Samples collected at D0 and D1 were characterized by low temperature values, which was expected, as well as low TAN, SRP and CDOM ($a_{325nm}$) concentrations and by high pH and SR values. These samples corresponded to the nauplius (Nii) larval stage. When larvae grew to zoea stage from D2 (Z1) to D4 (Z2), samples were characterized by expected higher temperature values while pH, SR, TAN, SRP and CDOM values remained similar to the first samples. Finally, during the late zoea (Z3) and all the mysis stages, from D5 to D9, physicochemical parameters of the samples shifted and were characterized by decreasing SR and pH values and by increasing TAN, SRP and CDOM concentrations. Thus, it appeared that physicochemical parameters of the rearing water samples varied according to the rearing day and the larval stage which are 2 closely related factors. In order to evaluate if these changes of the rearing environment could impact the microbiota associated with the water and the larvae in the rearing tanks, we performed microbiota analyses.

## Microbial compositions of all the samples

We first observed how samples clustered according to their global microbial composition. A hierarchical clustering of all the samples highlighted 7 clusters (Fig. 4A). The control and the rearing water samples collected on Day 0 (D0) were gathered in cluster 1 (C1). The second cluster (C2) was composed by the control samples collected from D2 to D9. The third cluster (C3) grouped the larvae samples collected on D0 as well as all the water and the larvae samples from D1 to D2. All the egg samples were also gathered in this cluster. Thus clusters 1, 2 and 3, which belonged to the same large cluster, gathered all the rearing water and the larvae samples from D0 to D2 as well as all the control and the egg samples. All the rearing water samples collected on D8 and D9 as well as all the water samples of the larval rearing cycle A from D6 to D7 were clustered in cluster 4 (C4). The fifth cluster (C5) gathered all the larvae samples collected from D5 to D9 for both rearing cycles. The water samples from the rearing cycle B and collected from D4 to D7 were grouped in the sixth cluster (C6). Finally, the last cluster (C7) was composed by all the larvae samples collected on D3 and D4 as well as water samples from the first rearing cycle (A) from D3 to D5 and from D3 of the second rearing cycle (B). Thus, clusters 4 to 7 formed a large group which gathered all the water and the larvae samples from D3 to D9. Thus, this first clustering analysis showed that microbial compositions of the samples changed after the second day of rearing. In addition, the water and the larvae samples were mixed in the clusters associated to the first rearing days (D0 to D2), while they were clustered in separate groups from D3 to D9. Furthermore, during that period, another microbial composition shift seemed to occur between D4 and D6 in all the samples. In order to investigate these observations, we conducted ordinations of the water and the control samples (Fig. 4B) as well as the larvae and the egg samples (Fig. 4C) using the Principal Coordinates Analysis (PCoA) method and a Bray-Curtis dissimilarity matrix.

The ordination of the water and the control samples showed a first cluster gathering all the control samples as well as the rearing water samples collected from D0 to D2 (Fig. 4B). The ordination of the egg and the larvae samples also clustered all the samples collected from D0 to D2 as well as all the egg samples (Fig. 4C). On these rearing days, larvae were at nauplii and zoea 1 stages. Furthermore, in both ordinations, samples collected from D3 to D5 clustered together and corresponded to the rearing days where larvae were at zoea stage. Samples collected from D7 to D9, which corresponded to the rearing days where the larvae were at mysis and post-larvae stage, grouped together in the 2 constructed ordinations. In order to understand these clusters, we investigated the major bacterial orders composing the considered samples (Fig. 4D and 4E). We first observed that *Rhodobacterales* were present
throughout the whole experiment in all the water samples (Fig. 4D). On D0, homogeneous proportions of *Flavobacteriales*, *Oceanospirillales*, *Rhodospirillales* and *SAR11* were also observed in the control and the rearing tanks. Abundances of *Rhodospirillales* and *SAR11* drastically decreased in the rearing water samples on D1 while variable abundances of *Oceanospirillales* and *Flavobacteriales* persisted throughout the experiment. On D1, proportions of *Alteromonadales* increased then decreased from D2 to D5. During those rearing days, a shift occurred as abundances of *Chitinophagales* increased after *Alteromonadales* decreased. Then, as for *Oceanospirillales*, the proportion of *Chitinophagales* grew until the end of the experiment. Between D3 and D6, some bacterial communities peaked as high abundances of *Cytophagales* and *Bdellovibrionales* were only observed during that time period. Thus, the microbial composition of the water samples dramatically changed between D2 and D6. The microbial composition of the control water samples, which did not receive larvae nor antibiotic addition, was identical to the microbial composition of the rearing water samples on D0 and D1. However, this changed from D2 to D9 as *Cellvibrionales* largely increased in the control tank while they had a very low relative abundance in the rearing tanks.

The larvae and the egg samples showed 12 main bacterial orders with a total relative abundance higher than 1% and were majorly composed by *Alteromonadales* as well as *Rhodobacterales* in a smaller proportion (Fig. 4E). *Flavobacteriales* as well as *Vibrionales* and *Oceanospirillales* were also highlighted in the eggs. From D0 to D2, the bacterial compositions of the larvae samples were more heterogeneous. For example, some samples collected during the first larval rearing cycle (A) from D0 to D2 showed high abundances of *Burkholderiales* which were not highlighted in the other samples collected (rearing cycle B) on the same rearing day. In the same way, one sample collected on the 2^nd^ day of rearing during the larval rearing cycle B was majorly composed by *Micrococcales* which were less abundant in the other samples of the rearing cycle A collected at the same time. *Chitinophagales* also had heterogeneous proportions in the larvae on D0 and D1 but their abundance increased in all the larvae samples on D2 and they were highlighted from D3 to D6. From D3 to the end of the experiment, the larvae samples had more homogeneous compositions even though compositions drastically changed between D3 and D9. Indeed, on D3 and D4, proportions of *Cytophagales* and *Caulobacterales* increased while they were less abundant from D6 to D9. In the same way, *Bdellovibrionales* peaked on the 5^th^ and the 6^th^ days of rearing. *Cellvibrionales* and *Alteromonadales* increased from D5 to the end of the experiment while *Flavobacteriales* decreased. Thus, as for the water samples, the composition of the larval microbiota drastically changed between D2 and D6 which corresponded to the time period where larvae were at zoea stage. As for the physicochemical parameters considered earlier, the microbial composition of the larvae and their rearing water seemed to be affected by the rearing day and the larval stage. Thus, we conducted LEfSe in order to further study the microbiota of the water and the larvae samples according to these 2 factors.
Chapitre V – Évolution des communautés procaryotiques associées à un élevage larvaire en
bonne santé

<figure><img src="image_89.png" /></figure>

<figure><img src="image_90.png" /></figure>

<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

<figure><img src="image_93.png" /></figure>
<caption>**Figure 4** – Clustering and global microbial composition of the water and the larvae samples.</caption>

Two larval rearing cycles were analyzed during the experiment and are indicated as A and B. (A) Hierarchical clustering based on a Bray-Curtis dissimilarity matrix and the Ward method groups the water, the larvae, the egg and the control (Ctrl) samples in 7 clusters (C1 to C7) when considering a threshold of 3 represented by the black dotted line. Each sample type is represented by a colored circle and rearing days are noted under the circles. (B) Ordination of the control (Ctrl) and the water samples based on the PCoA method and a Bray-Curtis dissimilarity matrix. (C) Ordination of the egg and the larvae samples based on the PCoA method and a Bray-Curtis dissimilarity matrix. In figures 4B and 4C, clusters are represented by black circles. (E) Histogram of the 11 bacterial orders with a relative abundance higher than 1% in the control and the water samples. (D) Histogram of the 12 bacterial orders with a total relative abundance higher than 1% in the egg and the larvae samples. In figures 4D and 4E, all orders with a relative abundance lower than 1% are summed in the "Other" category. In figures 4B to 4E, all rearing days are indicated by the letter D followed by the number of the rearing day (from 0 to 9).

## Enriched ASVs in the water and the larvae samples according to the rearing day and the larval stage

The first LEfSe was conducted to analyze the ASVs enriched in the rearing water samples according to their rearing day (Fig. 5A). We then analyzed the relative abundances of the highlighted ASVs in all the samples in order to observe if they were present since the beginning of the rearing process in the water and if they were also highlighted in the larvae and the food sources up to the considered rearing day (Fig. 5B). On the first day of rearing (D0), samples were dominated by 2 ASVs affiliated to the *Rhodobacteraceae* family and the *AEGEAN 169 marine group* (Fig. 5A). These ASVs were also found in the rearing reservoir with a relative abundance higher than 1% (Fig. 5B). The following day (D1), 4 ASVs were highlighted and were affiliated to the *Aestuariibacter*, *Alteromonas*, and *Cognatishimia* genera. On the second day of rearing (D2), 3 ASVs dominated the samples and were affiliated to the *Nautella* genus, the *Flavobacteriales* order and the *Rhodobactercaeae* family. On D3, 3 ASVs were highlighted and were affiliated to the *Cryomorphaceae* and the *Rhodobacteracea* families as well as the *Saprospirax* genus. A total of 5 ASVs dominated the water samples collected on D4. These ASVs were affiliated to 4 distinct genera: *Fabibacter*, *Aestuariicoccus*, *Owenweeksia* and *Lewinella*. On the fifth day (D5), the water samples showed 5 ASVs affiliated to *Ruegeria*, *Pseudoalteromonas* and *Nioella* as well as to the *Saprospiraceae* family. ASVs affiliated to the *OM27 clade* and the *Alcanivorax* genus dominated the rearing water samples collected on D6. The following day (D7), 2 ASVs were highlighted and were affiliated to the *Saprospiraceae* family and the *Marivita* genus. On D8, only 1 ASV belonging to the *Phaeodactylibacter* genus was highlighted. Finally, on the last rearing day (D9), ASVs affiliated to *Pseudoalteromonas*, *Litoricola* and *Leisingera* dominated. All the ASVs showed by the LEfSe were found in the water reservoirs, the larvae and the rearing water samples. Their relative abundance increased in these samples throughout the rearing days. All the ASVs had a relative abundance lower than 1% in the food samples except for ASV52 which was not found in the food and for ASVs 1 and 2 which had a relative abundance higher than 1% especially in F4 (Fig. 5B).

An LEfSe was also conducted considering the differentially abundant ASVs in the egg and the larvae samples according to their developmental stage (Fig. 6). As for the first LEfSe, we analyzed the relative abundances of these ASVs in all the samples in order to observe if
they were present since the beginning of the rearing process in the larvae and if they were also highlighted in the water and the food sources up to the considered rearing day. The egg samples were dominated by 2 ASVs which were affiliated to the *Rhodobacteraceae* family and the *Vibrio* genus (Fig. 6A). These 2 ASVs had a relative abundance higher than 0.1% in the hatching reservoirs (Fig. 6B). The nauplii (Nii) stage (from D0 to D1) was dominated by a *Saprospira* which was found in the hatching and the rearing reservoirs as well as in the eggs and the rearing waters on Days 0 and 1 (D0 and D1). The first zoea stage (Z1) on D2, was characterized by 5 ASVs affiliated to 4 distinct genera: *Aestuariibacter*, *Alteromonas*, *Saprospira* and *Thalassolituus*. All these ASVs were found in the water and the larvae samples prior to the second rearing day and their relative abundances increased constantly. Larvae on D3 were transitioning from stages zoea 1 (Z1) to zoea 2 (Z2) and were dominated by 2 ASVs; one was affiliated to the *Rhodobacteraceae* family while the other one was affiliated to the *Owenweeksia* genus. The Z2 stage (D4) was dominated by 3 ASVs affiliated to the *Fabibacter*, *Hyphomonas* and *Pseudoooceanicola* genera. The zoea 3 (Z3) stage on D5 was characterized by 2 ASVs which belonged to the *Thalassotalea* and *Leisingera* genera. On the sixth day of rearing, larvae were transitioning from stages zoea to mysis 1 (M1) and were dominated by 5 ASVs from the *OM27 Clade*, and the *Fluviicola*, *Thalassotalea* and *Halioblogus* genera, as well as the *Saprospiraceae* family. During the first mysis stages (M1, M2) on D7, 2 ASVs were highlighted and affiliated to *Phaeodactylibacter* and *Roseobacter*. Only 1 ASV was highlighted when considering the larvae at M2 and M3 stages on D8. It was affiliated to the *Oleiphilus* genus. Finally, when larvae were transitioning from mysis to post-larvae (PL) on the last day of rearing (D9), 4 ASVs were highlighted. Among these ASVs, 3 were affiliated to the *Pseudoalteromonas* genus and 1 to *Thalassotalea*. All the highlighted ASVs were found in the reservoir and the egg samples as well as in all the larvae and the water samples collected prior to the considered sampling time. Relative abundances in these samples increased throughout the rearing cycle. The ASVs were also found in the food samples but almost always had a relative abundance inferior to 0.1%. Thus, we highlighted microbial biomarkers up to the genus level, when possible, of the rearing day and the larval stage for the water and the larvae samples and all these microbial strains were present in the water reservoirs since the beginning of the experiment. We finally investigated if any correlation existed between these evidenced microbial strains and the considered physicochemical parameters.
<figure><img src="image_94.png" /><figcaption>Figure 5 – Differentially abundant ASVs in all the rearing water samples (larval rearing cycles A and B) according to their rearing day and relative abundance of these ASVs in all the samples.</figcaption></figure>

(A) Linear Discriminant Analysis (LDA) Effect Size (LEfSe) showing ASVs significantly more abundant in the rearing water samples according to their rearing day, from Day 0 (D0) to Day 9 (D9). For each ASV, the lowest phylogenetic level reached for affiliation is specified in brackets. (B) Relative abundance of the ASVs highlighted by the LEfSe in all the samples prior to the rearing day during which they had been identified. For all the samples, the mean relative abundance per sample type is represented. The water and the larvae samples are respectively designated by W and L. The relative abundances of the ASVs in the lagoon seawater, the decantation reservoir and the rearing reservoir are averaged in RR. The samples collected from the hatching reservoir for the larval rearing cycles A and B are averaged in HR.
<figure><img src="image_95.png" /><figcaption>**Figure 6** – Differentially abundant ASVs in all the egg and the larvae samples (larval rearing cycles A and B) according to their larval stage and relative abundance of these ASVs in all the samples.</figcaption></figure>

(A) Linear Discriminant Analysis (LDA) Effect Size (LEfSe) showing ASVs significantly more abundant in the egg and the larvae samples according to their larval stage: egg, nauplii (nii), zoea 1 (Z1), zoea 2 (Z2), zoea 3 (Z3), mysis 1 (M1), mysis 2 (M2), mysis 3 (M3) and post-larvae (PL). For each ASV, the lowest phylogenetic level reached for affiliation is specified in brackets. (B) Relative abundance of the ASVs highlighted by the LEfSe in all the samples prior to the larval stage in which they had been identified. For all the samples, the mean relative abundance per sample type is represented. The larvae and the water samples are respectively designated by L and W. The samples collected from the hatching reservoir for the larval rearing cycles A and B are averaged in HR. The relative abundances of the ASVs in the lagoon seawater, the decantation reservoir and the rearing reservoir are averaged in RR.

## Correlations between the microbial biomarkers and the physicochemical parameters

In the water samples, 19 out of the 30 ASVs highlighted by the LEfSe were significantly positively or negatively correlated with at least one environmental factor (Fig. 7A). Temperature was negatively correlated with members of the *Rhodobacteraceae* family and the *AEGEAN 169 marine group*, which were both enriched on D0, as well as with an ASV affiliated to *Litoricola* on D9. All the ASVs enriched in the water samples on D1 and D2 were negatively correlated with ammonium, phosphorus and CDOM concentrations (TAN, SRP and $a\lambda_{325nm}$). On the contrary, they were positively correlated with pH and SR. ASVs which were highlighted by the LEfSe from D7 to D9 showed an opposite profile as they were positively correlated with organic matter concentrations and negatively correlated with pH and SR;
exception made for ASV1 affiliated to *Pseudoalteromonas*. Upon 15 ASVs identified as microbial biomarkers between D3 and D6, only 5 were correlated with physicochemical parameters. Indeed, ASVs affiliated to the *Rhodobacteraceae* family and to the *Alcanivorax* genus were respectively highlighted on D3 and D6 and were both inversely correlated with CDOM concentration (aλ<sub>325nm</sub>) and pH. CDOM concentration was also correlated with a member of the *Cryomorphaceae* family on D3 while pH was correlated with a *Saprospira* on D3. Finally, ASV8 affiliated to *Ruegeria* and enriched on D5 was negatively correlated with ammonium concentration (TAN).

In the larvae samples, 25 ASVs were highlighted by the LEfSe analysis and 18 of them were correlated with the considered physicochemical parameters (Fig. 7B). As for the water samples, 3 ASVs highlighted by the LEfSe of the larvae samples were negatively correlated with the water temperature: an *Aestuariibacter* and an *Alteromonas* at zoea 1 stage as well as a *Thalassotalea* during the transition between zoea and mysis stages. ASVs highlighted at zoea 1 stage were negatively correlated with TAN, SRP and CDOM concentration (aλ<sub>325nm</sub>) but positively correlated with pH and SR. Opposite trends were highlighted for ASVs enriched during the transitioning phase between mysis 1 and 2 as well as between mysis 3 and post-larvae transformation. They showed the same correlation profile as ASVs 13 (*Leisingera*) and 26 (*Halioglobus*) respectively highlighted in the zoea 3 larvae and during the zoea / mysis transitioning phase. The 2 ASVs affiliated to the *Saprospira* genus and enriched at nauplius and zoea 1 stages were only positively correlated with pH. CDOM concentration, pH and SR were also correlated with 3 ASVs highlighted during the zoea stage. One ASV was affiliated to the *Rhodobacteraceae* family while the 2 others belonged to the *Fabibacter* and the *Leisingera* genera.
A

<figure><img src="image_96.png" /></figure>

B

<figure><img src="image_97.png" /></figure>

**Figure 7 – Pearson correlations between the microbial biomarkers and the physicochemical parameters of the rearing water.**

Correlations between the physicochemical parameters of the rearing water: temperature (Temp), total ammonia nitrogen (TAN) concentration, soluble reactive phosphorus (SRP) concentration, colored dissolved organic matter concentration (aλ<sub>325nm</sub>), slope ratio (SR) and pH values were considered and the microbial biomarkers highlighted by the LEfSe analyses in (A) the water samples and (B) the larvae samples. Rearing days are represented by the letter D followed by the considered rearing day (from 0 to 9) Larval developmental stages are designated as follows: nauplii (Nii), zoea 1 (Z1), zoea 2 (Z2), zoea 3 (Z3), mysis 1 (M1), mysis 2 (M2), mysis 3 (M3) and post-larvae (PL). Negative correlations are represented in blue while positive correlations are represented in red. Significant results are designated by an asterisk symbol: p-values comprised from 0.01 to 0.05 are represented by *, p-values comprised from 0.001 to 0.009 by ** and p-values below 0.0009 by ***.
## Discussion

### Temporal evolution of the physicochemical parameters in the rearing water

In order to evaluate the larval health status of the considered rearing cycles, the larval survival rate (LSR) and the larval stage indexes (LSI) were daily calculated (Fig. 2A and 2B) and compared to reference values (Fig. S1). These references were estimated by averaging LSR and LSI values for each rearing day using 10 years of successful rearing data (Pham, comm. pers.). During this experiment, despite varying LSR values due to day-to-day count discrepancies, LSR and LSI values in the rearing tanks mimicked the reference values meaning that all larvae survived and were able to transition from nauplii to post-larvae in 9 days, as expected. Thus, both larval rearing cycles were considered healthy. We then characterized the physicochemical parameters of the water in the rearing tanks throughout these two rearing cycles (Fig. 2C to 2H).

Even though temperature was intentionally increased from D0 to D2 in order to help larval transitioning from nauplius to zoea (Fig. 2C), a variation of 3°C could have influenced the microbial communities associated with the rearing water and the larvae. As a consequence, temperature was the first considered physicochemical parameter. As room temperature was not monitored and as water temperature was only controlled by heating resistances directly placed in the water, water temperature values fluctuated around 30°C. This also explains the temperature variations observed in the control tank. Ammonium (TAN) and phosphorus (SRP) concentrations progressively increased in the rearing water (Fig. 2D and 2E). Even though these nutrients are known to be essential to the development of most organisms, they can be harmful if present in excess. For example, ammonium and phosphorus pollution may disturb growth, metamorphosis and survival of several *Penaeid* shrimp species at post-larvae, juvenile and adult stages [38,39,40,41]. In this study, ammonium, phosphorus and organic matter accumulation suggested a eutrophication of the rearing water throughout the experiment. As no water renewal was performed during the whole rearing cycle, this eutrophication could be explained by the accumulation of uneaten food, excreted feces and larval molts as previously described during the larval rearing of *P. stylirostris* [18]. A shift of CDOM concentration ($a_{325nm}$), pH and SR values occurred between D4 and D5 as CDOM concentrations drastically increased (Fig. 2F) while pH and SR values decreased (Fig. 2G and 2H) during those rearing days. Overall, the environmental conditions drastically changed between the beginning and the end of the rearing process through a global eutrophication of the water and a major environmental shift occurred between D4 and D5, after antibiotic and *Artemia* sp. addition, when larvae were transitioning from zoea 2 to zoea 3 (Fig. 3). As eutrophication of the rearing environment can be linked to microbial dynamics in the water and in the host [42], we investigated if the microbial communities associated with the water and the larvae samples also exhibited major modifications.
<figure><img src="image_98.png" /><figcaption>**Supplementary figure 1 – Reference values for larval survival rate (LSR) and larval stage index (LSI).**</figcaption></figure>

A) Larval survival rate (LSR) and B) Larval stage index (LSI) values expected for each rearing day. Rearing days are represented on the horizontal axis by the letter D followed by the considered rearing day (from 0 to 9).

## Temporal evolution of the microbial communities in the rearing water and the larvae

Hierarchical clustering analysis showed that samples clustered according to their rearing day regardless of their rearing cycle (A or B). Therefore, the rearing tanks were considered to be suitable replicates. For all the constructed ordinations (Fig. 4A to 4C), samples collected from D0 to D2 grouped together as for samples collected from D3 to D5 and from D7 to D9. Samples collected on D6 were the only ones which could not be assigned to a unique cluster. Larvae in all the rearing tanks were at nauplius and zoea 1 stages from D0 to D2, at zoea 2 and zoea 3 stages from D3 to D5 and at mysis and post-larvae stages from D7 to D9. On D6, larvae were transitioning from zoea 3 to mysis stage. Thus, as for the physicochemical parameters previously analyzed, it appeared that the microbial composition of the water and the larvae samples changed according to the rearing day and thus, to the developmental stage of the larvae in the rearing tanks. Histograms allowed to highlight the major bacterial orders associated with the water and the larvae samples according to the rearing day and displayed major shifts in the microbial diversity (Fig. 4D and 4E).

