e c o l o g i c a l i n d i c a t o r s 9 ( 2 0 0 9 ) 9 8 2 – 9 9 1

Simple tools for assessing water quality and trophic status intransitional water ecosystems

G. Giordani a,*, J.M. Zaldıvar b, P. Viaroli a

aDepartment of Environmental Sciences, University of Parma, Via Usberti 11/A, I-43100 Parma, ItalybEuropean Commission, Joint Research Centre, Institute for Health and Consumer Protection, Via E. Fermi 2749, TP 272. I-21027 Ispra

(VA), Italy

a r t i c l e i n f o

Article history:

Received 4 March 2008

Received in revised form

10 November 2008

Accepted 12 November 2008

Keywords:

Phanerogams

Opportunistic macroalgae

Phytoplankton chlorophyll-a

Nutrients

Oxygen

Water quality index

a b s t r a c t

In this study we have developed an index for assessing trophic status and water quality in

transitional aquatic ecosystems of Southern Europe. The index has been developed from the

water quality index of the U.S. National Sanitation Foundation and integrates the main

causal factors (inorganic nutrients), the key biological elements (primary producers) and

indicator of effects (oxygen) of eutrophication. Six main variables have been used: relative

coverage of benthic phanerogams and opportunistic macroalgae species, and concentra-

tions of dissolved oxygen, phytoplankton chlorophyll-a, dissolved inorganic nitrogen and

phosphorus. Non-linear functions are used to transform each measured variable into its

quality value. Each quality value is then multiplied by a weighting factor, to take into

account the relative contribution of each variable to the overall water quality. Finally, the

index value is calculated as the sum of the weighted quality values, ranging from 0 (poorest

state) to 100 (best condition). The index has been tested and validated in six transitional

water ecosystems which differ in anthropogenic pressures and eutrophication levels, for

which data sets were available from 1989 to 2004: Sacca di Goro (Northern Adriatic Sea, Italy),

Lesina Lagoon (Southern Adriatic Sea, Italy), Ria Formosa (Algarve, Southern Portugal), Mar

Menor (Murcia, Southern Spain), Etang de Thau (Herault, Southern France) and Gulf of Gera

(Lesvos Island, Greece). The index assessments have been compared with evaluations from

the IFREMER (French Research Institute for the Exploitation of the Sea) classification scheme

(France) and the trophic index TRIX (Italy), which are currently used for national monitoring

of coastal waters and lagoons. Based on the conclusions of this study we suggest to use the

index for monitoring water quality in shallow coastal transitional waters, where benthic

vegetation controls primary productivity, which makes indices based on phytoplankton

only (e.g., TRIX) unsuitable.

# 2008 Elsevier Ltd. All rights reserved.

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1. Introduction

Several indicators and indices are available for assessing

trophic status and quality of aquatic ecosystems and their

evolution under different anthropogenic pressures and inher-

ent threats (Jørgensen et al., 2005; Niemeijer and de Groot,

* Corresponding author. Tel.: +39 0521 905976; fax: +39 0521 905402.E-mail address: giordani@nemo.unipr.it (G. Giordani).

1470-160X/$ – see front matter # 2008 Elsevier Ltd. All rights reservedoi:10.1016/j.ecolind.2008.11.007

2008). Nonetheless, little attention has been given to the

development of specific tools for transitional waters (TW),

despite their ecological and economical importance (Basset

et al., 2006). TW comprise shallow aquatic ecosystems, which

are highly dynamic and heterogeneous (McLusky and Elliott,

2007), thus indicators and indices developed for deeper

d.

e c o l o g i c a l i n d i c a t o r s 9 ( 2 0 0 9 ) 9 8 2 – 9 9 1 983

ecosystems (e.g., coastal marine waters and lakes) are

unsuitable for monitoring TW. Furthermore, the existing tools

specific for TW are generally currently under new develop-

ment or their validity is restricted to areas/habitats where they

have been developed (Jørgensen et al., 2005; Magni et al., 2005).

The implementation of the Water Framework Directive

(WFD) in the European Union is supporting the development of

assessment tools specific for TW, here considered as an

ecosystem distinct from coastal waters (McLusky and Elliott,

2007) and from which they have to be evaluated separately for

their ecological and chemical status (Borja, 2005). TW

monitoring requires indicators and descriptors that incorpo-

rate the specific ecosystem features, mainly shallowness and

degree of confinement. Moreover, they have to consider

ecosystem status and vulnerability to specific perturbations

such as nutrient loadings, contamination of toxic compounds

and resource exploitation, e.g., aquaculture (Rice, 2003).

Presently, aggregated indices would meet most of above

criteria, depending on the selected variables and algorithms

used in the integration processes. Among them, a series of

water quality indices (WQIs) have been developed for aquatic

ecosystems in which simple quality vectors are obtained from

several measured variables (Stambuk-Giljanovic, 1999; Cude,

2001). WQIs are user-friendly and can be easily handled in

automatic systems and computational tools (Mocenni et al., in

press).

The idea to use an integrated index that reflects the

composite influence of significant variables on water quality

was firstly proposed by Brown et al. (1970) and later improved

by the National Sanitation Foundation (McClelland, 1974). This

index, called the National Sanitation Foundation Water

Quality Index (NSFWQI), is actually used for water quality

monitoring of different U.S. water supplies. Recently, many

different types of WQIs have been developed but there is still a

need to develop new WQIs based on fewer variables which can

be used to compare sites with similar water quality char-

acteristics (Said et al., 2004).

Vollenweider et al. (1998) developed a trophic index (TRIX)

which integrates oxygen saturation, phytoplankton chloro-

phyll-a, nitrogen and phosphorus concentrations to assess the

trophic state of coastal marine waters, this is presently also

applied to coastal lagoons. TRIX is founded on the assumption

that eutrophication processes depend primarily on phyto-

plankton community, and assumes the reference system

proposed by Vollenweider et al. (1992) and Nixon (1995).

