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Benthic Diatoms in River Biomonitoring-Present and Future Perspectives within the Water Framework Directive

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WATER
卷 13, 期 4, 页码 -

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MDPI
DOI: 10.3390/w13040478

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phytobenthos; biological quality indices; ecological status; surface waters; water quality

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The benthic diatoms, commonly used as a biological quality element in surface water monitoring for the European Water Framework Directive, have shown effectiveness in detecting eutrophication, organic pollution, and acidification. Despite their widespread use, there are uncertainties related to species biogeography in taxonomy-based quality indices, and further development of trait-based, DNA sequencing, and predictive modeling indices may offer more accurate results in biomonitoring but require validation and intercalibration among EU Member States.
The European Water Framework Directive 2000/60/EC (WFD) has been implemented over the past 20 years, using physicochemical, biological and hydromorphological elements to assess the ecological status of surface waters. Benthic diatoms (i.e., phytobenthos) are one of the most common biological quality elements (BQEs) used in surface water monitoring and are particularly successful in detecting eutrophication, organic pollution and acidification. Herein, we reviewed their implementation in river biomonitoring for the purposes of the WFD, highlighting their advantages and disadvantages over other BQEs, and we discuss recent advances that could be applied in future biomonitoring. Until now, phytobenthos have been intercalibrated by the vast majority (26 out of 28) of EU Member States (MS) in 54% of the total water bodies assessed and was the most commonly used BQE after benthic invertebrates (85% of water bodies), followed by fish (53%), macrophytes (27%) and phytoplankton (4%). To meet the WFD demands, numerous taxonomy-based quality indices have been developed among MS, presenting, however, uncertainties possibly related to species biogeography. Recent development of different types of quality indices (trait-based, DNA sequencing and predictive modeling) could provide more accurate results in biomonitoring, but should be validated and intercalibrated among MS before their wide application in water quality assessments.

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