# A multi-trait evaluation framework to assess the consequences of polyculture in fish production: An application for pikeperch in recirculated aquaculture systems

Nellya Amoussou, Thomas Lecocq, Camille Fourrier, Renaud Nivelle, Clémentine Fleck, Pascal Fontaine, Alain Pasquet, Marielle Thomas

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Nellya Amoussou, Thomas Lecocq, Camille Fourrier, Renaud Nivelle, Clémentine Fleck, et al. A multi-trait evaluation framework to assess the consequences of polyculture in fish production: An application for pikeperch in recirculated aquaculture systems. Aquaculture Reports, 2022, 27, pp.101349. 10.1016/j.aqrep.2022.101349. hal-05050583

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# A multi-trait evaluation framework to assess the consequences of polyculture in fish production: An application for pikeperch in recirculated aquaculture systems

Nellya Amoussou<sup>a,b</sup>, Thomas Lecocq<sup>a,b,*</sup>, Camille Fourrier<sup>a</sup>, Renaud Nivelle<sup>c</sup>, Clémentine Fleck<sup>a</sup>, Pascal Fontaine<sup>a</sup>, Alain Pasquet<sup>a</sup>, Marielle Thomas<sup>a,b</sup>

## ARTICLE INFO

**Keywords:**

Fish polyculture

Recirculated aquaculture systems

Multivariate analysis

Integration method

## ABSTRACT

Polyculture offers an alternative to monoculture in recirculated aquaculture systems (RAS). Nevertheless, RAS polyculture can potentially result in beneficial as well as detrimental consequences. This places a premium on assessing consequences of a particular polyculture prior to its implementation in farming production. In fish aquaculture, most of previous assessments of polyculture consequences have been carried out through multi-trait approach by analysing independently species traits related to a single biological function. Yet, polyculture can impact many biological functions and the overall consequences are the result of multiple interactions from different functions. Therefore, an integrative analysis is required to synthesise the polyculture consequences at the multi-trait levels. Here we propose a multi-trait evaluation framework (i.e. considering traits related to multiple functions), for the polyculture consequence assessment. We analyse this framework through multi-variate analysis prior integrating results for each trait. As test case, we used pikeperch (*Sander lucioperca*), reared in monoculture and in polyculture with European perch (*Perca fluviatilis*), common carp (*Cyprinus carpio*) and black-bass (*Micropterus salmoides*). Our results revealed the importance of applying multi-trait framework to obtain a reliable assessment of fish polycultures. The divergence that might occur in the expression of some traits in the same polyculture has been highlighted to support this argument. Through the integration method, it was possible to find the suitable combination(s) of fish and to exclude the unsuitable ones. For future researches, we suggest to support the multi-trait assessment by weighting the traits involved in the multivariate analysis.

## 1. Introduction

Indoor intensive recirculated aquaculture system (RAS) is increasingly favoured for aquaculture development in Europe, North America and Australia (Martins et al., 2010; Espinal and Matuli, 2019). Indeed, RAS allows reducing (i) reliance on local environments and their constraints (water shortage, production seasonality, predation risk by wild animals), (ii) negative impacts of production effluents on wild ecosystems and (iii) competition with other land uses (Martins et al., 2010; Calone et al., 2019; Ahmed and Turchini, 2021), although downsides have been highlighted (e.g. issues with disease or water treatment: Delves-Broughton and Poupard, 1976; Kuhn et al., 2010; Badiola et al., 2012; Almeida et al., 2019). Nowadays, RAS is mainly used to produce a

single species (i.e. monoculture), mostly species with a high commercial value (e.g. salmon, pangasius, turbot, seabass: Ngoc et al., 2016; Badiola et al., 2018). Yet, monoculture can limit food conversion ratio (Kozłowski et al., 2014), which compromises the water quality of the system by increasing nutrient concentration (generated by non-digested feed) in the effluents (Tacon et al., 2010). Moreover, monoculture has also been criticised from human food security and economic viewpoints, as it might have a low resistance to pest attacks (Ball, 1980) and a low adaptation potential to face changes in environmental and socio-economic contexts (Medeiros et al., 2017).

