EC WFD)

Ecological Indicators 93 (2018) 316–332

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Original Articles

Development of ecological classification criteria for the Biological Quality Element phytoplankton for Adriatic and Tyrrhenian coastal waters by means of chlorophyll a (2000/60/EC WFD)

T

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Franco Giovanardia, Janja Francéb, , Patricija Mozetičb, Robert Precalic ISPRA – Italian National Institute for Environmental Protection and Research, present address: Via Aquilino Bresolin, 11, 21049 Tradate, VA, Italy National Institute of Biology, Marine Biology Station, Fornače 41, 6330 Piran, Slovenia c Ruđer Bošković Institute, Centre for Marine Research, Giordano Paliaga 5, 52210 Rovinj, Croatia a

b

A R T I C LE I N FO

A B S T R A C T

Keywords: Chlorophyll a Phytoplankton Ecological classification Water Framework Directive Adriatic Sea Tyrrhenian Sea

This paper describes the data processing that led to the definition of ecological classification criteria for the Biological Quality Element (BQE) phytoplankton in the coastal waters (CW) of the Adriatic and Tyrrhenian seas, according to the Water Framework Directive (2000/60/EC). The chosen metric was the annual geometric mean of chlorophyll a concentrations owing to the log-normal nature of chlorophyll a distribution. The sensitivity of this metric to the gradient of pressures was tested by adopting an empirical statistical approach. The dilution factor (F_dil), which is the share of freshwater in a sample of seawater, was introduced as a rough, but realistic proxy of nutrient loads from the continent. Correlations between F_dil and trophic indicators (i.e. nitrogen and phosphorus concentrations in seawater and the respective N:P ratio) were then evaluated. The F_dil approach was also used to derive reference conditions for each typology of coastal waters. Functional relationships between chlorophyll a, as phytoplankton biomass indicator, and nutrient concentrations, as pressure indicators, were computed by means of regression techniques. The classification scale for the BQE phytoplankton was based on the TRIX scale of water quality conditions. Reference conditions, pressure/impact relationships, boundary setting and classification criterion definition were treated separately and discussed for each of the CW Types: Type I, Type II A Adriatic, Type II A Tyrrhenian and Type III W for both the Adriatic and Tyrrhenian seas. Due to the lack of a functional relationship between the gradient of pressures and chlorophyll a, and a narrow range of annual chlorophyll a concentrations, only one threshold value was set for Type III W instead of the whole classification scale. Specific topics dealing with the log-normal model adopted for chlorophyll a data and the adequacy of classification criteria are discussed in the Appendix.

1. Introduction The Water Framework Directive (WFD, 2000/60 EC) establishes a framework for the protection of all European waters; inland surface waters, transitional waters, coastal waters and groundwater. Its objectives are numerous, but its overall purpose is to prevent further deterioration of water resources and to ensure their improvement if necessary. However, the WFD’s ambitious goal of achieving good ecological status for all European waters by 2015 was not entirely met. Therefore, the second River Basin Management Plans that are currently underway, have to deal with the fact that a lot of progress has to be achieved by around 2021. In coastal waters, the biological quality elements used for the assessment of the ecological status include macroalgae, benthic

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invertebrates, and phytoplankton as the only planktonic element. The WFD underlines the need to use several parameters of each of the biological quality elements to assess the ecological quality of coastal waters. In the case of phytoplankton, these parameters include biomass, community composition and abundance, as well as bloom frequency and intensity. Several attempts have been made to develop an integrated assessment of the ecological quality of coastal waters based on more than one if not all of these attributes in different European regions, where phytoplankton has long been considered in the assessment systems required by regional conventions such as OSPAR, HELCOM, and the Barcelona and Bucharest conventions. In the Baltic region, chlorophyll a is involved in the eutrophication assessment tool for the Baltic Sea (HELCOM EUTROPHICATION ASSESSMENT TOOL – HEAT 3.0) and, apart from nutrient concentrations, water transparency and

Corresponding author. E-mail addresses: [email protected] (F. Giovanardi), [email protected] (J. Francé), [email protected] (P. Mozetič), [email protected] (R. Precali).

https://doi.org/10.1016/j.ecolind.2018.05.015 Received 30 January 2018; Received in revised form 3 May 2018; Accepted 5 May 2018 1470-160X/ © 2018 Elsevier Ltd. All rights reserved.

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oxygen debt, is the only phytoplankton parameter taken into account (Fleming-Lehtinen et al., 2015). In the North-East Atlantic region, two phytoplankton parameters are used in the OSPAR Comprehensive Procedure to assess the status of waters with regard to anthropogenic eutrophication: chlorophyll a concentrations and elevated levels of nuisance or toxic phytoplankton indicator species (Foden et al., 2011). In the Black Sea region almost all WFD recommended phytoplankton parameters are used for the assessment of the ecological status of Romanian and Bulgarian coastal waters with an Integrated Biological Index (IBI) – Phytoplankton (Moncheva and Boicenco, 2011). Within the framework of the Mediterranean Geographical Intercalibration Group (Med-GIG), extensive discussions have taken place on the use of different phytoplankton attributes for the assessment of the ecological quality of coastal waters. Although some integrated indices have been proposed in the literature by various national groups (Pachés et al., 2012; Romero et al., 2013; Spatharis and Tsirtsis, 2010), a number of constrains have been raised by the Med-GIG group. Phytoplankton community composition in Adriatic coastal waters is seasonally and interannually highly variable, lacking a direct relationship with the nutrient status of the water body (Pugnetti et al., 2007). Moreover, no opportunistic phytoplankton species or substantial differences in the phytoplankton community structure were found when examining sampling stations located along a transect of increasing distance from the river mouth (Francé, 2009). As regards abundance, taking into account the wide range of cell dimensions, it can greatly underestimate or overestimate phytoplankton biomass. To overcome this constraint, carbon biomass or biovolume could be used (Cozzoli et al., 2017), although data-series containing either one or both parameters are extremely rare for coastal waters. The frequency of intense phytoplankton blooms with subsequent adverse effects in the area of interest is low, with the exception of the Emilia-Romagna region on the Italian coast (Colella et al., 2016; Vollenweider et al., 1992). On the other hand, blooms of harmful algae in Adriatic Sea coastal waters occur mainly as low biomass DSP events (Francé and Mozetič, 2006; Ninčević Gladan et al., 2008) and do not seem related to eutrophication pressure. To the best of our knowledge, these events cannot be related to the ecological status of Adriatic coastal waters. The use of chlorophyll a as a status indicator, however, has a lot of advantages: both spectrophotometric and fluorimetric analytical methods are time and cost-effective and reproducible, while the results are easily comparable among datasets (Domingues et al., 2008). Besides, there are a lot of records relating to the sensitivity of chlorophyll a to nutrient concentrations in the water column (Håkanson and Eklund, 2010; Harding et al., 2013; Mozetič et al., 2012). Therefore, it is not surprising that chlorophyll datasets are among the most widely used in assessment systems (Höglander et al., 2013). On the other hand, there are also some disadvantages as regards the use of chlorophyll a, e.g. the variable relationship between chlorophyll a concentration and species biomass (Kruskopf and Flynn, 2006). Finally, the recently adopted Commission Decision 2018/229/EU considers the parameter indicative of biomass, i.e. chlorophyll a, as the only classification criterion for Biological Quality Element (BQE) Phytoplankton for the coastal waters in 67 out of 72 national classification systems that have been intercalibrated for different water types in all Member States (MSs). The remaining five national classification systems comprise also other phytoplankton parameters besides chlorophyll a, namely Germany and Poland use total biovolume of phytoplankton as an additional expression of biomass for some specific water types in the Baltic Sea (Henriksen et al., 2013). In addition, Germany adopts also biovolume of Cyanophytes and Chlorophytes for one specific Baltic coastal water type (Henriksen et al., 2013). The Phytoplankton tool used in part of the United Kingdom coastal waters (Devlin et al., 2013) also comprises all the phytoplankton parameters, besides the above mentioned IBI in the Black Sea. The aim of this paper is to present a robust and straightforward definition of the assessment methods and classification criteria for the

Fig. 1. Map of the study area showing the locations of sampling stations in the Adriatic and Tyrrhenian Seas and their respective Type.

BQE Phytoplankton in the coastal waters of the Adriatic and Tyrrhenian Seas. The robustness of this approach was achieved by its development at a larger regional scale namely, the Adriatic Sea, and additionally tested in the Tyrrhenian Sea. The three coastal MSs – Croatia (HR), Italy (IT) and Slovenia (SI) agreed to develop a methodological approach based on a common dataset, which relied on the comprehensive knowledge of the eutrophication problem in the area. All the key issues in this process, i.e. the testing of the sensitivity of the metrics (chlorophyll a) to the pressure gradient and the derivation of the type specific reference conditions and boundaries setting, were based on a thorough evaluation of the behaviour of chlorophyll a as a proxy for phytoplankton biomass over the wide trophic gradient found in Adriatic and Tyrrhenian coastal waters. 2. Materials and methods 2.1. Area description The map of the study area is presented in Fig. 1. 2.1.1. Adriatic Sea The Adriatic Sea is an enclosed regional sea, extending from the Otranto Strait in the south-east to the Gulf of Trieste in the north. It is about 800 km long and, on average, 200 km wide, covering a surface of 138,600 km2. The hydrographic characteristics of the Adriatic Sea are quite variable in time and space and are depicted by a general cyclonic circulation pattern, running along the eastern coasts to the north and pointing southwards along the Italian coasts (Oddo and Guarnieri, 2011; Zavatarelli et al., 1998). The Western Adriatic Current (WAC) is mostly confined to the shallow Italian shelf and is driven by the accumulation of light waters due to runoff and heating. On the opposite side, the wider and meandering Eastern Adriatic Current (EAC) replaces the lost water with water from the Ionian Sea (Poulain and CushmanRoisin, 2001). Seasonally and interannually variable re-circulation cells can be found connected to the coastal water currents, with southern and middle gyres being the most permanent (Zavatarelli et al., 1998). Freshwater inputs mainly control the oceanographic conditions of the coastal waters of the Adriatic Sea, with almost one third coming from the Po River in the northwest (Gačić et al., 2001). A north to south 317

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Table 1 Description of common intercalibration types applicable for BQE phytoplankton only. Category: Coastal Waters. Geographical Intercalibration Group: Mediterranean Sea (modified according to Commission Decision 2008/915/EC). Type

Description

Annual mean salinity (p.s.u.)