All the considered water and larvae samples exhibited large proportions of *Rhodobacterales* and *Alteromonadales* throughout the experiment. *Rhodobacterales* are oligotrophic and metabolically diverse microorganisms colonizing various ecological niches within the marine environment [43,44,45]. As a result, they can represent up to 30% of the total marine bacterial communities in surface seawaters where they are essential to biogeochemical cycling [45]. Like *Rhodobacterales*, *Alteromonadales* are able to produce many different metabolites allowing them to adapt to various ecological niches and are, thus, found in very diverse ecosystems ranging from soil to seawater [46]. *Rhodobacterales* and *Alteromonadales* members have been identified in the rearing water and the post-larvae of
the Pacific white shrimp *P. vannamei* [47,48] as well as in the intestinal microbiota of adult Chinese shrimps *Fenneropenaeus chinensis* [49,50] and in the rearing water of *P. stylirostris* larvae [18]. Recently, studies focusing on the larval microbiota of *P. vannamei* and *P. stylirostris* have also highlighted microorganisms affiliated to these 2 bacterial orders [9,10,51,52,53]. Taken together, our results reinforce the hypothesis that *Rhodobacterales* and *Alteromonadales* members, originating from the rearing environment, partially form the core microbiota of several shrimp species where they play essential roles in the physiology of their hosts during the whole life cycle.

On D0, when the rearing tanks were filled, the water samples were partly composed by *Flavobacteriales*, *Oceanospirillales*, and *SAR11* members. This was not surprising as they are very common in the marine environments where they play key roles in organic matter transformation and nutrient turnover [54,55,56]. *Rhodospirillales* were also highlighted in the water samples on D0. This bacterial order has previously been isolated in the stomach of healthy adult shrimps, which had been challenged with *Vibrio parahaemolyticus*, a bacteria known to induce the acute hepatopancreatic necrotic disease [57]. Due to their antagonist activity with various *Vibrio* lineages, *Rhodospirillales* were suggested as potential probiotic strains and the authors even suggested that they could have a protective role in shrimps under rearing conditions [57]. Furthermore, *Rhodospirillales* members have previously been identified in the rearing water of *P. stylirsotris* larvae at nauplius stage and before larval mass mortalities, which could confirm their protective role during the early larval stages of the rearing process [18]. On the next rearing day (D1), proportions of *Alteromonadales* largely grew in the water samples while the other bacterial orders decreased. This was consistent with the α-diversity indexes which showed that evenness of the microbial communities in the water decreased between D0 and D1 while richness remained constant (Fig. S2). Indeed, during that rearing period, Shannon and Inverse Simpson indices dropped while Chao1 and ACE indices remained stable. *Alteromonadales* rapidly decreased in the rearing water from D2 to D5 while *Chitinophagales* abundances increased from D4 to D9. *Chitinophagales* belong to the *Bacteroidetes* phyla and are known to produce various exogeneous enzymes allowing them to degrade complex nutrient sources such as carbon polymers [58]. As suggested by their name, they are involved in the biodegradation of chitin, a polysaccharide composing the exosqueletton of crustaceans and therefore of shrimps [59]. To our knowledge, studies focusing on the microbiota associated with various shrimp species and their rearing environment have highlighted large abundances of *Bacteroidetes* at all developmental stages but never such high proportions of *Chitinophagales* [47,60]. As our study is the first to consider the active microbiota associated with healthy larvae of *P. stylirostris* and their surrounding environment, these contrasting *Chitinophagales* abundances could be explained by differences in studied shrimp species (different ontogenesis for example) and rearing environments (different antibiotics used or different water treatment and renewal protocols for example). Furthermore, dissimilarities in the choice of DNA (total microbial communities) or RNA (active microbial communities) extraction as well as extraction technique, sequencing method and downstream analyses could also explain the observed differences [61]. In any case, a shift between *Alteromonadales* and *Chitinophagles* communities occurred between D3 and D5 during the zoea stage in the water samples, at the same rearing time as the previously observed physicochemical shifts. Beyond larval transitioning, antibiotic as well as *Artemia* sp. larvae were added during this rearing period and could explain the observed modifications of the microbiota associated with the rearing water. However, antibiotic addition was shown to majorly impact the active rare biosphere of the surrounding water of
P. stylirostris at the beginning of the larval rearing [18] potentially rolling out the influence of oxytetracycline (OTC) addition on the observed microbial changes in this study. Furthermore, microbial composition of Artemia sp. larvae was dominated by Vibrionales members and by an unclassified Bacteria (ASV86) while Chitinophagales only accounted for 0.2% of the total relative abundance (Fig. S3). Thus, microbial profiles were very different between this feeding source and the rearing water. Even though, these observations tend to also rule out the influence of Artemia sp. larvae addition on the microbiota of the rearing water, it is important to notice that food samples were collected before addition to the tanks. As a consequence, active microbial communities associated with food sources could have increased when in contact with the rearing water or some dormant microorganisms may have been activated after addition to the tanks. Finally, larval transitioning may have led to the observed increase of Chitinophagales. Indeed, larval molting occur during all transitioning phases and could have resulted in a progressive increase of chitin in the rearing water [53,62]. Chitin is a high molecular weight linear assembly of N-acetylglucosamine residues [63]. Thus, chitin accumulation could explain the organic matter concentration increase showed by the high CDOM concentration ($a_{\lambda_{325nm}}$) values observed on D5 (Fig. 2F). Furthermore, as high SR values involve low molecular weight of the dissolved organic matter, the low SR values observed during our experiment (Fig. 2G) could also be explained by the increase of high molecular weight chitin [25,26]. This hypothesis is also supported by the increase of Cytophagales occurring at the same time. Indeed, Cytophagales also belong to the Bacteroidetes phyla and are known to degrade biomacromolecules explaining why they are very abundant in habitats rich in organic material [64]. Thus, an increase of chitin due to larval molting could also explain the increase of Cytophagales proportions. When Chitinophagales and Cytophagales increased in the rearing water samples, large proportions of Bdellovibrionales were also highlighted. Microorganisms affiliated to this bacterial order are Bdellovibrio and like organisms (BALOs) with an obligatory predatory lifestyle against Gram-negative bacteria [65,66]. They are considered as living antibiotics and water clean-up as well as biocontrol agents and are currently studied for their potential use as probiotics in aquatic, poultry and farm animals. For example, in shrimp farming, BALOs have been suggested as potential biocontrol agents to prevent Vibrio infections [66]. During shrimp larval rearing, Bdellovibrionales may regulate some bacterial lineages in order to preserve microbial balance.

In the egg samples, prior to the beginning of the larval rearing, members of the Flavobacteriales, Oceanospirillales and Vibrionales orders were highlighted. This was expected as members of these bacterial orders have previously been identified in the eggs, the nauplii and the breeders of P. stylirostris where they formed the core microbiota of all the considered samples [10]. Even though their abundance varied throughout the experiment, these bacterial orders were maintained in the larval microbiota during the whole rearing process. As for Alteromonadales and Rhodobacterales, our results suggest that Flavobacteriales, Oceanospirillales and Vibrionales members could form the core microbiota of P. stylirostris larvae. Furthermore, during the first 2 days of rearing, when larvae were at nauplius stage, they showed heterogeneous microbial compositions. Symbiotic microorganisms acquired from the breeders have been suggested to modulate the microbiota of several larval marine species and such a vertical transmission has been hypothesized to influence the establishment of the microbiota of P. stylirostris larvae [9,10,67]. Thus, the microbial variations observed between the larval samples at nauplius stage (D0 and D1) could be explained by this influence. However, at zoea stage (from D2 to D5), microbial compositions became more homogeneous. This was also shown by Wang et al. (2020) [53]
who found that microbial compositions of *P. vannamei* larvae at nauplius stage were more heterogeneous than at zoea and mysis stage. The authors explained that, after the mouth opening occurring at zoea 1 stage, the larvae could have selected particular microbial communities with specific functions in order to help digestion through their immature digestive tract. Furthermore, diet and environmental microbiota have been shown to influence the gut microbiota associated with fish [4,6,8]. Therefore, the observed homogenization of the larval microbial communities at zoea stage could be explained by a growing influence of the rearing environment on the larval microbiota. As for the water samples, high proportions of *Cytophagales* and *Bdellovibrionales* were associated with the larvae at zoea stage (from D2 to D5). Abundances of *Caulobacterales* also increased during that rearing period. Members of this bacterial order have previously been identified as enriched in healthy *P. vannamei* and *P. stylirostris* larvae [10,51]. After the zoea stage, during the mysis stage (from D6 to D8), proportions of *Cellvibrionales* largely increased in the larvae samples. *Cellvibrionales* members have previously been highlighted in the gut microbiota of healthy *P. vannamei* adults which had been fed with probiotic *Bacillus* strains and challenged with *Vibrio* infection [68]. Therefore, *Caulobacterales* and *Cellvibrionales* may also be important for maintaining larval health during shrimp rearing. Interestingly, *Cellvibrionales* abundances greatly increased in the control tank suggesting that they could proliferate under non-rearing conditions and that observed variations in the rearing tanks were due to rearing conditions and not to a natural evolution of the seawater. Thus, microbial communities in the water and the larvae samples changed according to the rearing day and the larval stage and several potential probiotic sources were highlighted throughout the experiment. In order to further investigate these microbial communities, we conducted LEfSe to highlight specific microbial biomarkers.
<figure><img src="image_99.png" /><figcaption>**Supplementary figure 2 - α-diversity indices in all the samples.**</figcaption></figure>

(A) Chao1, (B) ACE, (C) Shannon and (D) Inverse Simpson (InvSimpson) indexes in all the samples. LS stands for lagoon seawater, DR for decantation reservoir, RR for rearing reservoir, Ctrl for control and HR for hatching reservoir. All the water samples are designated by a W while all the larvae samples are designated by an L. The rearing days are represented by the letter D followed by the considered rearing day (from 0 to 9).

<figure><img src="image_100.png" /><figcaption>**Supplementary figure 3 - Microbial composition of the Artemia larvae.**</figcaption></figure>

Top 5 bacterial orders (when possible) with a relative abundance higher than 1% associated with the Artemia sp. larvae.
## Identifying microbial biomarkers in the water and the larvae samples

The predominance of *Rhodobacterales* in the water samples was confirmed by the LEfSe as 10 ASVs affiliated to this bacterial order were identified (Fig. 5A). Indeed, on D1 and D4, *Cognatishimia* and *Aestuariicoccus* were enriched in the rearing water (Fig. 5A). They are both aerobic marine bacteria therefore it was not surprising to identify them in the rearing environment of our experiment [69,70]. In the same way, *Marivita*, which were evidenced on D7 (Fig. 5A), were previously highlighted as associated with adult and larvae of *P. vannamei* as well as with their rearing environment [27,52,71]. More interestingly, *Nautella*, which were highlighted on D2 in the water samples (Fig. 5A), have been found associated with diseased *P. vannamei* larvae and were suggested as potential biomarkers of shrimp health status [51,72]. However, they have also been identified in healthy shrimp larvae at zoea and mysis stages as well as in the intestinal microbiota of healthy adult organisms [52,57]. In this study, we have also highlighted the *Nautella* genus in the rearing water of healthy shrimp larvae (Fig. 5A). Thus, under the considered rearing conditions, presence of these microorganisms in shrimp rearing may not necessarily be a sign of upcoming mortalities. However, they may be opportunistic pathogens if the health status of the shrimp deteriorates. In the same way, *Nioella* which were enriched on D5 in the rearing water during this experiment (Fig. 5A), have been identified in *P. vannamei* affected by the white feces syndrome and were suggested to be part of a polymicrobial pathogens infection [73]. As for *Nautella*, *Nioella* may be opportunistic pathogens which could lead to a microbial dysbiosis under specific conditions [12]. On the contrary, *Ruegeria* and *Leisingera*, which were respectively predominant in the water samples on D5 and D9 (Fig. 5A) as well as in the larvae samples on D5 (*Leisingera*) (Fig. 6A), have been reported as potential probiotics for shrimp and fish larval rearing [67,68] as they both exhibit antagonist activity against members of the *Vibrio* genus [74,75]. In this study, *Ruegeria* and *Leisingera* were identified in healthy *P. stylirostris* larvae and in their rearing environment at different rearing periods suggesting that they could play a defensive role in the surrounding water and in the larvae by limiting proliferation of opportunistic pathogens or r-strategist microorganisms at key moments during the rearing process. Other bacterial strains considered to be beneficial microorganisms were highlighted during the rearing process in the water samples. Indeed, members of the *AEGEAN 169 marine group*, belonging to the previously mentioned *Rhodospirillales* order, were enriched on D0 (Fig. 5A). This marine group has already been proposed to form the core microbiota of the eggs and the nauplii of *P. stylirostris* [9]. We have previously suggested that *Rhodospirillales* could be a potential source of probiotic strains in the rearing water and further on suggest that members of the *AEGEAN 169 marine group* could be beneficial to shrimp larval rearing. However, this group is very diverse and further metagenomic studies will be necessary to identify precise microbial genera [76,77]. *Flavobacteriales* and *Oceanospirillales* were also abundant on D0 in the water samples (Fig. 4E) and were maintained until the end of the experiment as *Owenweeksia*, *Alcanivorax* and *Litoricola* were highlighted by the LEFSe on D4, D6 and D9 (Fig. 5A). These genera have previously been identified during shrimp and salmon rearing so it was not surprising to find them in the rearing water of shrimp larvae [71,78]. On D1, proportions of *Alteromonadales* were greatly increased in the water samples as shown by the LEFSe which highlighted *Aestrauriibacter* and *Alteromonas* as predominant during that rearing time (Fig. 5A). *Aestuariibacter* and *Alteromonas* have been suggested to be part of the core microbiota associated with the eggs, the nauplii and the rearing water of *P. stylirostris* and this seems to be confirmed by this study [9]. Other *Alteromonadales* members were highlighted on D5 and D9 as *Pseudoalteromonas* were identified by the LEFSe on those rearing days (Fig. 5A).
Interestingly, *Pseudoalteromonas* are able to produce antibacterial compounds and have been identified as potential probiotic lineages for larval rearing of *P. stylirostris*. The NC201 strain is currently tested at the Saint Vincent experimental hatchery (Boulouparis, New Caledonia) [20, 79, 80]. Interestingly, *Pseudoalteromonas* members have also been identified in the surrounding environment of *P. stylirostris* larvae subjected to high mortalities and were hypothesized to restrain microbial dysbiosis of the rearing water in order to prevent the development of opportunistic pathogens [18]. Finding this genus in the rearing water of healthy larvae of the same species strengthens the potential probiotic role of *Pseudoalteromonas* members. During zoea stage, *Alteromonadales* decreased while *Chitinophagales* increased in the water samples (Fig. 4E). This was also highlighted by the LEfSe as *Saprospira*, *Lewinella* and *Phaeodactylibacter* were enriched from D3 to D8 (Fig. 5A). *Saprospira* were found in the eggs of the vent shrimp *Rimicaris exoculata* [81] while *Lewinella* have been identified in the water and the intestines of *P. vannamei* adults [47]. *Phaeodactylibacter* were highlighted during the larval rearing of *P. vannamei* where they originated from algal feed [71]. During our experiment, no algae were fed to the larvae. However, *Phaeodactylibacter* were abundant in the fourth type of microparticles (F4) given on D7 (Fig. 5B). As water samples were daily collected before the first feeding of the day, this could explain why *Phaeodactylibacter* were enriched in the rearing water on the following day (D8). Nevertheless, *Saprospira*, *Lewinella* and *Phaeodactylibacter* belong to the *Chitinophagales* order and are, as previously discussed, capable of degrading the chitin composing the cuticle of the larvae. These bacterial strains could limit chitin accumulation in the rearing water occurring during the larval transformation. The LEfSe also highlighted *Fabibacter*, affiliated to the *Cytophagales* order, in the rearing water on D4, during the zoea stage (Fig. 5A). *Fabibacter* are known to degrade complex organic molecules and have also been found associated with the cuticle of crustaceans [64] which could confirm our previous hypothesis that abundance of *Cytophagales* members increased due to larval molting and chitin release in the rearing water during zoea stage. As for *Saprospira*, *Lewinella* and *Phaeodactylibacter*, *Fabibacter* may also restrict chitin concentrations in the larval environment at zoea stage. At the same time, *OM27 clade* members, which are *Bdellovibrio* and like organisms (BALOs), were enriched in the water samples on D5 (Fig. 5A). We have previously suggested that *Bdellovibrioales* could be a potential source of beneficial microbial strains for shrimp larval rearing and further on suggest that members of the *OM27 clade* could be of interest. However, as for the *AEGEAN 169 marine group*, the *OM27 clade* is very diverse and further analysis will be necessary to identify more accurate taxa [82].

In the egg samples, the LEfSe highlighted an ASV affiliated to the *Rhodobacteraceae* family and to the *Vibrio* genus (Fig. 6A) which is consistent with what has been previously highlighted [9,10]. The *Rhodobacteraceae* family was suggested as potentially essential to shrimp larval rearing and as a source of potential probiotic taxa [53] which strengthens the theory that they form the core microbiota of healthy shrimp larvae. Throughout the whole rearing process, the abundance of *Rhodobacterales* stayed relatively high in the larvae samples which also displayed large proportions of *Alteromonadales*. This was illustrated by the LEfSe focusing on the enriched microbial strains in the different larval stages (Fig. 6A). Indeed, ASVs affiliated to the *Rhodobacterales* order were highlighted at Z2, Z3 and M1-M2 stages (D4, D5 and D7): *Pseudooceanolicola*, *Leisingera* and *Roseobacter*. To our knowledge, this study is the first to identify *Pseudooceanolicola* in larvae of a marine organism and further studies will be necessary to understand their role in *P. stylirostris* larvae. On the other hand, as previously mentioned, *Leisingera* have been considered as potential probiotics because of
their ability to produce indigoidine, a compound demonstrating antimicrobial activity against Vibrio [75]. In the same way, *Roseobacter* have also been hypothesized to be probiotic microorganisms in marine animals as they produce a diverse range of secondary metabolites [74,83]. The larvae LEfSe also highlighted various genera affiliated to the *Alteromonadales* in the larvae samples. Indeed, *Aestuariibacter*, *Alteromonas* and *Thalassotalea* were highlighted at Z1, Z3, M1 and M3-PL stages (D2, D5, D6 and D9) (Fig. 6A). They have been previously identified as members of the core microbiota of the eggs and the nauplii of *P. stylirsotris* [9]. In the same way, *Pseudoalteromonas*, which were enriched in the larvae samples during the mysis / post-larvae transitioning phase (D9) (Fig. 6A), were found in the rearing water, the eggs, the nauplii and the breeders of *P. stylirsotris* [10]. Thus, our experiment strengthens the hypothesis that *Aestuariibacter*, *Alteromonas*, *Thalassotalea* and *Pseudoalteromonas* belong to the core microbiota of shrimp larvae. From D3 to D6, *Cytophagales*, *Caulobacterales* and *Bdellovibrionales* peeked in the larval microbiota (Fig. 4F). This was supported by the LEfSe which highlighted *Fabibacter* and *Hyphomonas* on D4 (Z2 stage) as well as *OM27 clade* on D6 (Z3-M1 stages) (Fig. 6A). We have already discussed the implication of *Fabibater* and *OM27 clade* in shrimp larval rearing water. The *Hyphomonadaceae* family has been identified in shrimp larvae and more specifically in the nauplii of the Pacific blue shrimp where they were suggested to originate from the rare or inactive microbiota of the eggs and the rearing water [10]. As low abundances of *Hyphomonas* were also identified in the eggs and in the reservoir of the hatching tanks (Fig. 6B), our results reinforce this hypothesis. Interestingly all microbial taxa highlighted by the LEfSe in the water and the larvae samples were found in the reservoir waters of the hatching and the rearing tanks as well as in the eggs, as shown by the constructed scatterplots (Fig. 5B and 6B). The abundance of all taxa increased throughout the rearing days and the larval stages until reaching their maximum. This indicated that microorganisms present since the beginning of the experiment found more and more favorable conditions to their development throughout the rearing process. Furthermore, as microbial profiles were very different between the beginning and the end of the experiment (Fig. 4) in the rearing water and the larvae samples, it seemed that the rearing day and the larval stage influenced the microbiota. Thus, we highlighted microbial biomarkers of each rearing day in the water samples and of each larval developmental stage in the larvae samples (Fig. 5A and 6A). Interestingly, several bacterial lineages identified as potential probiotics were highlighted during the experiment (Fig. 5A and 6A) and their abundance varied greatly throughout the larval rearing (Fig. 5B and 6B). However, all the identified potential probiotics were found in the reservoir waters of the hatching and the rearing tanks suggesting that all these favorable microorganisms were present since the beginning of the rearing and originated from the natural lagoon seawater (Fig. 5B and 6B). As a consequence, we suggest that the classic use of probiotics in New-Caledonian hatcheries [20] (i.e., a unique bacterial strain regularly inoculated throughout the rearing process) should be adapted by using a mix of various probiotic stains or by managing bacterial communities naturally present in the rearing environment. This could also be applied to other aquaculture facilities using only one bacterial strain as a probiotic source [84,85] and could lead to a more sustainable world aquaculture. Indeed, our results suggest that several potential beneficial microbial strains with different functions are involved throughout the rearing at key moments of the larval development. Further analyses will be necessary to confirm the beneficial role of the highlighted microbial biomarkers, to better understand the cellular and molecular mechanisms involved and to establish better microbial management during shrimp larval rearing. In this context, we analyzed the correlations existing between the highlighted
microbial biomarkers and the considered physicochemical parameters to understand how the microbial communities in the water and the larvae samples could be influenced.

## Correlations between environmental parameters and microbial communities

More than half of the ASVs highlighted by the LEfSe of the rearing water samples were correlated with at least one parameter related to organic matter concentration (Fig. 7A). This showed that the progressive eutrophication of the surrounding environment could influence the microbiota of the rearing water. This was expected as eutrophication was proved to alter bacterioplankton assemblies in the culture ponds of adult *P. vannamei* [86] and in the rearing water of *P. vannamei* larvae [71]. More generally, with growing impacts of human activities, such as agricultural activities and pollutant discharge, on fresh and seawater ecosystems, several studies have highlighted the influence of nitrogen and phosphorus enrichments on microbial communities [87,88]. Eutrophication has also been hypothesized to increase bacterial growth and biomass, leading to an enhanced consumption of dissolved oxygen and, furthermore, to an enlarged CO₂ concentration in the water causing the pH to decrease [89]. During our experiment, we indeed observed a decline of pH values. However, as intensive bubbling of the rearing water was implemented to maintain oxygen levels, the observed pH decrease was limited. Nevertheless, as pH variations and eutrophication are linked, it was not surprising that the ASVs positively correlated with organic matter concentrations were also negatively correlated with pH and *vice versa*. As for organic matter concentrations and pH, temperature is also known to strongly influence microbial compositions in all ecosystems [90]. Interestingly, a study focusing on the microbiota associated with the intestines of adult *P. vannamei* as well as with their rearing water and sediments showed that pH, temperature and total nitrogen most influenced the microbial communities of all the considered samples [91]. The authors showed that pH was the environmental factor most influencing the microorganisms in the water while temperature had the biggest impacts on the microbial communities in the shrimps and in the sediment, suggesting that environmental factors not only impacted the microorganisms associated to the water but also the microbiota of the animals. This was also highlighted by in our findings as several ASVs enriched in the larvae samples were correlated with the considered physicochemical parameters (Fig. 7B). More specifically, larvae associated ASVs were strongly correlated with eutrophication parameters. In natural freshwater ecosystems, eutrophication of the water due to anthropogenic activities was proved to affect the microbial diversity of the skin microbiota of the wild gudgeon *Gobio occitanie* therefore influencing the epibiota of the animals [92]. Furthermore, the gastrointestinal microbiota of several fish species is known to be influenced by several environmental factors [4]. These results suggest that the epibiota and the endobiota of *P. stylirostris* larvae could be influenced by the changing environmental conditions throughout the rearing process.

