A Preliminary Stochastic Model for Managing Microorganisms in a Recirculating Aquaculture System

Ann Microbiol (2015) 65:1119–1129 DOI 10.1007/s13213-014-0958-0

ORIGINAL ARTICLE

A preliminary stochastic model for managing microorganisms in a recirculating aquaculture system

Songzhe Fu & Ying Liu & Xian Li & Junling Tu & Ruiting Lan & Huiqin Tian

Received: 12 May 2014 /Accepted: 5 August 2014 /Published online: 22 August 2014 # Springer-Verlag Berlin Heidelberg and the University of Milan 2014

Abstract Predicting the growth of key microorganisms is Acinetobacter baumannii (R2 =0.9970), Sphingomonas essential to improve the efficiency of wastewater treatment paucimobilis (R2=0.9086), Vibrio natriegens (R2=0.9993), of recirculating aquaculture systems (RAS). We have devel- Lutimonas sp. (R2 =0.9872) and Bacillus pumilus (R2 = oped a stochastic model to assess quantitatively the microbial 0.9816). Bacterial population structure was determined by populations in RAS. This stochastic model encompassed the the construction of 16S rRNA gene libraries. A regular vari- growth model into the Monte Carlo simulation and was con- ation trend was observed for the dominant groups during the structed with risk analysis software. A modified logistic model entire process, with a decrease of Cytophaga– combined with the saturation growth-rate model was success- Flavobacterium–Bacteroidetes from 37.6 to 18.7 % and an fully developed to regress the growth curves of six microor- increase in Gammaproteobacteria from 8.5 to 30.6 %. The ganisms. Monte Carlo simulation was employed to model the predicted model agreed well with observed values except for effects of chemical oxygen demand (COD) on the maximum Flavobacterium sp., and the results can be applied to predict specific growth rate. Probabilistic distributions and predic- key microorganisms in actual environments. The results of tions under the different COD ranges were generated for each this study provide a method to monitor the dynamics of key simulated scenario. The coefficient of determination (R2)and microorganisms, which can also help to evaluate the impacts bias factor (Bf) were used to assess the performance of an of microorganisms on the operations of RAS. established model. Logistic model produced a good fit to the growth curve of Flavobacterium sp. (R2 =0.9511), Keywords Recirculating aquaculture systems . Biofilter . Monte Carlo simulation . Stochastic model

