What I presented to you, ladies and gentlemen, is the common mode of entry by which a person might get infected by marine parasitic worms and some symptoms that manifest with the condition “Anisakiasis”.
Anisakiasis is named after the nematode which causes the disease. Worms of the genus Anisakis are common parasites of a wide range of aquatic organisms. Public interest with them can be phrased as the following question: “How the hell did it get into the fish in my dish?”
We know for a fact that parasites rely on their hosts to be able to complete their life cycles. To understand how Anisakis infections are acquired and to be able to manage them, Kuhn and colleagues (2016) modelled the distribution of nine Anisakis species using the Maxent approach.
When we lack of knowledge on how an organism is distributed in the environment, we turn to models to predict how they “might” occur in nature. Maxent is one approach by which we can predict the distribution of organisms. Maxent is a software program that aims to predict where species occur by finding the distribution where they are most spread out, while taking into account the environmental variables of their known locations and prioritizing them in terms of their influence on distribution. Maxent is useful for Anisakis as the development and dispersal of its eggs are influenced by environmental factors such as salinity, temperature, and ocean currents (Kuhn et al., 2016).
To refine their model, the authors included data on each parasite’s definitive hosts, or the host organisms where the parasite is able to reproduce. This is because the distribution of the intermediate and adult stages of Anisakis is shaped by the movement of its different hosts in those life stages; which are crustaceans, fishes, and ultimately, marine mammals as definitive hosts. The resulting Maxent model would then map the distribution of each Anisakis species as influenced by both their environment and their specific hosts.
Six environmental factors were found to be important drivers of distribution in the Anisakis species: distance from land, mean sea surface temperature, depth, salinity, range of sea surface temperature; as well as primary productivity (or how fast producers convert energy from the environment into food for different organisms).
Among these variables, temperature-related factors and salinity highly influence distribution because they affect how eggs of Anisakis hatch and how long they live. Higher temperatures favor faster egg hatching but shorten their life spans, while salinity lengthens the lives of the individuals. Meanwhile, distance from land is linked directly to the definitive hosts rather than the parasites.
The “hotspots” where most of the Anisakis species are located are in waters of the Mediterranean, around Japan, the North American coasts, as well as in the waters of the North Atlantic; areas with extensive fishing and where many economically-important fishes are caught.
One species they modelled, Anisakis simplex, has never been reported in the South Atlantic even if the area is highly suited to the species, based on their model. These can be due to limitations of egg dispersal in A. simplex or its associated definitive host species, which is a marine mammal.
Fish are intermediate hosts of the parasites and in a way, anisakiasis can be framed as if we are mistaken by the parasites as their definitive hosts, which if you recall, are also mammals. Thus, we end as “accidental hosts” of the parasites.
Reliable data on the intermediate hosts’ locations often come from areas which are more accessible and studied. These regions do not always overlap with those where the parasites are obtained, and this disparity affects the mapping of the parasite’s distribution.
Sampling disparities are also the reason why the authors emphasized that the inputted data on parasite and hosts distributions affect the Maxent model, regardless of its promise. Since some regions are more studied than others, this affects Maxent’s algorithm, which builds upon the known distribution of the parasite and host species being examined.
Hence, the authors warned that the diversity hotspots for Anisakis and its hosts might differ from the actual occurrences in nature. This inaccurate representation would then reduce the reliability of their model, and so they reminded that interpretation of the results of any approach or model should be handled carefully.
Nonetheless, this study provides valuable insight into the biogeography of marine parasites; a field brimming with questions (Rohde, 2016) waiting to be answered.
Literature Cited
Kuhn T, Cunze S, Kochmann J, Klimpel S. (2016). Environmental variables and definitive host distribution: a habitat suitability modelling for endohelminth parasites in the marine realm. Scientific Reports. 6: 30246. doi: 10.1038/srep30246.
Rohde, K. (2016). Ecology and Biogeography, Future Perspectives: Example Marine Parasites. Geoinformatics & Geostatistics: An Overview. 4. 10.4172/2327-4581.1000140.
Blog owner's note: As a culminating activity to my MS Biology class in Biogeography, I asked my students to write a blog post on a topic in biogeography. We welcome constructive comments on this student piece.
