Perjantai 3.7.2020 klo 18:00 - Mikko Nikinmaa
Mirella Kanerva, Kristiina Vuori and us others have recently published a study about the fitness of salmon during their feeding migration in different parts of the Baltic Sea (Kanerva et al. Environmentally driven changes in Baltic salmon oxidative status during marine migration, Science of the Total Environment, in press, https://doi.org/10.1016/j.scitotenv.2020.140259). The study is extremely difficult, since it tried to evaluate, how the physiological status of a commercially important fish species in natural environment is affected by food, water temperature and environmental pollution. It is noteworthy that we were able to show that factors affecting the oxidative status of the fish affected the fitness and seawater survival of the salmon. It was also possible to show that increased toxicant load, elevated temperature and cyanobacterial blooms already in the present Baltic Sea induce changes, which are measurable with physiological parameters, and are likely to affect recruitment of salmon.
The point about physiological measurements being able to predict changes in fitness and recruitment is revolutionary for fisheries biology. This is because earlier one has based all the models for stock estimations on retrospective observations on catches and spawning success. The findings of our study indicate that physiological expertise can add a predictive component to recruitment models.
Our results also indicate, which parts of the Baltic Sea are most contaminated affecting the oxidative status of salmon. It is no surprise that effects are observed in the Gulf of Finland. However, these findings show that similar parameters could be used elsewhere to evaluate, if environmental contamination is serious enough to affect preferred fisheries species. Again, this adds a predictive component to earlier estimations based on retrospective data.Hitherto, fish physiology has remained a small field, but our results indicate that it could play a major role in modelling fish stocks, because it adds a predictive component to models and thereby gives possibilities for more rapid fisheries decisions than are currently possible.