Surprisingly, during zoea stage, less correlations were highlighted between the physicochemical parameters and the microbial communities highlighted in the water and the larvae samples suggesting that other unconsidered parameters might have come into play. In *P. vannamei* larvae, differential microbial lineage distributions were evidenced between nauplii and zoea stages as larval microbial colonization shifted from dispersal to host selection processes after the mouth opening [53]. More generally, a meta-analysis conducted on several freshwater and marine shrimp species showed that environmental factors influenced the microbiota of the animals but that the developmental stage was also an important factor shaping these microbial communities [60]. Furthermore, many factors are involved when
considering the evolution of host microbiota. Indeed, many biotic and abiotic factors, such as rearing conditions, diet, vertical transmission and antibiotic addition, are known to influence symbiotic microbial communities [4,60]. As a consequence, it is currently recognized that microbial community assemblies in aquaculture systems result from deterministic and stochastic processes [93,94]. Indeed, many deterministic processes, also called non-random and niche-based mechanisms, such as species traits, interspecies interactions (competition, predation, mutualisms...) and environmental conditions are known to influence microorganisms in the environment [95]. However, many studies focusing on the microbiota of marine larvae and their environment have found that other mechanisms could be involved and that deterministic processes were not sufficient to explain the observed microbial patterns [71,96,97]. Therefore, stochastic processes have been suggested to also be involved in the assembly of microbial communities. Assuming that all species are ecologically functionally equivalent, these ecological mechanisms, ranging from probabilistic dispersal, random speciation or extinction to ecological drift, are considered to generate microbial diversity patterns identical to those randomly generated [95]. It is important to note that some environmental factors may not have been considered during our experiment explaining why few correlations were evidenced between the microbial communities and the physicochemical parameters during the zoea stage in the water and the larvae samples. However, it is undeniable that stochastic processes may also be involved. In zebrafish, deterministic processes influence the establishment of the microbiota while stochastic processes further shape the commensal gut microorganisms during the development [98]. Thus, the same alternation between stochastic and deterministic processes may also explain the microbial assembly of the Pacific blue shrimp larvae and their rearing water.

## Conclusion

In conclusion, we were able to characterize the microbiota associated with healthy *P. stylirostris* larvae and their rearing environment. We described the temporal evolutions of the physicochemical parameters of the rearing water as well as the microbial communities associated with the larvae and their surrounding environment (Fig. 8). We show a global eutrophication of the surrounding rearing water implying drastic environmental changes between the beginning and the end of the rearing process with important shifts occurring during the zoea stage. Microbial communities associated with the water and the larvae also strongly changed throughout the experiment according to the rearing day and the larval developmental stage. However, our results confirm that members of the *Alteromonadales*, *Rhodobacterales*, *Flavobacteriales*, *Oceanospirillales* and *Vibrionales* bacterial orders could belong to the core microbiota of shrimp larvae. As these microbial lineages were also found in the rearing water throughout the experiment, we hypothesize that these essential microorganisms may be acquired by the larvae from the rearing environment. Furthermore, several microbial variations were also observed and we identified, in the water and the larvae samples, several potential probiotic microbial taxa affiliated to the *AEGEAN 169 marine group* and the *OM27 clade* as well as to *Ruegeria*, *Leisingera*, *Pseudoalteromonas* and *Roseobacter*. As the abundance of these microorganisms varied throughout the experiment, we suggest that several beneficial microbial strains could be involved during shrimp larval rearing, each playing a key role at a very specific time leading to rethink probiotic use in aquaculture. Finally, we evidenced several correlations between the most abundant ASVs and the physicochemical parameters of the rearing water which could confirm that deterministic
# Chapitre V – Évolution des communautés procaryotiques associées à un élevage larvaire en bonne santé

processes are involved in the assembly of microbial communities during the larval rearing of *P. stylirostris*. However, as few correlations were evidenced during the zoea stage, stochastic mechanisms may also come into play. Overall, important microbial modifications occurred in the water and in the larvae during this larval stage suggesting that the zoea phase is crucial during shrimp larval development.

<table><caption>**Figure 8 – Enriched microbial lineages and their correlation with environmental factors.**</caption><thead><tr><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>ASV15<br>T°C</td><td>Nautella, ASV39,<br>ASV31<br>TAN, SRP, aλ<sub>325nm</sub><br>pH, SR</td><td>Alcanivorax<br>aλ<sub>325nm</sub>, pH</td><td>ASV5, Marivita,<br>Phaeodactylibacter,<br><strong>Leisingera</strong><br>TAN, SRP, aλ<sub>325nm</sub><br>pH, SR</td></tr><tr><td><strong>AEGEAN 169</strong><br><strong>marine group</strong><br>T°C, SRP, aλ<sub>325nm</sub></td><td>ASV33<br>aλ<sub>325nm</sub></td><td></td><td>Litoricola<br>TAN, SRP<br>T°C</td></tr><tr><td>Aestuariibacter,<br>Alteromonas,<br>Cognatishimia<br>TAN, SRP, aλ<sub>325nm</sub><br>pH, SR</td><td>Saprospira<br>pH<br><br>ASV25<br>aλ<sub>325nm</sub>, pH<br><br><strong>Ruegeria</strong><br>TAN</td><td><strong>OM27 Clade</strong></td><td><strong>Pseudoalteromonas</strong></td></tr><tr><td></td><td>Fabibacter,<br>Aestuariicoccus,<br>Owenweeksia,<br>Lewinella,<br>ASV32, Nioella,<br><strong>Pseudoalteromonas</strong></td><td></td><td></td></tr><tr><td>D0 to D1<br>Nauplii</td><td>D2 to D5<br>Zoea</td><td>D6<br>Zoea - Mysis</td><td>D7 to D9<br>Mysis</td></tr><tr><td></td><td>Owenweeksia,<br>Hyphomonas,<br>Pseudooceanicola,<br>Thalassotalea</td><td><strong>OM27 Clade</strong,<br>Fluviicola, ASV24</td><td></td></tr><tr><td></td><td><strong>Leisingera</strong><br>SRP, aλ<sub>325nm</sub>, pH, SR</td><td></td><td>Oleiphilus<br>SRP</td></tr><tr><td></td><td>Fabibacter, ASV25<br>aλ<sub>325nm</sub>, pH, SR</td><td></td><td>Phaeodactylibacter<br>aλ<sub>325nm</sub>, pH, SR</td></tr><tr><td></td><td>Saprospira<br>pH</td><td></td><td>Roseobacter,<br><strong>Pseudoalteromonas</strong>,<br>Thalassotalea<br>TAN, SRP, aλ<sub>325nm</sub><br>pH, SR</td></tr><tr><td></td><td>Thalassolituus<br>TAN, SRP, aλ<sub>325nm</sub><br>pH, SR</td><td>Halioglobus<br>TAN, SRP, aλ<sub>325nm</sub><br>pH, SR</td><td></td></tr><tr><td></td><td>Aestuariibacter,<br>Alteromonas<br>T°C, TAN, SRP, aλ<sub>325nm</sub><br>pH, SR</td><td>Thalassotalea<br>T°C</td><td></td></tr><tr><td>Saprospira<br>pH</td><td></td><td></td><td></td></tr></tbody></table>

Enriched microbial lineages (up to the genus when possible) evidenced by the LEfSe in the rearing water samples (in the upper blue frame) and in the larvae samples (in the upper orange frame). Environmental factors significantly correlated with these microbial lineages are noted in blue (negative correlations) or in red (positive correlations). Temperature (T°C), Total Ammonium Nitrogen (TAN), Soluble Reactive Phosphorus (SRP), Colored Dissolved Organic Matter (CDOM, aλ<sub>325nm</sub>), pH and Slope Ratios (SR) were considered. Microbial lineages noted in grey boxes were not correlated with the considered environmental factors. Taxa in bold represent potential probiotic strains.
## Declarations

### Ethics approval and consent to participate

Not applicable

### Consent for publication

Not applicable

### Availability of data and materials

The datasets generated and analyzed during the current study are available in the NCBI SRA repository (BioProject PRJNA736535, SPR21099695 to SPR21099837).

### Competing interests

The authors declare that they have no competing interests.

### Funding

This work was supported by the RESSAC project (LEAD-NC, Ifremer New-Caledonia) within the framework agreement with the New Caledonian Provinces and Government and by the Pacific Doctoral School of the University of New-Caledonia.

### Acknowledgements

The authors would like to thank Florence Antypas and Dominique Ansquer for their help with material preparation and for their laboratory support during chemical and biological experiments. We also thank Jean-Sébastien Lam and Jean-René Mailliez for their help with zootechnical material preparation. We further on express our gratitude to Séverine Duval for her support and for coordinating this experiment. Finally, we greatly thank all the teams working in the new-caledonian private hatcheries for welcoming us and helping us during our experiments.

## References

1. Whiteside SA, Razvi H, Dave S, Reid G, Burton JP. The microbiome of the urinary tract—a role beyond infection. Nat Rev Urol. 2015; doi:10.1038/nrurol.2014.361

2. Ikeda-Ohtsubo W, Brugman S, Warden CH, Rebel JMJ, Folkerts G, Pieterse CMJ. How Can We Define “Optimal Microbiota?”: A Comparative Review of Structure and Functions of Microbiota of Animals, Fish, and Plants in Agriculture. Front Nutr. 2018; doi:10.3389/fnut.2018.00090

3. Dhanjal DS, Dhole S, Singh R, Chopra C. Microorganisms-diversity analysis and approaches. Plant Arch. 2021; doi:21:576-582

4. Nayak SK. Role of gastrointestinal microbiota in fish. Aquac Res. 2010; doi:10.1111/j.1365-2109.2010.02546.x

5. Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH, Chinwalla AT, et al. Structure, function and diversity of the healthy human microbiome. Nature. 2012; doi:10.1038/nature11234

6. Ursell LK, Metcalf JL, Parfrey LW, Knight R. Defining the human microbiome. Nutr Rev. 2012; doi:10.1111/j.1753-4887.2012.00493.x

7. Moloney RD, Desbonnet L, Clarke G, Dinan TG, Cryan JF. The microbiome: Stress, health and disease. Mamm Genome. 2014; doi:10.1007/s00335-013-9488-5

8. Sun F, Xu Z. Significant Differences in Intestinal Microbial Communities in Aquatic Animals from an Aquaculture Area. J Mar Sci Eng. 2021; doi:10.3390/jmse9020104

9. Giraud C, Callac N, Beauvais M, Mailliez J, Ansquer D, Selmaoui-Folcher N, et al. Potential lineage transmission within the active microbiota of the eggs and the nauplii of the shrimp Litopenaeus stylirostris: possible influence of the rearing water and more. PeerJ. 2021; doi:10.7717/peerj/12241

10. Giraud C, Callac N, Boulo V, Lam J-S, Pham D, Selmaoui-Folcher N, et al. The Active Microbiota of the Eggs and the Nauplii of the Pacific Blue Shrimp Litopenaeus stylirostris Partially Shaped by a Potential Vertical Transmission. Front Microbiol. 2022; doi:10.3389/fmicb.2022.886752

11. Wippler J, Kleiner M, Lott C, Gruhl A, Abraham PE, Giannone RJ, Young JC, Hettich RL, Dubilier N. Transcriptomic and proteomic insights into innate immunity and adaptations to a symbiotic lifestyle in the gutless marine worm Olavius algarvensis. BMC Genomics. 2016; doi:10.1186/s12864-016-3293-y

12. Petersen C, Round JL. Defining dysbiosis and its influence on host immunity and disease. Cell Microbiol.
# Chapitre V – Évolution des communautés procaryotiques associées à un élevage larvaire en bonne santé

2014; doi: 10.1111/cmi.12308

13. Egan S, Gardiner M. Microbial dysbiosis: Rethinking disease in marine ecosystems. Front Microbiol. 2016; doi: 10.3389/fmicb.2016.00991

14. Vadstein O, Attramadal KJK, Bakke I, Olsen Y. K-selection as microbial community management strategy: A method for improved viability of larvae in aquaculture. Front Microbiol. 2018; doi:10.3389/fmicb.2018.02730

15. Infante-Villamil S, Huerlimann R, Jerry DR. Microbiome diversity and dysbiosis in aquaculture. Reviews in Aquaculture. 2021; doi:10.1111/raq.12513

16. Cornejo-Granados F, Lopez-Zavala AA, Gallardo-Becerra L, Mendoza-Vargas A, Sánchez F, Vichido R, et al. Microbiome of Pacific Whiteleg shrimp reveals differential bacterial community composition between Wild, Aquacultured and AHPND/EMS outbreak conditions. Sci Rep. 2017; doi:10.1038/s41598-017-11805-w

17. Xiong J, Wang K, Wu J, Qiuqian L, Yang K, Qian Y, et al. Changes in intestinal bacterial communities are closely associated with shrimp disease severity. Appl Microbiol Biotechnol. 2015; doi: 10.1007/s00253-015-6632-z

18. Callac N, Boulo V, Giraud C, Beauvais M, Ansquer D, Ballan V, Maillez JR, Wabete N, Pham D. 2022. Microbiota of the rearing water of Penaeus stylirostris larvae influenced by laggon seawater and specific key lineages of larval stage and survival. Spectrum Microbiology. doi: 10.1128/spectrum.04241-22

19. https://agence-rurale.nc/filieres/peche-et-aquaculture/crevettes/. 2021.

20. Pham D, Ansquer D, Chevalier A, Dauga C, Peyramale A, Wabete N, et al. Selection and characterization of potential probiotic bacteria for Litopenaeus stylirostris shrimp hatcheries in New Caledonia. Aquaculture. 2014; doi:10.1016/j.aquaculture.2014.04.031

21. Pham D, Charmantier G, Wabete N, Boulo V, Broutoi F, Mailliez J-R, et al. Salinity tolerance, ontogeny of osmoregulation and zootechnical improvement in the larval rearing of the Caledonian Blue Shrimp, Litopenaeus stylirostris (Decapoda, Penaeidae). Aquaculture. 2012; doi:10.1016/j.aquaculture.2012.07.026

22. Maddox MB, Manzi JJ. The Effects Of Algal Supplements On Static System Culture Of Macrobrachium rosenbergii (De Man) Larvae1,2. Proc Annu Meet - World Maric Soc. 1976; doi:10.1111/j.1749-7345.1976.tb00096.x

23. Holmes RM, Aminot A, Kérouel R, Hooker BA, Peterson BJ. A simple and precise method for measuring ammonium in marine and freshwater ecosystems. Can J Fish Aquat Sci. 1999; doi: 10.1139/f99-128

24. Murphy J, Riley JP. A modified single solution method for the determination of phosphate in natural waters. Anal Chim Acta. 1962; doi:10.1016/S0003-2670(00)88444-5

25. Helms JR, Stubbins A, Ritchie JD, Minor EC, Kieber DJ, Mopper K. Erratum: Absorption spectral slopes and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric dissolved organic matter. Limnol Oceanogr. 2009; doi:10.4319/lo.2009.54.3.1023

26. Sadeghi-Nassaj SM, Catalá TS, Álvarez PA, Reche I. Sea cucumbers reduce chromophoric dissolved organic matter in aquaculture tanks. PeerJ. 2018; doi:10.7717/peerj.4344

27. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011; doi:10.1073/pnas.1000080107

28. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016; doi:10.1038/nmeth.3869

29. RStudio Team. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. 2020. http://www.rstudio.com/

30. McMurdie PJ, Holmes S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS One. 2013; doi: 10.1371/journal.pone.0061217

31. Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. 2016; https://ggplot2.tidyverse.org

32. Thioulouse J, Dray S, Dufour A-B, Siberchicot A, Jombart T, Pavoine S. Multivariate Analysis of Ecological Data with ade4. New York, NY: Springer New York; 2018. http://link.springer.com/10.1007/978-1-4939-8850-1

33. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. CRAN - Package vegan. 2020; https://cran.r-project.org/web/packages/vegan/index.html

34. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011; doi: 10.1186/gb-2011-12-6-r60

35. Cao Y. microbiomeMarker: microbiome biomarker analysis toolkit. 2020. R package version 0.0, 1.
https://yiluheihei.github.io/microbiomeMarker/

36. Liu C, Cui Y, Li X, Yao M. microeco: an R package for data mining in microbial community ecology. FEMS Microbiol Ecol. 2021; doi:10.1093/femsec/fiaa255/6041020

37. Kassambara A. rstatix R package. 2021. https://cran.r-project.org/web/packages/rstatix/index.html

38. Chen J-C, Kou Y-Z. Effects of ammonia on growth and molting of Penaeus japonicus juveniles. Aquaculture. 1992; doi:10.1016/0044-8486(92)90207-2

39. Xia LZ, Yang LZ, Yang MC. Nitrogen and phosphorus cycling in shrimp ponds and the measures for sustainable management. Environ Geochem Health. 2004; doi:10.1023/B:EGAH.0000039587.64830.43

40. Magallon Barajas FJ, Servin Villegas R, Portillo Clark G, Lopez Moreno B. Litopenaeus vannamei (Boone) post-larval survival related to age, temperature, pH and ammonium concentration. Aquac Res. 2006; doi:10.1111/j.1365-2109.2006.01455.x

41. Zhao M, Yao D, Li S, Zhang Y, Aweya JJ. Effects of ammonia on shrimp physiology and immunity: a review. Rev Aquac. 2020; doi:10.1111/raq.12429

42. Sehnal L, Brammer-Robbins E, Wormington AM, Blaha L, Bisesi J, Larkin I, et al. Microbiome Composition and Function in Aquatic Vertebrates: Small Organisms Making Big Impacts on Aquatic Animal Health. Front Microbiol. 2021; doi:10.3389/fmicb.2021.567408

43. Garrity GM, Bell JA, Lilburn T. Rhodobacteraceae fam. nov. In: Bergey's Manual of Systematics of Archaea and Bacteria. Wiley. 2015; doi:10.1002/9781118960608.fbm00173

44. Haggerty JM, Dinsdale EA, Davies J. Distinct biogeographical patterns of marine bacterial taxonomy and functional genes. Glob Ecol Biogeogr. 2017; doi:10.1111/geb.12528

45. Simon M, Scheuner C, Meier-Kolthoff JP, Brinkhoff T, Wagner-Döbler I, Ulbrich M, et al. Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. ISME J. 2017; doi:10.1038/ismej.2016.198

46. Ivanova EP, Mikhailov VV. A new family, alteromonadaceae fam. nov., including marine proteobacteria of the genera alteromonas, pseudoalteromonas, idiomarina, and colwellia. Mikrobiologiya. 2001; doi: 10.1023/a:1004876301036

47. Hou D, Huang Z, Zeng S, Liu J, Weng S, He J. Comparative analysis of the bacterial community compositions of the shrimp intestine, surrounding water and sediment. J Appl Microbiol. 2018; doi:10.1111/jam.13919

48. Yan M, Zhang X, Hu L, Huang X, Zhou Q, Zeng G, et al. Bacterial Community Dynamics During Nursery Rearing of Pacific White Shrimp (Litopenaeus vannamei) Revealed via High-Throughput Sequencing. Indian J Microbiol. 2020; doi:10.1007/s12088-019-00853-7

49. Liu H, Wang L, Liu M, Wang B, Jiang K, Ma S, et al. The intestinal microbial diversity in Chinese shrimp (Penneropenaeus chinensis) as determined by PCR-DGGE and clone library analyses. Aquaculture. 2011; doi:10.1016/j.aquaculture.2011.04.008

50. Huang Z, Li X, Wang L, Shao Z. Changes in the intestinal bacterial community during the growth of white shrimp, Litopenaeus vannamei. Aquac Res. 2016; doi:10.1111/are.12628

51. Zheng Y, Yu M, Liu J, Qiao Y, Wang L, Li Z, et al. Bacterial Community Associated with Healthy and Diseased Pacific White Shrimp (Litopenaeus vannamei) Larvae and Rearing Water across Different Growth Stages. Front Microbiol. 2017; doi:10.3389/fmicb.2017.01362

52. Wang H, Huang J, Wang P, Li T. Insights into the microbiota of larval and postlarval Pacific white shrimp (Penaeus vannamei) along early developmental stages: a case in pond level. Mol Genet Genomics. 2020; doi:10.1007/s00438-020-01717-2

53. Wang Y, Wang K, Huang L, Dong P, Wang S, Chen H, et al. Fine-scale succession patterns and assembly mechanisms of bacterial community of Litopenaeus vannamei larvae across the developmental cycle. Microbiome. 2020; doi:10.1186/s40168-020-00879-w

54. Morris RM, Rappé MS, Connon SA, Vergin KL, Siebold WA, Carlson CA, et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature. 2002; doi:10.1038/nature01240

55. Bernardet J. Flavobacteriaceae. In: Bergey's Manual of Systematics of Archaea and Bacteria. Wiley. 2015; doi:10.1002/9781118960608.fbm00069

56. Garrity GM, Bell JA, Lilburn T. Oceanospirillaceae fam. nov. In: Bergey's Manual of Systematics of Archaea and Bacteria. Wiley. 2015; doi:10.1002/9781118960608.fbm00229

57. Restrepo L, Domínguez-Borbor C, Bajaña L, Betancourt I, Rodríguez J, Bayot B, et al. Microbial community characterization of shrimp survivors to AHPND challenge test treated with an effective shrimp probiotic (Vibrio diabolicus). Microbiome. 2021; doi:10.1186/s40168-021-01043-8

58. Fernández-Gómez B, Richter M, Schüler M, Pinhassi J, Acinas SG, González JM, et al. Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J. 2013; doi: 10.1038/ismej.2012.169
59. McIlroy SJ, Nielsen PH. The Family Saprospiraceae. In: The Prokaryotes. Berlin, Heidelberg: Springer Berlin Heidelberg. 2014; doi: 10.1007/978-3-642-38954-2_138

60. Cornejo-Granados F, Gallardo-Becerra L, Leonardo-Reza M, Ochoa-Romo JP, Ochoa-Leyva A. A meta-analysis reveals the environmental and host factors shaping the structure and function of the shrimp microbiota. PeerJ. 2018; doi:10.7717/peerj.5382

61. Hugerth LW, Andersson AF. Analysing Microbial Community Composition through Amplicon Sequencing: From Sampling to Hypothesis Testing. Front Microbiol. 2017; doi:10.3389/fmicb.2017.01561

62. Sathish Kumar T, Vidya R, Kumar S, Alavandi SV, Vijayan KK. Zoea-2 syndrome of Penaeus vannamei in shrimp hatcheries. Aquaculture. 2017; doi:10.1016/j.aquaculture.2017.07.022

63. Souza CP, Almeida BC, Colwell RR, Rivera ING. The Importance of Chitin in the Marine Environment. Mar Biotechnol. 2011; doi: 10.1007/s10126-011-9388-1

64. Reichenbach H. The Order Cytophagales. In: The Prokaryotes. New York, NY: Springer New York. 1992; doi:10.1007/978-1-4757-2191-1_37

65. Snyder AR, Williams HN, Baer ML, Walker KE, Stine OC. 16S rDNA sequence analysis of environmental Bdellovibrio-and-like organisms (BALO) reveals extensive diversity. Int J Syst Evol Microbiol. 2002; doi: 10.1099/ijs.0.02261-0

