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The metacommunity approach provides insights into how the biological communities are assembled along the environmental variations. The current study presents the importance of water quality on the metacommunity structure of algal communities in six river-connected lakes using long-term (8 years) monitoring datasets. Elements of metacommunity structure were analyzed to evaluate whether water quality structured the metacommunity across biogeographic regions in the riverine ecosystem. The algal community in all lakes was found to exhibit Clementsian or quasi-Clementsian structure properties such as significant turnover, grouped and species sorting indicating that the communities responded to the environmental gradient. Reciprocal averaging clearly classified the lakes into three clusters according to the geographical region in river flow (upstream, midstream, and downstream). The dispersal patterns of algal genera, including Aulacoseira, Cyclotella, Stephanodiscus, and Chlamydomonas across the regions also supported the spatial-based classification results. Although conductivity, chemical oxygen demand, and biological oxygen demand were found to be important variables (loading > |0.5|) of the entire algal community assembly, water temperature was a critical factor in water quality associated with community assembly in each geographical area. These results support the notion that the structure of algal communities is strongly associated with water quality, but the relative importance of variables in structuring algal communities differed by geological regions.

The metacommunity approach provide insights into how the biological communities are assembled along the environmental variations. The current study presents the importance of water quality on the metacommunity structure of algal communities in six, river-connected lakes using long-term (8 years) monitoring datasets. Elements of metacommunity structure were analyzed to evaluate whether water quality structured the metacommunity across biogeographic regions in the riverine ecosystem. The algal community in all lakes was found to exhibit Clementsian or quasi-Clementsian properties, indicating that the communities responded to the environmental gradient. Reciprocal averaging clearly classified the lakes into three clusters according to the geographical region in river flow (upstream, midstream, and downstream). The dispersal patterns of algal species, including Aulacoseira, Cyclotella, Stephanodiscus, and Chlamydomonas across the regions also supported the spatial-based classification results. Although conductivity, chemical oxygen demand, and biological oxygen demand were found to be important variables (loading > |0.5|) of the entire algal community assembly, temperature was a critical factor in water quality associated with community assembly in each geographical area. These results support the notion that the structure of algal communities is strongly associated with water quality, but the relative importance of variables in structuring algal communities differed by geological regions.

Abstract. The aims of this study were to determine the groundwater quality index (GQI) using an averaged neural network and evaluate its field applicability with two-dimensional (2D) spatial analysis. The GQI was computed using 29 water quality parameters obtained at 3,552 portable groundwater wells used as drinking water sources. The GQI was divided into the following three grades: ‘worrisome’, <0.89 (20.1 % of the wells); ‘good’, 0.89–0.94 (62.8 %); and ‘very good’, >0.94 (17.1 %). Based on the random forest, the most important water quality parameters were general bacteria, turbidity and nitrate. The 2D spatial analysis confirmed notable differences in the GQI grades among regions. The 10-year long-term groundwater quality monitoring in the ‘worrisome’ grade showed the nitrate and chloride concentrations have continuously increased. These results indicate that the coupling of the GQI with 2D spatial analysis is a promising approach that can be applied in groundwater management and vulnerability assessment.

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