Polyculture (i.e. production of two or more fish species in the same physical space at the same time; adapted from Stickney, 2013) is an age-old rearing practice in pond aquaculture. Implementation of this

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

<footnote><sup>b</sup> LTER 'Zone Atelier Moselle', Vandœuvre-lès-Nancy, France</footnote>

<footnote><sup>c</sup> University of Liège, Aquaculture Research and Education Center, Tihange, Belgium</footnote>

<footnote>* Correspondence to: University of Lorraine, Boulevard des Aiguillettes, BP 70 239, 54506 Vandœuvre-lès-Nancy, France.
E-mail address: thomas.lecocq@univ-lorraine.fr (T. Lecocq).</footnote>
rearing practice in RAS has been suggested as a way to improve on-growing phase of fish farming (Thomas et al., 2020). Indeed, polyculture can improve the use of available resources in RAS for instance, by co-rearing species that use the same trophic resource, but in different spatial niches (Kozłowski et al., 2014; Thomas et al., 2020) or by developing species community in which co-products of one species are recycled by another (Chopin et al., 2001; Nhan et al., 2007; Flickinger et al., 2020; Campanati et al., 2021). Overall, this can decrease environmental impacts, enhancing the sustainability and resilience of production systems, while diversifying the production (Medeiros et al., 2017; Boyd et al., 2020; Dumont et al., 2020). However, polyculture implementation in RAS is challenging because it can also lead to detrimental consequences affecting survival, growth (Reddy et al., 2002; Kadir et al., 2007), immunity or health (Goodwin, 1999; Abbas et al., 2014) and behaviour (Gibbard et al., 2009; Oladi et al., 2017) of fish. Actually, polyculture can be beneficial for production in RAS provided species compatibility (i.e. ability of species to live in the same production system while minimising detrimental interactions, or competition for available resources [e.g. trophic, spatial]) or species complementarity (i.e. co-farmed species can use different portions of resources or display commensal/mutualistic interactions) occur among co-farmed taxa (Thomas et al., 2021). This places a premium on the development of an evaluation framework to assess polyculture consequences of a particular species combination based on, at least, their compatibility. By this assessment, it is possible to find the suitable combination(s) of fish and to exclude the unsuitable ones.

Most of previous studies of polyculture consequences (Kozłowski et al., 2014; Mihailov et al., 2020) performed such an assessment by considering few traits (e.g. specific growth rate and fish weight), related to a single biological function (e.g. growth). Yet, a more efficient assessment should be based on multi-trait approach including traits involved in different biological functions, since polyculture can impact many different aspects of the organism biology and the system functionality. Furthermore, assessing the ability of a species combination to meet stakeholders expectations requires considering several key traits and biological functions impacting the aquaculture potential (Toomey et al., 2020a) of species combined in polyculture. Evaluation of a species combination through such a multi-trait evaluation framework implies to integrate the results for each trait to obtain an overview of this polyculture. To our knowledge, a multi-trait assessment framework for polyculture providing such an overview has not been proposed to date.

Hence, we propose a multi-trait evaluation framework considering survival rate and traits related to growth and relations functions, for the polyculture consequence assessment. We analyse pieces of information from this framework through multivariate analysis prior integrating results for each trait through a ranking procedure of the alternative tested modalities. As an example, we focused on the pikeperch (Sander lucioperca), a species of interest for the development of the European RAS production (Kestemont et al., 2015) and combined it in RAS with three other species (European perch, *Perca fluviatilis*; common carp, *Cyprinus carpio* and black-bass, *Micropterus salmoides*) during the on-growing phase. These species were selected for three main reasons. First, pikeperch feeds in the water column (Kestemont et al., 2015). Therefore, feed falling to the bottom is not consumed (Kozłowski et al., 2014). This results in feed loss and water quality issues (Thomas and Piedrahitza, 1998) as well as increases the disease hazard (Kozłowski et al., 2008). Therefore, adding a bottom feeder fish species in rearing tanks, such as the common carp, could improve pikeperch production in RAS. Second, pikeperch mainly occupies the lower parts of the water column. Therefore, combining pikeperch with another economically valuable species occupying the upper part of the water column, such as the black-bass (Pénka et al., 2021), could maximise the use of spatial resources in the rearing tanks along with a production diversification. Third, pikeperch is mainly active during twilight or at night (Ali et al., 1977). Therefore, combining it with another species primarily active in the daytime, such as the European perch, allows diversifying the

production, while minimising competition.

## 2. Material and methods

### 2.1. Compliance with ethical standards

This study was conducted at the Aquaculture Experimental Platform (AEP, registration number for animal experimentation D54-547-18) of the University of Lorraine (Nancy, France). It was carried out in accordance with the national and European guidelines for protection of animal welfare (Directive 2010/63/EU), respecting the physiological and behavioural requirements of the fish. This protocol has received a favourable opinion from the Ethics Committee for Animal Experimentation and has obtained the approval number APAFIS29317-2021012516289687 V3 from the Ministry of Higher Education, Research and Innovation of the French government.

