# Effects of biofilter management modalities on the microbiological status in RAS water to avoid recording capacities of biofilms

Al Dakdouki Salam, Nivelle Renaud, Yannick Ledoré, Fontaine Pascal, Frédérique Changey

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Al Dakdouki Salam, Nivelle Renaud, Yannick Ledoré, Fontaine Pascal, Frédérique Changey. Effects of biofilter management modalities on the microbiological status in RAS water to avoid recording capacities of biofilms. Aquacultural Engineering, 2025, 110, pp.102522. 10.1016/j.aquaeng.2025.102522. hal-05004126

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# Effects of biofilter management modalities on the microbiological status in RAS water to avoid recording capacities of biofilms

Al Dakdouki Salam<sup>a</sup>, Nivelle Renaud<sup>a</sup>, Ledoré Yannick<sup>a</sup>, Fontaine Pascal<sup>a</sup>, Frédérique Changey<sup>b,*</sup>

## ARTICLE INFO

### Keywords:

RAS

Microbial communities

Biofilm

Cleaning and disinfection protocols

## ABSTRACT

Recirculating Aquaculture Systems (RAS) are increasingly seen as a breeding system of the future, the development of RAS requires understanding the microbial communities, as they play a significant role in the system functioning, water quality and therefore fish welfare and organoleptic characteristics. However, some aspects remain insufficiently explored, such as information regarding the residual microorganisms persisting in the system and originating from the past rearing conditions. Any studies on microbial communities in RAS mention specific protocols to obtain a similar initial microbial state in their systems before conducting their new studies. In this context, controlling the initial RAS microbial status (before starting a breeding cycle) and having a reliable, standardized protocol for obtaining this state in a reproducible way is essential. In contrast to a standard protocol (i.e. only raw cleaning), this study aimed to evaluate the effects of two treatments that differ in the way of management of biofilter media. The water microbial communities' structure was assessed through the 16S rRNA gene over two weeks' post-treatment. The results show that the standard protocol does not shift the microbial past of the tank and is responsible for strong inter-tanks heterogeneity. The testing protocol number T1 is the most reliable for homogenising initial microbial composition of tanks while preserving the microbial communities of interest.

## 1. Introduction

Driven by the increased demand for protein, aquaculture has become one of the fastest-growing food industries worldwide. According to FAO (2020), aquaculture production is expected to increase from 77 million tons in 2018–109 million tons in 2030, corresponding to a 32 % increase. Recirculating aquaculture systems (RAS) are a type of aquaculture systems based on innovative technology used for intensive fish production that offers the greatest control of rearing parameters and recirculate and reuse 90–99 % of the water used in rearing tanks after treatment (Kamali et al., 2022; Moschos et al., 2022) to limit the supply of water in the system. The water treatment in RAS involves multiple steps through advanced mechanical and biological filtration systems, which remove solid and dissolved waste, ammonia, and carbon dioxide, ensuring optimal water quality for fish health (Aich et al., 2020). Faced with new challenges (climate change, food security, and sovereignty), these farming systems are increasingly seen as a future solution for

developing sustainable aquaculture. In continental environments such as Central Asia and Europe, this farming system is used for the production of various fish species, including percids, pikeperch *Stizostedion lucioperca*, and Eurasian perch *Perca fluviatilis* (Dalsgaard et al., 2013).

Microorganisms play a significant role in RAS, particularly regarding nutrient cycling, water quality, disease control, and the environmental quality of the effluent (Rurangwa and Verdegem, 2013). For example, the nitrifying bacteria of the biofilter transformed toxic ammonia excreted by fish into nitrate, which is less toxic for fish. Biofilters media, responsible for biological filtration, provide a surface for biofilm development which makes it the largest reservoir of microorganisms in RAS and therefore essential to ensuring the well-being of fish. (Moschos et al., 2022; Rurangwa and Verdegem, 2013). However, some microorganisms present in RAS could have undesirable effects. Certain pathogens, especially the opportunistic bacteria of the *Aeromonas*, *Flavobacterium* genus and Alteromonadaceae family, can cause fish diseases (Rud et al., 2017), while other bacteria are responsible for

<footnote><sup>a</sup> University of Lorraine, INRAE, L2A, Nancy 54000, France</footnote>

<footnote><sup>b</sup> Université de Lorraine, CNRS, LCPME, Nancy F-54000, France</footnote>

<footnote>* Correspondence to: LCPME (Laboratoire de Chimie Physique et Microbiologie pour les Matériaux et l'Environnement - UMR 7564 CNRS - Université de Lorraine). Campus Brabois Santé, Bat. AB, 3e étage, 9 avenue de la Forêt de Haye BP 20199, Vandœuvre-lès-Nancy F-54505 Cedex, France.

