Cristina Munari

Michele Mistri
email: michele.mistri@unife.it

Effect of copper on the scope for growth of clams (Tapes philippinarum) from a farming area in the Northern Adriatic Sea

Keywords: Antifouling Paints, Copper, Scope for Growth, Tapes philippinarum, Adriatic Sea

Copper is currently the most common biocide in antifouling paints. Levels of this metal were measured in the water, particulate matter and sediments from a shellfish farming area in the Sacca di Goro (Northern Adriatic Sea) over one year. With respect to the 1980s, copper environmental level increased two-fold. The release of copper from shellfish farmer boats was also estimated to be > 250 kg Cu y -1 . Clams Tapes philippinarum were collected in the same area and seasonally exposed to a sublethal (10 µgCu l -1 ) concentration of copper.

Physiological traits were significantly affected by copper exposure (scope for growth declined as a result of reduced clearance rate, increased oxygen consumption and a generally lower absorption efficiency). Results of this study are cause for concern for shellfish farming activities at least in Northern Adriatic, where shellfish farming is a monoculture of T. philippinarum. A strict interpretation of the precautionary principle might suggest that more rigorous regulatory action to control copper inputs in the field would be justified.

Introduction

Copper is essential for biological life, since it is a cofactor of the prion protein and many metalloenzymes, such as cytochrome-c oxidase and copper-zinc superoxide dismutase [START_REF] White | On the metabolic requirements for copper and zinc in Molluscs and Crustaceans[END_REF][START_REF] Geret | Response of antioxidant system to copper in the gills of the clam Ruditapes decussatus[END_REF]. On the other hand, copper at high concentration is extremely toxic: because of its high affinity for thiol groups, copper is able to bind to cysteine with a consequent inactivation of the proteins themselves [START_REF] Freedman | The role of glutathione in copper metabolism and toxicity[END_REF]. Copper pollution can arise from mining, brass manufacturing, electroplating and excessive use of copper-based agrochemicals. After the ban of the use of tributyltin as antifouling agent on small (< 25 m length) boats, copper has become the most common biocide in many antifouling paints [START_REF] Yebra | Antifouling technology-past, present and future steps towards efficient and environmentally friendly antifouling coatings[END_REF], and is the predominant toxicant among a number of other biocides [START_REF] Okamura | Toxicity evaluation of new antifouling compounds using suspension-cultured fish cells[END_REF]. Recent investigations have concluded that boat traffic may be a significant source of metal contamination, e.g. through the use of zinc anodes and copper-based antifouling paints [START_REF] Matthiessen | Sources and potential effects of copper and zinc concentrations in the estuarine waters of Essex and Suffolk, United Kingdom[END_REF][START_REF] Karlsson | New biocide-free antifouling paints are toxic[END_REF][START_REF] Schiff | Copper emissions from antifouling paint on recreational vessels[END_REF]. Bivalves accumulate copper in direct proportion to their environmental levels [START_REF] Roesijadi | Influence of copper on the clam Protothaca staminea: effects on the gills and occurrence of copper-binding proteins[END_REF]. Responses to copper exposure (80 -500 µg Cu l - 1 ) have been shown to vary from a reduction in filtration rate [START_REF] Manley | The effects of copper on the behaviour, respiration, filtration and ventilation activity of Mytilus edulis[END_REF] to profound effects on protein metabolism [START_REF] Viarengo | Effects of copper on the uptake of amino acids, on protein synthesis and on ATP content in different tissues of Mytilus galloprovincialis (Lam)[END_REF], and to reductions in calcium homeostasis [START_REF] Gnassia-Barelli | Effects of Cadmium and Copper contamination on Calcium content of the bivalve Ruditapes decussatus[END_REF]. At even lower concentration (10 µg Cu l -1 ), activities of antioxidant enzymes are reduced [START_REF] Isani | Effect of copper exposure on the antioxidant enzymes in bivalve mollusc Scapharca inaequivalvis[END_REF], and growth performance is affected [START_REF] Sobral | Effects of copper exposure on the scope for growth of the clam Ruditapes decussatus from Southern Portugal[END_REF].

Tapes philippinarum is an exotic species that was deliberately introduced in the 1980s in the Sacca di Goro, a brackish lagoon in the Po River deltaic area (northern Adriatic Sea, Italy), for the relaunch of a mollusc fishery. Today, about half of the bottom of the Sacca di Goro is dedicated to clam culture-based fishery. There is an abundant literature on the ecological characteristics of the species, largely because of its commercial interest (e.g. [START_REF] Goulletquer | Marine molluscan production trends in France: from fisheries to aquaculture[END_REF]. However, interactions and energetics of T. philippinarum in the new habitat have only recently been addressed [START_REF] Mistri | Effects of Musculista senhousia mats on clam mortality and growth: much ado about nothing?[END_REF]Sgro et al., 2005a;2005b). T. philippinarum is both a deposit feeder (i.e. it filters resuspended sediment) and a facultative filter feeder, and is available from farming grounds all year round.

Copper, though currently receiving less attention than organotin, will probably face similar restrictive legislation in the future. To date, one developed country, Sweden, has banned all toxic metal antifouling coatings in the Swedish Baltic Sea area (Rittschof, 2001).

The main objective of this study was to determine the effect of a sublethal concentration of copper on the energetics of T. philippinarum. Related goals were to determine whether, due to the ban of TBT in antifouling paints, copper contamination in clam farming areas exists, and to estimate the release of copper from shellfish farmers' boats.

Materials and Methods

Physiological measurements

Tapes philippinarum were collected from a farming ground (44°48'55.01''N, 12°18'19.44''E), and allowed to acclimate to laboratory conditions for one week prior to experiments. Clams were exposed to a sublethal copper concentration of 10 µg l -1 , obtained by diluting a stock solution of 1 g l -1 of CuCl 2 . There were two copper exposure tanks and two control tanks with 30 clams per tank (in 10 l of aerated seawater). Clams were fed daily on a culture of T-Isochrysis (batch: TISO15-1307839; BlueBiotech ® Germany). Water was renewed every two days. Tanks and glassware were acid-washed prior to use, and tanks rinsed thoroughly with seawater prior to addition of clams. Sampling times were at 0, 2, 5, 9, 14 and 20 days. At each time point, four randomly chosen clams from each tank (two controls and two treatments) were taken for physiological measurements. Measurement methods are fully described in Sgro et al. (2005a), and followed the procedures outlined by [START_REF] Widdows | Practical procedures for the measurement of scope for growth[END_REF]. Briefly, clearance rate was measured in plastic beakers (2 l) of filtered (0.45 µm) seawater, with two clams per beaker plus one control (without clams). Initial T-Iso concentration was ca. 25000 cells ml -1 . Each trial started when the clams started to filter again, i.e. when they extended their siphons. Samples were collected from each beaker every 20 min for the duration (2 h) of the experiment. Clearance rate was measured as the volume of water cleared of chlorophyll-a per unit time, and was related to the dry tissue weight of clams (oven drying of soft parts for 48 h at 80°C). Oxygen consumption was measured individually in four closed chambers (200 ml) plus one control, with a calibrated OxyGuard ® Handy-γ oxygen electrode, at the start and after 2 h incubation period. Oxygen consumption was also corrected for the dry weight of clams, and converted to energy equivalents according to [START_REF] Gnaiger | Calculation of energetic and biochemical equivalents of respiratory oxygen consumption[END_REF]: 1 µmol O 2 = 0.456 J. The absorption efficiency was established by the ratio method of [START_REF] Conover | Assimilation of organic matter by zooplankton[END_REF], which measures the efficiency with which organic material is absorbed from the ingested food, as the ratio of the fractions of food and faeces lost on ashing (4 h at 450°C on pre-weighed Whatman ® GF/C filters). Faeces were collected from the beakers at the end of each clearance measurement. The scope for growth provides an instantaneous measure of the energy status of the clam: the energy that clams have available for growth and reproduction was finally determined by the equation of [START_REF] Warren | Laboratory studies on the feeding, bioenergetics, and growth of fish[END_REF] which is based on the difference between energy absorbed from food consumed and energy losses via respiration. In bivalves, clearance and respiration rates depend on the physiological status, food ration, and to a lesser extent, season and temperature [START_REF] Bayne | Physiology: I[END_REF][START_REF] Sobral | Effects of increasing current velocity, turbidity and particlesize selection on the feeding activity and scope for growth of Ruditapes decussatus from Ria Formosa, southern Portugal[END_REF][START_REF] Riisgård | Regulation of opening state and filtration rate in filter-feeding bivalves (Cardium edule, Mytilus edulis, Mya arenaria) in response to low algal concentration[END_REF]. The first set of experiments was carried out in November 2003, and then experiments were repeated in March, May and July 2004. Physico-chemical water parameters were: temperature 10° to 25°C, salinity 26 to 28 and pH 8.0 to 8.2. A total of 480 clams was used (31.5 mm mean shell length + 0.6 SD).

Environmental measurements

Field surveys focused on sampling of water, sediment and suspended particulate matter (SPM), and covered nine monthly sampling periods from February to December 2004.

One fixed station (44°48'55.27''N, 12°18'18.89''E; depth 1.5 m), close to the farming ground, was chosen. At each sampling date, 50 l of seawater were collected and immediately vacuum-filtered onto Millipore ® HA 0.45 µm membranes. Two litres of filtered water were retained and acidified with suprapur HNO 3 to 1-2 pH (2 ml l -1 ). Membranes with SPM were transferred in weighed bottles and sealed. Sediment samples were collected with a corer; the first 2 cm of three replicate cores (i.d. 5 cm) were retained. All samples were ice-chilled in a cooler on the boat, and received at the laboratory within 2 h after collection. Water samples were analyzed through DPASV (Differential Pulse Anodic Stripping Voltammetry; [START_REF] Wang | Analytical Electrochemistry[END_REF] with an AMEL433 instrument. This technique is particularly useful when analyzing seawater. Membranes with SPM were oven dried (100°C) to constant weight, treated with suprapur HNO 3 , and mineralized in a microwave oven (Milestone ETHOS 900: 350 w: 15 min, 400 w: 10 min). After dilution with Milli-Q water, copper analyses were carried out through plasma spectrometry (Perkin-Elmer Optima 3100 XL). Sediment samples were treated and analyzed like SPM samples. Adequate quality assurance/quality control measures were followed in all aspects of the study, and intercalibration exercises were carried out within the TAQC-WFD (ISO/IEC 17025). The analytical procedures were checked using certified reference material (2704 of National Bureau of Standards) and allowed agreement with the certified values higher than 95%.

