Background and aims Ecosystem respiration (R eco) is controlled by thermal and hydrologic regimes, but their relative importance in defining the CO 2 emissions in peatlands seems to be site specific. The aim

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Nutrition is one of the most important factors influencing quantitative and qualitative plant yield. This study examined the effect of manganese (Mn) in nutrient solution on photosynthetic activity parameters, and the relations between photosynthetic activity parameters, yield and plant nutrient status in tomato (Solanum lycopersicum L.). Mn supplementation significantly modified the nutrient content of leaves. Macronutrient content varied less than micronutrient content. The optimal Mn concentration differed between the studied cultivars. Both Mn deficit and Mn excess caused a decrease of tomato yield. Gas exchange parameters, relative water content (RWC) and specific leaf area (SLA) were measured in fully expanded tomato leaves. Certain levels of Mn were found to be needed for proper plant function and future yield, and toxic effects of excess Mn were noted. Changes in P N (net photosynthetic rate) were found to be the first signal of plant response to higher Mn supply, while yield was as for optimal Mn concentrations. Under Mn treatment, uptake of some nutrients increased. A higher level of absorbed Mg led to a higher photosynthesis rate and increased stomatal opening. P N and g s (stomatal conductance) also increased, while C i (intercellular CO 2 concentration) decreased, indicating proper CO 2 consumption during the assimilation process.K Ke ey y w wo or rd ds s: : Macroelement, microelement, Solanum lycopersicum L., photosynthetic activity, manganese stress.

The study focused on modelling of macropyte indices against physico-chemical parameters of waters by artificial neural networks. Several macrophyte diversity indices were analysed (species richness-N, the Shannon index-H 0 , the Simpson index-D, and the Pielou index-J) as well as the ecological status index (the Macrophyte Index for Rivers-MIR). The aim of the study was to verify knowledge about potential application of macrophytes in the environmental monitoring. A Multi-Layer Perceptron type of network was used in the analyses. The study included 260 river sites located throughout Poland. Alkalinity, conductivity, pH, nitrate and ammonium nitrogen, reactive and total phosphorus, and biochemical oxygen demand were used as the explanatory variables. The quality of the constructed models was assessed using calculated errors (RMSE and NRMSE) and r Pearson's linear correlation coefficient. The neural network for the MIR index was characterised by the highest quality. Neural networks for other diversity indices (N, H 0 , D, and J) did not provide adequate results for modelling, which shows their ineffectiveness biological monitoring. Sensitivity analysis revealed the influence of each variable to the models. It indicated that modelled values of MIR are most strongly influenced by total phosphorus and alkalinity.

The variation of a number of parameters characterizing aquatic plant assemblages in rivers across a wide trophic gradient was investigated to evaluate their usefulness for a Polish national river monitoring system. Analyses were conducted at 100 sites included in the national river monitoring system, representing a uniform river type, i.e., small- and medium-sized lowland rivers with a sandy substrate. Results of botanical surveys, which were supplemented with comprehensive monthly quality records, were obtained from the national monitoring database. By analyzing the Jaccard distances of the botanical metrics using the adonis function, the variation in species composition between rivers of different trophic status was determined. The group consisting of the most degraded rivers was the most homogeneous in terms of botanical composition. The cleanest rivers displayed a high level of heterogeneity within their group, as numerous different unique species were found there at low frequencies. The variation of the macrophyte metrics used to assess the ecological status (Macrophyte Index for Rivers (MIR) and River Macrophyte Nutrient Index (RMNI)) reflected a trophic gradient. We confirmed that vegetation diversification along a trophic gradient is evident enough to detect degradation in a five quality class system.