66. Bratanis E, Andersson T, Lood R, Bukowska-Faniband E. Biotechnological Potential of Bdellovibrio and Like Organisms and Their Secreted Enzymes. Front Microbiol. 2020; doi:10.3389/fmicb.2020.00662

67. Nyholm SV. In the beginning: egg-microbe interactions and consequences for animal hosts. Philos Trans R Soc B Biol Sci. 2020; doi:10.1098/rstb.2019.0593

68. Amoah K, Huang Q, cheng, Dong X hui, Tan B ping, Zhang S, Chi S yan, et al. Paenibacillus polymyxa improves the growth, immune and antioxidant activity, intestinal health, and disease resistance in Litopenaeus vannamei challenged with Vibrio parahaemolyticus. Aquaculture. 2020; doi:10.1016/j.aquaculture.2019.734563

69. Feng T, Jeong SE, Kim KH, Park HY, Jeon CO. Aestuariicoccus marinus gen. nov., sp. nov., isolated from sea-tidal flat sediment. Int J Syst Evol Microbiol. 2018; doi:10.1099/ijsem.0.002494

70. Wirth JS, Whitman WB. Phylogenomic analyses of a clade within the roseobacter group suggest taxonomic reassignments of species of the genera Aestuariivita, Citreicella, Loktanella, Nautella, Pelagibaca, Ruegeria, Thalassobius, Thiobacimonas and Tropicibacter, and the proposal. Int J Syst Evol Microbiol. 2018; doi: 10.1099/ijsem.0.002833

71. Heyse J, Props R, Kongnuan P, De Schryver P, Rombaut G, Defoirdt T, et al. Rearing water microbiomes in white leg shrimp (Litopenaeus vannamei) larviculture assemble stochastically and are influenced by the microbiomes of live feed products. Environ Microbiol. 2021; doi:10.1111/1462-2920.15310

72. Zheng Y, Yu M, Liu Y, Su Y, Xu T, Yu M, et al. Comparison of cultivable bacterial communities associated with Pacific white shrimp (Litopenaeus vannamei) larvae at different health statuses and growth stages. Aquaculture. 2016; doi:10.1016/j.aquaculture.2015.09.020

73. Lu J, Zhang X, Qiu Q, Chen J, Xiong J. Identifying Potential Polymicrobial Pathogens: Moving Beyond Differential Abundance to Driver Taxa. Microb Ecol. 2020; doi:10.1007/s00248-020-01511-y

74. Porsby CH, Nielsen KF, Gram L. Phaeobacter and Ruegeria species of the Roseobacter clade colonize separate niches in a Danish turbot (Scophthalmus maximus)-rearing farm and antagonize Vibrio anguillarum under different growth conditions. Appl Environ Microbiol. 2008; doi: 10.1128/AEM.01738-08

75. Gromek SM, Suria AM, Fullmer MS, Garcia JL, Gogarten JP, Nyholm S V., et al. Leisingera sp. JC1, a bacterial isolate from hawaiian bobtail squid eggs, produces indigoidine and differentially inhibits vibrios. Front Microbiol. 2016; doi:10.3389/fmicb.2016.01342

76. Cram JA, Xia LC, Needham DM, Sachdeva R, Sun F, Fuhrman JA. Cross-depth analysis of marine bacterial networks suggests downward propagation of temporal changes. ISME J. 2015; doi:10.1038/ismej.2015.76

77. Reintjes G, Tegetmeyer HE, Bürgisser M, Orlić S, Tews I, Zubkov M, et al. On-Site Analysis of Bacterial Communities of the Ultraoligotrophic South Pacific Gyre. Nojiri H, editor. Appl Environ Microbiol. 2019; doi:10.1128/AEM.00184-19

78. Lavoie C, Wellband K, Perreault A, Bernatchez L, Derome N. Artificial rearing of atlantic salmon juveniles for supportive breeding programs induces long-term effects on gut microbiota after stocking. Microorganisms. 2021; doi:10.3390/microorganisms9091932

79. Sorieul L, Wabete N, Mailliez J, Pallud M, Zhang C, Lindivat M, Boulo V, Pham D. Survival improvement conferred by the Pseudoalteromonas sp. NC201 probiotic in Litopenaeus stylirostris exposed to Vibrio nigripulchritudo infection and salinity stress. Aquaculture. 2018; doi:
10.1016/j.aquaculture.2018.06.058

80. Sorieul L, Rückert C, Al-Dilaimi A, Winkler A, Kalinowski J, Pham D, Wabete N, Boulo V. Whole-genome sequence of Pseudoalteromonas sp. NC201, a probiotic strain for Litopenaeus stylirostris hatcheries in New Caledonia. Microbiol Resour Announc. 2019; doi: 10.1128/MRA.00477-19

81. Methou P, Hernández-Ávila I, Aube J, Cueff-Gauchard V, Gayet N, Amand L, et al. Is It First the Egg or the Shrimp? – Diversity and Variation in Microbial Communities Colonizing Broods of the Vent Shrimp Rimicaris exoculata During Embryonic Development. Front Microbiol. 2019; doi:10.3389/fmicb.2019.00808

82. Orsi WD, Smith JM, Liu S, Liu Z, Sakamoto CM, Wilken S, et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 2016; doi: 10.1038/ismej.2016.20

83. Buchan A, González JM, Moran MA. Overview of the marine Roseobacter lineage. Appl Environ Microbiol. 2005; doi:10.1128/AEM.71.10.5665

84. Balcázar JL, Blas I de, Ruiz-Zarzuela I, Cunningham D, Vendrell D, Múzquiz JL. The role of probiotics in aquaculture. Vet Microbiol. 2006; doi:10.1016/j.vetmic.2006.01.009

85. Dawood MAO, Koshio S, Abdel-Daim MM, Van Doan H. Probiotic application for sustainable aquaculture. Rev Aquac. 2019; doi:10.1111/raq.12272

86. Yang W, Zheng C, Zheng Z, Wei Y, Lu K, Zhu J. Nutrient enrichment during shrimp cultivation alters bacterioplankton assemblies and destroys community stability. Ecotoxicol Environ Saf. 2018; doi:10.1016/j.ecoenv.2018.03.043

87. Qin BQ, Gao G, Zhu GW, Zhang YL, Song YZ, Tang XM, et al. Lake eutrophication and its ecosystem response. Chinese Sci Bull. 2013; doi: 10.1007/s11434-012-5560-x

88. Liu Q, Zhang Y, Wu H, Liu F, Peng W, Zhang X, et al. A review and perspective of edna application to eutrophication and hab control in freshwater and marine ecosystems. Microorganisms. 2020; doi:10.3390/microorganisms8030417

89. Smith VH, Schindler DW. Eutrophication science: where do we go from here? Trends Ecol Evol. 2009; doi: 10.1016/j.tree.2008.11.009

90. Aragno M. Responses of Microorganisms to Temperature. Physiol Plant Ecol I. 1981; doi: 10.1007/978-3-642-68090-8_12

91. Huang F, Pan L, Song M, Tian C, Gao S. Microbiota assemblages of water, sediment, and intestine and their associations with environmental factors and shrimp physiological health. Appl Microbiol Biotechnol. 2018; doi: 10.1007/s00253-018-9229-5

92. Côte J, Jacquin L, Veyssière C, Manzi S, Etienne R, Perrault A, et al. Changes in fish skin microbiota along gradients of eutrophication in human-altered rivers. FEMS Microbiol Ecol. 2022; doi:10.1093/femsec/fiac006/6517683

93. De Schryver P, Vadstein O. Ecological theory as a foundation to control pathogenic invasion in aquaculture. ISME J. 2014; doi:10.1038/ismej.2014.84

94. Vadstein O, Attramadal KJK, Bakke I, Forberg T, Olsen Y, Verdegem M, et al. Managing the Microbial Community of Marine Fish Larvae: A Holistic Perspective for Larviculture. Front Microbiol. 2018; doi:3389/fmicb.2018.01820

95. Zhou J, Ning D. Stochastic Community Assembly: Does It Matter in Microbial Ecology? Microbiol Mol Biol Rev. 2017; doi:10.1128/MMBR.00002-17

96. Bakke I, Coward E, Andersen T, Vadstein O. Selection in the host structures the microbiota associated with developing cod larvae (Gadus morhua). Environ Microbiol. 2015; doi:10.1111/1462-2920.12888

97. Giatsis C, Sipkema D, Smidt H, Heilig H, Benvenuti G, Verreth J, et al. The impact of rearing environment on the development of gut microbiota in tilapia larvae. Sci Rep. 2015; doi: 10.1038/srep18206

98. Yan Q, van der Gast CJ, Yu Y. Bacterial Community Assembly and Turnover within the Intestines of Developing Zebrafish. Bereswill S, editor. PLoS One. 2012; doi:10.1371/journal.pone.0030603
## V.3- Synthèse des résultats et perspectives

Les bacs d'élevage larvaire considérés lors de cette étude ont tous montré une eutrophisation de l'eau d'élevage au cours du développement des animaux. En effet, en conséquence de l'accumulation de nourriture non ingérée, de fèces et de mues de larves, une augmentation de la concentration en sels nutritifs (ammonium et phosphate) et en matière organique (CDOM, $a\lambda325$) a été observée. En parallèle, une diminution du SR a également été mise en évidence dans l'ensemble des eaux d'élevage. L'implication des variations du SR a précédemment été discutée dans le chapitre IV (partie IV.1.3). Enfin, une acidification du pH a été constatée. Elle peut potentiellement être liée à une augmentation de la biomasse et de la croissance bactérienne conduisant à des concentrations en $CO_2$ plus importante dans le milieu (Smith & Schindler 2009). Des modifications drastiques du milieu d'élevage, pouvant moduler l'écosystème microbien associé à l'eau et aux larves, ont donc été mises en évidence. Les ordonnancements (dendrogramme et PCoA) construits à partir des données de diversité procaryotique ont d'ailleurs montré 3 groupes distincts composés des échantillons de J0 à J2 (stades nauplius et zoé 1), de J3 à J5 (stades zoé 2 et 3) et de J7 à J9 (stades mysis et post-larve). Ces résultats suggèrent un potentiel effet du stade de développement et du milieu d'élevage sur les communautés procaryotiques environnementales et larvaires. Il est également possible que l'ajout d'antibiotiques à J4 et les modifications de séquence alimentaire (ajout d'Artémias à J4 et changement de microparticules à J2, J5 et J7) puissent moduler les communautés procaryotiques associées aux élevages. Malgré ces variations, plusieurs bactéries affiliées aux ordres *Alteromonadales*, *Rhodobacterales*, *Flavobacteriales*, *Oceanospirillales* et *Vibrionales* ont été mises en évidence dans l'ensemble des échantillons d'eau et de larves. Ces ordres bactériens ont également été identifiés dans la littérature chez d'autres espèces de crevettes. Ces observations renforcent les hypothèses faites précédemment concernant une potentielle transmission horizontale de certaines communautés microbiennes et une persistance de microorganismes spécifiques tout au long de la vie des animaux. Les variations d'abondances bactériennes au cours des élevages ont été analysées à l'aide de LEfSe. Aussi bien dans l'eau que dans les larves et à différents moments de l'élevage, ces analyses ont montré un enrichissement de plusieurs bactéries potentiellement probiotiques affiliées aux groupes AEGEAN 169 (*Rhodospirillales*) et OM27 (*Bdellovibrionales*) ainsi qu'aux genres *Ruegeria*, *Leisingera*, *Pseudoalteromonas* et *Roseobacter*. Un probiotique est défini comme un microorganisme vivant qui exerce des effets positifs sur la santé de son hôte (stimulation du système immunitaire, amélioration de la nutrition, etc.) lorsqu'il est ingéré en quantité suffisante (FAO 2001). En aquaculture, la définition s'élargit et réfère également à un microorganisme vivant capable d'améliorer la qualité de l'eau d'élevage (dégradation de certains composés physico-chimiques, compétition avec des microorganismes opportunistes ou pathogènes, etc.) (Verschuere et al. 2000). Ainsi, il semblerait que diverses bactéries potentiellement bénéfiques soient impliquées à différents moments de l'élevage larvaire de *P. stylirostris*. Elles pourraient chacune jouer un rôle à des périodes clés du développement larvaire. Ces observations sont en faveur d'une adaptation de l'usage des probiotiques dans les systèmes aquacoles en Nouvelle-Calédonie (classiquement une seule souche est utilisée au cours de l'élevage) (Pham et al. 2014). Il est également intéressant de noter que l'ensemble des ASVs enrichis dans l'eau et dans les larves étaient présents dans les eaux de réserve et dans les œufs montrant l'importance de la mise en place du microbiote larvaire sur le devenir de l'élevage ainsi que l'importance de la composition microbienne de l'eau de mer lagonaire. Des corrélogrammes ont enfin permis
Chapitre V – Évolution des communautés procaryotiques associées à un élevage larvaire en
bonne santé

de mettre en évidence des corrélations existantes entre les paramètres physico-chimiques de
l'eau d'élevage et les ASVs enrichis au cours du développement larvaire suggérant
l'implication de processus déterministes dans la modulation de l'écosystème microbien
associés aux élevages. Cependant, le faible nombre de corrélations observées entre les
bactéries enrichies au stade larvaire zoé et les paramètres environnementaux suggèrent
également l'implication de processus stochastiques. Il est néanmoins possible que certains
facteurs, non pris en compte lors de cette étude, puissent également expliquer ces
observations. En conclusion, l'ensemble des résultats témoignent de changements radicaux
entre le début et la fin du développement larvaire tant au niveau du milieu d'élevage que du
microbiote de l'eau et des larves.
# CHAPITRE VI

Caractérisation des communautés procaryotiques de l'eau d'élevage au cours de mortalités larvaires : identification de biomarqueurs
# VI- Chapitre VI – Caractérisation des communautés procaryotiques de l'eau d'élevage au cours de mortalités larvaires : identification de biomarqueurs

## VI.1- Introduction

L'intensification des pratiques aquacoles au cours des dernières décennies a contribué à l'émergence d'épisodes de mortalités massives dont les causes sont souvent inexplicuées (Balcázar et al. 2006; Egan & Gardiner 2016). En effet, la complexité de distinguer les microorganismes pathogènes des saprophytes chez des animaux moribonds ou morts ainsi que l'émergence de maladies polymicrobiennes complexifient la mise en évidence d'un unique responsable des mortalités. De plus, certains pathogènes opportunistes peuvent appartenir au microbiote de leur hôte et ne devenir néfastes que sous certaines conditions. Enfin, des phénomènes de dysbiose peuvent également survenir (Egan & Gardiner 2016). Ces facteurs, et notamment la dysbiose, ont un impact sur la santé de crevettes Penaeus vannamei adultes (Xiong et al. 2015; Cornejo-Granados et al. 2017). Dans ce contexte, des modifications des communautés microbiennes associées à l'eau et aux larves de P. stylirostris ont été suspectées d'être impliquées dans les mortalités larvaires massives constatées dans les écloseries de Nouvelle-Calédonie (chap. I, partie I.1.2.c). L'article 3 présenté dans le chapitre V (partie V.2) a permis de caractériser les communautés procaryotiques associées à l'eau et aux larves de P. stylirostris au cours d'élevages en bonne santé. Il montre également une succession de taxa bactériens potentiellement bénéfiques durant le développement avec une mise en évidence de ces microorganismes en amont des élevages, dans les eaux de remplissage (provenant du lagon) et de réserve (alimentant les bacs d'élevage). À partir de ces connaissances et dans l'objectif de vérifier si un phénomène de dysbiose cause les mortalités larvaires observées, 2 études ont été conduites (article 4, partie VI.2 et article 5, partie VI.3). Elles visent à caractériser les communautés procaryotiques associées à des élevages en mauvaise santé, présentant donc d'importantes mortalités larvaires, et à mettre en évidence de potentiels biomarqueurs bactériens de la santé de ces élevages. La première étude (article 4) se concentre sur la dynamique des communautés procaryotiques associées à l'eau traitée des élevages avec (TAO) et sans (TSA) antibiotique de la campagne M3 réalisée en novembre 2018 à la Station Aquacole de Saint Vincent (SASV) (Tab. 6, chap. II, partie II.1.1a). Une analyse de la qualité physico-chimique de ces échantillons d'eau a également été réalisée par dosages des sels nutritifs et évaluation de la concentration en matière organique dissoute colorée (CDOM) (chap. II, parties II.4.2 et II.4.3). La seconde étude (article 5) vise à comparer, pendant l'élevage larvaire, l'évolution des communautés procaryotiques de l'eau non traitée des élevages avec (NTAO) et sans (NTSA) antibiotique de la campagne M4 réalisée en février 2019 à la SASV (Tab. 6, chap. II, partie II.1.1a). Pour chaque étude, la survie et le stade larvaire ont été estimés quotidiennement. Des échantillons de larves ainsi que d'eau de remplissage (eau provenant du lagon et stockée dans la réserve primaire) et de réserve (eau traitée dans la réserve secondaire et alimentant les bacs d'élevage) ont également été considérés (chap. II, partie II.1.1.a). Après extraction des ARNs et rétrotranscription en ADNc, des séquençages Illumina HiSeq (2 x 150pb) ont été réalisés (chap. IV, partie IV.1.1). Les données de séquençage ont été traitées avec le pipeline FROGS (Escudié et al. 2018) (chap. III, partie III.3.1) dans l'article 4 et avec le pipeline DADA2 (Callahan et al. 2016) (chap. III,
# Chapitre VI – Caractérisation des communautés procaryotiques de l’eau d’élevage au cours de mortalités larvaires : identification de biomarqueurs

partie III.3.2) dans l'article 5. Dans les 2 cas, les tables de comptage (OTU ou ASV) obtenues ont été normalisées par la méthode CPM (chapitre III, partie III.4.2.b) avant les analyses d'ordonnancement (dendrogramme, NMDS, PCoA). Des histogrammes, des diagrammes de Venn et des LEfSe ont également été effectués (chap. II, partie II.5.3). Dans l'article 5, une PERMANOVA a été réalisée sous RStudio.
## VI.2- Article 4 – Microbiota of the rearing water of Penaeus stylirostris larvae influenced by lagoon seawater and specific key lineages of larval stage and survival

<figure><img src="image_101.png" /></figure>

AMERICAN
SOCIETY FOR
MICROBIOLOGY

<figure><img src="image_102.png" /></figure>

Microbiology
Spectrum

RESEARCH ARTICLE

<figure><img src="image_103.png" /></figure>

Check for
updates

# Microbiota of the Rearing Water of Penaeus stylirostris Larvae Influenced by Lagoon Seawater and Specific Key Microbial Lineages of Larval Stage and Survival

© Nolwenn Callac,ª Viviane Boulo,ª* Carolane Giraud,ª,ᵇ Maxime Beauvais,ª,>S Dominique Ansquer,ª Valentine Ballan,ª Jean-René Maillez,ª Nelly Wabete,ª Dominique Phamª

**ABSTRACT** Aquacultured animals are reared in water, where they interact with microorganisms which can be involved in their development, immunity, and disease. It is therefore interesting to study the rearing water microbiota, especially in the hatcheries of the Pacific blue shrimp *Penaeus stylirostris*, where larval mass mortalities occur. In this study, using HiSeq sequencing of the V4 region of the 16S rRNA molecule coupled with zootechnical and chemical analyses, we investigated whether any microbial lineages could be associated with certain mortality rates at a given larval stage. Our results indicate that the active microbiota of the rearing water was highly dynamic throughout the rearing process, with distinct communities influenced by progressive water eutrophication, larval stage, and survival rate. Our data also highlighted the role of the lagoon seawater on the rearing water microbiome, as many operational taxonomic units (OTUs) specific to a given larval stage and survival rate were detected in the primary reservoir which contained the lagoon water. We also identified biomarkers specific to water eutrophication, with *Alteromonadaceae*, *Vibrionaceae*, and *Methylophilaceae*, respectively, linked to increases in ammonia, nitrogen, and soluble reactive phosphate, or to increases in colored dissolved organic matter in the rearing water; other biomarkers were specific to certain larval stages and survival rates. Indeed, the *Marinobacteraceae* were specific to the Nauplii, and the *Thalassospiraceae* and *Saprospiraceae* to the Zoea Good condition; when mortality occurred, the *Litoricolaceae* were specific to the Zoea Bad, *Microbacteraceae* to the Mysis Bad, and *Methylophilaceae* to the Mysis Worst condition. Thus, these biomarkers might be used as potential early warning sentinels in water storage to infer the evolution of larval rearing to improve shrimp larval rearing.

**IMPORTANCE** In New Caledonia, rearing of *P. stylirostris* is one of the main economic activities; unfortunately, mass larval mortalities cause important production decreases, involving major economic losses for the farmers and the Territory. This phenomenon, which has occurred at any larval stage over the past decade, is poorly understood. The significance of our research is in the identification of biomarkers specific to larval stage and survival rate, with some of these biomarkers being already present in the lagoon water. This enhances the role of the lagoon on the active microbiota of the rearing water at various larval stages and survival rates. Together, our results help us understand which active microbial communities are present in the rearing water according to larval stage and health. This might lead to broader impacts on hatcheries by helping to develop useful tools for using the water—lagoon, reservoir, or rearing—to test for the presence of these biomarkers as an early monitoring strategy.

**KEYWORDS** active microbiota, rearing water, core microbiome, specific microbiome, larvae, shrimp

**Editor** John M. Chaston, Brigham Young University

**Copyright** © 2022 Callac et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

Address correspondence to Nolwenn Callac, nolwenn callac@ifremer.fr.

*Present address: Viviane Boulo, IHPE, Université de Montpellier, CNRS, Ifremer, Université de Perpignan via Domitia, Montpellier, France.

†Present address: Maxime Beauvais, Sorbonne Université, UMR 7261, Laboratoire d'Océanographie Microbienne, Observatoire Océanologique de Banyuls-sur-Mer, CNRS, Banyuls-sur-Mer, France.

The authors declare no conflict of interest.

Received 17 October 2022

Accepted 9 November 2022

Published 23 November 2022

<footnote>ªIfremer, IRD, Université de la Nouvelle-Calédonie, Université de La Réunion, CNRS, UMR 9220 ENTROPIE, Nouméa, New Caledonia</footnote>

<footnote>ᵇInstitut des Sciences Exactes et Appliquées (ISEA), University of New Caledonia, Nouméa, New Caledonia</footnote>
Aquatic animals are influenced by the physical, chemical, and microbiological composition of their surrounding water, with microorganisms playing the greatest role. Indeed, they are involved in various biogeochemical cycles, such as the carbon and nitrogen cycles, through organic matter degradation, remineralization, and organic matter turnover (1-3). In addition, microorganisms are also involved in animal development, physiology fitness, and immunity, as some microorganisms may help protect against diseases or, inversely, be pathogenic (4-7). Aquacultured animals are reared in water and are therefore continuously surrounded by the water microbiota, with which they share close relationships (8-11). Several studies have shown that microbial exchanges occur between the water and various tissues of reared aquatic animals such as the gills or intestines. Thus, the host can enrich its own microbiota by selecting microorganisms from its environment (12-14). It has also been proven that the microbiome of the larvae, especially for fish such as cod or tilapia, shared more common taxa with the surrounding rearing water than with the feed products (12, 15, 16). Many studies have argued the importance of the microbiota of the rearing water for both the health of the animal and the modulation of its microbiome during all life cycle stages (17, 18). Research on the health of reared aquatic animals or the evolution of their rearing water usually focuses on the most abundant taxa. However, for many macroorganisms, such as humans or aquatic organisms (corals or fish), it has been established that, except for a conserved core microbiome, the overall microbiota changes in response to environmental disturbances while the rare taxa are cornerstone microorganisms which shape the community structure (19-22). In addition, more and more studies have proven that the low-abundance taxa have a crucial role in the ecosystem, playing substantial roles in biogeochemical cycles and often driving concealed functions of the microbiome (22, 23).