E-mail address: frederique.changey@univ-lorraine.fr (F. Changey).</footnote>
off-flavors and the otherwise poor quality of marketed products (Moschos et al., 2022). For, example Sorangium, Actinomycetales and Myxococcales are known to be geosmin producers (Lukassen et al., 2017). Other microorganisms are able to produce Hydrogen Sulphide in aquaculture and belonging to four bacterial families: Dethiosulfati-bacteraceae, Fusobacteriaceae, Vibrionaceae and Desulfovibrionaceae (Nguyen-tiet et al., 2025).

The pathogenic components of microbial communities in RAS constitute a significant challenge for the fish farm industry. Microorganisms in RAS can be introduced through different pathways: make-up water, air, animal and insect exposure, feed, stocked fish, equipment used and staff (Blancheton et al., 2013). Moreover, the structure of the microbial community in RAS is affected by numerous factors including water contamination with excreted material of fish and food residues, water pH and chemical parameters (the level of nitrates, nitrites...) and the population density of aquatic organisms (Sergaliev et al., 2021). In RAS, bacteria organized in biofilms are considered a potential reservoir for pathogenic bacteria. Once released from the biofilms, these pathogenic bacteria can cause recurring diseases (King et al., 2008). As the biofilms mature, they become reservoirs for microbial diversity, storing a record of past aquaculture conditions. Different management approaches are currently practiced in RAS to reduce opportunistic pathogen species, including raw cleaning normally applied to clean tanks and chemical disinfection treatments in case of disease outbreaks and sanitary problems such as the use of chloramine T and hydrogen peroxide (Bigarré et al., 2017; Schreier et al., 2010). In aquaculture, chloramine T is highly used to inactivate bacteria and viruses because of its fast microbicidal action but especially because of its low bio-accumulation potential due to its high solubility in water and great biodegradability (Quezada-Rodriguez et al., 2022). Also, Hydrogen peroxide ($H_2O_2$) is another effective disinfectant used in aquaculture and regarded as "green" due to its decomposition into non-toxic and environmentally friendly by-products (Bogner et al., 2021). For all these reasons, monitoring the effectiveness of cleaning and disinfection treatments on RAS microbial communities is crucial to the future of this promising aquaculture system.

In that context, a recent prospective study by Fourier et al. (2022) aimed to compare the rearing performance in RAS of pikeperch reared in monoculture or biculture with tench (Tinca tinca) and to assess RAS planktonic microbial communities using a metabarcoding approach. As expected, Fourier et al. (2022), identified a significant effect of the fish species but also a strong "tank effect". The authors observed high intra-treatment and inter-tank variabilities that persist even though a raw cleaning of the tanks was performed before conducting the study. This finding indicated that the history of fish tanks, probably linked to the memory of bacterial biofilms formed over the years, may underscore their ability to reintroduce microbial diversity into the system and influence the water microbiome even after a raw cleaning of the system. It's believed that bacterial biofilms within RAS exhibit a unique capability to record the microbiological diversity of previous farming practices within the system, essentially storing a record of the past aquaculture conditions. This "tank" effect generated a high level of initial residual variability, which interfered with the interpretation of the results of Fourier et al. (2022). In literature, no information was found regarding the acquisition of an initial identical microbial state across replicates before conducting studies on microbial communities in RAS. Studies on microbial ecology in RAS do not mention any precautions or specific disinfection recommendations before conducting experiments on microbial communities. This observation leads us to consider this issue very seriously. To better study the effects of new farming practices (mixed farming) on the microbial communities in RAS, returning tanks to a similar initial microbial state and limiting variability due to breeding history is essential.

The present study supports the hypothesis of a connection between the tanks' history and the composition of microbial communities in the water after cleaning and refilling. Therefore, the main purpose was

evaluate and identify a reliable cleaning and standardizing protocol in order to achieve a homogeneous initial tank microbial status with the lowest inter-tanks variability and prevent microbial recontamination of tanks originating from their individual history. To accomplish this three modalities based on cleaning protocols of tanks and biofilter management were tested without being followed by any fish addition to the system. Then the microbial community's structure was studied using a 16S rRNA gene metabarcoding approach and by analyzing α- and β-diversity. Thus, this study's objective was to test and select a cleaning and standardizing procedure reliable enough to avoid tank-dependent microbial recontamination and to smooth out global heterogeneity.