Estimation of copper inputs from shellfish farmer fleet

The estimate of copper inputs from boats was conducted calculating the wetted hull area (WHA) of boats, as WHA = L x (B + D), where L is the length, B the beam, and D the draft. Then, the leaching rate of copper from antifouling paints was calculated considering the input for all boats (IB) per day, as IB = Number of boats x WHA x release rate of copper. The release rate of copper was drawn from literature (6.24 µg cm -2 d -1 ; [START_REF] Matthiessen | Sources and potential effects of copper and zinc concentrations in the estuarine waters of Essex and Suffolk, United Kingdom[END_REF].

Results

No mortality occurred during the physiological experiments. Clearance rates were always lower in the copper-exposed clams (Fig. 1). Two-way ANOVAs (Table 1) showed a significant (all p < 0.001) initial decline in clearance rates of exposed clams, which tended to stabilize between the fifth and the eleventh day of exposure, depending on the period of experimentation. Clearance rates also differed with time of exposure (all p < 0.001). Oxygen consumption was always higher in exposed clams (Fig. 2; all p < 0.001 except November, p < 0.05). Absorption efficiency followed a more complicated pattern (Fig. 3). In two out of four experiments (November and March), a compensation seems evident in the earlier period of exposure, since less food was ingested (lower clearance rates) but it was absorbed more efficiently (higher absorption efficiency). However, all experiments showed a consistent decline in absorption efficiency after 9 d exposure. The results presented in Figures 1, 2 and3 shows that there was generally a consistent physiological response to Cu in all four seasonal experiments. Scope for growth was calculated for each measurement period, and was always higher for control clams, independently from different environmental conditions and physiological status of the clams (Fig. 4). In March and July, negative values of the scope for growth indicate that Cu exposed clams had to use their body reserves to meet metabolic requirements.

Remarkable variations of copper values in the field were found during the study period. Table 2 summarizes copper concentration in water, SPM, and sediments. Briefly, water concentrations ranged between 2 and 7.5 µg Cu l -1 ; in the SPM between 70 and 287 µg Cu g -1 , while in the sediment variations were between 7.5 and 53.5 µg Cu g -1 .

At Goro, shellfish farmers' boats are quite standard in size, and parameters for the calculation of WHA were: L = 5 m, B = 1.5 m, and B = 0.5 m. The fleet consists of about 1200 boats [START_REF] Irepa | Osservatorio economico sulle strutture produttive della pesca marittima in Italia 2001-2002[END_REF]. Thus, the leaching rate of copper from antifouling paints was estimated at 0.75 kg Cu d -1 , equalling 273 kg Cu yr -1 . However, this copper input must be considered a very conservative estimate, since it assumes one antifouling treatment per year (most shellfish farmers treat their boats twice a year), and does not consider the recreational fleet (about 200 yachts of various sizes) which is moored all year round in the touristic harbour of Goro.

Discussion

After their ban for the use on small boats and aquaculture, triorganotin antifoulants have been superseded by antifouling products based mainly on copper metal oxides. Although the deleterious effects of excess copper on aquatic biota are known, very few data are available regarding the potential increase in copper concentration following an increase in popularity of such products amongst small boat users. Pre-TBT-ban copper concentration in the water of the Sacca di Goro (1987)(1988) was between 0.2 and 1.6 µg Cu l -1 [START_REF] Fagioli | Valutazione del carico di metalli nelle acque, nei sedimenti e nei principali organismi accumulatori[END_REF]. It seems evident that in the last 15 y, the concentration of copper in the water has greatly increased. Of course we can not state whether this is due only to the use of copperbased antifoulants, or to a generalized increase of contamination of the environment (e.g. through the discharge of continental water from the catchment basin of the Po di Volano river). [START_REF] Helland | Transport and sedimentation of Cu in a microtidal estuary, SE Norway[END_REF], however, reported that during a period of 90% reduction in the industrial input of copper in a Norwegian estuary, the very high concentrations of copper found were caused by leaching from antifouling paints. We estimated very conservatively a leaching rate of copper from antifouling paints of about 270 kg Cu yr -1 . This amount is comparable to figures reported in many major marinas and yacht moorings in U.K. [START_REF] Matthiessen | Sources and potential effects of copper and zinc concentrations in the estuarine waters of Essex and Suffolk, United Kingdom[END_REF], which are cause for concern. The sampling site chosen in the Sacca di Goro, being close to the shellfish farming ground, should be considered a "clean" site.

Unfortunately, there are no data about pre-industrial levels of copper in the Sacca. However, from the 1980s, an increased concentration of copper in the water (up to 7 µg Cu l -1 ), SPM (up to 250 µg Cu g -1 ) and sediment (up to 50 µg Cu g -1 ) is evident. Moreover, sedimentary copper concentrations were often above the threshold effect level (TEL: 18.7 µg Cu g -1 ) and effect range Low (ERL: 34 µg Cu g -1 ) of the Florida Department of Environmental Protection (FDEP;[START_REF] Macdonald | Approach to the assessment of sediment quality of Florida coastal waters[END_REF] and the National Oceanic and Atmospheric Administration (NOAA; [START_REF] Long | Predicting toxicity in marine sediments with numerical sediment quality guidelines[END_REF] sediment quality guidelines respectively. Copper is known to preferentially adsorb to organic matter, and such an adsorption might reduce the reactivity and hence the toxicity of copper [START_REF] Gardner | The range of copper complexing ligands in the Tweed estuary[END_REF]. Clams, however, can accumulate contaminants directly from seawater, from sediments and from contaminated food (Coelho et al., 2002a;2002b;[START_REF] Coughlan | Detecting genotoxicity using the Comet assay following chronic exposure of Manila clam Tapes semidecussatus to polluted estuarine sediments[END_REF]. Such accumulation can result in the production of initiators of cytotoxic injury with degeneration of lysosomal function [START_REF] Kurelec | The genotoxic disease syndrome[END_REF], decreased scope for growth, as shown by the present study, faster aging, and finally, decreased reproduction which reduces the fecundity of the population [START_REF] Coughlan | Detecting genotoxicity using the Comet assay following chronic exposure of Manila clam Tapes semidecussatus to polluted estuarine sediments[END_REF].

Most bivalves can reduce the toxic effects of metals by producing metal-binding proteins [START_REF] Roesijadi | Influence of copper on the clam Protothaca staminea: effects on the gills and occurrence of copper-binding proteins[END_REF]. However, the increased energy expenditure recorded in the Cu exposed clams was probably due in part to the increased cost of metal-binding protein synthesis. When combined with the reduction in food energy consumption, due to the toxic effects of copper on the ciliary feeding activity, this resulted in very low and even negative values of scope for growth in 10 µg Cu l -1 exposed clams. Even at the end of the 20 d exposure period, when physiological responses seemed to recover slightly, the overall energetics of the clams was still greatly affected. This suggests that any detoxification mechanism allowed only partial recovery or only served to reduce the potential toxic effects of Cu exposure. In fact, at the end of the 20 d long exposure, clearance rates were, on average, 54% of control rates, respiration was high at about 120% of control, and scope for growth was reduced to about 15% of the initial value. These figures are in accordance with [START_REF] Sobral | Effects of copper exposure on the scope for growth of the clam Ruditapes decussatus from Southern Portugal[END_REF], who studied the effect of copper on the clam Ruditapes decussatus. It has been suggested [START_REF] Hawkins | The metabolic/physiological basis of genotype-dependent mortality during copper exposure in Mytilus edulis[END_REF]) that a positive relationship may exist between the growth rate of individual bivalves and heterozygosity measured at polymorphic enzyme loci.

In the blue mussel (Mytilus edulis), exposure to copper in the laboratory revealed genotypedependent mortality, with those individuals expressing a higher degree of heterozygosity surviving longest [START_REF] Hawkins | The metabolic/physiological basis of genotype-dependent mortality during copper exposure in Mytilus edulis[END_REF]. The hypothesis that heterozygosity is associated with improved survivorship in contaminated conditions is also supported by [START_REF] Nevo | Genetic diversity and resistance to marine pollution[END_REF], who reported that for marine molluscs, broad-niche and highly heterozygous species display significantly higher survivorship than narrow-niche, genetically poor, congeneric species, after exposure to inorganic pollutants. Unfortunately, the level of heterozygosity of Adriatic Tapes philippinarum populations is unknown. However, considering that all Adriatic populations, from Venice to Ravenna, derive just from three successive introductions (1984, 1985 and 1986) of only about 20 kg of seed imported from the same British hatchery [START_REF] Pellizzato | Chapter 8: Acclimatization of the Tapes philippinarum species and the first experimental rearing basins in Italy[END_REF], it seems reasonable to suspect that the level of heterozygosity should not be particularly high. The increasing environmental levels of copper, as well as other toxic environmental contaminants, poses a threat to populations with reduced heterozygosity and how it will affect the species' fitness and distribution.

In summary, the data presented in this study give sufficient concern about possible environmental damage to further justify restrictive legislation for copper use in antifouling coating. A strict interpretation of the precautionary principle might suggest that more rigorous regulatory action to control copper inputs in the field would be justified.