Songzhe Fu and Ying Liu contributed equally to this paper. Electronic supplementary material The online version of this article Introduction (doi:10.1007/s13213-014-0958-0) contains supplementary material, which is available to authorized users. Aquaculture is becoming one of the world’s fastest growing : S. Fu J. Tu food production sectors (FAO 2012). Due to the lower water Nanchang Center for Disease Control and Prevention, No.833 Lijing requirements, lower risk of creating adverse environmental road, Honggutan district, Nanchang 330038, China impacts and increased biosecurity, the recirculating aquacul- Y. Li u : X. Li ture system (RAS) is becoming a promising option for modern Institute of Oceanology, Chinese Academy of Sciences, aquaculture. However, how to control the water quality and Qingdao 266701, China maintain the good health of fish in an aquaculture system is a major concern in the industry (Ioannis and Aikaterini 2008). A S. Fu (*) : R. Lan School of Biotechnology and Biomolecular Sciences, University of key unit for efficient water treatment in RAS is the biofilter. New South Wales, Sydney, New South Wales, Australia Chemical oxygen demand (COD), which reflects the level of e-mail: [email protected] organic matter, and ammonia are two key parameters which are monitored during wastewater treatment. For biofilters, H. Tian Fisheries College, Ocean University of China, Qingdao 266701, maintaining efficient wastewater treatment is highly depen- China dent not only on the adequate removal of organic waste and 1120 Ann Microbiol (2015) 65:1119–1129 efficient ammonia nitrification, but also on the control of removal of COD and nitrogen transformation. Based on pre- microbial populations that colonize the biofilter itself. Tradi- vious studies, we chose six species representing different tionally, farmers rely on the observation that the fish go off functions in the biofilter: Flavobacterium sp., Acinetobacter their feed or refuse to eat as the first signs of a water-quality baumannii and Sphingomonas paucimobilis which are re- problem or disease. However, due to the time-lag effect, sponsible for the removal of COD, Lutimonas sp. as the problems may have existed for a few days before these signs heterotrophic ammonia-oxidizing microorganism, Vibrio manifest. Information has recently become available on the natriegens as the opportunistic pathogen, and Bacillus evolution of the microbial community and the dynamics of pumilus as probiotics. A stochastic approach which key microorganisms during biofilm development which pro- encompassed the growth model into the Monte Carlo simula- vides a new insight on the management of RAS (Michaud tion was developed to characterize the growth of these micro- et al. 2009). The degree to which microbial populations spe- organisms in a marine RAS and to explore the potential use of cialize in RAS and whether this specialization is indicative of microorganisms for evaluating the performance of RAS. specific environmental factors is not yet fully understood, but some studies have demonstrated an explicit relationship between the presence of distinct microbial populations and en- Materials and methods vironmental conditions in RAS. For example, Based on fluorescence in situ hybridization (FISH) studies, changes in both Bacterial strains and culture media the abundance of ammonia-oxidizing bacteria and its species composition have been postulated as in situ indicators of the The bacterial strains used in this study are listed in Table 1. biological impact of ammonia (Okabe et al. 1999;Biesterfeld The six indigenous bacteria were isolated from a pilot-scale et al. 2003). Michaud et al. (2014) further revealed a negative marine RAS and had been identified previously (Fu et al. relationship between the nitrification efficiency and carbon/ 2009; Gao et al. 2011). Bacterial strains were stored in 10 % nitrogen (C/N) ratio through the construction of cDNA clone (w/v) glycerol broth at −70 °C. Sterilized synthetic wastewater libraries. Bar-coded 16S rRNA pyrosequencing and denatur- [in g/L seawater: NH4Cl, 1.377; NaHCO3, 3.5; K2HPO4, ing gradient gel electrophoresis have also been used to deter- 1.53; Na2HPO4,1.59;MgSO4·7H2O, 3.6; FeCl3·6H2O, mine the potential pathogen level in the RAS (Martins et al. 0.005] was used as the culture media (Zhu and Chen 2001). 2013). Kruse et al. (2013) reported that the composition of the Prior to use, synthetic wastewater was diluted to achieve a microbial community varied with changes in two operational final concentration of total ammonium chloride of 1.0 mg/L. conditions, suggesting a strong species-specific effect on wa- Sucrose (C12H22O11) was used as a carbon source to maintain ter bacterial communities in RAS. However, current research the concentration of COD between 0.5 and 8 mg/L. tools that examine microbial populations in RAS are time- consuming and unable to reflect the dynamics of bacterial Determination of the growth curves in single-culture assay communities over time, which limit the ability to monitor and co-culture assay the key microorganisms. Moreover, it is difficult to measure the growth of individual species over a range of water qualities Population means of the viable counts (log10 CFU/mL) were due to the inherent difficulties in determining the accurate used for developing growth curves at each experimental COD real-time value of COD in a very dynamic RAS. as described in Fu et al. (2013). Flavobacterium sp. DY-7, Predictive microbiology models have been used previously Sphingomonas paucimobilis DY-1, Lutimonas sp. H10, within the Monte Carlo simulation to estimate changes in levels Acinetobacter baumanni DW-1, Vibrio natriegens FS-1, and of microbial hazards in microbial risk assessment (Cassin et al. Bacillus pumilus N3-6 were initially grown in 100 mL of 1998). Simulation programs (e.g., @RISK and ModelRisk; Luria Broth (LB) in 250-mL flasks, and starvation of cells Palisade Corp., Ithaca, NY) can utilize the growth model and was ensured using procedures described in Fu et al. (2012). Monte Carlo simulation to produce probability distributions of Starved cells were added to the sterilized culture media men- microbial level. This approach has been extended to perform tioned above at final concentration of 104 CFU/mL with sensitivity analyses to identify points in the supply chain where different COD concentrations (0.5, 2, or 8 mg/L) and then remedial actions can be made (Fernandez-Piquer et al. 2013). stored at 16 °C for 72 h. This step ensured that the behavior of In our previous studies, bacterial diversity, community bacterial cells would be similar to that in nature seawater. structure and function were also elucidated by construction Sampling was generally carried out at 3-h intervals, with 24 of 16S rRNA gene libraries to provide information on the data points obtained after each trial. A 1-mL sample was taken shifts in populations during biofilm development and the roles at each interval and treated by homogenization as described in of different bacterial species in the inorganic nitrogen removal Fu et al. (2013). The homogenates were used for direct plat- process (Gao et al. 2011). The results identified a number of ing. Three independent trials were conducted for each COD microorganisms that are believed to play important roles in the concentration. Ann Microbiol (2015) 65:1119–1129 1121

Table 1 Bacterial strains used in this study Bacteria Family Class or phylum

Vibrio natriegens FS-1 Vibrionaceae Gammaproteobacteria Bacillus pumilus N3-6 Bacillaceae Firmicutes Flavobacterium sp. DY-7 Flavobacteriaceae Flexibacter Sphingomonas paucimobilis DY-1 Sphingomonadaceae Gammaproteobacteria Lutimonas sp. H10 Flavobacteriaceae Flexibacter Acinetobacter baumannii DW-1 Moraxellaceae Gammaproteobacteria

