Microbiome monitoring for risk assessment in recirculating aquaculture systems

Last changed: 24 June 2024

Harmful bacteria have a negative impact in aquaculture operations, resulting in increased fish mortality and reduced yield. This project aims to understand the factors leading to their proliferation and develop a monitoring strategy together with commercial partners.

Recirculating aquaculture systems (RAS) are a promising development in land-based fish farming that reduces environmental impact by minimising water usage. However, the recirculation of water coupled to the high stocking densities mean that these systems are prone to the buildup of organic matter and the development of harmful bacterial blooms. Traditional pathogen control methods, such as disinfection or the use of antibiotics, are costly and can pose environmental risks, resulting in a less sustainable operation. Instead, monitoring the microbiome associated to aquaculture facilities can help us develop strategies to prevent disease and promote beneficial bacteria. However, our knowledge of microbial dynamics in aquaculture systems is still limited.

In this project, we will develop culture-independent indicators to classify RAS bacteria into different threat levels based on their genomic traits, and study how different types of organic matter affect the risk level of RAS operations. Coupled to machine learning and in situ portable sequencing techniques, this can develop into a routine monitoring tool to be adopted by aquaculture companies.

To achieve this, we will fulfill the following objectives:

  1. Perform a genome-level characterization of the microbial species present in different compartments (water, tank wall and biofilter biofilms) of a RAS pilot facility and evaluate their potential to cause operational problems (blooms, disease outbreaks) based on their genomic traits. Assess the role of tank wall and biofilter biofilms as reservoirs of potentially harmful bacteria.
  2. Monitor microbiome dynamics at in a set of replicate RAS units, and explore the response of bacteria to different amounts and qualities of organic matter.
  3. Integrate these results to identify the factors leading to the proliferation of potentially harmful bacteria in RAS, and use machine learning to identify indicators of an impending outbreak.
  4. Translate these results to a Swedish aquaponics company, which couples a RAS with the growth of plants to increase production and sustainability. We will implement an in situ microbiome monitoring approach using a mobile laboratory and a portable sequencing device, and determine how the growth of different plants affect the microbiome.


Schematic illustration of the project.

The water microbiome as a biosensor in aquaculture. Illustration by Nhat Ton Nguyen.



FORMAS Annual open call for Early Career Researchers, 2023

Funding period: 2024-2027


Fernando Puente-Sánchez

Nhat Ton Nguyen