Annual mean density (sigmat)

Type I (IT) Type II A Adriatic (IT, SI, HR) Type II A Tyrrhenian (IT) Type III W (IT, HR)

Highly influenced by freshwater input Moderately influenced by freshwater input (continent influence)

< 34.5 34.5–37.5

< 25 25–27

Continental coast, not influenced by freshwater input (Western Mediterranean Sea Basin)

> 37.5

> 27

eutrophic systems. For that purpose, the Adriatic Sea chlorophyll a database with data obtained by means of similar sampling and analytical methodology from the three MSs, was built and used to develop a common assessment methodology for the basin as a whole. The first database of the intercalibration exercise covered the period from 2007 to 2009 and contained 436 data records from Italian (26 Adriatic and 12 Tyrrhenian sampling stations), 86 data records from Croatian (5 sampling stations), and 120 from Slovenian (5 sampling stations) coastal waters. Subsequently, the Italian database was expanded to include a total of 22 transects for the Adriatic and 35 for the Tyrrhenian Sea, with sampling stations located at 500 m and 1000 m from the shore1 (Fig. 1). Croatia also supplemented the original database by increasing the number of stations from 5 to 27. It is worth pointing out that, for each sampling station, the available data records, i.e. chlorophyll a along with the supporting environmental parameters, namely water temperature, salinity, Secchi depth and concentrations of inorganic nutrients and dissolved oxygen, cover an entire annual cycle, which makes it possible to calculate both the annual means of parameters of interest and the corresponding overall average per individual station. Following the recommendations of the WFD (Carletti and Heiskanen, 2009), sampling stations were assigned to three common intercalibration types of coastal waters (Table 1). In Slovenian coastal waters (Gulf of Trieste), the five sampling stations considered cover all national coastal water bodies, which all belong to Type II A Adriatic. As regards the Croatian sampling stations, 22 are located in the Northern Adriatic and 5 in the Central Adriatic. Only 5 stations correspond to Type III W, the others were assigned to Type II A. The Italian Adriatic sampling stations were mostly assigned to Type II A (stations belonging to the 18 transects in the three coastal areas of the Northern and Central Adriatic Sea – Veneto, Marche and Abruzzi Regions) and to a lesser extent to Type I. The latter are found in the eutrophic coastal belt of the Emilia Romagna Region (4 transects) directly affected by the Po River inputs. Due to the peculiarity of this coastal zone, the number of stations along the 4 transects was increased to 15, by considering also the stations located further than one nautical mile from the shore. The Tyrrhenian sampling stations are related to 35 transects assigned to Type II (12 transects) and Type III W (23 transects). In particular, about 40 stations representative of Type III W are uniformly distributed throughout the basin, in areas not affected by river water inputs. From the remaining transects attributed to Type II, only 19 sampling stations were chosen for data processing, i.e. considering only those potentially affected by fluvial inputs of some importance.

decreasing trend of nutrient concentrations, which fuel the phytoplankton biomass, controlled by the Po River discharge and the circulation pattern (Zavatarelli et al., 1998), also dictates the west to east trophic gradient in the Adriatic Sea (Artegiani et al., 1997). The general characterisation of coastal waters as eutrophic along the western coasts and oligotrophic along the eastern coasts (Fonda Umani, 1996) is the major feature of such a pattern. Primary production in the Adriatic Sea has long been considered as phosphorus-limited (Maestrini et al., 1997; Pojed and Kveder, 1977). Moreover, a decreased input of phosphorus to the Adriatic Sea, following the ban on its use for the production of detergents (de Wit and Bendoricchio, 2001), together with the reduction of freshwater discharges from some major rivers in the northern Adriatic (Comici and Bussani, 2007; Zanchettin et al., 2008) recently, increased phosphorus limitation in some parts of the Adriatic Sea (Mozetič et al., 2012, 2010). One of the consequences of high nutrient loads and corresponding high phytoplankton growth in Adriatic coastal waters are hypoxic and anoxic events in the bottom layer (Djakovac et al., 2015), which mainly affect the NW Adriatic waters (Degobbis et al., 2000) and also the northernmost Gulf of Trieste (Faganeli and Ogrinc, 2009), especially in the past century. While the frequency of these events has decreased markedly at the northern Adriatic basin scale, hypoxic/anoxic events in the area directly affected by the Po River discharges did not show a similar decrease (Djakovac et al., 2015). 2.1.2. Tyrrhenian Sea The Tyrrhenian Sea is the deepest regional basin of the Western Mediterranean with a surface of approximately 222,000 km2 (Astraldi and Gasparini, 1994). It forms a triangle between the western coast of Italy and the islands of Corsica, Sardinia, and Sicily. Through the Tuscan Archipelago in the northwest, it is connected to the Ligurian Sea, and through the Strait of Messina in the southeast to the Ionian Sea. Moreover, Tyrrhenian Sea coastal waters are affected by important freshwater inputs, especially due to the discharges of the River Tiber affecting the Latium coasts, and those of the rivers Arno and Ombrone in Tuscany. Despite these contributions, the coastal environment of the Tyrrhenian basin, as a whole, is considered oligotrophic (Caldeira et al., 2012; Giovanardi et al., 2006; Innamorati and Giovanardi, 1992; Marchese et al., 2015). The differences between the Tyrrhenian and Adriatic coastal waters of Emilia-Romagna trophodynamic systems have already been studied in the past (Giovanardi et al., 2006; Giovanardi and Vollenweider, 2004). In fact, the Tyrrhenian coastal system seems to be limited by factors other than nutrients in determining productivity, i.e. nutrients are not utilized to their maximum potential, as will be further clarified below, based on the functional relationships between trophic levels and available phosphorus. 2.2. Dataset

1 This was made possible by the availability of the Sidimar database that provides a full set of data relating to environmental variables of hydrological, physical-chemical and chemical type plus chlorophyll a concentrations and phytoplankton determinations. Sidimar contains the data from coastal monitoring programs carried out, from 2001 to 2009, by the fifteen Italian maritime regions, in agreement with the Italian Ministry for the Environment (General Direction for the Protection of Nature and the Sea – PNM).

The WFD requirements for a coordinated and intercalibrated methodology for the assessment of the ecological quality of coastal waters revealed the need to obtain phytoplankton data that would cover a wide range of trophic conditions, from pristine areas to 318

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elaborations in order to allow comparison between two metrics. Additional explanations of the calculation and use of the 90th percentile and the requirement of the metrics’ discrimination limits are presented in the Appendix.

2.3. Statistical methods Statistical analyses were performed using the R, ver. 3.2.3, statistical packages (R Development Core Team, 2015). Data processing involved the use of the regression analysis techniques provided by the stats package. The lm function was used to fit linear models and perform regressions. The predict function allowed to calculate confidence intervals (with confidence levels P = 0.95 and P = 0.99) for the estimated values of the dependent variable. The step command was used to perform stepwise regression analysis. The stepwise search mode was chosen as direction “backward”. The following diagnostic tests were used: (i) Shapiro-Wilks test (command shapiro.test(residuals), from the stats package), which ensures that the errors (i.e. residuals) distribution approaches normality, (ii) Breusch-Pagan test (command bptest from the lmtest package) against heteroskedasticity of residuals variances, and (iii) Durbin-Watson test (command dwtest from the lmtest package) on absence of serial correlations among the residuals. For more details on these topics, see Ricci (2006). Finally, in the case of stepwise regression, the risk of multicollinearity was controlled using the vif (Variance Inflation Factor) function, taken from the faraway package.

3.2. Introduction to the development of the assessment methodology In testing the sensitivity of the metrics (G_mean and 90th percentile of chlorophyll a concentrations) to the gradient of pressures and establishing the boundaries among classes, we adopted an empirical statistical approach, quite similar to the approach already adopted for lakes by the OECD (Vollenweider and Kerekes, 1982): (i) correlations between nutrient loads released from the tributary basins and nutritional conditions in coastal areas, in terms of nitrogen and phosphorus concentrations at sea and N:P ratio variation, and (ii) regressions between biomass indicator (i.e. chlorophyll a) and nutrient concentrations as direct indicators of pressure. Nevertheless, coastal areas cannot be compared to confined systems such as a single lake with an identifiable catchment area, from where quantitatively well-defined nutrient loads enter the lake. Therefore, we decided to use the dilution factor of seawater (F_dil: percent content of freshwater in a sample of seawater; Yentsch, 1975), as a pressure indicator for terrestrial origin nutrient loads (Giovanardi and Tromellini, 1992a). The assessment of the ecological status for BQE phytoplankton has been juxtaposed with the water quality conditions described by the TRIX scale (Table 2). The TRIX Index and its scale were developed for the eutrophic waters of the Emilia Romagna coastline (NW Adriatic Sea – Italy) and it has been used for a long time as a management tool (Vollenweider et al., 1998). Prior to implementation of the WFD, this index was required by Italian Legislation for the trophic classification of the coastal environment and consequently used to set quality objectives to be achieved. TRIX has been also adopted by UNEP-MAP (2003) in the

3. Results and discussion 3.1. Selection of the appropriate phytoplankton biomass metrics Regardless of water type, chlorophyll a data distributions are always functionally related to multiplicative type phenomena, such as biomass growth and nutrient uptake and release. Therefore, the statistical distributions of chlorophyll a and nutrients tend towards log-normality. We quote verbatim from Margalef (1965): “a logarithmic transformation often proves appropriate for parameters referring to populations (chlorophyll content, production, number of cells) and to environmental factors strongly influenced by organisms (nutrient concentration). Multiplication and diffusion in a non-uniform environment lead commonly to a type of distribution in which density of population decreases exponentially with increasing distance from a center of maximum density. If samples are taken with a regular spacing, or a regular periodicity, chances are that in any series of samples, not the actual densities, but the logarithms of the densities approach normal distribution”. Regarding these topics, other references and technical details can be found in Innamorati and Giovanardi (1992). The goodness of logtransformation had also been verified by means of the Box-Cox procedure (Sokal and Rohlf, 1981), applied to parameters such as nutrients, chlorophyll a, Secchi depth, etc. in Giovanardi and Vollenweider (2004). When a set of values shows a sufficiently strong central tendency, it is usual to characterize the set by the related moments, defined by the sums of integer powers of the individual values: variance, skewness and kurtosis, they are just as many higher-order moments of the distributions. The arithmetic mean is a 1st order moment. Since statistical distributions of chlorophyll a and nutrients tend towards log-normality, the parameter that better estimates the value around which central clustering occurs, is represented by the geometric mean (G_mean), i.e. the arithmetic mean of chlorophyll a log-data reconverted into numbers. The normalization of the chlorophyll a distributions by means of logtransformation stabilizes the variance, with a standard deviation (sd) practically constant, around 0.3–0.4, in the case of decimal log-transformation (Giovanardi and Tromellini, 1992b). All these statistical properties led us to select annual G_mean of chlorophyll a as the metric for the classification criterion of BQE phytoplankton in the Adriatic and Tyrrhenian Seas. Since other MSs adopted the 90th percentile of chlorophyll a as the metric in their assessment systems, this statistical parameter of chlorophyll a data distributions is also presented in our

Table 2 Reference values for TRIX annual means, corresponding trophic status and related coastal water quality conditions (from Rinaldi and Giovanardi, 2011). TRIX annual means

Trophic status

<4

Elevated (oligotrophy)

4–5

Good (mesotrophy)

5–6

Mediocre (eutrophy)

>6

Bad (hypereutrophy)

Water quality conditions productive waters • Scarcely water transparency • Good of anomalous water colour • Absence of oxygen under-saturation • Absence conditions in the bottom waters productive waters • Moderately water turbidity • Occasional anomalous water colour • Occasional • Occasional bottom water hypoxia productive waters • Very water transparency • Low anomalous water colour. • Frequent and occasional anoxic • Hypoxic episodes in the bottom layers degradation of benthic • Some communities productive waters • Strongly water turbidity • High and persistent anomalies in • Diffuse water colour and persistent hypoxic/ • Diffuse anoxic episodes in the bottom waters

mortality rate of benthic • High organisms of the benthic • Alteration communities and strong decrease of the biodiversity

