/eveOQH3nkkfgVwNb4j51TIaHCuejWeFEH+8fOKh215ZZbxpHTR96/iid//8MIslvcs2fP+Pljssd58Xdxw/7UBKm3tekLAeLrbHtTDLbVWxPqRnoaQgt4fxAN20iVY6P8hB4g316g7deLRbZ/TXjxsT4rJiSi3J6tYiRJvD3FkbxsPFEWm9133z2+nCPkhCeKd7J8fUc5RhLFZpNF4TUMTB5d4xfXLV9CEnlM5PPKC3eiaF1DmQjlUFZvifwYn0RWeBhb87z57CcMeCyFzklkifdc7LFz7YhXN6fLWDB8lAuBEWaTwhpSHS/hWvCEu2iDXJJZJNQvEuV65JNXBklnVIj5ZgBoh9FF5l2PnBVFEb/uQuHbQdC2uuaf/ppn5iOvvOslz9Z2d2sYqNOoCckn837pDDhKnhdvflEUjTr0pnH5zDJZFe5l7OTPnBCSJk9IjlCvVVZZJX7thcHJC+29MEYnuXKdRCcnItJ0gwY6OOGEE2LoWnlI8rzAbi0TtsXgh4XE08qpV67f0Y7Fy9N11myYcJDYIYYDTmSHiGPCr/K0Pg0J3Nyz1hMnjLXWRxA4M2Fi9wzR53yxLgjrsjvmS2l2e9Uhf3Cu9fENbv9qgtTbRkfqkVrElJL0CbHBHVw9XG+7FoEQDlBJevSfBe+zgMiGbU0hN/KrJXFlyInF19YcoS7Xo0ARFvgSdO1RHkio77cyHoQ1eHHQpLBIla/vKMe88r4C4h0HWCGGwqOMnxEmX4Inwidf4oHzxRWxj4yvSvzVqYeE8BqfRGl6Cak1A5shSDYpT/Ulc5nc+pQqg1GeXSWeufL4LV6IuDAPX6JBzsvlDCMLGUNVG74H7n0Q/UMU3BMxJ/dC1swluyWendAhbTFKvA/hemSfnJN5i4KXZeVrX7y+F4MZFo7LBpmxuKdvjWuz0ZOdlxTmZPeFzPtl2MBLksdr3ehYGJ9dOGuTkBFjN+/JplBJ84NckY20U2qurLXWWoETRVgYuXKdZE6Qe+02YrKGwKs8NuuatcQ8Ev5gfaEj4em9JXiV63e0Y7s7wg5xHzJibiVZso5zXNiF9KvcmtQRHQxJLnz1x7wT8mZ9SJhw7Ngh6tGjR0gy6Nf8JGvWhYRraqsRfweJ1Ns+441ry2TryctatjlZYm3Zdi23xatjzFK1cXuzW75yCUatjYcXSB3J9rBQkHJdHhJlrH51pcr2eTPVkWyvl68f9OM3YhxlvVxvK89WOwxghSimvpefl7CQlJ9+0zXVylKdWv9NcpLGUilH5f6X8Uj14acO3ORJZK2ynXK5OtUws6WqTBLKo10ya8teXno+2qaX5PnVtrr6Ik9yjf7KF8bjWvn65tfc8iuf10c9iZFMP5lTzhs9GSfcjNMvzPxWPgu6Qp1GT8ZZ1sGwMGYyQl6E2zlPKdVP+jPJlbrp2lS30X7NPXOxPC7zlgfVvJXvHBZSmqfyO3KCA+xgQv8mLMgW/USm/CqvpidT/Y7wS74SVvCQ4ARDZdZuuiphQZ+pY76mvCH9K35/aBkMg0TqbZfzkOV0WMgYZAyyDDS2DAgdoPN8vSQ/6yH8rA/L7WcZyzKQZaC+ZUCodF2Ren98w0KX01nxRuiRXAAAAR5JREFUs1wZh4xDloEsA1kGsgxkGcgykGWgfWSglnH2bl1dkfqh1dl834xARiAjkBHICGQEMgIZgYxARqAlAoMUftOymZyTEWgUBPI4MgIZgYxARiAjkBHICNQfApnU198zyz3OCGQEMgIZgaGNQL5/RiAjkBGoMQQyqa+xB5K7kxHICGQEMgIZgYxARiAj0BgItOcoMqlvT7TzvTICGYGMQEYgI5ARyAhkBDICQwCBTOqHAKi5yYxA+yCQ75IRyAhkBDICGYGMQEbgLwQyqf8Lh/xvRiAjkBHICGQEGhOBPKqMQEagQyCQSX2HeMx5kBmBjEBGICOQEcgIZAQyAo2MwOCS+kbGJo8tI5ARyAhkBDICGYGMQEYgI1AXCPwfAAD//yujNeMAAAAGSURBVAMA+UAHa6i3kZQAAAAASUVORK5CYII=\"\u003e\u003c/em\u003e\u003cbr\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study aimed to assess the influence of both, the freshwater rearing system, and the feed regime provided during the grow-out phase, on the incidence of sexual maturation of rainbow trout in sea cages in Norway. Results showed that the freshwater rearing system was the determinant factor for the occurrence of sexual maturation at sea, irrespective of the feed regime. Apart from maturation, the freshwater rearing system also influenced growth, the incidence of some welfare issues, and mortality, while the feed regime exerted a modulatory effect on growth, condition factor, feeding efficiency or harvest results, but not on sexual maturation. However, stating that \u0026ldquo;the rearing system used during the freshwater phase\u0026rdquo; was the main factor influencing sexual maturation at sea is too unspecific. The concept \u0026ldquo;freshwater rearing system\u0026rdquo; is too wide and general. It comprises or is related to many different parameters including temperatures, photoperiod, feeds used, growth rate attained during that period, water quality parameters, or even size reached at transfer to sea as well as season of this transfer. As such, an adequate discussion on the effects of the freshwater rearing system on sexual maturation at sea requires taking all these aspects into consideration.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 The freshwater rearing system appeared determinant for trout maturation at sea\u003c/h2\u003e \u003cp\u003eRainbow trout produced in RAS during the freshwater phase showed more tendency to mature in the sea (12% of fish maturing) than trout produced in FT (0% maturation). This is consistent with previous research in Atlantic salmon, which has linked production in RAS with occurrence of early maturation (Crouse et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fraser et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Good and Davidson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pino Martinez et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In those studies, elevated constant water temperatures and higher growth rates in RAS were the main suspicious factors linked to the higher occurrence of sexual maturation in RAS-produced fish. The constant and elevated water temperatures are known to induce an early activation throughout all components of the BPG axis in Atlantic salmon, evident at pituitary (gonadotropin upregulation, see Pino Martinez et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e) and gonad level (downregulation of inhibitory factors, early signs of mitotic proliferation, see Pino Martinez et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, such higher and constant temperatures used in RAS induce higher growth rates (Handeland et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and as a result the fish can attain sufficient size or energy resources for sexual maturation earlier (Thorpe, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). All considered, fish reared at elevated constant temperatures are more likely to have further developed their reproductive capacity and