Figure 1 .

 1 Figure 1. Mean clearance rates (CR) by clams (Tapes philippinarum) in response to Cu exposure (10 µg Cu l -1 ) and control conditions (mean + SD).

Figure 2 .

 2 Figure 2. Mean rates of oxygen consumption by clams (Tapes philippinarum) in response to Cu exposure (10 µg Cu l -1 ) and control conditions (mean + SD).

Figure 3 .

 3 Figure 3. Mean absorption efficiency (AE) by clams (Tapes philippinarum) in response to Cu exposure (10 µg Cu l -1 ) and control conditions (mean + SD).

Figure 4 .

 4 Figure 4. Scope for growth for control and exposed clams (Tapes philippinarum) at each

  Figure

Table 2

 2 Values of copper concentration (mean + standard deviation) at the study site.

		Water	SPM		Sediment
		(µg Cu l -1 )	(µg Cu g	-1 )	(µg Cu g	-1 )
	February	3.3+0.2	108.1+4.0	16.4+1.5
	March	3.0+0.2	187.7+4.7	46.3+5.5
	April	2.0+0.1	101.0+3.5	21.9+4.0
	May	3.2+0.2	100.7+4.5	15.0+3.2
	June	7.3+0.2	74.3+2.1	7.5+2.0
	July	3.5+0.1	70.0+2.5	8.6+2.2
	August	ND	ND		ND
	September	ND	ND		ND
	October	3.0+0.3	100.5+4.0	18.0+4.6
	November	2.5+0.2	201.6+5.8	26.3+3.7
	December	4.7+0.2	287.3+6.0	53.5+10.1

Acknowledgemets

This study was supported by Regione Emilia Romagna (2376Romagna ( /2002)). We are grateful to A. Pagnoni (Dept. of Chemistry, University of Ferrara) for copper analyses. J. Widdows and an anonymous reviewer are acknowledged for constructive criticism.


R Simonini

I Ansaloni

P Bonini

V Grandi

F Graziosi

M Iotti

G Massamba-N'siala

M Mauri

G Montanari

M Preti

N De Nigris

D Prevedelli

Simonini Roberto
email: simonini.roberto@unimore.it

Recolonization and recovery dynamics of the macrozoobenthos after sand ex traction in relict sand bottoms of the Northern Adriatic Sea

Keywords: Environmental impact, Dredging, Sand extraction, Benthos, Recolonisation, Recovery, Northern Adriatic Sea, Relict sands

Recolonization and recovery dynamics of the macrozoobenthos after sand extraction in relict sand bottoms of the Northern Adriatic Sea

Introduction

The impact of marine sand extraction on the characteristics of the seabed and the changes in benthic fauna have been widely investigated [START_REF] Newell | The impact of dredging work in coastal waters: a review of the sensitivity to disturbance and subsequent recovery of biological resources on the sea bed[END_REF][START_REF] Sardà | Changes in the dynamics of shallow sandy bottoms assemblages due to sand extraction in the Catalan Western Mediterranean Sea[END_REF]Van Dalfsen, Essink, Toxvig Madsen, Birklund, Romero, & Manzanera, 2000). The dredging operations may influence the physical and biological characteristics of the impacted areas both directly, through removal, smothering and damage caused by the dredge head, and indirectly: surface and bottom plumes, changes in plankton bloom seasons, the release of nutrients and chemicals, as well as sound, can affect both the sea bottom and the water column in the immediate area around the dredging site [START_REF] Boyd | The effects of marine sand and gravel extraction on the macrobenthos at a commercial dredging site (results 6 years post-dredging)[END_REF][START_REF] Newell | The impact of dredging work in coastal waters: a review of the sensitivity to disturbance and subsequent recovery of biological resources on the sea bed[END_REF]Van Dalfsen et al., 2000).

Most studies concerning the impacts of sand dredging on macrozoobenthos were performed in shallow, dynamic sandy bottoms. In these cases, the recovery of the benthic assemblages appeared to be linked to the alterations in the seabed, in terms of grain size, organic content and morphology induced by sand extraction. Major alterations in the sediment and grain size characteristics over a long period favoured the settlement of benthic communities that were different in composition and structure from the pre-operational ones and this difference could be maintained for a long time [START_REF] Boyd | Preliminary observations of the effects of dredging intensity on the recolonization of dredged sediments off the south-east coast of England (Area 222)[END_REF][START_REF] Boyd | The effects of marine sand and gravel extraction on the macrobenthos at a commercial dredging site (results 6 years post-dredging)[END_REF]Van der Veer, Bergman, & Beukema, 1985). In contrast, when there was minimal variation in the seabed characteristics, benthic communities recovered within a few years in terms of biodiversity and biomass [START_REF] Boyd | Preliminary observations of the effects of dredging intensity on the recolonization of dredged sediments off the south-east coast of England (Area 222)[END_REF]Kenny & Rees, 1994, 1996;Kenny, Rees, Greening, & Campbell, 1998;[START_REF] Robinson | Impacts of aggregate dredging on sediment composition and associated benthic fauna at an offshore dredge site in the southern North Sea[END_REF]Van Dalfsen & Essink, 1997).

The largest nourishment operation in the Mediterranean Sea took place in 2002, when approximately 800,000 m 3 of sand were dredged from off-shore relict sand deposits in the Northern Adriatic Sea. These were destined for the nourishment of several beaches along the Emilia-Romagna coast (Italy) [START_REF] Simonini | The effects of sand[END_REF]. Besides the geographical location, this operation was unusual because the seabed of the area 1) did not show active sedimentation, 2) was primarily composed of relict sand, 3) was far from the coast (55 km) and 4) at a greater depth (40-42 m) compared with the other previous dredging operations, where the maximum dredging depth was usually around 30 metres (North Sea Foundation, 2005).

The analysis of the short-term impacts of sand extraction on sediment and macrozoobenthos during the first 12 months after dredging, highlighted that the activities did not significantly influence the granulometry and the total organic carbon content (%TOC) of the substratum, but caused almost complete defaunation at the dredging stations. Yet, at the end of this survey period, the recolonisation of the communities at the impacted stations was at an advanced stage [START_REF] Simonini | The effects of sand[END_REF].

In the present paper, we analysed the impact of relict sand extraction on benthic communities and the degree of recolonisation of macrobenthos at the end of the monitoring program, up to 30 months after the completion of the dredging. The aim was to establish the recolonization-recovery state of the macrozoobenthos and obtain a reference model for the effects of sand extraction on relict sand bottoms.

Materials and methods

Study area and sampling

The dredged area is located approximately 55 Km off the coast of Ravenna, in an area characterized by the occurrence of relict offshore sandy deposits (Fig. 1; [START_REF] Preti | Ripascimento di spiagge con sabbie sottomarine in Emilia-Romagna[END_REF].

These deposits, mainly composed of sand and coarse detritus, are the remains of coastal structures that were formed during the last Adriatic marine transgression. This area is outside the main current circuit of the Northern Adriatic, and as a result is characterized by a limited amount of sedimentation of fine material during the summer period that become re-suspended and dispersed during the winter [START_REF] Preti | Ripascimento di spiagge con sabbie sottomarine in Emilia-Romagna[END_REF][START_REF] Preti | Stato del litorale emiliano-romagnolo all'anno[END_REF][START_REF] Simonini | The effects of sand[END_REF]. The tidal excursion is negligible and the currents are weak and with variable direction. In fact, the main factors that affect the sediment transport and re-suspension in the area are the storms driven by the NE and SE winds (Bora and Scirocco) that frequently occur in the Northern Adriatic Sea from December to late March and probably lead to significant sediment re-suspension (Matteucci and Frascari, 1997;Wang, 2002). Water temperature in the bottom showed marked seasonal variation ranging from 8°C in February to 26°C in August [START_REF] Montanari | The currents of Emilia-Romagna coastal strip during the period 1995-2002[END_REF]. Surveys and sampling activities were performed with the oceanographic M/S "Daphne II" of ARPA (Regional Agency for Prevention and the Environment of Emilia-Romagna). The sampling design provided for samples at three impacted stations within the extraction area and at seven reference stations outside it. The stations were positioned along two transects perpendicular to and intersecting each other in the centre of the dredging zone (Fig. 1). The reference sites were chosen at large enough distances so as not to be affected by the extraction activity and deposition of suspended sediment. The direction and extension of the plume during the extraction were monitored by ARPA Daphne. During the extraction the currents were weak and variable and the plume never reach the inner reference stations.

The dredging operations took place in

Multibeam profile, Side Scan Sonar and video camera images were used to measure the depth of the excavation pits and holes.

The sampling for macrozoobenthos was carried out using a van Veen grab with a surface area of 1250 cm 2 and sampling volume of 24 dm 3 . Three replicates were collected at each station. For each station, analyses for sediment granulometry and %TOC were also performed (ICRAM, 2001).

The samples for macrozoobenthos analysis were sieved (mesh size 0.5 mm) and the retained material was preserved in 4% CaCO 3 buffered formalin in seawater.

Data analysis

The samples and the data were treated and analysed using the same techniques described in [START_REF] Simonini | The effects of sand[END_REF]. The residual material obtained from the sample sieving was sorted in the laboratory and the macrofauna was preserved in 70% ethylic alcohol. Most of the organisms were identified to species level. Data from each sampling station were obtained from the sum of their respective replicates.

The data collected during the different surveys were integrated by obtaining a total matrix of the abundances and the main ecological indices (number of species/taxa, abundance and Shannon-Wiener diversity) were calculated.

Multivariate analyses were performed with original (untrasformed) and 4 th root transformed abundance data in order to analyze the patterns of variation of benthic assemblages in terms of numerically dominant species and species composition.

