# Aquaculture potential evolution during early domestication trials in a model species (Danio rerio) with different breeding strategies

E. Diakos, Pascal Fontaine, Sophie Lambert, Yannick Ledoré, Jérôme Lambert, A. Magitteri, Md. Shahjahan, P. Kestemont, Marc Vandeputte, Thomas Lecocq

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E. Diakos, Pascal Fontaine, Sophie Lambert, Yannick Ledoré, Jérôme Lambert, et al.. Aquaculture potential evolution during early domestication trials in a model species (Danio rerio) with different breeding strategies. Aquaculture, 2026, 612, pp.743194. 10.1016/j.aquaculture.2025.743194. hal-05462726

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# Aquaculture potential evolution during early domestication trials in a model species (*Danio rerio*) with different breeding strategies

E. Diakos<sup>a,*</sup>, P. Fontaine<sup>a</sup>, S. Lambert<sup>a</sup>, Y. Ledoré<sup>a</sup>, J. Lambert<sup>c</sup>, A. Magitteri<sup>a</sup>, Md. Shahjahan<sup>b</sup>, P. Kestemont<sup>c</sup>, M. Vandeputte<sup>d</sup>, T. Lecocq<sup>a,*</sup>

## ARTICLE INFO

### Keywords:

Heritability

Evolution

Integrative assessment

Phenotypic traits

Selective breeding programs

## ABSTRACT

Domestication is a cornerstone for diversifying aquaculture, and the feasibility of domesticating a wild fish population can be assessed through its aquaculture potential (i.e., a measure based on key phenotypic traits related to growth and reproduction in captivity while meeting stakeholders' expectations). As domestication modifies these traits, aquaculture potential is expected to evolve over generations, influenced by breeding strategies. However, little is known about how different management approaches may shape this evolution during early domestication. In this study, we tracked changes in aquaculture potential over four captive generations of wild-caught zebrafish (*Danio rerio*), subjected to three breeding strategies: no selection (NS), single-function selective breeding program (S-SBP; growth-related traits), and multi-function selective breeding program (M-SBP: growth, reproduction, welfare). S-SBP and M-SBP employed within-family selection based on a phenotypic index. We developed a standardized Aquaculture Potential Score (APS) integrating growth (standard length, height/length ratio, specific growth rate), reproductive (fecundity, sperm motility), and welfare traits (stress-induced cortisol, Fulton's condition factor). Trait evolution was assessed using linear model predictions and genetic trends based on estimated breeding values and heritability derived from pedigrees. Selective breeding (S-SBP and M-SBP) rapidly improved growth and morphometric traits, whereas NS lines showed minimal change. Reproductive and welfare traits, however, remained largely static due to both low heritability (h² < 0.20) and environmental modulation. Consequently, APS remained stable across generations, with growth gains offset by stagnation in other domains. These findings underscore that (i) early selection may effectively improve high-heritability traits; (ii) composite metrics like APS may obscure divergent trait responses.

## 1. Introduction

Aquatic organisms have long been one of humanity's primary sources of food, and their importance has grown significantly in recent decades due to the rising of global demand for aquatic products (FAO, 2024). While fishing efforts intensified after the 1950s to meet the demand, overfishing had pushed natural ecosystems to their limits by the late 1980s (FAO, 2024). In response, aquaculture (i.e., farming of aquatic organisms such as algae, crustaceans, fish, and mollusks) has emerged as a vital solution to supplement or replace wild fish catches and sustainably meet the global demand (Duarte et al., 2007; Subasinghe et al., 2009).

Current global fish aquaculture production relies heavily on a narrow range of species (~20 species), with most production occurring in Asia (FAO, 2024; Metian et al., 2020; Teletchea, 2018). This reliance raises ecological, economic, and food security concerns. First, many of the most widely farmed species are produced outside their natural ranges, threatening wild native fauna through biological invasions and pathogen spill-over (Troell et al., 2014). Second, this reliance on a few species jeopardizes food security by increasing aquaculture vulnerability to disruptions, such as epizootic outbreaks (e.g., Godoy et al., 2008). Third, the low species diversity undermines the economic resilience of aquaculture by limiting its ability to adapt to environmental changes or shifting consumer demands, ultimately threatening its long-

<footnote><sup>a</sup> University of Lorraine, L2A, INRAE, Nancy, France</footnote>

<footnote><sup>b</sup> Bangladesh Agricultural University, Department of Fisheries Management, Mymensingh, Bangladesh</footnote>

<footnote><sup>c</sup> Research Unit in Environmental and Evolutionary Biology, University of Namur, Namur, Belgium</footnote>

<footnote><sup>d</sup> MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, INRAE, 34250 Palavas-les-Flots, France</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: emmanouil.diakos@univ-lorraine.fr (E. Diakos), thomas.lecocq@univ-lorraine.fr (T. Lecocq).</footnote>
term sustainability (Fontaine et al., 2009; Metian et al., 2020). As a result, diversifying the range of farmed species, particularly by promoting the production of native species, has been recognized as a solution to enhance the sustainability and resilience of aquaculture (Cai et al., 2023; FAO, 2018; Harvey et al., 2017; Metian et al., 2020).

Domestication is a key strategy for diversifying aquaculture as it enables the production of new fish species (Milla et al., 2021; Teletchea and Fontaine, 2014). Domestication is a mutualistic, multigenerational evolutionary process in which humans provide controlled environments and sustained care in exchange for reliable resources or services from the domesticated species (adapted from Brooker and Feeney, 2019; Purugganan, 2022). In fish, it is a gradual process that begins with acclimating captured wild organisms to a farming environment to establish fish stocks for initial production and parental broodstock populations (Teletchea and Fontaine, 2014). It progresses through the successful completion of consecutive life cycles in captivity and culminates in the application of artificial selection to enhance desirable phenotypic traits (Milla et al., 2021; Teletchea and Fontaine, 2014).

Domestication is a lengthy and challenging process that often ends in failure despite years of effort (Diamond, 2002; Metian et al., 2020; Teletchea, 2021; Teletchea and Fontaine, 2014). This difficulty stems not only from technological and socio-economic constraints but also from the inherent biological characteristics of the target species, which can limit their suitability for domestication (Diamond, 2002; Driscoll et al., 2009; Jobling, 2010; Lecocq and Toomey, 2021; Teletchea and Fontaine, 2014).

Phenotypic traits present at the outset of domestication play a pivotal role in determining its success, either facilitating progress or creating significant obstacles (Braithwaite and Salvanes, 2010; Diamond, 2002; Teletchea, 2021; Teletchea, 2018). To increase the success of new domestication programs, several approaches have been proposed to identify wild species or populations best suited for fish aquaculture (Cacot and Lazard, 2009; Lal et al., 2023; Le François et al., 2002; Oboh, 2022; Quéméner et al., 2002; Toomey et al., 2020a). These approaches typically emphasize the importance of favorable expression of key traits related to reproduction, growth, generation intervals, nutrition, stress tolerance, disease resistance, aggressiveness toward conspecifics, and stakeholder expectations. A population is more likely to be domesticated if it can successfully reproduce (reproductive and behavioral functions), grow (growth and behavioral functions), feed (nutritional and behavioral functions), and cope with stress (e.g., disease resistance, immune and behavioral functions) within a controlled environment, while also meeting stakeholders' expectations (e.g., product quality traits) (Metian et al., 2020; Teletchea, 2016; Toomey et al., 2020b). Accordingly, three primary groups of traits (i.e., growth, reproduction, and welfare) are often considered when assessing an organism's relevance for aquaculture.

Growth is the most critical consideration, because it directly impacts production efficiency and profitability. Growth-related traits measured through morphometric traits such as weight and length are thus generally prioritized in early selective breeding programs (SBPs) (Gjedrem, 2012; Zenger et al., 2019). Achieving greater and faster growth leads to higher production yields, a shorter production cycle, reduced maintenance costs, and improved feed conversion efficiency (Gjedrem and Baranski, 2009; Kause et al., 2022; Vandeputte et al., 2022).

Reproduction is another key consideration, as it directly influences the ability to produce the next generation under captive conditions and ensures the efficiency and sustainability of fish production (Toomey et al., 2020b). Reproductive performance relies on the quality of both male and female gametes. High-quality sperm is crucial for effective fertilization, while high-quality eggs (i.e., not undergoing atresia, and with adequate protein, lipid, essential fatty acids, vitamins and minerals composition) ensure proper embryonic development and offspring survival (Bobe, 2015; Kestemont and Dabrowski, 2015; Kowalski and Cejko, 2019; Rurangwa et al., 2004).

Fish welfare has emerged as an increasingly important consideration. As fish are sentent beings capable of experiencing stress, pain, and discomfort (Broom, 2007; Gjedrem, 2012; Kristiansen et al., 2020), it is important to ensure fish welfare for ethical reasons. However, it is also vital for production, since poor welfare negatively impacts growth rate, feed conversion efficiency, reproductive performance, and survival (Ellis et al., 2012; Øverli et al., 2007; Øverli et al., 2006). Moreover, stress-induced elevations in cortisol (i) impair fish immune function, increasing susceptibility to infections and disease outbreaks, and (ii) change meat quality (Daskalova, 2019) which ultimately reduces productivity and profitability (Schreck and Tort, 2016). With growing consumer demand for ethical food production, farms that uphold high welfare standards tend to gain greater market trust (MSC, 2018). Consequently, integrating of welfare-related traits into SBPs is becoming more common, aiming to improve both fish welfare and aquaculture sustainability by decreasing the stress response (Kristiansen et al., 2020; Øverli et al., 2002; Vandeputte et al., 2016).

The concept of aquaculture potential refers to the quantification of how favorably key traits/functions (i.e., biological and socio-economic traits) are expressed for domestication and subsequent production (Toomey et al., 2020b). This quantification is based on measurements of multiple traits related to growth, reproduction, and welfare, obtained during experimental trials conducted under conditions similar to those envisioned for aquaculture production (Toomey et al., 2021; Toomey et al., 2020b). Aquaculture potential can be used to compare different candidate species for the launch of a new aquaculture production (Toomey et al., 2021, Toomey et al., 2020b). In this context, the quantification is synthesized into an aquaculture potential score (APS), which facilitates the ranking of different organism groups according to their suitability for aquaculture, thereby helping to identify the best candidates for domestication programs (Toomey et al., 2021, Toomey et al., 2020b).