## 2. Materials and methods

The study was carried out at the Experimental Platform for Aquaculture - EPA (agreement number C54-547-18) of the Faculty of Sciences and Technologies (University of Lorraine, Nancy, France) (registration number for animal experimentation D54-547-18) from October 24th (day 1, D1) to November 22th 2022 (day 30, D30).

### 2.1. Experimental facilities

12 independent temperature-controlled rooms "ecotrons" were used for this experiment. Each ecotron has its own closed system with its own water recirculating system. It contained an experimental unit of fiber and polyester resin (fish tank, 2400 L, L = 2 m; l = 2 m; h = 0.8 m), connected to a water recycling system with a flow rate of 3000 L/h. The outlet of the fish tank was directed towards the mechanical filter (drum filter, mesh size: 63 µm), then water successively crosses a 300 L buffer tank (recovery tank for pumping), a 55 W Ultraviolet sterilization reactor and finally a fluidized biological bed filter of 450 L filled with media (Helix type). The management of the ecotrons has not changed since the creation of the PEA in 2014 (for all the tanks same origin of water: tape water of Nancy, same equipment and water retention time).

### 2.2. Experimental design

Taking as a basis the ecotron disinfection protocols used at the PEA (instruction N°AFPA-INS-ANI-23, March 27th, 2020), either routinely in the case of minor risk (standard protocol) or occasionally in the case of viral infection (Bigarré et al., 2017; reinforced protocol), two cleaning/disinfection treatments were designed by integrating high-pressure cleaning of the basins, chemical treatments with hydrogen peroxide and chloramine T, two powerful disinfectants, physical shocks (high temperature and drying of the system) (Linley et al., 2012; Arvin and Pedersen, 2015; Quezada-Rodriguez et al., 2022), and biofilter management. These treatments were applied with 4 replicates (n = 4) randomly defined (Fig. 1). The standard protocol is applied one day, whereas the reinforced protocol is based on several actions carried out over 2 weeks:

Standard treatment SP (control): 4 tanks were treated on day one (D15) by a simple cleaning of rearing tank with a high-pressure cleaner (raw cleaning; no action on biofilter media, no chemical treatment, no thermal shock) followed by water emptying and filling of the tanks (temperature = 25°C) on day 15 (D15).

Treatment T1 (Standard + chemical disinfection + biofiltration media mix: 4 tanks were treated in 15 days. On day 1 (D1) raw cleaning, removal and homogenization/mixing of biofiltration media of the 4 biofilters in one separate tank and redistribution of the mix in the 4 tanks; followed by addition of 80 ml/m³ of hydrogen peroxide 30 % and 7.5 g/m³ of chloramine T, both left to incubate. On day 3, draining and drying the system then filling and increasing water temperature to 45°C on days 9–10 and finally decreasing the temperature to 30°C (D11-D14) then to 25°C on day 15 (thermal shock).

Treatment T2 (Standard + chemical disinfection + replacement of biofiltration media) T2: 4 tanks were treated in 15 days. This treatment
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Experimental design. 12 independent temperature-controlled rooms "ecotrons" including 4 replicates (R1, R2, R3, R4) for each treatment were randomly selected.</figcaption></figure>

included the same steps as the T1 treatment except for the management of the filtration media. In the T2 treatment, the biofiltration media of the 4 biofilters were removed on day 1 and replaced with new media for each ecotron on day 15 after the thermal shock.

## 2.3. *Sampling strategy*

The experiment lasted 30 days with two distinct periods: firstly, the cleaning/disinfection 15 days' period (day 1-day 15) dedicated to the application of the treatments T1 and T2 (only day for T1, day 15) and secondly a post-disinfection 15 days' period focused on the evaluation of the effects of the disinfection treatments on the tank microbial communities. Volumes of 500 ml of water were sampled in triplicates in each tank selected for the experiment. The samples were collected at different times: day 1 (D1, initial time), day 16 (D16, beginning of the post-treatment period), day 23 (D23, one week after the end of the treatment period) and day 30 (D30, two weeks after the end of the treatment period). Water samples were stored at -20°C.