For Flavobacterium sp. DY-7, S. paucimobilis DY-1, the parameter ‘a’ and ‘b’ are constant coefficients. To linearize Lutimonas sp. H10 and A. baumannii DW-1, the viable counts the equation, we inverted it to give a linear regression equa- were estimated by preparing 10-fold serial dilutions and then tion. Therefore, the equation can be written in terms of the inoculating 0.1 mL from each dilution into Marine Agar (Difco new variables 1/μ and 1/COD. Laboratories, Detroit, MI), followed by incubation at 30 °C for 1=μ = = = 72 h. For V. natriegens FS-1 and B. pumilus N3-6, the viable ¼ ðÞÂa 24 ðÞþ1 COD ðÞb 24 ð2Þ counts were obtained by preparing 10-fold serial dilutions and Once the μ was calculated by Eq. (1), the relationship inoculating 0.1 mL from each dilution onto thiosulfate-citrate- max between μ and COD was linearized in Eq. (2). bile salts-sucrose (TCBS) agar and Mannitol-Egg-Yolk-Poly- max myxin (MYP) agar, respectively. Only statistically valid plates, Monte Carlo simulation Monte Carlo simulation for six se- i.e., those with between 25 and 250 colonies per plate, were lected species was constructed in an Excel (Microsoft, Red- considered for the determination of viable counts. Each data mond, WA) spreadsheet and was simulated using ModelRisk point represented the average of three trials. version 4.0 (Vose Software BVBA, Gent, Belgium), a spread- For the co-culture assay, B. pumilus N3-6 and V. natriegens sheet add-in program. The input settings of COD, including FS-1 were pre-cultured separately in LB at 30 °C for 24 h at distributions in Excel, were based on historical data from a the stationary phase. Then V. natriegens FS-1 and B. pumilus recirculating aquaculture system of Atlantic salmon in China N3-6 were inoculated into sterilized synthetic wastewater at between 2010 and 2013. Data were fitted using the PERT an initial cell density of approximately 104 CFU/mL and method and normal distributions. The root mean square error incubated at 16 °C. Samples were withdrawn every 3 h in (RMSE) was used as a criterion for selecting probability duplicate and inoculated onto TCBS agar and MYP agar for distributions based on goodness of fit. The predictive model the determination of each species’ density. The plates were developed here for growth was used outside the Monte Carlo incubated at 30 °C for 24 h. simulation to convert data of environmental factors into input settings. ModelRisk’s "Add Output" button was used to nom- inate the outputs. The scenario of the Monte Carlo simulation Construction of the predictive model was simulated with ModelRisk settings of Latin Hypercube sampling. Iteration was set at 10,000. The probability distri- Primary model For growth curves, the data were fitted by the bution was generated after the calculation [Electronic Supple- modified logistics equation [Eq. (1)], which is often used to mentary Material (ESM) Fig. S1]. The growth rates in the describe bacterial growth curves (Zwietering et al. 1990). In range of 98 % probability were considered as the growth range this equation [Eq. (1)], the parameter A and μ , which are max of microorganisms in RAS and chosen as input of Eq. (1); the constant values for each experiment, represent the maximum predicted values were obtained at a specific time. The data of increase in microbial cell density (log10 CFU/mL) and max- − six species were fitted by the modified logistics equation imum specific growth rate (h 1), respectively. Y(t) represents [Eq. (1)] and further linearized by inverting the values of the the increase in microbial cell density at a certain time (log10 specific growth rate and COD [Eq. (2)], respectively. CFU/mL), with t the time of incubation. The model was simplified so that there was no growth lag time (λ)atthe Operation conditions of test RAS initial stage of biofilm formation. = μ = λ− YtðÞ¼A fg1 þ exp½ðÞÂ 4 max A ðÞþt 2 ð1Þ In the present study, to simplify the component of COD, the biofilter was set up to treat recirculating water in a marine To describe a proportional relationship between the specific aquaculture