A new general dynamic model to predict biomass and production of bacterioplankton in lakes

The main aim of this work is to present a new dynamic model to predict bacterioplankton production and biomass. This model has been developed as a submodel within the framework of a more comprehensive lake ecosystem model, LakeWeb, which is based on nine key functional groups of organisms. Beside bacterioplankton, LakeWeb accounts for phytoplankton, two types of zooplankton (herbivorous and predatory), two types of fish (prey and predatory), as well as zoobenthos, macrophytes and benthic algae. The model uses ordinary differential equations and gives seasonal (weekly) variations and accounts in a general way for all major abiotic/biotic interactions and feedbacks for entire lakes (the ecosystem approach). The new dynamic model has not been calibrated and tested in the traditional way using data from one or a few well-investigated lakes. Instead, it has been calibrated using empirical regressions based on data from many lakes. We have presented empirical reference models utilising data from a new database, which includes many lakes situated in the former Soviet Union. They were investigated during the Soviet period and those results have been largely unknown in the West. The basic aim of the dynamic model is that it should capture typical functional and structural patterns in many lakes. We have given algorithms for (1) bacterioplankton production, (2) elimination (related to the turnover time of bacterioplankton), (3) bacterioplankton consumption by herbivorous zooplankton, and the factors influencing these processes/rates. We have demonstrated that the new dynamic model gives predictions that agree well with the values given by the empirical reference models, and also expected and requested divergences from these regressions when they do not provide sufficient resolution. The new dynamic model is driven by data easily accessed from standard monitoring programs or maps and meant to be of practical use in lake management.