## 2.4. *Water quality*

Physico-chemical parameters were measured to ensure that our protocols will not have effects on water quality in this study and therefore on fish welfare in future studies. Over the D15-D30 period, basic physico-chemical parameters of water were measured twice a week, in the morning before any manipulations on the system. Temperature (24.2 - 25.2°C), dissolved oxygen (> 6 mg.L⁻¹) and pH (7.3-8.2) are measured daily with a multi-parameter probes WTW pH/Oxi 340i. The water concentrations of N-NH₄⁺ (< 0.01 mg.L⁻¹) and N-NO₂ (< 0.05 mg.L⁻¹) were measured spectrometrically.

## 2.5. *DNA extraction and amplicon sequencing*

Three 100 ml replicates of water were filtered through Whatman® Nucleopore™ Track-Etched membranes of 0.22 µm pore size before extraction. DNA extraction was performed using DNeasy PowerWater

kit (Qiagen, Hilden, Germany) according to the manufacturer recommendations and were eluted in 100 µL of DNase-free water. DNA concentration was measured by the QuBit4 Fluorometer (Invitrogen, Waltham, USA) method. In addition, purity of the DNA extractions was assessed by measuring both 260/280 nm and 260/230 nm ratios with a NanoDrop spectrophotometer (Thermo Scientific, Waltham, USA). DNA was stored at -20°C until further analysis.

## 2.6. *Amplicon sequencing*

Metabarcoding targeted the hypervariable V3-V4 region of the bacterial 16S rRNA gene and was carried out by the GIGA Institute (Liège University, Belgium), using the universal primers 341 F and 806 R. Briefly, variable regions V3 and V4 of the 16S rRNA gene are amplified using the primer pair 357F-806R in a dual-barcoding approach according to Kozich et al. (Kozich et al., 2013).

## 2.7. *Bioinformatic analysis*

Data were subsequently imported into the FROGS pipeline (Find Rapidly OTU with Galaxy Solution) implemented on a galaxy instance (v.2.3.0) (http://sigenae-workbench.toulouse.inra.fr/galaxy/) (Escudié et al., 2018). After a quality control, 16S paired-end sequences were merged into contigs with VSEARCH (Rognes et al., 2022). Sequences were dereplicated before being clustered using SWARM algorithm (v.2.1.5) (Mahé et al., 2022) with a first denoising step using an aggregation distance equal to 1 and a second one equal to 3. Chimera were removed using VSEARCH (Rognes et al., 2022). Filters were applied to remove clusters which are not present in at least 3 samples or with an abundance below a 0.005 % threshold (Bokulich et al., 2013). The taxonomic assignation of each OTU was performed using the BLAST tools against (Camacho et al., 2009) the database SILVA 132 16S (Pruesse et al., 2007). Phyloseq (1.26.1) R package was used to identify community composition analysis, to normalize and to generate α-diversity indexes (richness and evenness) after a rarefaction curve using the transform counts method (McMurdie and Holmes, 2013).
## 2.8. Statistical analysis

All statistical analyses were carried out using R software version 3.3.1 (R Development Core Team, http://www.R-project.org). We first compared α-diversity indexes to summarize the structure microbial communities with respect to its richness (number of Amplicon Sequence Variant (ASV)) and Shannon diversity index (distribution of abundances of the ASVs) with Phyloseq (1.26.1) (McMurdie and Holmes, 2013). After normality and homoscedasticity verifications (Shapiro and Bartlett test respectively), ANOVA (significance was declared at $P < 0.05$) were performed on richness and Shannon indexes and Tukey HSD test was used for pairwise comparisons using “agricolae” package. The β-diversity, to assess the degree of community differentiation, was estimated by using the relative abundance matrices and multivariate analyses. The dissimilarities (weighted Unifrac Distance) among treatments were assessed using Non-Metric Multidimensional Scaling, (NMDS) were performed followed by PERMANOVA using “adonis” function on vegan 3.0.2 R package to test for significant differences in sample clustering patterns across sampling point (9999 permutations). Plots were drawn using the R packages ggplot2 (3.1.1). Heatmaps were plotted with R package ampvis2 (2.5.1) (Andersen et al., 2018).

# 3. Results

## 3.1. Water quality

Temperature (24–25°C) and pH (8.02–8.04) remained constant and stable in all tanks (Table 1). A higher mean level of dissolved oxygen level was recorded using the standard protocol; however, it remained higher than 7.5 mg O₂/L in all tanks indicating well-oxygenated water in all tanks. Concerning ammonia concentrations, a higher concentration was recorded using treatment T1, only explained by a higher concentration on day 1. After day 1 the ammonia level was zero as in other treatments. Regarding the nitrite level, the concentration was continuously higher in tanks of T2 treatment.

