Effect of substrate type on the response of diatoms to environmental gradients: Implication for bioassessment and biomonitoring programs

The distribution of epipelic, epilithic and epiphytic diatom communities in relation to the environmental variables was studied in River Nile, Rosetta and Damietta branches to assess the best substrate type most correlate with these measured environmental variables. Chemical and biological characters were studied at 40 locations during autumn-2013 and spring-2014. Eleven environmental variables; temperature, pH, electrical conductivity, total dissolved salts, Nitrogen-Nitrate, Nitrogen-Ammonium, Nitrogen-Nitrite, soluble reactive phosphorus, total phosphorus, reactive silicate and biochemical oxygen demand were measured and used for assessing the diatom-environmental relationship. The sites especially those of Rosetta were characterized by high nutrient concentrations as well as total dissolved salts (TDS) and electrical conductivity (EC) compared to the main stem of the river. A total of 224 diatom species belonging to 26 genera were recorded. Out of them, 61 species were considered as the most frequent species. Common diatom species relatively varied among different substrates and were not exclusively found upon a specific substrate. Diversity and evenness had higher values at the southern sites with partial and non-significant variations, respectively, among substrates which slightly decreased northward. However, the diversity average was slightly higher in epilithic than epiphytic and epipelic communities. Three Canonical Correspondence Analysis (CCA) related epipelic, epilithic and epiphytic diatom community structures to simultaneous effect of predictor environmental variables. CCA analyses explained 51%, 43% and 39% of the diatom species variance in epipelic, epilithic and epiphytic communities, respectively, the mantel tests enforced these results. These relationships mirrored that epipelic diatom communities in River Nile and its branches were the most related communities to the environmental variables and are the best fit for the monitoring program.