Addition of commercial probiotic in a biofloc shrimp farm of Litopenaeus vannamei during the nursery phase: Effect on bacterial diversity using massive sequencing 16S rRNA

Accepted Manuscript Addition of commercial probiotic in a biofloc shrimp farm of Litopenaeus vannamei during the nursery phase: Effect on bacterial diversity using massive sequencing 16S rRNA

José Alberto Huerta-Rábago, Marcel Martínez-Porchas, Anselmo Miranda-Baeza, Mario Nieves-Soto, Martha Elisa Rivas-Vega, Luis Rafael Martínez-Córdova PII: DOI: Reference:

S0044-8486(18)31544-8 https://doi.org/10.1016/j.aquaculture.2018.12.055 AQUA 633777

To appear in:

aquaculture

Received date: Revised date: Accepted date:

17 July 2018 16 December 2018 17 December 2018

Please cite this article as: José Alberto Huerta-Rábago, Marcel Martínez-Porchas, Anselmo Miranda-Baeza, Mario Nieves-Soto, Martha Elisa Rivas-Vega, Luis Rafael Martínez-Córdova , Addition of commercial probiotic in a biofloc shrimp farm of Litopenaeus vannamei during the nursery phase: Effect on bacterial diversity using massive sequencing 16S rRNA. Aqua (2018), https://doi.org/10.1016/ j.aquaculture.2018.12.055

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ACCEPTED MANUSCRIPT Addition of commercial probiotic in a biofloc shrimp farm of Litopenaeus vannamei during the nursery phase: effect on bacterial diversity using massive sequencing 16S

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rRNA

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José Alberto Huerta-Rábagoa,b, Marcel Martínez-Porchasc, Anselmo Miranda-Baezaa,*

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[email protected], Mario Nieves-Sotod, Martha Elisa Rivas-Vegaa, Luis Rafael

Universidad Estatal de Sonora (UES), Navojoa, Sonora, 85875, México

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a

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Martínez-Córdovae

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Maestría en Sistemas de producción biosustentables; Universidad Estatal de Sonora

(UES), Navojoa, Sonora, 85875, México

Centro de Investigación en Alimentación y Desarrollo, A.C. (CIAD), Hermosillo, Sonora,

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c

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83304, México d

Universidad Autónoma de Sinaloa (UAS), Mazatlán, Sinaloa, 80000, México

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DICTUS, Universidad de Sonora, Hermosillo, Sonora, 83000, México

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*Corresponding author at: Universidad Estatal de Sonora (UES), Navojoa, Sonora 85875, Mexico. E-mail address: (A. Miranda-Baeza).

ACCEPTED MANUSCRIPT Abstract The aim of this study was to describe the effect of the addition of commercial probiotics on the bacterial diversity in biofloc generated in a commercial farm of whiteleg shrimp Litopenaeus vannamei. The experiment consisted of a simple random design with three

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treatments by triplicate (two commercial probiotics: PA and PB, and one control, C). The

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PA was composed by a mixture (50:50) of Efinol PT and Mix Laboratory Robles; the

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second, coded as PB was composed by the mixture (50:50) of Epicin Ponds and Epicin Hatcheries. The control treatment, coded as C (natural endemic microbial consortium),

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consisted of the traditional biofloc system without probiotic application. The samples were

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obtained during tree periods of the shrimp nursery (beginning, medium and final). The abundance and diversity indexes (alpha, beta and gamma) were calculated. The shrimp

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productive parameters were determined. The ponds receiving the PA mixture registered 22

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phyla, being Proteobacteria (49.99-53.66%), Planctomycetes (9.62 -18%) and Bacteroidetes (11.41 - 29.66%) the most abundat. The ponds receiving the PB mixture, registered 19

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phyla, and Proteobacteria (49.09-60.81%), Bacteroidetes (8.18-26.56%) and

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Planctomycetes (6.38-25.05%) were the most abundant. The control ponds had 19 phyla, and Proteobacteria (43.15-73.88%), Bacteroidetes (16.29-25.56%) and Planctomycetes

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(5.03-9.37%) registered the highest abundance. During the tree sampling periods, the Shannon index (alpha diversity) varied from 1.54 -1.40 in PA; 1.34 to 1.41 in PB and 0.88 to 1.59 in C. The beta diversity indicated 86% of similarity among PA-C; and 90% among PB-C. At the end of the study, the gamma diversity in bioflocs depends 96% of alpha diversity and 3.70% of beta diversity. The autochthonous bacteria had the greatest influence on the diversity. The productive parameters did not show significant differences among treatments (P<0.05).

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Keywords: bacterial diversity, bacterial ecology, biofloc, probiotics, shrimp farms.