## 3.2. Microbial analysis

Sequencing of the amplified rDNA yielded 5.95 E10⁶ sequences over 150 samples, with an average of 39,696 reads per sample.

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

Water quality parameters (mean ± SE) of water in RAS (n = 4) measured during the post-treatment period from D16 to D30 between the three tested treatments: standard treatment SP, treatment T1 (standard + chemical disinfection + biofiltration media mix) and treatment T2 (standard + chemical disinfection + replacement of biofiltration media).

<table><thead><tr><th>Water quality parameters</th><th>Standard protocol SP (control)</th><th>Treatment T1</th><th>Treatment T2</th><th>Test statistic and p-Value</th></tr></thead><tbody><tr><td>Temperature (°C)</td><td>24.8 ± 0.2 a</td><td>24.9 ± 0.2 a</td><td>25.0 ± 0.1 a</td><td>ANOVA, p-Value = 0.611</td></tr><tr><td>O2 (mg.L<sup>-1</sup>)</td><td>7.98 ± 0.10 a</td><td>7.66 ± 0.10 b</td><td>7.72 ± 0.10 b</td><td>ANOVA, p-Value = 0.009</td></tr><tr><td>pH</td><td>8.04 ± 0.02 a</td><td>8.03 ± 0.01 a</td><td>8.02 ± 0.03 a</td><td>ANOVA, p-Value = 0.309</td></tr><tr><td>NH<sup>4</sup><sup>+</sup> (mg.L<sup>-1</sup>)</td><td>0.000 ± 0.000 a</td><td>0.033 ± 0.000 b</td><td>0.000 ± 0.000 a</td><td>Kruskal-wallis, p-Value = 0.004</td></tr><tr><td>NO<sup>2</sup><sup>-</sup> (mg.L<sup>-1</sup>)</td><td>0.000 ± 0.000 a</td><td>0.001 ± 0.002 a</td><td>0.034 ± 0.006 b</td><td>Kruskal-wallis, p-Value = 0.009</td></tr></tbody></table>

## 3.2.1. Alpha-diversity

Bacterial ASV richness was reported as the total number of amplicon sequence variants (ASVs) per sample (Fig. 2). There was significant variability between the tanks, regardless of the treatment used, and no significant difference was recorded on Day 1. There were significant differences in the number of ASV in the tanks treated with T2 compared with the number of ASV in the tanks treated with the other two treatments (SP and T1). Mixing biofiltration media already colonized by bacteria (T1) allowed for a significantly higher richness of planktonic microbial communities in the tanks than new biofiltration media (T2). Furthermore, no significant differences were observed between the different sampling times, except in tanks treated with modality 3, where microbial richness decreased significantly at D16 (when the new biofiltration media was used).

No significant differences in ASV diversity (approximated by Shannon index calculation) (Fig. 3) were found with either treatment. Moreover, no significant differences in kinetic alpha diversity measurements were observed throughout the experiment.

## 3.2.2. Beta Diversity

Non Metric Multidimensional Scaling (NMDS) using weighted Unifrac Distance was performed to make a quantitative Beta diversity measurement, established that the water microbiomes clustered by treatment but not by sampling time (Fig. 4). A permutational multivariate analysis of variance (PERMANOVA) test indicated that the difference between treatments was statistically significant Fig. 4 shows that treatments for T1 and T2 sites clustered separately but were away from the SP treatment. This experiment confirmed that the routine washing and filling of water tanks results in the recolonization of the water by biofilm microbial communities. It found that routine washing and filling the water tanks resulted in the recolonization of the water by biofilm microbial communities (since no significant difference was observed between the initial treatment and modality 1) because the tanks shared common microbiomes at the endpoint (P = 0.4) The inter-tank heterogeneity of the microbiome structure was lower in the water sampled in the tanks treated with T1 (disinfection and media mix).

## 3.2.3. Microbial community composition

The distribution of bacterial orders among the three treatments was examined to identify the general composition of the water microbiome and the orders preferentially associated with the treatments and sampling times. Order distributions are shown as a heat map (Fig. 5), from which it can be observed that the treatment affects the microbial