While the initial aquaculture potential is crucial at the outset of a domestication program, it is equally important to consider how this potential could evolve throughout the domestication process to ensure the development of viable and socio-economically valuable production. During domestication, evolutionary changes occur due to “natural selection” for adaptation to the human-controlled environment, as well as stochastic and demographic events such as genetic drift and inbreeding (Gjedrem and Baranski, 2009; Mignon-Grasteau et al., 2005; Price, 1999). In fish, these forces drive numerous behavioral, morphological, physiological, and phenological modifications (Milla et al., 2021; Pasquet, 2019). Domesticated fish tend to be tamer, exhibiting reduced fear and predator avoidance compared to their wild counterparts (Alvarez and Nicieza, 2003; Sundström et al., 2004). Most of the time, they display lower behavioral and physiological responses to stress (Awata et al., 2011; Diakos et al., 2024; Douxfils et al., 2011; Lepage et al., 2000; Pasquet, 2019), improved tolerance to confinement (Douxfils et al., 2015) and altered foraging, exploratory, and risk-taking behavior (Pasquet, 2019; Reinhardt et al., 2001; Thodesen et al., 1999). Aggressiveness also often changes, decreasing in some species while increasing in others (Diakos et al., 2024; Pasquet, 2019; Ruzzante and Doyle, 1991). Growth rates typically improve during domestication due to enhanced feed intake, digestion and adaptation to farming-related stress (reviewed by Milla et al., 2021). However, they may remain unchanged (Vandeputte et al., 2009) or even decline in certain cases (Brummett et al., 2004). Reproductive traits are often negatively impacted, with reduced ovulation, hatching, and survival rate documented in parental broodstock across several species, although improvements have been documented in certain species (reviewed by Milla et al., 2021). Immune system changes are variable, with no consistent immunostimulatory or immunosuppressive effects observed across species (Milla et al., 2021). Some of these phenotypical changes are influenced or directed by humans through strategies for managing the breeding population. These strategies range from minimal intervention to more advanced methods, such as inbreeding control and SBPs that are designed to enhance the
expression of key traits for aquaculture (Gjedrem, 2005; Holtsmark et al., 2008). These SBPs may focus on improving a single trait (e.g., specific growth rate), a set of traits related to a particular function (i.e., single-function SBP), or multiple traits or functions either successively (e.g., tandem selection, where a different trait is selected at each generation) or simultaneously (i.e., simultaneous multifunction SBP) (Gjedrem, 2005; Gjedrem and Baranski, 2009).

Given that domestication alters the expression of phenotypic traits, including those that shape aquaculture potential, this potential is expected to change over generations. Observed trends in fish domestication suggest that aquaculture potential may be either enhanced or degraded over generations (Anastasiadi et al., 2018; de Verdal et al., 2019; Geffroy et al., 2021; McKenzie et al., 2021; Milla et al., 2021; Ramos and Gonçalves, 2019; Vandeputte et al., 2022; Zhao et al., 2020). However, specific changes during early domestication have not yet been thoroughly assessed (Milla et al., 2021). In this study, we track the change of key traits for fish production and APS over the first four generations of domestication in a model aquaculture species (Ribas and Piferrer, 2014), the zebrafish (Danio rerio, Danionidae, Cypriniformes). We use zebrafish because it meets the key criteria of a suitable model species for aquaculture research (Ribas and Piferrer, 2014): (i) it shares the fundamental biological characteristics of the most important cultured species, (ii) it exhibits physiological responses comparable to those of farmed fish, (iii) it has a short life cycle and is easy and inexpensive to breed, and (iv) it benefits from extensive research resources and published phenotyping protocols that facilitate experimentation (von Jørgensen, 2020; Ribas and Piferrer, 2014; Ulloa et al., 2014). Starting from a wild base population, we developed three SBPs, each managed using a different strategy (i.e., no selection: NS; single-function SBP focusing on growth: S-SBP; simultaneous multifunction SBP considering all traits included in the APS: M-SBP). Our objective is to identify evolutionary trends in key traits and aquaculture potential across different strategies applied to manage breeding populations. This research aims to provide insights into aquaculture potential change during domestication and if this evolutionary process tends to enhance aquaculture potential.

# 2. Materials and methods

## 2.1. Ethics statement

No specific permits were required for the sampling of wild zebrafish, as collection did not take place in privately-owned areas and protected locations, or involve protected species. All the procedures adhered to national and international guidelines for the animal welfare (Directive 2010/63/ EU). This study was approved by the Animal Care Committee of Lorraine (CELMA n°66) and the French Ministry of Higher Education, Research, and Innovation (APAFIS #38648-2022092215545630 v5). The research was conducted at the facilities of the University of Lorraine (Plateforme Expériementale en Aquaculture, Vandœuvre-lès-Nancy, France; registration number for animal experimentation D54-547-18).

## 2.2. Animal collection, acclimatization, and housing facilities

The zebrafish is a freshwater teleost fish species. It typically inhabits rivers, small streams, and channels, as well as stagnant or slow-moving pools near streams and rice paddies in Southeast Asia (i.e., India, Bangladesh, and Nepal) (Spence et al., 2008).

Between June 17 and 28, 2022, wild zebrafish were collected using net from rice paddies within a 10 km radius around the Bangladesh Agricultural University (WGS84: 24°43'21.20" N, 90°26'1.45" E, Mymensingh, Mymensingh Division, Bangladesh). The sites were selected for their suitability for fish capture (shallow depth, minimal or no current) and their proximity to the university, which facilitated rapid transport and ensured that the fish could be quickly transferred to appropriate holding conditions. The collected fish were then held in

aquariums at the Bangladesh Agricultural University for 5 to 11 days (i. e., depending on sampling time) before being shipped. The fish were shipped from Bangladesh on June 29 and arrived at the Plateforme Expériementale en Aquaculture on July 1.

The fish were acclimatized to captive conditions in four 240 L aquariums to prepare them for the main housing system (Table 1). This initial population was designated as the F0 generation, assumed to be unrelated and non-inbred. After a 5-month acclimatization period (July to December 2022), the F0 fish were transferred to the main housing system: a set of 3.5 L tanks within an automatized rack system (ZebTEC Active Blue Stand Alone Tecniplast®). This rack system was chosen for its suitability and is commonly used as breeding system in zebrafish culture (Weisbroth et al., 2020). Moreover, this kind of rearing device corresponds to a recirculating aquaculture system (Hammer, 2020), which is increasingly used in aquaculture production (Ahmed and Turchini, 2021). Housing conditions were based a consensus between the recommendations in the literature (Avdesh et al., 2012) and the abiotic conditions observed in the sampling zone, the latter obtained by on-site measurement and completed by AquaDesign V1 (Butruille et al., 2022) (Table 1).

## 2.3. Fish management over the generations

After F0 individuals were transferred to the main housing rack system, they were prepared for reproduction (see details below). The offspring of the F0 constituted the F1 generation (i.e., first generation born under captive conditions, Fig. 1). F1 was produced by randomly pairing F0 males and females, with no selection criterion applied at this stage other than their ability to reproduce in captivity. Ten pairs successfully reproduced, providing sufficient offspring for the experiment (Fig. 1).

The offspring of each of these 10 F0 pairs formed a distinct family (Fig. 1). Each family was randomly divided into three equal groups. Each group was randomly assigned to one of the three SBPs (Fig. 1) to which different selection would later be applied (see details below). Consequently, in F1, there were 10 families per SBP. For each SBP, the families were reared separately (i.e., kept in different 3.5 L tanks).

From the F1 onwards, SBPs were implemented to select the breeders that would produce the next generation (F2). At each generation, a target number of 10 families (realized: 7 to 10) were produced within each SBP, in a within family selection scheme. To avoid the risk of inbreeding, a mating plan between families within each breeding

<caption>**Table 1**</caption>

Acclimatization and rearing conditions of the domestication environment. Summary of the water quality parameters and husbandry conditions applied during the acclimatization of wild-caught zebrafish (F0) and the rearing of subsequent captive-born generations (F1 to F4).

<table><thead><tr><th>Environmental conditions</th><th>F0 acclimatization</th><th>Rearing racks (F0 to F4)</th></tr></thead><tbody><tr><td>Water temperature (°C)</td><td>27.5 ± 0.5</td><td>27.5 ± 0.5</td></tr><tr><td>pH</td><td>7.5 ± 0.5</td><td>7.5 ± 0.5</td></tr><tr><td>Conductivity (µS/cm)</td><td>500 ± 50</td><td>500 ± 50</td></tr><tr><td>NO2- (mg/L)</td><td>~0.02</td><td>~0.02</td></tr><tr><td>NH4+ (mg/L)</td><td>~0.03</td><td>~0.03</td></tr><tr><td>Fish feeding</td><td>4× daily</td><td>4× daily</td></tr><tr><td>> 14 dpf</td><td>(3× dry +1× Artemia)</td><td>(3× dry +1× Artemia)</td></tr><tr><td>Tank cleaning regime</td><td>Twice a week</td><td>Monthly</td></tr><tr><td>Juvenile/adults</td><td>(clean and siphon)</td><td>(wash tanks)</td></tr><tr><td>Adult fish rearing density (fish/L)</td><td>1–2</td><td>5</td></tr><tr><td>Light cycle with 30 min dawn & dusk</td><td>LD 13:11</td><td>LD 13:11</td></tr><tr><td>Light intensity (lx)</td><td>300–3000</td><td>200–400</td></tr><tr><td>Dissolved oxygen</td><td>> 7 mg/L</td><td>> 7 mg/L</td></tr></tbody></table>

Diets: Dry; Gemma Micro ZF (Skretting, France) and Artemia nauplii (EG > 225).

Abbreviations: dpf, days post fertilization; L, light; D, dark.
<table><caption>**Fig. 1.** Overview of generations and selective breeding programs. Wild-caught zebrafish (F0) were bred to produce four generations (F0-F4). Progeny from 10 F0 pairs were divided into three selective breeding programs (SBPs): No Selection (NS; green), Single-function (S-SBP; blue), and Multifunction (M-SBP; red). Each SBP comprised 10 families (18 fish/family, equal sex ratio). From F1 to F4, selection occurred within families, followed by interfamilial crosses within each SBP.</caption><thead><tr><th></th><th colspan="3">F0 parental pairs (n = 10)</th></tr></thead><tbody><tr><th>F1</th><td>M - SBP</td><td>S - SBP</td><td>NS</td></tr><tr><th></th><td>Family 1 to 10</td><td>Family 1 to 10</td><td>Family 1 to 10</td></tr><tr><th>F2</th><td>M - SBP</td><td>S - SBP</td><td>NS</td></tr><tr><th></th><td>Family 1 to 10</td><td>Family 1 to 10</td><td>Family 1 to 10</td></tr><tr><th>F3</th><td>M - SBP</td><td>S - SBP</td><td>NS</td></tr><tr><th></th><td>Family 1 to 10</td><td>Family 1 to 9</td><td>Family 1 to 10</td></tr><tr><th>F4</th><td>M - SBP</td><td>S - SBP</td><td>NS</td></tr><tr><th></th><td>Family 1 to 7</td><td>Family 1 to 7</td><td>Family 1 to 7</td></tr></tbody></table>

program was applied from the F1. This mating plan allowed to reach the F4 generation without mating related individuals (i.e., tracing back to the F0). The same selection and mating scheme were consistently followed to produce the subsequent generations, F3 and F4, under the respective SBPs. Pedigrees (Table S1) were established and managed using PyRAT (Scionics, Germany), a lab animal management software.