### 2.2. Studied fishes

The four selected species are compatible on the basis of their thermal range (European perch 10–31°C: Hokanson, 1977; Jobling, 1981, common carp 3–41°C: Horoszewicz, 1973; Edward et al., 1984, black-bass 10–32°C: Ferguson, 1958; Strawn, 1961 and pikeperch 10–28°C: Hokanson, 1977; Frisk et al., 2012). For the trial, we used juvenile pikeperch (weight: 52.2 ± 1.1 g), European perch (weight: 50.7 ± 0.7 g), common carp (weight: 46.5 ± 2.6 g) and black-bass (weight: 120.2 ± 5.4 g). Pikeperch were provided by the Belgian company (INAGRO, Belgium). Juveniles of European perch, common carp and black-bass were supplied by professional fish farmers (SARL Lucas Perches, Domaine de Lindre [France] and EI Jérôme Bernard [France] respectively). We performed a three-week acclimatisation in four RAS (2 m³ tanks, one for each species) (see the trial planning in Supplementary information S1). Abiotic conditions were set based on previous studies on pikeperch in RAS (Fontaine et al., 1997; Baekelandt et al., 2018; Thomas et al., 2020): water temperature (20.9 ± 0.2 °C), photoperiod (10 L: 14 D) and light intensity (white light; luminosity of 20 lx). These conditions are also suitable for the three other fish species (Bochert, 2020; Pénka et al., 2021; Tabarrok et al., 2020). During the acclimatisation period, fish were fed ad libitum based on different granulometry formulated feeds (Gouessant NEO START – coul 3 mm, Le Gouessant, France, for pikeperch, European perch and common carp and Gouessant NEO START – coul 5 mm, Le Gouessant, France, for black-bass).

### 2.3. Trial design

Pikeperch monoculture was selected as the baseline because we are looking to implement polycultures which provide an advantage to pikeperch monoculture in RAS (Thomas et al., 2020). Overall, we considered four modalities: one monoculture (S = pikeperch [S. lucioperca]), two polycultures with two species (SC = pikeperch - common carp [C. carpio] and SM = pikeperch - black-bass [M. salmoides]) and a polyculture with three species (SCP = pikeperch - common carp - European perch). Three replicates were done for each modality with a total of 12 experimental units (EU). Each EU was an independent RAS and consists of a tank operating in a closed circuit including a 2 m³ tank, a mechanical drum filter (63 µm filtration plate), a UV steriliser (55 W), a buffer tank (350 L), a fluidised bed biological filter (480 L, including 250 L of media), as well as a temperature and light control centralised technical management. We applied the same physicochemical parameters (i.e. mean values: temperature: 20.9 ± 0.2 °C; dissolved oxygen: 7.4 ± 0.6 mg L⁻¹; pH: 7.5 ± 0.2; ammonia: 0.02 ± 0.05 mg L⁻¹ and nitrites: 0.07 ± 0.18 mg L⁻¹) (Supplementary information S3), light conditions (white light; luminosity of 20 lx) and photoperiod (10 L: 14 D) to all EU. Anti-jump grids, placed above each tank, prevent fish from escaping.
## 2.4. Trial protocol

At the beginning of the trial, a biomass of 4 kg m⁻³ was applied in each EU, with a repartition of 100 % pikeperch in S, 50 % pikeperch – 50 % common carp in SC, 50 % pikeperch – 50 % black-bass reared in SM and 33 % pikeperch – 33 % common carp – 33 % European perch in SCP (Supplementary information S2). The day of the allotment was considered as the beginning of the trial phase (D0). During the trial, which lasted 90 days (D90), we recorded daily the fish mortality and manually fed them one time a day (at 9:00 A.M.) during the photophase with a standardised food ration (1.5 % of the total biomass of each EU) of the same formulated pellets used during acclimatisation. The rations were readjusted depending on total biomass of each EU, each 20 days after a growth control. Unconsumed feed was collected by syphoning. The physicochemical parameters of the water of each EU were monitored twice a week.

## 2.5. Assessment of polyculture consequences through multi-trait approach

### 2.5.1. Biological traits datasets

To propose a framework providing an overview of the consequences of polyculture, we focused on traits that are relevant for the on-growing phase of a RAS production and regarded as important for fish production (Toomey et al., 2020a). It includes survival rate and traits related to the production objectives (e.g. growth traits: Frimpong and Angermeier, 2009; Brosse et al., 2021) and those involved in specific responses to stress (e.g. physiological traits: Milla et al., 2010 and behavioural traits: Huntingford et al., 2012). The latter category of traits is especially important for pikeperch which is a species with a high stress sensitivity (Kestemont et al., 2015).