acquired energy stores to sexually mature earlier compared to fish reared at lower temperature. However, paradoxically, in our study the rainbow trout from RAS experienced a similar, even slightly lower mean temperature than the fish from FT during their period in the land-based facility (8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6ᵒC in RAS, versus 9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6ᵒC in FT). In addition, the growth rates during the freshwater period were very similar in both groups (~\u0026thinsp;1.5% of body weight gained per day). This appears to contradict our findings (maturation observed only in rainbow trout from RAS) and the previously provided knowledge about conditions in RAS and their relation to sexual maturation in salmonids. Because of this, we must then pay closer attention to the details.\u003c/p\u003e \u003cp\u003eLooking at the figure of the two freshwater temperature profiles over time, it is evident that they were similar after hatching, displaying only a 3-month phase or delay between them. The major difference, however, was in the temperature profiles experienced during the last 3\u0026ndash;4 months, until both groups were transferred to seawater in late March. From December 2023 to late March 2024, the RAS fish experienced a significantly higher mean temperature than the FT fish (11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4ᵒC versus 7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4ᵒC respectively), and this temperature in RAS followed an increasing trend over time. It is likely that this higher and increasing temperature during the last 3 to 4 months induced a higher determination in the RAS group to mature later at sea, due to both higher stimulation of the BPG axis and higher growth rate during this last period. As mentioned, it is well known that elevated temperatures increase growth rates and induce a tendency to mature earlier in Atlantic salmon and other salmonids (Atkinson, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Fjelldal et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pino Martinez et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Thorpe, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e1994b\u003c/span\u003e). Specifically in rainbow trout, some studies have shown higher proportion of maturation in individuals experiencing higher temperatures. For example, Wilkinson et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) reported that elevated \u0026ldquo;winter-spring\u0026rdquo; temperatures increased the incidence of sexual maturation in female rainbow trout of up to 600 g, although with the photoperiod playing a more important role in triggering the process. Similarly, after a series of trials with temperature and photoperiod, Davies and Bromage (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) found that generally, female trout that experienced elevated mean temperatures matured in higher proportion, although with minor differences and with photoperiod acting as the main regulatory cue. This considered, the period of 3 to 4 months at an elevated (and increasing) temperature that trout in the RAS facility experienced pre-transfer may have been sufficient to induce that a certain percentage of them matured later at sea. This is also consistent with findings in Atlantic salmon reported by Pino Martinez et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e), who found that an early period of 2 months at constant high temperature (15ᵒC) was stimulating enough to later induce early maturation in post-smolts, even if they had been subsequently subjected to a severe drop in temperature (to 8ᵒC). However, while elevated temperature generally seems to stimulate sexual maturation in rainbow trout (Davies and Bromage, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wilkinson et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), this link does not seem as clear as in Atlantic salmon. This is illustrated by studies with rainbow trout that reported three times lower proportions of maturing females at elevated (20ᵒC) than at lower (12ᵒC) temperature (Zelennikov and Golod, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), or lower proportion of male maturation as well as detrimental effects on spermatozoa development also at elevated temperature (Butzge et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a result, more research is needed to understand how the constant high temperatures typical of land-based facilities impact on rainbow trout maturation. A projected fully controlled, small-scale experiment with smaller rainbow trout grown under the appropriate temperature conditions will help determine the specific effects that elevated temperatures have on the decision to mature in this species. Other factors during the freshwater phase of our study, like photoperiod or feeds, are unlikely to have exerted any important influence on the differences in maturation observed at sea between the two groups (RAS and FT), since the mentioned factors were very similar for both groups\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Mechanisms behind sexual maturation of rainbow trout from RAS\u003c/h2\u003e \u003cp\u003eThe inducing effect