## 2.4. Breeding protocol and rearing from eggs to adults

Animals were bred at an age of 130–150 days post-fertilization (dpf), except for F0, where the exact age of the animals was unknown, but was more than 263 dpf (i.e., time between the field capture and the reproduction success in captivity). Reproduction was attempted through a protocol adapted from literature (see supporting information 1). If no reproduction occurred, the process was repeated daily. It is important to note that not all families were produced on the same day. The asynchronous production of offspring among parental pairs can be attributed to natural variability in spawning frequency and environmental adaptation. Individual differences in acclimation to captivity, reproductive cycles, and pair compatibility resulted in staggered spawning events, preventing all pairs from producing offspring simultaneously. Eventually, the maximum interval between the youngest and oldest families (18 days, established in the F1 generation) was maintained consistently across generations for each SBP. Fish pairs unable to reproduce within this timeframe were discarded, leading to a gradual reduction in the number of families within SBPs during domestication. Egg incubation and larval rearing up to 14 dpf were performed using adapted protocols from literature (supporting information 1).

## 2.5. Fish tagging

To phenotype and follow-up our juveniles and adults at the individual level, we tagged them at 70 dpf using color combinations of visible implant elastomers - VIE tags (Northwest Marine Technology, Inc., Anacortes, WA, USA) (see supporting information 1 for details). This age was chosen because fish had reached the recommended total length of 22 mm which minimizes mortality associated with VIE tagging in zebrafish (Rácz et al., 2021). Additionally, by 70 dpf, individuals were above the size threshold for female puberty initiation (standard length > 1.8 cm), consistent with the onset of previtellogenic follicle development and the emergence of secondary sexual characteristics (Chen and Ge, 2013). Sexual dimorphisms such as the more rounded body

shape of females compared to the streamlined form of males, became apparent, allowing for visual sex determination.

## 2.6. Fish phenotyping

Across all generations, 10 randomly chosen individuals (i.e., five males and five females) from each family within each SBP were phenotyped for three groups of traits of aquaculture importance: (i) growth (Standard Length [SL], Specific Growth Rate [SGR], Height/Length ratio (H/L), (ii) reproduction (Sperm Concentration, Total Motility of sperm, Progressive Motility of sperm, Total Fecundity, Fertilization Rate, Hatching Rate), and (iii) welfare-related traits (Fulton's Condition Factor [FCF], Waterborne Cortisol) (Table 2; see detailed phenotyping protocols in supporting information 1 and importance of studied traits for aquaculture in Table S2). For a given trait, phenotyping was performed at the same age for each fish, taking into account the staggered spawning events (i.e., up to 18 days).

### 2.6.1. Applied selective breeding programs

From the reproduction of F1 breeders, within-family selection was performed at each generation within every SBP to produce the next generation. Three SBPs were developed: (i) NS, in which breeders were randomly chosen (i.e. the only pragmatic criterion applied is the ability to reproduce); (ii) S-SBP, in which the breeders were selected based on their performance for the growth-related traits, with the objective of improving growth; (iii) M-SBP, in which the breeders were selected based on their performance across multiple trait categories, including growth, reproduction, and welfare, with the goal of enhancing overall aquaculture potential. At each generation and for each SBP and sex independently, parental ranking and selection (i.e., identifying the best male and female per family) were conducted using a phenotypic selection index (SI) tailored to each SBP.

For S-SBP, the SI calculation was adapted from an approach previously used in the assessment of fish polycultures (Lecocq et al., 2024). SI was computed in a three-dimensional space, where each dimension (axis) represented a growth trait: SGR, SL, and HL (Fig. 2A). In this space, the position of candidate breeders was determined by coordinates derived from their standardized values for each trait (Fig. 2A). The mathematical space and candidate breeder positions were calculated in R V4.3.3 (R Core Team, 2020) after polarizing the traits (± according to the desired direction of trait expression) and standardizing all values (scaling between 0 and 1). The polarization was performed in order to
<caption>**Table 2**</caption>

Traits studied according to the trait group, the applied phenotyping method, and the respective phenotyping time point (in days post-fertilization, dpf). Sex-specific traits are specified for males (♂) and females (♀).

<table><thead><tr><th>Trait Group</th><th>Trait (Abbreviation if used) (unit)</th><th>Phenotyping method</th><th>Time Point</th></tr></thead><tbody><tr><td rowspan="3">Growth</td><td>Standard Length (SL) (mm)</td><td>Based on photos through ImageJ (Schneider et al., 2012)</td><td rowspan="3">70 and 90 dpf</td></tr><tr><td>Specific Growth Rate (SGR)</td><td>Formula 100 x (logSL<sub>70</sub> - logSL<sub>70</sub>)/20) (Crane et al., 2020)</td></tr><tr><td>Height/Length ratio (H/L)</td><td>Body height at 90 dpf divided by SL at 90 dpf</td></tr><tr><td rowspan="5">Reproduction</td><td>♂ Sperm Concentration (million/ml)</td><td>Obtained by stripping males and using computer-assisted sperm analysis (CASA)</td><td rowspan="5">130-140 dpf</td></tr><tr><td>♂ Total Motility (%)</td><td></td></tr><tr><td>♂ Progressive Motility (%)</td><td></td></tr><tr><td>♀ Total Fecundity (Total number of eggs)</td><td>Each female was mated with two males at a 3-day interval. Eggs were counted using a stereomicroscope (SZX7; Olympus)</td><td rowspan="3">90-120 dpf</td></tr><tr><td>♀ Fertilization Rate (%)</td><td>Calculated by determining the proportion of fertilized eggs</td></tr><tr><td>♀ Hatching Rate (%)</td><td>Evaluated post-fertilization by monitoring the proportion of larvae that successfully hatch</td></tr><tr><td rowspan="2">Welfare</td><td>Fulton's Condition Factor (FCF) (mg/ mm<sup>3</sup> × 100)</td><td>Formula adapted from Morgan et al. (2018) using SL as a parameter (W<sub>90</sub>/SL<sup>3</sup>) × 100</td><td rowspan="2">90 dpf</td></tr><tr><td>Waterborne Cortisol (ng/mg)</td><td>Non-invasive 'beaker test': Individual fish placed in 50 mL of water for 30 min. Post-stress cortisol extracted from the collected water via solid-phase extraction (SPE) and quantified using an ELISA kit (Cayman Chemical Company, #500360). Results scaled by fish weight (ng/mg).</td></tr></tbody></table>

take into account that for one trait (i.e., Waterborne Cortisol) the higher expression is not desirable for aquaculture purpose. The theoretical optimal fish for the set under consideration thus had coordinates (1,1,1). For each fish, its Euclidean distance from the origin (0,0,0) was used as the SI: $SI = \sqrt{SL^2 + SGR^2 + H/L^2}$, where SL, SGR, and H/L value were standardized. It is important to note that in this configuration, an individual with a high SI could perform very well for one trait but poorly for another. To avoid this, we implemented a sliding-threshold approach to select fish with balanced performance across the three traits. Specifically, we defined a minimum threshold of 0.9 for each axis. If only one fish exceeded this threshold, it was selected as the best breeder. If multiple fish exceeded the threshold, the Euclidean distance (SI) was calculated, and the fish with the greatest distance was chosen as the best breeder. If no fish met the threshold, a lower threshold (i.e., 0.8) was applied, and the process was repeated until a best breeder was identified (i.e., using successively thresholds of 0.7 and 0.6). Selection of the best breeder was performed independently for each family and for each sex within each family.

For M-SBP, SI calculation followed a similar logic but in two steps

(Fig. 2B). First, spaces were constructed and each individual's position calculated as for S-SBP, with each space corresponding to one of the three trait groups studied: growth (3D space), reproduction (3D space), and welfare (2D space) (Fig. 2B). In each space, the Euclidean distance from the origin was calculated for each candidate breeder: Growth = $\sqrt{SL^2 + SGR^2 + H/L^2}$; male reproduction = $\sqrt{Sperm Concentration^2 + Total Motility^2 + Progressive Motility^2}$; female reproduction = $\sqrt{Total Fecundity^2 + Fertilization Rate^2 + Hatching Rate^2}$; welfare = $\sqrt{FCF^2 + Waterborne Cortisol^2}$, where trait values were polarized and standardized. Second, the three Euclidean distances obtained for the three trait groups were independently standardized (range: 0–1) to give them equal weight. These standardized distances were then used to construct a new three-dimensional space, where each axis corresponded to a trait group (Growth, Reproduction, and Welfare). In this space, the best breeder was identified independently for each family and sex within the family using the same sliding-threshold procedure as for S-SBP, applied to this new space. The M-SBP SI was thus calculated as $\sqrt{Growth^2 + Reproduction^2 + Welfare^2}$ (Fig. 2B).

### 2.6.2. Assessing the aquaculture potential

The aquaculture potential of each family was measured by an APS based on the eleven key traits related to growth, welfare, and reproduction.

First, within each family at each generation for every SBP, the measured trait values were averaged. For growth and welfare traits, the average was computed using data from both sexes combined. For sex-specific traits, each family was assigned an average value for six reproduction-related traits (i.e., the combined performance of males and females), although only individuals of the corresponding sex were considered. As a result, families within each SBP served as replicates (n = 10) for each SBP at each generation.

Second, the APS of families was determined using the same calculation method as for the M-SBP SI, but it was computed on mean values for each family across all SBPs and generations combined. Therefore, the potential of each family is expressed relative to that of all families, SBPs, and generations combined. Overall, the APS of each family was then calculated as the overall Euclidean distance from the origin.

## 2.7. Data analyses

### 2.7.1. Linear models with baseline

To evaluate changes in each trait over generations and across SBPs, we applied a linear model framework in R. For each trait, a set of alternative models was developed to systematically identify the optimal structure for explaining variation, resulting in one best-fitting model per trait. Model selection followed a hierarchical set of criteria (i.e., marginal $R^2$ [$R^2_m$] and conditional $R^2$ [$R^2_c$] coefficients of determination, Akaike Information Criterion [AIC], cross-validated Root Mean Square Error [cross-validated RMSE] and likelihood ratio tests [LRT]), with the interaction model retained to explicitly test for SBP-dependent divergence; see supporting information 1).