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Microbial alpha-diversity indexes between treatments (Standard protocol SP (control), treatment T1 (Standard + chemical disinfection + biofiltration media mix) and treatment T2 (Standard + chemical disinfection + replacement of biofiltration media)) measured at D1(initial time before any treatment), D16 (just after the treatment period), D23 and D30 (one and two weeks after the treatment period). Alpha-diversity is presented as a measure of species richness (number of ASVs). Stars above the box-plots indicate significant differences between compartments; \*p < 0.05, \*\*p < 0.005 and \*\*\*p < 0.001.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Alpha-diversity between treatments (Standard protocol SP (control), treatment T1 (Standard + chemical disinfection + biofiltration media mix) and treatment T2 (Standard + chemical disinfection + replacement of biofiltration media)) measured at D1(initial time before any treatment), D16 (just after the treatment period). D23 and D30 (one and two weeks after the treatment period). The alpha diversity is presented as a measure of Shannon diversity index (biodiversity index). Stars above the box-plots indicate significant differences between compartments; \*p < 0.05, \*\*p < 0.005 and \*\*\*p < 0.001.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Non Metric Multidimensional Scaling (NMDS) of microbial communities' structure among treatments (Standard protocol SP (control), treatment T1 (Standard + chemical disinfection + biofiltration media mix) and treatment T2 (Standard + chemical disinfection + replacement of biofiltration media) measured at D1(initial time before any treatment), D16 (just after the treatment period), D23 and D30 (one and two weeks after the treatment period) based on weighted Unifrac Distance. The colors of symbols are blue: before any treatment (D1), red: treatment SP, green: treatment T1 and violet: treatment T2.</figcaption></figure>

composition. Water sampled for the T2 and T1 treatments seemed to host microbial orders at different relative abundances than the SP treatment. The difference in microbial composition between T2 and T1 treatments dealt, for example, with the *Gammaproteobacteria* order, which was significantly more abundant in the samples taken from the T2 tanks. The results also indicated that T2 treatment removed some bacterial orders by replacing media, such as *Clostridia*, *Oligoflexia*, and *Planctomycetes*. The percentage of *Clostridia* in the T2 tanks decreased from 2.3 at D1 to 0.4 at D16. However, a greater decrease in the percentage of *Anaerolineae*, from 3.5 at D1 to 0 at D16, was observed. The same heat map was performed at genus and family level (data not shown) and shows, on the one hand, that a large proportion of reads are unaffiliated, but, on the other hand, that some organisms with positive or negative functions in aquaculture (according to the literature) show different relative abundance profiles depending on the treatments. At the end of the experiment, the *Nitrospira* genus was significantly less represented in treatment T3 than in SP and T2. Conversely, bacteria from the *Flavobacterium* genus and the Alteromonadaceae family were relatively more abundant in the T3 treatment.

### 3.2.4. Venn Diagram

The percentage of shared ASVs between the tanks of SPand T1 treatments significantly increased from 5 % to 15 % at D30. ASV was

shared between the three treatments on days 1 and 30 (Fig. 6). The percentage of shared ASVs between the tanks of the SP and T1 treatments significantly increased from 5 % to 15 % at D30. Meanwhile, the core microbiota between the tanks of the three treatments at D30 was 58 %, significantly lower than the initial percentage of shared ASVs, which was 72 %.

## 4. Discussion

First, this study corroborates the hypothesis of an association between the tanks' history and the water microbial communities after refilling. The β-diversity representation permitted highlighting a high degree of heterogeneity in the initial microbial communities' structure, as graphically illustrated by the splitting of the points associated with these samples in the multivariate analysis. In addition, despite performing SP treatment (consisting of routine raw cleaning), this heterogeneity remained unchanged throughout the experiment. Finally, as the PERMANOVA results showed no difference between the communities before and after cleaning, we can conclude that the planktonic communities were inherited from the former sessile communities. Biofilms, the aggregates of microbial layers (Costerton et al., 1999), have been considered to be more resistant to antimicrobial than free-living bacteria (Lewis, 2001). The propensity of biofilms to contaminate the surrounding environment is a well-known medical phenomenon (Whitehouse et al., 1991) but also in food contamination (Abebe, 2020). Previous studies of microbial diversity assessment in RAS have been performed, but none mention this recolonization phenomenon or the option of using a decontamination protocol.

As previously discussed, SP treatment had a relatively minor effect compared to the difference between the initial time and decontaminated treatments (T2 and T1). A decrease in tank-dependent heterogeneity could be observed on the cloud of points illustrating tanks treated with the T1 modality. A significant decrease in microbial richness was observed in tanks treated with T2. This decrease was linked to the use of new media and the microbial loads being much lower than those of native media. Specific orders showing a difference in their relative abundance were identified. For example, the *Parcubacteria* order was underrepresented in T2 treatment. Moreover, tanks treated with T2 appear to host a less abundant nitrifying community (indicated by a drop in the relative abundance of *Nitrospira* bacteria at the end of the experiment). Conversely, groups such as the *Flavobacterium* genus or the Alteromonadaceae family (whose species include pathogenic bacteria) are more abundant in the T2 treatment.