that the RAS system had on the reproductive axis of rainbow trout was noticeable already in the first sampling in early June, at pituitary and gonad level. At that time, although we did not classify any fish as sexually maturing based on GSI, we visually detected early signs of ovary development in some females from RAS; this, in turn, resulted in significantly elevated female mean GSI (although still under the established threshold value for maturity of 0.16%). But more importantly, on the same date, we observed a clear upregulation in pituitary transcription of the gonadotropins (primarily \u003cem\u003efshb\u003c/em\u003e but also \u003cem\u003elhb\u003c/em\u003e) in individuals of both genders. Gonadotropins are key hormones for initiation and coordination of sexual maturation, regulating the process in both genders from the initial mitotic phase of gonad development to sex steroid production and gametogenesis (Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Schulz et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Taranger et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The gonadotropic upregulation observed only in the RAS groups suggests that only individuals from RAS were ready or \u0026ldquo;had made the decision\u0026rdquo; to mature. This is, there was a differential activation of gonadotropin production in the pituitary depending on the early conditions experienced. Considering the relatively low proportion of maturing fish in the full trial and in the last sampling, it is possible that not all individuals showing that early upregulation of \u003cem\u003efshb\u003c/em\u003e transcription would have completed sexual maturation. An increase in Fsh production can be seen in salmonids in response to inducing environmental cues like increasing photoperiod, but sometimes this does not result in sexual maturation; this is a phenomenon known as \u0026ldquo;dummy run\u0026rdquo; (Maugars and Schmitz, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Okuzawa, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Prat et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and rather appears as if individuals are \u0026ldquo;testing\u0026rdquo; the capacity of their reproductive system. But in any case, the fact that such upregulation was not present in FT groups suggests that these fish did not even need to test their reproductive capacity in June, meaning that they had already discarded or inhibited the process of sexual development (Thorpe et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn subsequent samplings, more individuals and with a more pronounced upregulation of both gonadotropins were found only in the RAS group, never in the FT groups. This was consistent with the increasing number of individuals of both genders displaying increased GSI, which is highly indicative of gonad development and sexual maturation. The trigger for this reproductive activation was most likely the increasing photoperiod that the fish experienced from March until June, as well as the steep rise in temperature during the same period (from 6ᵒC to 18ᵒC in surface water up to 5 m depth). Rainbow trout is very responsive to increasing photoperiod, maybe more than to temperature, and several studies have demonstrated that the increasing daylength typical of spring has a very strong effect in entraining sexual maturation in this species (Bon et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Davies et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Davies and Bromage, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wilkinson et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Similar findings have been consistently reported in Atlantic salmon (Fraser et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Pino Martinez et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Skjold et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As such, the combination of a more advanced developmental status and a stimulated BPG axis in RAS-produced trout, together with a spring transfer to seawater (which entails increasing daylength and temperature) seemed to have caused that a number of individuals in this group, but not in the FT groups, ended up sexually maturing during the production cycle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 May the different size at transfer have confounded our results?\u003c/h2\u003e \u003cp\u003eDespite the consistent differences in the maturation responses between RAS and FT rainbow trout, we cannot overlook that other factor, which in our study was intrinsically linked to the freshwater rearing systems, might be confounding our results. This factor is age/fish size at transfer. Thus, trout reared in RAS hatched in early January 2023 and weighed 450 g at seawater transfer, while trout reared in FT hatched in late April 2023 and weighed 179 g. This size difference at transfer could be important for maturation, in addition to the different rearing conditions experienced. Size can be seen as a proxy for development and energy availability, and thus larger fish most likely have had the opportunity to further advance their reproductive development and accumulate more energy than smaller fish. As a result, larger fish