Candidate models included combinations of fixed effects such as SBP, generation, sex, tank, and their interactions (e.g., additive models [SBP + generation + sex + tank], models excluding tank effects, and models with or without SBP × Generation interactions). Growth and welfare traits were analyzed using data from both sexes, while sex-specific traits were restricted to individuals of the corresponding sex.

To standardize comparisons across SBPs, trait values were transformed relative to the F1 baseline within each SBP. For each trait, the mean value in F1 individuals was calculated independently in each SBP and used as the reference. Individual trait values from generations F1–F4 were then expressed as percent deviation from the F1 mean: Transformed trait value = ((trait value - F1 mean)/F1 mean) × 100. This transformation centers F1 values at zero and enables standardized comparisons of relative changes across generations and SBPs.

Trait changes were analyzed using linear mixed-effects models
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Schematic representation of the calculation of selection indices (SI) in single-trait (A) and multi-trait (B) selective breeding programs. The best breeders (colored dots) are selected within families to produce the next generation. For each fish, the SI is computed as the Euclidean distance from the origin (0,0,0) in a multidimensional trait space, where each axis represents a standardized value for one trait or trait category (growth, reproduction, welfare). Threshold planes at values of 0.9, 0.8, 0.7, and 0.6 (parallel to each trait axis) are shown within the cube as reference zones for breeder selection. The Euclidean distance reflects an individual's overall performance. Candidates exceeding the selection threshold (see Materials and Methods) are ranked by SI, with the greatest distance corresponding to the highest index and thus the most balanced and superior performance across traits (e.g., 3B2GG for S-SBP SI and 2A1YY for M-SBP SI). SIs are calculated separately for males and females within each family and SBP.</figcaption></figure>
(LMMs), fitted with the *lme4* V1.1–37 R-package (Bates et al., 2015) in R. Fixed effects included SBP, generation, and sex, while tank was included as a random intercept to account for non-independence among individuals. The explanatory power of the fixed effects alone and combined with random effects was quantified by $R_m^2$ and $R_c^2$, respectively, using the *MuMln* V1.46.0 R-package (Barton, 2020). The statistical significance of individual fixed effects was assessed by performing an analysis of variance (ANOVA) on LRT comparing nested models using the *lmerTest* V3.1-3 R-package (Kuznetsova et al., 2017). Estimated marginal means (EMMs) for each SBP × Generation combination were obtained using the *emmeans* V1.11.2-8 R-package (Lenth, 2025). For comparability, EMMs were centered by subtracting the model-based F1 mean within each SBP, thereby expressing all means as relative deviations from the baseline.

Trait evolution was visualized using line plots generated with *ggplot2* V3.5.2 R-package (Wickham, 2016), displaying centered EMMs across generations for each SBP, with error bars representing the standard error of the estimates. Pairwise comparisons among SBP × Generation groups were performed using Tukey-adjusted contrasts (emmeans R-package), with significance thresholds set at *p* < 0.05 and *p* < 0.01. To summarize divergence patterns across generations, significant contrasts were visualized in heatmaps constructed with *ggplot2* R-package.

### 2.7.2. Statistical analysis of APS evolution across generations

To evaluate generational trends in APS under the three SBPs, APS was calculated at the family level, with each data point representing the mean APS of a single family. To standardize comparisons, across SBPs and generations, family-level APS values were transformed to express percent change relative to the F1 baseline within each SBP. Specifically, the mean APS of all F1 families within a given SBP was used as the baseline. For each family and generation, percent change was computed by subtracting the SBP-specific F1 mean from the observed APS value, dividing by the same F1 mean, and multiplying by 100 (consistent with the formula in Section 2.7.1).

Generational APS trends were analyzed using a LMM fitted with the *lme4* R-package. The model included SBP, generation, and their interaction as fixed effects, with family as a random intercept to account for repeated measures within families. Model selection prioritized hierarchical criteria (i.e., $R_m^2$ and $R_c^2$, AIC, cross-validated RMSE, and LRT), with the interaction model retained to explicitly test for SBP-dependent divergence. Model-based EMMs for each SBP × Generation combination were obtained using the *emmeans* R-package. To enable comparisons relative to baseline, EMMs were centered by subtracting the model-based F1 mean within each SBP.

APS changes were visualized using the *ggplot2* R-package, with centered EMMs plotted against generation (x-axis) and percent change (y-axis), grouped by SBP. Error bars represent the standard errors of the EMMs. To assess pairwise differences among SBP × Generation groups, post hoc comparisons of EMMs were conducted using emmeans R-package, applying significance thresholds of *p* < 0.05 and *p* < 0.01. Results were visualized as heatmaps to highlight patterns of significance across groups.

### 2.7.3. Genetic evaluation: heritability, estimation of breeding values, and genetic correlations

Fish domestication involves both heritable genetic changes (driven by selection, drift, and bottlenecks) and non-heritable modifications (e. g., behavioral plasticity, learning, epigenetic regulation) (Milla et al., 2021). Phenotypic expression reflects the interaction of genetic inheritance with environmental factors such as temperature, photoperiod, feed, density, and water flow (Johnston et al., 2024). Narrow-sense heritability ($h^2$; i.e., the proportion of phenotypic variance explained by additive genetic effects) is central to both evolutionary biology and aquaculture, as it determines trait evolvability, response to selection, and genetic constraints under domestication. In practice, $h^2$ is essential for assessing domestication outcomes and designing SBPs: traits with $h^2$

> 0.20 are most suitable for improvement (Gjedrem and Baranski, 2009). Reliable estimates also help optimize selection efficiency by balancing genetic gains with environmental influences.

To obtain genetic parameters (heritability and genetic correlations), variance components of all traits were estimated by Restricted Maximum Likelihood using mixed models of the form $y = X\beta + Z\alpha + e$, where *y* is on the vector of phenotypes for the trait(s), *β* is the vector of fixed effects (Generation, SBP, Sex, Tank), *α* is the vector of random animal additive genetic values, and *e* the vector of random residual effects. *X* and *Z* are incidence matrices of *β* and *α*. All variance component analyses were performed using VCE V6.0 (Groeneveld and Mielenz, 2010), with the pedigree relationship matrix computed from the full pedigree file.

Single-trait models were used to estimate heritability and estimated breeding values (EBVs). Heritability was calculated as $h^2 = \sigma_a^2/(\sigma_a^2 + \sigma_e^2)$ where $\sigma_a^2$ is the variance of the animal additive genetic effect and $\sigma_e^2$ is the error variance. Heritability estimates were classified as low (0.05–0.15), medium (0.20–0.40), high (0.45–0.60), or very high (>0.65) (Elalfy et al., 2021). EBVs for all animals in the pedigree were obtained from the solutions for the animal additive genetic values. EBV reflect an individual’s genetic merit (Johnston et al., 2024).

For each trait, EBVs corresponding to the random animal effect were extracted, and genetic trends were computed as the mean ± SE of EBVs per SBP-generation combination. Trends were visualized using *ggplot2* R-package, with line plots illustrating SBP-specific trajectories of genetic merit over generations.

Pairwise comparisons of EBVs between SBP × Generation groups were performed using a dual-path R workflow. Assumptions of normality (Shapiro-Wilk test) and variance homogeneity (Levene's test) were first assessed using the *stats* and *car* V3.1–3 (Fox et al., 2024) packages, respectively. If met (*p* ≥ 0.05), parametric two-way ANOVA with Tukey HSD post-hoc tests were applied using the package *rstatix* V0.7.1 (Kassambara, 2023). For violated assumptions, non-parametric Aligned Rank Transform (ART) analysis followed by ART-ANOVA and interaction contrasts with Benjamini-Hochberg adjustment was used using the *ARTTool* V0.11.2 R-package (Kay and Wobbrock, 2021). Comparisons were visualized as a symmetric heatmap using the *ggplot2* R-package.

The two-trait models were used to estimate phenotypic and genetic correlations. Genetic correlations were computed as $r_{a(x,y)} = \sigma_{axay}/\sqrt{(\sigma_{ax}^2 \sigma_{ay}^2)}$ where $\sigma_{axay}$ is the additive genetic covariance between traits *x* and *y* and $\sigma_{ax}^2$ and $\sigma_{ay}^2$ are the additive genetic variances of the same traits. Standard errors (SE) of genetic correlations were derived, and significance was assessed by evaluating whether zero was excluded from the interval defined as estimates ± 2 × SE. Correlations with confidence intervals not overlapping zero were considered significantly different from zero, indicating meaningful genetic associations. Genetic correlations were not estimated when heritability was near zero for one or both traits, as such estimates would lack biological and statistical relevance.

## 3. Results

In total, 1006 fish were phenotyped across all generations and SBP combined (Table 3, Table S3).

### 3.1. Growth traits

Overall, results based on growth-trait models (Table 4; Fig. 3) revealed intergenerational changes from the F₁ baseline across all SBPs. Statistically significant changes in SL were exclusive to M-SBP, which accumulates a + 8 % increase by F4 and differed significantly from both S-SBP and NS. Although S-SBP showed transient SL increases at F2 and F3, it did not maintain a net gain by F4. For SGR, expressions significantly tended to decrease between F1 and F4 for all SBPs, except M-SBP (Fig. 3). NS and S-SBP experienced a decline (−68.7 % and −93.6 %, respectively), whereas the M-SBP did not decrease (Fig. 3), between F1
<caption>**Table 3**</caption>

Result of fish phenotyping. The results shown are mean ± standard deviation of raw trait values and sampling size (n), across families, per generation for each of the three breeding strategies (SBP): no selection (NS), single-function SBP (S-SBP; growth-related traits), and multi-function SBP (M-SBP: growth, reproduction, welfare). F1, F2, F3, and F4 are successive generations of captive-born fish. Traits related to growth performance are Standard Length (SL), SGR (Specific Growth Rate) and Height/Length ratio (H/L). Traits related to reproductive performance are Sperm Concentration, Total Motility, Progressive Motility, Total Fecundity, Fertilization Rate, and Hatching Rate. Traits related to welfare are Fulton's Condition Factor (FCF) and Waterborne Cortisol (post-stress waterborne cortisol concentration corrected by individual body weight).