ACCEPTED MANUSCRIPT 1. Introduction Aquaculture is the food industry sector with the highest growth rate in the world. The whiteleg shrimp Litopenaeus vannamei is the sixth most cultured species (FAO, 2016). In recent decades, aquaculture and other animal production activities have used diverse

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substances to increase production. The most used include hormones, vitamins, antibiotics

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and immunostimulants, and recently, probiotics. To reduce the shrimp diseases, commercial

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farms started to culture shrimp in biofloc technology systems (BFT). The biofloc is a term used to designate the formation of aggregates of particles in a colloidal dispersion. These

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aggregates contain diverse bacteria, microalgae, fungi and detritus, as well as flagellates,

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ciliates, rotifers and nematodes (Emerenciano et al., 2017). In some cases to promote and maintain the biofloc, the shrimp farms use commercial probiotics (Crab et al., 2012;

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Vargas-Albores et al., 2017).

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Probiotics are defined as "live microorganisms, which, administered in adequate doses confer a health benefit to the host" (FAO, 2001). In aquaculture, and particularly in shrimp

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farming, probiotics began to be used as an alternative to reduce the use of antibiotics. The

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microorganisms commonly used include acidolactic bacteria, bifidobacteria and yeast. Several studies have reported that the use of probiotics in shrimp and fish increases growth

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and survival, resulting in better digestion and stimulation of the immune system (Verschuere et al., 2000; Kesarcodi-Watson et al., 2008; Vargas‐ Albores, et al., 2016). The natural water ecosystems contain a great diversity of bacteria, which are essential in the recycling of organic matter and play an important roles in biogeochemical cycles. Despite probiotics are widely used in aquaculture, there are no systematic studies to evaluate their effect on the diversity of pre-existing native bacteria of farms (MartínezPorchas et al., 2017). During the water exchange or harvest processes, the bacteria

ACCEPTED MANUSCRIPT contained in commercial probiotics could reach the natural environment. The diversity of native species is essential to maintain the stability of the ecosystems. Thus is a highly relevant topic for biological conservation (Vargas-Albores et al., 2017). Until a few years ago, studies of bacterial diversity were limited to culture dependent

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techniques, and only 1-15% of bacteria were detected (Streit and Schmitz 2004; Ortiz-

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Estrada et al., 2018). However, current molecular techniques including high throughput

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sequencing of metagenomics DNA or taxonomic biomarker genes provide insights into the structure of microbial communities, including the non-cultivable species.

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Tracking bacterial diversity changes during the probiotic supply in the farms allow

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estimating possible changes in the natural environment. In biodiversity studies, one of the most used approaches was proposed by Whittaker in 1960 which analyses the diversity of a

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community considering three components (alpha, beta and gamma), and this approach is

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still valid (Whittaker et al., 2001). The objective of this study was to describe the effect of the addition of commercial probiotics on the bacterial diversity of the bioflocs generated in

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a commercial shrimp farm.

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2. Material and methods 2.1. Experimental design

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The experiment was developed in the facilities of the company Proveedora de Larvas S.A. de C.V., located in Sinaloa, Mexico. The hyper-intensive farm has ponds of 70 m3 and 2,000 m3 (volume). This experiment was developed in 70 m3 ponds and consisted of a simple random design with three treatments by triplicate (commercial probiotics: PA and PB, and one control, C). The PA was composed by a 50:50 mixture of Efinol PT (containing Bacillus spp., lactic acid, Lactobacillus spp., Saccharomyces spp.) and Mix Laboratory Robles (containing a native microbial consortia). PB was composed by the a

ACCEPTED MANUSCRIPT 50:50 mixture of Epicin Ponds and Epicin Hatcheries (the labels of both products declare the content of non-toxic compounds, natural microbial cultures, and enzymes with stabilizers and growth stimulants). The control treatment coded as C (natural endemic microbial consortium), consisted of the traditional biofloc system without probiotics

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application. 2.2. Probiotic use

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Two bioreactors of 1,000 L (operative volume) were used to activate and incubate the

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bacterial consortia. Each bioreactor received vigorous aeration by microporous tube placed at the bottom. The water (33-35 ‰) was filtered (5 microns) and sterilized with sodium

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hypochlorite, neutralized with sodium thiosulfate and after 12 h. The respective bioreactors

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were inoculated with probiotics in a dose of 100 g/m3 , and 1 kg of molasses/m3 as carbon source, as well as a mix of micronutrients with other compounds (not revealed by the

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company; it was roughly composed by trace metals, vitamins and pH stabilizers). The

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probiotics were incubated at 30 ± 3 °C during 36 h. Under these conditions the viable heterotrophic bacteria reached concentrations ranging from 80 to 120 x 106 CFU/mL

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(determined in marine agar; plate spread at 30°C in 24 h).

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2.3. Culture conditions

The whiteleg shrimp postlarvae (PL 12) were obtained from the laboratory of the same company (Proveedora de larvas S.A. de C.V.). The experimental specimens (7.3 mg/ind) were stocked in ponds (70 m3 volume) at density of 500 ind/m2 . The ponds were covered with a high-density polyethylene (HDPE). The nine experimental units were placed in the greenhouses and the natural photoperiod was maintained. The aeration was supplied by electric aerators during the experiment the aeration power varied from 70-100 Hp/Ha.