are more likely to initiate sexual maturation than smaller fish in response to stimulating cues like increasing daylength and temperature. This is supported by findings in McMillan et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who reported that the probability of male rainbow trout to mature early increased a 49% for every 5 mm increase in length, and 33% for each 0.5% increase in lipid content. Further support can be found in Taylor et al., (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), who found that both size and growth rate in late spring-early summer was determinant for female rainbow trout to initiate sexual maturation. Finally, in a study with sibling Atlantic salmon, Pino Martinez et al., (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e) subjected groups of different sizes to \u0026ldquo;winter signal\u0026rdquo; photoperiods for 5 weeks (LD12:12, introduced at 70 g, 110 g, 180 g or no photoperiod changes), followed by daylength increases to constant light. The authors reported that the larger Atlantic salmon were more likely to become jacks in response to the photoperiod change, while maturation was not present in fish exposed to a winter signal at a smaller size. Other studies that have suggested a larger probability of maturation in larger than in smaller salmon in response to a photoperiod cue are Fraser et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or Melo et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While in our study the conditions during the freshwater phase surely influenced the occurrence of maturation in RAS-produced trout and not in FT trout, the different age/size at transfer to sea water must also have contributed to this. To clarify the influence of size at transfer on maturation, a projected study will transfer to sea water two groups of rainbow trout that differ only in size, but not in early rearing conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4 The season of seawater transfer must have influenced maturation to a high degree\u003c/h2\u003e \u003cp\u003eIn addition, the fact that the fish were transferred to sea water in early spring, when daylength and temperature start to rise, surely influenced the occurrence of maturation in the RAS groups. Rainbow trout maturation is very responsive to photoperiod and increasing daylength, and therefore if the groups had been transferred in the fall instead of the spring, probably sexual maturation would not have been observed until the following spring (Bon et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Choi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Davies et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Davies and Bromage, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wilkinson et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The relevance of this combined effect of \u0026ldquo;environmental cues at transfer \u0026ndash; size \u0026ndash; freshwater origin\u0026rdquo; on maturation is also obvious in the fact that FT groups did not show any sign of sexual maturation at any time, even when they reached the same market size as the RAS groups (but in February 2024, this is, 3 to 4 months later). This suggests that size itself was not the driving factor for sexual maturation, and instead this was triggered by the combination of previous stimulatory conditions experienced, large size, and exposure to appropriate changes in environmental factors like photoperiod and temperature in spring. This highlights the potential importance of the season of seawater transfer to induce or skip sexual maturation during the production cycle. With high probability, if kept until summer 2025 in the cages instead of only February (experiencing increasing daylength and temperatures), individuals in the FT groups would also have initiated maturation; however, in that case, maturation in FT trout would be occurring one year later than in RAS trout, which aligns with the hypothesis of gonadal growth inhibition put forward for Atlantic salmon by Thorpe et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5 A restricted feed ration did not help reduce incidence of maturation\u003c/h2\u003e \u003cp\u003eAs previously mentioned, providing a restricted feed ration during the grow-out phase did not influence the proportion of rainbow trout maturing in the RAS treatments, but it reduced fish growth and negatively impacted feed utilization and harvest results. Similar findings were reported by Pino Martinez et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e) in a study with Atlantic salmon post-smolts reared under different temperatures and feed regimes. In that study, a 67% feed ration did not induce a significant reduction in the proportion of maturing males reared at constant high temperatures (18ᵒC, highly stimulatory for sexual maturation), and physiologically only modulated the process by slightly reducing pituitary \u003cem\u003efshb\u003c/em\u003e transcription and sex steroid production. In contrast, at lower temperature (12ᵒC, less stimulating for sexual maturation) the effect of the feed restriction was comparatively much larger, significantly affecting proportion of maturation and its physiological regulation, growth and