<table><thead><tr><th rowspan="2">Trait (units)</th><th colspan="4">NS</th><th colspan="4">S-SBP</th><th colspan="4">M-SBP</th></tr><tr><th>F1</th><th>F2</th><th>F3</th><th>F4</th><th>F1</th><th>F2</th><th>F3</th><th>F4</th><th>F1</th><th>F2</th><th>F3</th><th>F4</th></tr></thead><tbody><tr><td>SL (mm)</td><td>24.12 ± 2.39<br>(n = 79)</td><td>24.57 ± 1.73<br>(n = 91)</td><td>25.12 ± 2.07<br>(n = 95)</td><td>23.88 ± 1.54<br>(n = 67)</td><td>23.66 ± 2.01<br>(n = 83)</td><td>25.26 ± 1.93<br>(n = 94)</td><td>25.28 ± 2.13<br>(n = 88)</td><td>24.93 ± 1.30<br>(n = 66)</td><td>23.72 ± 2.14<br>(n = 89)</td><td>25.12 ± 2.01<br>(n = 96)</td><td>25.85 ± 1.71<br>(n = 88)</td><td>25.80 ± 1.86<br>(n = 70)</td></tr><tr><td>SGR</td><td>0.58 ± 0.29<br>(n = 79)</td><td>0.43 ± 0.23<br>(n = 91)</td><td>0.61 ± 0.51<br>(n = 95)</td><td>0.26 ± 0.16<br>(n = 67)</td><td>0.67 ± 0.49<br>(n = 83)</td><td>0.50 ± 0.27<br>(n = 94)</td><td>0.53 ± 0.39<br>(n = 88)</td><td>0.35 ± 0.17<br>(n = 66)</td><td>0.56 ± 0.28<br>(n = 89)</td><td>0.40 ± 0.27<br>(n = 96)</td><td>0.64 ± 0.48<br>(n = 88)</td><td>0.51 ± 0.35<br>(n = 70)</td></tr><tr><td>H/L</td><td>0.22 ± 0.02<br>(n = 79)</td><td>0.22 ± 0.02<br>(n = 91)</td><td>0.23 ± 0.01<br>(n = 95)</td><td>0.22 ± 0.02<br>(n = 67)</td><td>0.21 ± 0.02<br>(n = 83)</td><td>0.22 ± 0.01<br>(n = 94)</td><td>0.23 ± 0.02<br>(n = 88)</td><td>0.23 ± 0.01<br>(n = 66)</td><td>0.22 ± 0.02<br>(n = 89)</td><td>0.22 ± 0.01<br>(n = 96)</td><td>0.23 ± 0.01<br>(n = 88)</td><td>0.23 ± 0.01<br>(n = 70)</td></tr><tr><td>Sperm<br>Concentration<br>(millions/ml)</td><td>4573.47 ±<br>4797.67 (n =<br>44)</td><td>2033.67 ±<br>1711.95 (n =<br>43)</td><td>1170.42 ±<br>1910.14 (n =<br>44)</td><td>3688.86 ±<br>2850.55 (n =<br>35)</td><td>3283.65 ±<br>5081.67 (n =<br>43)</td><td>2504.51 ±<br>2291.43 (n =<br>44)</td><td>1553.52 ±<br>1959.51 (n =<br>43)</td><td>2423.48 ±<br>1815.62 (n =<br>33)</td><td>5119.13 ±<br>6273.80 (n =<br>49)</td><td>2625.58 ±<br>2677.18 (n =<br>46)</td><td>1937.41 ±<br>2853.24 (n =<br>43)</td><td>3097.52 ±<br>1912.44 (n =<br>35)</td></tr><tr><td>Total Motility (%)</td><td>30.42 ±<br>19.47 (n =<br>44)</td><td>24.94 ±<br>13.24 (n =<br>43)</td><td>24.64 ±<br>27.67 (n =<br>44)</td><td>55.22 ±<br>29.49 (n =<br>35)</td><td>22.92 ±<br>19.69 (n =<br>43)</td><td>26.28 ±<br>10.29 (n =<br>44)</td><td>27.27 ±<br>29.09 (n =<br>43)</td><td>44.53 ±<br>23.08 (n =<br>33)</td><td>32.63 ±<br>19.01 (n =<br>49)</td><td>26.67 ±<br>14.10 (n =<br>46)</td><td>33.96 ±<br>30.16 (n =<br>43)</td><td>52.29 ±<br>20.05 (n =<br>35)</td></tr><tr><td>Progressive Motility<br>(%)</td><td>7.74 ± 5.78<br>(n = 44)</td><td>6.24 ± 4.07<br>(n = 43)</td><td>8.48 ± 11.64<br>(n = 44)</td><td>10.96 ± 8.72<br>(n = 35)</td><td>5.75 ± 6.04<br>(n = 43)</td><td>6.75 ± 3.23<br>(n = 44)</td><td>12.03 ±<br>17.20 (n =<br>43)</td><td>9.16 ± 6.90<br>(n = 33)</td><td>8.27 ± 5.88<br>(n = 49)</td><td>6.73 ± 5.92<br>(n = 46)</td><td>13.95 ±<br>15.94 (n =<br>43)</td><td>10.87 ± 5.04<br>(n = 35)</td></tr><tr><td>Total Fecundity<br>(N°)</td><td>19.74 ±<br>46.07 (n =<br>35)</td><td>33.19 ±<br>54.40 (n =<br>48)</td><td>47.55 ±<br>119.17 (n =<br>51)</td><td>47.72 ±<br>70.64 (n =<br>32)</td><td>13.35 ±<br>33.01 (n =<br>40)</td><td>38.98 ±<br>99.55 (n =<br>50)</td><td>68.44 ±<br>122.90 (n =<br>45)</td><td>63.27 ±<br>95.83 (n =<br>33)</td><td>32.83 ±<br>50.85 (n =<br>40)</td><td>36.91 ±<br>74.67 (n =<br>50)</td><td>30.38 ±<br>60.12 (n =<br>45)</td><td>36.34 ±<br>57.37 (n =<br>35)</td></tr><tr><td>Fertilization Rate<br>(%)</td><td>15.89 ±<br>33.29 (n =<br>35)</td><td>17.66 ±<br>34.03 (n =<br>48)</td><td>20.50 ±<br>35.33 (n =<br>51)</td><td>28.16 ±<br>39.07 (n =<br>32)</td><td>12.18 ±<br>27.61 (n =<br>40)</td><td>15.68 ±<br>34.83 (n =<br>50)</td><td>22.66 ±<br>35.23 (n =<br>45)</td><td>26.16 ±<br>38.50 (n =<br>33)</td><td>20.51 ±<br>31.07 (n =<br>40)</td><td>17.72 ±<br>34.12 (n =<br>50)</td><td>21.90 ±<br>41.69 (n =<br>45)</td><td>24.66 ±<br>39.95 (n =<br>35)</td></tr><tr><td>Hatching Rate (%)</td><td>18.94 ±<br>38.74 (n =<br>35)</td><td>24.35 ±<br>42.06 (n =<br>48)</td><td>25.47 ±<br>42.74 (n =<br>51)</td><td>40.02 ±<br>49.18 (n =<br>32)</td><td>16.28 ±<br>35.99 (n =<br>40)</td><td>15.31 ±<br>34.30 (n =<br>50)</td><td>32.79 ±<br>46.96 (n =<br>45)</td><td>36.05 ±<br>48.43 (n =<br>33)</td><td>28.27 ±<br>40.13 (n =<br>40)</td><td>19.56 ±<br>37.57 (n =<br>50)</td><td>26.42 ±<br>44.31 (n =<br>45)</td><td>33.49 ±<br>47.13 (n =<br>35)</td></tr><tr><td>FCF</td><td>1.65 ± 0.27<br>(n = 79)</td><td>1.80 ± 0.25<br>(n = 91)</td><td>1.91 ± 0.22<br>(n = 95)</td><td>1.80 ± 0.25<br>(n = 67)</td><td>1.65 ± 0.27<br>(n = 83)</td><td>1.78 ± 0.20<br>(n = 94)</td><td>1.99 ± 0.27<br>(n = 88)</td><td>1.79 ± 0.16<br>(n = 66)</td><td>1.66 ± 0.27<br>(n = 89)</td><td>1.74 ± 0.21<br>(n = 96)</td><td>1.85 ± 0.24<br>(n = 88)</td><td>1.61 ± 0.34<br>(n = 70)</td></tr><tr><td>Waterborne Cortisol<br>(ng/mg)</td><td>0.07 ± 0.06<br>(n = 79)</td><td>0.06 ± 0.04<br>(n = 91)</td><td>0.09 ± 0.07<br>(n = 95)</td><td>0.15 ± 0.16<br>(n = 67)</td><td>0.08 ± 0.06<br>(n = 83)</td><td>0.05 ± 0.04<br>(n = 94)</td><td>0.11 ± 0.12<br>(n = 88)</td><td>0.18 ± 0.16<br>(n = 66)</td><td>0.07 ± 0.06<br>(n = 89)</td><td>0.08 ± 0.05<br>(n = 96)</td><td>0.09 ± 0.08<br>(n = 88)</td><td>0.15 ± 0.15<br>(n = 70)</td></tr><tr><td>APS</td><td>1.11 ± 0.19<br>(n = 10)</td><td>1.14 ± 0.21<br>(n = 10)</td><td>1.18 ± 0.16<br>(n = 10)</td><td>1.15 ± 0.16<br>(n = 7)</td><td>0.98 ± 0.19<br>(n = 10)</td><td>1.15 ± 0.24<br>(n = 10)</td><td>1.26 ± 0.25<br>(n = 9)</td><td>1.11 ± 0.15<br>(n = 7)</td><td>1.12 ± 0.2 (n<br>= 10)</td><td>1.1 ± 0.21 (n<br>= 10)</td><td>1.28 ± 0.16<br>(n = 10)</td><td>1.2 ± 0.27 (n<br>= 7)</td></tr></tbody></table>
**Table 4**

Summary of linear mixed effects model performance across trait groups with individual trait details. The marginal R² (R²_m) is the variance explained by the fixed effects, and the conditional R² (R²_c) is the variance explained by both fixed and random effect. Significant likelihood ratio test (LRT) p-values (<0.05) indicate that the inclusion of fixed effects significantly improves model fit. Traits related to growth performance are Standard Length (SL), SGR (Specific Growth Rate) and Height/Length (H/L) ratio. Traits related to reproductive performance are Sperm Concentration, Total Motility, Progressive Motility, Total Fecundity, Fertilization Rate, and Hatching Rate. Traits related to welfare are Fulton's Condition Factor (FCF) and Waterborne Cortisol (post-stress waterborne cortisol concentration corrected by individual body weight).

