A comparison between biotic indices and predictive models in stream water quality assessment based on benthic diatom communities

Abstract

Freshwater Diatoms Indices Predictive model a b s t r a c tDiatoms are widely used in stream bioassessment due to their broad distribution, extraordinary variability and the ability to integrate changes in water quality. The indices Specific Polluosensitivity Index (SPI), standardized Biological Diatom Index (BDI), European Economic Community Index (CEC) and Generic Diatom Index (GDI), originally developed in France, are often applied in Portugal to evaluate stream ecological quality based on diatom communities. Alternatively, predictive models resulting from the comparison between the communities of the study site and those of a set of reference sites representing undisturbed or the best available conditions of a given region have been proposed as valuable methods for evaluating the ecological status of streams. In the present study, we applied the four above-mentioned widely used diatom-based indices (SPI, BDI, CEC and GDI) and a predictive model (MoDi) to 54 sites located in central Portugal to assess the sensitivity of the five methods to a range of anthropogenic disturbances cumulatively affecting streams and represented by 27 variables (e.g., organic enrichment, changes in morphology of the channels, integrity of the riparian corridor, land use in the catchment). The results were analyzed comparatively through Spearman correlations, Boxplots and Stepwise Discriminant Analysis. This study confirmed the sensitivity of diatoms to organic and nutrient contamination (showed by the MoDi, BDI, CEC and SPI) and revealed the importance of suspended solids (through the MoDi, GDI, and SPI). The relevance of modifications in land use to diatoms was shown by all methods applied, except for the GDI. The MoDI also revealed the importance of changes in the structure and morphology of the reach and the channel, like the construction of artificial walls or embankments and connectivity; the BDI also related its assessments with the riparian zone integrity; and the SPI was not useful in detecting morphological pressures. The GDI produced the most divergent assessments and was less effective in revealing the anthropogenic disturbances. The use of the predictive model (MoDi) is therefore a good method for the assessment of streams in central Portugal because it expresses a great diversity of quantitative and qualitative changes in freshwater systems reflected in the structure (species richness and abundance) of diatom communities.

Recent views on river pollution and diatoms

“…Multivariate models were also used to assess river quality in Portugal on the basis of diatom assemblages (Feio et al, 2007). They even furnish good results in comparison with already existing diatom indices (Feio et al, 2009).…”

Application of index number theory to the construction of a water quality index: Aggregated nutrient loadings related to the areal extent of hypoxia in the northern Gulf of Mexico

“…A recent survey of approaches for constructing indices (Bierman et al, 2011) cite the techniques of cluster analysis (Hargiss et al, 2008;Khalil et al, 2010;Styers et al, 2010), principal components analysis, factor analysis (Blocksom and Johnson, 2009;Leunda et al, 2009;Liou et al, 2004;Ou et al, 2010;Tran et al, 2010), discriminant analysis (Feio et al, 2009;Kane et al, 2009), and fuzzy logic (Ghosh and Mujumdar, 2010). This work has been extended to include errors in measurement of the constituent variables used to construct the water quality index (Beamonte Córdoba et al, 2010;Castoldi et al, 2009;Ghosh and Mujumdar, 2010;Sin et al, 2009;Taheriyoun et al, 2010).…”

“…Water quality indices have been developed and used to characterize a wide variety of phenomena including drinking water (Beamonte Córdoba et al, 2010), bioassessment (Aguiar et al 2014;Blanchet et al, 2008;Kanno et al, 2010;Stoddard et al, 2008), fresh water habitat (Pinto et al, 2009;Simaika and Samways, 2011), effect of agriculture on stream water (Justus et al, 2010;Shiels, 2010), river water quality (Feio et al, 2009;Navarro-Llácer et al, 2010), ecological condition (Jordan et al, 2010;Marchini et al, 2009;Seilheimer et al, 2009;Tran et al, 2008) and variable reduction for selection of variables to monitor (Kantoussan et al, 2010). A recent survey of approaches for constructing indices (Bierman et al, 2011) cite the techniques of cluster analysis (Hargiss et al, 2008;Khalil et al, 2010;Styers et al, 2010), principal components analysis, factor analysis (Blocksom and Johnson, 2009;Leunda et al, 2009;Liou et al, 2004;Ou et al, 2010;Tran et al, 2010), discriminant analysis (Feio et al, 2009;Kane et al, 2009), and fuzzy logic (Ghosh and Mujumdar, 2010).…”

Ecosystem Services and River Basin Ecohydrology

“…The downstream portions of the catchment area are densely populated while the upper and middle basin regions experience low to moderate human impacts (Feio et al 2009). The downstream portions of the catchment area are densely populated while the upper and middle basin regions experience low to moderate human impacts (Feio et al 2009).…”