body condition. But apart from these effects related with freshwater rearing conditions/temperature, the absence of effects of the feed restriction may be related to the date in which it was introduced (late June). Before that date, we had already carried out the first sampling (in early June) in which, as mentioned, signs of sexual activation had been observed. This means that when the feed restriction commenced, individuals from RAS appeared already sexually activated, and this is consistent with the time of the year and environmental conditions present then (with a peak in daylength and temperature). If the decision to mature had already taken place in some individuals and therefore the process was ongoing, it would be expected that a late introduction of a caloric restriction would not arrest it. This is because the process may be reversible only in the very early stages (Schulz et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and once it is triggered, it tends to gain evolutionary priority as a life strategy (Thorpe, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1994a\u003c/span\u003e; Thorpe et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), progressing even if the energy input is reduced as shown by Pino Martinez et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Thus, a feed restriction should have been introduced earlier, possibly during the freshwater stage, in order to induce accumulative effects to influence maturation later at sea more clearly. All considered, we do not perceive a reduction in feeding intensity as a good solution to reduce the risk of early maturation in rainbow trout during the seawater phase. Our view is aligned with previous studies (Crespo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pino Martinez et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e) that reached similar conclusions; furthermore, our conclusion is also related to the negative effects on growth, feed utilization and harvest results that the feed restriction induced in this study and that are discussed in the last sub-section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Female and male rainbow trout have a similar tendency to mature early\u003c/h2\u003e \u003cp\u003eBased on our results, the tendency to mature during the grow-out phase appears similar in male and female rainbow trout. This seems different to Atlantic salmon, whose early maturation during aquaculture production is primarily a male phenomenon (Fjelldal et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pino Martinez et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). The absence of early maturation in female Atlantic salmon has been linked to the higher energy investments required to produce eggs compared to sperm (Adams and Thorpe, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Schulz et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and thus it is rare to find maturing females before they reach harvest size (Taranger et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A revision of the fish weight attained by Atlantic salmon in a number of small-scale studies on early maturation published in the last decade (Fjelldal et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fraser et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Imsland et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Melo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pino Martinez et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e), may suggest that the size salmon reached (roughly 100\u0026ndash;800 g) would be insufficient for females to mature. In contrast, in the present study, the female trout maturing in sampling 2 (late August) weighed 2.5 kg on average, and 3.1 kg in sampling 3 (late September), a much larger size. Similarly, a study with Atlantic salmon where females reached a size in that range also reported female sexual maturation, although in less numbers than males (Kadri et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Thus, while there might be some specific differences between Atlantic salmon and rainbow trout females in their tendency to mature early, in our study the large size that trout achieved in summer probably contributed significantly to the occurrence of female maturation. Whether sexual maturation can also be consistently induced in female rainbow trout at smaller size (0.5-1 kg) as occurs with salmon males (\u0026ldquo;jacks\u0026rdquo;) will be discerned in future small-scale trials where fish will be subjected to stimulatory conditions for maturation at a smaller size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Aquaculture implications of using a specific freshwater rearing system and feeding regime at sea\u003c/h2\u003e \u003cp\u003eFinally, the convenience of using a specific freshwater rearing system and a restricted feed regime during the grow-out phase of trout aquaculture must be discussed from a production perspective, taking into consideration risks associated to sexual maturation, fish performance and its implications. On the negative implications linked to RAS production, the RAS groups showed a certain tendency to sexually mature, and displayed a higher incidence of welfare issues (i.e. dorsal and caudal fin damage, spinal deformity). As stated, a high incidence of sexual maturation is detrimental for the producer\u0026rsquo;s economy since that fish is downgraded or discarded at harvest. Welfare issues are also negative for the fish, for public perception of the industry, and represent a risk of economic losses for the producer (i.e. downgrading of deformed fish). However, on the positive side, trout from RAS grew faster in the sea, displayed lower mortality, and was harvested 3 to 4 months earlier than trout from FT. As a result, by stocking RAS-produced rainbow trout, the company had a market-size product available earlier and shortened the production period, thus reducing potential risks linked to a long seawater residence such as delousing, disease outbreaks, etc. These positive and negative implications represent a trade-off that farmers must consider when choosing their ideal early rearing system. Producers need to analyze the benefits (i.e. fast growth, less mortality, shorter period at sea) obtained from rearing juveniles in intensive systems like RAS, and weigh them against the economic risks linked to the potential occurrence of early maturation or welfare issues at sea that may result in the downgrade of a percentage of fish at harvest.\u003c/p\u003e \u003cp\u003eIn relation to the choice of feed regime at sea, our results indicate that providing a full feed regime was more efficient than a restricted ration. Irrespective of the freshwater rearing system of origin, the groups that received a full regime displayed faster growth, higher feeding efficiency and better harvest results compared to their counterparts receiving a restricted ration. This implies that by providing a full ration, a market-size product was available earlier, less feed resources were wasted, and higher income was obtained due to harvesting a higher share of trout with superior quality. In addition, the restricted ration did not produce the results that were expected in terms of sexual maturation, this is, did not reduce its incidence, and had hardly any other beneficial effect. All considered, some discussion among producers may occur about the ideal freshwater system to produce rainbow trout juveniles, and the best choice will be dependent on the many aspects discussed and others (i.e. fish size at transfer to sea, season of transfer, temporal changes in trout market price, etc.). However, in regard to the choice of feed regime, very few reasons seem to justify providing a restricted ration for the whole duration of a production cycle.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe intensity of the rearing conditions during the freshwater phase can have an important influence on the probability of sexual maturation of rainbow trout later at sea, affecting both males and females. Thus, fish produced in RAS under elevated temperatures may later mature at sea in higher proportion, due to their more advanced development and growth rate, that result in the activation of the reproductive axis. But maturation may also occur in FT-produced juveniles if similar conditions are applied. Other factors like body size at seawater transfer and the season of transfer will also contribute to initiating or inhibiting sexual maturation. Restricting the feed ration seems unlikely to exert a major effect on the process, maybe unless introduced much earlier than maturation is initiated. Producers must assess the benefits of using intensive conditions in freshwater (i.e. increased growth) against the economic risks linked to higher probability of early maturation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSH and Eide Fjordbruk AS established the project, agreed on the funding and designed this study. EPM, JISM, CP and SH performed the samplings. CP carried out lab analyses. EPM performed data analysis, drafted and wrote the manuscript. SH provided assistance during the writing process. All authors critically reviewed the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was funded through a research concession granted by the Norwegian Directorate of Fisheries (Fiskeridirektoratet) to Eide Fjordbruk AS and University of Bergen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could influence the work included in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Erlend Eide, Nikolai Eide, Stein Inge Holstad and all the staff at Eide Fordbruk AS’s marine site Eikebærånæ, for their continuous support to this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAas, T.S., \u0026Aring;sg\u0026aring;rd, T., Ytrest\u0026oslash;yl, T., 2022. 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Aquaculture 86, 315\u0026ndash;326. https://doi.org/10.1016/0044-8486(90)90122-4\u003c/li\u003e\n \u003cli\u003eTrombley, S., Mustafa, A., Schmitz, M., 2014. Regulation of the seasonal leptin and leptin receptor expression profile during early sexual maturation and feed restriction in male Atlantic salmon, Salmo salar L., parr. Gen. Comp. Endocrinol. 204, 60\u0026ndash;70. https://doi.org/10.1016/j.ygcen.2014.04.033\u003c/li\u003e\n \u003cli\u003eVan Doornik, D.M., Berejikian, B.A., Campbell, L.A., 2013. Gene flow between sympatric life history forms of oncorhynchus mykiss located above and below migratory barriers. PLoS One 8, 1\u0026ndash;12. https://doi.org/10.1371/journal.pone.0079931\u003c/li\u003e\n \u003cli\u003eWickham, H., 2016. ggplot2: elegant graphics for data analysis. Springer.