<table><thead><tr><th>Trait Group</th><th>Trait</th><th>Model</th><th>R<sup>2</sup><sub>m</sub></th><th>R<sup>2</sup><sub>c</sub></th><th>LRT p-Value</th></tr></thead><tbody><tr><td rowspan="3">Growth</td><td>SL</td><td>SBP ×<br>Generation<br>+ (1|Tank)<br>+ Sex</td><td>0.217</td><td>0.539</td><td>&lt; 2.2e-16</td></tr><tr><td>SGR</td><td>SBP ×<br>Generation<br>+ (1|Tank)<br>+ Sex</td><td>0.235</td><td>0.758</td><td>&lt; 2.2e-16</td></tr><tr><td>H/L</td><td>SBP ×<br>Generation<br>+ (1|Tank)<br>+ Sex</td><td>0.372</td><td>0.582</td><td>&lt; 2.2e-16</td></tr><tr><td rowspan="4">Reproduction<br>(d)</td><td>Sperm<br>Concentration</td><td>SBP ×<br>Generation<br>+ (1|Tank)</td><td>0.097</td><td>0.199</td><td>2.787e-06</td></tr><tr><td>Total Motility</td><td>SBP ×<br>Generation<br>+ (1|Tank)</td><td>0.179</td><td>0.33</td><td>5.76e-13</td></tr><tr><td>Progressive<br>Motility</td><td>SBP ×<br>Generation<br>+ (1|Tank)</td><td>0.067</td><td>0.258</td><td>0.003114</td></tr><tr><td>Total<br>Fecundity</td><td>SBP ×<br>Generation<br>+ (1|Tank)</td><td>0.078</td><td>0.136</td><td>0.0001252</td></tr><tr><td rowspan="3">Reproduction<br>(e)</td><td>Fertilization<br>Rate</td><td>SBP ×<br>Generation<br>+ (1|Tank)</td><td>0.028</td><td>0.149</td><td>0.3541</td></tr><tr><td>Hatching Rate</td><td>SBP ×<br>Generation<br>+ (1|Tank)</td><td>0.046</td><td>0.15</td><td>0.03678</td></tr><tr><td>FCF</td><td>SBP ×<br>Generation<br>+ (1|Tank)<br>+ Sex</td><td>0.327</td><td>0.546</td><td>&lt; 2.2e-16</td></tr><tr><td rowspan="2">Aquaculture<br>potential</td><td>Waterborne<br>Cortisol</td><td>SBP ×<br>Generation</td><td>0.143</td><td>0.143</td><td>&lt; 2.2e-16</td></tr><tr><td>APS</td><td>SBP ×<br>Generation<br>+ (1|Family)</td><td>0.17</td><td>0.41</td><td>0.00149</td></tr></tbody></table>

and F4. Pairwise comparisons at F4 confirmed that M-SBP differed significantly from both NS and S-SBP. Importantly, the SGR trajectory in M-SBP and NS was non-linear (Fig. 3). H/L ratio remained largely stable only M-SBP, while S-SBP exhibited a significant +10% increase and NS a decrease from F1 to F4. Only S-SBP diverged significantly from the other programs at F4.

All traits, exhibited high (SGR: h² = 0.535, SE = 0.08) to very high heritability (H/L ratio: h² = 0.718, SE = 0.09; SL: h² = 0.628, SE = 0.08), indicating substantial additive genetic variance available for selection (Table 5).

EBV analyses showed that only M-SBP experienced genetic gain in SGR between F1 and F4 and diverged from other SBP (Fig. 3). For H/L ratio and SL, M-SBP and S-SBP both experienced genetic gain between F1 and F4 and diverged from NS (Fig. 3).

## 3.2. Reproduction traits

### 3.2.1. Male reproductive traits

Male reproductive traits followed divergent trajectories across four generations (Fig. 4). Sperm concentration remained stable over generations in all SBP (i.e., non-significant changes between F1 and F4), although a transient decrease was observed for NS and M-SBP at F3 (Fig. 4). In contrast, Total Motility exhibited pronounced, statistically significant gains at F4, except for M-SBP. Total motility increased by +101.8% in S-SBP, +79.6% in NS, relative to the F1 baseline (Fig. 4). Only M-SBP was different from other SBPs at F4. No differences were observed for progressive motility.

Trait heritability was negligible (sperm concentration: h² = 0.008, SE = 0.10), low (Total Motility: h² = 0.148, SE = 0.16), or very high (Progressive Motility: h² = 0.657, SE = 0.22) (Table 5). The EBV of the two first was thus not further considered, as we regarded them as entirely or largely driven by environmental factors. EBV analysis for progressive motility showed few statistically significant changes: mainly divergence between NS and S-SBP at F4. (Fig. 4).

### 3.2.2. Female reproductive traits

Overall, no change was observed between and within SBP over the four generations (Fig. 5).

The heritability for Total Fecundity was negligible (h² ≈ 0.00000005489, SE ≈ 0.0004844), suggesting non-genetic factors overwhelmingly shape it. In contrast, for Fertilization and Hatching rates were estimated very high heritabilities (h² ≈ 0.99389, SE ≈ 0.29623 / h² ≈ 1.000, SE ≈ 0, respectively) (Table 5). For Fertilization Rate, although no changes were observed, the trait had a very high heritability estimate (h² ≈ 0.99, SE = 0.30) (Table 5). The genetic trends indicated a gain in Hatching rate for NS up to F3 (Fig. 5). Further, for the S- and M-SBPs, a significant decrease in Hatching Rate was observed from F1 to F4. A significant difference was found at F4 between NS and the other two SBPs.

## 3.3. Welfare traits

For welfare-related traits, linear models (Table 4) revealed contrasting generational trends between FCF and the stress indicator (Waterborne Cortisol), but not between SBP (Fig. 6). FCF improved steadily in every SBP from F1 to F3, then all dropped at F4, except for S-SBP. Only S-SBP sustained an overall increase between F1 and F4 (around 13.8%) for FCF while NS and M-SBP in F4 returned to the same level as in F1. Waterborne Cortisol levels rose progressively from F1 through F4 (around +120%), with all SBPs diverging significantly from their F1 baselines by F4, but not diverging between them (Fig. 6). Waterborne Cortisol levels rose progressively from F1 through F4 (around +120%), with all SBPs diverging significantly from their F1 baselines by F4, but not diverging between them (Fig. 6).

Heritability of welfare-related traits were contrasting: null for waterborne cortisol and very high for FCF (h² = 0.745, 0.09) (Table 5). The EBV of waterborne cortisol was thus not further considered, as we regarded it as entirely governed by environmental factors. EBV analysis for FCF showed significant gain between F1 and F4 only for S-SBP and NS (Fig. 6).

## 3.4. Phenotypic and genetic correlations

Overall, we observed no strong phenotypic or genetic correlations between traits (i.e., all were r < 0.7) and only one significant medium or moderate genetic correlation (i.e., 0.51 < r < 0.69) between SL and SGR. No significant and at least medium or moderate negative correlation was observed (Table 5).
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Growth trait trends. (A), (B), (C) show centered estimated marginal means (EMMs) from linear mixed-effect models for Standard length (SL), SGR (specific growth rate) and Height/Length (H/L), respectively. Error bars represent the standard error of the EMMs. For comparability, EMMs were centered by subtracting the model-based F1 mean within each SBP, thereby expressing all means as relative deviations from the baseline F1. Heat maps show pairwise comparisons among SBP × Generation groups obtained using Tukey-adjusted contrasts, with significance thresholds set at *p* < 0.05 and *p* < 0.01. (D), (E), (F) display the mean estimated breeding values (EBVs) for SL, SGR, and, H/L, respectively, with error bars indicating the standard error.</figcaption></figure>

### 3.5. Aquaculture potential score

The linear model analyzing generational trends in APS (Table 4; Fig. 7) showed a significant divergence between F1 – F3 for S-SBP only. More specifically, APS increased from F1 to F3 (S-SBP: + ~28 %) followed by a drop at F4 and a return to a level similar to that of F1.

## 4. Discussion

Overall, these zebrafish domestication trials demonstrate that growth and body-shape traits can respond rapidly to domestication from the earliest generations, whereas the welfare-related and reproductive traits we studied exhibit more complex trajectories that most likely are modulated by environmental variation when their heritability is low. The magnitude and direction of change depend both on whether selection is applied and on the selection strategy (S-SBP versus M-SBP). While both S-SBP and M-SBP improved growth performance, they did not consistently enhance welfare indicators or reproductive parameters. Moreover, the APS was not ultimately altered by S-SBP and M-SBP. Importantly, however, the APS was maintained during the first few generations of domestication in all SBPs.

### 4.1. Potential biases and limitations

Our experimental design has four potential limitations for interpreting the effects of domestication and stock-management strategies on the first generations of captive-born fish.

First, we tracked changes from F1 to F4 but did not phenotype the F0. This decision precludes direct comparison with the ancestral stock and limits our ability to contextualize early domestication dynamics, such as genetic adaptation, proportional to the heritable genetic diversity, under domestication-selection regimes (Christie et al., 2016; Lorenzen et al., 2012). However, comparing subsequent generations to F0 is inherently challenging since the age and the life history of F0 fish was unknown and would thus create bias in our measurements. In addition, early-life experiences in the wild (e.g., social interactions, exposure to predators and prey, and habitat complexity) can markedly shape the F0 phenotype under captive conditions (Huntingford, 2004; Price, 1999). As a result, following a single cohort through multiple generations risks conflating genuine domestication-driven changes with phenotypic shifts induced by those initial environmental influences.

Second, monitoring only four generations may not capture the full trajectory of domestication seen in successful domestication programs. In species such as rainbow trout and Atlantic salmon, more than 12 generations of selection have produced steady improvements that continue beyond early cohorts (Teletchea, 2018). Adaptation during the
<table><caption>**Table 5**