system without aquatic animals and operated growth rate (μ) and COD, a saturation-growth rate model was from day 0 to day 30. Three pilot-scale biofilters (height employed as the secondary model (Fu et al. 2013). In Eq. (2), 1.2 m, diameter 0.40 m, with a working volume of 80 L) in 1122 Ann Microbiol (2015) 65:1119–1129 combination with three 100-L feeding tanks were set up to 3′) (Weller et al. 2000), BP15 (5′- GGATCAAACTCTCCGA form three independent RAS. A mechanical filter (drum filter GG-3′) (Mohapatra and La Duc 2012), GV (5′-AGGCCACA with 20-mm mesh) was used for particle removal. The biore- ACCTCCAAGTAG-3′) (Giuliano et al. 1999), SPH120 (5′- actor compartment was packed to a height of 1.0 m with GGGCAGATTCCCACGCGT-3′) (Neef et al. 1999), S-G-Lni- porous Maifan mineral stones (diameter 10 mm). Biofilters 1237-a-A-18 (5′-TTAAGGATTCGCTCCCCC-3′) (Fu et al. were operated with a 0.5-h hydraulic retention time. The 2009), and Z93435 (5′-GCTTGCTACCGGACCTAGCGGC- temperature was controlled at 16.0±1 °C by aquarium heaters 3′) (Wong et al. 2007)wereusedtodetectFlavobacteria, (typical temperature in Atlantic salmon aquaculture system). Bacillus pumilus, Vibrio sp., Sphingomonas sp., Lutimonas The dissolved oxygen concentrations in the reactors were sp., and Acinetobacter sp., respectively. always maintained above 6 mg/mL and pH was maintained between 7.0 and 7.6. Every 72 h, diluted synthetic wastewater Construction and analysis of 16S rRNA gene libraries (Zhu and Chen 2001) and sucrose (C12H22O11) were added to for the biofilm samples the biofilters, with a final ammonium chloride concentration of 1.0 mg/L and a COD of 8 mg/L. Biofilm samples were taken from three biofilters as described by Fu et al. (2009) and the genomic DNA extracted following Fluorescence in situ hybridization the procedure of Lyautey et al. (2005). The 16S rRNA genes of the DNA extracted from the biofilm samples were ampli- Fluorescence in situ hybridization was used to measure the fied by PCR with the universal primers 27 F (5′-AGA GTT abundance of total bacteria and individual species in biofilm TGA TCC TGG CTC AG-3′) and 1492R (5′-GGT TAC CTT samples. The protocol of Lydmark et al. (2006) was used for GTT ACG ACT T-3′)(Lane1991). The amplified products sample fixation and cryosectioning. Biofilm specimens were were recovered from the gel using a gel extraction kit examined under an epifluorescence microscope (Nikon model (Tiangen Biotech Corp, Beijing, China), cloned into the 600i; Nikon Corp., Tokyo, Japan). Cell volumes were also pBS-T vector (Tiangen Biotech Corp, Beijing, China) and determined under the epifluorescence microscope. Images of transformed into Escherichia coli TOP 10 in accordance with stained samples were captured by a photometric image video the manufacturer’s instructions. Randomly selected clones camera (Axiocam; Carl Zeiss, Jena, Germany) and digitized on were sequenced by the Shanghai Shengong Company (Shang- a personal computer using Axiovision 3.1 software as de- hai, China). Alignments of the 16S rRNA gene sequences scribed by Michaud et al. (2009). The Domain Bacteria was were carried out with Cluster X software, and sequence sim- detected using the eubacterial universal probe EUB-338I (5′- ilarities were obtained by performing BLASTN searches GCTGCCTCCCGTAGGAGT-3′)(Amannetal.1990). A neg- (http://www.ncbi.nlm.nih.gov/BLAST/). Coverage values ative control probe, non-EUB (5′-ACTCCTACGGGAGGCA were calculated in order to determine how efficiently the GC-3′), was also used for non-specific probe binding. Results clone libraries described the complexity of the original with this negative control probe, which accounts for the auto- bacterial community. The coverage value (Good 1953)is fluorescence of cells and nonspecific probe binding, were given as C=1 − (n1/N), where n1 is the number of clones subtracted from the percentages detected with probes for the which occurred only once in the library (singletons). The bacterial groups. CFB563 (5′-GGACCCTTT AAACCCAAT- diversity of 16S rRNA gene libraries was measured by the