Biofilm plays also a central role in the conversion of nitrogen metabolites in recirculating aquaculture systems, avoiding the accumulation of toxic form for fish (Eddy and Williams, 1987). Thus, even if T2 treatment was reliable for erasing tanks' history (by using new biofiltration media), it could be responsible for a decrease in richness and an imbalance in microbial communities, especially those involved in nitrogen cycle. β-diversity analyses showed a greater phylogenetic and structural similarity between the initial communities of tanks before any treatment and the communities hosted in tanks treated with T1 than those treated with the T2 treatment. Moreover, the Venn diagram analysis indicated that at the end of the experiment, the percentage of shared ASVs between the tanks treated with T1 and SP was the most important of the total microbiome. This treatment was, therefore, likelier to maintain operating conditions (the nitrogen cycle and system resilience) since it was most similar to the initial system from a microbial perspective. This phenomenon can be largely explained by the fact that the particularity of T1 treatment was to mix the media from the different tanks. This allowed the initial bacterial community to be maintained, while making biofilm recontamination more homogeneous.

Samples obtained from the T1 modality showed higher microbial richness than other modalities, meaning that mixing biofiltration media allowed for disseminating many microbial species. The higher the biodiversity of a bacterial community, the more resilient and resistant it
<table><caption>**Fig. 5.** Heatmap visualizing the microbial community composition and relative abundances of the most abundant bacterial orders (n = 15) among treatments (Standard protocol SP (control), treatment T1 (Standard + chemical disinfection + biofiltration media mix) and treatment T2 (Standard + chemical disinfection + replacement of biofiltration media)) measured at D1(initial time before any treatment), D16 (just after the treatment period), D23 and D30 (one and two weeks after the treatment period) among sampling point and lines. Colors are scaled from highest (red) to lowest (blue) values within columns.</caption><thead><tr><th></th><th colspan="3">D1 - Initial Time</th><th colspan="3">D16</th><th colspan="3">D23</th><th colspan="3">D30</th></tr></thead><tbody><tr><td>Alphaproteobacteria -</td><td>22.7</td><td>23.8</td><td>16.1</td><td>28.8</td><td>17.8</td><td>26.9</td><td>20.5</td><td>20.5</td><td>29.5</td><td>19.5</td><td>19.7</td><td>24.3</td></tr><tr><td>Parcubacteria -</td><td>26.6</td><td>24.7</td><td>13.5</td><td>17.6</td><td>34.3</td><td>0.3</td><td>7.9</td><td>27.6</td><td>1.1</td><td>12.6</td><td>28.9</td><td>17</td></tr><tr><td>Gammaproteobacteria -</td><td>10.1</td><td>5.5</td><td>20.6</td><td>6.4</td><td>13.4</td><td>45.9</td><td>9.9</td><td>12</td><td>29.6</td><td>10.5</td><td>12</td><td>18.2</td></tr><tr><td>Bacteroidia -</td><td>12.8</td><td>14.5</td><td>17.8</td><td>12.9</td><td>9.2</td><td>13.7</td><td>14.7</td><td>9.1</td><td>17.2</td><td>14.5</td><td>9.1</td><td>14.3</td></tr><tr><td>Bdellovibrionia -</td><td>4.6</td><td>3.8</td><td>4.5</td><td>1.7</td><td>7.1</td><td>4.4</td><td>7.2</td><td>9.4</td><td>13.5</td><td>5.8</td><td>10.3</td><td>12.4</td></tr><tr><td>Phycisphaerae -</td><td>1</td><td>0.9</td><td>1.2</td><td>1.2</td><td>3.3</td><td>1.2</td><td>5.8</td><td>5.2</td><td>1.8</td><td>5.2</td><td>4.9</td><td>1.4</td></tr><tr><td>Planctomycetes -</td><td>1.4</td><td>1.8</td><td>2.5</td><td>1.6</td><td>2.8</td><td>0.5</td><td>4.7</td><td>2.9</td><td>1.4</td><td>3.2</td><td>2.7</td><td>2.4</td></tr><tr><td>Saccharimonadia -</td><td>2</td><td>1.5</td><td>0.2</td><td>1.2</td><td>0.1</td><td>0.1</td><td>5.8</td><td>0.5</td><td>0.3</td><td>8.2</td><td>1</td><td>2.5</td></tr><tr><td>Clostridia -</td><td>0.4</td><td>0.1</td><td>2.3</td><td>12.3</td><td>0.2</td><td>0.4</td><td>0.7</td><td>0.1</td><td>0</td><td>0.5</td><td>0</td><td>0</td></tr><tr><td>Verrucomicrobiae -</td><td>0.5</td><td>0.9</td><td>1.5</td><td>0.4</td><td>1.5</td><td>0.1</td><td>0.6</td><td>1.4</td><td>1</td><td>0.5</td><td>0.9</td><td>3.4</td></tr><tr><td>Anaerolineae -</td><td>3.4</td><td>0.9</td><td>3.5</td><td>1.2</td><td>0.1</td><td>0</td><td>1.6</td><td>0.1</td><td>0</td><td>1.3</td><td>0.1</td><td>0</td></tr><tr><td>Oligoflexia -</td><td>1.7</td><td>1.9</td><td>1.6</td><td>0.4</td><td>0.6</td><td>0.1</td><td>1.2</td><td>0.9</td><td>0.1</td><td>1.2</td><td>1</td><td>0.1</td></tr><tr><td>Rubrobacteria -</td><td>0.1</td><td>5.2</td><td>0.3</td><td>3.2</td><td>0.1</td><td>0.3</td><td>0.5</td><td>0.7</td><td>0.2</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Blastocatella -</td><td>0.4</td><td>0.9</td><td>0.4</td><td>0.5</td><td>1.4</td><td>0.1</td><td>1.4</td><td>1</td><td>0.2</td><td>1.7</td><td>1.4</td><td>0.3</td></tr><tr><td>Polyangia -</td><td>0.4</td><td>0.5</td><td>0.9</td><td>0.3</td><td>0.8</td><td>1.3</td><td>0.9</td><td>0.5</td><td>1.4</td><td>0.8</td><td>0.4</td><td>0.8</td></tr></tbody><tfoot><tr><td></td><td>SP</td><td>T1</td><td>T2</td><td>SP</td><td>T1</td><td>T2</td><td>SP</td><td>T1</td><td>T2</td><td>SP</td><td>T1</td><td>T2</td></tr></tfoot></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Venn diagram visualizing the amplicon sequence variant ASV shared between the three modalities D1(initial time before any treatment) and D30 (two weeks after the treatment period). Colors of symbols are blue: Standard protocol SP (control), green: treatment T1 (Standard + chemical disinfection + biofiltration media mix) and beige: treatment T2 (Standard + chemical disinfection + replacement of biofiltration media).</figcaption></figure>