Outputs from non-metric Multidimensional Scaling (nMDS) ordination models of the Bray-Curtis similarity matrix were obtained for all the stations/surveys. Differences among the extraction and reference stations during different surveys were tested using a two-way crossed ANOSIM (ANalysis Of SIMilarity) randomisation-permutation test. One-way ANOSIM was performed to compare a posteriori the extraction area stations with the reference stations, and the reference stations of the various surveys.

The species making the greatest contribution to dissimilarity among the impacted and reference stations for the different surveys were investigated using the SIMilarity PERcentage breakdown procedure (SIMPER). The analyses were carried out using PRIMER V5 software (Clarke & Warwick, 2001).

Results

Sediment characteristics

Both the reference and impacted sites did not differ for the sediment characteristics during the whole examined period. In all the samples analysed the percentage of sand varied from 85% to 98%, with the prevalence of fine sands (250-125 µm), and the %TOC was low (0.3-0.8%). The recorded sailing tracks of the dredger and the bathymetric changes in the sea bed after sand extraction revealed that the operations were carried out mainly in the southern and central parts of the dredged area, corresponding to sampling stations 4 and 3 (Fig. 1,2). During the extraction period the currents were weak and directed toward S9, but the dredged-sediment plumes were confined to within 1 km of the sand extraction area. The reconstruction of the bottom profile based on multi-beam data collected immediately after the extraction showed that pits with a depth of 1.5 m had formed on the seabed of the impacted area, due to the extraction activities (Fig. 3). The surface area and the depth of these "holes" tended to decrease over time: their mean depth was about 0.30 m in the the A.-Ex. 24 months surveys (Fig. 3).

Taxonomic analysis

The taxonomic analysis of the samples collected during the eight surveys led to the identification of about 42000 individuals, belonging to 183 taxa and distributed among 12 phyla (in order of abundance: annelids, arthropods, molluscs, nemertines, echinoderms, sipunculids, phoronids, poripherans, cnidarians, echiurians, chordates and hemichordates). The polychaetous annelids were the most abundant group (43% of the taxa, 59% of the individuals), followed by the molluscs (mainly bivalves and gastropods, 24% of the taxa and 4% of the individuals), the arthropods (mostly crustaceans, 18% of the taxa and 33% of the individuals) and the echinoderms (echinoids, stellaroids and ophiuroids, 9% of the taxa and 2% of the individuals). All the other phyla together represented 7% of the taxa, but only 1% of the individuals.

The species were characteristic of offshore sandy-muddy bottoms. No mass settlement of opportunistic species or species typical of disturbed bottoms was observed in the impacted areas. The list of the taxa found during the whole monitoring program was reported in Appendix 1.

Abundance data analyses

The multivariate analyses performed on original and 4 th root transformed data highlighted that the sand extraction affected the macrozoobenthic communities of the impacted stations during and after sand extraction, both in terms of species composition and of numerically dominant species (ANOSIM, Table 1).

The MDS ordination plots of the untransformed and 4 th transformed data relating to each station and sampling date (Fig. 4a,b) showed the stations 3 and 4 of the Ex. and A.-Ex. 1 surveys to be segregated towards one side, while the reference and the dredging station of the last three A.-Ex. Surveys surveys) were grouped on the other side, very close to each other. Between these two extremes, there were the points indicative of stations 2-4 of the A.-Ex. 6 and A.-Ex.

12 months surveys) (Fig. 4a,b). The MDS sub-plot of the impacted station from the different surveys (Fig. 4c,d) showed the same patterns as the whole plot.

In the Ex and A.-Ex. 1 survey, the average dissimilarities of impacted station with respect to the reference station and B-Ex Imp. stations were very high and significant (80-90% with untransformed data, 70-75% with 4 th root transformed data) (Table 2-3). The stations 3 and 4 of the Ex. and A.-Ex. 1 surveys underwent almost complete defaunation after the sand extraction, evidenced by the drastic reduction in the number of taxa, abundances and Shannon-Wiener diversity with respect to references stations (Table 2; Fig. 5). Station 2, located in the northern part of the extraction area, was subject to a lower impact and showed a smaller reduction in the number of taxa and abundances than station 3 and 4 (Table 4; Fig. 5).

The intermediate position of the stations 2-4 of the A.-Ex. 6 and A.-Ex. 12 surveys in the MDS plots of Fig. 3a-d suggested that recolonisation was taking place at these stations. In fact the average dissimilarity of impacted station in the Ex and A.-Ex. 1 survey, with respect to B-Ex Imp. stations was 63-83% (untransformed data) and 45-66% (4 th root transformed data) (Table 23). Moreover, the dredged stations showed abundances that were about 40-50% lower than the reference stations, whereas during the A.-Ex. 1 survey the mean difference was about 90%. In addition, the number of taxa discovered at the extraction area stations was 60-70% of that observed at the reference stations, whereas during the surveys carried out during and 1 month after extraction this percentage was only 26% (Table 4; Fig. 5).

Although the reference sites of each survey are tightly grouped on one side of the global MDS plots (Fig. 4a,b), the MDS subplots of the reference station from the different surveys highlighted the great temporal variability of the community, throughout the eight surveys both in term of species composition and, overall, in term of numerically dominant species (Fig. 4 e-f). In particular, the MDS plots showed the reference station of the A-Ex 12 survey segregated separately and more dispersed than the others, due to the lower abundances and number of taxa found (Table 4).

With the exception of the A-Ex 12 survey, the dissimilarities among surveys were lower than could be ascribed to the impact of the extraction activity, ranging from 25 to 42% (4 th root transformed data) and from 33 to 62% (untransformed data). Yet the results of the one-way ANOSIM tests between the references station of the eight surveys showed that the communities varied from one survey to another (Table 2, 3), probably because the high within-groups similarities (60-70% with untransformed data, 70-80% with 4 th root transformed data). Most of the dissimilarities among the reference stations of different surveys were due to variations in the abundances of some of the more highly represented and/or characteristic species, and to the occurrence of low-density, rare species that characterize one or more surveys (SIMPER, [START_REF] Simonini | The effects of sand[END_REF]. In fact only about the 50%-60% of the species found throughout the whole investigation were common to the reference station of all the surveys but these species represented 90-95% of the total abundance.

The high R-values suggested that the community of the B-Ex. Imp. station and the impacted station from the other survey are different from one survey to another, despite their dissimilarity decrease over time after the dredging (Table 2, 3). The SIMPER analysis performed on the 4 th root-transformed data from the B-Ex or the A-Ex 30 impacted stations has emphasized the role of some rare species with very low density (Appendix 2). The same analysis performed without data transformation highlighted that the differences in the communities of the impacted station before and 30 month after dredging were mostly due to variations in the abundances of the more highly represented and characteristic species of the relict sands, such as the polychaetes Aricidea claudiae, Spiophanes kroyeri kroyeri, Monticellina dorsobranchialis and Jasmineira elegans, and the crustaceans Ampelisca diadema and Apseudes latreilli (Appendix 2). As observed for the reference station of the different survey, the communities of the impacted station before and 30 month after sand extraction have a number (52%) of species in common. These species contributed to the 89% and the 91% of the total abundances of the B-Ex impacted station and the A-Ex. 30 impacted station.

Discussion

Despite the extraction depth and the considerable volume of sand removed, negligible change in sediment structure were observed at the relict sand extraction sites in the Northern Adriatic Sea. The deep depressions created by the extraction have gradually been levelled out by the winter storm activity and by the sedimentary regime (predominantly erosive) of the area that did not allow the accumulation of appreciable quantities of fine material in the depressions.

The variability in the composition and structure of the macrozoobenthic communities at the reference station, throughout the different surveys makes it difficult to analyze the recovery of the benthic community. A number of studies have highlighted the considerable intra-and inter-annual variability in composition and density of the community of sandy and muddy bottoms of the Northern Adriatic (Ambrogi et al., 1990;Bonvicini Pagliai et al., 1996;Bonvicini Pagliai et al., 1999;Prevedelli et al., 2001;Occhipinti Ambrogi et al., 2002;Simonini et al. 2004). It is likely that the fluctuations in density observed during the different surveys are linked to the biological cycles of different species and to the high seasonal variation in physical conditions in the Northern Adriatic Sea. For example, the reduction in terms of abundances and number of taxa that was observed during the A-Ex 12 survey can be related to the anomalous low temperature of the water (4°C in the bottom) that occurred in the northern Adriatic sea during the winter 2002 and caused the mortality of several benthic species (Gismondi e Rinaldi, 2002).

The temporal variability among the surveys was low and could not be attributed to the impact of the extraction activity. The effects of sand extraction on the macrozoobenthic communities were particularly intense, especially during and immediately after the extraction at station 3 and 4, and were mainly due to the removal of fauna from the seabed. The recolonisation at the impacted station began early by larval settlement (most polychaetes, molluscs, echinoderms and other sedentary organisms) and adult migration (vagile taxa, such as the dominant crustaceans A. latreilli and A. diadema). In other cases larval settlements were noted to have been the most important source of new recruits, rather than the migration of adults from nearby [START_REF] Boyd | Preliminary observations of the effects of dredging intensity on the recolonization of dredged sediments off the south-east coast of England (Area 222)[END_REF].

In contrast to the MDS and dissimilarity analyses, the high R value derived from the pair-wise ANOSIM test suggested that the community at the A-Ex impacted station were different from those found prior to the dredging operation, even 30 months after sand extraction. We suggest that the failure of the R statistic in detecting the recovery of the community could be the result of the low (spatial) variability among stations for each sampling survey relative to the natural seasonal and inter-annual variability of the benthic communities.