Table 2 Parameter values of maximum specific growth rate and chemical oxygen demand and formulas for the growth models of six bacterial species

Species A (CFU/mL)a Formula R2 SD SEE

b Vibrio natriegens FS-1 2.24±0.061 μmax =24×COD /(6.313+5.82× COD) 0.9993 0.011 0.0202 b Bacillus pumilus N3-6 2.6±0.14 μmax=24×COD /(9.4964+4.535× COD) 0.9816 0.0095 0.0060 c V. natriegens FS-1 1.41±0.15 μmax =24×COD /(3.475+11.63× COD) 0.9999 0.031 0.0080 c B. pumilus N3-6 1.56±0.082 μmax =24×COD /(5.44+11.892× COD) 0.9985 0.017 0.0071

Sphingomonas paucimobilis DY-1 1.81±0.16 μmax =24×COD /(3.862+14.28× COD) 0.9086 0.043 0.0268

Flavobacterium sp. DY-7 1.63±0.32 μmax =24×COD /(12.707+9.22× COD) 0.9970 0.021 0.0043

Lutimonas sp. H10 1.13±0.23 μmax=24×COD /(16.514+17.59× COD) 0.9872 0.010 0.0029

Acinetobacter baumannii DW-1 2.34±0.54 μmax =24×COD /(4.97+8.69× COD) 0.9511 0.014 0.0030

SD, Standard deviation; SEE, standard error of the estimate μmax maximum specific growth rate a Maximum increase of microbial cell density b In single-culture assay c In co-culture assay Ann Microbiol (2015) 65:1119–1129 1123

Shannon index (H=−∑Pi × ln Pi), the Simpson index (D=1 − (a) ∑Pi2) and the Margalef richness index [R=(S − 1)/ln N), 60 2 respectively (Shannon 1948; Simpson 1949;Margalef 50 R = 0.9872 1958). In the above-mentioned equations, H, D,andR are 40 the species diversity index, S is the number of species, N is the R2 = 0.997 total number of individuals for all species and Pi is the 30 R2 = 0.9086 proportion of individuals of each species. The percentage (h)1/µ 20 abundance of each species (Pi) was calculated as Pi=ni/N, in 2 10 R = 0.9511 which ni represents the number of clones for each species in the library. 0 0 0.5 1 1.5 2 2.5 1/COD Data analysis (b) The performance of the model was evaluated using the coef- 20 R2 = 0.9999 ficient of determination (R2) and standard deviation (SD) provided by CURVE EXPERT 1.3 software (Hyams Devel- 15 2 opment Inc, Hixson, USA) The parameter μmax, R and SD were calculated for each trial, and the results of three trials 10 R2 = 0.9993 were averaged. The Student’s t test was employed to deter- 1/µ (h) mine differences (P<0.05) between the predicted value and 5 observed data. Models are fitted to data using the standard error of the estimate (SEE). All statistics were performed with 0 SPSS for Windows, version 11.5 (SPSS Inc, Chicago, IL). 0 0.5 1 1.5 2 2.5 The bias factor (Bf), which was defined by Eq. (3)(Ross 1/COD 1996), was used as the overall measure of model prediction (c) bias. Where n is the number of prediction cases used in the 25 2 calculation, Xpredicted and Xobserved are the predicted and R = 0.9985 observed values of each species, respectively. 20 Bf ¼ 10 Â ∑log 10ðÞ Xpredicted=Xobserved =n ð3Þ 15 2 1/µ (h) 1/µ 10 R = 0.9816 5 Results and discussion 0 0 0.5 1 1.5 2 2.5 Primary growth modeling of microorganisms in seawater 1/COD Fig. 1 Relationships between the specific growth rate (μ)andchemical A total of 432 experimental data points on the growth of six oxygen demand (COD). × Flavobacterium sp. DY-7, filled diamond species in seawater under different conditions (COD was set at Acinetobacter baumannii DW-1, open diamond Sphingomonas 0.5, 2 or 8 mg/L) were obtained. The impact of organics on the paucimobilis DY-1, + Lutimonas sp. H10, open triangle Vibrio natriegens FS-1 in single-culture assay, filled triangle V. natriegens FS- specific growth rate was evaluated by inoculating individual 1 in co-culture assay, open circle Bacillus pumilus N3-6 in single-culture species into seawater under these three different COD con- assay, filled circle B. pumilus N3-6 in co-culture assay centrations. Growth curves were fitted from experimental data using Eq. (1), and the maximum specific growth rates were also calculated. Among the six species, V. natriegens showed Verschuere et al. (2000)reportedthatBacillus sp. had an the highest maximum specific growth rates (μmax), which inhibitory effect against the growth of Vibrio sp. We therefore ranged from 0.0541, 0.1137 to 0.1480 h−1 when COD was investigated the specific growth rates of B. pumilus N3-6 and equal to 0.5, 2, and 8 mg/L, respectively. The values of μmax V. natriegens FS-1 in co-culture to see if these differed from were lowest for Lutimonas sp., with 0.019, 0.042, and those in the single-culture assay. The values of μmax for 0.047 h−1 at the corresponding COD concentration, respec- V. natriegens FS-1 and B. pumilus N3-6 in the co-culture tively. These results reveal that specific growth rates increased assay varied between 0.0538 and 0.0827 h−1 and between markedly with an increase of COD. 0.044 and 0.0807 h−1, respectively. However, it is notable that 1124 Ann Microbiol (2015) 65:1119–1129