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Fig. 2. Relationship between sampling station G_means of TRIX and its components (TP, DIN, Chl-a) and orthophosphate (P-PO4) in the Adriatic and Tyrrhenian Seas.

two distinct typologies of Type II A coastal waters for the Adriatic and Tyrrhenian, with their respective distinct classification criteria.

context of the eutrophication risk in the Mediterranean region. TRIX is formulated as a linear combination of four fundamental trophic status indicators: chlorophyll a (Chl-a), nitrogen as Dissolved Inorganic Nitrogen (DIN), phosphorus as Total Phosphorus (TP) and oxygen absolute deviation from saturation (aD_O). As TRIX represents a combination of pressure (DIN and TP) and impact indicators (Chl-a and aD_O), it is not consistent with the WFD “philosophy”. Nevertheless, the way in which the TRIX trophic scale addresses water quality conditions is consistent with the WFD requirements for BQE phytoplankton in coastal waters, thus justifying the use of TRIX as “control variable”. Any relationship between TRIX and each of its components will of course be strongly affected by spurious correlation. Inevitably, if we wish to assign the same descriptive water quality scale as identified by TRIX to the concentration gradient of nutrients and chlorophyll a, we would need to plot TRIX values against the concentrations of its components. In this way, we use TRIX to arrange the corresponding nutrients and chlorophyll a boundary values that define the limits of the classification criterion that we wanted to develop (see Tables 7–9). We were able to distinguish different trophic regimes characterizing Tyrrhenian and Adriatic coastal systems (Fig. 2), where the relationships between TRIX values and dissolved nutrients (P-PO4 and DIN), TP and chlorophyll a are presented. These results (Fig. 2) confirm a typical behaviour already discussed in Giovanardi and Vollenweider (2004), where similar results were obtained with data from the previous decade (1996–1999). A divergence between the Adriatic and Tyrrhenian coastal systems was observed as regards the use of phosphorus to produce biomass, showing higher efficiency of the first one. From here arose the need to assume

3.3. Establishing type specific reference conditions (RC) for Types I and II A As established by the WFD CIS Guidance Document No. 5 (2003), Reference Conditions (RC) represent “a description of the biological quality elements that exist, or would exist, at high status. That is, with no, or very minor disturbance from human activities. The objective of setting reference condition standards is to enable the assessment of ecological quality against these standards”. Since we have assigned the meaning of comprehensive pressure indicator to the dilution factor, specifically related to the potential transport of nutrients (natural loads plus anthropogenic loads) from the mainland to the sea, we can also use this indicator to measure, albeit roughly, this transport and to verify the eventual absence of pressures of some importance exerted by human activities that may affect the BQEs. In other words, we can use this indicator to derive RC standards. The dilution factor is formulated as: F_dil = [(S-s)/S] * 100, where S = open sea salinity, s = measured salinity at a given coastal sampling point (Giovanardi and Vollenweider, 2004). According to this definition, F_dil does not represent a true pressure indicator; however, it is indisputable that the input of nutrients in a coastal area should be strictly related to the fresh waters of continental origin. In a given contribution of freshwater from the continent, the amount of nutrients associated with these inputs can differ from region to region, depending on several factors, most notably land use and the degree of waste water treatment and nutrient removal in the catchment areas. The complexity 320

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with data points and the area with no data points. For each fixed value of the F_dil indicator, corresponding chlorophyll a values (as annual G_means) can range from a minimum identified by the separation line to a maximum, which in turn will depend on the weight of the nutrient loads burdening the coastal systems. This separation line can be interpreted as the threshold between natural and anthropogenic pressures. We assume that the nutrient loads, either natural or generated by minor human activities, determine a response of the coastal systems that is well-represented by concentrations of chlorophyll a lying on the curve (Fig. 3). Thus, in our case, the assessment of RC for BQE phytoplankton does not derive from theoretical considerations or expert judgments, but refers to real situations occurring along the Adriatic and Tyrrhenian coasts. According to this approach, chlorophyll a RC is treated as a continuous variable functionally related to a wide spectrum of salinities that characterize different typologies of seawater. In order to define more accurately chlorophyll a RC for each type, the data corresponding to individual Adriatic and Tyrrhenian Types were considered separately. Then it is possible to plot the curves separately for the three types (Fig. 4), which now represent the RC for each type. The best functional relationships between chlorophyll a RC and F_dil were always exponential. The equations describing these relationships have been used to derive a unique chlorophyll a RC per type corresponding to the mean value of F_dil (as required by the WFD to assess Ecological Quality Ratios – EQRs for BQE phytoplankton). Table 3 summarizes the results. Type I RC corresponds to a value of F_dil % = 7.9, which is the overall average of the F_dil values calculated on the whole Type I Adriatic data set. The Chl-a = 1.4 µg/L RC is comparable with the average chlorophyll a concentrations at sampling station N. 319, located 3 km off the Cattolica transect (90 km south of the Po River delta), already regarded as a reference station by the Environment Agency of the Emilia Romagna Region (Arpa Emilia-Romagna, 2015). The RC for Type II A Adriatic was calculated from the corresponding value of F_dil = 4.96 (i.e. the overall average of the dilution values, on the whole Type II A Adriatic data set). The Chl-a = 0.33 µg/L RC is very close to the chlorophyll a concentrations characterizing, on average, the Adriatic Type III W coastal waters (see Fig. 11). For Type II A Tyrrhenian, the RC was calculated from the corresponding value of F_dil = 2.47 (i.e. the overall average of the dilution factor values, on the whole Type II Tyrrhenian data set). The Chla = 0.32 µg/L RC is very close to the one already assigned to Type II Adriatic. We can conclude that, in the case of Type II A waters, both for the Adriatic and Tyrrhenian seas, the actual natural conditions that may be taken as a “reference“ standard for the BQE phytoplankton, are those approaching the average conditions characterizing Type III W coastal areas (see Section 3.5).

Fig. 3. Scatter plot of annual G_means of chlorophyll a (Chl-a) against the dilution factor (F_dil) for Types I and II A. The curve marks the boundary of the lower limit of chlorophyll a values (RC). (Data obtained from the IT-SI-HR common data set, plus Tyrrhenian data from the Sidimar database).

Fig. 4. Reference conditions for chlorophyll a (Chl-a) corresponding to different Types, depending on the gradient of the dilution factor (F_dil).

is then increased by the dynamics of the currents at local scale, where the mixing of the freshwater input from the rivers with open seawaters can be strongly affected by the prevailing current direction, vertical and horizontal advection, eddy formations, wind stress, tidal cycles, and so on. However, for our purposes, the quantification of these effects is not necessary, since F_dil can be calculated from measurements of salinity, used as a tracer (Officer, 1976). The role of the F_dil indicator in assigning the chlorophyll a RC is depicted in Fig. 3. The data points refer to coastal areas belonging to all typologies of water bodies in the Adriatic and Tyrrhenian seas, in order to ensure maximum variation range for the related water quality parameters. As we were not interested in finding a significant relationship between chlorophyll a and F_dil, no regression line is plotted on the graph. Instead, we have drawn a boundary line between the area

3.4. Testing the sensitivity of the metrics to the gradient of pressures for Types I and II A To test the sensitivity of the selected metrics to different pressure indicators, multiple regression analysis with linear models (LMs) was performed first of all. By means of this stepwise regression technique,

Table 3 Summary table for BQE phytoplankton reference conditions (RC) based on chlorophyll a. Type

Functional relationships

F_dil (%) mean value

RC – Chl-a (µg/L) as G_mean

RC – Chl-a (µg/L) as 90th percentile*

Type I Type II A Adriatic Type II A Tyrrhenian

y = 0.388 e0.162x y = 0.109 e0.221x y = 0.146 e0.315x

7.9 4.96 2.47

1.40 0.33 0.32

3.9 0.87 0.77

* Based on a theoretical sd value of the Log-transformed Chl-a data distributions. The recommended calculation procedure is better explained in the Appendix. 321

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implemented for a long time to confront coastal eutrophication in the northern Adriatic Sea (de Wit and Bendoricchio, 2001; Haddrill et al., 1983; Pagnotta et al., 1995). In order to improve exploration of the effects of terrestrial nutrient loads on the trophic regime of the coastal systems of interest, the Adriatic and Tyrrhenian coastal waters were analyzed separately, starting from the important meaning assigned to the F_dil indicator in the above analyses.

Table 4 Results of the stepwise regression applied to Type I coastal waters data. For each regression coefficient (Estimate), the value of Student’s test (under hypothesis β = 0), the relative P-value and the degree of significance expressed by the number of asterisks, are provided.

(Intercept) F_dil aD_O TP DIN

Estimate (β)

t value

Pr(> |t|)

−2.4536 0.1598 0.3212 3.6530 −0.1100

−4.705 4.296 5.241 8.021 −5.646

3.380E−04 7.390E−04 1.250E−04 1.330E−06 6.040E−05

*** *** ***

3.4.3. Comparison between nutrient utilization in Adriatic and Tyrrhenian coastal waters The relationships between the dilution factor and nutrients in Type I and II A Adriatic coastal waters are depicted in Fig. 5. These relationships represent a statistical continuum, which connect Type I and Type II A waters in the Adriatic Sea, implying a close relationship between nutrient concentrations in seawater and freshwater inputs from the continental catchment areas generating nutrient loads. Since DIN is more conservative and soluble if compared to TP, the functional relationships with the dilution factor show significance levels higher for DIN than for TP (R2 = 0.88; P = 2.2 10−16 and R2 = 0.69; P = 2.43 10−13, respectively), as expected. In fact, unlike nitrogen, the decay of phosphorus associated with river waters entering the sea must be attributed not only to physical dilution, but also to the removal from the system due to sedimentation and/or chemical precipitation (Giovanardi and Tromellini, 1992a). Based on these results, both nitrogen and phosphorus concentrations in seawater were used to test the pressure-impact relationship, using chlorophyll a as an indicator that documents the response of coastal systems to nutrient availability. The graphical representations of these pressure-impact relationships are presented in Fig. 6. As with the stepwise regression analysis, where the data were grouped according to the coastal water types, chlorophyll a was also found to be strongly related to TP; in this case, data were grouped at Adriatic Sea level. In all cases, with Types I and II A separated or combined, the correlation was always highly significant, with P = 4.11 10−7 for Type I, P = 3.94 10−10 for Type II and P = 2.2 10−16 for combined Types (Fig. 6, right panel). Overall, along the coasts of the Adriatic Sea with these water typologies, almost 90% of the chlorophyll a variability is explained by phosphorus that was, therefore, accepted as the principal pressure indicator in relation to BQE phytoplankton. Conversely, when relating chlorophyll a to inorganic nitrogen as a pressure indicator (Fig. 6, left panel), the correlation was notably weaker when considering Adriatic data as a whole (P = 2.8 10−13). While there was no correlation at all for Type I waters (R2 = 0.004; P = 0.771), it was hardly acceptable though still significant for Type II A waters (R2 = 0.448; P = 6.9 10−5). This confirms what has been widely reported in the literature, namely, that the Adriatic Sea is primarily a phosphorus-limited sea (Chiaudani and Vighi, 1982; Maestrini et al., 1997; Solidoro et al., 2009). Due to the high N:P ratios in Adriatic coastal areas (N:P > 50), nitrogen does not limit algal growth (i.e. excess nitrogen is not utilized by phytoplankton), which is expressed as low or zero correlation in the charts. In order to evaluate the pressure-impact relationships for the Tyrrhenian Sea, data from 19 sampling stations belonging to Type II A were used, corresponding mainly to coastal areas directly affected by the main rivers (Tiber, Arno, Ombrone) and also by minor rivers that, however, cause relevant seasonal salinity fluctuations, at least at a local scale. As already mentioned, Tyrrhenian coastal systems react less efficiently to the availability of nutrients and tend to produce less biomass (in terms of chlorophyll a concentrations). Unlike the Adriatic, where P-limitation practically represents a generalized condition, in Tyrrhenian coastal environments P-limitation conditions are influenced mainly by riverine inputs. The most noticeable effect attributable to freshwater from rivers is represented by an increase in the N:P ratio, which in itself is a useful indicator of pressure, although maximum values rarely exceed N:P = 30 (Fig. 7).