Pettine et al. (2007) recently developed a new TRIX version in

order to fulfil the WFD requirements for marine coastal waters

dominated by phytoplankton. In the last few years, other

multi-metric indices were developed including benthic com-

ponents, however, they required high numbers of variables to

be measured. A comprehensive review on indicators and tools

for assessing eutrophication and water quality in transitional

aquatic ecosystems is reported by Zaldıvar et al. (2008).

In the present study, we implemented an index that is

tailored for transitional ecosystems, adopting the WQI

approach: the Transitional Water Quality Index (TWQI). The

TWQI approach has been tested and validated at different

temporal and spatial scales in six transitional water ecosys-

tems which differ in levels of eutrophication: Sacca di Goro

and Lesina lagoons (Italy), Etang de Thau (France), Ria Formosa

(Portugal), Mar Menor (Spain) and Gulf of Gera (Greece). The

TWQI assessments were then compared with the evaluations

from the IFREMER (French Research Institute for the Exploita-

tion of the Sea) classification scheme and the TRIX index,

considering also other eight French Mediterranean lagoons

described by Souchu et al. (2000).

2. Materials and methods

2.1. Assumptions and metrics of Transitional WaterQuality Index (TWQI)

TWQI was implemented using six variables, namely: dissolved

oxygen (DO), phytoplankton chloropyll-a (Chl-a), dissolved

inorganic nitrogen (DIN) and phosphorus (DIP) concentrations

plus coverage of benthic phanerogams (Ph) and opportunistic

macroalgae (Ma) species. These variables represent the main

causal factors (inorganic nutrients), the key biological ele-

ments (primary producers) and indicator of effects (oxygen) of

eutrophication and water quality in shallow transitional

waters (Vollenweider et al., 1998; Orfanidis et al., 2003; Viaroli

et al., 2008). As for the other WQIs, non-linear functions were a

priori established and applied to transform each measured

variable into its quality value (QV). Each QV was then

multiplied by a weighting factor, to take into account the

relative contribution of each variable to the overall water

quality. Both utility functions and weighting factor were

derived from literature and expert assessment. Finally, TWQI

was calculated as the sum of the weighted QVs, ranging from 0

(poorest state) to 100 (best condition).

The QV assigned to DO (QVDO) increased from 0 to 100,

spanning complete anoxia to 100–125% oxygen saturation

(Fig. 1a). DO values higher than 125%, common in highly

productive transitional ecosystems, were also considered

critical, since oversaturation is often coupled to phytoplank-

ton or macroalgal biomass accumulation. The accumulated

biomass fuels respiration processes, which in turn can lead to

complete anoxia (Viaroli and Christian, 2003). Therefore, at

oxygen saturation greater than 125%, QVDO decreased with

QVDO = 0 at 250%, according to previous studies (Vollenweider

et al., 1998; Stambuk-Giljanovic, 1999; Cude, 2001).

Chl-a is a measure of the active phytoplankton biomass,

although the cellular Chl-a content is often species-specific

and depends on the physiological status of phytoplankton

cells (Felip and Catalan, 2000). In this study, we used

thresholds and fixed boundaries from common classification

criteria for eutrophication (Vollenweider and Kerekes, 1982).

QVchla = 0 was attained at concentrations Chl-a > 30 mg m�3,

whilst optimal conditions (QVchla = 100) were assigned to Chl-

a < 6 mg m�3 (Fig. 1b). The concentration range proposed here

is typical of Mediterranean coastal lagoons and continental

estuaries (Giordani et al., 2005; EPA, 2005).

DIN and DIP concentrations result from external loadings

and internal recycling and have been widely used as criteria

for assessing trophic status in lentic water bodies (Vollen-

weider and Kerekes, 1982). DIN is now recognised as the

main driver of coastal eutrophication, whilst DIP is often

assumed as the main limiting factor (de Jonge et al., 2002;

Howarth and Marino, 2006). For this reason, simple models

e c o l o g i c a l i n d i c a t o r s 9 ( 2 0 0 9 ) 9 8 2 – 9 9 1984

have been developed which allow for estimating the net

ecosystem metabolism from DIP as a measures of lagoon

trophic potential (Giordani et al., 2008a). Utility functions for

DIN and DIP were established considering the main criteria

for trophic status classification (Vollenweider and Kerekes,

1982; Vollenweider et al., 1998). QVDIN was assumed to be

inversely related to DIN concentrations with QVDIN = 100 at

DIN = 0 mM and QVDIN = 0 at DIN > 100 mM (Fig. 1c). The most

significant decrease of QVDIN was imposed in the 0–20 mM

range, because the main transformation in the productivity

assets usually occurs within this range (Viaroli et al., 2008).

Fig. 1 – Relationships among analytical measurements of (a) di

dissolved inorganic and total nitrogen (DIN-TN), (d) dissolved in

coverage (Ma), (f) phanerogam coverage (Ph) and respective TW

calculation.

Moreover, other classification schemes report DIN = 20 mM

as a critical threshold for coastal lagoons (Souchu et al.,

2000; EPA, 2005). A similar utility function was set for DIP,

with QVDIP = 100 at DIP = 0 mM and QVDIP = 0 at DIP > 6 mM

(Fig. 1d). The use of total nitrogen (TN) and phosphorus (TP),

in place of DIN and DIP, would be recommended if such data

are available, because dissolved organic and particulate

species can greatly contribute to the nitrogen and phos-

phorus bulk. Utility functions for TN and TP follow similar

patterns as for DIN and DIP, except for the wider ranges

(Fig. 1c and d).

ssolved oxygen saturation (DO), (b) chlorophyll-a (Chl-a), (c)

organic and total phosphorus (DIP-TP), (e) macroalgal

QI Q values (QV). wf: weighting factors used in TWQI

Table 1 – Trophic state, geographical and morphometric data of the TW ecosystems considered in this study. t: theoreticalwater residence time.