The % dissimilarity between Before-Extraction and After-Extraction impacted station decreased of over time and, starting from 18 month after dredging, the community of impacted station grouped together with the reference station and B-Ex station on one side of the MDS plots. These finding suggested that starting from 24 months after dredging the composition and structure of the macrozoobenthic assemblages becomes similar to that observed prior to sand extraction. In particular, the difference in the community of the B-Ex and the A-Ex 30 impacted stations are due to 1) some rare species exclusive to the first or the last survey; and 2) differences in the abundances of common species, characteristic of the relict sands biocoenosis, that can be related to the natural temporal variability of the communities. The species in common between the two surveys constituted about 90% of the whole abundances at both B-Ex and the A-Ex 30 impacted stations. Therefore it could be assumed that the communities of the dredged area returned to the original condition 30 months after sand extraction. This pattern of recolonisation-recovery fits well with the commonly encountered scenario where the substratum merely remains unchanged after marine aggregate excavation [START_REF] Boyd | Preliminary observations of the effects of dredging intensity on the recolonization of dredged sediments off the south-east coast of England (Area 222)[END_REF][START_REF] Boyd | The effects of marine sand and gravel extraction on the macrobenthos at a commercial dredging site (results 6 years post-dredging)[END_REF]ICES, 2000). In most of these cases, the extraction area exhibited a strong hydrodynamic activity, which could obstruct the deposition of fine sediment, or the depth of the extraction was low, so the change in sediment structure of the dredged areas was limited over time and the complete recovery of the communities required a period of between 2 and 4 years.

In order to contrast coastal erosion along the Mediterranean coasts, the local governments and the environmental protection agencies of several regions and countries are planning new nourishment operations exploiting the relict sands (see INTEREG IIIC-BEACHMED project at http://www.beachmed.it). This monitoring programme has provided the opportunity to derive useful information for the evaluation of the consequences of relict sand extraction over sufficiently long time scales, from impact to recovery. Firstly, in order to separate the effects of sand extraction from the effects of the high inter-and intra-annual variability, the monitoring survey must be repeated a number of times both before and after dredging (Underwood, 1994)

  April-May 2002. The monitoring activities started in March 2001 and ended in December 2004. Eight sampling surveys were carried out: March 2001 (before extraction [B.-Ex.]), April 2002 (during extraction [Ex.]), June 2002 (1 month after extraction [A.-Ex. 1]), December 2002 (6 months after extraction [A.-Ex. 6]), June 2003 (12 months after extraction [A.-Ex. 12]), December 2003 (18 months after extraction [A.-Ex. 18]), June 2004 (24 months after extraction [A.-Ex. 24]) and December 2004 (30 months after extraction [A.-Ex. 30]).Because of the lack of information on the effects of sand extraction in the Northern Adriatic Sea, most of the efforts were devoted to the after-dredging surveys (7 survey), in order to analyze the major recolonization-recovery patterns of the macrozoobenthos, as opposed to the characterization on the temporal variability of community before dredging (1 survey).

  . And secondly, to evaluate the outcome of the recolonisationrecovery processes the post-dredging survey might be performed 2-3 years after sand extraction Acknowledgements The Authors thank the crew of the oceanographic M/S "Daphne II" (Struttura Oceanografica Daphne, ARPA Emilia-Romagna), Mr. Marco Papperini and Mr. Dino Pagan, Dr M. Gabellini and his staff (ICRAM, Rome) for the coordination of the different monitoring activities, and Dr. Katie Henry for English language The research was financially supported by the Emilia-Romagna Region Project "Programma pluriennale di monitoraggio dell'area a mare da cui sono state prelevate le sabbie per il ripascimento delle spiagge emiliano-romagnole'' ("Multiannual monitoring program of the offshore area of sand extraction for the nourishment of the Emilia-Romagna beaches", convention between ARPA Ingegneria Ambientale and Dipartimento di Biologia Animale, Università degli studi di Modena e Reggio Emilia). Boyd, S.E., & Rees, H.L. (2003). An examination of the spatial scale of impact on the marine benthos arising from marine aggregate extraction in the central English Channel. Estuarine & Coastal Shelf Science, 57, 1-16. Clarke, K.R., & Warwick, R.M. (2001). Change in marine communities: an approach to statistical analysis and interpretation. 2nd edition, Primer-E, Plymouth, UK. ICES (2000). Section 16 of the ACME Report 2000 -Meeting of the Biodiversity Committee (BDC), Vlissingen. ICRAM (2001). Intervento di messa in sicurezza dei tratti critici del litorale emiliano romagnolo mediante ripascimento con sabbie sottomarine: caratterizzazione dell'area di prelievo in mare. Relazione preliminare. ICRAM, Roma. Kenny, A.J., & Rees, H.L. (1994). The effects of marine gravel extraction on the macrobenthos; early post-dredging recolonisation. Marine Pollution Bulletin, 28(7), 442-447. Kenny, A.J., & Rees, H.L. (1996). The effects of marine gravel extraction on the macrobenthos. Results 2 years post-dredging. Marine Pollution Bulletin, 32, 615-622. Kenny, A.J., Rees, H.L., Greening, J., & Campbell, S. (1998). The effects of gravel extraction on the macrobenthos at an experimental dredge site off North Norfolk, UK (results 3 years post-dredging). ICES CM1998/V 14, 1-7.Matteucci, M., & Frascari, F. (1997). Fluxes of suspended materials in the north Adriatic sea (po prodelta area). Water,Air and Soil Pollution, 99,[557][558][559][560][561][562][563][564][565][566][567][568][569][570][571][572]

Figure legend Fig. 1 .

 legend1 Figure legend

Fig. 2 .

 2 Fig. 2. Grain size, and % total organic carbon (TOC; mean ± 2 S.D.) at impacted and

Fig. 3 .

 3 Fig. 3. Reconstruction of the bottom profile immediately after (A Ex 1 survey) and 24

Fig. 4 .

 4 Fig. 4. 2D-nMDS ordination of: a) impacted (Imp) and reference (Ref) station at all

Fig. 5 .FIGUREFig. 4

 54 Fig. 5. Number of taxa, total abundance and Shannon-Wiener diversity index (mean ±

Table 1 .

 1 Results of two-way ANOSIM for the effects of sand extraction at different surveys, performed on untrasformed and 4 th root transformed macrofaunal abundance data.

	Transformation	none		4 th
	Statistics	R	P	R	P
	Factors
	Extraction	0.532	<0.001	0.635	<0.001
	Survey	0.791	<0.001	0.732	<0.001

Table 2 .

 2 Pair-wise test (One-way ANOSIM, untransformed data) and % average dissimilarity between the impacted stations vs reference stations for each survey, the impacted station from the different surveys vs the pre-dredging station Imp) and the reference stations of the different surveys. Significant R values (P<0.05) are reported in bold.

	Groups	R	% dissimilarity	Groups	R	% dissimilarity
	A-Ex 30: Imp. vs Ref.	0.075	33.5	A-Ex 30 vs A-Ex 24	0.841	41.3
	A-Ex 24: Imp. vs Ref.	0.460	40.6	A-Ex 30 vs A-Ex 18	0.21	33.0
	A-Ex 18: Imp. vs Ref.	0.508	35.8	A-Ex 30 vs A-Ex 12	1.000	78.1
	A-Ex 12: Imp. vs Ref.	0.611	57.2	A-Ex 30 vs A-Ex 6	0.760	51.0
	A-Ex 6: Imp. vs Ref.	0.742	57.7	A-Ex 30 vs A-Ex 1	1.000	61.0
	A-Ex 1: Imp. vs Ref.	0.948	83.0	A-Ex 30 vs Ex	1.000	59.1
	Ex: Imp. vs Ref.	0.861	84.5	A-Ex 30 vs B-Ex	0.995	56.3
	B-Ex: Imp. vs Ref.	-0.136	40.0	A-Ex 24 vs A-Ex 18	0.653	38.5
				A-Ex 24 vs A-Ex 12	1.000	79.4
				A-Ex 24 vs A-Ex 6	0.832	55.5
	B-Ex Imp. vs Ex Imp.	0.444	89.2	A-Ex 24 vs A-Ex 1	1.000	61.7
	B-Ex Imp. vs Ex 1 Imp.	1.000	91.1	A-Ex 24 vs Ex	1.000	60.3
	B-Ex Imp. vs Ex 6 Imp.	0.850	65.8	A-Ex 24 vs B-Ex	1.000	57.8
	B-Ex Imp. vs Ex 12 Imp. 1.000	83.2	A-Ex 18 vs A-Ex 12	1.000	78.0
	B-Ex Imp. vs Ex 18 Imp. 1.000	51.9	A-Ex 18 vs A-Ex 6	0.715	46.2
	B-Ex Imp. vs Ex 24 Imp. 1.000	58.9	A-Ex 18 vs A-Ex 1	1.000	59.0
	B-Ex Imp. vs Ex 30 Imp. 1.000	54.3	A-Ex 18 vs Ex	1.000	56.7
				A-Ex 18 vs B-Ex	0.998	53.0
				A-Ex 12 vs A-Ex 6	0.949	72.1
				A-Ex 12 vs A-Ex 1	0.922	64.3
				A-Ex 12 vs Ex	0.962	67.2
				A-Ex 12 vs B-Ex	0.988	75.8
				A-Ex 6 vs A-Ex 1	0.741	50.7
				A-Ex 6 vs Ex	0.771	53.1
				A-Ex 6 vs B-Ex	0.631	48.6
				A-Ex 1vs Ex	0.896	49.3
				A-Ex 1 vs B-Ex	0.963	53.8
				Ex vs B-Ex	0.954	53.1

Table 3 .

 3 Pair-wise test (One-way ANOSIM, 4 th root transformed data) and % average dissimilarity between the impacted stations vs reference ones of each survey, the impacted station from the different surveys vs the pre-dredging station (B-Ex Imp) and the reference stations of the different surveys. Significant R values (P<0.05) are reported in bold.

