Use of environmental DNA (eDNA) and water quality data to predict protozoan parasites outbreaks in fish farms
Journal Publication ResearchOnline@JCUAbstract
Globally, disease accounts for around 40% of lost aquaculture production. Commonly, disease occurs due to an inability of farm managers to accurately quantify disease risk due to the abundance of the pathogenic agent in their production systems, along with a poor understanding of how outbreaks are linked to changes in water quality parameters. Environmental DNA (eDNA) sampling methodology associated with molecular techniques is suited to rapidly assess the background presence of pathogens in fish farms, thereby providing managers with critical information that can be used to mitigate disease threats. Adopting the ciliate protozoan Chilodonella hexasticha as a model, this study examined the relationship between environmental DNA of C. hexasticha, critical water parameters and the occurrence of disease outbreaks on a commercial barramundi Lates calcarifer farm, where water was sampled monthly over a 1 year timeframe. A qPCR assay based on the small subunit ribosomal DNA gene was used to monitor the abundance of C. hexasticha in pond water (SSU-rDNA copies/μL). Increased C. hexasticha eDNA levels were found to be highly correlated with occurrence of later fish mortality events (r = 0.402; P < 0.001) and also with size of fish (r = − 0.189; P < 0.05); smaller fish were more prone to being impacted by an epizootic of the parasite. However, no correlations were found between any of the water quality parameters measured (rainfall, water temperature and dissolved oxygen) and abundance of this parasite, although there were significantly more fish mortalities observed during the warmer, wetter monsoonal season compared to the cooler, dry season (1280 vs. 135 mortalities, respectively; P < 0.05). This study highlights the potential of an eDNA approach as a management tool to quickly assess parasite loads in water and minimise the risk of disease outbreaks in aquaculture systems.
Journal
Aquaculture
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Volume
479
ISBN/ISSN
1873-5622
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Pages Count
7
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Publisher
Elsevier
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EISSN
N/A
DOI
10.1016/j.aquaculture.2017.06.021