System Country Symbol Latitudeand longitude

t (d) Area(km2)

Meandepth (m)

Trophiclevel

Reference

Sacca di Goro Italy SG 44.78–44.848N 3 26 1.5 Very high Viaroli et al. (2006)

12.26–12.398E

Lagoon of Lesina Italy LE 41.85–41.928N 100 52 0.8 Low Manini et al. (2005)

15.31–15.578E

Ria Formosa Portugal RF 36.96–37.168N 1 105 3.5 Medium Newton and

Mudge (2005)

08.25–07.518W

Mar Menor Spain MM 37.63–37.828N 190 135 3.6 High Perez-Ruzafa

et al. (2005)

00.72–00.868W

Etang du Thau France ET 43.33–43.468N 56 75 4.5 Medium Plus et al. (2006)

03.53–03.708E

Gulf of Gera Greece GG 39.00–39.128N 8 43 10 Very low Arhonditsis et al. (2003)

26.44–26.538E

e c o l o g i c a l i n d i c a t o r s 9 ( 2 0 0 9 ) 9 8 2 – 9 9 1 985

The main functional groups of the benthic vegetation

community and their relative coverage were considered for

TWQI, assuming that each of them was associated with

different stages of the eutrophication development in TW

(Orfanidis et al., 2003; Nielsen et al., 2004; Hauxwell and

Valiela, 2004). The general assumption was that phanerogams

prevailed in pristine and unaltered ecosystems, whilst

opportunistic macroalgae species became dominant in

eutrophic and dystrophic TW (Schramm, 1999; Viaroli et al.,

2008). The opportunistic macroalgae species considered in this

study were among those listed in the ESGII group of the

Ecological Evaluation Index (Orfanidis et al., 2003). We

considered the presence and abundance of the functional

groups as sufficient to discriminate the trophic status,

although we recognize that the eco-physiological conditions

of benthic vegetation could add information on buffering

capacity, resistance and resilience of benthic community

(Juanes et al., 2008). The coverage by macroalgae and

phanerogams (Ph) was expressed as % of surface area

colonised by a permanent meadow/stand using an ordinal

transform scale based on an extended Braun-Blanquet cover-

abundance scale (Braun-Blanquet, 1964). Both macroalgae and

phanerogams are known to out-compete phytoplankton, thus

measures of their density and biomass were considered as

complementary to Chl-a measurements. Furthermore, we

assumed that the maximum coverage by benthic vegetation

could not exceed 80% of the total lagoon surface, 20% being

accounted for as unsuitable areas, e.g. deep canals, intertidal

mudflats, etc. For this reason, QVMa = 0 was assigned to a

macroalgal coverage >80% (Fig. 1e). Conversely, the highest

QVMa was assigned to a coverage range <10%, assuming that

small amounts of opportunistic macroalgae can be found

under pristine conditions. The utility function representing

phanerogam coverage was set symmetric of that of macro-

algae, with QVPh = 100 corresponding to 80% coverage, and

QVPh = 0 at 0–10% coverage (Fig. 1f).

Weighing factors were selected based on the ecological

relevance of the considered variables. The highest values (0.23)

were set for benthic vegetation, assuming that it represented

the main driver of the lagoon water quality and trophic status

(Viaroli et al., 2008). A lower weight (0.15) was used for

phytoplankton chlorophyll-a, phytoplankton being less

important due to the shallow depth. Also DO was rated with

0.15, assuming that it depends primarily on benthic vegetation

and phytoplankton. The lowest weight (0.12) was assigned to

dissolved nutrients, as they represent the causal factor of

vegetation status rather than a direct estimate of trophic

status and water quality. DIN and DIP concentrations are also

linked to several biogeochemical processes.

TWQI was then obtained as the sum of weighted QVs. The

sum was considered more appropriate than the unweighted

harmonic mean (Cude, 2001), because QVs close to zero – as

often occurs for QVMa or QVPh – can result in unsuitable values

(TWQI < 1).

2.2. Data sources for TWQI testing

TWQI was tested in six coastal lagoons in the Southern

European Arc, which differ in trophic status and water quality,

namely Sacca di Goro (SG), Lagoon of Lesina (LE), Etang du

Thau (ET), Ria Formosa (RF), Mar Menor (MM) and Gulf of Gera

(GG) (Table 1).

A detailed analysis of spatial and temporal variations of

TWQI was performed in SG. Complete datasets for TWQI

applications to the whole SG were available for seven periods

from 1991 to 1994. QVs were obtained from averages of

variables measured at eight stations representative of the

lagoon (Colombo et al., 1994) and benthic vegetation coverage

was obtained from Viaroli et al. (2006).

A more detailed analysis of the seasonal evolution of TWQI

in SG was performed for a fixed station (st. 17) located in the

confined eastern sub-basin of the lagoon where huge blooms

of floating macroalgae occurred (Viaroli et al., 2006).

TWQI and TRIX responses were tested and compared with

the 0D biogeochemical model developed for SG by Zaldıvar

et al. (2003a,b). Simulations were run with input data from

1997, which represented the average meteorological condi-

tions, nutrient loads and water flushing values of the last two

Fig. 2 – TWQI values estimated for the whole Sacca di Goro

Lagoon on 18/6/91, 27/11/91, 5/5/92, 27/10/92, 24/11/93, 16/

3/94, 7/6/94. Legend as Fig. 1.

e c o l o g i c a l i n d i c a t o r s 9 ( 2 0 0 9 ) 9 8 2 – 9 9 1986

decades (Viaroli et al., 2005, 2006). Additional data were

provided by the Regional Environmental Protection Agency

(http://www.arpa.emr.it) and the Coastal Waters and Fishery

unit of the Province of Ferrara (http://www.provincia.fe.it/

acquecostiere).