#### 2.5.1.1. Survival and growth traits.
We have used survival rate [SR] and growth traits of pikeperch (mean final weight [Wf], weight heterogeneity [CV], specific growth rate [SGR] and Fulton condition index [FCF] (See Table 1 for the formulas) because they are key traits for aquaculture production (Ruane et al., 1999; Adeogun et al., 2016). Both were measured at D90. Survival rate were estimated and growth traits were measured on specimens previously anesthetised with tricaine (MS-222: 150 mg L⁻¹) buffered with sodium bicarbonate.

#### 2.5.1.2. Physiological traits.
The physiological traits analysed were related to fish stress (cortisol [Cort], glucose [Glu], haematocrit [Hct], serotonin [Ser] and dopamine [Dop]). All fish were starved one day before and only pikeperch were sampled on D90 for the physiological analyses. To assess Cort, Glu and Hct, four individuals were sampled randomly from each EU (total: n = 48) and anesthetised with tricaine (MS-222; 150 mg L⁻¹). Blood (< 1 mL) was collected on each fish in less than 5 min by caudal vein puncture with heparinised syringes and centrifuged at 7,500 rpm for 10 min. Plasma (~ 600 µL) was stored at 80 °C. To assess Ser and Dop levels, another four individuals were removed randomly from each EU (total: n = 48). They were euthanised

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

Formulas used to compute biological traits (survival rate [SR], mean final weight [Wf], weight heterogeneity [CV], specific growth rate [SGR] and Fulton condition index [FCF]. Nf and Ni were the number of fish at the end and the beginning of the trial period, respectively. Wi and Wf are respectively the initial and final mean weight of all fish of an EU and ΔT the duration of the trial. Lif is the total length of this individual fish.

<table><thead><tr><td>Traits</td><td>Formulas</td></tr></thead><tbody><tr><td>SR (%)</td><td>(Nf/Ni) * 100</td></tr><tr><td>Wf (g)</td><td>Final weight of all fish of an EU</td></tr><tr><td>SGR (% Day<sup>-1</sup>)</td><td>((ln (Wf) - ln (Wi)) * ΔT - 1) * 100</td></tr><tr><td>CV</td><td>SD of Wf * 100)/mean of Wf</td></tr><tr><td>FCF</td><td>(Wi * Lif<sup>-3</sup>) * 100</td></tr></tbody></table>

by tricaine overdose (MS-222, 300 mg L⁻¹). The brain without the brain stem was removed in less than 5 min and placed in a cryotube, which was immersed immediately in liquid nitrogen and stored at – 80 °C until analysis. Brain and blood samples from the same fish were not taken because of the time constraint for the sampling.

#### 2.5.1.2.1. Cortisol, glucose and haematocrit analyse.
Plasma Cort was assayed in duplicate using a "cortisol ELISA kit (KAPDB270)", according to the manufacturer instructions (DiaSource, Belgium). Plasma Glu was assayed in duplicate using a "Glucose Assay Kit (MAK263)" and the manufacturer instructions (Sigma-Aldrich). Hct level was performed immediately after blood (0.1 mL) collection, using haematocrit micro tubes. The blood samples were centrifuged at 12,000 rpm for 5 min, using the Hct centrifuge (Haematocrit 200, Hettich). After the centrifugation, the percentage of Hct was read using a graduated plate supplied with the device.

#### 2.5.1.2.2. Serotonin and dopamine analyse.
Serotonergic activity was assayed in duplicate using a DRG® Serotonin Research ELISA kit (EIA-4642) according to the manufacturer instructions (DiaSource, Belgium). As serotonin activity, the dopamine activity has been described as a good indicator of stress in fish (Messias et al., 2016; de Abreu et al., 2020). Dop activity was assayed in duplicate using a "Dopamine ELISA Research kit (EIA-4824)" according to the manufacturer instructions (DiaSource, Belgium).