Table 3 Variables and formulas used in the Monte Carlo simulation

Variable Type Distribution Formula

COD (mg/L) Input PERT =VosePert(0.44, 2.22, 8. 42) Vibrio natriegens FS-1 a Output Normal =VoseOutput()+24×COD /(6.313+5.82× COD) Bacillus pumilus N3-6 a Output Normal =VoseOutput()+24×COD /(9.4964+4.535× COD) V. natriegens FS-1b Output Normal =VoseOutput()+24×COD /(3.475+11.63× COD) B. pumilus N3-6b Output Normal =VoseOutput()+24×COD /(5.44+11.892× COD) Sphingomonas paucimobilis DY-1 Output Normal =VoseOutput()+24×COD /(3.862+14.28× COD) Lutimonas sp. H10 Output Normal =VoseOutput()+24×COD /(16.514+17.59× COD) Flavobacterium sp. DY-7 Output Normal =VoseOutput()+24×COD /(12.707+9.22× COD) Acinetobacter baumannii DW-1 Output Normal =VoseOutput()+24×COD /(4.97+8.69× COD) a In single-culture assay b In co-culture assay the growth rates of B. pumilus N3-6 and V. natriegens FS-1 in Comparison the modeling results with FISH the single-culture assay were significantly higher than those in the co-culture assay. The equations in Table 2 were further inputted into ModelRisk 4.0 to run the Monte Carlo simulation. The variables, formulas, and input settings used in the Monte Carlo simulation are Secondary growth modeling of microorganisms in seawater shown in Table 3. In our study, PERT and normal distributions were compared for the COD data from the Atlantic almon In the secondary modeling, Eq. (2) was carried out to deter- aquaculture system (Fig. 2). The RMSE was lower for the −6 mine the relationship between μmax and COD. The values of PERT distribution (3.1×10 ) than for the normal distribution −5 μmax obtained in Eq. (1) and the corresponding COD were (7.8×10 ). Another advantage of the PERT distribution is inverted to give a linear regression equation. Based on the that its shape is flexible as it can vary in shape from a normal performance of the calculated equations, we determined SD distribution to a lognormal distribution. The PERT distribu- and SEE (see Table 2). These data showed a good fit to tion was selected for both reasons. The box-plot of the prob-

Eq. (2). Our results suggest that 1/μmax and 1/COD were ability distribution of growth rate of the six species by Monte adequately described by a linear regression equation for all Carlo simulation is shown in ESM Fig. S1. Growth rates in the species (Fig. 1). The R2 value for Flavobacterium sp., range of 98 % probability were chosen as input of Eq. (1), and A. baumannii, S. paucimobilis, V. natriegens, Lutimonas sp. the predicted values were obtained at a specific time. It is and B. pumilus was 0.9511, 0.9970, 0.9086, 0.9993, 0.9872 noteworthy that the box-plot had lines extending vertically and 0.9816, respectively (Fig. 1). The R2 value for from the box, indicating variability outside the upper and V. natriegens and B. pumilus in co-culture assay was 0.9999 lower quartiles, which means that the occurrences were rare and 0.9985, respectively (Fig. 1b, c). These results support our and negligible. The prediction values from six species were hypothesis that the specific growth rate and COD are linearly calculated based on Eq. (1) and compared with the observed related in the saturation-growth rate model. values on day 1, 5, 15, and 30, respectively. It can be seen that

Fig. 2 Comparison of PERT and 0.6 Data normal probability density Pert distributions for the dynamics of 0.5 Normal COD in the recirculating 0.4 aquaculture system (RAS). Historical data were derived from 0.3 the RAS of Atlantic salmon in China between 2010 and 2013 0.2 Distribution density Distribution 0.1

0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 COD (mg per l) Ann Microbiol (2015) 65:1119–1129 1125 the predictive model reflected the actual growth of individual Flavobacterium sp.. However, the Bf value for V. natriegens species in the biofilm formation process (Fig. 3). The accuracy and B. pumilus in the single-culture assay was extremely high, of the prediction is acceptable if the Bf value ranges from 0.75 with 8.65 for V. natriegens and 4.285 for B. pumilus,indicat- to 1.25 (Ross 1996). Bf values for A. baumannii, ing the need to consider microbial interaction in predictive Flavobacterium sp., S. paucimobilis, Lutimonas sp., modeling. Most species had reached their maximum cell V. natriegens,andB. pumilus in co-culture assay were 1.19, density on day 15. At the end of day 30, 7.4×102 CFU/mL 2.96, 1.016, 1.13, 1.22, and 1.077, respectively, which means Bacillus species and 3.0×103 CFU/mL Vibrio species were that the predictions agreed well with observed data except for also detected in the rearing water.