In spring and summer 2004, TWQI was further compared

between SG and LE. Four stations were selected in each lagoon

in order to discriminate among the main pressures and

trophic conditions (Giordani et al., 2008b). In LE, station LE1

was close to the town of Lesina, and had bare sediment with

high nutrient concentrations in the water column. Station LE2

was mainly influenced by marine water inflow. Stations LE3

and LE4 were colonized by dense meadows of Zostera noltii and

Ruppia cirrhosa. In SG lagoon, station SG1 was located in the

plume zone of the Po di Volano river, station SG2 was in the

central part of the lagoon, where mussel farming was

performed until 2002, station SG3 was close to the sea mouth

in a sandy area exploited for clam farming, and station SG4

was located in a sheltered and muddy-sand zone impacted by

both clam farming and macroalgal blooms.

A wider comparison was performed among LE, SG, RF, MM,

ET and GG. The data source for the latter five lagoons was the

EU-funded DITTY project (Aliaume et al., 2007; Giordani et al.,

2008b; Table 1).

Relationships between TWQI and IFREMER quality scheme

and TWQI and TRIX were analysed for RF, MM, ET, SG, GG and

for additional 13 sites in 8 coastal lagoons located in the

Southern Mediterranean French coast, namely: Etang de l’Or

(E, W), Grec, Ingril (N, S), Bages (N, C, S), Campignol, l’Ayrolle,

Gruissan and Leucate (N, S). Details on the IFREMER

Fig. 3 – Seasonal evolution of (a) dissolved oxygen (DO), (b) phy

nitrogen (DIN), (d) dissolved inorganic phosphorus (DIP), (e) mac

17 of the Sacca di Goro lagoon from 1990 to 1992. (f) comparison

nitrogen and phosphorus (DIN,DIP) and total nitrogen and phos

classification scheme and the French lagoons considered in

this study are reported by Souchu et al. (2000).

3. Results

3.1. TWQI estimations in the Sacca di Goro lagoon

The TWQI evaluation of the whole SG lagoon for the 1991–1994

period is shown in Fig. 2. TWQI spanned 39–48 in the May–June

periods when macroalgal blooms occurred. Values slightly

toplankton chlorophyll-a (Chl-a), (c) dissolved inorganic

roalgal coverage (Ma) and the respective Q values at station

between TWQI estimations based on dissolved inorganic

phorus (TN,TP).

Fig. 4 – Annual evolution of Ulva coverage, TWQI and TRIX

values simulated with the 0D model of Zaldıvar et al.

(2003a,b) for a normal year in the Sacca di Goro lagoon.

The TRIX scale is reversed to have higher quality values

upwards for both indexes.

e c o l o g i c a l i n d i c a t o r s 9 ( 2 0 0 9 ) 9 8 2 – 9 9 1 987

higher (60 < TWQI < 64) were found in the other periods.

TWQI was very sensitive to macroalgal blooms, whilst

phanerogams had negligible effects, as they have nearly

disappeared since the late 80s. QVs of the other variables

underwent opposite patterns. The lowest QVDIN and QVDIP

were attained in autumn and winter (i.e., highest concentra-

tions), whilst DO and Chl-a had low QVs in spring and

summer.

The contribution of individual QVs to TWQI and their

timing over three years (1990–1992) were analyzed at station 17

in SG (Fig. 3). Overall, TWQI was driven by macroalgal biomass,

with water quality deterioration during bloom events and

subsequent dystrophic outbreaks. The total TWQI was kept

low by QVMa in spring and by QVDO and QVDIP in early summer.

Summer phytoplankton blooms also affected TWQI with

QVchla close to zero. By contrast, in late autumn and winter,

TWQI was influenced by high DIN loadings with very low

QVDIN. Basically, one can argue that TWQI responses to the

within system variability were robust and captured the

Fig. 5 – TWQI values in 4 stations of the SG in May (M) and Augus

and, in the right box, TWQI values based on annual mean valu

(ET98), SG in 1990–1993 (SG90-93) and GG in 1996 (GG96). Legen

essential dynamics of the water quality and trophic status.

Water quality varied seasonally with 25 < TWQI < 75. The

lowest values were detected in early summer each year.

Overall, these findings conformed to previous assessments

achieved with conventional descriptors, namely community

productivity and respiration (Viaroli and Christian, 2003;

Viaroli et al., 2005) and sedimentary biogeochemical indicators

(Azzoni et al., 2005; Giordani et al., 2008b).

The 0D model runs for 1997 also provided evidence that

under typical meteorological and hydrological conditions

TWQI ranged from 42 to 65 (Fig. 4), which was close to values

estimated from experimental data (Fig. 2). As usual, the lowest

TWQI occurred in June, during the early phase of the

dystrophic outbreak (Viaroli et al., 2005). QVMa, QVcha and

QVDO were the main drivers of water quality in the lagoon.

QVMa and QVcha were complementary, whilst QVDO was

mainly associated with QVMa.

3.2. TWQI application to lagoons with differenteutrophication levels

The comparison of SG and LE provide evidence of clear

differences between lagoons, with 40 < TWQI< 70 in SG and

TWQI> 70 in LE, in agreement with the expected trophic status

of the two ecosystems (Fig. 5; Table 1). Differences between

lagoons were greater insummer, when inSG TWQIdecreased to

40 at SG1, SG2 and SG4, due to phytoplankton and Ulva blooms,

whilst at SG3, TWQI was almost constant, likely due to marine

water flushing. InLE,noseasonal differences were found,whilst

within-lagoon differences were observed with lower TWQI at

LE1 and LE2, these stations receiving sewage from the urban

area of Lesina, and TWQI peaks up to 98 at LE3 and LE4, where

large meadows of benthic phanerogams developed out-com-

peting phytoplankton and leading to oxygen saturation.

