To quantify only nitrite, the cadmium reduction column was not incorporated into the Auto Analyzer. RAS operators recorded all other chemical parameters from submerged probes measuring temperature, pH, and oxidation-reduction potential.
Per the laboratory standard operating procedure, RAS operators used Hach colorimetric kits to measure rearing tank concentrations of ammonia and nitrite. To maximize read depth for a temporal study of the biofilter surface communities, we used the illumina HiSeq platform and targeted the V6 region of the 16S rRNA gene for Archaea and Bacteria separately. In total, we obtained community data from 15 dates for the temporal analysis. To interrogate changes in the spatial distribution of taxa across depth in the biofilter and obtain increased taxonomic resolution, we used 16S rRNA gene V4-V5 region sequencing on an illumina MiSeq.
Sample metadata are listed in Table S1. Sequence run processing and quality control for the V6 dataset are described in Fisher et al. Trimmed reads were merged using Illumina-Utils as described previously Newton et al. MED uses information uncertainty calculated via Shannon entropy at all nucleotide positions of an alignment to split sequences into sequence-similar groups Eren et al. The sequence datasets were decomposed with the following minimum substantive abundance settings: bacterial V6, ; archaeal V6, ; bacterial V4-V5, The minimum substantive threshold sets the abundance threshold for MED node i.
Minimum substantive abundances were calculated by dividing the sum total number of 16S rRNA gene sequences per dataset by 50, as suggested in the MED best practices sequence counts are listed in Table S2.
Primer sequences were identified from the consensus using Primer3Plus Untergasser et al. The pmoA forward primer Luesken et al. Multiple endpoint PCR approaches were used to investigate the nitrifying community composition of the RAS fluidized sand biofilter for amoA Gammaproteobacteria, Betaproteobacteria, Archaea , and comammox Nitrospira , nxrA Nitrobacter spp. The primer sets and reaction conditions used are listed in Table 1. DNA samples of biofilter water and sand from four different rearing cycle time-points were used to construct clone libraries of archaeal amoA and Nitrospira sp.
One sample from the center of the sand biofilter was used to construct clone libraries for betaproteobacterial amoA and comammox amoA. The center biofilter sample was chosen as it produced well-defined amplicons suitable for cloning target amoA genes.
Comammox amoA sequences from this study were aligned with those from van Kessel et al. The Nitrospira nxrB sequences generated in this study were significantly shorter than those used for nxrB phylogenetic reconstruction in Pester et al. Instead, the UWM Biofilter and Candidatus Nitrospira nitrificans sequences were added to the majority consensus tree from Pester et al.
This tool uses sequence similarity to add sequences to pre-existing trees without changing the tree topology. Quantitative PCR assays were designed to differentiate two Nitrospira nxrB genotypes and two Nitrosomonas amoA genotypes in our system.
Primer concentrations and annealing temperatures were optimized for specificity to each reaction target. The newly designed primers were tested for between genotype cross-reactivity using the non-target genotype sequence in both endpoint and real time PCR dilution series. After optimization, all assays amplified only the target genotype.
The two closely related sequence types were pooled in equimolar amounts for reaction standards. A comammox amoA qPCR primer set was developed using the same methods as the other assays presented in this study. All assay conditions are listed in Table 1. Cloned target genes were used to generate standard curves from 1.
All reactions were carried out in triplicate, with melt curve and endpoint confirmation of assays qPCR standard curve parameters and efficiency are listed in Table S3. MED nodes were used in all sample diversity metrics. Kruskal—Wallis rank sum tests were performed in the R base statistics package R Core Team, to test whether the populations of the aforementioned genes were stratified by depth. To determine whether the observed ammonia removal could provide the energy needed to support the number of potential ammonia-oxidizing microorganisms AOM in the biofilter as quantified via qPCR, we modeled steady-state biomass concentration from measured ammonia oxidation with the following equation:.
Y AO is the growth yield of ammonia oxidizers, and b AO is the endogenous respiration constant of ammonia oxidizers, which were estimated as 0. To calculate X AO , or biomass concentration, we used the mean cell diameter 0. The modeled biomass concentration was plotted vs. The results of all amoA qPCR assays were combined to estimate total ammonia-oxidizing microorganism biomass in copy numbers per gram wet weight sand.
The mean biofilter influent concentrations of ammonia and nitrite were, respectively, 9. Biofilter effluent ammonia concentrations 3. On occasion, nitrite accumulated above the recommended threshold of 0.
No major fish illnesses were reported during the RAS operational period. Environment and operations data are listed in Table S1. At family-level taxonomic classification, the biofilter sand-associated community was distinct from the water community.
A MED-based bacterial community composition comparison Figure 1 supported the patterns observed using broader taxonomic classification indicating that the biofilter sand-associated community was distinct from the assemblage present in the biofilter water.
In contrast to the large diversity in the bacterial community, we found the archaeal community to be dominated by a single taxonomic group, affiliated with the genus Nitrososphaera. The initial biofilter community composition characterization revealed distinct communities between the biofilter sand and decanted biofilter water Figure 2. Based on this data and that fluidized-bed biofilter nitrification occurs primarily in particle-attached biofilms Schreier et al.