ACCEPTED MANUSCRIPT Aeration grills (elaborated with porous tube) were placed at the bottom of the tanks to supply oxygen and maintain the solids in suspension. The culture protocol of the company consisted of two phases, the first one suggests a zero water exchange (30 d) and the second (60 d) allow performing a low water exchange (3-5%

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/day). To avoid interference on bacterial community by the effect of the natural microbiota

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present on influent water, this study was focused on the zero water exchange phase. Three days previous to stocking, the ponds were filled with filtered seawater (5 microns).

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Commercial food containing 35% of protein (Purina ®, Agribrands Purina Mexico, S.A. de

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C.V.) was used, and the daily ration varied from 20 to 8 % according to the shrimp weight. The feeding frequency varied from 12 to 8 times during 24 h. In all treatments the carbon:

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were daily added as carbon source.

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nitrogen (C:N) ratio was of 12:1, to maintain this balance different amounts of molasses

During the experimental period, the physicochemical characteristics of the culture ponds

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were: dissolved oxygen, 5.22-5.59 mg/L; water temperature, 32.4-32.7 °C; salinity, 35.3-

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36.8 ‰; pH, 7.0-8.3 and settable solids, 2.53-3.12 mL/L.

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2.4. Shrimp productive parameters At the end of the study, each tank was sampled to determine growth, survival and feed conversion rate. The specific growth rate of the shrimp was estimated as follows:

𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝑔𝑟𝑜𝑤𝑡ℎ 𝑟𝑎𝑡𝑒 =

[ 𝐿𝑛 𝑓𝑖𝑛𝑎𝑙 𝑤𝑒𝑖𝑔ℎ𝑡 (𝑔) − 𝐿𝑛 𝑖𝑛𝑖𝑡𝑖𝑎𝑙 𝑤𝑒𝑖𝑔ℎ𝑡 (𝑔)] 𝑐𝑢𝑙𝑡𝑢𝑟𝑒 𝑡𝑖𝑚𝑒 (𝑑)

ACCEPTED MANUSCRIPT 2.5. Collect of bacteria samples During weeks 1, 3 and 6 of the experiment (periods: initial, medium and final), water samples (200 mL) were taken from all the experimental units. The samples were collected in sterile Whirl-Pak® bags of 250 mL capacity, which were immediately placed on ice and

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transported to the laboratory. Once in the laboratory, the supernatant was removed and the

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precipitated biofloc was placed in 15 mL falcon tubes. For each treatment, a mixture (of the

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tree replicates) was obtained. The tubes with the corresponding samples were placed in an ultrafreezer (Thermo) at -80 ° C until processing.

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2.6. DNA isolation

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To isolate DNA from the collected bioflocs, the commercial DNA extraction kit PowerBiofilm® (MO BIO Laboratories, Solana Beech, CA, USA) was used, and DNA free

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of inhibitors and high molecular weight was obtained. For this, a biofloc mass of 0.20 mg

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of each sample (previously thawed) was homogenized in a Fisher Scientific ™ vortex. From this step and forward the procedure indicated by the manufacturer of the DNA

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isolation kit was strictly followed.

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The quality of the extracted DNA was evaluated by capillary microelecrophoresis in a 2200 Tape Station micro-electrophoresis device (Agilent, Palo Alto CA, USA). Briefly, 1 μL of

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the isolated DNA solution was taken and mixed with 10 μL of gDNA buffer (Agilent, USA). For reference, 1 μL of gDNA Ladder (Agilent, USA) was used. After the mixture, 1 μL of each sample was taken and inserted into a microfluidic chip (gDNA ScreenTape, Agilent, USA) to measure DNA quantity and quality, the DNA fragments of 200 to> 60 000 bp were considered. Finally, the microfluidic chip with the nucleotide samples was introduced in the 2200 Tapestation (Agilent, USA). All samples with DIN (DNA integrity number) ratings greater than 7, were considered for the preparation of the library.

ACCEPTED MANUSCRIPT 2.7. Library preparation For the preparation of the amplicon library, the "Library preparation guide for 16S gene sequencing" of Illumina was used. In short, the protocol is designed to carry out the amplification, purification and sequencing of the fragment containing the V3 and V4

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regions, considering the primers reported by Klindworth et al. (2012), which were

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combined with the barcode of sequencing adapters and dual indicators.

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Adapter Forward of amplicon 16S for PCR + adapter over hang =

5 'TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG 3'

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Adapter Reverse amplicon 16S for PCR + adapter Over hang = 5'

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GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAAT CC 3'.

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The first amplification was carried out in 25 ul reactions, using 2x KAPA HiFi HotStart

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ReadyMix (KAPA Biosystems, USA), by PCR under the following thermal cycle conditions: an initial denaturation at 95 ° C for 3 min; a pairing of 25 cycles of 95 ° C for

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10 s, 55 ° C for 30 s, 72 ° C for 30 s, and a final extension at 72 ° C for 5 min. Thereafter,

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the resulting amplicons between 450 and 550 bp were subjected to a cleaning process using the AMPure XP magnetic bead protocol (Beckman Coulter, USA) to remove free primers,

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as well as primer dimers species, following the manufacturer's specifications. After cleaning, the indexing of samples continued, using dual indexes and sequencing adapters of the Nextera Index XT kit (Illumina, San Diego, CA, USA) and 2x KAPA HiFi HotStart Readymix (KAPA Biosystems). A second PCR was carried out by repeating the same thermal cycle and finishing a new cleaning process through the aforementioned process.