In New Caledonia, the Pacific blue shrimp *Penaeus stylirostris* is farmed, and its semi-intensive production achieved up to 1,444 tons in 2019. Around 55% of this production is exported to the rest of the world, making this sector the second biggest economic source for the territory after the nickel industry (https://www.agence-rurale.nc/filières/peche-et-aquaculture/crevettes/) (24, 25). In 2019, 128 million post-larvae were produced, compared to 167 million in 2005. This dramatic drop is due to the huge mortality issues occurring at all larval stages which hatcheries have been facing for the past decade. This phenomenon is still poorly understood, and the causes are still unidentified (24). Water quality has been considered as a possible cause, but the discrepancy in larval survival during the same rearing cycle in the same hatchery eliminates this hypothesis. Bacterial infection has often been suspected, but an analysis conducted by the Neo-Caledonian Network of Shrimp Epidemiological Vigilance (REC-DAVAR) revealed no evidence of larval septicemia. A multifactorial cause seems to prompt mass larval mortality. Our hypothesis is that a shift in the active microbial diversity of the rearing water could lead to microbial dysbiosis in both water and larvae, which could then hamper the larvae and lead to the mass mortality observed. To address whether the microbiome of the rearing water could be linked to lower larval survival rates, we especially investigated whether any microbial lineages or families were associated with certain mortality rates at given larval stages and whether the composition of the rearing seawater had any effect. Because antibiotics are used for prophylactic reasons under veterinary advice during shrimp larval rearing in New Caledonian hatcheries, we also investigated the effect of the addition or absence of erythromycin on the rearing water microbiota and larval survival. Thus, we followed the evolution of the dissolved organic matter, dissolved total nitrogen, reactive phosphorus, and active microbial diversity from the storage water to the rearing tanks during the first 10 days of larval rearing. A core microbiome of all the rearing waters was found and most of these operational taxonomic units (OTUs) were previously present in the water storage, highlighting the huge influence of the lagoon seawater on the larval rearing. Several biomarkers at the family level were specific to a given larval stage and survival rate or specific to the rearing water eutrophication. Furthermore, in the context of our study, we highlighted potential
bacterial lineages present during early rearing that could either be putative larvae pathogen or potential indicators of upcoming and progressive larval mortality.

RESULTS

**Time-series analyses of the chemical parameters and larval development.**
Throughout the 9 rearing days, the temperature and salinity of all tanks remained constant at 30° ± 1°C and 35 ± 0.2 ppt, respectively. Total ammonia nitrogen (TAN), soluble reactive phosphate (SRP), and the absorption coefficient at 325 nm (Aλ<sub>325</sub>, proxy for CDOM [chromophoric dissolved organic matter]) remained low and constant in the control waters whereas they increased constantly in the larval rearing waters, with or without antibiotic treatment (Fig. 1). Indeed, in the rearing water, TAN concentrations increased around 7-fold between day 3 (D3) and D9 in both treatments (with and without antibiotic) (Fig. 1A). The same trend was observed for SRP, with the same magnitude (Fig. 1B). The CDOM tripled from D0 to D9 in all larval rearing water tanks (Fig. 1C). Compared to the reference, significant mortality was observed between D6 and D9 regardless of the treatment. Overall, the two treatments resulted in survival rates below 45% on D9, except for the third tank with antibiotic (samples with D9-3), with a survival rate of 57% (Fig. 1D). At the end of the experiment, a delay in larval growth was observed because all larvae in all tanks were still in the mysis stage (Fig. 1E) compared to the reference, where at least half of the shrimp cohort usually reached the post-larvae stage at D9 (the stage reference is an average of the daily larval stage calculated using 10 years of data for successful rearing; Pham, personal communication). Mann-Whitney tests showed that the addition of antibiotic had no significant effect on TAN, SRP or CDOM concentrations, with *P* values of 0.851, 0.860, and 0.649, respectively. Furthermore, the addition of antibiotic did not impact larval development or larval survival, with *P* values of 0.276 and 0.786, respectively.

**Dynamic of the active microbial diversity in the rearing water.** We used a meta-barcoding approach to characterize the prokaryotic communities associated with the water samples, resulting in, after filtration, chimera, chloroplast and mitochondrial removal, and taxonomic assignment, a total of 4,612,207 reads clustered into 1,563 OTUs according to the database Silva 132-16S. The OTUs were spread into 21 phyla, 3 from Archaea and 18 from Bacteria. The alpha diversity index values, shown in Table S1, indicated that the richness estimated using ACE and Chao1 was generally higher in the rearing water samples without antibiotic than in the rearing water with antibiotic and the control water. The richness was higher in the reservoirs than in the control water. The same trend was observed for the evenness estimated with the Shannon and Inverse Simpson indexes. The Mann-Whitney tests performed on the alpha-diversity indices between water samples on D1 with and without antibiotic indicated that diversity was not significantly different; whereas considering the entire set of samples (from D1 to D9) with and without antibiotic, the alpha diversity indices were significantly different, with *P* < 0.05.

We first visualized how the samples clustered together according to the entire normalized OTU table. The dendrogram displayed 10 clusters at a threshold of 1, with clear distinction among the samples regarding the water type: control, rearing water with antibiotic, rearing water without antibiotic, and reservoir water. Clusters 1 and 8 were the exception, as they included samples with and without antibiotic (Fig. 2A). Indeed, cluster 1 encompassed all the rearing water samples without antibiotic collected on D8 and D9 and all the rearing water with antibiotic samples between D7 and D9. Cluster 2 pooled the water samples without antibiotic collected on D6 and D7. The water reservoirs (primary [GR] and secondary [RC and C]) were gathered together in cluster 3. Clusters 4, 5, and 6 encompassed all the rearing waters without antibiotic sampled from D2 to D9; while clusters 7, 9 and 10 gathered all the rearing waters with antibiotic sampled from D2 to D9. Cluster 8 was composed of all the rearing water samples collected on D1. As pictured in Fig. 2A, the clustering was mostly consistent with the larval stages, while survival status did not influence grouping.

Nonmetric multidimensional scaling (NMDS) displayed a clear distinction between the water reservoirs (cluster reservoirs), the control water (cluster control), and the rearing water samples with (clusters A, F, G, and H) or without antibiotic (clusters B, C, D,
Microbiota of the Rearing Water of Shrimp Larvae

Microbiology Spectrum

<figure><img src="image_104.png" /></figure>

<figure><img src="image_105.png" /></figure>

<figure><img src="image_106.png" /></figure>

<figure><img src="image_107.png" /></figure>

<figure><img src="image_108.png" /></figure>

**FIG 1** Evolution of hydrochemical values, larval survival rates, and development according to the days of the rearing period. Evolution of (A) total ammonia nitrogen (TAN), (B) soluble reactive phosphate (SRP) (for the control, both the TAN and SRP values were around 0 µM throughout the rearing), and (C) chromophoric dissolved organic matter (CDOM) concentrations using the A<sub>λ325</sub> proxy, measured during the 10 days of rearing. Each bar of the bar plot

(Continued on next page)
<figure><img src="image_109.png" /></figure>
Microbiota of the Rearing Water of Shrimp Larvae

Microbiology Spectrum

<figure><img src="image_110.png" /></figure>

**FIG 3** Microbial communities associated with specific and core microbiomes on D1 in the rearing waters supplemented or not supplemented with antibiotic. (A) Venn diagram of shared OTUs between the rearing waters with and without the antibiotic erythromycin. Blue ellipse represents OTUs common to the rearing water from the tanks without antibiotic collected on D1; yellow ellipse represents OTUs common to the rearing waters from the tanks with antibiotic on D1. The overlapping area between the blue and yellow ellipses represents the OTUs common to all water samples collected on D1. The numbers inside the ellipses and in the overlapping zones correspond to the total number of OTUs present under each condition. (B) Top 17 families with a relative abundance higher than 1% corresponding to the specific microbiome of the rearing water with antibiotic. (C) Top 15 families (when possible, higher level otherwise) with a relative abundance higher than 1% corresponding to the specific microbiome of the rearing water without antibiotic. (D) Top 12 families with a relative abundance higher than 1% corresponding to the core microbiome common to the rearing water with and without antibiotic collected at D1. Shown in blue are the families present in the core microbiome on D1 and in the specific microbiomes of the rearing water collected on D1 with and without antibiotic.

biotic samples collected on D1 and D2. However, like the dendrogram, it showed a daily evolution according to the sampling time and no clear grouping according to larval survival rate.

The active microbiota of the water samples showed different patterns according to the presence or absence of larvae or antibiotic (Fig. 2C). The primary reservoir (GR) which contained the lagoon seawater was mostly composed of members of *Alphaproteobacteria*, *Gammaproteobacteria*, and *Bacteroidota* classes and lineages related to the *Rhodobacteraceae* and *Alteromonadaceae* families. The secondary reservoirs (RC and C), before (RC) and after (C) recirculation through filters, exhibited the same outlines, with a high abundance of the *Gimesiaceae* family, which comprised around or less than 1% of the relative abundance in the rearing water sample and the control (Fig. 2C). The control water without larvae, antibiotic, or food was mainly composed of *Hyphomonadaceae*, *Alteromonadaceae*, and *Saprospiraceae*, with their relative abundances varying over the 10 days. The microbial composition of the rearing water with and without antibiotic displayed a homogeneous distribution among the replicates throughout the whole rearing, as shown in Fig. 2C. They were both mostly composed of *Rhodobacteraceae*, *Alteromonadaceae*, *Saprospiraceae*, *Litoricolaceae*, and *Cryomorphaceae* (Fig. 2C). On D2, and only on D2, the *Beggiatoaceae* were highly present in the water without antibiotic and the *Kordiimonadaceae* were found in the rearing water with antibiotic.

**Specific and core microbiomes of the rearing water.** To determine the impact of the antibiotic erythromycin on the microbiome of the rearing water, we made a Venn diagram of the rearing waters on D1 with and without antibiotic (Fig. 3A). On D1, the
<figure><img src="image_111.png" /><figcaption>**FIG 4** Microbial communities associated with the specific and core microbiomes of the entire rearing experiment in the rearing water. (A) Venn diagram of shared OTUs among rearing water samples. The overlapping area between all the ellipses, represented by a salmon circle, corresponds to the core microbiome composed of the 54 OTUs common to all samples. The numbers inside the ellipses and in the overlapping zones correspond to the total number of OTUs present under each condition. (B) Venn diagram of shared OTUs among the core rearing water samples and water storages. The numbers inside the ellipses and in the overlapping zone correspond to the total number of OTUs present under each condition. (C) Histogram of the families with relative abundance higher than 1%, constitutive of the core microbiome of all samples. All families with a relative abundance lower than 1% are included as "Other.". Barplot was constructed by averaging all rearing water samples.</figcaption></figure>

rearing water hosted the nauplii which had a 100% survival rate, and these originated from the same nauplius batch; thus, we mostly expected to highlight the effect of the antibiotic on the rearing water microbiota. The water with antibiotic had 80 specific OTUs. These were mostly composed of members of *Coxiellaceae*, *Nitrincolaceae*, *Rhodobacteraceae*, and *Hyphomonadaceae* families and the SAR 116 clade (Fig. 3B). A total of 222 OTUs were specific to the water without antibiotic on D1, accounting for an average of 4% of the relative abundance, and these were dominated by an unknown family of the *Bradymonadales* and by *Rhodobacteraceae* (Fig. 3C). A core microbiome of 268 OTUs was shared in the rearing waters without or with antibiotic. This core microbiome encompassed an average of 97% of the reads. These 268 OTUs were mostly related to the *Marinobacteraceae*, *Alteromonadaceae*, and *Rhobodacteraceae* (Fig. 3D).

As described previously, the water samples were grouped according to larval stage and sampling day regardless of health status (Fig. 2A and B). However, we wondered whether several OTUs were specific to a given larval stage and health status and whether all the rearing water samples shared a core microbiome. Thus, in addition to the core microbiome on D1, several core microbiomes specific to a given condition were compared (Fig. 4): water hosting the zoea with a good survival rate (Zoea Good), water with the zoea with a bad survival rate (Zoea Bad), water with the mysis with a good survival rate (Mysis Good), water with the mysis with a bad survival rate (Mysis Bad), and rearing water with the mysis with a low survival rate (Mysis Worst). A core microbiome of 54 OTUs was co-owned by all
<figure><img src="image_112.png" /></figure>
while of the 5 OTUs specific to the "Mysis Worst" condition, only 1 was also found in the water reservoirs. These observations indicated that many OTUs detected as specific lineages for a given condition were present in the lagoon and the water reservoirs and were reactivated during rearing according to water quality, larval stage, and/or larval health status.

**Differential analyses of microbial communities.** An initial linear discriminant analysis (LDA) effect size (LEfSe) analysis was performed to investigate the families which were significantly more abundant in the water according to rearing day. On D1, 3 families were prevalent in the rearing water, *Hyphomonadaceae*, *Marinobacteraceae*, and *Alteromonadaceae*. On D2, only one family, *Thalassospiraceae*, was enriched. The following day (D3), one biomarker was highlighted with the *Saprospiraceae* family. On D4, the samples were dominated by *Rhodobacteraceae*, while no biomarker was found on D5. On D6, the samples were enriched in *Pseudoalteromonadaceae*. The following day (D7), two biomarkers, *Microbacteriaceae* and *Pseudohongiellaceae*, dominated the rearing water. On D8, *Methylophilaceae* and *Vibrionaceae* were enriched in the rearing water; while at the end of the larval rearing, on D9, the water samples were enriched in *Cryomorphaceae*.

A second LEfSe analysis was conducted to expose the families which were significantly more abundant in the rearing water according to larval stage and health status. When the nauplii with a good survival rate were in the rearing water, the most prevalent biomarkers in the samples were the same as those exhibited at D1 in the previous LEfSe: *Hyphomonadaceae*, *Marinobacteraceae*, and *Alteromonadaceae*. The rearing water hosting the zoea with a good survival rate had 3 enriched families: *Saprospiraceae*, *Sphingomonadaceae*, and *Thalassospiraceae*. Except for *Sphingomonadaceae*, all of these were detected in the first LEfSe, with the *Saprospiraceae* highlighted on D2 and the *Thalassospiraceae* on D3. The *Litoricolaceae* and *Rhodobacteraceae* were enriched in the rearing water hosting the zoea with a bad survival rate. *Rhodobacteraceae* was also the prevalent family on D4. The biomarker specific to the mysis with a good survival rate in the rearing water was *Pseudohongiellaceae*, previously detected as prevalent biomarker on D7. When the mysis had a bad survival rate, the samples were dominated by the *Cryomorphaceae* and *Microbacteriaceae*, families which were previously detected as biomarkers on D9 and D7, respectively. Three families were significantly enriched in the rearing water when it hosted the mysis with the worst survival rate, *Methylophilaceae*, *Pseudoalteromonadaceae*, and *Vibrionaceae*, previously detected in the first LEfSe on D8 for *Methylophilaceae* and D6 for *Vibrionaceae* and *Pseudoalteromonadaceae*.

Next, we performed a Pearson correlation to determine which factor, hydrochemical parameters or larval stage and status, shaped the biomarkers highlighted with the LEfSe, the most abundant families in the rearing water. The correlogram displayed that the "Mysis Good" condition was not correlated with any family and that the *Cryomorphaceae* were correlated with all factors tested. The "Nauplii Good" condition was positively correlated with the *Marinobacteraceae*, *Alteromonadaceae*, and *Hyphomonadaceae*; however, these families positively correlated with TAN and SRP concentrations, making it difficult to untangle the effect of the nauplii on the rearing water. However, because the *Marinobacteraceae* exhibited a strong coefficient correlation with the nauplii, we suggest that they are linked to the presence of the nauplii in the rearing water. The "Zoea Good" factor was strongly positively correlated with the *Saprospiraceae* and *Thalassospiraceae*, slightly positively correlated with the *Alteromonadaceae*, and negatively correlated with the *Litoricolaceae* and *Vibrionaceae*. Because the *Thalassospiraceae* were slightly positively correlated with TAN and SRP and the *Saprospiraceae* were negatively correlated with SRP, we inferred that both the *Thalassospiraceae* and *Saprospiraceae* were linked to the "Zoea Good" condition. The "Zoea Bad" condition was positively correlated with the *Rhodobacteraceae* and *Litoricolaceae*, families that either did not correlate or slightly correlated with other factors. The "Mysis Bad" condition was slightly positively correlated with the *Microbacteraceae* and the *Vibrionaceae*. The *Microbacteraceae* were also moderately correlated positively with the
CDOM proxy, while the *Vibrionaceae* were greatly correlated positively with both the "Mysis Worst" condition and the CDOM proxy. In addition, the "Mysis Worst" condition had a significant positive correlation with the *Methylophiliaceae*, which were also positively cor- related with the CDOM proxy. Regarding the correlation coefficient, the *Methylophilaceae* appeared to be linked to the "Mysis Worst" condition. The *Alteromonadaceae* appeared to be positively correlated with increasing TAN and SRP, while the *Vibrionaceae* were posi- tively correlated with increased CDOM (Aλ<sub>325</sub> proxy).

DISCUSSION

To explore whether the microbiome of the rearing water was linked to the survival rate of *P. stylirostris* larvae, we tracked the active microbial diversity of the water storage and rearing water of 7 tanks during larval development: 3 with larvae treated with antibiotic, 3 with larvae not treated with antibiotic, and 1 with only water. The meta-analysis aimed to investigate whether specific OTUs were key lineages and whether families could be used as biomarkers of good or bad survival rates. Hydrochemical analyses were also performed to monitor the behavior of the dissolved organic matter, dissolved total nitrogen, and reactive phosphorus throughout the larval rearing.

In shrimp hatcheries worldwide, antibiotics are regularly used for prophylactic purposes (under veterinary advice) or to prevent larval mortality due to pathogenic *Vibrio* species (26, 27). At the Station Aquacole de Saint Vincent shrimp hatchery, erythromycin was added into the rearing water of 3 tanks on day 0 (before nauplii addition) and days 3, 5, 7 and 9 to determine whether it could increase larval survival and larval stage metamorphosis. In this experiment, we evaluated that neither larval development nor larval survival rates were dependent on the addition of antibiotic, likewise for the alpha diversity indices of the microbiota of the rearing water on D1 with the nauplii. This is interesting because erythromycin is known to target a large spectrum of bacteria, inhibit bacterial growth, and exhibit bacteriostatic effect (28). In our case, at least in the water on D1, only the rare biosphere was affected, as less than 3% of the active microbiome was specific to the condition with or without antibiotic. At the time of writing, only one study has examined the effect of antibiotic on the microbiome of the shrimp *Macrobrachium rosenbergii* at the zoea stage (29). The authors showed that the diversity indices did not show any significant differences between the water control and the water in which zoea had been reared during 3 days in the presence of oxytetracycline; this was similar to what we found in our samples on D1. Studies on erythromycin resistance in both bacterial strains and biofilms have shown that several gammaproteobacterial strains are less sensible to erythromycin than other taxa related to the *Actinobacteria*, *Alphaproteobacteria*, or Bacteroidetes (30). This may explain why taxa affiliated with the *Gammaproteobacteria* were more abundant in the rearing water with antibiotic on D1 (Fig. 2 and 3). However, this changed when we considered the whole rearing microbiome because alpha diversity indices were significantly different between the samples with or without antibiotic. This was consistent with the dendrogram and NMDS (Fig. 2). Indeed, both the dendrogram and NMDS grouped all the rearing water samples with antibiotic together into several clusters (with a threshold of 0.5 for cluster 1 in the dendrogram). However, as larval mortalities began to really occur at the Zoea stage on D3 to D4 in all tanks with or without antibiotic, it became difficult to untangle the effect of antibiotic from that of larval mortality on the rearing water microbiome.

Throughout the larval rearing, a global eutrophication of the water was observed com-
pared to that in the control water which remained in the oligotrophic condition. Here, eutro-
phication was evaluated in the dissolved fraction with CDOM, TAN, and SRP measurements
and was mostly due to particles from the larval leftover feed, feces, and exoskeleton as well
as dead larvae and absence of water renewal. The CDOM, TAN, and SRP concentrations in
the larval tanks were irrespective of antibiotic presence, larval stage, or larval survival rates
(Fig. 1). The active microbial diversity dynamic over the rearing period seemed to be linked
to the gradual water eutrophication and the larval stages as exhibited by the dendrogram,
NMDS (Fig. 2A), LEfSe, and correlogram (Fig. 6A and C). This is linked to an overall increase
in the proportion of *Bacteroidia* accompanied by decreases in *Alpha-* and *Gammaproteobac-*
Microbiota of the Rearing Water of Shrimp Larvae

Microbiology Spectrum

<figure><img src="image_113.png" /></figure>

<figure><img src="image_114.png" /></figure>

<figure><img src="image_115.png" /></figure>

**FIG 6** Differentially abundant families according to the rearing day, to the larval growth and survival status; and Pearson correlations between the biomarkers, hydro-chemical parameters of the rearing
(Continued on next page)
teria (Fig. 2C); these classes were also predominant in the rearing water of *P. vannamei* (8) and *P. monodon* breeding (9). At the beginning of the experiment, the predominant families were *Alteromonadaceae* and *Marinobacteraceae*, and at the end, it was *Cryomorphaceae*. The fact that the *Alteromonadaceae* were highly represented in the water at the start of the rearing, positively correlated with TAN and SRP concentrations and negatively correlated with the CDOM proxy (Fig. 6C), and were even present in the control water is quite unusual because they are generally found in nutrient-sufficient to nutrient-rich environments (31, 32). This family has been found in one other shrimp larval rearing water, but at lower proportions than in our study (8). In this study, the *Alteromonadaceae* could play a significant role in larval health and/or in carbon biogeochemical cycle in the rearing water by metabolizing and removing some of the labile or semi-labile dissolved organic compounds and by breaking down organic matter molecules (33–35). Members of the family *Cryomorphaceae* are often detected in organic-rich oceanic water (36), so it was not surprising that they encompassed the most lineages at the end of the experiment in the eutrophic water (Fig. 2C and 6A). The relative abundance of the *Rhodobacteraceae* was highly distributed (>10% in most of the samples) during the entire rearing period; this was similar, but at a lower magnitude, to the findings of Zheng et al. 2017 (8). Members of the *Rhodobacteraceae* were found throughout all steps of the rearing process, from the storage water to all samples and even in the control, which was not surprising because *Rhodobacteraceae* has been defined as a keystone lineage in aquatic ecosystems (37). That fits with the suggested significant role of the *Rhodobacteraceae* as keystone taxa in the rearing water proposed by Zheng et al. (8). We have also shown that through the entire rearing, several taxa present in the primary reservoir, so in the lagoon, and in the secondary reservoirs, were not active in some intermediate sampling points and were found back active later in the rearing water highly suggesting a role of the lagoon water composition during the rearing. These data also suggested the activation of certain lineage according to the rearing condition such as the eutrophication of the water, larval development and/or survival (Fig. 5). Then, it appeared that the larval development and health also seemed to drive the dynamic of the active microbiome. In order to distinguish the effect of the rearing conditions hydro-chemical parameters or larval stage and health, on the main families highlighted with the LEfSe, we performed Pearson correlations (Fig. 6). The correlogram shows that the *Alteromonadaceae* and *Vibrioaceae*, respectively, were linked to increased ammonia, nitrogen, and soluble reactive phosphate or to increased colored dissolved organic matter in the rearing water. It also shows that the *Rhodobacteraceae*, for example, were positively correlated with the "Zoea Bad" condition. This contrasts with the keystone role of *Rhodobacteraceae* in the rearing water as described above. However, this correlation was made from both the core microbiome and the LEfSe data, meaning that when we only examined the shared microbiota among the samples, the *Rhodobacteraceae* were statistically enriched in the "Zoea Bad" condition. In addition, one could not deny that, for the entire rearing, the *Rhodobacteraceae* were always present regardless of larval stage or health status, highlighting their significant role.