*** ***

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Multiple R-squared: 0.8886, F-statistic: 27.93 on 4 and 14 DF, P-value: 1.533E−06.

the chlorophyll a concentration variations were tested against the pressure indicators provided by the Adriatic and Tyrrhenian data set, that is to say, nutrient concentrations in their various forms, oxygen saturation (as aD_O), dilution factor and Secchi depth, with a total of 20 and 25 available records, for Type I and Type II respectively. Annual geometric means of the parameters were used in the analysis. 3.4.1. Type I coastal waters Among all the possible combinations, the stepwise regression technique provided the following linear model:

lm (formula = Chl-a ∼ F dil + aDO + TP + DIN ,data = TypeI )

(1)

The numerical output of the linear model is presented in Table 4. Multiple R-squared and F statistics with the relative P-value, which assess the overall significance of the regression model, show that the fitted linear model explains 89% of the total chlorophyll a variability. The maximum weight in determining this variability is taken by TP; the other regressors have lower effects, although their regression coefficients are all significantly ≠ 0. The diagnostic tests performed on the regression residuals ensured that the remaining chlorophyll a variability was not affected by other independent variables not included in the model. 3.4.2. Type II A coastal waters (Adriatic and Tyrrhenian data combined) The linear model provided by the stepwise regression technique was:

lm (formula = Chl-a ∼ F dil + TP,data = TypeIIA)

(2)

The linear model is quite simple, only two regressors were chosen with a largely dominant weight of TP over the weight of F_dil (Table 5). Moreover, multiple R_squared shows that the amount of chlorophyll a variability explained by this model is 78%. As TP accounts for the maximum weight in determining the variability of chlorophyll a, for both Type I and Type II A Adriatic and Tyrrhenian coastal waters, this parameter can be considered as the most eligible indicator of the pressure gradient. Since the phosphorus pool in the water column (TP) can be considered as an internal measure of external phosphorus enrichment, the above results confirm the correctness of control policies and phosphorus removal that have been Table 5 Results of the stepwise regression applied to Type II A data.

(Intercept) F_dil TP

Estimate (β)

t value

Pr(> |t|)

−0.0097 0.0414 1.6219

−0.167 3.323 4.089

0.8692 3.231E−03 5.250E−04

n.s. ** ***

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Multiple R-squared: 0.7758, F-statistic: 36.33 on 2 and 21 DF, P-value: 1.521E−07. 322

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Fig. 5. Functional relationships between G_means of the dilution factor (F_dil) and overall G_means of DIN (left panel) and TP (right panel) for Type I and Type II A Adriatic sampling stations combined. Confidence intervals for the estimated values of the dependent variable are shown with confidence levels P = 0.95 and P = 0.99.

Due to the high sensitivity of the N:P ratios to the F_dil gradient, as presented in Fig. 7 (R2 = 0.829; P = 1.58 10−7), we assume that the value of the ratio is dominated by the variability of the numerator (DIN), rather than the denominator (P-PO4). The diagrams presented in Fig. 8 show (in the left panel) highly correlated variations of nitrogen vs the dilution factor (P = 5.56 10−8), with an increase from one to 10 µmol/L of DIN, corresponding to a 5% increase of the F_dil parameter. On the contrary, the relationships between orthophosphate and the dilution factor (Fig. 8, right panel) appear to be weaker, although the correlation is statistically significant (P = 9.53 10−6). It should be noted that in the same variation range of the dilution factor, the orthophosphate concentrations increase only from 0.1–0.4 µmol/L. By replacing the orthophosphate with total phosphorus (TP) we obtain similar relationships vs dilution factor with an even weaker although significant correlation (R2 = 0.629; P = 4.44 10−4). Despite the high correlation obtained in the case of the DIN vs F_dil relationship, the test applied to check the normality of the regression residuals fails. This confirms the high heterogeneity of nitrogen inputs from the catchment areas in the Tyrrhenian Sea as there is no unique major source of fresh water with effects comparably important to those determined by the Po River throughout most of the Adriatic basin. This heterogeneity is due mainly to the sampling stations located in the Gulf

Fig. 7. Relationship between the dilution factor (F_dil) and the N:P ratio in Type II A Tyrrhenian coastal waters. Data represent overall G_means.

of Naples, represented in the diagram by the data points above the main sequence (Fig. 8, left panel). By removing these points from the analysis, we get a much higher R2 > 0.95. The same applies to the

Fig. 6. Functional relationships between overall G_means of chlorophyll a (Chl-a) and G_means of DIN (left panel) and TP (right panel) for Adriatic coastal waters: Type I (dashed blue line), Type II A (dashed red line) and both types combined (solid black line). Confidence intervals for the estimated values of the dependent variable with confidence levels P = 0.95 and P = 0.99, are shown for combined Adriatic Types only. (For interpretation of the references to colour in this figure caption, the reader is referred to the web version of this article.) 323

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Fig. 8. Relationship between the dilution factor (F_dil) and dissolved inorganic nitrogen (DIN; left panel) and orthophosphate (P-PO4; right panel) in Type II A Tyrrhenian coastal waters. Data represent overall G_means.

Fig. 9. Relationship between chlorophyll a and dissolved inorganic nitrogen (DIN; left panel) and total phosphorus (TP; right panel) in Type II A Tyrrhenian coastal waters. Data represent overall G_means.

which provides the highest degree of correlation. The equations in rows 1) to 4) of Table 6 serve to prove the potentiality and the goodness of the dilution factor approach. The equations in row 5) were obtained from the inverse relationship between the TRIX index and its component TP. For Type I and II A Adriatic, which were combined in Fig. 2, these equations were prepared separately per Type, using the same data as those used to assess the next functional relationships between TP and chlorophyll a. Finally, equations in row 6) exploit the relationship between TP and chlorophyll a, with the aim of fixing the limits among the ecological quality classes of the classification criterion.

relationship between the N:P ratios and the dilution factor (Fig. 7), where this heterogeneity is also evident, although the regression diagnostic tests provided acceptable results. Regardless of the discrepancies observed in the reactions of different parts of the Tyrrhenian Sea to the input of freshwater nutrients, the relationships between chlorophyll a and nutrient concentrations in seawater were similar to those in the Adriatic Sea, with a highly significant correlation between TP and chlorophyll a results (Fig. 9, right panel; R2 = 0.845; P = 3.29 10−8). A lower degree of correlation was found for dissolved inorganic nitrogen (Fig. 9, left panel; R2 = 0.541; P = 3.24 10−4). The causes for this weak functional link might be due to the effects already highlighted and discussed for the Adriatic situation; with prevailing P-limitation in coastal environments, the available inorganic nitrogen does not significantly affect the changes in chlorophyll a concentrations.

3.5. Type III W waters case Type III W coastal waters, common to both the Tyrrhenian and Adriatic Sea, were treated separately in this paper due to the practical inability to find a suitable proxy of pressures and to determine a functional relationship between the gradient of the pressures (as nutrient-related parameters) and the impact (as chlorophyll a concentrations). Following the same approach used for Type I and II A waters, overall G_means of nutrient concentrations were related to the dilution factor (Fig. 10). The data refer to 41 sampling stations in the Southern Tyrrhenian Sea (Campania, Sicily and Sardinia coasts), Northern Tyrrhenian and Ligurian Sea (Tuscany and Liguria), and Adriatic Sea (Croatian coast). No correlation was found for DIN (R2 = 0.05; P = 0.303. Fig. 10, left panel), while for the TP the relationship was even inverse to the one expected (Fig. 10, right panel).

3.4.4. Summary of the quantitative functional links All the above relationships showed that chlorophyll a sensitivity, considered as the response of coastal systems to the availability of nutrients in terms of phytoplankton biomass production, is largely controlled by total phosphorus, which can therefore assume the role of the main pressure indicator. The important regression equations used subsequently for the construction of the ecological classification criteria are summarized in Table 6. The nature of these relationships is almost always log-log type, 324

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Table 6 List of functional relationships of interest to this work, per Type. For each regression equation, the sample size N and the R-squared values are provided. Functional link (1) DIN vs F_dil (2) TP vs F_dil (3) N:P vs F_dil (4) P-PO4 vs F_dil (5) TP vs TRIX (6) Chl-a vs TP

Type II A Tyrrhenian

Type I 2.009

[DIN] = 0.343 [F_dil] N = 19; R2 = 0.831; P = 5.56 10−8 [TP] = 0.318 [F_dil]0.505 N = 19; R2 = 0.629; P = 4.44 10−4 [N:P] = 17.440 ln(F_dil) + 0.650 N = 19; R2 = 0.829; P = 1.58 10−7 [P-PO4] = 0.0553[F_dil]1.033 N = 19; R2 = 0.704; P = 9.53 10−6 [TP] = exp [(TRIX–5.363)/1.305] N = 19 [Chl-a] = 1.656 [TP]1.178 N = 19; R2 = 0.845; P = 3.29 10−8

Not tested

Type II A Adriatic [DIN] = 0.318 [F_dil]1.312 N = 54; R2 = 0.882; P = 2.2 10−16 [TP] = 0.083 [F_dil]0.559 N = 49; R2 = 0.692; P = 2.43 10−13 Not tested

n.s. relationships

n.s relationships

[TP] = exp [(TRIX – 6.064)/1.349] N = 15 [Chl-a] = 10.591 [TP]1.237 N = 15; R2 = 0.835; P = 4.45 10−6

[TP] = exp [(TRIX – 6.148)/1.583] N = 52 [Chl-a] = 3.978 [TP]1.347 N = 52; R2 = 0.896; P = 2.2 10−16

If the nutrients are replaced with the corresponding chlorophyll a G_means (Fig. 11), the scattering of the data points reflects the general trophic characteristics that strictly differentiate the oligotrophic waters of the southern Tyrrhenian Type III W coastal waters, including Sardinian waters (with chlorophyll a G_means < 0.3 µg/L), from the slightly more productive Ligurian, northern Tyrrhenian and Adriatic Type III W coastal waters (with chlorophyll a G_means not exceeding 0.4 µg/L). However, there is a clear absence of correlation between chlorophyll a G_means and the dilution factor as a proxy of the anthropogenic pressures for both groups of waters. A different distinction between two Type III W water areas is depicted in Fig. 12, where the N:P ratio is confronted with the dilution factor. Here, Tyrrhenian and Adriatic Type III W waters, although always belonging to the same Type, differ trophodinamically in terms of nutrient limitation, a feature already observed with the Type II A waters of both basins. The Type III W coastal waters of the Tyrrhenian and Ligurian Seas are characterised by N-limitation, with minimum N:P values of around 8 at the sampling stations of the southern part of the Tyrrhenian Sea. These N:P values are well comparable with the data reported for surface Tyrrhenian waters (Innamorati and Giovanardi, 1992) and appear to be typical of coastal areas largely unimpacted by nutrient inputs. We can assume that, in the case of Tyrrhenian coastal waters, the shift from conditions of N-limitation to P-limitation also marks the shift from Type III W to Type II A waters. Conversely, the high N:P ratios characterizing Adriatic Type III W coastal areas confirm the influence of the Po River, although chlorophyll a concentrations are very low. The Po River discharge tends to affect the whole Adriatic basin and determines P-limiting conditions both spatially and temporally, also in the case of strictly oligotrophic areas (Chiaudani and Vighi, 1982; Maestrini et al., 1997; Pojed and Kveder, 1977).

