Environmental Quality of Catalan Coastal Waters Based on Macroalgae: The Interannual Variability of CARLIT Index and Its Ability to Detect Changes in Anthropogenic Pressures over Time

Abstract

CARLIT is a Water Framework Directive-compliant methodology permitting a rapid assessment of water quality using rocky-shore macroalgae as biological quality elements. Here we present the water quality assessment of 32 coastal water bodies of Catalonia (Northwestern Mediterranean) during a period of 14 years (1999-2012) applying CARLIT. The averaged ecological status of the water bodies ranges between high and poor and the Ecological Quality Ratio shows a significant negative relationship with a modified LUSI index, thus providing further evidence on the utility of CARLIT to detect anthropogenic pressures. The lowest interannual variability in water quality was found in water bodies having most of their shore covered by natural rocks, while the highest variability was observed in water bodies situated in semi-confined environments or located close to freshwater discharges. In spite of the multiple advantages of CARLIT as a monitoring methodology, it can show strong disagreements in water quality assessment with other methodologies using other biological quality elements (i.e., macroinvertebrates). These discrepancies mainly occur in water bodies with reduced extension of rocky shores, questioning the use of CARLIT in these situations.

Ecological status improvement over a decade along the Ligurian coast according to a macroalgae based index (CARLIT)

“…In addition, two pressure indices, the Land Uses Simplified Index (LUSI [33]) and the modified LUSI Index (MA-LUSI-WB [34]), were calculated for each WB in order to relate anthropogenic pressures and the EQR values.…”

Statistical analysis of the distribution of infralittoral Cystoseira populations on pristine coasts of four Tyrrhenian islands: Proposed adjustment to the CARLIT index

“…Therefore, according to the CARLIT index, the Mediterranean algal communities of the upper infralittoral zone dominated by Cystoseira species, as well as the build-ups (trottoirs) of Lithophyllum byssoides (Lamarck) Foslie, indicate undisturbed environments, those dominated by Dictyotales and geniculate or crustose Corallinales indicate slightly polluted environments, and those dominated by Ulva, Cladophora and Cyanobacteria indicate extremely polluted environments (Ballesteros et al, 2007; see also Arevalo et al, 2007;Pinedo et al, 2007;Mangialajo et al, 2008a). To date, the CARLIT index has been applied in Spain (Ballesteros et al, 2007;Bermejo et al, 2013Bermejo et al, , 2014Cavallo et al, 2016;Torras et al, 2016), Italy (Mangialajo et al, 2007;Asnaghi et al, 2009;Guala et al, 2010;Sfriso and Facca, 2011; the present paper), France, Malta (Blanfuné et al, 2011(Blanfuné et al, , 2017, already available on http:/dx.doi.org/10.1016/j.ecolind.2016.07.049), Croatia (Nikolić et al, 2013) and Albania (Blanfuné et al, 2016).…”

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)

“…Previous theoretical studies proposed mixed models to estimate uncertainties in monitoring data, considering numerous different sources of variation that could affect an indicator, from the uncertainty related to sampling and analysis, to spatial and temporal variations and their interactions. However, when real data were considered, if some sources of uncertainty were small, they were disregarded by analysis [20][21][22][23][24][25][26]. In the current study, temporal variations were not considered, as the application of the MaQI index foresees to merge both spring and autumnal data collected in the year of monitoring, avoiding contribution from intra-annual variations to uncertainty [9].…”

“…However, when uncertainty is estimated from a larger dataset, pooling observations from multiple ecosystems with similar characteristics, with several spatial, temporal, and analytical method variations, it might be desirable to quantify every component at different scales [3,22]. In addition, as soon as data from subsequent monitoring cycles are available, inter-annual variability may also be assessed [7,[24][25][26].…”