#### 2.5.1.3. Behavioural traits.
Specimens of the four species were collected using a sub-sample for each of the four modalities, respecting the interspecies ratios. They were transferred to 80 L aquariums operated in closed circuit because it is impossible to record their behaviour in the EU. The transfer of fish from the tanks to the aquaria occurred on the growth control day (D40) (Supplementary information S1). Fish were acclimatised during one week under rearing conditions (e.g. light condition, photoperiod, physicochemistry and food ration) similar to those applied in the tanks. Two replicates of each of the four modalities were considered. The mean biomass contained in each replica was 600 g (7.5 kg m⁻³). The video recording session started on D48 and the behaviour of fish was recorded during two days. Each day, the video recording started at 9:00 A.M., lasted 1h20 and were centred on the feeding period, with 30 min before, 20 min during and 30 min after. The first 10 min were considered as an acclimatisation period, corresponding to the potential disturbance caused by the camera set-up and were not used for the analysis. Three cameras (Sony Handycam DCR-SR72E) were used simultaneously, and placed in front of the aquarium. To analyse fish behaviour, only observable fish within a 15 cm depth of field from the front window were considered. This volume was a strategic place where the feed was distributed. According to Thomas et al. (2020) fish behaviours were observed throughout the interactions between pikeperch associated or not with the two other species.

To analyse inter and intraspecific interactions of pikeperch, three individuals per species and per replica were tracked. They were randomly selected during two-minute sequences, every five minutes of a 30 min video. In total, 24 min were thus analysed per film. The occurrences of the behaviours of pikeperch were done with BORIS software (Behavioural Observation Research Interactive Software v.7.10.2 - 2021-01-16). These behaviours were grouped into two categories of traits: (i) agonistic (Ag) that include bites, advance with contact, or without contact, open mouth approach, frontal demonstration, pursuit and (ii) flight (Flg) (see Supplementary information S1 and S4 for more information).

### 2.5.2. Statistical analyse

#### 2.5.2.1. Data processing.
Degree of expression sought for aquaculture purpose are different between traits. For instance, higher expression of SR, Wf, FCF and SGR are sought according to the needs of fish farmers while low expression of CV, Hct, Cort, Glu, Ser, Dop, Ago and Flg are
valuable for production. Therefore, the value of each trait was polarised (multiplied by “± 1”) according to expectations for aquaculture purpose. All traits were considered in this study at the EU level. For the analysis of behavioural traits (inter and intraspecific interactions), the raw occurrence data were converted by dividing the number of observed pikeperch behaviours by the total average number of individuals (all species) in each replica (see Supplementary information S5). Missing values of some behavioural traits were filled in by mode values (Kang, 2013). We used R version 4.1.1 (2021) and we considered p-value below than 0.05 as threshold of significance.

### 2.5.2.2. Multivariate analysis.
Multivariate analysis was applied because it facilitates the decision-making process (Sahu and Nayak, 2018). On the basis of the mean and variance, it makes use of co-variances or correlations which reflect the degree of the relationships among the variables (Huberty and Morris, 2004; Saccenti et al., 2014; Ivanova et al., 2021). The principle is to reduce the dimensionality of datasets (Hasan and Abdulazeez, 2021), improving interpretability without losing few information. The multivariate analysis might promote the selection of traits and makes a compromise between several conflicting traits.

To perform the multivariate analysis, all trait datasets were combined in one matrix and were standardised to facilitate comparison of traits value, even if those values were measured on different scales. The standardised value corresponds to the centred scaled value (i.e. ranged between 0 and 1 (R-package [scales])) attributed to the replicate for each trait.

We assessed influence of modalities on traits through a principal component analysis (PCA) using a correlation matrix on which was performed a multivariate permutation tests (MRPP; permutations = 999) from R-package [vegan]. We used a MRPP because parametric test assumptions for normality for a multivariate dataset (mshapiro.test from R-package [mvnormtest]) were not met. The PCA and MRPP were performed by comparing only pikeperch in four combinations. We considered that the traits were impacted by the modalities if a significant differentiation is highlighted by MRPP test. In this case, pairwise comparisons MRPP with a Bonferroni correction were performed, provided that polyculture(s) was/were significantly different from monoculture. In order to integrate the results providing by the multivariate analysis, all modalities were ranked using Rank function (-ws) (R-Package [MCDA]), where ws corresponded to the sum of all trait values of a particular modality. By applying the Rank function (-ws), the modality with the lowest ws is ranked in first position. For more information, see Bigaret et al. (2017). Here, it allowed highlighting among alternative fish combinations, those with overall the best expression of studied traits for aquaculture purpose.