ACCEPTED MANUSCRIPT Finally, the resulting library was quantified and graded by electrophoresis (2200 Tapestation, Agilent, USA) using microfluidic chips (D1000 Screen Tape, Agilent, USA) with a range of analysis from 35 to 1000 bp. For this, 2 μL of the sample from the indexed library were taken and mixed with 2 μL of High Sensitivity D1000 buffer. Finally the

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samples were inserted in Screentapes High Sensitivity D1000 and analyzed with the 2200

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Tapestation equipment to know the concentration and size of purified amplicons and

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continue with the sequencing. 2.8. Sequencing

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The libraries were adjusted to a concentration of 4 nM using 10 mM Tris (pH 8.5) as a

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diluent. Then, the libraries were denatured with 0.2 N NaOH. At the same time, a standard library of PhiX Control (Illumina) was denatured, which was used as an internal control,

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this allows to calculate the error rates, since it is a short genome sequence well defined.

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Both the denatured library and the PhiX Control were adjusted to a concentration of 8 pM and were mixed (95% of the library + 5% PhiX Control). Finally, the mixture was heated at

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96 ° C for 2 min and immediately cooled on ice for 5 min. The samples were loaded into a

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MiSeq v3 Reagent Tray (Illumina) cartridge, which contained a MiSeq v3 Flow Cell (Illumina) with a capacity of 25 million readings. The results were obtained after 602

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cycles (2 x 301).

2.9. Data analysis and estimation of diversity indexes Primers, linkers and adaptors were trimmed from the sequences and sequences shorter than 50 bp were removed. The resulting files were uploaded to Basespace (Illumina) for the automatized pipeline and the resulting sequences were analyzed and classified with Kraken 1.0.0 (Illumina; basespace.illumina.com; Wood and Salzberg, 2014).

ACCEPTED MANUSCRIPT In this study we considered the phyla with abundance superior to 0.001% of the total. The diversity estimators were calculated using multivariate techniques based on sampling patterns. To evaluate the effectiveness of the sampling, the ACE, CHAO 1, JACK 1, Boostrap and Unique estimators were used, which were determined with the software

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EstimateS 9.1.0 (Colwell, 2013). Alpha beta and gamma diversity were calculated using the

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indexes of: Shannon, Pielou, Jaccard, and Schluter and Ricklefs, as described by Moreno

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(2001) and Carmona-Galindo and Carmona (2013); according the following equations:

𝑆

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Alpha diversity (Shannon-Wiener index):

𝐻´ = − ∑ 𝑃𝑖 𝑙𝑛 (𝑃𝑖 )

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𝑖=1

Where;

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S = number of species (or Phyla); Pi = proportion of individuals of species i; H '= 0 when

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the sample has only one species; H 'has a maximum value when all S species are represented by the same number of individuals.

𝐽´ =

𝐻´ 𝐻´𝑀𝑎𝑥

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Equitability was estimated with the Pielou index:

Where; H’max = ln (S); H = value of the Shannon-Wiener index To estimate the β diversity, the similarity index of Jaccard was used: 𝐼𝑗 =

𝑐 𝑎 +𝑏 −𝑐

Where; a is the number of species in site A; b is the number of species in site B; c is the number of species present in both sites. The range of this index varied from zero (when there are no shared species), to 1 (when both sites share the same species).

ACCEPTED MANUSCRIPT The gamma diversity was estimated using the Schluter and Ricklefs method: 𝛾 = 𝛼𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑥 𝛽 𝑥 𝑑𝑖𝑚𝑚𝑒𝑛𝑠𝑖𝑜𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑠𝑎𝑚𝑝𝑙𝑒 Where; average alpha diversity = average number of species in a community; beta diversity = inverse of the specific dimension, that is 1 / average number of communities occupied by

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a species; sample size = total number of communities.

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The calculation was made based on the Shannon index, using the following equation: 𝐻´𝑏𝑒𝑡𝑎 = − ∑ 𝑃𝑖 𝑙𝑛𝑃𝑖 − ∑ 𝑞𝑗 𝐻𝑗 𝑗

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𝑖

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Where;

𝑃𝑖 = ∑ 𝑞𝑗 𝑝𝑖𝑗

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𝑗

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which represents the average frequency of species i in the set of communities, weighted according to the importance of the communities (qj). In this study, the weighting was done

3. Results

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for each community.