Fig. 11. Relationships between the dilution factor (F_dil) and chlorophyll a (Chl-a) for Type III W coastal waters of the southern Tyrrhenian Sea, including Sardinia (circles), and the northern Tyrrhenian, Ligurian and Adriatic Seas (triangles). Data represent overall G_means.

Another important reason for treating these waters separately is the very narrow range of chlorophyll a concentrations characterizing this typology. In fact, as shown in Fig. 11, overall G_means of chlorophyll a values range from around 0.1 to around 0.4 µg/L. Since the ecological classification scheme set by the WFD consists of 5 ecological quality classes, the discrimination limit between two contiguous chlorophyll a annual G_means would not be suitable for proper and safe classification. A single threshold value is therefore proposed for Type III W coastal

Fig. 10. Relationship between the dilution factor (F_dil) and dissolved inorganic nitrogen (DIN; left panel) and total phosphorus (TP; right panel) in Type III W coastal waters. Data represent overall G_means of the sampling stations. 325

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Table 7 Reference conditions and boundaries of ecological quality classes expressed by different parameters for Type I coastal waters. Normalized EQRs are to be used in ecological quality assessment. Boundaries

Reference Conditions H/G G/M M/P P/B

TRIX

Chl-a EQRs

Chl-a EQRs

µg/L

TP annual G_mean µmol/L

actual

normalized

1.40

3.93

–

1

1

2.0 5.0 12.6 25.0

5.6 14.1 35.2 70.1

0.26 0.55 1.15 2.00

0.70 0.28 0.11 0.06

0.85 0.62 0.38 0.20

Chl-a annual G_mean µg/L

–

4.25 5.25 6.25 7

Chl-a 90th percentile*

* Based on a theoretical sd value of the Log-transformed Chl-a data distributions equal to 0.35. The recommended calculation procedure is better explained in the Appendix.

Fig. 12. Type III W waters-N:P ratios (as overall G_means) vs dilution factor gradient (F_dil).

Table 8 Reference conditions and boundaries of ecological quality classes expressed by different parameters for Type II A Adriatic coastal waters. Normalized EQRs are to be used in ecological quality assessment.

waters. The reader is referred to the Appendix, where these issues are discussed in detail.

Boundaries

3.6. Setting the boundaries between ecological quality classes With the definition of RC for Type I and Type II A coastal waters and the unveiling of their pressure/impact relationships, we have provided all the necessary tools for defining the Classification criteria for BQE phytoplankton in Adriatic and Tyrrhenian coastal waters. The first step in setting the boundaries was the definition of the most important boundary: Good/Moderate (G/M) boundary, which delimits the need for taking measures in case of good ecological status failure. Firstly, the boundary was set for TP, as it appeared to be the best pressure indicator for phytoplankton in this study. The G/M boundary for TP was calculated using the equations in row 5) of Table 6, at the corresponding TRIX boundary between Good and Mediocre Trophic Status (TRIX = 5; see Table 2), which matches the transition from mesotrophic to eutrophic conditions in the coastal ecosystem. This boundary was used for both Type II A Adriatic and Tyrrhenian, giving the values of 0.48 and 0.76 µmol TP/L, respectively. For Type I, the value of TRIX for deriving the G/M boundary was increased to 5.25, in order to take into account the nutrient loads originating from natural sources carried by the Po River into the Adriatic Sea, presumably in not negligible amounts. In this way, the G/M boundary for TP was set at 0.55 µmol/L for Type I. We believe that in such a way, the resulting boundaries allow for more realistic and effective management policies. Having set the boundary between G/M for the pressure parameter TP, it was possible to calculate the corresponding G/M boundary for the impact parameter chlorophyll a, using the equations in row 6) of Table 6, for the corresponding Types. The functional relationship between TP and chlorophyll a gives the following mandatory G/M boundaries: 1.50 µg/L and 1.20 µg/L for Types II A Adriatic and Tyrrhenian, respectively, and 5.00 µg/L for Type I. In the same way, the other boundaries for Types II A Adriatic and Tyrrhenian were also set at TRIX values delimiting the trophic scale in Table 2: 4 for High/Good (H/G) boundary, 6 for Moderate/Poor (M/P) boundary and 7 for Poor/Bad (P/B) boundary. For Type I, the TRIX values used to derive the H/G and M/P boundaries were increased by a quarter of a point, likewise for the G/M boundary, while for the P/B boundary TRIX = 7 was kept. The corresponding TP and chlorophyll a boundaries were calculated using the equations in rows 5) and 6) of Table 6 and are presented in Tables 7–9 for the different Types. The identified P/B boundaries refer to “virtual” conditions, since it was not possible to detect real situations relating to ecological class “Bad” in any of the datasets analysed in this work. TP concentrations characterizing “Bad” ecological class have been extrapolated from the

Reference Conditions H/G G/M M/P P/B

TRIX

Chl-a EQRs

Chl-a EQRs

µg/L

TP annual G_mean µmol/L

actual

normalized

0.33

0.87

–

1

1

0.64 1.5 3.5 8.2

1.7 4.0 9.3 21.7

0.26 0.48 0.91 1.71

0.52 0.22 0.09 0.04

0.82 0.61 0.40 0.19

Chl-a annual G_mean µg/L

–

4 5 6 7

Chl-a 90th percentile*

* Based on a theoretical sd value of the Log-transformed Chl-a data distributions equal to 0.33. The recommended calculation procedure is better explained in the Appendix. Table 9 Reference conditions and boundaries of ecological quality classes expressed by different parameters for Type II A Tyrrhenian coastal waters. Normalized EQRs are to be used in ecological quality assessment. Boundaries

Reference Conditions H/G G/M M/P P/B

TRIX

Chl-a EQRs

Chl-a EQRs

µg/L

TP annual G_mean µmol/L

actual

normalized

0.32

0.78

–

1

1

0.48 1.2 2.9 7.3

1.17 2.9 7.1 17.6

0.35 0.76 1.63 3.51

0.66 0.27 0.11 0.04

0.84 0.62 0.40 0.18

Chl-a annual G_mean µg/L

–

4 5 6 7

Chl-a 90th percentile*

* Based on a theoretical sd value of the Log-transformed Chl-a data distributions equal to 0.30. The recommended calculation procedure is better explained in the Appendix.

functional relationships extended to the area of the diagrams not actually covered by observations. It is impossible to predict how coastal systems would behave with such high concentrations of phosphorus, especially since we are talking about annual averages. We therefore consider this class as indicative, but not strictly necessary for proper ecological classification of the BQE phytoplankton based on chlorophyll a concentrations. The next step in developing the assessment methodology was the designation of the ecological quality ratios (EQR) for each boundary. 326

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Fig. 13. Relationships between actual and normalized EQR values for the three types of coastal waters with corresponding conversion functions.

I and II A Adriatic and Tyrrhenian, a single threshold approach is proposed for Adriatic and Tyrrhenian Type III W coastal waters. The threshold value for annual geometric means of monthly concentrations of chlorophyll a (Chl-a annual G_mean), delimiting between good and non-good ecological status, was set using the H/G boundary of the related Types II A Adriatic and Tyrrhenian. The threshold values for the two Types are presented in Table 10. The reasons for selecting this approach are: a) practical unfeasibility of defining the boundaries of all five WFD ecological quality classes with a very narrow annual range of chlorophyll a concentrations; b) difficulty of proving, for this Type, the sensitivity of chlorophyll a concentrations to the gradient of pressures. This threshold value could assume the same meaning and be subject to the same constraints as those that the WFD assigns to the G/M boundaries set for all other typologies.

The EQR, which is set by the WFD as the relative deviation from the reference conditions (RC), was then calculated for every boundary using the simple equation: (3)

EQRactual = RC / Chl-a annual Gmean,

where for Chl-a annual G_mean the chlorophyll a concentrations defined for every boundary were used. As chlorophyll a concentrations are derived using non-linear relationships, the corresponding EQRs are not on a linear equidistant scale as required by WFD. In order to calculate the EQRs values normalized to the scale from 0 to 1 (EQR_norm) and set more or less equidistantly, with respect to the above calculated values (designated as EQR_actual), the following conversion functions were used:

EQRnorm = 0.259 ln (EQRactual ) + 0.947 for Type I,

(4)

EQRnorm = 0.246 ln (EQRactual ) + 0.981 for Type II A Adriatic,

(5) 4. Conclusions

EQRnorm = 0.244 ln (EQRactual ) + 0.946 for Type II A Tyrrhenian. (6)

In this work, we built a methodology for the assessment of the ecological quality of coastal waters by means of BQE phytoplankton for water Types shared by Italy, Slovenia and Croatia. Despite the need, expressed by WFD, to use different phytoplankton parameters such as biomass, abundance and community structure, we developed the methodology based on a single metric: annual geometric mean of chlorophyll a concentrations. A similar decision for the assessment methodology, based solely on the chlorophyll a parameter, has been adopted by most of European MSs (Commission decision 2018/229/ EU), based on a trade-off between the necessity of long-term, reliable, fast and easily attainable data and the minimum requirement of the WFD. Efforts have been devoted to quantitatively link phytoplankton composition to environmental variables within the Med-GIG and broader. Garmendia et al. (2013) conclude that the difficulty of establishing significant pressure-impact relationships is one of the principal aspects that hinder the inclusion of such indicators into the assessment systems. This difficulty most probably originates in the intrinsic variability of phytoplankton assemblages (Giovanardi, unpublished data). Although the road ahead is still long, we acknowledge the necessity of developing indicators addressing multiple phytoplankton parameters. At this stage, a sound method for the most used parameter, i.e. chlorophyll a, is a prerequisite for a subsequent upgrade from a singleparameter index to the formulation of multimetric indices, more consistent with the WFD philosophy. The selection of the geometric mean as the statistically most appropriate metric is quite unique among the intercalibration groups, as most other countries agreed to use the 90th percentile of chlorophyll a concentrations. We believe that our decision for a different metric is highly justified, since we once again demonstrated the log-normal

