Introduction
Microorganisms are omnipresent and key players in aquaculture, especially in recirculating aquaculture systems (RAS), where water is reused continuously. Monitoring and managing microorganisms in RAS rearing water is essential for optimal water quality, avoidance of microbial diseases, and sustainable fish production (1). Online sensors for multiple physicochemical parameters are standard in the industry; however, knowledge of microbial water quality has been hampered by the lack of tools available to fish farm managers.
Tracking and understanding microbial dynamics and the drivers of water microbiome disturbances requires fast-paced sampling and analysis, ideally in real-time, allowing for immediate reaction. Traditional methods have long time lags between water sampling and results and they are labor-intensive, time-consuming, and costly for implementation in a fish farm. onCyt Microbiology offers a real-time and fully automated microbial monitoring system based on flow cytometry that allows fast, accurate, and reproducible quantification and differentiation of total and intact microbial cells in aquaculture rearing water (2).
Materials and Methods
A fully automated flow cytometry monitoring system was used to monitor microbial concentration at multiple points across a European Perch RAS for 51 days. In the initial phase (biofilter start-up phase), water samples were drawn from the biofilter tank for 16 days every 30 minutes. In the second phase (fish farming phase), samples were drawn from up to 4 locations (2 fish tanks and their corresponding water inlets) for 35 consecutive days. Each water sample was automatically mixed with fluorescent stains, incubated, and then analyzed using flow cytometry. The resulting data sets (Approx. 4’000 data points) were batch-processed using onCyt proprietary software to determine total cell concentration (TCC) and intact cell concentration (ICC). Time series decomposition analysis was performed using R, version 4.21, and correlated with RAS operational and physicochemical sensor data.
Results
The onCyt automated flow cytometry system successfully delivered meaningful information on the microbial dynamics of the RAS. In the first 6 days, during the biofilter start-up phase, TCC and ICC remained relatively stable with an average of 1.5 x106 cells/ml and 1.1 x106 cells/ml, respectively. Over the following 2 days, a considerable increase (62%) in microbial concentration was observed, reaching a maximum TCC value of 4.1 x106 cells/ml, followed by a decrease and stabilization phase. Changes in microbial dynamics during this phase were attributed to operational changes, including the opening of the water recirculation and the start of water temperature regulation.
During the fish farming phase, measurements started one day after the perch entered the RAS. The first 10 days of this phase were highly dynamic and characterized by a massive increase in TCC and ICC at the three locations measured (two fish tanks and one water inlet), with tank 2 reaching up to 6.1 x106 cells/ml TCC (Fig. 1A). This expansion event was followed by a quick and sharp decrease in concentration to the order of 105 cells/ml (Fig. 1A). Thereafter, daily average microbial concentration remained relatively constant for the rest of the measurement period in all four locations, with water inlets showing similar trends but slightly lower concentration as the tanks they supply. Interestingly, a time series decomposition of selected 3 days revealed microbial trends linked to light and dark cycles, feeding times, and specific physicochemical parameters (oxygen and pH) (Fig. 1B).
Conclusions
Our findings provide strong evidence of the potential of advanced microbial monitoring for understanding the underlying mechanisms and operational consequences driving microbial dynamics in a RAS. The implementation of this tool in routine aquaculture operations can massively enhance microbial process management, improve operational efficiency, and reduce the risk of microbial infections.
References
1. Rojas-Tirado P, Pedersen PB, Vadstein O, Pedersen LF. Changes in microbial water quality in RAS following altered feed loading, Aquacultural Engineering 81: 80-88.
2. Besmer MD, Weissbrodt DG, Kratochvil BE , Sigrist JA , Weyland MS and Hammes F (2014). The feasibility of automated online flow cytometry for in-situ monitoring of microbial dynamics in aquatic ecosystems. Frontiers in Microbiology 5:265.