ARCSTA Our research

Our research

Our overarching goal will be to deliver the genetic knowledge to instigate or further advance world-leading and highly productive breeding programs and to establish a novel understanding of the genetic basis of disease resistance and how the production environment interfaces with the bacterial microbiome, pathogens and water quality to cause disease.

The vision of the Hub will be delivered through three integrated and cross-cutting programmatic themes; (i) Breeding for Productivity; (ii) Improved Aquatic Animal Health; and (iii) Decoding Production Environments and Microbiomes.

This theme will develop the quantitative genetic and genomic framework for partner companies to instigate world-leading breeding programs for their species, resulting in elite genetic lines selected for increased growth, disease tolerance, product quality and/or bioactives. It will work with companies to refine and develop new advanced genomic, reproductive and hatchery technologies, relevant to breeding programs that ensure reliable control of seedstock production, rapid genetic gains and maintenance of genetic diversity

This theme will develop industrial-scale challenge trial methodologies for commercially important pathogens and select for new genetic lines with increased resistance to disease. This theme will also produce and implement new diagnostic environmental DNA and immunoassay technologies to detect and monitor pathogens/bacterial toxins on-farm and to link the susceptibility of hosts with pathogen loads in the culture system.

This theme will deliver new knowledge on the interplay of bacterial microbiomes and the genes they express that impact on the productivity and health of farmed aquatic species. It will achieve this by elucidating how microbial communities link to hatchery and production environments, pathogen prevalence, management and host susceptibility. It aims to ultimately integrate all relevant data streams to develop on-farm decision support applications based on artificial intelligence and machine learning algorithms to help farms manage their systems proactively, instead of reactively in the face of challenges.

Research ponds.