Fig. 3 Prediction values and the (a) observed values of Vibrio natriegens FS-1(VN), 6 Flavobacterium sp DY-7 (F), 5 Sphingomonas paucimobilis DY- 1(SP), Acinetobacter baumannii 4 DW-1 (AB), Lutimonas sp. H10 3 (L), and Bacillus pumilus N3-6 2 (BP)atday1(a), day 5 (b), day 15 (c), and day 30 (d). Filled bars 1

Measured concentrations of g per Log number 0 individual species; open bars AB F BP SP VN L predictions values without microbial interactions, stippled bars prediction values with (b) microbial interactions. 6 5 4 3 2 1

Log number per g g per Log number 0 AB F BP SP VN L

(d) . 6 5 4 3 2 1

Log number per g 0 AB F BP SP VN L 1126 Ann Microbiol (2015) 65:1119–1129

Bacterial community structure With the detection of just two clones representing Nitrobacter sp. and Nitrosomonas sp. on days 15 day 30, The shifts in populations and the dynamics of individual respectively, the bacterial abundance values of ammonia- species were also elucidated based on 16S rRNA gene librar- oxidizing bacteria and nitrite-oxidizing bacteria obtained in this ies. In this study, bacterial diversity and population richness study were comparable with those previously reported by Gao were increased gradually, as revealed by the Shannon index et al. (2011). However, clones related to Planctomycetes were (from 2.92 on day 1 to 3.52 in day 30), Simpson's index (from detected in all samples at various percentages in the library, 0.92 on day 1 to 0.95 on day 30), and Margalef's index (from indicating the presence of anammox bacteria that convert nitrite

7.14 on day 1 to 10.79 on day 30) (Table 4). These results may and ammonia into N2 gas (Strouss et al. 1997). One possibility suggest that the biofilm had been naturally domesticated and is that on a highly irregular surface, such as the porous mineral was composed of an increased number of functional microbial material used for the biofilter, the presence of many micro- populations. niches may allow the establishment of anoxic microenviron- The operational taxonomic units (OTU) detected from day ments that would support the growth of such organisms. How- 1 to day 30 are shown in Fig. 4. Members of the Cytophaga– ever, with the development of the biofilm, this group shrunk Flavobacterium–Bacteroides (CFB) group were well repre- from 10.2 % on day 1 to 4.4 % on day 30, suggesting a decrease sented on day 1 (37.6 %), followed by the of anoxic microenvironments in the biofilter. Alphaproteobacteria (22.2 %), and Planctomycetes (10.3 %). However, in terms of the domestication of biofilm, Advantages and improvements of the stochastic model the CFB accounted for only 18.7 % on day 15 (Fig. 4b) and 17.5 % on day 30 (Fig. 4c). Despite being present at a very Although there have been significant research efforts on de- low abundance at the beginning, both the individual veloping a predictive model for pathogens, a general modeling Gammaproteobacteria species targeted and the of key microorganisms for RAS is still lacking. In an intensive Gammaproteobacteria group displayed high growth rates. RAS, the high concentration of waste produced by these The percentage of Gammaproteobacteria gradually increased animals’ normal metabolic processes may stimulate the rapid from8.5%onday1to30.6%onday30,whichwas growth of microorganisms, such as Vibrio sp. Therefore, the consistent with the results of a previous study (Zhu et al. key aspect for improving the sustainability of the biofilter is to 2012). Despite representing a small percentage of marine acquire the ability to ‘manage’ these bacterial populations, bacterioplankton, this group can be abundant or even become especially those microorganisms that are responsible for the the dominant microbial communities under certain conditions biodegradability. However, the real-time value of COD is very (Ferrera et al. 2011). In addition, the percentage of dynamic in RAS, resulting in difficulties in measuring the Alphaproteobacteria showed a slight decrease, from 22.2 % growth of microorganisms by the traditional method. In our on day 1 to 11.7 % on day 30. study, we developed a predictive model to characterize the The clones closely related to Acinetobacter sp. showed a growth of some common microorganisms in a marine RAS by significant increase with the development of the biofilm, from using a stochastic approach. We further compared the predict- 2.56 % on day 1 to 15.94 % on day 15 and then to 20.59 % on ed values with observed values in a pilot-scale RAS. The day 30, which was also consistent with the prediction stochastic model, which encompassed the growth model into (Fig. 3d). Flavobacterium sp. accounted for a decreasing the Monte Carlo simulation, successfully predicted the growth fraction of the gene library (5.3 % on day 1, 3.5 % io day range of the selected microorganisms. This stochastic ap- 15, 2.8 % on day 30). The proportion of B. pumilus peaked on proach produces a range of outcomes in terms of probability day 15 in both predicted and observed values and then grad- of microbial levels—not as average or fixed results. Monte ually decreased, while the trend was the reverse for Vibrio Carlo simulation is a useful measure of dependence because it species. is easy to estimate from data and maintains the marginal

