A report on trophic interaction between nematodes (Anisakidae) and Antarctic toothfish (Dissostichus mawsoni) in the Ross Sea region, Antarctic

Parasites are an important but overlooked component in the marine ecosystem, however, the study on parasites of Antarctic fish, especially Antarctic toothfish (TOA, Dissostichus mawsoni), remains limited. Parasitic infection is closely linked to the economic value of TOA, which is a commercial fishery resource, while the presence of parasites also contributes to the complexity of marine food webs. Investigating host-parasite interactions is thus essential for understanding their important roles in marine ecosystem. This study integrates baseline biological information of TOA in the Ross Sea region with infection status to examine the characteristics of parasitic infections. Furthermore, stable isotope analysis and trace element analysis were employed to further explore host-parasitic interactions. The Ross Sea TOA population emerged as a susceptible host assemblage; however, infection prevalence varied markedly with specific prey taxa. Analysis of stomach-content data identified the principal intermediate/paratenic hosts responsible for transmitting anisakids to TOA. Beyond diet composition and intake, infection risk and intensity were further shaped by habitat and fish maturity. Consistent with this, infections were associated with putative shifts in host energetics/metabolic status and physiological condition, with downstream effects on stable-isotopic signatures. Our findings also suggest that stomach-dwelling anisakids preferentially absorb host-derived metabolites, indicating a relationship that departs from a simple predator–prey framework; hosts and parasites occupy distinct isotopic niche spaces. Incorporating parasites into food web models can substantially improve the accuracy of trophic dynamics and provide a more integrated understanding of ecosystem functioning in the Ross Sea. We therefore recommend that parasitic infection cannot be neglectable while structuring the ecosystem modelling.