The zooplankton of 18 reservoirs of South China was investigated in 2000. 61 Rotifera species, 23 Cladoceras and 14 Copepodas were identified. The most frequent Rotifera genera were Keratella, Brachionus, Trichocerca, Diurella, Ascomorpha, Polyarthra, Ploesoma, Asplanchna, Pompholyx and Conochilus. Bosmina longirostris, Bosminopsis deitersi, Diaphanosoma birgei, D. brachyurum and Moina micrura were typical of Cladocera in the reservoirs. Phyllodiaptomus tunguidus, Neodiaptomus schmackeri and Mesocyclops leuckarti were the most frequent Copepoda and M. leuckarti dominated Copepoda in most reservoirs. High zooplankton species richness with low abundance was characteristic of the throughflowing reservoir, whereas low species richness with low abundance was found in the reservoir with the longest retention time. Relative high abundance and medium species diversity were the distinction of intermediate retention time reservoirs.

In this study, a 1D model of reservoir hydrodynamics DYRESM has been applied to Sau Reservoir, a river valley reservoir in the North-Eastern Spain. Simulation is undertaken for 3 years (1995-1997). Meteorological input data measured at the dam are only available from May of 1997. In this case the simulation results fit measured temperatures very well. In the remaining periods, some meteorological data (radiation, wind and rainfall) were obtained from two nearby stations. Simulated temperature distribution in 1996 is close to the observed one. In 1995, however, the simulated result is far from the observed data. Inflows, outflow and local meteorological events such as storms and gusts of wind seem to be responsible for the differences. By changing some parameters, the effects of flow, light extinction coefficient and outlet elevation on thermal stratification are investigated. Simulations demonstrate that the inflow with high temperature is the main factor controlling the thermal structure in Sau Reservoir and demonstrate that the effect of residence time on thermal stratification is manifested mainly by the changes in the depth of thermocline.

Empirical biomass spectra in which biomass is measured in logarithmically equal body size intervals are different from those measured in linearly equal size intervals. Moreover, the scales of body size used by different authors may differ, e. g., length, volume, equivalent!sphere diameter and body mass. The discrete models derived to explain the regularity of the empirical spectra are dependent on the choice of size-scales and size!intervals. Hence, evaluating the effect of size scales and intervals on biomass spectra is helpful for understanding the size!structures of ecosystems. In the present contribution, we analyse the relationships between the size measures used frequently in expressing the empirical data and discuss the difference between the biomass spectra organized in logarithmically equal size intervals and those in linearly equal size intervals. On this basis, we present the distribution function of biomass spectral density and transformation to different size scales. After dexthe effect of size intervals on the distribution functions of biomass spectral density, we give an example of the calculation of this effect by assuming that the distribution function of biomass spectral density is an allometric relationship. Finally, we explore the influence of size intervals on the validity of three discrete models developed by Kerr, Sheldon and co!workers and Borgman.

nity or group, which characterizes the mean rate of energy consumption in a community. Models 1 and 2 generate peaked distributions of biomass spectral density whereas Model 3 generates a flat distribution. In Model 4, the distributions of biomass spectral density and of abundance spectral density depend on the Lagrangian multipler (λ2). When λ2 tends to zero or equals zero, the distributions of biomass spectral density and of abundance spectral density correspond to those from Model 3. When λ2 has a large negative value, the biomass spectrum is similar to the empirical flat biomass spectrum organized in logarithmic size intervals. When λ2>0, the biomass spectral density increases with body mass and the distribution of abundance spectral density is an unimodal curve.

In consequence of interactions between compartments, the matter or energy residence time in an econetwork is in nature distinct from that in a compartment. Based on the analysis of econetwork structure, a strategy is developed to calculate the matter or energy residence time in a general econetwork and the effects of self-, direct-and indirect interaction on econetwork residence time. Two typical examples are used to illustrate the strategy, the results show that total residence time equals the ratio of total standing stock to total system outflow or total system inflow instead of the ratio of total standing stock to total system throughput.