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according to the culture area, since all treatments had the same, so the weighting was 0.333

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A total of 5,727,411 readings were obtained from the biofloc samples; 82% were classified within a taxonomic level, whereas the rest corresponded to unidentified readings (in some cases correspond to uncompleted sequences). In the analysis we used the classified readings. The ponds that received the mixture of probiotics A, registered 22 phyla, and the most abundant were Proteobacteria (49.99-53.66%), Planctomycetes (9.62 -18%) and Bacteroidetes (11.41 - 29.66%) (Fig. 1). The ponds that received the mixture of probiotics B, showed 19 phyla, where Proteobacteria (49.09-60.81%), Bacteroidetes (8.18-26.56%)

ACCEPTED MANUSCRIPT and Planctomycetes (6.38-25.05%) were the most abundant. The ponds not receiving inoculation of commercial probiotics (control), had 19 phyla, the most abundant were Proteobacteria (43.15-73.88%), Bacteroidetes (16.29-25.56%) and Planctomycetes (5.039.37%).

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At the beginning of the experiment, treatment C obtained the lowest alpha diversity

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(Shannon index H = 0.88 nits / ind); although at the end of the study increased to: H = 1.59

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nits / ind. The values of the equitability index (Pielou index, J) presented the same trend as alpha diversity respect to the beginning and end of the study (Table 1).

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The beta diversity was calculated using the Jaccard similarity index (Ij), which allowed a

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comparison per couple of treatments, PA - C and PB - C. The highest similarity was observed between PB - C treatments with 0.90 (90% of similarity in bacterial diversity),

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while between PA - C the value was 0.86 (86% similarity in bacterial diversity) (Table 2).

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The gamma diversity index (H' gamma; which consider the whole community; 3 treatments) was the same between weeks 1 and 3, however, in the week 1 it was composed

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of 95% diversity α, and 5% diversity β; while for week 3, the contribution of α diversity

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increased to 97.6% and diversity β decreased to 2.4%. By week 6, the gamma diversity index increased slightly reaching 1.52, and was composed of 96.3% by diversity α and by

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3.7% by diversity β (Table 3). The predictors of bacteria richness, determined by the different mathematical models used, had a high coincidence with the number of phyla recorded during the sampling (sobs: observed phyla; Fig. 2). In the PA treatment 22 phyla were observed, while the models predicted an interval from 22 to 24. According to the mathematical models (predictors) in this treatment the most rigorous estimator was the first order Jacknife (Jack 1). The highest precision of the estimators was presented in the PB treatment, all predicting 19 phyla, the

ACCEPTED MANUSCRIPT same amount as those recorded during the sequencing. In the control treatment, 19 phyla from the interval 19-21 expected according to the estimators were collected. In this case, the two most rigorous estimators were Jack 1 and ICE. In all treatments Uniques (phyla that only had one reading) tended to descend according to

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the sampling periods. These values were 4, 0 and 3 for PA, PB and C respectively.

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Both the estimators and the Uniques indicate that in the PB treatment, the best sampling

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was performed, although in the PA and C treatment the estimators were very close to the real data and in both cases the Uniques tended to descend.

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In all treatments, only five genus constituted between 46 and 78% of the total abundance

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(Fig. 3). At the end of the study, the most abundant genera that were repeated in the 3 treatments were: Rhodopirellula, Ketogulonicigenium, Ruegeria, Sulfurimonas (Fig. 3)

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which the study was developed.

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which can be considered as the most important in the biofloc system under the conditions in

The productive parameters did not show significant differences among treatments (P<0.05);

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by the end of the study (30 d) the shrimp weight (g/ind) was: 1.54±0.25 (PA), 1.46±0.33

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(PB) and 1.44±0.04 (C). The specific growth rate (SGR %/day): 14.17±0.34 (PA), 14.03±0.48 (PB) and 14.05±0.05 (C). The survival (%): 92.0±4.35 (PA), 85.0±8.66 (PB)

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and 92.3±6.54 (C). The feed conversion rate (FCR): 0.98±0.05 (PA), 0.90 ± 0.04 (PB) and 0.93 ±0.05 (C). 4. Discussion In all treatments the phyla with the greatest presence in the bioflocs were Proteobacteria (50-73%), Bacteroidetes (9-27%), Planctomycetes (10-25%), Actinobacteria (4-11%) and Firmicutes (0.5-2%). The proteobacteria phylum is usually the most abundant in

ACCEPTED MANUSCRIPT aquaculture environments, considering biofloc, biofilm and recirculation systems (shrimp or tilapia) (Martins et al., 2013, Lee et al., 2016, Martínez-Córdova et al., 2016). At the beginning of the study, the control treatment showed the highest abundance of Proteobacteria (73%), which could be correlated with its abundance in natural environment.

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In the ocean, species belonging to this phylum constitute up to 79% of the bacterial

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biomass from the bottom and 64% from the water column; while in freshwater they

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represent up to 40% of the bacterial diversity (Battistuzzi and Hedges, 2009). Proteobacteria have a wide variety of metabolic pathways to obtain energy, among which

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phototrophic, chemo-trophic and chemoganotrophic routes are abundant (Madigan et al.,

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1997); therefore, they play an important role in the nutrient cycle and in the mineralization of organic compounds (Kirchman, 2002; Kersters et al., 2003).