	Groups	R	% dissimilarity	Groups	R	% dissimilarity
	A-Ex 30: Imp, vs Ref,	0,046	22.7	A-Ex 30 vs A-Ex 24	1.000	29.8
	A-Ex 24: Imp, vs Ref,	0,698	27.7	A-Ex 30 vs A-Ex 18	0.697	25.0
	A-Ex 18: Imp, vs Ref,	0,690	27.8	A-Ex 30 vs A-Ex 12	0.857	57.1
	A-Ex 12: Imp, vs Ref,	0,683	56.1	A-Ex 30 vs A-Ex 6	0.890	37.6
	A-Ex 6: Imp, vs Ref,	0,651	37.9	A-Ex 30 vs A-Ex 1	1.000	41.7
	A-Ex 1: Imp, vs Ref,	0,917	68.2	A-Ex 30 vs Ex	1.000	39.2
	Ex: Imp, vs Ref,	0,849	72.8	A-Ex 30 vs B-Ex	0.996	39.0
	B-Ex: Imp, vs Ref,	-0,062	32.2	A-Ex 24 vs A-Ex 18	0.974	29.7
				A-Ex 24 vs A-Ex 12	0.992	59.0
				A-Ex 24 vs A-Ex 6	0.983	41.5
	B-Ex Imp. vs Ex Imp.	0.444	74.1	A-Ex 24 vs A-Ex 1	1.000	42.7
	B-Ex Imp. vs Ex 1 Imp.	0.815	74.3	A-Ex 24 vs Ex	1.000	41.0
	B-Ex Imp. vs Ex 6 Imp.	0.815	45.5	A-Ex 24 vs B-Ex	1.000	41.6
	B-Ex Imp. vs Ex 12 Imp.	0.963	65.6	A-Ex 18 vs A-Ex 12	0.863	57.0
	B-Ex Imp. vs Ex 18 Imp.	1.000	39.1	A-Ex 18 vs A-Ex 6	0.688	34.7
	B-Ex Imp. vs Ex 24 Imp.	1.000	40.5	A-Ex 18 vs A-Ex 1	1.000	41.4
	B-Ex Imp. vs Ex 30 Imp.	1.000	40.3	A-Ex 18 vs Ex	1.000	38.2
				A-Ex 18 vs B-Ex	0.992	37.8
				A-Ex 12 vs A-Ex 6	0.734	52.6
				A-Ex 12 vs A-Ex 1	0.688	50.8
				A-Ex 12 vs Ex	0.715	51.5
				A-Ex 12 vs B-Ex	0.729	53.2
				A-Ex 6 vs A-Ex 1	0.793	35.1
				A-Ex 6 vs Ex	0.741	34.4
				A-Ex 6 vs B-Ex	0.642	33.8
				A-Ex 1vs Ex	0.773	32.9
				A-Ex 1 vs B-Ex	0.821	34.3
				Ex vs B-Ex	0.704	32.2

Table 4 .

 4 Number of taxa, density and Shannon diversity index calculated at each station of the different survey.

					Number of taxa
	Survey		B-Ex	Ex	A-Ex 1	A-Ex 6	A-Ex 12	A-Ex 18	A-Ex 24	A-Ex 30
	Extraction	Station
	impact	2	62	42	28	60	31	72	91	73
		3	65	5	10	42	22	75	80	60
		4	71	6	14	31	17	73	100	78
	reference	1	68	81	60	70	54	77	90	77
		5	69	54	68	59	45	83	95	75
		6	72	64	73	60	31	82	103	72
		7	54	69	55	52	32	76	97	78
		8	58	72	56	60	48	68	91	79
		9	72	64	62	63	22	79	92	75
		10	66	67	65	70	35	80	90	75
					Density [ind m -2 ]
	Survey		B-Ex	Ex	A-Ex 1	A-Ex 6	A-Ex 12	A-Ex 18	A-Ex 24	A-Ex 30
	Extraction	Station
	impact	2	1506	358	189	858	233	1878	2450	2481
		3	1561	14	42	483	219	1856	2911	2492
		4	2406	19	67	408	175	2236	2897	2308
	reference	1	1492	1389	881	1864	533	1944	3508	1903
		5	1442	767	942	1369	550	2533	2858	2919
		6	1811	861	939	1492	214	2083	2800	1906
		7	2058	1156	661	550	286	2053	2392	1797
		8	1556	1161	703	1497	403	2333	2408	2678
		9	1817	1250	986	1600	258	2617	2581	2036
		10	1768	869	1092	1786	342	2106	2664	1856
					Shannon diversity H'
	Survey		B-Ex	Ex	A-Ex 1	A-Ex 6	A-Ex 12	A-Ex 18	A-Ex 24	A-Ex 30
	Extraction	Station
	impact	2	3.35	3.31	2.95	3.45	2.76	2.94	3.64	3.36
		3	3.25	1.61	2.15	3.19	2.35	3.32	2.48	3.15
		4	3.19	1.75	2.40	3.11	2.16	2.93	2.87	3.25
	reference	1	3.32	3.75	3.42	3.44	3.28	3.52	2.99	3.61
		5	3.41	3.56	3.69	3.44	2.81	3.28	3.17	3.35
		6	3.22	3.71	3.81	3.30	2.99	3.46	3.36	3.41
		7	3.11	3.78	3.53	3.46	3.00	3.62	3.69	3.65
		8	3.35	3.66	3.57	3.28	3.43	3.39	3.63	3.47
		9	3.38	3.54	3.65	3.42	2.54	3.46	3.74	3.57
		10	3.28	3.65	3.55	3.62	3.04	3.55	3.60	3.56

extraction on the macrobenthos of a relict sands area (northern Adriatic Sea): results 12 months post-extraction. Marine Pollution Bulletin, 50, 768-777. Underwood, A.J., 1994. On beyond BACI: sampling designs that might reliably detect environmental disturbances. Ecological Application, 4, 3-15.

Van der Veer, H.W., Bergman, M.J.N., & Beukema, J.J. (1985). Dredging activities in the Dutch Wadden Sea: effects on macrobenthic infauna. Netherlands Journal of Sea Research, 19,[183][184][185][186][187][188][189][190] Van Dalfsen, J.A., Essink, K., Toxvig Madsen, H., Birklund, J., Romero, J., & Manzanera, M. (2000). Differential responses of macrozoobenthos to marine sand extraction in the North Sea and the Western Mediterranean. ICES Journal of Marine Science, 57, 1439-1445. Van Dalfsen, J.A., & Essink, K. (1997). Risk analysis of coastal nourishment techniques in the Netherlands (RIACON). National Institute for Coastal and Marine Management/RIKZ. Whang, X.H., & Pinardi N. (2003). Modeling the dynamics of sediment transport and resuspension in the northern Adriatic Sea. Journal Of Geophysical Research, 107, 1-23.


Cristina Munari

Michele Mistri
email: michele.mistri@unife.it

Evaluation of the applicability of a fuzzy index of ecosystem integrity (FINE) to characterize the status of Tyrrhenian lagoons

Keywords: Benthos, Environmental quality, Multimetric index, Fuzzy logics, Tyrrhenian Sea

The new index FINE, a multimetric, fuzzy-based index for the evaluation of environmental quality for Mediterranean transitional waters, was calculated using biotic data gathered between 2000 and 2006 at 15 stations in 4 Tyrrhenian transitional water ecosystems (Orbetello Lagoon, Padrongiano Delta, Stagno di S. Teodoro, and Stagno di Tortolì), i.e. in a different biogeographic sub-province respect to the one in which the index was developed and validated. The rationale of FINE is that certain attributes, selected on the basis of established principles of benthic ecology, are fundamental for lagoon ecosystem function. A set of other indices (Simpson's 1-', the W-statistics, AMBI, and BOPA) was also calculated and compared to FINE outputs. FINE, 1-', and the W-statistics were significantly correlated with sedimentary organic matter content. Some stations were unequivocally assigned to the same ecological status, independently from the index used; for others, some discrepancies were evident. Mediterranean transitional waters probably share ecosystem complexities which are not yet fully captured by indices developed and validated elsewhere.

Introduction

The European Water Framework Directive (WFD, 2000/60/EC) recommends the development of biotic indices which, if they constitute an extreme in information reduction, are most straightforward and easy to present to potential end users. Although the absolute values of an individual biotic index may be specific to a given ecoregion, theories and methods used in development should be applicable across systems. A universal index that works in all systems or even in systems of the same ecological type, however, is unrealistic because communities are complex and geographically diverse. Most indices so far proposed for the implementation of the WFD are based on the Pearson-Rosenberg paradigm [START_REF] Pearson | Macrobenthic succession in relation to organic enrichment and pollution of the marine environment[END_REF]: the AMBI / M-AMBI [START_REF] Borja | A marine biotic index to establish the ecological quality of soft-bottom benthos within European estuarine and coastal environments[END_REF]Muxica et al., 2007), the BENTIX [START_REF] Simboura | Benthic indicators to use in ecological quality classification of Mediterranean soft bottom marine ecosystems, including a new biotic index[END_REF], the BQI [START_REF] Rosenberg | Marine quality assessment by use of benthic species-abundance distribution: a proposed new protocol within the European Union Water Framework Directive[END_REF], and the BOPA [START_REF] Dauvin | Polychaete/amphipod ratio revisited[END_REF] are the most recently proposed biotic indices. AMBI, as well as the related BENTIX, accounts for the transition towards the dominance of mostly sensitive species during secondary succession. BQI is the product of two terms, one accounting for the relative dominance of tolerant versus sensitive species, and the second one for species richness. BOPA considers the total number of individuals, and the frequency of opportunistic polychaetes and amphipods.

