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This work presents a dynamic model to predict phytoplankton biomass and production. The model has been developed as an integral part within the framework of a more comprehensive lake ecosystem model, LakeWeb, which also accounts for production and biomass of bacterioplankton, two types of zooplankton (herbivorous and predatory), two types of fish (prey and predatory), as well as zoobenthos, macrophytes and benthic algae. The LakeWeb-model is based on ordinary differential equations (the ecosystem perspective) and gives seasonal variations (the calculation time, dt, is 1 week and Euler’s integration method has been applied). The sub-model for phytoplankton presented in this work is meant to account for all fundamental abiotic/biotic interactions and feedbacks (including predation by herbivorous zooplankton) for lakes in general. The model has not been tested in the traditional way using data from a few well investigated lakes. Instead, it has been tested using empirical regressions based on data from many lakes. The basic aim of this dynamic model is that it should capture typical functional and structural patterns in many lakes. It accounts for how variations in (1) lake phosphorus concentrations, (2) water clarity, (3) lake morphometry, (4) water temperature, (5) lake pH and (6) predation by herbivorous zooplankton influence production and biomass of phytoplankton. An important demand for this model is that it should be driven by variables easily accessed from standard monitoring programs and maps (the driving variables are: total phosphorus, colour, pH, lake mean depth, lake area, and epilimnetic temperatures). We have demonstrated that the new model gives predictions that agree well with the values given by the empirical regressions, and also expected and requested divergences from these regression lines when they do not provide sufficient resolution. The model has been tested in a very wide limnological domain: TP values from 3 to 300 μg/l, which covers ultraoligotrophic to hypertrophic conditions, colour values from 3 to 300 mg Pt/l, which covers ultraoligohumic to highly dystrophic conditions, pH from 3 to 11, which covers the entire natural range, and lake areas from 0.1 to 100 km2. [Copyright &y& Elsevier]

This work presents a dynamic model to predict two fundamental functional categories of zooplankton in lakes, herbivorous and predatory zooplankton. The model has been developed as an integral part within the framework of a more comprehensive lake ecosystem model, LakeWeb, which also accounts for phytoplankton, bacterioplankton, two types of fish (prey and predatory), as well as zoobenthos, macrophytes and benthic algae. The new model is based on ordinary differential equations, the ecosystem scale and gives seasonal variations (the calculation time is 1 week). It is meant to account for all important factors regulating the production and biomass of zooplankton and the predation on zooplankton in lakes. The model has not been calibrated and tested in the traditional way using data from a few well investigated lakes. Instead, it has been tested using empirical regressions based on data from many lakes. We have also presented new data sets and regressions based on those data for zooplankton. The basic aim of the dynamic model is that it should capture general functional and structural patterns in lakes. We have demonstrated by several model tests along limnological gradients (total phosphorus concentrations, pH, lake colour, latitude and lake size) that the new model gives predictions that agree well with the values given by the empirical regressions, and also expected and requested divergences from these regressions when they do not provide sufficient resolution. It would have been very difficult indeed to carry out such tests regarding ecosystem responses using traditional methods with extensive field studies in a few lakes. We have given algorithms for (1) production of herbivorous and predatory zooplankton, (2) elimination (related to the turnover time of zooplankton), (3) zooplankton consumption by prey fish, and the factors influencing these processes/rates. The model is driven by data easily accessed from standard monitoring programs or maps and meant to be of practical use in lake management. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

Macrophytes play several important roles in lake ecosystems, e.g. proving shelter for small fish, binding nutrients and influencing secondary production by creating habitats for bacteria, benthic algae and zooplankton. However, the quantitative role of macrophytes in lakes is poorly known because few general, validated models yielding high predictive power for macrophyte production, cover and biomass have been presented. There are probably many reasons for this, e.g. related to the costs and efforts required to obtain relevant data. This work is based on a new database established by us from published sources. Many of the lakes included in this study are situated in the former Soviet Union. They were investigated during the Soviet period and those results have been largely unknown in the West. With this new database, we have presented empirical models for macrophyte cover and production yielding predictions close to the theoretical maximum values, as determined by the uncertainty in the empirical data. Using data from 35 lakes covering a wide domain in lake characteristics, we have ranked the factors influencing macrophyte cover and demonstrated that the ratio Secchi depth to mean depth can statistically explain about 40% of the variability among these lakes in macrophyte cover. Other important factors are latitude (related to lake temperature), maximum depth and area of the lake shallower than 1 m. A new regression model based on these four factors can statistically explain 82% of the variation in macrophyte cover among these lakes. We have also presented a dynamic model for macrophyte production and biomass and several critical tests of that model. The dynamic model gives better predictions and a more general structure then the empirical model. We have given algorithms for: (1) the macrophyte production rate; (2) the elimination rate (related to the macrophyte turnover time); (3) the rate of macrophyte consumption by animals; and (4) the rate of macrophyte erosion. Our results indicate that macrophyte production is highly dependent on latitude and temperature, morphometry and sediment character, as well as water clarity, and less dependent on nutrient concentration. Qualitatively, this has been known or suggested before, but this work gives new quantitative support to such conclusions and also a practically useful model for predictions of macrophyte production and biomass. [Copyright &y& Elsevier]