Results of genetic evaluation: heritability and phenotypic/genetic correlations. Heritability estimates (± standard error) are shown in **bold** on the diagonal, phenotypic correlations in *italics* on the lower diagonal, and genetic correlations (± standard error) on the upper diagonal. Underlined values indicate significant genetic correlations, where zero was excluded from the interval defined as estimate ±2 × standard error. NE denotes cases where genetic correlations were not estimated because one or both traits had near-zero heritability. Traits related to growth performance are Standard Length (SL), Specific Growth Rate (SGR), and Height/Length ratio (H/L); traits related to reproductive performance are Sperm Concentration, Total Motility, Progressive Motility, Total Fecundity, Fertilization Rate, and Hatching Rate; traits related to welfare are Fulton's Condition Factor (FCF) and post-stress Waterborne Cortisol concentration (corrected for body weight).</caption><thead><tr><th></th><th>SL</th><th>SGR</th><th>H/L</th><th>Sperm Concentration</th><th>Total Motility</th><th>Progressive Motility</th><th>Total Fecundity</th><th>Fertilization Rate</th><th>Hatching Rate</th><th>FCF</th><th>Waterborne Cortisol</th></tr></thead><tbody><tr><td>SL</td><td>0.63 ± 0.08</td><td>0.53 ± 0.02</td><td>-0.09 ± 0.06</td><td>NE</td><td>-0.15 ± 0.30</td><td>-0.24 ± 0.22</td><td>NE</td><td>0.11 ± 0.14</td><td>-0.07 ± 0.15</td><td>-0.41 ± 0.06</td><td>NE</td></tr><tr><td>SGR</td><td>0.29</td><td>0.53 ± 0.08</td><td>-0.03 ± 0.14</td><td>NE</td><td>-0.11 ± 0.26</td><td>-0.24 ± 0.11</td><td>NE</td><td>0.19 ± 0.34</td><td>0.06 ± 0.30</td><td>-0.48 ± 0.15</td><td>NE</td></tr><tr><td>H/L</td><td>-0.06</td><td>0.02</td><td>0.72 ± 0.09</td><td>NE</td><td>-0.21 ± 0.28</td><td>-0.12 ± 0.11</td><td>NE</td><td>0.34 ± 0.31</td><td>0.20 ± 0.27</td><td>0.45 ± 0.11</td><td>NE</td></tr><tr><td>Sperm Concentration</td><td>0.15</td><td>0.20</td><td>-0.16</td><td>~0 ± 0.10</td><td>NE</td><td>NE</td><td>NE</td><td>NE</td><td>NE</td><td>NE</td><td>NE</td></tr><tr><td>Total Motility</td><td>-0.02</td><td>-0.04</td><td>-0.07</td><td>0.42</td><td>0.15 ± 0.16</td><td>0.69 ± 0.28</td><td>NE</td><td>0.62 ± 0.48</td><td>-0.48 ± 0.34</td><td>-0.08 ± 0.10</td><td>NE</td></tr><tr><td>Progressive Motility</td><td>-0.10</td><td>-0.15</td><td>-0.08</td><td>0.05</td><td>0.59</td><td>0.66 ± 0.22</td><td>NE</td><td>NE</td><td>NE</td><td>NE</td><td>NE</td></tr><tr><td>Total Fecundity</td><td>0.43</td><td>-0.03</td><td>0.48</td><td>-0.14</td><td>-0.12</td><td>-0.04</td><td>0.46</td><td>0.51 ± 0.36</td><td>0.22 ± 0.30</td><td>0.12 ± 0.26</td><td>NE</td></tr><tr><td>Fertilization Rate</td><td>0.09</td><td>0.19</td><td>0.18</td><td>0.22</td><td>0.41</td><td>0.46</td><td>0.12</td><td>0.99 ± 0.30</td><td>0.22 ± 0.30</td><td>0.04</td><td>NE</td></tr><tr><td>Hatching Rate</td><td>-0.06</td><td>0.06</td><td>0.03</td><td>0.14</td><td>0.08</td><td>-0.38</td><td>-0.10</td><td>0.40</td><td>1.00 ± 0</td><td>0.07</td><td>NE</td></tr><tr><td>FCF</td><td>-0.29</td><td>-0.19</td><td>0.48</td><td>-0.16</td><td>-0.10</td><td>-0.05</td><td>0.50</td><td>0.08</td><td>-0.23</td><td>0.75 ± 0.09</td><td>NE</td></tr><tr><td>Waterborne Cortisol</td><td>-0.08</td><td>-0.01</td><td>-0.03</td><td>0.03</td><td>0.03</td><td>0.03</td><td>0.06</td><td>0.05</td><td>0.04</td><td>-0.07</td><td>~0 ± 0</td></tr></tbody></table>

first 5–10 generations can exceed 5 % genetic gain per generation, yielding substantial total change over that period (Gilligan and Frankham, 2003). It is proposed that up to six generations of selective breeding have been shown to double production metrics in fish and shellfish (e.g., 20 % percent improvement in feed conversion ratio over five generations of Atlantic salmon) (Gjedrem et al., 2012; Thodesen et al., 1999). Selection in captive populations depends heavily on the number of generations in captivity and the degree of preadaptation to culture conditions, so early domestication phases may experience intense selection pressures that later stabilize (Price, 1999). Accordingly, the transient peak in APS at F3 and the limited divergence among our SBPs likely reflect incomplete adaptation and latent trade-offs that require additional generations to resolve. Extending this work beyond four generations and incorporating genomic analyses to partition environmental plasticity from heritable variation (i.e., particularly for highly heritable traits such as fertilization rate) would better mirror the domestication trajectories of commercial aquaculture stocks.

Third, by retaining only the top 20 % of individuals per sex within each family, our SBP designs imposed a moderate selection intensity (i). In quantitative-genetic theory, selection intensity is inversely related to the proportion of selected individuals (Falconer and Mackay, 1996). Therefore, choosing the best 20 % corresponds to i ~ 1.40 (Falconer and Mackay, 1996). This moderate intensity (when compared to stronger ~2.6 for the top 1 % and weaker ~0.8 for the top 50 %; Falconer and Mackay, 1996) likely constrained the rate of genetic gain. In addition, our mating strategy strictly controlled breeders' relatedness across generations to minimize inbreeding. While effective in avoiding inbreeding, such strict avoidance may have reduced the efficiency of mating decisions and thereby constrained genetic progress. In contrast, standard breeding programs typically allow limited inbreeding (within acceptable thresholds) to balance genetic gain with long-term population viability. Future studies could enhance response by applying more stringent within-family thresholds or by integrating between-family selection, while tolerating a controlled level of inbreeding to maximize overall selection intensity and genetic gain.

Fourth, we used phenotypic rather than EBV-based selection to deliberately mimic common aquaculture practices, especially in small-to medium-scale operations where advanced genetic infrastructure is rarely available. Although EBV estimation can increase selection accuracy, it was less feasible in our design because the small number of families limited the precision needed for robust multi-trait EBV selection. In contrast, our within-family phenotypic approach enabled practical implementation across asynchronously reared families in different tanks, while allowing strict control of inbreeding (i.e., a critical constraint given the limited number of families per SBP across four generations). This strategy prioritized experimental feasibility and real-world applicability, even though it may have reduced selection intensity compared to optimal genetic improvement.

## 4.2. Evolution of key phenotypic traits during domestication

### 4.2.1. Growth traits

Domestication without intentional selection does not inherently lead to phenotypic improvement in animal production (Brummett et al., 2004; Vandeputte et al., 2009). However, passive adaptation to captive environments can also lead to improvement of growth and morphometric traits in animals, driven by indirect selection, relaxed ecological pressures, and optimized husbandry conditions (Lorenzen et al., 2012; Milla et al., 2021). Selective breeding accelerates these gains and stabilizes them over generations, as seen in species such as Atlantic salmon (Solberg et al., 2013; Thodesen et al., 1999) and European seabass (Vandeputte et al., 2009). Conformingly, both M-SBP and S-SBP produced clear, cumulative improvements in growth rate and body-shape metrics (i.e., in M-SBP, SL increased while H/L remained unchanged across generations; in S-SBP, H/L increased while SL remained stable, but later declined), whereas the NS exhibited only minimal change or
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Male reproduction trends. (A), (B), (C) show centered estimated marginal means (EMMs) from linear mixed-effect models for sperm concentration, total motility, and progressive motility, respectively. Error bars represent the standard error of the EMMs. For comparability, EMMs were centered by subtracting the model-based F1 mean within each SBP, thereby expressing all means as relative deviations from the baseline F1. Heat maps show pairwise comparisons among SBP × Generation groups obtained using Tukey-adjusted contrasts, with significance thresholds set at $p < 0.05$ and $p < 0.01$. (D), (E), (F) display the mean estimated breeding values (EBVs) for sperm concentration, total motility, and progressive motility, respectively, with error bars indicating the standard error (SE).</figcaption></figure>

decrease, reflecting the additive impact of selection on traits with a strong additive genetic basis ($h^2 \approx 0.72$ for H/L and $h^2 \approx 0.63$ for SL). This underscores that directed selection, rather than passive exposure, is the primary driver of growth enhancement under fish domestication (Solberg et al., 2013; Vandeputte et al., 2009).

In contrast to the other two traits, SGR was only maintained in M-SBP, while its improvement was only transient in NS and it deteriorated over generations in S-SBP. Given our carefully managed pedigrees and uniform breeding environment (i.e., constant feed, density and water parameters), we can rule out inbreeding depression or environmental saturation as the main causes. As S-SBP showed an improvement in SL but not SGR, the most likely explanation is that S-SBP mostly improved early growth (up to 70 dpf) but not late growth (from 70 to 90 dpf).

### 4.2.2. Reproductive traits

Domestication often disrupts natural reproductive rhythms in fish, leading to a bottleneck during the first captive generations (Milla et al., 2021; Teletchea and Fontaine, 2014). Indeed, many species show reduced ovulation rates (Kris'tan et al., 2012; Kujawa et al., 2011; Osure and Phelps, 2006), impaired gamete quality (Kujawa et al., 2011), and lower fertilization or hatching rate (Crespel et al., 2008; Kris'tan et al., 2012; Lund et al., 2008; Salze et al., 2005) when initially reared under artificial conditions (Milla et al., 2021). These reproductive issues are often due to loss of spawning or breeding behavior due to mismatches

between endogenous physiological rhythms (i.e., shaped by wild environments) and the novel stressors of captivity, such as altered photo-periods, artificial diets, excessive handling, and confinement, which can impede final oocyte maturation or spermatogenesis (Mylonas et al., 2017; Mylonas et al., 2010; Zupa et al., 2017). Conversely, some domestication programs have preserved or even enhanced reproductive performance. Gilthead seabream (*Sparus auratus*) has been proposed as a good example of domestication, where broodstocks gradually lost their dependence on exogenous hormones, and modern domesticated lines now spawning daily throughout the season (Zohar and Mylonas, 2001). Likewise, domesticated tench (*Tinca tinca*) spawn readily under controlled conditions without hormonal induction and exhibit shortened spawning latency, while total motility and embryo survival remain indistinguishable from wild-caught stocks (Krejszeff et al., 2010; Kujawa et al., 2011). In our zebrafish trials, both male and female reproductive capacities remained essentially unchanged through the first generations of domestication, regardless of whether a SBP was applied. This stability likely reflects two factors. First, key studied reproductive traits in zebrafish (i.e., sperm concentration, total motility and total fecundity) exhibit low heritability estimates (already observed in other fish species, e.g., Khaw, 2015) which can only lead to minor genetic gains through selection. Second, we maintained optimal, standardized rearing conditions known to support zebrafish reproduction (Weisbroth et al., 2020), thereby minimizing environmental variance and preserving
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Female reproduction trends. (A), (B), (C) show centered estimated marginal means (EMMs) from linear mixed-effect models for total fecundity, fertilization rate, and hatching rate, respectively. Error bars represent the standard error of the EMMs. For comparability, EMMs were centered by subtracting the model-based F1 mean within each SBP, thereby expressing all means as relative deviations from the baseline F1. Heat maps show pairwise comparisons among SBP × Generation groups obtained using Tukey-adjusted contrasts, with significance thresholds set at p < 0.05 and p < 0.01. (D), (E), (F) display the mean estimated breeding values (EBVs) for total fecundity, fertilization rate, and hatching rate, respectively, with error bars indicating the standard error (SE).</figcaption></figure>

reproductive performance across generations.