Despite their obvious ecological and economical interests, transitional water bodies have received little attention about development of ecological status indicators. Recently, a multimetric index for transitional waters, the Fuzzy Index of Ecosystem Integrity (FINE: Mistri and Marchini, 2007), was proposed. In the development of FINE, a different approach from the sensitivity/tolerance one was chosen, because a) the classification of the different species or taxa are subjectively made and may vary between scientists and geographical areas [START_REF] Rosenberg | Marine quality assessment by use of benthic species-abundance distribution: a proposed new protocol within the European Union Water Framework Directive[END_REF], b) there are problems with making this operational due to our current lack of understanding of the links between the effects of human activities and changes in populations of such species [START_REF] Ices | Report of the Study Group on Ecological Quality Objectives for sensitive and for opportunistic benthos species[END_REF], and c) identification errors can lead to incorrect classifications and false interpretations of the indices [START_REF] Dauvin | Expertise in coastal zone environmental impact assessment[END_REF]. FINE takes into account 7 ecosystem attributes (variables or metrics), each having ecological relevance for lagoon ecosystems: 1) biomass of seaweeds, 2) presence of seagrass, 3) biomass of macrobenthos, 4) macrobenthic diversity (as the classical Shannon's H'), 5) macrobenthic functional diversity (as Shannon's H' on functional groups), 6) abundance of macrobenthos, 7) number of macrobenthic taxa. The 7 variables are combined in a system of 768 logic rules, and the result is a number, ranging from 0 to 100, which expresses the ecological quality of the considered sample. FINE 100 corresponds to the optimal conditions for all variables, whereas FINE 0 indicates that all the variables are displaying their worst modality; therefore, FINE 100 and FINE 0 can be regarded as reference for HIGH and BAD ecological quality (WFD, 2000/60/EC), respectively. All the algorithms for FINE calculation are based on the fuzzy set theory, which has repeatedly been proposed as a useful method to develop ecological models and indices of environmental conditions [START_REF] Salski | Fuzzy logic in ecological modelling[END_REF][START_REF] Silvert | Ecological impact classification with fuzzy sets[END_REF]2000;[START_REF] Lu | Diagnosing reservoir water quality using self-organizing maps and fuzzy theory[END_REF]Liuo et al., 2003;[START_REF] Adriaenssens | Fuzzy rule-based models for decision support in ecosystem management[END_REF]. The online version of the index is freely available at: http://web.unife.it/progetti/FINE/.

Recently, the FINE index was successfully tested in two Adriatic lagoons (Mistri et al., 2007). Aim of this study was to evaluate its applicability and effectiveness in transitional waters from the Tyrrhenian Sea (Figure 1), i.e. a different biogeographic sub-province respect to the one in which FINE was developed and validated. A set of indices was also calculated and compared to FINE outputs: Simpson's 1-', the W-statistics, AMBI, and BOPA. BENTIX and BQI were not used since the former is closely related to AMBI, and the latter is not applicable to very shallow transitional environments, where depth rarely exceed 1.5 m even at high tide.

Materials and Methods

Study areas

The Orbetello lagoon (southern Tuscany, eastern Tyrrhenian Sea) has a surface area of

Environmental and biotic data collection

At each station, water parameters (salinity, temperature, dissolved oxygen) were collected with an OxyGuard® Mk III probe and an ATAGO S/Mill-E refractometer. Sediment cores (4.5 cm i.d.) were also taken for total organic matter content (weight loss after drying and incineration at 450°C). Three replicate benthic samples were collected for the analysis of the macrofaunal community using a Van Veen grab (sampling area: 0.027 m 2 ); fauna retained on a 0.5 mm screen were identified to the species level and counted. The biomass of fauna was obtained by oven-drying to constant weight, and incineration (ash-free dry weight, gAFDW m -2 ). Seaweeds and seagrass, when present, were also collected and weighed to obtain wet weight.

Data analysis

FINE was calculated using the FINE software (http://web.unife.it/progetti/FINE/).

Besides FINE, the following indices were also applied to macrofaunal data: Simpson's 1-', the W-statistics [START_REF] Warwick | A new method for detecting pollution effects on marine macrobenthic communities[END_REF][START_REF] Clarke | Comparison of dominance curves[END_REF], the marine biotic index AMBI [START_REF] Borja | A marine biotic index to establish the ecological quality of soft-bottom benthos within European estuarine and coastal environments[END_REF], and the benthic opportunistic polychaetes/amphipods index, BOPA [START_REF] Dauvin | Polychaete/amphipod ratio revisited[END_REF]. The W-statistics condenses the information embedded in the ABC method (Abundance-Biomass Comparison, [START_REF] Warwick | A new method for detecting pollution effects on marine macrobenthic communities[END_REF] reflecting the degree of overlapping of kdominance curves for biomass and abundance of benthos. The theoretical basis of AMBI is that of the ecological strategies of the r, K and T [START_REF] Pianka | On r-and K-selection[END_REF], and the progressive steps in stressed environments [START_REF] Pearson | Macrobenthic succession in relation to organic enrichment and pollution of the marine environment[END_REF]. BOPA considers the total number of individuals collected in the samples, the frequency of opportunistic polychaetes, and the frequency of amphipods. An increase in the values of FINE, 1-' and W-statistics is understood as an improvement of environmental quality, while it means exactly the contrary in the case of AMBI and BOPA. 1-' and the W-statistics were calculated using the PRIMER ® package, AMBI using the AMBI ® software (http.//www.azti.es), while BOPA was calculated as:

BOPA = log [(f P / f A +1) +1]
where f P and f A are the opportunistic polychaetes and amphipods (excluding Jassa, but this opportunistic amphipod was not present at our study sites) frequencies, respectively. The purpose was to compare the performance of the different indices in distinguishing between different environmental scenarios. The response of each index as a function of different environmental variables was assessed through regression analyses, and regression ANOVAs.

Using multivariate analysis, we tried to assess which index was able to capture most of the information embedded in the macrofaunal species/abundance matrices. We used only AMBI, BOPA and FINE because of their 5-category (High, Good, Moderate, Poor, Bad) quality classification system. For each index, a matrix was constructed assigning to each study station its ecological quality (calculated with that index) in numerical form (5 = High, 4 = Good, 3 = Moderate, 2 = Poor, 1 = Bad). A second-stage MDS [START_REF] Clarke | Change in marine communities: an approach to statistical analysis and interpretation[END_REF] was then performed on these 3 one-row matrices (AMBI, BOPA, FINE) and the matrix based on abundance of species from all stations (SPECIES), using Euclidean distance to measure similarities. The idea was that "classification subsets" (i.e. AMBI, BOPA and FINE classifications) whose multivariate pattern links well to the information given by the biotic data will be represented by points on the second-stage MDS which lie close to the SPECIES point. Paired comparisons between indices and the SPECIES matrix were then performed through RELATE analysis [START_REF] Clarke | PRIMER v 6: User Manual Tutorial[END_REF].

Results

ACCEPTED MANUSCRIPT

In Table 1, physico-chemical characteristics of water and sediments at the various stations are shown. Table 2 summarizes the biotic data collected. Ecological conditions among stations varied greatly, as shown by the values of the input variables: 1) biomass of seaweeds varied from 0 to 25000 gWW m -2 , 2) biomass of seagrass from 0 to 890 gWW m -2 , 3) biomass of macrobenthos between 0.8 and 606.5 gAFDW m -2 , 4) macrobenthic alphadiversity between 0.42 and 2.69, 5) macrobenthic functional diversity between 0.39 and 1.49, 6) abundance of macrobenthos between 2002 and 48901 ind m -2 , 7) number of macrobenthic taxa between 5 and 62. The gradient of impact represented by the data set ranged from very impacted (e.g., STEO-BMJun and STEO-DJun, characterized by extremely depauperated benthic communities and huge amount of seaweeds), to high environmental quality (e.g. many stations at Tortolì, characterized by luxuriant seagrass prairies). Table 3 shows FINE outputs, together with the different grades of membership associated to each ecological status class. Table 4 shows the values of the other indices estimated at the sampling stations. While some stations resulted unequivocally assigned to the same ecological status, independently from the index used (e.g. PADR-SJul), for others some discrepancies were evident. For example, the ecological quality at station PADR-LMar should be considered disturbed from 1-', moderately disturbed from the W-statistics, good from AMBI, and high from BOPA. In Table 5 regression ANOVAs are summarized. Significant negative correlations were found between sediment organic matter content and FINE (r=-0.57; p<0.01), 1-' (r=-0.55; p<0.01), and W-statistics (r=-0.49; p<0.01).

Figure 2 shows the second-stage MDS plot. The AMBI, BOPA and FINE points were segregated in different zones of the plot, while the SPECIES point was grossly in its centre. It seemed that the FINE point lies closer to the SPECIES point compared to the BOPA and AMBI points. Table 6 shows the quantitative results of paired comparisons, through RELATE analysis, between indices (AMBI, BOPA and FINE) and SPECIES matrix: the highest correlation (Spearman's Rho=0.263) was found with the FINE classification.

Discussion

The successful development of a system capable of assessing the quantity, quality and functional value of aquatic systems is not a trivial task. It will require a holistic ecosystembased approach that at the same time focuses on individual species and habitats as well as accounting for those factor that affect these components at various temporal and spatial scales [START_REF] Diaz | A review of approaches for classifying benthic habitats and evaluating habitat quality[END_REF]. A number of indices have been developed in the few past years; Diaz et al. recently developed indices require a species identification that is as precise as possible, while the problems of species identification for research consultancies conducting environmental assessments has been examined by [START_REF] Dauvin | Expertise in coastal zone environmental impact assessment[END_REF]. From this viewpoint, FINE (as well as BOPA; [START_REF] Dauvin | Polychaete/amphipod ratio revisited[END_REF] has some advantages over other methods: there is no need for a deep taxonomic expertise or the painstakingly detailed analysis of species, since, instead of identifying and counting, say, Gammarus aequicauda, G. insensibilis, Echinogammarus veneris and Neogammarus adriaticus, one can count as Gammaridae sp. 1, sp. 2, sp. 3 and sp. 4, simply on the basis of morphological differences, since for FINE calculation, it is the number of species and not their names which is important. This is also true for assigning animals to broad functional categories, since, with a few exceptions (e.g., the detritivorous amphipod Corophium insidiosum), species belonging to the same lower taxonomic level (e. g. family) usually feed in the same manner.

