Balance between the Reliability of Classification and Sampling Effort: A Multi-Approach for the Water Framework Directive (WFD) Ecological Status Applied to the Venice Lagoon (Italy)
| Autor: | A. Bonometto, Federica Cacciatore, Rossella Boscolo Brusà, Paolo Parati, Elisa Paganini, Massimo Gabellini, Marta Novello, Adriano Sfriso |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
Settore BIO/07 - Ecologia lcsh:Hydraulic engineering Transitional waters Geography Planning and Development Macrophyte Quality Index (MaQI) transitional waters 010501 environmental sciences Aquatic Science 01 natural sciences Biochemistry Multivariate interpolation Macrophyte Quality Index (MaQI) lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 Statistical inference uncertainty analysis Reliability (statistics) Uncertainty analysis 0105 earth and related environmental sciences Water Science and Technology lcsh:TD201-500 Ecology 010604 marine biology & hydrobiology Sampling (statistics) Macrophyte Water Framework Directive confidence interval Confidence interval Kernel standard error Environmental science Spatial variability |
| Zdroj: | Water, Vol 11, Iss 8, p 1572 (2019) Water Volume 11 Issue 8 |
| ISSN: | 2073-4441 |
| Popis: | The Water Framework Directive (WFD) requires Member States to assess the ecological status of water bodies and provide an estimation of the classification confidence and precision. This study tackles the issue of the uncertainty in the classification, due to the spatial variability within each water body, proposing an analysis of the reliability of classification, using the results of macrophyte WFD monitoring in the Venice Lagoon as case study. The level of classification confidence, assessed for each water body, was also used as reference to optimize the sampling effort for the subsequent monitorings. The ecological status of macrophytes was calculated by the Macrophyte Quality Index at 114 stations located in 11 water bodies. At water body scale, the level of classification confidence ranges from 54% to 100%. After application of the multi-approach (inferential statistics, spatial analyses, and expert judgment), the optimization of the sampling effort resulted in a reduction of the number of stations from 114 to 84. The decrease of sampling effort was validated by assessing the reliability of classification after the optimization process (54&ndash 99%) and by spatial interpolation of data (Kernel standard error of 22.75%). The multi-approach proposed in this study could be easily applied to any other water body and biological quality element. |
| Databáze: | OpenAIRE |
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