Nevertheless, two notable exceptions suggest that additional factors may influence evolutionary change. Total motility was the only reproductive trait to show consistent improvement across generations despite its low heritability. This could suggest the possibility of indirect selection, hidden environmental effects, or correlated genetic responses. Although the broader evolutionary observation showed that traits under strong selection can still exhibit low heritability yet evolve due to genetic correlations with other traits or structured pleiotropy (i.e., genetic link between associated traits) (Roff and Fairbairn, 2007). However, we can rule out this potential explanation as we did not observe high genetic correlations with other traits displaying significant change over generations. Conversely, Fertilization and Hatching rates remained unchanged within and between SBPs despite high heritability estimates (although the large standard error questions the statistical reliability for fertilization rate) that could suggest that antagonistic genetic correlations, environmental covariances, or low selection intensity may be constraining gains (Kruuk, 2004; Roff and Fairbairn, 2007). We can rule out potential antagonistic correlations as we detected any. Most likely, EBV estimation based solely on pedigree and phenotypes with limited sample size, and no genomic data, likely reduced power to detect real change (Kruuk, 2004).

### 4.2.3. Welfare traits

Overall, fish domestication tends to have positive effects on stress response (Milla et al., 2021). However, domestication does not necessarily translate into better overall welfare in fish (Saraiva et al., 2019), as our results also suggest.

In our experiment, improvements were observed only for FCF. This improvement may be attributed to the high heritability of this trait, which has been documented in other species and is considered amenable to enhancement through domestication (Glover et al., 2009; Vandeputte et al., 2004), particularly via selective breeding (Thodesen et al., 1999). The absence of improvement in F4 for M-SBP compared to NS and S-SBP could be due to the fact that FCF correlates more strongly with body weight than with body length (Dupont-Nivet et al., 2008). This may explain the significantly lower FCF observed in M-SBP, which had the greatest SL. As seen in Atlantic salmon, rapid increases in body length can outpace weight gain, resulting in a reduced condition factor (Thodesen et al., 1999).

Our Waterborne Cortisol results suggest potential negative effects of domestication on stress responsiveness in zebrafish. The apparent lack of improvement across generations may be explained by the low heritability of this trait and by the fact that any additive variation lost in F1 cannot be recovered in a closed program (Evans et al., 2004). In European seabass, confinement-induced cortisol, measured under conditions comparable to our beaker test, has shown substantial additive variation
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Welfare trait trends. (A), (B) show centered estimated marginal means (EMMs) from linear mixed-effect models for Fulton's Condition Factor (FCF) and post-stress waterborne cortisol, respectively. Error bars represent the standard error of the EMMs. For comparability, EMMs were centered by subtracting the model-based F1 mean within each SBP, thereby expressing all means as relative deviations from the baseline F1. Heat maps show pairwise comparisons among SBP × Generation groups obtained using Tukey-adjusted contrasts, with significance thresholds set at $p < 0.05$ and $p < 0.01$. (C), (D) display the mean estimated breeding values (EBVs) for FCF and cortisol, respectively, with error bars indicating the standard error (SE).</figcaption></figure>

($h^2 = 0.33 \pm 0.09$; Vandeputte et al., 2016), a figure markedly higher than the earlier estimate of $h^2 = 0.08 \pm 0.06$ based on mostly offspring from ten sires and two dams (Volckaert et al., 2012). This contrast suggests that while cortisol response may be heritable, program design and limited effective population size can mask genetic gains in stress traits. Instead, transgenerational maternal effects likely drove the increase: excess glucocorticoid transferred from stressed mothers can “program” offspring HPI-axis development and elevate stress responsiveness in zebrafish larvae (Best et al., 2017; Nesan and Vijayan, 2016). Finally, the novelty of the beaker confinement test for rack-acclimated fish can provoke larger cortisol surges (Barton, 2002) that may be unrelated to genetic change.

### 4.3. Selection to improve the aquaculture potential

Our results show that directed selection, rather than passive exposure, is the primary driver of improvements in key traits during fish domestication, making SBP the most relevant strategy to enhance aquaculture potential. The S-SBP SI functions as an effective selection index but captures only a single functional dimension, and therefore does not encompass all components of aquaculture potential. By contrast, the M-SBP integrates growth, welfare, and reproductive traits into a composite score that aligns with APS, making it a seemingly relevant tool for improving aquaculture performance. However, because M-SBP SI combines traits with widely varying heritabilities, from high (e.g., SL, H/L, SGR) to near-zero (e.g., cortisol response, fecundity), its capacity to reflect sustained genetic improvement becomes inherently limited. According to index theory, the expected response to selection in a composite trait depends on the weighted sum of each trait's heritability and phenotypic variance, as well as on the genetic correlations between the traits (Falconer and Mackay, 1996). Thus, the inclusion of low-heritability traits (e.g., fecundity) may dilute progress by using part

of the selection intensity on traits that elicit no selection response. The limited improvement may also reflect weak genetic correlations between growth and reproductive traits, also observed in our study (Table 5), and previously observed in a GIFT tilapia population, where harvest weight showed low and nonsignificant correlations (0.01–0.31) with reproductive traits such as fecundity and fry number (Khaw, 2015), further suggesting that composite indices aggregating such trait categories face inherent constraints on genetic gain. In addition, domestication can lead to fixation of maladaptive alleles (Lynch and O'Hely, 2001) as seen in inbred zebrafish stocks with reduced hatchability (Lewandowski et al., 2022). To enhance aquaculture potential, the M-SBP SI should be (a) regularly recalibrated using trait weights toward actively selected, heritable traits, (b) applied for direct selection within trait categories where needed, (c) and adapted as genetic parameters shift over generations. The version applied here may be effective in early generations, when weighting and parameter estimation are less critical, but its efficiency is likely to decline in later generations (e.g., F4) as genetic constraints limit selection response.

Overall, improving aquaculture potential is challenging due to the involvement of traits for which genetic gain may be slow or constrained, improving APS over the course of domestication demands a balanced management of genetic strategies, breeding and optimized rearing conditions (e.g., husbandry, nutrition, environment) to achieve sustainable improvements.

### 4.4. Implications for domestication and aquaculture diversification

Overall, only a few traits showed significant improvement during the first generations of domestication. The stability of APS demonstrates that early gains are limited. To accelerate progress, it is therefore important to pre-select species or populations exhibiting favorable phenotypes (e.g., robust growth, stress resilience, or reproductive
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Aquaculture potential evolution across generations at the population level. It is expressed by the aquaculture potential score (APS), calculated by the mean family performance for each of the traits at each generation per SBP with families per SBP serving as replicates. The plot shows centered estimated marginal means (EMMs) from linear mixed-effect models for APS. Error bars represent the standard error of the EMMs. For comparability, EMMs were centered by subtracting the model-based F1 mean within each SBP, thereby expressing all means as relative deviations from the baseline F1. Heat maps show pairwise comparisons among SBP × Generation groups obtained using Tukey-adjusted contrasts, with significance thresholds set at $p < 0.05$ and $p < 0.01$.</figcaption></figure>

performance) before initiating domestication (Liao and Huang, 2000; Quéméner et al., 2002; Toomey et al., 2020a). By choosing candidates already pre-adapted to production in captive conditions, fish farmers can maximize success and reduce the time and resources required to develop new production.

Nevertheless, some improvement is achievable even in the first captive-born generations by targeting traits with high heritability. Such traits (e.g., growth rate, Fulton's condition factor, and fertilization rate) should be prioritized for direct selection (Gjedrem, 2005; Gjedrem et al., 2012). This approach has yielded rapid gains in species such as Atlantic salmon and Nile tilapia (Gjedrem and Baranski, 2009; Ponzoni et al., 2011). However, when multiple traits are combined into a selection index, it is essential to monitor genetic correlations and periodically re-estimate heritability, to account trade-offs among traits of divergent genetic architecture than can impede progress (Gjedrem, 2005).

Conversely, traits with low heritability (e.g., fecundity, stress-induced cortisol responses) require rigorous environmental control (e.g., consistent feeding regimes, minimal handling stress) to reveal underlying genetic differences. Even slight fluctuations in rearing conditions can mask genetic signals, making genomic tools indispensable for partitioning genetic from environmental variance (Milla et al., 2021).

## 5. Conclusion

This study demonstrates that early domestication of zebrafish rapidly improves growth and morphometric traits under selective breeding (S-SBP, M-SBP), but welfare and reproductive traits exhibit limited or no genetic gain due to low heritability and environmental modulation. It is worth mentioning, that the APS showed limited variation across SBPs, though its stability in early generations suggests managed domestication can preserve, although not enhance, overall aquaculture potential. To establish definitive conclusions, however, genomic validation is

required. SNP-based genotyping, coupled with analyses of selection pressures, population genetic structure, and genetic drift, would provide critical evidence for the effectiveness of different selection strategies and confirm whether the observed phenotypic changes truly reflect genetic progress. Future research should therefore extend multigenerational selection beyond F4 while integrating genome-wide data to partition genetic versus environmental variance, identify pleiotropic loci influencing APS components, and refine selection targets. By anchoring programs in robust broodstock, prioritizing high-heritability traits, and optimizing rearing environments, domestication programs will be better positioned to overcome bottlenecks that have limited progress in the past.

## CRediT authorship contribution statement

**E. Diakos:** Writing – review & editing, Writing – original draft, Visualization, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **P. Fontaine:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Methodology, Investigation, Data curation, Conceptualization. **S. Lambert:** Writing – review & editing, Investigation, Data curation. **Y. Ledoré:** Writing – review & editing, Methodology, Investigation. **J. Lambert:** Writing – review & editing, Visualization, Supervision, Investigation, Data curation. **A. Magitteri:** Writing – review & editing, Investigation. **Md. Shahjahan:** Resources, Writing – review & editing. **P. Kestemont:** Writing – review & editing, Resources, Methodology, Investigation, Conceptualization. **M. Vandeputte:** Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing – review & editing. **T. Lecocq:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Methodology, Investigation, Data curation, Conceptualization.
# Declaration of competing interest

The authors declare no competing interests.

# Acknowledgements

This research is a part of an international research partnership between University of Lorraine and University of Namur, funded by Lorraine Université d'Excellence (LUE). We thank Jabed Hasan for his help in sampling wild fish. In addition, all the students that worked in fish phenotyping including Sophie Genaux, Arina Kravchenko, Theo Leclerc, and Erwann Locatelli.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.aquaculture.2025.743194.

# Data availability statement

The datasets are available in Tables S1&3 (Diakos et al., 2025).

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