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The second most dominant phyla found in this study and that agreeing with the reports of

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Lee et al. (2016) and Porchas-Cornejo et al. (2017) was Bacteroidetes; it is common to find it colonizing macroscopic particles of organic matter (Woebken et al., 2007). Additionally,

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it is found naturally in the bottom of the sea, where it represents approximately 8%

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bacterial diversity, while on the surface it reaches 9%; however, its presence increases remarkably in humid soils, reaching up to 19% (Battistuzzi and Hedges, 2009). It can be

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assumed that the biofloc has physico-chemical characteristics suitable for the proliferation of this phylum, allowing it to remain as the second most dominant; besides that, the relative abundance of Bacteridetes increased in the PB and C treatments from the first to the last sampling. Despite the great variety of phyla thriving in the natural environment, the particular conditions of the microenvironments favor the presence of some of them. For example, in soils or solid surfaces nine abundant phyla can be found, but four of them (Proteobacteria,

ACCEPTED MANUSCRIPT Acidobacteria, Actinobacteria and Bacteroidetes) accumulate 90% of the total diversity (Tsai et al., 2009). Comparing these statistics with the present study, it was found that about 90% of the bacterial diversity was represented by four phyla: Proteobacteria, Bacteroidetes, Planctomycetes and Actinobacteria.

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The analysis of the 16S rRNA gene sequences allows knowing the bacterial diversity with

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high accuracy (Porteous et al., 1997, Stackebrandt and Ebers, 2006). In a similar study, Lee

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et al. (2016) evaluated the bacterial diversity thriving in six different points of a recirculation aquaculture system; the Shannon-Wiener indexes indicated that the biofilter

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index was four times higher compared to that obtained in the fish tanks. The high diversity

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in the biofilter could be related with the numerous biological processes influencing the consumption of organic matter and the transformation of nitrogen compounds.

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Evaluating the effectiveness of the sampling is relevant for biodiversity studies. There are

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several models that allow determining the species richness and construct an asymptote of the species accumulation curve through various functions (Gotelli and Colwell, 2011).

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These models are called diversity estimators or predictors (Colwell et al., 2012); the

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similarity among the estimated and the observed value, indicates that the sampling is efficient and representative of the bacterial community. In a study of bacterial diversity

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indicating fecal contamination in incoming tide (FLD) and outgoing tide (EBB) in a wetland, Dorsey et al. (2013) obtained approximately 51 species of bacteria, their sampling was evaluated using the EstimateS software (Colwell, 2013), the estimators, that came closest to the observed values were: Bootstrap (approximately 49-50 for EBB and 50-52 for FLD) and Jacknife (between 55-58 for EBB and 59-61 for FLD). While the most rigorous estimators predicted higher values: Chao 1 (79-81 for EBB and 114-116 for FLD) and ICE (19-21 for EBB and 160-162 for FLD), such differences indicated that the bodies of water

ACCEPTED MANUSCRIPT had a greater number of species than those detected in their samplings. In the same study a Jaccard similarity index was used, and it was concluded that between EBB and FLD there was a 40% of shared species, considered as a medium or moderate similarity. In this study, the number of phyla present in the biofloc collected during the samplings was

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very similar to that predicted by the estimators. The results of the estimators indicate that

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the sampling was sufficient to determine with precision the richness of the phyla in the

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three treatments.

Taking as a reference the bacterial diversity developed in the control treatment, the

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similarity between PA-C and PB-C was evaluated using the Jaccard index. The PA-C index

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was 0.86, which indicates an 86% match; the index between PB-C was 0.90, that is, 90% of bacterial similarity between both treatments.

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The gamma diversity index allowed determining the impact of local diversity (α) and the

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replacement of species (β) within the bacterial community (by treatment and by sampling). In week 1 the gamma index was 1.32, of which 95% corresponded to diversity α and 5%

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diversity to β, while in weeks 3 and 6 the values were very similar to the first sampling

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(1.32 and 1.52 ), as well as the contributions of diversity α and β. Evidently the replacement of phyla between the treatments (diversity β) was almost

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negligible compared to the richness of the phyla in each treatment (α diversity). The results make sense when observing that 19 of the 22 phyla recorded by metagenomic analysis were shared among the treatments. The effect found with the addition of commercial probiotics was contrary to expectations. Local diversity had the greatest contribution to the diversity of the community. This is result could be associated to a better adaption of the local diversity to the marine environment compared that contained in both probiotic mixtures.

ACCEPTED MANUSCRIPT In the RAS, biofilm or biofloc systems, the Proteobacteria and Bacteroidetes phyla tend to dominate (Martins et al., 2013; Monroy-Dosta et al., 2013; Martínez-Córdova et al., 2016, Lee et al., 2016). While biofloc systems have a large number of bacteria, few genera dominate. Since the sum of the 5 most abundant genera accumulated between 46.43 and

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78.29% of the total readings.

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The genus with greater presence was Rhodopirellula, which is abundant in the marine

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environment; Žure et al. (2015) indicate that these microorganisms apparently play an important role in the metabolism of nitrogen compounds. Carbohydrates are the main

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source of carbon and energy for this genus (Schlesner et al., 2004), but also play a

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significant role in carbon cycling (Glöckner et al., 2003). It can be considered that the biofloc developed in aquaculture systems provide conditions for the proliferation of these

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microorganisms, their abundance in oceanic waters is around 1.4% (Porchas-Cornejo et al.,

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2017).