is (to invasion, for example (Mallon et al., 2015). The microbial communities of mixed biofilms will converge while adapting to their environment, as has already been reported in the literature in natural ecosystems (Fechner et al., 2012). All these data allowed us to select this modality as the most reliable cleaning and standardizing protocol to overcome the issue of the effect of tanks' history. This modality will be our new routine procedure for treating tanks before performing future studies on microbial communities in RAS.

## 5. Conclusion

To conclude, our research provides the first detailed description of the effect of a tank's history. Microbial diversity varied from one tank to another, as each fish species introduced a particular microbial flora (linked physiological status and origin) through intimate interaction with its surrounding water. This work is the first to offer preventive measures to avoid the recolonization effects of biofilms, which can be considered recorders of the previous microbial life of rearing tanks.
## CRediT authorship contribution statement

**Al Dakdouki Salam:** Writing – original draft, Investigation, Formal analysis, Data curation. **Frédérique Changey:** Writing – review & editing, Writing – original draft, Software, Methodology, Investigation, Data curation, Conceptualization. **Fontaine Pascal:** Writing – review & editing, Supervision, Resources, Project administration. **Ledoré Yannick:** Writing – review & editing, Validation, Methodology, Formal analysis. **Nivelle Renaud:** Writing – review & editing, Validation, Formal analysis, Data curation.

## 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.

## Acknowledgments

We are thankful to the Toulouse Midi-Pyrenees bioinformatics platform (Genotoul) and Sigenae group for storage and computing resources. The authors want to thank Camille Fourrier for technical assistance. We gratefully acknowledge the financial supports received from the regional programme V2A « Vers une Aquaponie Autonome » (Région Grand-Est) and the research programmes Interreg GR VA Perciponie (073-4-08-191) and PolyRAS (INTGR0100025).

## Data availability

Data will be made available on request.

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