Towards an in situ non-lethal rapid test to accurately detect the presence of the nematode parasite, Anguillicoloides crassus, in European eel, Anguilla anguilla.

Anguillicoloides crassus is an invasive nematode parasite of the critically endangered European eel, Anguilla anguilla, and possibly one of the primary drivers of eel population collapse, impacting many features of eel physiology and life history. Early detection of the parasite is vital to limit the spread of A. crassus, to assess its potential impact on spawning biomass. However accurate diagnosis of infection could only be achieved via necropsy. To support eel fisheries management we developed a rapid, non-lethal, minimally invasive and in situ DNA-based method to infer the presence of the parasite in the swim bladder. Screening of 131 wild eels was undertaken between 2017 and 2019 in Ireland and UK to validate the procedure. DNA extractions and PCR were conducted using both a Qiagen Stool kit and in situ using Whatman qualitative filter paper No1 and a miniPCR DNA Discovery-System™. Primers were specifically designed to target the cytochrome oxidase mtDNA gene region and in situ extraction and amplification takes approximately 3 h for up to 16 individuals. Our in-situ diagnostic procedure demonstrated positive predictive values at 96% and negative predictive values at 87% by comparison to necropsy data. Our method could be a valuable tool in the hands of fisheries managers to enable infection control and help protect this iconic but critically endangered species.

Genetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classification approach.

Caligid sea lice represent a significant threat to salmonid aquaculture worldwide. Population genetic analyses have consistently shown minimal population genetic structure in North Atlantic Lepeophtheirus salmonis, frustrating efforts to track louse populations and improve targeted control measures. The aim of this study was to test the power of reduced representation library sequencing (IIb-RAD sequencing) coupled with random forest machine learning algorithms to define markers for fine-scale discrimination of louse populations. We identified 1286 robustly supported SNPs among four L. salmonis populations from Ireland, Scotland and Northern Norway. Only weak global structure was observed based on the full SNP dataset. The application of a random forest machine-learning algorithm identified 98 discriminatory SNPs that dramatically improved population assignment, increased global genetic structure and resulted in significant genetic population differentiation. A large proportion of SNPs found to be under directional selection were also identified to be highly discriminatory. Our data suggest that it is possible to discriminate between nearby L. salmonis populations given suitable marker selection approaches, and that such differences might have an adaptive basis. We discuss these data in light of sea lice adaption to anthropogenic and environmental pressures as well as novel approaches to track and predict sea louse dispersal.