The sensitivity of TWQI was assessed through its applica-

tion in six lagoons with a wide range of trophic conditions

(Table 1). The highest TWQI were found in GG (TWQI = 99), RF

(TWQI = 95) and LE (TWQI = 85), which conformed to hydro-

dynamics and the healthy status of benthic vegetation (Fig. 5).

t (A) 2004, in 4 stations of the LE in May (M) and July (J) 2004

es for RF in 1999 (RF99), MM in 1988 (MM88), ET in 1998

d as Table 1.

e c o l o g i c a l i n d i c a t o r s 9 ( 2 0 0 9 ) 9 8 2 – 9 9 1988

Intermediate conditions were observed in ET (TWQI = 77),

which was assessed as mesotrophic, being colonized by large

meadows of Z. marina and Z. noltii, but with an increasing

impact of chlorophyceans (Plus et al., 2005). Lower TWQI was

estimated for MM (TWQI = 68) due to high DIP concentrations

and significant losses of benthic vegetation following the huge

development of the urban area, tourism and agriculture. The

worst conditions were found in SG (TWQI = 52), conforming to

the observations reported above.

Fig. 7 – Relationships between TRIX and TWQI for systems

not colonized by benthic vegetation (bare) and colonized

by macroalgae (Ma) or phanerogams (Ph) with coverage

>10%. Data relative to the systems considered in this study

listed in Fig. 5. TRIX scale is reversed to have the higher

quality values upwards for both indexes.

4. Discussion and conclusions

4.1. Comparison of TWQI with other indices

TWQI was compared with the IFREMER classification scheme

and with TRIX index. The IFREMER classification scheme was

implemented as an operational tool to assess the eutrophica-

tion level in French Mediterranean lagoons (Souchu et al.,

2000). This tool adopted a classification grid with five classes,

spanning ‘‘very good’’ to ‘‘very bad’’, according to the EU Water

Framework Directive (2000/60/EC). The IFREMER tool com-

prises of 21 descriptors referring to phytoplankton, macro-

phytes, macro-zoobenthos, sediment and water column. A

simplified version was also developed and applied to the

lagoons listed in Table 1, except for LE (Austoni et al., 2004). On

average TWQI was directly comparable with the IFREMER

quality classes, with RF as an exception, it being only included

in the IFREMER ‘‘sufficient’’ class, whereas, it obtained a high

TWQI value (Fig. 6). The IFREMER classification is based on the

worst partial value among the descriptors mentioned above.

Although water quality was good on average, in summer

ammonium concentrations significantly increased causing a

decrease from good to sufficient status (Duarte et al., 2007). A

similar scenario was obtained by comparing summer TWQI

with the IFREMER classification in 13 sites of 8 Mediterranean

coastal lagoons (Souchu et al., 2000). A good agreement was

found between IFREMER classes and TWQI, except for the sites

Fig. 6 – Relationship between TWQI values and IFREMER

quality classes estimated for DITTY sites from Austoni

et al. (2004) and 13 sites of 8 Mediterranean French Coastal

lagoons for water column compartment in summer 1999,

Souchu et al. (2000). IFREMER scheme classification ranges

from 1 to 5 which correspond to bad (1), sufficient (2),

discrete (3), good (4) and very good (5) conditions. Legend

as Table 1.

with the lowest quality (Fig. 6). Once again, differences

between tools depended on the different metrics, the IFREMER

approach being more restrictive and driven by single variables,

whilst TWQI was based on the integration of all the considered

variables.

TRIX integrates several state variables, mainly with linear

metrics (Vollenweider et al., 1998). The main assumption is

that eutrophication depends primarily on phytoplankton

community (Vollenweider et al., 1992; Nixon, 1995). Therefore,

TRIX and TWQI have similar data requirements, except for

benthic vegetation, which is used only in TWQI, and Secchi

depth which is used only in TRIX as an optional parameter.

Thus both indices can be applied using the same datasets with

some integrations. Responses of TRIX and TWQI show good

agreements for sites without benthic vegetation (bare sedi-

ments), whilst when benthic vegetation is present the two

indices give contrasting assessments, with TWQI peaks

coinciding with low TRIX values (Fig. 7). Basically, the

difference is due to the inclusion of benthic vegetation in

TWQI, which is not considered in TRIX, it being developed for

deep coastal waters rather than for shallow TW. In TW the

dominance of benthic vegetation within the primary producer

community can alter the TRIX response, due to oxygen

production and nutrient uptake by macroalgae (Viaroli and

Christian, 2003; Viaroli et al., 2005). For example, lagoons with

high Ulva biomass, which is detrimental for ecosystems

quality, can be rated as ‘‘good status’’ by TRIX due to high

oxygen, and low DIN and chlorophyll-a concentrations. TWQI,

which includes macroalgal coverage as a negative factor, can

lead to the opposite status, capturing the community

degeneration that can be caused by the occurrence of blooms

of opportunistic macroalgal species (Nielsen et al., 2004;

Viaroli et al., 2008). The different responses of TWQI and TRIX

can be more clearly shown using 0D model simulations

(Fig. 4). Whilst the TWQI signals a progressive water quality

e c o l o g i c a l i n d i c a t o r s 9 ( 2 0 0 9 ) 9 8 2 – 9 9 1 989

deterioration following macroalgal growth, TRIX indicates an

improvement of water quality during the growth season of

macroalgae. Furthermore, TWQI seems to better represent the

most critical period when the macroalgal biomass begin to

decompose and dystrophic crisis have the highest probability

of occuring. Also TRIX is less sensitive to changes driven by the

benthic component of the system and remains rather constant

when the benthic community undergoes sudden changes due

to the collapse of macroalgal stands. The main reason is that

TRIX does not contain indicators of benthic metabolism,

which are included in TWQI. Due to its structure, which is

tailored for benthic dominated TW, TWQI is less sensitive to

changes in nutrient and chlorophyll-a concentrations, which

can be induced by external inputs to TW. This behaviour effect

can be seen from late summer onwards when water quality is

mainly affected by DIN concentrations.

4.2. Reliability of TWQI for monitoring TW ecosystems

TWQI seems suitable for monitoring eutrophication processes

in transitional water ecosystems, where, due to the shallow

depth benthic vegetation controls primary productivity,

making ineffective indices based on phytoplankton only.