Table 4 Diversity indices and richness index of the librariesa

Library Shannon-Weiner index Simpson index Margalef richness index Coverage (%) Number of OTU

1 day 2.92±0.093 0.92±0.0043 7.14±0.128 90.5±0.73 73 15 days 2.70±0.054 0.95±0.0003 9.93±0.397 91.33±1.21 81 30 days 3.52±0.019 0.94±0.0046 10.79±0.375 91.30±0.97 87

OUT, Operational taxonomic unit a The diversity and richness of microbial community was measured by the Shannon index (H= −∑Pi × ln Pi, Simpson index (D=1−∑Pi2 ), and Margalef richness index [R=(S- − 1)/ln N]. The coverage of 16S rRNA gene libraries is given as C=1 - (n1/N) Ann Microbiol (2015) 65:1119–1129 1127

(a) (b)

15% 16% 16% 22% 1% 2% 3% 2% 9% 5% 10% 2% 2% 9% 1%

20% 28% 37%

(d)

25.00% 20.00% 15.00% Day 1 10.00% Day 15 5.00% Day 30 0.00% AB F BP SP VN L Fig. 4 The succession of the microbial community during biofilm de- the mid-stage biofilm (15 days), c microbial community of the mature velopment. ALF Alphaproteobacteria, GAM Gammaproteobacteria, biofilm (30 days), d change in the percentage of Vibrio natriegens (VN), BETA Betaproteobacteria, FIR Firmicutes ACT Actinobacteria,CFB: Flavobacterium sp. (F), Sphingomonas paucimobilis (SP), Acinetobacter Cytophaga-Flexibacter-Bacteroidetes, PLA Planctomycetes. a Microbial baumannii (AB), Lutimonas sp. H10 (L)andBacillus pumilus (BP)inthe community of the initial-stage biofilm (1 day), b microbial community of 16S rRNA library distributions of the correlated variables (Iman and Conover production. Thus, it would be possible to predict the probable 1982). The technique is 'distribution independent', which has level of microbiological safety in RAS. To our knowledge, no effect on the shape of the correlated distributions. There- this is the first time that modeling data from the laboratory fore, it is guaranteed that the distributions will still be repli- have been used together with field observations in an integrat- cated. A stochastic model that evaluates levels of microorgan- ed approach to provide a solution for real-time monitoring of isms in RAS can help to determine the effects of uncertainties microorganisms in RAS. The results obtained from the Monte and variability on microbial growth throughout the Carlo simulation were consistent with field observations, 1128 Ann Microbiol (2015) 65:1119–1129 suggesting the importance of controlling COD in RAS. Addi- Conclusion tionally, the predictive modeling of bacteria previously described as probiotics or producers of inhibitory compounds, In summary, the results of this study indicate the advantages of such as the Bacillus sp., could further improve our ability for using predictive and stochastic modeling for reducing the probiotic intervention and for controlling the development of uncertainties and variability of the operations in RAS. They pathogenic organisms in fish rearing systems. Should the indicate a good agreement between the predicted and ob- modeling of key microorganisms be developed in the real served microbial levels and would provide useful information RAS scenario, it will become a possible alternative to the for managers of aquaculture farms to manipulate the RAS and time-consuming and costly microbiological methods. eventually prevent disease outbreaks in the aquaculture Admittedly, predicting the real-time dynamics of microor- industry. ganisms in RAS remains a challenge since many environmental variables are involved. In the natural environment, the Acknowledgments This work was supported by grants from the Na- microbial ecology is extremely complex. Environmental con- tional Key Technologies R&D Program (2011BAD13B04), Public Ser- ditions, substrate, and inter-specific relationships significantly vice Sectors (Agriculture) Special Project (201003024), Natural Science influence the structure of microbial populations. A simple Foundation of Jiangxi Province (20132BAB215027), the earmarked fund for Modern Agro-industry Technology Research System (CARS-48), and model based on single-cultured bacteria has no ability to the National Natural Science Fund of China (No 41306152). The authors predict the dynamics of a microbial community. Furthermore, wish to thank Dr. Luigi Michaud and Dr. Kang Chen for their helpful the potential of microbial interaction and phage pressure on criticisms and valuable suggestions to improve the manuscript. This paper microorganisms are also not negligible (Fu et al. 2009)as is dedicated to the memory of Dr. Luigi Michaud. these are not necessarily associated with environmental variables and, therefore, not traditionally considered in the predictive model. However, the strength of stochastic modeling is References its iterative nature. 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