The actual and normalized EQRs for all boundaries of Types I, II A Adriatic and II A Tyrrhenian are shown in Tables 7, 8 and 9, respectively. The relationships between actual and normalized EQRs are shown in Fig. 13. For the assessment of the ecological status of coastal waters, the normalized EQR boundary values have to be compared to normalized EQRs calculated from the annual geometric means of concentrations of chlorophyll a (Chl-a annual G_mean) in a water body using the above equations. Among the types examined in this paper, only Type II A Adriatic involved the preparation of a common data set among three MSs (HR, IT, SI). In this respect, since the three MSs have used the same database, adopted the same metric (chlorophyll a annual geometric mean) and accepted a common methodology, there was no need for intercalibration, or even harmonization of the class limits, as is mandatory according to the WFD requirements in case of different methodologies adopted by MSs. Contrarily to the five ecological classes approach adopted for Types Table 10 Type III W- Threshold values between good and non-good ecological status. Type

Chl-a (µg/L) annual G_mean

Chl-a (µg/L) 90th percentile*

TP (µmol/L) annual G_mean

Type III W Adriatic Type III W Tyrrhenian

0.64 0.48

1.7 1.17

0.26 0.35

* Based on a theoretical sd value of the Log-transformed Chl-a data distributions, equal to 0.33 and 0.30 for Type III W Adriatic and Type III W Tyrrhenian, respectively. The recommended procedure is better explained in the Appendix. 327

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quality classes. Therefore, the use of the H/G boundary of Types II A pertaining to the Adriatic and Tyrrhenian basins as a single threshold between good and non-good ecological quality for Type III W is proposed, based on the assumption of a continuum of coastal water types in space and time. In this way, the problem of having such narrow class ranges as to exceed the discrimination limit for a certain sampling scheme is also overcome (see Appendix for clarifications). Finally, the following should be noted as regards sampling frequency, which is taken into account when calculating the discrimination limits in the Appendix. It is absolutely necessity to conduct sampling on a monthly basis, providing at least 12 measurements per year and per sampling station. Monthly sampling frequency was proven to be the minimum prerequisite for ecological studies, as it is supposed to capture the main phytoplankton variability (Winder and Cloern, 2010). In this way, one keeps the discrimination limit of the presented classification method at a reasonably low level, thus allowing assessment of the right ecological quality class of a certain water body.

nature of chlorophyll a distributions, based on a large common data set. The logarithmic nature of phytoplankton biomass is a universal pattern related to growth phenomena and thus this transformation is widely used in ecological and physiological phytoplankton research (Innamorati and Giovanardi, 1992; Poikane et al., 2014; Sauzède et al., 2015). The advantage of log-transformation is also of pure statistical nature, since its stabilization of variances and reduction of data ranges allows more space for further studies with relevant statistical elaborations. We can point to two continuums in the presented classification criteria. The first is the continuum of RC found with the novel use of the dilution factor as a proxy of pressures, which in coastal waters of Type I and II A can be seen mostly as river-borne nutrients. The visualization of chlorophyll a data against this pressure proxy exposed a line below which there are no chlorophyll a data. The lowest observed chlorophyll a concentrations constructing this line act as a continuum of RC shaped only by natural processes. In this way, RC can always be found in “reallife scenarios” and there is no need for theoretical assumptions or expert judgment. It was therefore not surprising that for coastal waters with so clearly defined natural reference conditions (Type I and II A), the pressure–impact relationships were also easy to find. The highly significant relationships between TP, as pressure parameter, and chlorophyll a permitted us to construct a robust and, in our opinion, reliable classification system. Nevertheless, data elaborations pointed once again to the different nature of the two Mediterranean basins: while primary production in the Adriatic Sea is generally P-limited, in the Tyrrhenian Sea it switches between N- and P-limitation in times/places of low and high riverine inputs, respectively. The second continuum can be tracked between the classification systems of Types II A Adriatic and Tyrrhenian, and those of Type III W, which is based solely on a single threshold value. In coastal waters belonging to Type III W, the approach successfully applied to other Types failed completely: we were unable to find a significant pressure upon which to construct the classification scheme with five ecological

Acknowledgements This study is based on a common data-set originating from the national monitoring programmes financed by the Slovenian Environment Agency of the Ministry of Environment and Spatial Planning, Italian Ministry of the Environment (MATTM: General Direction for the Protection of Nature and the Sea - PNM) and Ministry of Science, Education and Sport of the Republic of Croatia (project “Jadran”). Authors also wish to thank colleagues from the General Direction for Land Conservation and Water Protection - STA of the MATTM, from the Regional Agencies for the Environmental Protection (ARPA) and from the Italian National Institute for Environmental Protection and Research (ISPRA), who contributed to the success of the Intercalibration exercise of the Med-GIG to varying degrees. Final thanks go to the Joint Research Centre (JRC) and in particular, to Ms. Wendy Bonne and Ms. Fuensanta Salas Herrero who in these years have always followed and stimulated us with valuable advice.

Appendix: Statistical properties of chlorophyll a data and adequacy of the classification criteria A.1. About the use of the 90th percentile The metric often used for classification purposes by other IC Working Groups is the 90th percentile of chlorophyll a distributions, to be compared with the corresponding 90th percentile boundaries identifying the different ecological quality classes, in agreement with Commission Decision 2018/ 229/EU. By definition, the 90th percentile indicates the value below which 90% of observations in a group of observations may be found, or conversely, the value exceeded only by 10% of the observations. On the other hand, in the case of a normal distribution, the expected percentile values are fixed immediately, once the distribution parameters, mean and sd, have been defined. For easier comparison with other classification systems, the tables with the BQE phytoplankton classification criterion for Adriatic and Tyrrhenian Seas provide boundaries for both the chlorophyll a G_means and the corresponding 90th percentile values, to be meant not as observed, but as expected values (see Tables 7–9). We adopted the following calculation procedure:

Chl-a 90th percentile

(expected)

= 10(log10(Chl-a G mean) + 1.282 sd)

(A.1)

where 1.282 is the value assumed by the standard normal variable zc for which P(z > zc) = 10%. This calculation procedure is based on the assumption of log-normality of chlorophyll a data. In this sense, the use of the 90th percentile should not be a true alternative to the geometric mean, since it is the value of a percentile belonging to the same theoretical normal distribution of chlorophyll a log data, reconverted into a number. As for the value to assign to sd, as it emerged from the analysis of long-term chlorophyll a data series carried out according to the OECD recommendations (Vollenweider and Kerekes, 1982),2 standard deviations of the log10 chlorophyll a sample distributions ranged from 0.30 to 0.35 for all water types; in particular, for Type I, sd is close to 0.35; for Type II Adriatic and Type III W Adriatic, sd is close to 0.33; and for Type II Tyrrhenian and Type III W Tyrrhenian, sd is about 0.30. To give an example, the above calculation procedure was applied to the chlorophyll a annual data of two sampling stations that are quite representative of the average conditions in the northern Adriatic (station A) and the Tyrrhenian Sea (station B). The differences emerging between the distribution free assessment and log-normal model after decimal log-transformation of the data are quite substantial (Table A.1). We should also emphasize the effect of the OECD recommended screening on the range of the data and the good agreement between the observed and expected

2 In the preliminary screening of the rough chlorophyll a analytical data, OECD recommended the following empirical procedure: perform a base 10 log transformation; remove the data exceeding the mean ± 2 sd; re-calculate the new mean and related sd. In practical terms, by this method, only a few anomalous data are eliminated and the actual range of the resulting distributions covers an interval around 1.4–1.7 decimal log-units, in good agreement with a theoretical range of: mean ± 2 sd.

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Table A.1 Results obtained on two annual sample distributions of chlorophyll a data. Two different approaches are tested: the first without logarithmic transformation, the second following the log-normal model. Chlorophyll a data processing (Chl-a values in µg/L)

S.S. A

S.S. B

Distribution free assessment

Without log10 transformation

Sample size N Mean Median Actual range Observed 90th percentile

48 5.5 3.56 0.5–27.5 12.82

48 0.5 0.39 0.06–1.61 0.95

Log-normal model

After log10 transformation and OECD recommended screening

Sample size N sd (log10 data distribution) G_mean

47 0.354 (0.372)(*) 3.65 0.715–18.63

46 0.255 (0.291) (*) 0.41 0.13–1.33

0.5–22.7 10.87

0.12–1.50 0.854

10.38

0.873

10(x ± 2 sd) Actual range Observed 90th percentile Expected 90th percentile

(*) In this case the observed sd are used. Between brackets sd before OECD screening.

values with respect to the 90th percentile calculation using decimal log-transformation. A.2 Discrimination limits In order to verify whether the interval between the different classes is sufficiently wide to allow for a safe and reliable classification, a hypothesis test on the difference between the means can be applied. The validity of the classification criterion will depend on the statistical significance of the difference between two chlorophyll a G_means that correspond to the two contiguous boundaries, under the hypothesis that sampling distributions of the chlorophyll a data, previously log-transformed, belong to the same normally distributed population. If the sample sizes are at least N ≥ 50, the hypothesis test to be adopted is based on the values of the standard normal variable z. The difference between two contiguous log10(Chl-a) means (dM) will be significantly ≠ 0, if the value of the statistic:

z=

dM σxi − xj

(A.2)

exceeds the critical value zc. Since the test is a two-tailed test, the critical value zc is to be found on the standard normal distribution table, at an opportune significance level α/2 and corresponding probability P = 100(1-α). In the above formula the difference dM is scaled by the quantity:

σx 1 − x 2 =

(σ12/ N1) + (σ22/ N2)

(A.3)

representing the standard error of the difference between means. In order to estimate σ1 and σ2, the corresponding sample sd, s1 and s2, can be used. From Eq. (A.2) we can easily derive a Discrimination limit dMc:

dMc = ± z c ·σxi − xj

(A.4)

below which the difference dM must be considered = 0 (i.e. one cannot discriminate between the two G_means in this case). As an example of calculating the discrimination limits, the classification criterion for Type II A Tyrrhenian is used. The related class boundaries, presented also as log values are shown in Table A.2. Let us assume that each chlorophyll a value corresponding to the boundary represents an average of the annual sample distributions with the same sample size (e.g. N = 70). Suppose also that these sample distributions are characterized by a common standard deviation sd = 0.30, evaluated on the log10-transformed chlorophyll a data. The Discrimination limit among the class boundaries can be calculated as follows. With a significance level α/2 = 0.05 (P = 90%), we evaluate from Eq. (A.4):

dMc = z c ·σx1− x2 = 1.645∗0.05079 = |0.083|. By halving the significance level (α/2 = 0.025, with P = 95%), the discrimination limit becomes:

dMc = 1.960∗0.05079 = |0.099|.