In the sand samples, we observed a significant change in bacterial community composition MED nodes over time Table 2. Figure 2. Dendrogram illustrating the bacterial community composition relationships among biofilter sand and biofilter water samples. A complete-linkage dendrogram is depicted from Bray—Curtis sample dissimilarity relationships based on Minimum Entropy Decomposition node distributions among samples V6 dataset. The leaves of the dendrogram are labeled with the day count, where 0 represents the beginning of a fish rearing cycle.
Negative numbers are days prior to a new rearing cycle. The day count is followed by the date sampled mm. See Table S1 for sample metadata. Table 2. Environmental variable to bacterial community composition correlations. Figure 3. Non-metric multidimensional scaling plot of Bray—Curtis bacterial community composition dissimilarity between sample time points. The circle indicates samples taken after fish had grown to a size where feed type and amount were stabilized 3 mm pelleted feed diet and 3—7 kg of feed per day.
Using a second sequence dataset V4-V5 16S rRNA gene sequences , we examined the bacterial community composition associated with sand across a depth gradient surface, middle, bottom. The Planctomycetes were a larger portion of the community in the surface sand on average Figure 4. Depth comparison of bacterial biofilter community composition. The dendrogram represents Bray-Curtis dissimilarity between sample community composition. Sample IDs are listed and sample depth is indicated by on the plot next to the dendrogram.
Sample names correspond to sample metadata in Table S1. We also were unable to amplify Nitrobacter nxrA genes Figure S1 with a commonly used primer set Poly et al.
In addition to the 16S rRNA gene community data, we amplified, cloned, and sequenced nitrifying marker genes representing the dominant nitrifying taxa in the UWM biofilter. Both genotypes placed phylogenetically in the Nitrososphaera sister cluster Figure 5 , which includes the candidate genus, Nitrosocosmicus Lehtovirta-Morley et al.
Figure 5. Ammonia-oxidizing Archaea consensus tree. A consensus phylogenetic tree was generated from maximum likelihood and Bayesian inference phylogenetic reconstructions. Consensus tree support is indicated by colored circles at tree nodes. Collapsed nodes and assigned names are based off of Pester et al.
Clone and taxonomic names are followed by NCBI accession numbers. Ammonia-oxidizing archaea amoA sequences generated in this study are highlighted.
Figure 6. Ammonia-oxidizing Bacteria consensus tree. Collapsed nodes and assigned names are based off of Abell et al. Nitrospira nxrB uwm-1 formed a clade distinct from cultivated Nitrospira spp. Nitrospira nxrB uwm-2 clustered phylogenetically with Nitrospira spp. Because of the association of Nitrospira nxrB uwm—2 with comammox nxrB sequences, we further examined the biofilter for the presence of Nitrospira -like amoA genes.
We subsequently amplified a single Nitrospira -like amoA out of the biofilter samples, and phylogenetic inference placed this amoA on a monophyletic branch with currently known Nitrospira amoA sequences, but in a distinct cluster Figure 7B with a drinking water metagenome contig Pinto et al. Figure 7. For the nxrB phylogeny, the consensus tree from Pester et al.
For the amoA phylogeny, a consensus phylogenetic tree was generated from maximum likelihood and Bayesian inference phylogenetic reconstructions.
Clone names are followed by NCBI accession numbers or a manuscript citation. In both trees, sequences generated in this study are highlighted with colored boxes.
We investigated the temporal and spatial stability of the nitrifying organisms in the UWM biofilter by developing qPCR assays specific to identified amoA and nxrB genes. Within the ammonia-oxidizing community, the AOA and comammox- Nitrospira amoA assay had space-time abundance patterns distinct from that of the Nitrosomonas genotypes. Figure 8. Nitrification marker gene concentration over time. Standard deviation of triplicate qPCR reactions is indicated for each sample.
The x-axis indicates time, with timepoint 0 representing the beginning of one fish rearing cycle. Samples collected in the previous rearing cycle are labeled with negative values. Despite these abundance pattern similarities, the two genotypes had differential associations with other nitrifying taxa marker genes.
Figure 9. Heatmap of abundance pattern correlations for nitrifier genotypes. Pearson's correlation coefficient values r are listed and colored according to the strength of the abundance correlation between marker genes for each genotype.
Purple colors indicate stronger correlations and green colors indicate weaker correlations. The estimated cell densities for ammonia oxidizers in the biofilter were modeled as a function of mean cell residence time MCRT.
Since the biofilter MCRT was unknown, a range of values 1—30 days was used in the model. For example, the model indicates ammonia oxidizer biomass reaches near maximum by a mean cell residence time MCRT of 20 days Figure Figure Model output of ammonia-oxidizer cell concentration as a function of biofilter mean cell residence time MCRT. The red line indicates ammonia-oxidizer cell abundance estimates from the mean change in ammonia concentration across the filter matrix as a function of mean cell residence time.