## 3. Results

The mean values of biological traits in modalities tested were presented in Table 2. PCA showed that the first axis (PC1: inertia 38.3 %, eigenvalue 4.59) was composed of traits related to growth and stress. It opposed SGR, Wf, FCF and Dop to CV (Fig. 1A, B). The second axis (PC2: inertia 22.2 %, eigenvalue 2.56) was composed of survival rate and traits related to behaviour and stress. It opposed Glu, Ser, Ago and Hct to Flg and SR (Fig. 1A, C). The third axis (PC3: inertia 12 %, eigenvalue 1.43) was composed of traits related to behaviour and stress. It opposed Cort, Ago, Ser to Hct (Fig. 1B, C). The PCA highlighted divergences between the four modalities on the three first axes. The results were supported by MRPP (global MRPP, df = 3, A = 0.262, p-value < 0.05) and pairwise MRPP (Table 3), which return significant differences between modalities. We showed that (i) S was characterised by high SR, Wf, FCF, SGR CV, Hct, Glu and Ago with low Dop and Flg, (ii) SM was characterised by high Wf, FCF, SGR, CV and Flg with low SR, Hct, Glu and Ago (iii) SC was characterised by high Wf, FCF, SGR, Hct, Glu, Dop, Ago with low CV and

<caption>**Table 2**</caption>

Values (mean and standard deviation [SD]) of biological traits for tested polycultures. SR = survival rate, Wf = mean final weight, CV = weight heterogeneity, SGR = specific growth rate, FCF = Fulton condition index, Ago = agonistic, Flg = flight, Cort = cortisol, Glu = glucose, Ser = serotonin, Dop = dopamine and Hct = haematocrit. S, SC, SM and SCP correspond respectively to pikeperch, pikeperch - common carp, pikeperch - black-bass and pikeperch - common carp - European perch.

<table><thead><tr><th>Biological traits</th><th>S Mean (SD)</th><th>SC Mean (SD)</th><th>SM Mean (SD)</th><th>SCP Mean (SD)</th></tr></thead><tbody><tr><td>SR (%)</td><td>99 (0.62)</td><td>96 (2.06)</td><td>90 (3.78)</td><td>94 (2.01)</td></tr><tr><td>Wf (g)</td><td>79.09 (12.01)</td><td>79.33 (11.69)</td><td>91.95 (24.16)</td><td>56.91 (2.17)</td></tr><tr><td>SGR (% Day<sup>-1</sup>)</td><td>0.48 (0.17)</td><td>0.43 (0.15)</td><td>0.61 (0.31)</td><td>0.08 (0.04)</td></tr><tr><td>CV</td><td>0.67 (0.05)</td><td>0.53 (0.06)</td><td>0.76 (0.08)</td><td>0.27 (0.02)</td></tr><tr><td>FCF</td><td>0.70 (0.02)</td><td>0.67 (0.02)</td><td>0.69 (0.08)</td><td>0.65 (0.02)</td></tr><tr><td>Ago</td><td>0.12 (0.06)</td><td>0.26 (0.08)</td><td>0.12 (0.06)</td><td>0.05 (0.05)</td></tr><tr><td>Flg</td><td>0.16 (0.08)</td><td>0.20 (0.05)</td><td>0.76 (0.15)</td><td>0.5 (0.06)</td></tr><tr><td>Cort (ng mL<sup>-1</sup>)</td><td>110.31 (86.84)</td><td>131.23 (65.22)</td><td>112.69 (51.77)</td><td>86.84 (92.11)</td></tr><tr><td>Glu (ng mL<sup>-1</sup>)</td><td>566.63 (278.13)</td><td>490.26 (113.41)</td><td>383.45 (209.99)</td><td>276.15 (9.03)</td></tr><tr><td>Ser (ng g<sup>-1</sup>)</td><td>106.82 (21.46)</td><td>183.21 (27.03)</td><td>74.51 (29.26)</td><td>142.64 (17.94)</td></tr><tr><td>Dop (ng g<sup>-1</sup>)</td><td>61.94 (5.56)</td><td>78.04 (4.55)</td><td>62.85 (10)</td><td>83.06 (5.23)</td></tr><tr><td>Hct (%)</td><td>26.25 (3.93)</td><td>24.16 (3.54)</td><td>23.75 (5.06)</td><td>23.16 (4.11)</td></tr></tbody></table>

Flg and (iv) SCP was characterised by high Dop with low Wf, FCF, SGR, CV, Hct, Glu and Ago.

In ranking procedure, therefore, considering the results, the respective ws of each modality were S (pikeperch) = - 3.10; SM (pikeperch-black-bass) = - 2.22; SCP (pikeperch-common carp-European perch) = - 2.03 and SC (pikeperch-common carp) = - 1.94. The monoculture is ranked first. The best polyculture is thus SM (Fig. 1).