Table A.2 Boundaries between ecological quality classes for Type II A Tyrrhenian coastal waters presented as chlorophyll a G_means and their corresponding log values. Boundaries

H/G

G/M

M/P

P/B

Chl-a G_means (µg/L) log10 values

0.48 −0.319

1.2 0.079

2.9 0.462

7.3 0.8633

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Table A.3 Results of the calculation of the discrimination limits (dM) for chlorophyll a log10 units for different sample sizes with the significance level α/2 = 0.05 (P = 90%). N

t(1-a/2;2N-2)

2/N

dM

12 24 52

1.717 1.679 1.660

0.408 0.289 0.196

|0.21| |0.15| |0.10|

Table A.4 Results of the calculation of the discrimination limits (dM) for chlorophyll a log10 units for different sample sizes N with the significance level: α/2 = 0.025 (P = 95%). N

t(1-a/2;2N-2)

2/N

dM

12 24 52

2.074 2.013 1.983

0.408 0.289 0.196

|0.25| |0.17| |0.12|

Table A.5 Tentative boundaries between ecological quality classes for Type III W coastal waters presented as chlorophyll a G_means and their corresponding log values. Boundaries

RC

H/G

G/M

M/P

P/B

Chl-a G_means (µg/L) log10 values

0.1 −1

0.2 −0.774

0.3 −0.548

0.5 −0.323

0.8 −0.097

In both cases, the critical value for the difference between two chlorophyll a boundaries is < 0.1 log10 units, and is as thus suitable for a proper classification of Type II A Tyrrhenian, where the range among classes is around |0.4| log10 units (Table A.2). The “resolution power” provided by this test on the differences is of course strongly dependent on sample size. The important question is, therefore, how many samples are needed to guarantee a reliable estimate of the difference between two contiguous means of chlorophyll a data. In general, it is possible to a priori evaluate the optimum sample size to ensure an acceptable level of resolution or, vice versa, the level of resolution achievable depending on sample size. With small samples (N < 50), for two sample distributions randomly extracted from the same normal population, the following condition applies:

dMc = sp·t(1 − α /2;N1+ N2 − 2)· (1/ N1 + 1/ N2) ≠ 0

(A.5)

where dMc represents the discrimination limit expressed as an absolute value. In the case N1 = N2 = N, the degrees of freedom for the variable t become 2 N-2 and the term under root becomes 2/N. In this case, a two-tailed test has to be applied also. The critical value t(1-α/2) is to be found on the table of the upper percentage points of the t distribution. Assuming, therefore, a pooled sd for log10(Chl-a), sp = 0.30, at an opportune significance level, the results obtained for different small sample sizes are presented in Tables A.3 and A.4. The tested sample sizes N correspond to the number of data that would be obtained in a yearly monitoring program with monthly, biweekly and weekly sampling frequencies. The most conservative discrimination limit of |0.25| log10 units calculated for a monthly sampling scheme, which is probably the most common in the national monitoring programs, does not prove to be very suitable for classification purposes, since in the case under examination (Type II Tyrrhenian), as already mentioned, the differences between ecological quality classes are typically around |0.40| log10 units, too low to insure a safe classification. It is, therefore, necessary to have a minimum number of data greater than 12 per year, which can be easily obtained, e.g. by keeping the same monthly frequency, but aggregating the data of two or more stations that are representative of the same water body. With N ≥ 24, more secure and appropriate discrimination limits are obtained. The same procedure can be used to assess the particular case of Type III W coastal waters. The available Type III W chlorophyll a data are mainly collected in oligotrophic Tyrrhenian coastal waters. The annual geometric means of chlorophyll a do not exceed values of 0.2–0.3 µg/L (see Fig. 11), with maximum seasonal peaks that are unlikely to exceed 1 µg/L. An attempt to build a classification criterion based on chlorophyll a in these conditions would result in setting a full range of G_means values of chlorophyll a from 0.1–0.8 µg/L, with 4 intermediate boundaries. If the “alternative benchmarking” rule is adopted, i.e. the rule of the equidistant ranges applied to log-transformed chlorophyll a data as advised by WFD CIS Guidance Document No. 5 (2003), we obtain the tentative chlorophyll a boundaries between ecological quality classes for Type III W presented in Table A.5. Such a Type III W classification criterion, based on chlorophyll a G_means as a metric, would be inadequate, since the difference between two boundaries (|0.226| log10 units) is lower than the discrimination limit calculated with N = 12 (monthly sampling frequency, Table A.4). The situation does not improve even with a less stringent level of probability P = 90% than the one usually required in hypothesis testing (P = 95%) (Table A.3). By increasing sampling frequency, from monthly to bi-weekly (N = 24) and then to weekly (N = 52), the level of discrimination is reduced noticeably, but still not sufficiently for a proper classification of the ecological status. With weekly sampling, the discrimination limit, dMc = |0.12|, still exceeds half of the range between two adjacent boundaries. In conclusion, by assigning the meaning of “measure of the degree of adequacy” of the classification criteria adopted to the discrimination limit, it is shown that chlorophyll a is not a suitable indicator for Type III W coastal waters, as it does not enable an acceptable level of confidence in the assessment of the ecological status for BQE phytoplankton. Accordingly, for this typology of waters, the choice of a threshold value for chlorophyll a, which would mean just one level of attention (i.e. one threshold), instead of a formal subdivision into five classes, appears to be fully justified. 330

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experience in applying the Trophic Index TRIX to two areas of the Adriatic and Tyrrhenian seas. J. Limnology 63, 199–218. http://dx.doi.org/10.4081/jlimnol. 2004.199. Haddrill, M.V., Keffer, R., Olivetti, G.C., Polleri, G.B., Giovanardi, F., 1983. Eutrophication problems in Emilia Romagna, Italy. Water Res. 17, 483–495. http:// dx.doi.org/10.1016/0043-1354(83)90108-2. Håkanson, L., Eklund, J.M., 2010. Relationships between chlorophyll, salinity, phosphorus, and nitrogen in Lakes and Marine Areas. J. Coastal Res. 412–423. http://dx. doi.org/10.2112/08-1121.1. Harding, L.W., Batiuk, R.A., Fisher, T.R., Gallegos, C.L., Malone, T.C., Miller, W.D., Mulholland, M.R., Paerl, H.W., Perry, E.S., Tango, P., 2013. Scientific bases for numerical chlorophyll criteria in Chesapeake Bay. Estuar. Coasts 37, 134–148. http:// dx.doi.org/10.1007/s12237-013-9656-6. Henriksen, P., Jaanus, A., Kauppila, P., Grage, A., Jurgensone, I., Remeikaite-Nikiene, N., Olenina, I., Lysiak-Pastuszak, E., Dubinski, M., Walve, J., Höglander, H., Bonne, W., 2013. Baltic Sea GIG: Coastal waters – Phytoplankton. Intercalibration of biological elements for transitional and coastal water bodies. Water Framework Directive intercalibration technical report. p. 52. Retrieved on https://circabc.europa.eu/4.4. 2018. Höglander, H., Karlson, B., Johansen, M., Walve, J., Andersson, A., 2013. Overview of coastal phytoplankton indicators and their potential use in Swedish waters. Deliverable 3.3-1, WATERS Report no. 2013: 5. Havsmiljöinstitutet, Sweden. Innamorati, M., Giovanardi, F., 1992. Interrelationships between phytoplankton biomass and nutrients in the eu-trophied areas of the Northwestern Adriatic Sea. Sci. Total Environ. Supplement 235–250. http://dx.doi.org/10.1016/B978-0-444-89990-3. 50024-9. Kruskopf, M., Flynn, K.J., 2006. Chlorophyll content and fluorescence responses cannot be used to gauge reliably phytoplankton biomass, nutrient status or growth rate. New Phytol. 169, 525–536. http://dx.doi.org/10.1111/j.1469-8137.2005.01601.x. Maestrini, S.Y., Berland, B.R., Bréret, M., Bechemin, C., Poletti, R., Rinaldi, A., 1997. Nutrients limiting the algal growth potential (AGP) in the Po River Plume and an adjacent area, northwest Adriatic Sea: Enrichment bioassays with the test algae Nitzschia closterium and Thalassiosira pseudonana. Estuaries 20, 416–429. Marchese, C., Lazzara, L., Pieri, M., Massi, L., Nuccio, C., Santini, C., Maselli, F., 2015. Analysis of Chlorophyll-a and primary production dynamics in North Tyrrhenian and Ligurian Coastal–Neritic and Oceanic Waters. J. Coastal Res. 690–701. http://dx.doi. org/10.2112/jcoastres-d-13-00210.1. Margalef, R., 1965. Ecological correlations and the relationship between primary productivity and community structure. In: Proc. Symposium on Primary Productivity in Aquatic Environment, Pallanza, Italy, pp. 355–364. Moncheva, S., Boicenco, L., 2011. Compliance of national assessment methods with the WFD requirements (Romania and Bulgaria). WFD Intercalibration Phase 2: Milestone 4b report – Black Sea GIG, ECOSTAT Meeting,17–19 October, Ispra, 2011. Mozetič, P., France, J., Kogovšek, T., Talaber, I., Malej, A., 2012. Plankton trends and community changes in a coastal sea (northern Adriatic): bottom-up vs. top-down control in relation to environmental drivers. Est. Coast. Shelf Sci. 115, 138–148. http://dx.doi.org/10.1016/j.ecss.2012.02.009. Mozetič, P., Solidoro, C., Cossarini, G., Socal, G., Precali, R., France, J., Bianchi, F., De Vittor, C., Smodlaka, N., Fonda Umani, S., 2010. Recent trends towards oligotrophication of the northern Adriatic: evidence from chlorophyll a time series. Estuar. Coasts 33, 362–375. http://dx.doi.org/10.1007/s12237-009-9191-7. Ninčević Gladan, Ž., Skejić, S., Bužančić, M., Marasović, I., Arapov, J., Ujević, I., Bojanić, N., Grbec, B., Kušpilić, G., Vidjak, O., 2008. Seasonal variability in Dinophysis spp. abundances and diarrhetic shellfish poisoning outbreaks along the eastern Adriatic coast. Bot. Mar. 51, 449–463. http://dx.doi.org/10.1515/BOT.2008.067. Oddo, P., Guarnieri, A., 2011. A study of the hydrographic conditions in the Adriatic Sea from numerical modelling and direct observations (2000–2008). Ocean Sci. 7, 549–567. http://dx.doi.org/10.5194/os-7-549-2011. Officer, C.B., 1976. Physical Oceanography of Estuaries (and Associated Coastal Waters). J. Wiley and Sons, New York. Pachés, M., Romero, I., Hermosilla, Z., Martinez-Guijarro, R., 2012. PHYMED: an ecological classification system for the Water Framework Directive based on phytoplankton community composition. Ecol. Indic. 19, 15–23. http://dx.doi.org/10. 1016/j.ecolind.2011.07.003. Pagnotta, R., Caggiati, G., Piazza, D., Ferrari, F., 1995. Il controllo della qualità delle acque superficiali del bacino padano. Inquinamento 4, 8–14. Poikane, S., Portielje, R., van den Berg, M., Phillips, G., Brucet, S., Carvalho, L., Mischke, U., Ott, I., Soszka, H., Van Wichelen, J., 2014. Defining ecologically relevant water quality targets for lakes in Europe. J. Appl. Ecol. 51, 592–602. http://dx.doi.org/10. 1111/1365-2664.12228. Pojed, I., Kveder, S., 1977. Investigation of nutrient limitation of phytoplankton production in the Northern Adriatic by enrichment experiments. Thalassia Jugosl. 13, 13–24. Poulain, P.-M., Cushman-Roisin, B., 2001. Circulation. In: Cushman-Roisin, B., Gačić, M., Poulain, P.-.M., Artegiani, A. (Eds.), Physical Oceanography of the Adriatic Sea. Kluwer Academic Publishers, Nederlands, pp. 67–109. Pugnetti, A., Bastianini, M., Acri, F., Bernardi Aubry, F., Bianchi, F., Boldrin, A., Socal, G., 2007. Comunita' fitoplanctoniche e climatologia nell'Adriatico Settentrionale. In: Carli, B., Gavaretta, G., Colacino, N., Fuzzi, S. (Eds.), Clima e cambiamenti climatici: le attivita' di ricerca del CNR. CNR, Roma, pp. 551–556. R Development Core Team, 2015. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Ricci, V., 2006. Principali tecniche di regressione con R. Versione 0.3. https://cran.rproject.org/doc/contrib/Ricci-regression-it.pdf. Rinaldi, A., Giovanardi, F., 2011. Contribution of Richard A. Vollenweider toward