The genus Vibrio was the most abundant in the control treatment (22.22%) during the first

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week. This genus is part of the natural biota of aquatic environments. It may contain

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potentially pathogenic species, and it is known that the excess organic matter promotes its proliferation (Austin et al., 1995; Fuentes and Pérez, 1998). The biofloc is characterized by

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having high concentrations of organic matter which in theory, can encourage its proliferation; however, it is documented that this culture system favors the growth of beneficial bacteria that compete for resources (space and nutriments) that also generates complex substances inhibiting the growth of potential pathogens (Wu et al., 2012). In control treatment, the Vibrio inhibition could have occurred considering that it was not detected appear in the five most abundant genera.

ACCEPTED MANUSCRIPT Another abundant genus that appeared in the three treatments was Ketogulonigenium, which includes Gram-negative facultative anaerobic bacteria; these are used in the biotechnology industry for their ability to produce vitamin precursors, specifically 2-ketoL-gulonic acid for the formation of C vitamin (Urbance et al., 2001). On the other hand, it

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has been found that this genus has the ability to interact with a wide variety of bacterial

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groups (Cai et al., 2012) and use different carbon sources (Staats et al., 1999). Martínez-

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Córdova et al. (2016) found the specie Ketogulonigenium vulgare was the most abundant (44.9%) in the biofilm of an experimental shrimp culture.

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In this study Ketogulonigenium was recorded during the first week in the PA treatment,

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with an abundance of 9.43%; while, in the PB it appeared in the weeks 1 and 6 with 11.67

the most abundant in week 6 (14%).

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and 7.15% respectively; in the control it was the third most abundant in week 1 (14%) and

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The information related to the genus Ruegeria is scarce; Arora et al. (2012) isolated a strain (Ruegeria sp.) from the microalgae Tetraselmis indica, several laboratory studies indicated

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that these bacteria use carbohydrate from the cell wall of microalgae as metabolic

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substrates. In this study, Ruegeria ranked in the first place of abundance in week 6 in the treatments PA (23.82%) and PB (15.52%), and secondly in the control treatment (10.27%),

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due to its high presence. Its increase could be associated with the abundance of microalgae in the biofloc cultures. The genus Sulfurimonas seems to be responsible for a large part of the chemoautotrophic activity, with great importance in the cycle of elements in some habitats (Grote et al., 2008; Glaubitz et al., 2009; Perner et al., 2013). The importance of Sulfurimonas is attributed to its capacity to grow from inorganic ions, since it has a high metabolic versatility (Campbell et al., 2006; Grote et al., 2012). Some species of Sulfurimonas participate in an important

ACCEPTED MANUSCRIPT way in the mobility of Zn, Cu and Pb through denitrification by oxidation of sulfides in contaminated marine sediments (Shao et al., 2009). In the study of diversity in biofilm developed in shrimp culture, Martínez-Córdova et al. (2016), reported the species Sulfurimonas autotrophica, although it represented only 0.6% of the bacterial abundance,

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which coincides with the 0.6% reported by Porchas-Cornejo et al. (2017) in the influent

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water of a shrimp farm. However, it seems that the conditions prevailing in the biofloc

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culture systems favored its abundance considering that Sulfurimonas appeared in several samplings with abundances of 4.49 to 21.25%.

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In a previous study developed by Vargas-Albores et al. (2017) a total of 23 species of

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bacteria were detected in a commercial probiotic mixture; after the culture period, of these, 11 were detected in the intestine of the shrimp. The use of the probiotic mixture modified

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the bacterial profile of the shrimp intestine; however, most of the bacteria incorporated in

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the intestine were not from the marine environment, and there is no previous evidence of probiotic effects in any marine organism.

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It is important to remark that the physiological and metabolic characteristics as well as the

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beneficial properties of autochthonous bacteria thriving in the biofloc cultures are required to be addressed in further studies. In the same way, more studies are needed to evaluate the

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effect of commercial probiotics on the coastal environment where shrimp farms are located. 5. Conclusion

At the end of the study, the bacterial diversity (phyla and genera) in the PA and PB treatments was very similar to the control. In the biofloc system and under conditions present during the bioassay, it can be assumed that autochthonous bacteria had the greatest influence on the diversity. Acknowledgments

ACCEPTED MANUSCRIPT To the National Council of Science and Technology (CONACYT) by the financing through the projects: 222414 and 246529. The first author received a CONACYT scholarship (582300) to obtain the Master degree. The company Proveedora de larvas S.A. de C.V. contributed with the facilities and postlarvae.

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References Arora, M., Anil, A.C., Delany, J., Rajarajan, N., Emami, K., Mesbahi, E., 2012.

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ACCEPTED MANUSCRIPT marker. FEMS Microbiol. Lett. 362(17), fnv127.

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https://doi.org/10.1093/femsle/fnv127.