Our hypothesis to understand the mass larval mortality was that a shift in the active microbial diversity of the rearing water could hamper the larvae and lead to high larval mortality. We therefore explored whether biomarkers at the family level were associated with a certain mortality rate at a given larval stage and whether these biomarkers could ultimately be used to monitor larval health. The biomarker investigation revealed the prevalence of the *Hyphomonadaceae*, *Marinobacteraceae* and *Alteromonadaceae* in the "Nauplii Good" condition (Fig. 6B). These 3 families could be involved in the

**FIG 6** Legend (Continued)

water and larval growth and survival status. (A) LEfSe (linear discriminant analysis [LDA] effect size) showing the families which were significantly more abundant in the rearing water according to rearing day from days 1 to 9. (B) Family-level biomarkers significantly more abundant in the rearing water according to larval growth and survival status. (C) Correlogram comparing biomarkers at the family level highlighted by LEfSe, with the following hydrochemical parameters: TAN, SRP, and $A_{125}$ (stands for $A_{125}$) proxy of the CDOM. Larval growth and survival status: Nauplii Good, Zoea Bad, Zoea Good, Mysis Good, Mysis Bad, and Mysis Worst.
carbon, nitrogen, and phosphorus biogeochemical cycles by removing or transforming various substrates such as sugars, chitin, and organic acid (33–35, 38, 39) during the early stage of rearing. However, the Pearson correlation showed that only the *Marino-bacteraceae* were a biomarker related to the "Nauplii Good" condition (Fig. 6C). Two families were found as biomarkers of the "Zoea Good" condition, the *Thalasso-spiraceae* and the *Saprospiraceae* (Fig. 6B), as also confirmed by the Pearson correlation (Fig. 6C). They seemed to have an important ecological role in larval wellness. Indeed, members of the *Thalassospiraceae* are known to be able to degrade complex carbon sources (40) or hydrocarbon and xenobiotic molecules (41, 42). The "Zoea Bad" condition had 2 biomarkers, the *Litoricolaceae* and the *Rhodobacteraceae*, as shown by the LEfSe and correlogram (Fig. 6B and C). Few data are available on *Litoricolaceae* except that they grow on oligotrophic medium; this could explain why they were prevalent during this stage of rearing (43). Interestingly, it has been shown that marine *Rhodo-bacteraceae* can produce antibacterial compounds or secondary metabolites that influ-ence both the producing strain and the surrounding microbiota (44). Thus, the *Rhodobacteraceae* might contribute to the beginning of dysbiosis, as the larvae started to die under this condition; by producing antimicrobial compounds, they act on the rearing water microbiota to overcome r-strategist microorganisms and/or putative pathogens. The LEfSe displayed that the *Pseudohongiellaceae* were the biomarker for the "Mysis Good" condition; while the correlogram showed no correlation between any families and the "Mysis Good" condition. So far, *Pseudohongiellaceae* have been detected in the gut of the silver carp fish (45), in marine sediments (46), and in sea-water (47). According to the genome annotations of several *Pseudohongiellaceae* spe-cies, they can degrade bio-polymers (48). This could explain their enrichment at that point of the rearing, where the mortality rate was still low and did not affect the main families. The biomarkers related to the "Mysis Bad" condition were the *Cryomor-phaceae* and the *Microbacteraceae* (Fig. 6B). However, the Pearson correlation showed that only the *Microbacteraceae* were positively correlated with the "Mysis Bad" condi-tion (Fig. 6C). This is surprising because in a previous study by Zheng et al. (8), the *Microbacteraceae* were the main contributors during the post-larval stage of rearing. The "Mysis Worst" condition included 3 biomarkers, *Methylophilaceae*, *Pseudoaltero-monadaceae*, and *Vibrionaceae*, with the LEfSe (Fig. 7B), while the Pearson correlation showed a strong positive correlation with the *Methylophilaceae* and the *Vibrionaceae*; the latter was also highly positively correlated with the chromophoric dissolved or-ganic matter (Fig. 6C, A<sub>325</sub>). The biomarker *Pseudolateromonadaceae* was also positively correlated with the chromophoric dissolved organic matter (Fig. 6C). The *Methy-lophilaceae* are methylotrophs (49). They have been detected in the gut of the sea cucumber *Apostichopus japonicus*, where it has been suggested that they help degrade organic matter, reduce the toxic effects of certain intestinal contents, and maintain gut health by influencing the intestinal microbiota (50). In our study, the *Methylophilaceae* might also act to reduce the amount of organic matter in the rearing water, as they were also positively correlated with the CDOM proxy; however, this biomarker exhib-ited the best correlation with the "Mysis Worst" condition. The *Vibrionaceae* and *Methylophilaceae* were positively correlated with the "Mysis Worst" condition and the CDOM proxy (Fig. 6C). Vibrios are often associated with shrimp rearing, either in the rearing water and/or the shrimp microbiome at all life stages, from eggs and larvae to juveniles, adults and broodstock, and in both healthy and diseased shrimps (8, 9, 11, 17, 51). Several *Vibrio* species are known to be shrimp pathogens in shrimp culture (52, 53). In New Caledonia, 2 vibrios are known: *Vibrio nigripulchritudo* (5) and *V. penae cida* (54). They cause juvenile and adult mass mortality but do not affect larvae (55). In our study, members of the *Vibrionaceae* family were present in all samples from the lagoon/primary reservoir to the control and the rearing water supplemented or not with antibiotic (Fig. 2C). Because many *Vibrio* species are opportunistic bacteria with an r-strategy (56), they might have outcompeted the other communities that were hampered by the unbalanced microbiome of the rearing water due to the larval
Microbiota of the Rearing Water of Shrimp Larvae

Microbiology Spectrum

<figure><img src="image_116.png" /><figcaption>**FIG 7** Schematic representation of the evolution of the microbial communities and their predictive functions, according to larval survival rate during rearing. Green: families detected in the rearing water when the larvae had a good survival rate. Orange: families detected in the rearing water when the larvae had a bad survival rate. Red: families detected in the rearing water when the larvae had the worst survival rate.</figcaption></figure>

mortality since the zoea stage. The *Pseudoalteromonadaceae* were statistically highly enriched in the rearing water on D4 (Fig. 6A) and in the rearing water of the mysis with the worst survival rates (Fig. 6B). These, then, may contribute to hamper dysbiosis of the water microbiota, because the mortality started on D3, overcoming r-strategist microorganisms and/or putative pathogens by producing antimicrobial compounds. Indeed, they might have been released in the rearing water from the dead larvae as the number of dead larvae in the water increased. On the other hand, the *Pseudoalteromonadaceae* are found in various marine environments and many species produce molecules with bactericidal effects (24, 57). Previous studies have shown that taxa affiliated with the *Pseudoalteromonadaceae* are present in the gut of both healthy and diseased shrimps (58) and in their rearing environment (59). Other studies have shown that *Pseudoalteromonadaceae* are opportunistic bacteria, and they may have overpassed the core microbiota of the rearing water by the end of the rearing (60, 61). Like the *Vibrionaceae*, the *Pseudoalteromonadaceae* and the *Methylophilaceae* were present from the lagoon/primary reservoir throughout the whole rearing process (Fig. 2C), indicating that they found suitable growth conditions, like many other taxa that were present in the reservoirs and then activated again later during the rearing process. Altogether, these biomarker data allowed us to sum up the time series of the evolution of the biomarkers during the rearing process, as shown in Fig. 7.

Taken together, our results indicate that the active microbiota of the rearing water is highly dynamic throughout the rearing process, with distinct communities influenced by the progressive eutrophication of the water, larval stage, and larval survival rate. Microbial families related to water eutrophication were highlighted. The *Alteromonadaceae* were related to increased ammonia, nitrogen, and soluble reactive phosphate, while the *Vibrionaceae* and *Methylophilaceae* were linked to increased colored dissolved organic matter in the rearing water. Many OTUs were shared between the primary reservoir containing lagoon seawater, the secondary reservoirs (both RC and C samples), and the core microbiomes of the rearing waters. This provides evidence of the activation of certain lineages according to water composition and/or larval stage and health and is also evidence of the influence of the lagoon water initial composition
on the larval rearing water microbiome. Biomarkers at the family level were revealed according to a given larval stage, larval health status, and specific microbiome (Fig. 7). This suggests that each larval stage and status condition has its specific microbial sig-nature in the rearing water. In addition, our findings provide evidence of biomarkers which could be used as sentinels to monitor larval health during the early stages of rearing. Indeed, *Marinobacteraceae* were specific to the "Nauplii Good" condition, *Thalassospiraceae* and *Saprospiraceae* to the "Zoea Good," *Litoricolaceae* to the "Zoea Bad," *Microbacteraceae* to the "Mysis Bad," and *Methylophilaceae* to the "Mysis Worst." We eliminated the *Vibrionaceae* as biomarkers of the "Mysis Worst" condition because they had a stronger correlation with dissolved organic matter. The *Microbacteraceae* and *Methylophilaceae* could be potential announcers of upcoming and progressive larval mortality. Indeed, their prevalence and abundance in the rearing water, or even earlier in the lagoon or reservoir, might indicate the future fate of larval rearing. Thus, the highlighted biomarker families might be used as potential early warning sentinels in water storage to infer the evolution of the larval rearing during its early days. Potential future steps could be developed, such as designing PCR or qPCR primers spe-cific to these key lineages to test the lagoon water before rearing and during the early days of rearing. Ultimately, we may eventually be able to manage the active microbiota of the rearing water, which is a major concern in aquaculture (62), and select beneficial microorganisms to improve larval shrimp rearing.

## MATERIALS AND METHODS

**Larval shrimp rearing.** The experiments were carried out in November 2018 in an experimental shrimp hatchery hosted in a shrimp farming research facility at the Station Aquacole de Saint Vincent (Boulouparis, New Caledonia). The reproduction of *P. stylirostris* was performed by artificially inseminat-ing mature females as described by Pham et al. (63). The day after hatching, nauplii were transferred into 100-L rearing tanks at a density of 180 nauplii/L. The water salinity was 35 ppt, the temperature was 30°C, and a 14:10 h day:night photoperiod was applied. From the zoea 1 to zoea 2 stages, larvae were fed with microparticles six times per day and supplemented with frozen *Tetraselmis* sp. twice a day. For larvae from the zoea 3 stage to the post-larvae stage, in addition to microparticles, living *Artemia* sp. nauplii (from 20 to 40 nauplii/larvae/day) were administered twice per day instead of frozen *Tetraselmis* sp. Throughout the 10 days of the larval rearing, there was no water exchange. The seawater was col-lected from the Saint Vincent Bay (Boulouparis, New-Caledonia) to a primary reservoir through a pump-ing system with pores of 1 cm. Next, the water was filtered through a sand filter and a 10-µm filter prior to storage in a secondary reservoir, where it was continuously circulated through a skimmer and a series of 10- and 5-µm filters over 3 days. The day following the fecundations, the 100-L rearing tanks were filled with water which had passed through a 1-µm filter and been treated with UV. This water followed the same path as described by Giraud et al. (17). EDTA at 5 g·m⁻³ was added in all tanks. An increasing intensive bubbling was implemented in each tank to allow good oxygenation of the water. The experi-ment was performed in triplicate for each condition except for the control. Thus, 7 tanks were monitored during the experiment: 3 larval tanks with erythromycin added at 2 ppm a few hours after the EDTA (day 0) and on days 3, 5, 7 and 9; 3 tanks with larvae but without antibiotic; and 1 tank (control) without larvae, antibiotic, or food.

**Water sample collection and storage.** Prior to the start of the experiment, 1 L of lagoon seawater from the primary reservoir was collected for both chemical and microbial diversity analyses (GR sample). Next, just after being pumped, 1 L of water from the secondary reservoir was collected (RC sample) after an additional 3-day circulation through the skimmer and filters of the secondary reservoir (C sample). The tanks were filled the day after the fecundations and, after addition of EDTA, 1 L was collected in the control tank (sample control, D0). The nauplii were introduced to the tanks on D0 and sample collection began on D1. Throughout the 10 days of the experiment, 1 L of water was collected daily from each tank in the morning before the 1st feeding of the day (6 AM). Samples were collected in opaque contain-ers and immediately kept on ice until filtration. The water samples were filtered through 0.22-µm sterile membrane filters (S-Pak, MilliporeSigma, Burlington, MA). In order to examine the active microbial diver-sity, the filters were directly stored at -80°C. The filtrates were distributed and stored accordingly: 50 mL was poured into a pre-combusted (8 h, 450°C) glass bottle for chromophoric dissolved organic matter (CDOM) analysis and kept in the dark until further analysis done within the hour after the sample collection; 40 mL for the total ammonium determination and 40 mL for the soluble reactive phosphate, both stored in acid-washed plastic vials and put at -20°C until analysis. A total of 67 samples were col-lected throughout the rearing in addition to the 3 storage water samples. The rearing and control waters were named as follows: "control" for samples collected from the control tank; "with" for samples col-lected from tanks with antibiotic and "without" for those collected from tanks without antibiotic, fol-lowed by the sampling day (D0 indicating the day after the fecundation, which corresponds to the day where the nauplii were placed in the rearing tanks; to D9, indicating the 10th day of rearing) and the replicate numbers 1 to 3 corresponding to the triplicates.
**Daily determination of the zootechnical parameters: shrimp larval stages and survival rates.**

Every day, larval stages and survival rates were determined by examination under a binocular magnifying glass. The Larval Stage Index (LSI) was calculated as follows, based on the equation made by Maddox and Manzi (64) but modified to consider all life stages:

$$ \text{LSI} = ([0 \times N_{i1}] + [1 \times Z1] + [2 \times Z2] + [3 \times Z3] + [4 \times M1] + [5 \times M2] + [6 \times M3] + [7 \times PL])/N \quad (1) $$

where $N_{i1}$ is the number of larvae observed during the nauplius stage, Z1 indicates the number observed during the zoea 1 stage, Z2 is zoea 2, Z3 is zoea 3, M1 is mysis 1, M2 is mysis 2, M3 is mysis 3, PL is post-larval 1, and N is the total number of observed larvae (minimum 30). The Larval Survival Rate (LSR) was determined by averaging 3 direct counts of the living and dead larvae in 1 L of rearing water per tank and per day. The LSR was determined as follows: LSR =100 × counted living larvae/initial number of nauplii.

**Hydrochemical analysis.** The temperature and salinity in each tank were monitored twice a day (morning and afternoon) using the probe pH/Cond 3320 (Mettler Toledo, Columbus, OH). The absorption spectra of the chromophoric dissolved organic matter was recorded directly at wavelengths from 200 to 700 nm at 1-nm intervals using a 10-cm quartz tank and a Shimadzu UV-1800 spectrometer (65). MilliQ water was used as the reference. The absorption coefficients at 325 nm ($A_{325}$), which was a proxy for CDOM concentration, were used to describe the evolution of CDOM in the tanks (66). Concentrations of TAN and SRP in the filtered water were analyzed using the methods described by Holmes et al. (67) and Murphy and Riley, respectively (68).

**RNA extraction, reverse transcription, sequencing method, and sequence processing.** RNAs of the water samples were extracted from the filters using the RNAeasy Powerwater kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. Then, the RNAs were reverse-transcribed into cDNAs using 10 µL of RNA (200 ng/µL), 1 µL of the reverse transcriptase M-MLV at 200 u/µL (Promega, Madison, WI), 2 µL random hexamers at 50 µM, 4 µL of 5× buffer, 2 µL of a mix of deoxynucleoside triphosphate at 10 mM each, and 1 µL of RNase/DNase free water. The reverse transcriptions were carried out on a Veriti instrument (Applied Biosystems, Waltham, MA) using the following program: 10 min at 25°C, 2 h at 42°C, and 5 min at 85°C. The cDNAs were stored at -80°C until shipping to MrDNA (Molecular Research LP, Shallowwater, TX), where PCR, barcode indexing, and sequencing of the V4 hyper-variable region of the reverse-transcribed bacterial 16S rRNA molecule were conducted. PCRs were performed for each sample using the universal primer combination 515F and 806R (69, 70). The sequencing was performed using Illumina HiSeq technology with 2 × 300 bp chemistry following MrDNA protocols. The raw prokaryotic 16S rRNA gene sequencing data from HiSeq sequencing were processed using the pipline Find, Rapidly, OTU with the Galaxy Solution (FROGS) version 2.0.0 (71). In brief, all reads first were trimmed by length and assigned to a specific sample based on barcode recognition using the demultiplex tool in FROGS. Next, the sequences were clustered using the SWARM algorithm with an aggregation distance of 3. Chimeras were removed using the Vsearch algorithm. An additional filtering step for the abundance was used at a threshold of 0.005% (72) to select the most relevant OTU sequences. Taxonomic affiliation was assigned by BLASTn+ using the Silva database 132-16S (73).

Diversity analyses were performed after removing the chloroplast and the mitochondria sequences.

**Statistical analyses and Venn diagrams.** The alpha diversity, ACE, Chao 1, Shannon, and inverse Shannon indices were calculated for each sample using FROGSSTAT Phyloseq in Galaxy. Mann-Whitney tests were performed using XLSTAT (Addinsoft, Paris, France), a statistic tool appended to Microsoft Excel, on the alpha diversity indices to assess significant differences between the following treatments: rearing water with versus without antibiotic on D1, rearing water versus without antibiotic throughout the rearing, control versus rearing water with antibiotic, and control versus the rearing water without antibiotic. Mann-Whitney tests were also performed to determine the impact of the absence or presence of antibiotic on the chemical values (TAN, SRP, and CDOM) and on larval stages and survival rates. Results with a P value below 0.05 were considered statistically significant. Prior to microbiota analysis, the sequencing data were normalized with the count per million (CPM) method using the edgeR package in R Studio software. Beta diversity was visualized using an agglomerative hierarchical clustering method and an NMDS plot. The dendrogram was built using a Bray-Curtis dissimilarity matrix and the agglomeration method of Ward (74), using the Vegan package (75) in R Studio. The NMDS and Bray-Curtis distances were built using the packages phyloseq (76) and ggplot2 (77) in R Studio. For further statistical analyses, we divided the larval survival rates into 3 groups as follows: good survival rate corresponding to a larval survival rate (LSR) above, equal to, or slightly below the reference (less than 5%) (the reference is an average survival rate calculated for each day using data from 10 years of successful rearing; Ifremer data, Pham, personal communication); bad survival rate corresponding to a larval survival rate between 50% and 100% of the reference value (e.g., an LSR of 60% is a bad survival rate when the reference, on the same day, is at 80%); and worst LSR displaying a survival rate below 50% of the reference value. Several Venn diagrams (78) were constructed using the Jvenn interactive tool (79) to highlight specific microbial communities and particular patterns specific to given conditions. An initial Venn diagram was made to compare the impact of the antibiotic on the active microbial diversity in the rearing water on D1 with the nauplii before the start of larval mortality. From this diagram, we created a core microbiome of the rearing water hosting the nauplii with a good survival rate. Next, several core microbiomes were constructed specific to the following conditions: corresponding to the water hosting the zoea with a good survival rate (Zoea Good), corresponding to the water with the zoea with a bad survival rate (Zoea Bad), corresponding to the water with the mysis with a good survival rate (Mysis Good), corresponding to the water with the mysis with a bad survival rate (Mysis Bad), and
corresponding to the rearing water with the mysis with a low survival rate (Mysis Worst), were con-
structed. These 6 core microbiomes were ultimately compared together to highlight a core microbiome
of the rearing water present throughout the larval development and also specific microbiomes. These
core and specific microbiomes of interest were then compared to the water storage microbiomes to de-
cipher the role of the lagoon water on the rearing process.

The LEFse (80) was used to identify characteristic biomarkers of a specific condition based on their relative abundances and was performed using the microbiomeMarker package (81) in R Studio. LEFSe was calculated using the OTUs common to all rearing water samples to identify biomarkers common to specific conditions. A threshold of 4 was used for the logarithmic LDA score for discriminative features and a P value of 0.05 was set for the Kruskal-Wallis tests between the conditions. Then, a correlogram was built between the families identified in the LEFse, the hydrochemical parameters, and the growth and health status of the larvae present in the rearing water (Nauplii Good, Zoea Good, Zoea Bad, Mysis).

**Data availability.** The data sets generated and analyzed for this study can be found in the NCBI SRA repository under the BioProject ID PRJNA736535 (SRA accession no. SRR17333422 to SRR17333488).

SUPPLEMENTAL MATERIAL

Supplemental material is available online only.

SUPPLEMENTAL FILE 1, PDF file, 0.1 MB.

ACKNOWLEDGMENTS

We are grateful to the SASV teams for their indubitable help during the experiment,
especially their zootechnical and laboratory support. We especially thank Jean-Sébastien
Lam for his precious help in the hatchery and Florence Antypas, Marie-Madeleine Le Gall
and Mélanie Sérusier for their valuable contribution to the daily sample filtration, storage
for further analyses, and CDOM measurement. We also thank Anthony Muselli for his help
with figure shaping and editing.

This work was supported by the RESSAC project (LEAD-NC, Ifremer, New Caledonia) within the framework agreement with the New Caledonian Provinces and the Government.

We declare that the research was conducted in the absence of any commercial or
financial relationships that could be construed as potential conflicts of interest.

N.C., V.B., N.W., and D.P. conceived and designed the experiment. N.C., V.B., M.B., D.A., B.V., J.-R.M., N.W., and D.P. performed the experiment and acquired the data. N.C., V.B., C.G., M.B., N.W., and D.P. analyzed the data. N.C. prepared the figures and drafted the manuscript. N.C., V.B., C.G., N.W., and D.P. reviewed the draft. All authors approved the final version of the manuscript.

REFERENCES

1. Pajares S, Ramos R. 2019. Processes and microorganisms involved in the marine nitrogen cycle: knowledge and gaps. Front Mar Sci 6:739. https://doi.org/10.3389/fmars.2019.00739.

2. Jiao N, Herndl GJ, Hansell DA, Benner R, Kattner G, Wilhelm SW, Kirchman DL, Weinbauer MG, Luo T, Chen F, Azam F. 2010. Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean. Nat Rev Microbiol 8:593–599. https://doi.org/10.1038/nrmicro2386.