References Arpa Emilia-Romagna, 2015. Qualità ambientale acque marine in Emilia-Romagna. Rapporto annuale 2013. Struttura Oceanografica Daphne, pp. 147. Artegiani, A., Paschini, E., Russo, A., Bregant, D., Raicich, F., Pinardi, N., 1997. The Adriatic Sea general circulation. Part II: baroclinic circulation structure. J. Phys. Oceanogr. 27, 1515–1532. http://dx.doi.org/10.1175/1520-0485(1997) 027<1515:tasgcp>2.0.co;2. Astraldi, M., Gasparini, G.P., 1994. The seasonal characteristics of the circulation in the Tyrrhenian Sea. In: La Viollette, P.E. (Ed.), Seasonal and Interannual Variability of the Western Mediterranean Sea. American Geophysical Union, Washington, D.C., pp. 115–134. Caldeira, R.M.A., Couvelard, X., Casella, E., Vetrano, A., 2012. Assymmetric eddy populations in adjacent basins - a high resolution numerical study of the Tyrrhenian and Ligurian Seas. Ocean Sci. Discuss. 2012, 3521–3566. http://dx.doi.org/10.5194/osd9-3521-2012. Carletti, A., Heiskanen, A.-S., 2009. Water Framework Directive intercalibration technical report. Part 3: Coastal and Transitional waters, pp. 240. Chiaudani, G., Vighi, M., 1982. Multistep approach to identification of limiting nutrients in Northern Adriatic eutrophied coastal waters. Water Res. 16, 1161–1166. http://dx. doi.org/10.1016/0043-1354(82)90134-8. Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., Santoleri, R., 2016. Mediterranean ocean colour chlorophyll trends. PLoS One 11, e0155756. http://dx.doi.org/10. 1371/journal.pone.0155756. Comici, C., Bussani, A., 2007. Analysis of the River Isonzo discharge (1998–2005). Boll. Geofis. Teor. Appl. 48, 435–454. Cozzoli, F., Stanca, E., Selmeczy, G.B., Francé, J., Varkitzi, I., Basset, A., 2017. Sensitivity of phytoplankton metrics to sample-size: a case study on a large transitional water dataset (WISER). Ecol. Indic. 82, 558–573. http://dx.doi.org/10.1016/j.ecolind. 2017.07.022. de Wit, M., Bendoricchio, G., 2001. Nutrient fluxes in the Po basin. Sci. Total Environ. 273, 147–161. http://dx.doi.org/10.1016/S0048-9697(00)00851-2. Degobbis, D., Precali, R., Ivančić, I., Smodlaka, N., Fuks, D., Kveder, S., 2000. Long-term changes in the northern Adriatic ecosystem related to anthropogenic eutrophication. Int. J. Environ. Pollut. 13, 495–533. http://dx.doi.org/10.1504/IJEP.2000.002332. Devlin, M.J., Best, M., Bresnan, E., Scanlan, C., Baptie, M., 2013. Water Framework Directive: the development and status of phytoplankton tools for ecological assessment of coastal and transitional waters. United Kingdom. Update Report to UK Technical Advisory Group. Djakovac, T., Supić, N., Bernardi Aubry, F., Degobbis, D., Giani, M., 2015. Mechanisms of hypoxia frequency changes in the northern Adriatic Sea during the period 1972–2012. J. Mar. Syst. 141, 179–189. http://dx.doi.org/10.1016/j.jmarsys.2014. 08.001. Domingues, R.B., Barbosa, A., Galvão, H., 2008. Constraints on the use of phytoplankton as a biological quality element within the Water Framework Directive in Portuguese waters. Mar. Pollut. Bull. 56, 1389–1395. http://dx.doi.org/10.1016/j.marpolbul. 2008.05.006. Faganeli, J., Ogrinc, N., 2009. Oxic-anoxic transition of benthic fluxes from the coastal marine environment (Gulf of Trieste, northern Adriatic Sea). Mar. Freshw. Res. 60, 700–711. http://dx.doi.org/10.1071/MF08065. Fleming-Lehtinen, V., Andersen, J.H., Carstensen, J., Łysiak-Pastuszak, E., Murray, C., Pyhälä, M., Laamanen, M., 2015. Recent developments in assessment methodology reveal that the Baltic Sea eutrophication problem is expanding. Ecol. Indic. 48, 380–388. http://dx.doi.org/10.1016/j.ecolind.2014.08.022. Foden, J., Devlin, M., Mills, D., Malcolm, S., 2011. Searching for undesirable disturbance: an application of the OSPAR eutrophication assessment method to marine waters of England and Wales. Biogeochemistry 106, 157–175. http://dx.doi.org/10.1007/ s10533-010-9475-9. Fonda Umani, S., 1996. Pelagic production and biomass in the Adriatic Sea. Sci. Mar. 60, 65–77. Francé, J., 2009. Long-term Structural Changes of the Phytoplankton Community of the Gulf of Trieste, Biotechnical Faculty, Department of Biology. University of Ljubljana, Ljubljana, pp. 160. Francé, J., Mozetič, P., 2006. Ecological characterization of toxic phytoplankton species (Dinophysis spp., Dinophyceae) in Slovenian mariculture areas (Gulf of Trieste, Adriatic Sea) and the implications for monitoring. Mar. Pollut. Bull. 52, 1504–1516. http://dx.doi.org/10.1016/j.marpolbul.2006.05.012. Gačić, M., Poulain, P.-M., Zore-Armanda, M., Barale, V., 2001. Overview. In: CushmanRoisin, B., Gačić, M., Poulain, P.-M., Artegiani, A. (Eds.), Physical Oceanography of the Adriatic Sea. Past, Present and Future. Kluwer Academic Publishers, Dordrecht, pp. 1–44. Garmendia, M., Borja, Á., Franco, J., Revilla, M., 2013. Phytoplankton composition indicators for the assessment of eutrophication in marine waters: present state and challenges within the European directives. Mar. Pollut. Bull. 66, 7–16. Giovanardi, F., Finoia, M.G., Russo, S., Amori, M., Di Lorenzo, B., 2006. Coastal waters monitoring data: frequency distributions of the principal water quality variables. J. Limnology 65, 65–82. http://dx.doi.org/10.4081/jlimnol.2006.65. Giovanardi, F., Tromellini, E., 1992a. An empirical dispersion model for total phosphorus in a coastal area: the Po River-Adriatic system. Sci. Total Environ. Supplement 201–210. http://dx.doi.org/10.1016/B978-0-444-89990-3.50022-5. Giovanardi, F., Tromellini, E., 1992b. Statistical assessment of trophic conditions. Application of the OECD methodology to the Marine Environment. Sci. Total Environ. Supplement 211–233. http://dx.doi.org/10.1016/B978-0-444-89990-3.50023-7. Giovanardi, F., Vollenweider, R.A., 2004. Trophic conditions of marine coastal waters:

331

Ecological Indicators 93 (2018) 316–332

F. Giovanardi et al.

index. Environmetrics 98, 329–357. http://dx.doi.org/10.1002/(SICI)1099-095X (199805/06)9:3<329::AID-ENV308>3.0.CO;2-9. Vollenweider, R.A., Kerekes, J., 1982. Eutrophication of Waters. Monitoring, Assessment and Control, OECD Cooperative Programme on Monitoring of Inland Waters (Eutrophication Control). Environment Directorate. OECD, Paris, pp. 154. Vollenweider, R.A., Rinaldi, A., Montanari, G., 1992. Eutrophication, structure and dynamics of a marine coastal system: results of ten-year monitoring along the EmiliaRomagna coast (Northwest Adriatic Sea). Sci. Total Environ. Supplement 63–106. http://dx.doi.org/10.1016/B978-0-444-89990-3.50014-6. WFD CIS Guidance Document No. 5, 2003. Common Implementation Strategy for the Water Framework Directive (2000/60/EC). Transitional and Coastal Waters Typology, Reference Conditions and Classification Systems. Working Group 2.4 “COAST”, Office for Official Publications of the European Communities, Luxembourg. Winder, M., Cloern, J.E., 2010. The annual cycles of phytoplankton biomass. Philos. Trans. R. Soc. B: Biol. Sci. 365, 3215–3226. http://dx.doi.org/10.1098/rstb.2010. 0125. Yentsch, C.S., 1975. New England coastal waters – an infinite estuary. In: Church, T.M. (Ed.), Marine Chemistry in the Coastal Environment. American Chemical Society, pp. 608–617. Zanchettin, D., Traverso, P., Tomasino, M., 2008. Po River discharges: a preliminary analysis of a 200-year time series. Clim. Change 89, 411–433. http://dx.doi.org/10. 1007/s10584-008-9395-z. Zavatarelli, M., Raicich, F., Bregant, D., Russo, A., Artegiani, A., 1998. Climatological biogeochemical characteristics of the Adriatic Sea. J. Mar. Syst. 18, 227–263. http:// dx.doi.org/10.1016/S0924-7963(98)00014-1.

understanding eutrophication of the coastal Adriatic Sea. Aquat. Ecosyst. Health 14, 200–203. http://dx.doi.org/10.1080/14634988.2011.576990. Romero, I., Pachés, M., Martínez-Guijarro, R., Ferrer, J., 2013. Glophymed: An index to establish the ecological status for the Water Framework Directive based on phytoplankton in coastal waters. Mar. Pollut. Bull. 75, 218–223. http://dx.doi.org/10. 1016/j.marpolbul.2013.07.028. Sauzède, R., Lavigne, H., Claustre, H., Uitz, J., Schmechtig, C., D'Ortenzio, F., Guinet, C., Pesant, S., 2015. Vertical distribution of chlorophyll a concentration and phytoplankton community composition from in situ fluorescence profiles: a first database for the global ocean. Earth Syst. Sci. Data 7, 261–273. http://dx.doi.org/10.5194/ essd-7-261-2015. Sokal, R.R., Rohlf, F.J., 1981. Biometry – The Principles and Practice of Statistics in Biological Research, second ed. W. H. Freeman and Co., San Francisco. Solidoro, C., Bastianini, M., Bandelj, V., Codermatz, R., Cossarini, G., Melaku Canu, D., Ravagnan, E., Salon, S., Trevisani, S., 2009. Current state, scales of variability and decadal trends of biogeochemical properties in the Northern Adriatic Sea. J. Geophys. Res. 114, C07S91. http://dx.doi.org/10.1029/2008JC004838. Spatharis, S., Tsirtsis, G., 2010. Ecological quality scales based on phytoplankton for the implementation of Water Framework Directive in the Eastern Mediterranean. Ecol. Indic. 10, 840–847. http://dx.doi.org/10.1016/j.ecolind.2010.01.005. UNEP-MAP, 2003. Eutrophication monitoring strategy of MEDPOL, UNEP(DEC)/MED WG.231/14, 30 April 2003. Vollenweider, R.A., Giovanardi, F., Montanari, G., Rinaldi, A., 1998. Characterization of the trophic conditions of marine coastal waters, with special references to the NW Adriatic Sea: proposal for a trophic scale turbidity and generalized water quality

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