## 4. Discussion

### 4.1. The importance of the multi-trait approach to assess the consequences of polyculture

Our results highlight that focusing on one or few traits, related to a single biological function, could bias assessment of the overall potential for aquaculture of a particular polyculture. For instance, in the SC polyculture, pikeperch has a high survival rate (Fig. 1A), which is highly important in aquaculture (Blancheton, 2000; Rahman et al., 2006; Kozłowski et al., 2014; Thomas et al., 2020; Pěnka et al., 2021; Tongo and Erhunmwunse, 2022). However, in this polyculture, pikeperch displays high level of physiological stress indicators (i.e. Ser, Glu and Hct; Fig. 1A), which could lead to fish welfare issues.

When comparing alternative polycultures, relying on one or few traits can also bias the choice of the best polyculture for the target species. For instance, pikeperch has higher level of Wf, FCF and SGR in SM than in other polycultures. One could conclude that SM should be favoured in the development of RAS polyculture of pikeperch compared to SCP. However, in SM polyculture, pikeperch has high expression level for traits related to the dominance at the intra or interspecific level (i.e. high values of CV) or to stress (i.e. high Flg) (Nakano and Furukawa-Tanaka, 1994), suggesting deleterious impact on fish welfare. This confirms the importance of applying multi-trait framework to obtain a reliable assessment of fish polycultures. However, our results also show opposite expressions between key traits (e.g. CV versus SGR on the first axis of the PCA; Fig. 1A,B). This means that a particular polyculture could be promising for a specific trait, but constantly less valuable when considering another trait (see similar results for multi-trait assessment of monoculture in Toomey et al., 2020b). Such discrepancies in the multi-trait assessment requires applying integration methods to compare alternative polycultures and highlight the best one for aquaculture development.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Principal component analysis based on 12 traits compared between monoculture and alternative polycultures of pikeperch in recirculated aquaculture systems. S = pikeperch monoculture; SC = pikeperch - common carp polyculture; SM = pikeperch - black-bass polyculture; SCP = pikeperch - common carp - European perch polyculture. 72.5 % of the information (variances) contained in the data is retained by the first three principal components (PC1: 38.3 %, PC2: 22.2 % et PC3: 12 %). Contributions of each trait on each axis is plotted in grey. Ago = agonistic; Cort = cortisol; CV = weight heterogeneity; Dop = dopamine; FCF = Fulton condition index; Flg = flight; Glu = glucose; Hct = haematocrit; Ser = serotonin; SGR = specific growth rate; SR = survival rate; Wf = final weight. Traits which are preceded by “-” are those for which a lower expression is more valuable for aquaculture purpose. Thus, they are multiplied by “- 1” in the analysis, meaning that arrows indicate a low expression of these traits, which is better for fish farming. Traits which are preceded by “+” are those for which a higher expression is more valuable for aquaculture purpose. For these traits, arrows indicate a high expression, which is sought for fish farming. According to the ranking, the best modality is S, following successively by SM, SCP and SC.</figcaption></figure>

## 4.2. Integrating and comparing results from multi-trait assessment

Methods have been previously proposed to compare alternative practices or species for aquaculture development based on a multi-trait assessment and stakeholders expectations (Quéméner et al., 2002; Moroni et al., 2015; Toomey et al., 2020a). However, these methods statistically analyse each trait independently prior integrate univariate results through indicators. This can lead to several statistical issues such as using highly correlated traits to take a decision (Saccenti et al., 2014; Toomey et al., 2020b; Ivanova et al., 2021) or multiple correction problems (Saccenti et al., 2014). Here, we used Bigaret et al. (2017) approach performs in multivariate space to rank modalities according to

the expression of the polarised potentially conflicting traits. For pikeperch, our multi-trait assessment and ranking approach show that combining pikeperch with black-bass is the best of the here studied fish species combinations to develop pikeperch-centred polyculture in RAS. The overall advantage of this polyculture compare to others could result from a better resource sharing between pikeperch and black-bass and between pikeperch and other tested species. Indeed, although all tested polycultures combined species feeding in different parts of the water column or at different time, SM was feed with two different sizes of pellets, potentially favouring resource sharing.

Here, we considered that all traits studied have the same weight (i.e. equal importance) to produce our ranking. Since aquaculture
<caption>**Table 3**</caption>

Pairwise results of multivariate permutation tests (MRPP). S, SC, SM and SCP correspond respectively to pikeperch, pikeperch - common carp, pikeperch - black-bass and pikeperch - common carp - European perch.