3. Ying C, Chang MJ, Chang YT, Chao WL, Yeh SL, Hsu JT. 2020. Photosynthetic bacteria enhanced water quality and integrity of microbial community composition of integrated multitrophic aquaculture system of milk-fish *Chanos chanos* coastal farming. Fish Sci 86:329–338. https://doi.org/10.1007/s12562-019-01387-z.

4. Ganguly S, Prasad A. 2012. Microflora in fish digestive tract plays significant role in digestion and metabolism. Rev Fish Biol Fisheries 22:11–16. https://doi.org/10.1007/s11160-011-9214-x.

5. Goarant C, Reynaud Y, Ansquer D, de Decker S, Saulnier D, Le Roux F. 2006. Molecular epidemiology of Vibrio nigripulchritudo, a pathogen of cultured penaeid shrimp (*Litopenaeus stylirostris*) in New Caledonia. Syst Appl Microbiol 29:570–580. https://doi.org/10.1016/j.syapm.2005.12.005.

6. Carbone D, Faggio C. 2016. Importance of prebiotics in aquaculture as immunostimulants. Effects on immune system of *Sparus aurata* and *Dicentrarchus labrax*. Fish Shellfish Immunol 54:172–178. https://doi.org/10.1016/j.fsi.2016.04.011.

7. Wei H, Li X, Tang L, Yao H, Ren Z, Wang C, Mu C, Shi C, Wang H. 2020. 16S rRNA gene sequencing reveals the relationship between gut microbiota and ovarian development in the swimming crab *Portunus trituberculatus*. Chemosphere 254:126891. https://doi.org/10.1016/j.chemosphere.2020.126891.

8. Zheng Y, Yu M, Liu J, Qiao Y, Wang L, Li Z, Zhang XH, Yu M. 2017. Bacterial community associated with healthy and diseased Pacific white shrimp (*Litopenaeus vannamei*) larvae and rearing water across different growth stages. Front Microbiol 8:1362. https://doi.org/10.3389/fmicb.2017.01362.

9. Anghong P, Uengwetwanit T, Arayamethakorn S, Chaitongsakul P, Karoonuthaisiri N, Rungrassamee W. 2020. Bacterial analysis in the early developmental stages of the black tiger shrimp (*Penaeus monodon*). Sci Rep 10:4896. https://doi.org/10.1038/s41598-020-61559-1.

10. Sun F, Wang Y, Wang C, Zhang L, Tu K, Zheng Z. 2019. Insights into the in-
testinal microbiota of several aquatic organisms and association with the
surrounding environment. Aquaculture 507:196-202. https://doi.org/10
.1016/j.aquaculture.2019.04.026.

11. Wang Y, Wang K, Huang L, Dong P, Wang S, Chen H, Lu Z, Hou D, Zhang D. 2020. Fine-scale succession patterns and assembly mechanisms of bacterial community of *Litopenaeus vannamei* larvae across the developmental cycle. *Microbiome* 8:106. https://doi.org/10.1186/s40168-020-00879-w.

12. Bakke I, Coward E, Andersen T, Vadstein O. 2015. Selection in the host structures the microbiota associated with developing cod larvae (*Gadus morhua*). Environ Microbiol 17:3914–3924. https://doi.org/10.1111/1462-2920.12888.

13. Zhang Z, Li D, Xu W, Tang R, Li L. 2019. Microbiome of co-cultured fish exhibits host selection and niche differentiation at the organ scale. Front Microbiol 10:2576. https://doi.org/10.3389/fmicb.2019.02576.

14. Pratte ZA, Besson M, Hollman RD, Stewart FJ. 2018. The gills of reef fish support a distinct microbiome influenced by hostspecific factors. Appl Environ Microbiol 84:e00063-18. https://doi.org/10.1128/AEM.00063-18.
15. Giatsis C, Sipkema D, Smidt H, Heilig H, Benvenuti G, Verreth J, Verdegem M. 2015. The impact of rearing environment on the development of gut microbiota in tilapia larvae. Sci Rep 5:18206-18215. https://doi.org/10.1038/srep18206.

16. Vadstein O, Attramadal KJK, Bakke I, Forberg T, Olsen Y, Verdegem M, Giatsis C, Skjermo J, Aasen IM, Gatesoupe FJ, Dierckens K, Sorgeloos P, Bossier P. 2018. Managing the microbial community of marine fish larvae: a holistic perspective for larviculture. Front Microbiol 9:1820. https://doi.org/10.3389/fmicb.2018.01820.

17. Giraud C, Callac N, Beauvais M, Mailliez J-R, Ansquer D, Selmaoui-Folcher N, Pham D, Wabete N, Boulo V. 2021. Potential lineage transmission within the active microbiota of the eggs and the nauplii of the shrimp *Litopenaeus stylirostris*: possible influence of the rearing water and more. PeerJ e12241. https://doi.org/10.7717/peerj.12241.

18. Huang F, Pan L, Song M, Tian C, Gao S. 2018. Microbiota assemblages of water, sediment, and intestine and their associations with environmental factors and shrimp physiological health. Appl Microbiol Biotechnol 102: 8585-8598. https://doi.org/10.1007/s00253-018-9229-5.

19. Bhute SS, Ghaskadbi SS, Shouche YS. 2017. Rare biosphere in human gut: a less explored component of human gut microbiota and its association with human health, p 133-142. In: Mining of microbial wealth and Meta-Genomics. Springer Singapore, Gateway East, Singapore.

20. Ziegler M, Eguiluz VM, Duarte CM, Voolstra CR. 2018. Rare symbionts may contribute to the resilience of coral-algal assemblages. ISME J 12:161-172. https://doi.org/10.1038/ismej.2017.151.

21. Cheaib B, Seghouani H, Llewellyn M, Vandal-Lenghan K, Mercier P-L, Derome N. 2021. The yellow perch (*Perca flavescens*) microbiome revealed resistance to colonisation mostly associated with neutralism driven by rare taxa under cadmium disturbance. Anim Microbiome 3:3. https://doi.org/10.1186/s42523-020-00063-3.

22. Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, Küsel K, Rillig MC, Rivett DW, Salles JF, Van Der Heijden MGA, Youssef NH, Zhang X, Wei Z, Hol GWH. 2017. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J 11:853-862. https://doi.org/10.1038/ismej.2016.174.

23. Pester M, Bittner N, Deevong P, Wagner M, Loy A. 2010. A 'rare biosphere' microorganism contributes to sulfate reduction in a peatland. ISME J 4: 1591-1602. https://doi.org/10.1038/ismej.2010.75.

24. Pham D, Ansquer D, Chevalier A, Duga C, Peyramale A, Wabete N, Labreuche Y. 2014. Selection and characterization of potential probiotic bacteria for *Litopenaeus stylirostris* shrimp hatcheries in New Caledonia. Aquaculture 432:475-482. https://doi.org/10.1016/j.aquaculture.2014.04.031.

25. Beliaeff B, Chim L, Della Patrona L, Goyard E, Herlin J, Labreache Y, Walling E, Ansquer D, Brun P, Castex M, Coatanea D, Courties C, De Lorgeril J, Dufour R, Frappier J, Goarant C, Huber M, Lemaire P, Lemonnier H, Le Roux F, Loubersac L, Lucas R, Patrois J, Peignon J-M, Pham D, Ramage Y, Soulard B, Vic M, Vourey E, Wabete N. 2009. DEDUCTION: a research project for shrimp farming sustainability in New Caledonia.

26. Holmström K, Gräslund S, Wahlström A, Poungshompo S, Bengtsson BE, Kautsky N. 2003. Antibiotic use in shrimp farming and implications for environmental impacts and human health. Int J Food Sci Tech 38: 255-266. https://doi.org/10.1046/j.1365-2621.2003.00671.x.

27. Aftabuddin S, Kader A, Kamal AM, Zafar M. 2009. Present status on the use of antibiotics and grow-out ponds and their environmental implications in Bangladesh. AACL Bioflux 2:369-380.

28. Griffith RS, Black HR. 1970. Erythromycin. Med Clin North Am 54:1199-1215. https://doi.org/10.1016/S0025-7125(16)32587-1.

29. Ma R, Wang Y, Zhao L, Zhou J, Zhao S, Li X, Dai X, Fang W. 2020. Exploration of the death causes in zoea stages of *Macrobacium rosenbergii* based on microbial diversity analysis. Aquaculture 519:734710. https://doi.org/10.1016/j.aquaculture.2019.734710.

30. Moskot M, Kotlarska E, Jakóbkiewicz-Banecka J, Gabig-Cimińska M, Fari K, Wegrzyn G, Wróbel B. 2012. Metal and antibiotic resistance of bacteria isolated from the Baltic Sea. Int Microbiol 15:131-139. https://doi.org/10.2436/20.1501.01.166.

31. López-Pérez M, Rodriguez-Valera F. 2014. The family Alteromonadaceae, p 69-92. In Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (ed), The prokaryotes: Gammaproteobacteria. Springer Berlin Heidelberg, Berlin, Germany.

32. Maki T, Lee KC, Pointing SB, Watanabe K, Aoki K, Archer SDJ, Lacap-Bugler DC, Ishikawa A. 2021. Desert and anthropogenic mixing dust deposition influences microbial communities in surface waters of the western Pacific Ocean. Sci Total Environ 791:148026. https://doi.org/10.1016/j.scitotenv.2021.148026.

33. Gómez-Consarnau L, Needham DM, Weber PK, Fuhrman JA, Mayali X. 2019. Influence of light on particulate organic matter utilization by attached and free-living marine bacteria. Front Microbiol 10:1204. https://doi.org/10.3389/fmicb.2019.01204.

34. Pedler BE, Aluwihare LI, Azam F. 2014. Single bacterial strain capable of significant contribution to carbon cycling in the surface ocean. Proc Natl Acad Sci U S A 111:7202-7207. https://doi.org/10.1073/pnas.1401887111.

35. McCarren J, Becker JW, Repeta DJ, Shi Y, Young CR, Malmstrom RR, Chisholm SW, DeLong EF. 2010. Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea. Proc Natl Acad Sci U S A 107:16420-16427. https://doi.org/10.1073/pnas.1010732107.

36. Bowman JP, McMeekin TA. 2015. *Alteromonadaceae*. In Trujillo ME, Dedysh S, DeVos P, Hedlund B, Kämpfer P, Rainey FA, Whitman WB (ed), Bergey's manual of systematics of archaea and bacteria. John Wiley and Sons, Inc., New York, NY.

37. Banerjee S, Schlaeppi K, van der Heijden MGA. 2018. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol 16: 567-576. https://doi.org/10.1038/s41579-018-0024-1.

38. Varela MM, Rodriguez-Ramos T, Guerrero-Feijóo E, Nieto-Cid M. 2020. Changes in activity and community composition shape bacterial responses to size-fractionated marine DOM. Front Microbiol 11:586148. https://doi.org/10.3389/fmicb.2020.586148.

39. Stephens BM, Opalk K, Petras D, Liu S, Comstock J, Aluwihare LI, Hansell DA, Carlson CA. 2020. Organic matter composition at ocean station papa affects its bioavailability, bacterioplankton growth efficiency and the responding taxa. Front Mar Sci 7:10.3389/fmars.2020.590273. https://doi.org/10.3389/fmars.2020.590273.

40. McIlroy SJ, Nielsen PH. 2014. The family *Saprospiraceae*, p 863-889. In The prokaryotes: other major lineages of bacteria and the archaea. Springer Science and Business Media, Berlin, Germany.

41. Zhao B, Wang H, Li R, Mao X. 2010. *Thalassospira xianhensis* sp. nov., a polycyclic aromatic hydrocarbon-degrading marine bacterium. Int J Syst Evol Microbiol 60:1125-1129. https://doi.org/10.1099/ijss.0.013201-0.

42. Nogi Y, Yoshizumi M, Miyazaki M. 2014. *Thalassospira povalitica* sp. nov., a polyvinyl-alcohol-degrading marine bacterium. Int J Syst Evol Microbiol 64:1149-1153. https://doi.org/10.1099/ijss.0.058321-0.

43. Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F. 2013. The prokaryotes: Gammaproteobacteria. Springer Publishing, New York, NY.

44. Henriksen NNSE, Lindqvist LL, Wibowo M, Sonnenschein EC, Bentzon-Tilia M, Gram L. 2022. Role is in the eye of the beholder: the multiple functions of the antibacterial compound tropodithietic acid produced by marine *Rhodobacteraceae*. FEMS Microbiol Rev 46:fuac007. https://doi.org/10.1093/femsre/fuac007.

45. Khurana H, Sharma M, Bharti M, Singh DN, Negi RK. 2021. Gut milieu shapes the bacterial communities of invasive silver carp. Genomics 113:815-826. https://doi.org/10.1016/j.jygeno.2021.01.013.

46. Li A-Z, Han X-B, Zhang M-X, Zhou Y, Chen M, Yao Q, Zhu H-H. 2019. Culture-dependent and -independent analyses reveal the diversity, structure, and assembly mechanism of benthic bacterial community in the Ross Sea, Antarctica. Front Microbiol 10:2523. https://doi.org/10.3389/fmicb.2019.02523.

47. Nowinski B, Smith CB, Thomas CM, Esson K, Marin R, Preston CM, Birch JM, Scholin CA, Huntemann M, Clum A, Foster B, Foster B, Roux S, Palaniappan K, Varghese N, Mukherjee S, Reddy TBK, Daum C, Copeland A, Chen I-MA, Ivanova NN, Kyrpides NC, Glavina del Rio T, Whitman WB, Kiene RP, Eloe-Fadrosh EA, Moran MA. 2019. Microbial metagenomes and metatranscriptomes during a coastal phytoplankton bloom. Sci Data 6: 129. https://doi.org/10.1038/s41597-019-0132-4.

48. Francis B, Urich T, Mikolasch A, Teeling H, Amann R. 2021. North Sea spring bloom-associated *Gammaproteobacteria* fill diverse heterotrophic niches. Environ Microbiome 16:15. https://doi.org/10.1186/s40793-021-00385-y.

49. Chistoserdova L. 2011. Modularity of methylotrophy, revisited. Environ Microbiol 13:2603-2622. https://doi.org/10.1111/j.1462-2920.2011.02464.x.

50. Sun Y, Rabbi MH, Ma S, Wen Z, Li X, Mi R, Meng N, Li Y, Wang Q, Du X, Ren T. 2021. Effect of dietary *Cordyceps* polysaccharide supplementation on intestinal microflora and immune response of *Apostichopus japonicus*. Aquac Res 52:5198-5212. https://doi.org/10.1111/are.15389.

51. Vandenberghe J, Verdonck L, Robles-Arozarena R, Rivera G, Bolland A, Balladares M, Gomez-Gil B, Calderon J, Sorgeloos P, Swings J. 1999. *Vibrios* associated with *Litopenaeus vannamei* larvae, postlarvae, broodstock, and hatchery probionts. Appl Environ Microbiol 65:2592-2597. https://doi.org/10.1128/AEM.65.6.2592-2597.1999.
52. Kumar R, Ng TH, Wang H. 2020. Acute hepatopancreatic necrosis disease in penaeid shrimp. Rev Aquacult 12:1867-1880. https://doi.org/10.1111/raq.12414.

53. Zhang X-H, He X, Austin B. 2020. Vibrio harveyi: a serious pathogen of fish and invertebrates in mariculture. Mar Life Sci Technol 2:231-245. https://doi.org/10.1007/s42995-020-00037-z.

54. Goarant C, Merien F. 2006. Quantification of Vibrio penaeicida, the etiolog-ical agent of Syndrome 93 in New Caledonian shrimp, by real-time PCR using SYBR Green I chemistry. J Microbiol Methods 67:27-35. https://doi.org/10.1016/j.mimet.2006.02.013.

55. Goarant C, Régnier F, Brizard R, Marteau A-L. 1998. Acquisition of susceptibil-ity to Vibrio penaeicida in Penaeus stylirostris postlarvae and juveniles. Aquaculture 169:291-296. https://doi.org/10.1016/S0044-8486(98)00380-9.

56. Vadstein O, Attramadal KJK, Bakke I, Olsen Y. 2018. K-selection as micro-bial community management strategy: a method for improved viability of larvae in aquaculture. Front Microbiol 9:2730. https://doi.org/10.3389/fmicb.2018.02730.

57. Sorieul L, Rückert C, Al-Dilaimi A, Winkler A, Kalinowski J, Pham D, Wabete N, Boulo V. 2019. Whole-genome sequence of Pseudoalteromonas sp. NC201, a probiotic strain for Litopenaeus stylirostris hatcheries in New Caledonia. Micro-biol Resour Announc 8:e00477-19. https://doi.org/10.1128/MRA.00477-19.

58. Xiong J, Dai W, Qiu Q, Zhu J, Yang W, Li C. 2018. Response of host-bacter-rial colonization in shrimp to developmental stage, environment and dis-ease. Mol Ecol 27:3686-3699. https://doi.org/10.1111/mec.14822.

59. Huang L, Guo H, Chen C, Huang X, Chen W, Bao F, Liu W, Wang S, Zhang D. 2020. The bacteria from large-sized bioflocs are more associated with the shrimp gut microbiota in culture system. Aquaculture 523:735159. https://doi.org/10.1016/j.aquaculture.2020.735159.

60. Tamburini C, Garel M, Barani A, Boeuf D, Bonin P, Bhairy N, Guasco S, Jacquet S, Le Moigne FAC, Panagiotopoulos C, Riou V, Veloso S, Santinelli C, Armougom F. 2021. Increasing hydrostatic pressure impacts the pro-karyotic diversity during Emiliania huxleyi aggregates degradation. Water (Switzerland) 13:2616. https://doi.org/10.3390/w13192616.

61. Lydon KA, Lipp EK. 2018. Taxonomic annotation errors incorrectly assign the family Pseudoalteromonadaceae to the order Vibrionales in Green-genes: implications for microbial community assessments. PeerJ 6:5248. https://doi.org/10.7717/peerj.5248.

62. Rurangwa E, Verdegem MCJ. 2015. Microorganisms in recirculating aqua-culture systems and their management. Rev Aquacult 7:117-130. https://doi.org/10.1111/raq.12057.

63. Pham D, Charmantier G, Wabete N, Boulo V, Broutoi F, Mailliez JR, Peignon JM, Charmantier-Daures M. 2012. Salinity tolerance, ontogeny of osmoregulation and zootechnical improvement in the larval rearing of the Caledonian Blue Shrimp, Litopenaeus stylirostris (Decapoda, Penaei-dae). Aquaculture 362-363:10-17. https://doi.org/10.1016/j.aquaculture.2012.07.026.

64. Maddox MB, Manzi JJ. 2009. The effects of algal supplements on static system culture of Macrobrachium rosenbergii (De Man) larvae 1, 2. World Aquaculture Soc J 7:677-698. https://doi.org/10.1111/j.1749-7345.1976.tb00096.x.

65. Helms JR, Stubbins A, Ritchie JD, Minor EC, Kieber DJ, Mopper K. 2009. Erratum: absorption spectral slopes and slope ratios as indicators of mo-lecular weight, source, and photobleaching of chromophoric dissolved

organic matter (Limnology and Oceanography 53 955-969). Limnol Oce-anogr 54:1023. https://doi.org/10.4319/lo.2009.54.3.1023.

66. Sadeghi-Nassaj SM, Catalá TS, Álvarez PA, Reche I. 2018. Sea cucumbers reduce chromophoric dissolved organic matter in aquaculture tanks. PeerJ 6:e4344. https://doi.org/10.7717/peerj.4344.

67. Holmes RM, Aminot A, Kérouel R, Hooker BA, Peterson BJ. 1999. A simple and precise method for measuring ammonium in marine and freshwater ecosystems. Can J Fish Aquat Sci 56:1801-1808. https://doi.org/10.1139/f99-128.

68. Murphy J, Riley JP. 1962. A modified single solution method for the deter-mination of phosphate in natural waters. Anal Chim Acta 27:31-36. https://doi.org/10.1016/S0003-2670(00)88444-5.

69. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. 2011. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A 108:4516-4522. https://doi.org/10.1073/pnas.1000080107.

70. Hugerth LW, Wefer HA, Lundin S, Jakobsson HE, Lindberg M, Rodin S, Engstrand L, Andersson AF. 2014. DegePrime, a program for degenerate primer design for broad-taxonomic-range PCR in microbial ecology stud-ies. Appl Environ Microbiol 80:5116-5123. https://doi.org/10.1128/AEM.01403-14.

71. Escudié F, Auer L, Bernard M, Mariadassou M, Cauquil L, Vidal K, Maman S, Hernandez-Raquet G, Combes S, Pascal G. 2018. FROGS: Find, Rapidly, OTUs with Galaxy Solution. Bioinformatics 34:1287-1294. https://doi.org/10.1093/bioinformatics/btx791.

72. Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, Mills DA, Caporaso JG. 2013. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods 10:57-59. https://doi.org/10.1038/nmeth.2276.

73. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590-D596. https://doi.org/10.1093/nar/gks1219.

74. Ward JHJ. 1963. Hierarchical grouping to optimize an objective function. Am Stat Assoc 58:236-244. https://doi.org/10.1080/01621459.1963.10500845.

75. Oksanen J. 2022. Package "vegan" Title Community Ecology Package.

76. McMurdie PJ, Holmes S. 2013. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8: e61217. https://doi.org/10.1371/journal.pone.0061217.

77. Wickham H, Chang W. 2016. Package ggplot2: an implementation of the grammar of graphics.

78. Shade A, Handelsman J. 2012. Beyond the Venn diagram: the hunt for a core microbiome. Environ Microbiol 14:4-12. https://doi.org/10.1111/j.1462-2920.2011.02585.x.

79. Bardou P, Mariette J, Escudié F, Djemiel C, Klopp C. 2014. SOFTWARE Open Access jvenn: an interactive Venn diagram viewer. BMC Bioinformatics 15: 293. https://doi.org/10.1186/1471-2105-15-293.

80. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. 2011. Metagenomic biomarker discovery and explanation. Genome Biol 12: R60. https://doi.org/10.1186/gb-2011-12-6-r60.

81. Cao Y. Tools for microbiome marker identification. https://doi.org/10.5281/zenodo.3749415.
1 **Microbial biomarker detection in shrimp larvae rearing water as putative bio-surveillance proxies in shrimp aquaculture**

3

4 Nolwenn Callac¹, Carolane Giraud¹,², Viviane Boulo¹,³, Nelly Wabete¹, Dominique Pham¹

5

6 ¹Ifremer, IRD, Université de la Nouvelle-Calédonie, Université de La Réunion, CNRS, UMR

7 9220 ENTROPIE, 101 Promenade Roger Laroque, 98897, Nouméa, New Caledonia.

8 ²University of New Caledonia, Institut des Sciences Exactes et Appliquées (ISEA), Nouméa,

9 New Caledonia.

10 ³Present address: IHPE, Université de Montpellier, CNRS, Ifremer, Université de Perpignan via

11 Domitia, Montpellier, France.

12

13 Corresponding Author:

14 Nolwenn Callac

15 Station Aquacole de Saint Vincent

16 98812 Boulouparis

17 New-Caledonia

18 Email address: nolwenn callac@ifremer.fr

19

20

21 **Abstract**

22 **Background:** Aquacultured animals are reared in water hosting various microorganisms with
23 which they are in close relationships during their whole lifecycle as some of these

24 microorganisms can be involved in their host’s health or physiology. In aquaculture hatcheries,

25 understanding the interactions existing between the natural seawater microbiota, the rearing
26 water microbiota, the larval stage and the larval health status, may allow the establishment of

27 microbial proxies to monitor the rearing ecosystems. Indeed, these proxies could help to define

28 the optimal microbiota for shrimp larval development and could ultimately help microbial
29 management.