TWQI integrates the main causal factors (inorganic nutrients),

the key biological elements (primary producers) and an

indicator of eutrophication effects (oxygen). The metrics we

have adopted are simple and allow the contribution of each

component of the index to be taken into account, thus

capturing the intrinsic variability of each of them. The utility

functions we adopted also consider the potential variability of

index components. For example, oxygen concentration is

extremely sensitive to temporal changes due to the natural

dynamics of primary productivity and community respiration

(Viaroli and Christian, 2003). Thus, instantaneous oxygen

concentrations are not suited for assessing the oxygenation

status of a given aquatic ecosystem. Nonetheless, concentra-

tions measured around mid-day may be considered as a good

approximation of oxygen availability, as when oxygen

saturation is either low or very high, an oxygen deficit can

be predicted within the system (see Fig. 1a). Repeated

measurements throughout day-night cycles as well as auto-

mated equipments will ensure better assessment, but with

higher costs.

Cross measurements of oxygen concentration and benthic

vegetation or phytoplankton chlorophyll-a will highlight the

tendency of the system to become anoxic; TW with macro-

algae being more sensitive to oxygen consumption (de Wit

et al., 2001; Viaroli et al., 2008). However, one has to avoid the

risk of tautology, i.e., of using TWQI for assessing the status of

its own components, e.g., benthic vegetation (Bortone, 2005).

Inorganic nutrient species are per se not informative of

water quality in so shallow ecosystems. Nevertheless they can

be used not only for setting boundaries of potential trophic

status, but also for estimating the net ecosystem metabolism

(NEM), which is obtained from DIP concentrations (Giordani

et al., 2008a). However, relationships between TWQI and NEM

are still unclear (data not shown), probably due to the number

of assumptions made for estimating both indices. Further-

more, a much larger data set would be required for testing

possible correlations.

When using simple metrics, large and heterogeneous

datasets are likely to be generated; thus the final assessment

of environmental quality is often made considering large

intervals of variation, thus loosing much of the detailed

information collected (Viaroli et al., 2004). This is particularly

true for TW ecosystems, as has been discussed above, where

few specific indicators are available and, where often, those

that have been developed in other types of water bodies (rivers

and coastal waters) are manipulated to be used in TW.

We believe that the use of simple indices such as TWQI are

meaningful for rapid assessments and repeated surveillance

programs rather than for highly detailed quality evaluations.

Detailed quality assessments require more sensitive tools,

which are clearly very difficult to apply at large scale and over

repeated surveys (because of the need of highly trained

specialists, expensive instrumentation and due to their

generally time consuming application). Finally rapid and

simple assessment tools are especially useful in systems

having long time series of measurements of a few selected

variables of high ecological relevance.

Acknowledgements

This research was partially supported by the European

Commission under contract n8 EVK3-CT-2002-00084 ‘‘Devel-

opment of an Information Technology Tool for the Manage-

ment of European Southern Lagoons under the influence of

river-basin runoff (DITTY)’’ and by the Italian Ministry of

Research and Education under the PRIN project ‘‘Nuovi

Indicatori di stato Trofico e d’Integrita ecologica Di Ambienti

marini costieri e ambienti di transizione (NITIDA)’’. We are

very indebted to Nicholas Murray and the two anonymous

referees for reviewing and commenting on the manuscript.

r e f e r e n c e s

Aliaume, C., Do Chi, T., Viaroli, P., Zaldıvar, J.M., (Eds.), 2007.Coastal lagoons of Southern Europe: Recent changes andfuture scenarios. Transit. Waters Monogr. 1, 127 pp.

Arhonditsis, G., Eleftheriadou, M., Karydis, M., Tsirtsis, G., 2003.Eutrophication risk assessment in coastal embaymentsusing simple statistical models. Mar. Pollut. Bull. 46,1174–1178.

Austoni, M., Viaroli, P., Giordani, G., Zaldivar, J.M., 2004.Intercomparison among the test sites of the DITTY Projectusing the IFREMER classification scheme for coastallagoons. European Communities, EUR report n. 21286 EN,52 pp.

Azzoni, R., Giordani, G., Viaroli, P., 2005. Iron-sulfur-phosphorusinteractions: implications for sediment buffering capacityin a mediterranean eutrophic lagoon (Sacca di Goro, Italy).Hydrobiologia 550, 131–148.

Basset, A., Sabetta, L., Fonnesu, A., Mouillot, D., Do Chi, T.,Viaroli, P., Giordani, G., Reizopoulou, S., Abbiati, M.,Carrada, G.C., 2006. Typology in Mediterranean transitionalwaters: new challenges and perspectives. Aquat. Conserv.16, 441–455.

Borja, A., 2005. The European water framework directive: Achallenge for nearshore, coastal and continental shelfresearch. Cont. Shelf Res. 25, 1768–1783.

e c o l o g i c a l i n d i c a t o r s 9 ( 2 0 0 9 ) 9 8 2 – 9 9 1990

Bortone, S.A., 2005. Estuarine Indicators. CRC Marine SciencesSeries. CRC Press, 531 pp.

Braun-Blanquet, J., 1964. Pflanzensoziologie, Grundzuge derVegetationskunde, 3, Aufl., Wien (in German).

Brown, R.M., McClelland, N., Deininger, R.A., Tozer, R.G., 1970. Awater quality index: Do we dare? Water Sewage Works 117,339–343.

Colombo, G., Bisceglia, R., Zaccaria, V., Gaiani V., 1994.Variazioni spaziali e temporali delle caratteristiche fisico-chimiche delle acque e della biomassa fitoplanctonica nellaSacca di Goro. In: Bencivelli, S., Castaldi, N., (Eds.), StudioIntegrato sull’ecologia della Sacca di Goro, 28 anno diricerche, Provincia di Ferrara, FrancoAngeli editore, pp. 9–82(in Italian).

