Biofouling (the undesirable growth and accumulation of organisms on submerged surfaces) presents a set of challenges to the salmon aquaculture industry, with excessive fouling of nets posing risks to fish health, farm infrastructure and the broader environment. To manage this, industry currently undertakes visual estimations of biofouling to decide when net cleaning is required. Currently, these visual surveys are manual and time-consuming, and nets are generally still cleaned on a set schedule, regardless of the biofouling status of individual pens.
This project seeks to monitor and understand biofouling in a consistent, reproducible, time-efficient manner based on in situ sampling. Specifically, the use of machine learning and eDNA methods for rapid analyses of macro- and microbiological biofouling communities will be investigated, and how these might be applied as diagnostic and monitoring tools for industry. The four aims of this project are to:
It is hoped that introducing robust biofouling monitoring systems can contribute to reduced operational costs of net cleaning and increase farm efficiencies, as well as improving animal health outcomes.