30 **Methods:** In this context, we monitored the daily composition of the active microbiota of the
31 rearing water in a hatchery of the Pacific blue shrimp *Penaeus stylirostris*. Two distinct rearing

32 conditions were analyzed; one with antibiotics added to the rearing water and one without

33 antibiotics. During this rearing, healthy larvae with a high survival rate and unhealthy larvae with

34 a high mortality rate were observed. Using HiSeq sequencing of the V4 region of the 16S rRNA
35 gene of the water microbiota, coupled with zootechnical and statistical analysis, we aimed to
36 distinguish the microbial taxa related to high mortality rates at a given larval stage.

37 **Results:** We highlight that the active microbiota of the rearing water is highly dynamic whatever
38 the larval survival rate. A clear distinction of the microbial composition is shown between the
39 water harboring heathy larvae reared with antibiotics *versus* the unhealthy larvae reared without
40 antibiotics. However, it is hard to untangle the effects of the antibiotic addition and of the larval
41 death on the active microbiota of the rearing water. Various active taxa of the rearing water are
42 specific to a given larval stage and survival rate except for the zoea with a good survival rate.

43 Comparing these communities to those of the lagoon, it appears that many taxa were originally
44 detected in the natural seawater. This highlights the great importance of the microbial
45 composition of the lagoon on the rearing water microbiota. Considering the larval stage and
46 larval survival we highlight that several genera: *Nautella*, *Leisingera*, *Ruegerira*, *Alconivorax*,
47 *Marinobacter* and *Tenacibaculum*, could be beneficial for the larval survival and may, in the
48 rearing water, overcome the r-strategist microorganisms and/or putative pathogens. Members of
49 these genera might also act as probiotics for the larvae. While *Marivita*, *Aestuariicocccus*,
50 HIMB11 and *Nioella*, appeared to be unfavorable for the larval survival and could be associated
51 with upcoming and occurring larval mortalities. All these specific biomarkers of healthy or
52 unhealthy larvae, could be used as early routine detection proxies in the natural seawater and
53 then during the first days of larval rearing, and might help to manage the rearing water
54 microbiota and to select beneficial microorganisms for the larvae.

55

56 **Introduction**

57 In New-Caledonia, hatcheries of the Pacific blue shrimp *Penaeus stylirostris* face high larval
58 mortality rates (Beliaeff et al., 2009; Pham et al., 2012). The causes of such mortalities are not
59 yet understood and multi-factorial reasons seem to trigger larval death. For example, only 128
60 million post larvae were produced in 2019; while the production had reached up to 167 million
61 post larvae in 2005 (https://www.isee.nc/economie-entreprises/entreprises-secteurs-d-
62 activites/agriculture-peche-aquaculture, section *Les structures aquacoles et maritimes*). These larval
63 mortalities create many issues, as not enough post larvae are available to be spread among the
64 earthen ponds of the 18 farms of the territory, where they grow until they reach a certain weight
65 to be sold. This induces a commercial deficit and an economical loss for the farmers, the workers
and the territory. Several factors, such as water quality or bacterial infections, could play a role in the larval mortalities but these hypotheses have been denied. Indeed, no known pathogen was found, and various larval survival rates can be observed among larvae reared under the same conditions in the same rearing water. However, we think that dysbiosis of the rearing water microbiota could hamper the larval survival. Indeed, aquacultured animals are reared in water hosting various microorganisms with which they are in close relationships during their whole lifecycle as some of these microorganisms can be involved in their host’s health, physiology and fitness (Goarant et al., 2006; Ganguly & Prasad, 2012; Carbone & Faggio, 2016; Zheng et al., 2017; Sun et al., 2019; Wei et al., 2020; Wang et al., 2020b; Angthong et al., 2020). It has been shown that the rearing water microbiota could influence pre-feeding fish larvae (Bledsoe et al., 2016; Wilkes Walburn et al., 2019) and may contribute to larval health in cod larvae (Lauzon et al., 2010). Regarding shrimps, it has been proved that the rearing water microbiota can interact with the shrimps and that some microbial taxa originating from the water can be transmitted to the host microbiota at various lifecycle stages (Huang et al., 2018; Giraud et al., 2021). Thus, in order to establish microbial proxies to monitor the rearing ecosystems in shrimp hatcheries, it seems necessary to uncover the interactions existing between the natural seawater microbiota, the rearing water microbiota, the larval stage and the larval health status. To date, a few studies have been conducted on both diseased and healthy *Penaeus vannamei* shrimps at various lifecycle stages. In these studies, the authors managed to establish links between the microbial communities and the health status of the shrimp, and also identified bacterial indicators of diseased shrimps (Xiong, Zhu & Zhang, 2014; Zheng et al., 2017). However rearing methods of *P. vannamei* and *P. stylirostris* larvae slightly differ. Indeed, *P. vannamei* larvae are reared in oceanic water using both probiotics and antibiotics while, in New-Caledonia, larval rearing is performed using lagoon seawater and antibiotics are often only added until post-larval stage (Pham et al., 2012; Zheng et al., 2017). Thus, in order to overcome the larval deficit in New-Caledonia, it is imperative to establish microbial proxies to monitor the rearing ecosystems and to distinguish taxa that seem beneficial to larval health. Such data will ultimately help microbial management in shrimp hatcheries.

To this aim, we have monitored daily the microbial composition of the rearing water, containing larvae raised with or without antibiotics, to investigate if any microbial families were associated with a certain mortality rate at a given larval stage. We also investigated if the antibiotic addition
97 and the natural seawater microbiota influenced the active microbial communities inhabiting the

98 rearing water.

99 In the context of this study, we identified several specific biomarkers of a given larval stage and
100 health, that might be used as early routine detection proxies in the natural seawater and then
101 during the first days of larval rearing.

102

103 **Materials & Methods**

104 **Study design, water collection and storage**

105 The study was conducted in an experimental shrimp hatchery hosted in a shrimp farming
106 research facility at the Station Aquacole de Saint Vincent (Boulouparis, New-Caledonia). The
107 experiment was carried out in February 2019, where seawater from the water storages, the larval
108 rearing tanks and the control tank were collected during the same larval rearing cycle. Tanks in
109 the hatchery were filled with natural seawater collected from the Saint Vincent Bay. Natural
110 seawater was pumped through a 1cm pore size device into a primary reservoir (ResI). Seawater
111 was then filtered through a 10µm filter prior to storage into a 2m³ storage container implemented
112 with intensive bubbling (ResII). In New Caledonia, the reproduction of *Penaeus stylirostris* is
113 conducted by artificially inseminating mature females, as described by Pham et al. (2012). The
114 day these inseminations were performed, the hatchery tanks were filled with storage seawater in
115 which 5 g.m⁻³ of EDTA (ethylenediaminetetraacetic acid) were added and intensive bubbling
116 was implemented in all the tanks including the control tank that contained no larvae, antibiotics
117 nor food addition. Each tested condition was carried out in triplicates except for the control. One
118 control tank, three larval tanks without antibiotics addition and three larval tanks with
119 erythromycin (antibiotic) addition were considered. In the latter, 2 ppm of erythromycin were
120 added on Day 0 (D0) after EDTA addition and then on days 3, 5, 7 and 9. The larval feeding
121 protocol was as follow: from zoea 1 to zoea 2, microparticles were added five times per day and
122 frozen *Tetraselmis* sp. were given once a day; from zoea 3 to post larvae, microparticles were
123 added twice a day and living *Artemia* sp. nauplii (between 20 to 40 nauplii/larvae/day) twice a
124 day. No water exchange was applied during the first 10 days of the larval rearing.

125 Natural seawater from the primary reservoir was sampled before the insemination and seawater
126 from the storage container was sampled on the insemination day; while samples from the rearing
tanks were collected daily during 9 days, before the first feeding of the day. All the water
127
128 samples were collected using a 100µm mesh to avoid any larvae on the filter used for the RNAs
129 extractions. For each sample, 1L of water was filtered on 0.2µm sterile membrane filters (S-Pak,
130 Millipore). All filters were stored at -80°C until RNA extractions.

131

132 Daily determination of the zootechnical parameters

133 Daily larval survival rates were estimated by counting the number of larvae contained in three
134 distinct sub-samples of 100 mL per tank. The larval stages were determined by the observation
135 of 30 larvae per tank using a binocular magnifying glass. This allowed the calculation of the
136 Larval Stage Index (LSI), using the modified equation of Maddox and Manzi (Maddox & Manzi,
137 1976):

138 LSI = (0xNii+1xZ1+2xZ2+3xZ3+4xM1+5xM2+6xM3+7XPL) / N

139 where Nii is the number of larvae observed in the nauplius stage, Z1 in the zoea 1 stage, Z2 in
140 zoea 2, Z3 in zoea 3, M1 in mysis 1, M2 in mysis M2, M3 mysis M3, PL in post larvae 1; and N
141 corresponds to the total number of observed larvae. The Larval Survival Rate (LSR) was defined
142 by averaging 3 direct counts of the living and dead larvae in 1L of rearing water per tank and per
143 day. The LSR was determined as follow LSR: 100*(counted living larvae / initial number of
144 nauplii).

145

146 RNA extractions, reverse transcriptions, sequencing and sequence processing

147 RNA extractions were performed using the RNAeasy Powerwater kit (Qiagen) following the
148 manufacturer's information. RNA purity and concentration were checked with NanoDrop™
149 measurements. Then, the total RNAs were reverse transcripted into complementary DNA
150 (cDNAs) using 10 µL of RNA (200 ng/µl), 1 µl of the reverse transcriptase M-MLV at 200 u/µl
151 (PROMEGA), 2 µl random hexamers 50 µM, 4 µl of Buffer 5X, 2 µl of a mix of dNTP a 10 mM
152 each and 1 µl of Rnase/Dnase free water. All the reverse-transcriptions were carried out in a
153 Veriti™ instrument (Applied Biosystems), using the program: 10 min at 25°C, 2 h at 42°C and 5
154 min at 85°C. The cDNAs were stored at -80°C until shipping to MrDNA (Molecular Research
155 LP, Shallowater, Texas, United States) where the PCR, barcode indexing and sequencing of the
156 V4 hypervariable region of the 16S rRNA molecule were conducted using the universal primer
157 combination 515f-806R (Caporaso et al., 2011; Hugerth et al., 2014). An Illumina HiSeq
158 sequencing was performed using MrDNA protocols with a 2 x 150 bp paired-end run and an
159 average sequencing depth of 20k raw reads per sample. As described in Giraud et al, 2021, the
160 raw data were first demultiplexed using the fastqSplitter available on the MrDNA website
161 (https://www. Mrdnalab.com/mrdnafreesoftware/fastq-splitter.html) (Giraud et al., 2021). Then,
162 the reads were treated using the DADA2 (Callahan et al., 2016) package available in the Rstudio
163 software. We kept all the reads with a quality score above 30. The sequences were filtered using
164 a maximum excepted error (maxEE) set at 2, a maximum N (maxN) set at 0, and a truncation
165 based on quality scores (truncQ) set at 2. The sample inference was done by setting the “pool”
166 argument to true. The chimeras were removed using the consensus method. The taxonomy was
167 assigned using the Silva 138 SSU Ref NR99 database (Quast et al., 2013). Once the ASV table
168 was obtained, sequences with no affiliation or affiliated to the Eukaryota, Mitochondria or
169 Chloroplasts were removed before further analysis. All the 16S rRNA data are available in the
170 NCBI SRA repository (BioProject ID PRJNA736535, SRP324193 for all the sample except ResI
171 sample available in SRP14825206).

172

173 **Downstream microbial analysis**

174 The alpha diversity was estimated with the Chao 1, Shannon and Inverse Simpson (InvSimpson)
175 indexes using the phyloseq package in RStudio (McMurdie & Holmes, 2013), while the Good's
176 coverage was estimated using RStudio. The rarefaction curves were built using the Vegan
177 package in Rstudio (Jari Oksanen, 2022).

178 Before further microbial analysis, the ASV table was normalized with the Count Per Million
179 (CPM) method using the edgeR package under the RStudio software. The beta diversity was
180 determined by constructing a PCoA based on a Bray-Curtis dissimilarity matrix and the Ward
181 method using phyloseq packages (McMurdie & Holmes, 2013) in Rstudio. Clusters were
determined by the construction of ellipses using a confidence level for a multivariate t-
182 distribution set at 80%.

184 Prior to Venn diagram constructions, we established 5 groups of rearing water samples based on
185 the larval stage: nauplii, zoea or mysis; and on the larval survival rate: good or bad. We
186 considered a good larval survival rate when the daily larval counting was above, equal or slightly
187 below the reference (less than 5% in order to mitigate putative counting errors) (the reference is
188 an average of survival rates calculated for each rearing day using data of 10 years of successful
189 data; Ifremer data, Pham comm. pers.). The 5 final defined groups were the nauplii (Nauplii), the
190 zoea with a good survival rate (later named Zoea Good), the zoea with a bad survival rate (Zoea

191 Bad), the mysis with a good survival rate (Mysis Good) and the mysis with a bad survival rate

192 (Mysis Bad). Venn diagrams were then constructed using the Jvenn web application (Bardou et

193 al., 2014) (http://bioinfo.genotoul.fr/jvenn/example.html). In order to identify microbial

194 biomarkers, Linear Discriminant Enalysis (LDA) effect size (LEfSe) (Segata et al., 2011) were

195 performed with a threshold set at 4 using the microbiomeMarker package (Cao et al., 2022) in

196 RStudio.

197

198 **Results**

199 **Zootechnical parameters**

200 Contrasting survival rates were observed between the two treatments after D1 (Fig. 1). Larvae

201 reared with antibiotics showed the best survival rates on D9 with more than 70% of surviving

202 larvae, which is similar to the reference value on this rearing day. Inversely, larvae raised

203 without antibiotics did not manage to reach the 9th day of rearing as mortalities started to occur

204 on D2 in tanks A and B. In these tanks, larval survival rates only reached 75% on D4 against

205 85% for the reference. On D5 the larval survival rates in the three replicates without antibiotics

206 were all dramatically below the reference value.

207 Regarding the larval stage, none of the tanks, with or without antibiotics addition, reached the

208 post larvae stage on D9 but on D10 for all the tanks (except With-C, Table 1). All larvae

209 metamorphosed in mysis on D7, one day later compared to the reference. Except for the tanks B

210 and C reared without antibiotics (Without-B and Without-C in Table 1); all larvae stayed 2 days

211 in the zoea 3 stage (D3 and D4) while it usually only takes 1 day. Globally, compared to the

212 reference, regardless of the rearing condition, larvae had a delay in their metamorphosis. Indeed,

213 at D6 all larvae should have been in Mysis 1 stage while they were still in Zoea 3 stage.

214

215 **Time series of the active microbiota in the water**

216 After Eukaryota, Mitochondria, Chloroplasts and unassigned sequences were removed, a total of

217 21,749,911 reads, spread into 6707 ASVs were obtained from the HiSeq Illumina sequencing of

218 all the samples. The smallest and the largest libraries-were respectively composed of 207,221 and

219 650,143 reads and corresponded to the Control D3 and Control D6 samples (water without

220 larvae, antibiotics nor food).
221 Overall, the alpha diversity indexes (Table SI) were generally higher in the rearing water without antibiotics than in the rearing water with antibiotics and the control water. Also, the alpha diversity indexes of the control water were higher than the rearing water with antibiotics. The storage water samples (primary reservoir and container storage) were those exhibiting the higher alpha diversity indexes. Despite the rarefaction curves did not reach the plateau for all the samples (Fig. S1), the Good's coverage revealed an overall average above 99.8% of the total ASV table (Table S1), signifying that the sequencing depth was sufficient.

222
223
224
225
226
227
228 Using the whole microbial beta diversity, we visualized how the samples clustered together (Fig 2). The PCoA displayed 7 clusters (ellipses on Fig. 2), that were built using a confidence level for a multivariate t-distribution set at 80%. The first cluster grouped the control water samples along with the secondary reservoir (ResII). The second group gathered all the rearing water 231 hosting the nauplii. The third cluster encompassed the rearing water samples without antibiotics 232 collected on D2 and D3 and hosting the zoea with a good survival rate; while cluster 4 grouped 233 the rearing water samples without antibiotics collected from D4 to D6 (excepted one sample 234 collected on D4) and hosting the zoea with a bad survival rate. The rearing water samples 235 without antibiotics collected on D7 and D8, and hosting the mysis with a bad survival rate, were 236 all grouped in the cluster 5. The rearing water with antibiotics collected on D2 and hosting the 237 zoea with a good survival rate were apart from the cluster 6 that encompassed all the samples 238 collected in the rearing water with antibiotics and hosting the zoea with a good survival rate (D3 239 to D6). The last cluster grouped all the rearing water samples with antibiotics that hosted the 240 mysis with a good survival rate and corresponded to the samples collected from D7 to D9. The 241 primary reservoir that encompassed the lagoon seawater was aside all clusters (Fig. 2). In order 242 to confirm the PCoA and the clusters, a PERMANOVA along with a pairwise comparison were 243 done to evaluate the water treatment effect on the samples. The PERMANOVA displayed that 244 the sampling day, the treatment (control, antibiotics or not), the larval survival rate, the larval 245 stage, the larval stage and health as well as the day and the treatment together, explained the 246 variability among the samples (Table 2). Only 8.3% of the variability among the samples was not 247 explained by the tested factors. The main factors describing the variability were the sampling day 248 and the water treatment that respectively explained 36% and 25.4% of the variability. The water 249 treatment effect on the microbial diversity, was analyzed with a pairwise comparison using 250
251 Kruskal-Wallis tests which exhibited that all the treatments: control, antibiotics and not antibiotics, were significantly different (p-value < 0.001).

252
253 The active microbiota of the primary reservoir (ResI), that contained lagoon seawater, was
254 highly different from all the other samples, as most of its lineages belonged to families that were
255 not represented by more than 1% of the total abundance in the other samples (Fig. 3). On D0, the
256 microbial composition of the control water, which contained no larvae, antibiotics nor food, was
257 identical to those of the storage container ResII and of the rearing waters with and without
258 antibiotics collected on D1. The *Alteromonadaceae* and the *Rhodobacteraceae* were dominant in
259 all these samples. Their abundances remained high during the other nine days of rearing with,
260 however, an increased proportion of *Pseudohongiellacaea* and *Bdellovibrionaceae* (Fig. 3). The
261 active microbial compositions of the 3 replicate samples for each condition displayed
262 homogenous profiles through the whole rearing excepted on D8 for the samples collected in the
263 rearing water without antibiotics (Fig. 3). The active microbiota of the water samples exhibited
264 different compositions and dynamics according to the rearing day, as microbial shifts occurred
265 daily and according to the addition of antibiotics. However, from D2 to the end of the rearing, in
266 the presence or not of antibiotics, the *Rhodobacteraceae* had high abundances (Fig. 3). The
267 active microbial composition of the rearing water without antibiotics encompassed mostly
268 members of the *Rhodobacteraceae*, the *Alteromonadaceae*, the *Saprospiracaea* and the
269 *Litoricolaceae* on D1and on D2 (Fig. 3). On D3, a shift occurred with decreasing proportions of
270 *Alteromonacae* accompanied with increasing proportions of *Saprospiraceae*. Until D5, the
271 most abundant families were the *Rhodobacteraceae*, the *Alteromonadaceae* and the
272 *Saprospiracae*. From D6 to D8, *Alteromonadaceae* and *Saprospiraceae* decreased drastically.
273 On D7 the main families were the *Rhodobacteraceae* and an unknown family related to the
274 ASV19 affiliated to the *Gammaproteobacteria*. On D8, the dominant families varied among the
275 3 tanks, where in addition to the *Rhodobacteraceae*, the proportion of *Flavobacteriaceae*,
276 *Vibrionaceae* and/or *Pseudoalteromonadaceae* increased. In the rearing water with antibiotics,
277 on D1, the taxa affiliated to the *Rhodobacteraceae*, the *Alteromonadaceae* and the *Litoricolaceae*
278 were the most abundant; while on D2, the main families were the *Rhodobacteraceae*, the
279 *Alteromonadaceae* and the NS11-12 marine group (Fig. 3). On D3, a shift occurred with an
280 increased proportion of *Pseudoalteromonadaceae* and *Cryomorphaceae*, associated with a drop
281 of the *Alteromonadaceae*. On D4, the *Pseudoalteromonadaceae* decreased whereas the unknown
282 family UBA12409 increased. On D5, the microbiota was dominated by members of the

283 *Rhodobacteraceae* and the *Sapropsiraceae*. On D6, *Rhodobacteraceae* and *Cryomorphaceae*

284 greatly composed the microbiota. A prevalence of members of the *Rhodobacteraceae* and the

285 *Flavobacteraceae* was noticed on D7; while from D8 to D9, the main families of the rearing

286 water microbiota with antibiotics were the *Rhodobacteraceae*, the *Cryomorphaceae* and the

287 *Flavobacteraceae*.

288

289 **Specific, shared and core microbiomes among the water samples, larval stages, and health status**

290

291 In order to determine specific ASVs of a given rearing condition, specific to a larval stage and
292 survival rate, as well as common microbiomes, several Venn diagrams were constructed.

293 A first Venn diagram was built to compare the rearing water of all larval groups (Fig. 4). As the

294 larval mortalities mainly occurred in the rearing water without antibiotics after D4, the nauplii

295 group was composed by water hosting the nauplii from the 2 rearing conditions. In the same

296 way, the group *Zoea Good* encompassed all the samples collected in the rearing water with
297 antibiotics from D2 to D6 and samples collected on D2 and on D3 in the rearing water without

298 antibiotics. Thus, the nauplii-specific microbiome contained 658 ASVs. The diagram showed
299 that no ASV was specific to the *Zoea Good*, while the *Zoea Bad* gathered 95 ASVs. The Mysis

300 Good condition exhibited 79 common ASVs and the Mysis Bad had 141 specific ASVs. The
301 diagram also displayed a core microbiota composed by 137 ASVs. Specific microbiotas of a

302 given condition as well as a core microbiome were pointed out through the Venn diagram
303 comparisons. The natural lagoon seawater stocked in the primary reservoir and in the storage
304 container, was used for the rearing. These two water storages as well as the evidenced specific
305 microbiotas and the core microbiome were therefore compared. Consequently, the specific
306 microbiota of the rearing water hosting the nauplii had 85 unique ASVs, while 573 ASVs were

307 already present in the natural seawater (Fig. 4B). The comparison between the specific
308 microbiota of the *Zoea Bad* and the natural seawater highlighted that 58 ASVs were previously

309 not in the natural seawater whereas 37 ASVs from the natural seawater were only detected in this
310 condition (Fig. 4C). The Mysis Good condition had 48 specific ASVs and shared 31 ASVs with

311 the natural seawater (Fig. 4D). The Mysis Bad condition exhibited 79 specific ASVs and co-
312 owned 62 ASVs with the natural seawater (Fig. 4E). The comparison between the rearing water