Cude, C.G., 2001. Oregon Water Quality Index: a tool forevaluating water quality management effectiveness. J. Am.Water Resour. As. 37, 125–137.

de Jonge, V.N., Elliott, M., Orive, E., 2002. Causes, historicaldevelopment, effects and future challenges of a commonenvironmental problem: eutrophication. Hydrobiologia 475/476, 1–19.

de Wit, R., Stal, L.J., Lomstein, B.A., Herbert, R.A., van Gemerden,H., Viaroli, P., Ceccherelli, V.U., Rodrıguez-Valera, F., Bartoli,M., Giordani, G., Azzoni, R., Shaub, B., Welsh, D.T., Donnely,A., Cifuentes, A., Anton, J., Finster, K., Nielsen, L.B.,Underlien Pedersen, A.G., Neubauer, A.T., Colangelo, M.A.,Heijs, S.K., 2001. ROBUST: The ROle of BUffering capacitiesin STabilising coastal lagoon ecosystems. Cont. Shelf Res.21, 2021–2041.

Duarte, P., Azevedo, B., Ribeiro, C., Pereira, A., Falcao, M., Serpa,D., Bandeira, R., Reia, J., 2007. Management orientedmathematical modelling of Ria Formosa (South Portugal).Transit. Waters Monogr. 1, 13–51.

EPA, 2005. National Coastal Condition, Report II. Report of U.S.Environmental Protection Agency, 286 pp. Available at:http://www.epa.gov/owow/oceans/nccr/.

Felip, M., Catalan, J., 2000. The relationship betweenphytoplankton biovolume and chlorophyll in a deepoligotrophic lake: decoupling in their spatial and temporalmaxima. J. Plankton Res. 22, 91–105.

Giordani, G., Viaroli, P., Swaney, D.P., Murray, C.N., Zaldıvar,J.M., Marshall Crossland, J.I., (Eds.), 2005. Nutrient fluxes intransitional zones of the Italian coast. LOICZ Reports &Studies No. 28, LOICZ, Texel, the Netherlands, ii + 157 pp.

Giordani, G., Austoni, M., Zaldıvar, J.M., Swaney, D.P., Viaroli, P.,2008a. Modelling ecosystem functions and properties atdifferent time and spatial scales in shallow coastal lagoons:an application of the LOICZ biogeochemical model. Estuar.Coast. Shelf S. 77, 264–277.

Giordani, G., Azzoni, R., Viaroli, P., 2008b. A rapid assessment ofthe sedimentary buffering capacity towards free sulphides.Hydrobiologia 611, 55–66.

Hauxwell, J., Valiela, I., 2004. Effects of nutrient loading onshallow seagrass-dominated coastal systems: patterns andprocesses. In: Nielsen, S.L., Banta, G.T., Pedersen, M.F.(Eds.), Estuarine Nutrient Cycling: The Influence of PrimaryProducers. Kluwer Academic Publishers, Dordrecht, TheNetherlands, pp. 59–92.

Howarth, R.W., Marino, R., 2006. Nitrogen as the limitingnutrient for eutrophication in coastal marine ecosystems:Evolving views over three decades. Limnol. Oceanogr. 51,364–376.

Jørgensen, S.E., Constanza, R., Xu, F.L. (Eds.), 2005. Handbook ofEcological Indicators for Assessment of Ecosystem Health.CRC Press, Boca Raton, FL, p. 439 pp.

Juanes, J.A., Guinda, X., Puente, A., Revilla, J.A., 2008.Macroalgae, a suitable indicator of the ecological status ofrocky communities in the NE Atlantic. Ecol. Indic. 8,351–359.

Magni, P., Hyland, J., Manzella, G., Rumhor, H., Viaroli, P.,Zenetos, A., (Eds.), 2005. Indicators of stress in the marinebenthos. Proceedings of the Workshop ‘‘Indicators of Stressin the Marine Benthos’’, Torregrande-Oristano (IT). 8–9October 2004. IOC Workshop Reports 195, iv + 45 pp.

Manini, E., Breber, P., D’Adamo, R., Spagnoli, F., Danovaro, R.,2005. Lagoon of Lesina. In: Giordani, G., Viaroli, P., Swaney,D.P., Murray, C.N., Zaldıvar, J.M., Marshall Crossland, J.I.,(Eds.), Nutrient fluxes in transitional zones of the Italiancoast. LOICZ Reports & Studies No. 28, LOICZ, Texel, TheNetherlands, pp. 49–54.

McClelland, N.I., 1974. Water Quality Index Application in theKansas River Basin, US Environmental Protection Agency,Kansas City, MO, EPA-907/9-74-001.

McLusky, D.S., Elliott, M., 2007. Transitional waters: A newapproach, semantics or just muddying the waters? Estuar.Coast. Shelf S. 71, 359–363.

Mocenni, C., Casini, M., Paoletti, S., Giordani, G., Viaroli, P.,Zaldıvar, J.M. A decision support system for themanagement of the Sacca di Goro (Italy). In: Marcomini, A.,Suter II, G.W., Critto, A., (Eds.), Decision Support Systems forRisk Based Management of Contaminated Sites, in press.

Newton, A., Mudge, S.M., 2005. Lagoon-sea exchanges, nutrientdynamics and water quality management of the RiaFormosa (Portugal). Estuar. Coast. Shelf S. 62, 405–414.

Nielsen, S.L., Banta, G.T., Pedersen, M.F. (Eds.), 2004. EstuarineNutrient Cycling: The Influence of Primary Producers.Kluwer Academic Publishers, Dordrecht, The Netherlands,p. 303 pp.

Niemeijer, D., de Groot, R.S., 2008. A conceptual framework forselecting environmental indicator sets. Ecol. Indic. 8, 14–25.

Nixon, S.W., 1995. Coastal marine eutrophication: a definition,social causes and future concerns. Ophelia 41, 199–219.