ACCEPTED MANUSCRIPT Table 1. Indexes of α diversity (H), and equitability (J) of the bacteria phyla in the biofloc of each treatment, during three sampling periods. Pielou index (J) week 1 week 3 week 6

1.54

1.30

1.40

0.52

PB

1.34

1.28

1.41

0.45

C

0.88

1.30

1.59

0.48

CR

PA

T

Shannon index (H) week 1 week 3 week 6

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Treatment

0.45

0.49

0.47

0.55

Beta diversity (β) of the bacteria phyla registered in the biofloc of probiotics A

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Table 2.

AN

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0.32

0.46

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(PA) y Probiotics B (PB) in respect to the control (C).

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PA - C

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PB - C

Jaccard index (Ij) 0.86 0.90

ACCEPTED MANUSCRIPT Table 3. Variation of the gamma diversity index of the bacteria phyla in the bioflocs during the weeks 1, 3 and 6. Gamma diversity week 3

week 6

H’ gamma

1.32

1.32

1.52

%α %β

95.00 5.00

97.60 2.40

96.30 3.70

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CE

PT

ED

M

AN

US

CR

IP

T

week 1

ACCEPTED MANUSCRIPT

100

Others Verrucomicrobia

60

Actinobacteria Firmicutes

40

T

Chlamydiae

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Cyanobacteria

20

Planctomycetes

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Relative abundance (%)

PA 80

0 Week 3

AN

80

20

Week 1

Chlamydiae Firmicutes Actinobacteria

Planctomycetes Bacteroidetes

Proteobacteria Week 6

CE

C

80

Others Candidatus Sacc.

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Relative abundance (%)

100

Week 3

PB Cyanobacteria

ED

40

Proteobacteria

Others

M

60

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Relative abundance (%)

100

0

Week 6

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Week 1

Bacteroidetes

60

Cyanobacteria Chlamydiae

40

Firmicutes Actinobacteria

20

Planctomycetes

Bacteroidetes 0 Week 1

Week 3

Week 6

Proteobacteria

Fig. 1. Bacterial profile of the main bacteria phyla in the treatments, during the sampling periods.

ACCEPTED MANUSCRIPT Uniques

25

18

25

Bootstrap

25 24 23

23

22

10

18

19

5

4

17

ICE

Jack 1

18

19

18 17

ED

16 15

PT

1

Sobs

ICE

18

17

CE

19

16

AC

Number of phyla

22

16

Uniques

20

15

19

19

AN

18

M

Number of phyla

20 19

PA

Bootstrap

20

20

0

3

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Sobs

21

2

CR

1

20

5

IP

15

21

15

22

21

21

20

T

Number of phyla

23

Number of Uniques

Jack 1

10

5

2

Number of Uniques

ICE

0 0

2

Jack 1

PB

3

Bootstrap

Uniques

21 21 20

20 20 19 18 3

19

3

20 15 10 5

Number of Uniques

Sobs

16 15

0

1

2

Sampling period

3

C

Fig. 2. Estimators of species richness and Uniques in treatments during the tree sampling periods (determined by the software EstimateS 9.1.0; Colwell, 2013).

ACCEPTED MANUSCRIPT PA

Rhodopirellula (Planctomycetes) Rhodobacter (Proteobacteria) Planctomyces (Planctomycetes) Ketogulonicigenium (Proteobacteria)

50

Cyanobacterium (Cyanobacteria) Ruegeria (Proteobacteria)

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Sulfurimonas (Proteobacteria) Croceibacter (Bacteroidetes)

0 1

3

other

6

PB

100

Rhodopirellula (Planctomycetes)

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Rhodobacter (Proteobacteria) Planctomyces (Planctomycetes)

AN

Ketogulonicigenium (Proteobacteria)

M

50

0 1

ED

Relative abundance (%)

CR

weeks

3

PT

Control

Sulfurimonas (Proteobacteria) Croceibacter (Bacteroidetes) Teredinibacter (Proteobacteria) Cytophaga (Bacteroidetes)

other

Rhodopirellula (Planctomycetes)

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Rhodobacter (Proteobacteria) Ketogulonicigenium (Proteobacteria) Ruegeria (Proteobacteria) Sulfurimonas (Proteobacteria)

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Relative abundance (%)

100

Ruegeria (Proteobacteria)

Echinicola (bacteroidetes) 6

Weeks

50

Croceibacter (Bacteroidetes) Vibrio Muricauda Teredinibacter

0 1

3

Weeks

Fig. 3.

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Relative abundance (%)

100

6

Other

The five more abundant bacteria genus in each treatment registered during the

weeks 1, 3 and 6.

ACCEPTED MANUSCRIPT HIGHLIGHTS

T IP CR US AN M ED PT CE

 

The use of commercial probiotics has increased in aquaculture industry The effect of commercial probiotics on the bacterial diversity is scarcely studied Massive sequencing 16S rRNA permitted to detect 22, 19 and 19 bacterial Phyla in PA, PB and C treatments Biofloc culture systems favored the abundance of endemic bacteria The autochthonous bacteria had the greatest influence on the diversity

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  