The shaded gray region represents the range of cell abundance estimates from the minimum and maximum observed ammonia removal rates.
In this study, we generated data that deeply explored the microbial community composition for a production-scale freshwater RAS nitrifying biofilter, expanding our understanding of the complexity of these systems beyond previous reports Sugita et al. This deeper coverage gave us the power to examine temporal and depth distributions for both total bacterial and archaeal communities and the potential nitrifying member consortia therein. In previous studies of freshwater RAS biofilters, Actinobacteria, Gammaproteobacteria, Plantomycetes , and Sphingobacteria were identified as dominant taxa, while at more refined taxonomic levels Acinetobacteria, Cetobacterium, Comamonas, Flectobacillus, Flavobacterium , and Hyphomicrobium were common Sugita et al.
Some researchers have hypothesized that each RAS biofilter should have a unique microbial community composition shaped by operational controls and components implemented in the RAS Sugita et al. In support of this idea, many of the most abundant bacterial genera in our system e. While it is likely true that each microbial community assemblage will be unique among RAS biofilters, i. Different components of RAS are expected to have unique environmental selective pressures, and thus multiple distinct microbial communities should be present within a single RAS.
Our community data indicates there are consistent and significant differences in the biofilter sand and water communities. These differences included community members that were ubiquitous in, but nearly exclusive to the water samples. These taxa could be remnant members derived from previous components in the system e.
The water samples also had decreased representation of prominent sand-associated taxa, including most known nitrifiers, so studies sampling biofilter outflow water would not represent accurately the microbial assemblages associated with nitrification. These observations support previous observations to the same effect, further lending support to the idea that a transient planktonic microbial assemblage is constantly moving through RAS components while an independent community develops on the biofilter media Blancheton et al.
Recirculating aquaculture systems can be used where suitable land or water is limited, or where environmental conditions are not ideal for the species being cultured. This type of aquaculture production system can be used in marine environments; however, it is more commonly used in freshwater environments. There is a large cost involved in setting up and running a recirculation system and you will need to consider a number of factors in designing the system that will fit your needs.
The water in the system is recirculated through tanks and a series of water treatments to remove waste products. Unless the water is treated, fish will stress, resulting in retarded growth, increased pre-disposition to disease and finally death. You should have a good general knowledge of the principles of water chemistry and a good knowledge of the biology of the species being cultivated.
Production tanks vary in size and shape. Smooth, round tanks with sloping bottoms are useful as solids can be concentrated and subsequently removed from a centre drain. This design facilitates thorough cleaning and ensures aeration is evenly distributed. In simple recirculation systems, water may be treated by two processes: mechanical filtration to remove solids such as faecal matter, uneaten feeds, etc.
Depending on your location and the species you are planning on farming, you should consider including other components such as disinfection devices, foam fractionators or protein skimmers , dedicated aeration units and temperature control. You should also make sure you have access to water quality testing equipment, a purpose-built facility to accommodate bulk feeds and hygiene measures to limit the spread of disease.
The silt and water for cleaning is drained to the waste water sewage system in the farm. After this mechanical treatment, the system water flows to the next filter stage. Cleaning a sedimentation filter Project in Ghana. The system water coming from the fish tanks can also be mechanically cleaned using a drumfilter. Suspended solids are continuously and automatically removed from the system water and therefore the drumfilter requires no daily maintenance.
The downside of a drumfilter is the continuously need of electricity. The system water flow from the fish tanks in the drumfilter. In the drumfilter the system water passes the filter screen. In this process, the particles in the system water are blocked by the filter screen.
Due to the clogging of the filter screen the water level inside the drum will rise. As a result, the flushing mechanism is put into operation. The drum starts to rotate, and from the outside of the drum water is sprayed under high pressure from nozzles through the filter cloth, hereby flushing the waste particles from the screen. Together with the waste particles the water is discarded in the gutter. Different filter screens can be chosen depending on fish species and fish size. Example of an installed drumfilter Project: Dominican Republic.
NH4 or ammonium is produced by the fish during digestion of the feed. It is a waste product of protein digestion. The ammonium is toxic for fish.
Ammonium NH4 however is used by certain bacteria for energy production. These bacteria are present in the biotower of a recirculation system and transfer NH4 into nitrite, NO2, in an aerobe environment. Like ammonium, nitrite is toxic for fish at high levels, but will cause problems at lower concentrations already. Nitrite is also used as an energy source for certain bacteria in the biological filter. These bacteria transfer nitrite in the relatively harmless nitrate NO3. In a recirculating aquaculture system there are parts created where these bacteria can grow in optimal conditions.
Like the biotower and moving bed filter also known as upflow filter. From the pump tank the system water is first pumped to the biotower, also known as trickling tower or degassing tower. A biotower consists of polypropylene net filter blocks suited for bio-filtration. On top of the biotower the water is dispersed over the filter material.
The water flows through the filter packs to the biotower reception tank or directly back into the pump tank.
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