In our research conducted in the River Yarqon during 2003-2006, we identified 313 taxa of algae and cyanobacteria belonging to 8 taxonomical divisions. Out of these 313 taxa, 268 taxa (85.6%) were indicators of environmental conditions that characterised the river water as alkaline with medium mineralisation. In the rainy and dry seasons the algal taxonomic compositions were very different, with prevailing diatoms in the winter and cyabobacteria and greens in summer. Bio-indication shows that the taxonomic preference for the self-purification process was more intensive during the rainy season, while the low level of river water in the dry season stressed the algal community. By employing CCA analysis some indicators of highly mineralised water with high pH, anthropogenic pollution, and eutrophication were revealed. CCA analyses also helped to reveal various biosensor species sensitive to the advent of anthropogenic pollution. We therefore conclude that the combination of bioindicational methods with statistics is effective in the determination of the main factors influencing algal diversity. This combination is also helpful in indicating which biosensing species will influence the most important environmental parameters. The obtained results can be used for water quality assessment and in monitoring systems of Israeli and other Mediterranean coastal rivers.

In our research conducted in the River Yarqon during 2003-2006, we identified 313 taxa of algae and cyanobacteria belonging to 8 taxonomical divisions. Out of these 313 taxa, 268 taxa (85.6%) were indicators of environmental conditions that characterised the river water as alkaline with medium mineralisation. In the rainy and dry seasons the algal taxonomic compositions were very different, with prevailing diatoms in the winter and cyabobacteria and greens in summer. Bio-indication shows that the taxonomic preference for the self-purification process was more intensive during the rainy season, while the low level of river water in the dry season stressed the algal community. By employing CCA analysis some indicators of highly mineralised water with high pH, anthropogenic pollution, and eutrophication were revealed. CCA analyses also helped to reveal various biosensor species sensitive to the advent of anthropogenic pollution. We therefore conclude that the combination of bioindicational methods with statistics is effective in the determination of the main factors influencing algal diversity. This combination is also helpful in indicating which biosensing species will influence the most important environmental parameters. The obtained results can be used for water quality assessment and in monitoring systems of Israeli and other Mediterranean coastal rivers.