Orfanidis, S., Panayotidis, P., Stamatis, N., 2003. An insight tothe ecological evaluation index (EEI). Ecol. Indic. 3, 27–33.

Perez-Ruzafa, A., Fernandez, A., Marcos, C., Gilabert, J., Quispe,J.I., Garcıa-Charton, J.A., 2005. Spatial and temporalvariations of hydrological conditions, nutrients andchlorophyll a in a Mediterranean coastal lagoon (MarMenor, Spain). Hydrobiologia 550, 11–27.

Pettine, M., Casentini, B., Fazi, S., Giovanardi, F., Pagnotta, R.,2007. A revisitation of TRIX for trophic status assessment inthe light of the European Water Framework Directive:Application to Italian coastal waters. Mar. Pollut. Bull. 54,1413–1426.

Plus, M., Auby, I., Verlaque, M., Levavasseur, G., 2005. Seasonalvariations in photosynthetic irradiance response curves ofmacrophytes from a Mediterranean coastal lagoon. Aquat.Bot. 81, 157–173.

Plus, M., La Jeunesse, I., Bouraoui, F., Zaldıvar, J.M., Chapelle, A.,Lazure, P., 2006. Modelling water discharges and nitrogeninputs into a Mediterranean lagoon. Impact on the primaryproduction. Ecol. Model. 193, 69–89.

Rice, J., 2003. Environmental health indicators. Ocean Coast.Manage. 46, 235–259.

Said, A., Stevens, D.K., Sehlke, G., 2004. An Innovative Index forEvaluating Water Quality in Streams. Environ. Manage. 34,406–414.

Schramm, W., 1999. Factors influencing seaweed responses toeutrophication: some results from EU-project EUMAC. J.Appl. Phycol. 11, 69–78.

Souchu, P., Ximenes, M.C., Lauret, M., Vaquer, A., Dutriex, E.,2000. Mise a jour d’indicateurs du niveau d’eutrophisationdes milieux lagunaires mediterraneens, Ifremer-Creocean-Universite Montpellier II, 412 pp. (in French).

Stambuk-Giljanovic, N., 1999. Water quality evaluation by indexin Dalmatia. Water Res. 33, 3423–3440.

Viaroli, P., Christian, R.R., 2003. Description of trophic status ofan eutrophic coastal lagoon through potential oxygen

e c o l o g i c a l i n d i c a t o r s 9 ( 2 0 0 9 ) 9 8 2 – 9 9 1 991

production and consumption: defining hyperautotrophyand dystrophy. Ecol. Indic. 3, 237–250.

Viaroli, P., Bartoli, M., Giordani, G., Magni, P., Welsh, D.T., 2004.Biogeochemical indicators as tools for assessing sedimentquality/vulnerability in transitional aquatic ecosystems.Aquat. Conserv. 14, S19–S29.

Viaroli, P., Bartoli, M., Azzoni, R., Giordani, G., Mucchino, C.,Naldi, M., Nizzoli, D., Taje, L., 2005. Nutrient and ironlimitation to Ulva blooms in an eutrophic coastal lagoon(Sacca di Goro, Italy). Hydrobiologia 550, 57–71.

Viaroli, P., Giordani, G., Bartoli, M., Naldi, M., Azzoni, R., Nizzoli,D., Ferrari, I., Zaldıvar, J.M., Bencivelli, S., Castaldelli, G.,Fano, E.A., 2006. The Sacca di Goro lagoon and an arm of thePo River. In: Wangersky, P.J., (Ed.), The Handbook ofEnvironmental Chemistry. Volume H: Estuaries Springer-Verlag GmbH, Berlin, pp. 197–232.

Viaroli, P., Bartoli, M., Giordani, G., Naldi, M., Orfanidis, S.,Zaldivar, J.M., 2008. Community shifts, alternative stablestates, biogeochemical controls and feedbacks in eutrophiccoastal lagoons: a brief overview. Aquat. Conserv. 18,S105–S117.

Vollenweider, R.A., Kerekes, J.J., 1982. Eutrophication of Waters,Monitoring Assessment and Control. O.E.C.D. Paris, 154 pp.

Vollenweider, R.A., Rinaldi, A., Montanari, G., 1992.Eutrophication, structure and dynamics of a marinecoastal system: results of a ten year monitoring along

the Emilia-Romagna coast (Northwest Adriatic Sea). In:Vollenweider, R.A., Marchetti, R., Viviani, R. (Eds.), MarineCoastal Eutrophication, Proceedings of an InternationalConference, Bologna Italy 21–24 March 1990, pp.63–106.

Vollenweider, R.A., Giovanardi, F., Montanari, G., Rinaldi, A.,1998. Characterization of the trophic conditions of marinecoastal waters with special reference to the NW AdriaticSea: proposal for a trophic scale, turbidity and generalizedwater quality index. Environmetrics 9, 329–357.

Zaldıvar, J.M., Cattaneo, E., Plus, M., Murray, C.N., Giordani, G.,Viaroli, P., 2003a. Long-term simulation of mainbiogeochemical events in a coastal lagoon: Sacca di Goro(Northern Adriatic Coast, Italy). Cont. Shelf Res. 23,1847–1875.

Zaldıvar, J.M., Plus, M., Giordani, G., Viaroli, P., 2003b. Modellingthe impact of clams in the biogeochemical cycles of aMediterranean lagoon. In: Ozhan, E. (Ed.), Proceedings ofthe Sixth International Conference on the MediterraneanCoastal Environment, MEDCOAST 03, 7–11 October 2003,Ravenna, Italy, pp. 1291–1302.

Zaldıvar, J.M., Cardoso, A.C., Viaroli, P., Newton, A., De Wit, R.,Ibanez, C., Reizopoulou, S., Somma, F., Razinkovas, A.,Basset, A., Holmer, M., Murray, N.C., 2008. Eutrophication intransitional waters: an overview. Transit. Waters Monogr. 2,1–78.