Abstract

Relationships between biomass and ecological factors including trophic interactions were examined to understand the dynamics of six fish species in Lake Võrtsjärv, a large shallow eutrophic lake located in Estonia (north-eastern Europe). The database contained initially 31 predictive variables that were monitored in situ for nearly forty years. The strongest predictive variables were selected by three parallel approaches: single correlation (Pearson), a multivariate method (Co-inertia analyses), and a machine learning algorithm (Random Forests), followed by a Generalized Least Squares model to determine meaningful relationships with fish biomass. Models with both additive and interactive effects were constructed. The results revealed that the indicators of degraded ecological conditions (high cyanobacteria biomass and their proportion in total phytoplankton, high summer temperature, high nutrient concentration) were negatively correlated to fish biomass. Benthic macroinvertebrates and other biotic predictors (biomass of specific fish prey and predators) were also important contributors to fish biomass dynamics. Together, abiotic and biotic factors explained 40–60% of the variance of fish biomass, depending of the species. Our findings suggest that both abiotic and biotic factors control fish biomass changes in this eutrophic lake.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call