<table><thead><tr><th rowspan="2">Modalities</th><th colspan="4">Pairwise MRPP</th></tr><tr><th>S</th><th>SM</th><th>SC</th><th>SCP</th></tr></thead><tbody><tr><td>S</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SM</td><td>df = 3<br>A = 0.109<br>p-value < 0.05</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SC</td><td>df = 3<br>A = 0.155<br>p-value < 0.05</td><td>df = 3<br>A = 0.225<br>p-value < 0.05</td><td>-</td><td>-</td></tr><tr><td>SCP</td><td>df = 3<br>A = 0.265<br>p-value < 0.05</td><td>df = 3<br>A = 0.216<br>p-value < 0.05</td><td>df = 3<br>A = 0.267<br>p-value < 0.05</td><td>-</td></tr></tbody></table>

stakeholders most likely would not consider that all traits have the same importance (Gan et al., 2017), our current ranking between three alternative pikeperch polyculture, or even, our result that pikeperch monoculture is better than polyculture should be considered with caution. Indeed, we here did not explore potential solution to fine-tune tested polyculture (e.g. testing different species ratios, densities, foods, abiotic conditions) to maximise efficiency for aquaculture purpose.

### 4.3. Future developments

Although implementation of multi-trait assessment in development of fish polyculture in various systems could be valuable, further developments are needed.

First, our study focuses on a target species (i.e. pikeperch), disregarding the polyculture consequences for the other combined species. Yet, it cannot be ruled out that polyculture results in beneficial consequences for one species at the detriment of other species (Kohinoor et al., 1998; Gibbard et al., 2009). Therefore, an efficient multi-trait assessment should encompass the polyculture consequences for each combined species.

Second, the results are specific to our RAS. Indeed, the expression of trait impacting species compatibility can be modified by the environment (Sae-Lim et al., 2016). This implies that the best polyculture(s) for our rearing system may not be as interesting in different rearing conditions. Therefore, multi-trait assessment of polycultures should be always performed in a farming system as similar as possible of the target aquaculture environment. Extending conclusions to other rearing systems should be proposed with caution.

Third, we here focused on trait related to zootechnical and stress concerns. However, the potential for aquaculture depends on many different aspects, such as socio-economic interests, ethic concerns, robustness/resilience of farming systems, or regulations (Moroni et al., 2015; Jennings et al., 2016; Rossi et al., 2021). All of these aspects, as well as their relative importance according to the stakeholders expectations, should be integrated in the multi-trait assessment framework, as they have been included in approach aiming at finding best candidate species for aquaculture development (Moroni et al., 2015; Rossi et al., 2021).

## 5. Conclusion

Designing efficient fish polycultures implies to develop an evaluation framework to assess different candidate polycultures and highlight the best one(s) for a particular rearing system. Our study underlines the importance of considering traits related to multiple functions to shape this framework. Indeed, a particular polyculture could be promising for a specific trait, but less valuable for another trait. Discrepancies between traits raise the need of using an integration approach (i.e. ranking considering all traits) to facilitate decision-making and highlight best

polyculture(s) for a particular purpose. For pikeperch, our multi-trait assessment and ranking approach suggest that combining pikeperch with black-bass is the best of the here studied fish species combinations to develop pikeperch-centred polyculture in RAS. However, this result should be considered with caution since this study aims mainly at triggering further studies to develop an efficient multi-trait evaluation framework of fish polyculture.

### CRediT authorship contribution statement

MT, TL and PF found the financial acquisition. NA, TL, AP and MT conceived the original idea. NA, CF, TL, AP and MT performed the bioassays. NA, CF and RN performed and analysed the physiological analyse. NA, MT, AP, and TL performed statistical analyses. NA led the writing of the original draft. All authors discussed the results and contributed to the final manuscript, revising it critically for content.

### Funding sources

NA is supported by a grant from the French Ministère de l'Enseignement Supérieur et de la Recherche and the Zone Atelier du bassin versant de la Moselle (Centre national de la recherche scientifique, France). This study was supported by the Research Programme Interreg GR VA Perciponie (073-4-08-191).

### Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

The authors are grateful to (i) Sylvain Milla (University of Lorraine, Animal and Functionality of Animal Products, Team Domestication in Inland Aquaculture, Nancy, France) and Carole Rougeot (University of Liège, Aquaculture Research and Education Center, Tihange, Belgium) for their expertise and recommendations for the physiological analyses and (ii) Colin Lutz, Tiphaine Guionnet and Yannick Ledoré for their technical assistance throughout the bioassays at the Aquaculture Experimental Platform of the University of Lorraine (Nancy, France).

### Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.aqrep.2022.101349.

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