\u003c/li\u003e\n \u003cli\u003eWilkinson, R.J., Longland, R., Woolcott, H., Porter, M.J.R., 2010. Effect of elevated winter-spring water temperature on sexual maturation in photoperiod manipulated stocks of rainbow trout (Oncorhynchus mykiss). Aquaculture 309, 236\u0026ndash;244. https://doi.org/10.1016/j.aquaculture.2010.08.023\u003c/li\u003e\n \u003cli\u003eZelennikov, O. V., Golod, V.M., 2019. Gametogenesis of Rainbow Trout Parasalmo mykiss Cultivated from Hatching to Sexual Maturity at a Temperature of Approximately 20\u0026deg;С. J. Ichthyol. 59, 78\u0026ndash;89. https://doi.org/10.1134/S0032945219010181\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Recirculation Aquaculture Systems, Flow-Through, early puberty, gonadotropins, feed restriction","lastPublishedDoi":"10.21203/rs.3.rs-9042484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9042484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSexual maturation of rainbow trout during grow-out at sea poses economic risks for aquaculture due to reduced growth, feed intake, and downgraded harvest quality. This study evaluated the effects of freshwater rearing systems and feed rationing at sea on maturation in a commercial production site in Western Norway. Two batches of trout reared in Recirculating Aquaculture Systems (RAS) or Flow-Through (FT) systems were transferred to sea cages in March 2024 (mean sizes 450 g and 179 g, respectively). All fish received full rations for the first three months at sea, after which half of the cages from each system were subjected to feed restriction (one fasting day every three days; ~70% ration) until harvest. Fish reared in RAS showed a higher incidence of sexual maturation than FT fish irrespective of the feed regime (12% vs. 0%). This difference was associated with more intensive freshwater conditions, particularly elevated temperatures during the last period of the freshwater phase, and larger size at sea transfer. Gonadotropic upregulation in RAS trout was evident by June, prior to visible maturation, suggesting activation of the reproductive axis triggered by increasing photoperiod and temperature following spring transfer. Maturation occurred in both sexes, unlike in Atlantic salmon where is male-predominant. Feed restriction did not reduce maturation rates but negatively affected growth, feed efficiency, and harvest quality. Thus, ration reduction is not an effective strategy against early maturation and incurs additional costs. These results highlight a trade-off in freshwater rearing strategies: while intensive RAS conditions promote faster growth, they may increase the risk of early maturation and associated welfare issues at sea.\u003c/p\u003e","manuscriptTitle":"Freshwater history exerts a larger effect than the feeding regime at sea on the occurrence of rainbow trout sexual maturation in seawater cages","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 07:45:33","doi":"10.21203/rs.3.rs-9042484/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-15T18:50:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6771865200729978824616716731558017587","date":"2026-05-07T12:29:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52080301782186364111774443774005439604","date":"2026-05-07T00:55:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T11:02:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276948128930788895442383716014050280414","date":"2026-04-17T14:49:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94566813012966482732749273331037789423","date":"2026-03-23T07:45:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T23:56:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T13:24:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-16T11:45:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T08:16:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-11T11:41:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48d040fa-41d4-4a1c-bcc7-c4001d47b821","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T18:50:55+00:00","index":71,"fulltext":""},{"type":"reviewerAgreed","content":"6771865200729978824616716731558017587","date":"2026-05-07T12:29:54+00:00","index":70,"fulltext":""},{"type":"reviewerAgreed","content":"52080301782186364111774443774005439604","date":"2026-05-07T00:55:55+00:00","index":68,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T11:02:51+00:00","index":58,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64728063,"name":"Biological sciences/Ecology"},{"id":64728064,"name":"Earth and environmental sciences/Ecology"},{"id":64728065,"name":"Earth and environmental sciences/Ocean sciences"},{"id":64728066,"name":"Biological sciences/Physiology"},{"id":64728067,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2026-03-20T07:45:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 07:45:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9042484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9042484","identity":"rs-9042484","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}