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

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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 be- tween the presence of distinct microbial populations and en- Materials and methods vironmental conditions in RAS. For example, Based on fluo- rescence 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.
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