Many of the proposed recent indices provide information about the relative abundances of sensitive species faced with increasing organic matter in the sediment and those of the species that are resistant or indifferent to such increases, or even favored by such conditions.

Recently [START_REF] Dauvin | Polychaete/amphipod ratio revisited[END_REF] highlighted that all the so far proposed indices, which aim to determine anthropogenic stress, relate to abundances of stress tolerant species (i.e. species tolerant of natural stressors likely to occur in transitional waters). Moreover, such indices relate to anthropogenically organic-rich systems whereas transitional waters are naturally organic rich systems. These complex systems are characterized by low benthic diversity, and, given such natural impoverishment, certain indices might loose their effectiveness. The usefulness of AMBI in detecting impact gradients has been clearly demonstrated in the northeastern and south-western Atlantic coastal area [START_REF] Borja | The application of a Marine Biotic Index to different impact sources affecting soft-bottom benthic communities along European coasts[END_REF][START_REF] Muniz | Testing the applicability of a Marine Biotic Index (AMBI) to assessing the ecological quality of soft-bottom benthic communities, in the South America Atlantic region[END_REF][START_REF] Muxica | Using historical data, export judgement and multivariate analysis in assessing reference conditions and benthic ecological status, according to the European Water Framework Directive[END_REF]. In the Mediterranean ecoregion, and in its transitional waters [START_REF] Ponti | Quality assessment of transitional waters using a benthic biotic index: the case study of the Pialassa Baiona (Northern Adriatic Sea)[END_REF][START_REF] Occhipinti-Ambrogi | Biotic indices[END_REF], the usefulness of AMBI is still uncertain: a clear impact gradient due to cage culturing of fish was detected at 20-30 m water depth in Western Greece [START_REF] Muxica | Using historical data, export judgement and multivariate analysis in assessing reference conditions and benthic ecological status, according to the European Water Framework Directive[END_REF], while it showed discrepancies in detecting trawling impacts at 16-30 m water depth in the Gulf of Lion [START_REF] Labrune | Characterization of the ecological quality of the coastal Gulf of Lions (NW Mediterranean). A comparative approach based on three biotic indices[END_REF], and failed to detect the spatial differentiation of ecological quality along a gradient of anthropogenic disturbance in the Sea of Marmara [START_REF] Albayrak | Ecological quality status of coastal benthic ecosystems in the Sea of Marmara[END_REF]. The BOPA proved its effectiveness in coastal marine and polyhaline systems in the English Channel [START_REF] Dauvin | Polychaete/amphipod ratio revisited[END_REF], but to a lesser extent in Tyrrhenian lagoons. [START_REF] Dauvin | Polychaete/amphipod ratio revisited[END_REF], however, stressed the need for future adaptations of BOPA for mesohaline and oligohaline estuarine zones. We do not dispute the effectiveness of AMBI and BOPA for coastal and estuarine waters. Mediterranean transitional waters, however, probably share ecosystem complexities which are not yet fully captured by these indices, as shown by the second-stage MDS and by the RELATE analysis. As pointed out by [START_REF] Cognetti | Colonization of stressed coastal environments[END_REF], the detection of impact gradients in transitional waters on the basis of opportunistic and tolerant organisms is an extremely difficult task, since the functional phenotypical flexibility of genetic nature which is characteristic of alolymnobic species gives them a wider margin of response to environmental alterations [START_REF] Cognetti | Biodiversity and adaptive mechanisms in brackish water fauna[END_REF]. The finding that FINE, which is based on benthos data plus vegetation data, was closer to the SPECIES point (Figure 2) than other indices exclusively based on benthos data should not be a surprise. Aquatic vegetation (i.e. seagrass and seaweeds) is known as a key structural component and regulator in shallow water ecosystems [START_REF] Carpenter | Effects of submersed macrophytes on ecosystem processes[END_REF][START_REF] Jaynes | Effects of vascular and non-vascular macrophytes on sediment redox and solute dynamics[END_REF], and a key factor in driving pattern and structure of faunal benthic assemblages [START_REF] Edgar | The influence of plant structure on the species richness, biomass and secondary production of macrofaunal assemblages associated with Western Australian seagrass beds[END_REF][START_REF] Bachelet | Seasonal changes in macrophyte and macrozoobenthos assemblages in three coastal lagoons under varying degrees of eutrophication[END_REF][START_REF] Yamamuro | Ecosystem shift resulting from loss of eelgrass and other submerged aquatic vegetation in two estuarine lagoons, Lake Nakaumi and Lake Shinji, Japan[END_REF].

Environmental managers and policymakers require tools capable of distinguishing the degree of degradation to the biotic community. While the WFD asks for a 5-category quality classification system ("high", "good", etc.), to environmental managers and policymakers the only important boundary is that between "moderate" and "good", since if the environmental quality of an area is "moderate" they have to spend (a lot of) money to make it "good". We feel that an advantage of FINE is that areas of intermediate quality can be finely identified through its membership grades (i.e. the outputs resulting from "fuzzification" and processing across the 768 model rules by means of logic operators, such as "and", "if…then" and "or", of the input data; Mistri and Marchini, 2007) Let us consider, for example, the case of sites PADR-SMar and PADR-SJul (Table 2). FINE values obtained for these sites were quite similar (61 and 75), and both fell into the "good" (FINE range: 60-80) quality class. However, PADR-SMar had membership grade 0.563 to "moderate", and 0.437 to "good", (and it was slightly inclined towards the condition "moderate"), while PADR-SJul had 0.005 to "moderate" and 0.995 to "good" (and it was more inclined towards the condition "good"). A comparison of FINE values (and its membership grades) through time, should be able to reveal if the environmental quality of an area is improving or worsening.

In this paper, the effectiveness of FINE has been tested in 4 Tyrrhenian lagoons, i.e. in a different biogeographic sub-province respect to the one in which the index was developed and validated, and succeeded in producing an ecologically relevant classification of the considered areas. Notwithstanding the fuzzy-based FINE has proven its effectiveness, at least in some Italian transitional waters, it does not conform to the WFD. In fact, the FINE model includes seaweeds, seagrass and benthos, while the WFD states the need to evaluate each element separately, in order to determine the impacts from different pressures over each of the elements, and this cannot be assessed when all of them are evaluated together. Finally, although FINE does not require a precise identification of species, it requires the measurement of biomass for benthic fauna and seaweeds, thus a notably larger sampling effort. These are certainly some of the limitations of FINE.

  27 km 2 , and is embraced within two sandbars. A third incomplete spit, on which the town of Orbetello lies, is connected with Mount Argentario by a cause-way, which partially divides the lagoon into two basins. The western basin is linked to the sea by the shorter Nassa Channel, and to the mouth of the Albegna river by the longer Fibbia Channel. It is a shallow, non-tidal environment with weak hydrodynamics, which reduces the dilution potential of organic matter and nutrients discharged from urban areas, aquaculture facilities, and agriculture waste waters. Three stations representative of different areas in the western basin were sampled in March and July 2003: stations ORBE-F and ORBE-N were close to the Fibbia and Nassa channels respectively, while ORBE-C was in the centre of the lagoon. The Rio Padrongiano Delta (2.5 km 2 ) lies in the north-eastern coast of Sardinia (western Tyrrhenian Sea), and has depth of about 0.8 m. It receives marine waters from the Gulf of Olbia, and freshwater from the Rio Padrongiano, whose flow ranges from torrent-like to almost dry behaviour in summer. It also receives waters from the adjacent Olbia harbour, the most important industrial and tourist port of Western Sardinia. The area is characterized by coarse and poorly vegetated (Cymodocea nodosa, Z. noltii) sediments. Padrongiano deltaic area hosts fisheries and mollusc (clams and mussels) aquaculture activities. Three stations were sampled in March, July and October 2003. At stations PADR-L and PADR-S, beds of the bivalve Musculista senhousia were present, while PADR-C was on bare sediments. The Stagno di San Teodoro (22 km 2 ) is a heavily euthrophicated, shallow (average depth: 0.7 m) coastal pond in northeastern Sardinia (western Tyrrhenian Sea). It is characterized by sandy bottoms with granitic outcrops, and connects to the sea through a narrow (20 m wide) and shallow (0.3 m deep) mouth, which is often impounded by sand and Posidonia oceanica debris. The Stagno, which is scarcely exploited for fishing, receives municipal wastewaters from the town of San Teodoro, the most important tourist centre in northern Sardinia, and nutrient rich freshwater from the Rio San Teodoro and Rio Filicani. Massive algal blooms due to the discharge of untreated wastewaters caused recently dramatic mortalities to the biota, resulting in legal actions against the top management of the treatment plant (Info Regioni 2007: http://www.edipol.it/images/news/94-108.pdf; page 15 of the document). Three stations were sampled in June 2006, just before the mortality event. Station STEO-D was close to the outflow of wastewaters, in the southernmost area of the Stagno, station STEO-BM was in the centre of the Stagno, and station STEO-C was close to the sea mouth. The Stagno di Tortolì is a shallow (average depth: 1.0 m) coastal pond (25 km 2 ) in central-eastern Sardinia (western Tyrrhenian Sea). It is connected to the sea by two channels, and receives freshwater inputs from the Rio Mannu. Bottoms are sandy-muddy, and large areas are covered with seagrasses (mainly Z. noltii). The Stagno hosts a flourishing finfish and shellfish traditional fishery. Six stations were sampled in June 2000 and January 2001: stations TORT-1 (south-eastern basin), TORT-4 (central basin) and TORT-5 (north-western basin) were located on seagrass prairies, TORT-2 (southern basin) was close to a sea inflow, TORT-6 (northern basin) to the Rio Mannu outflow, and TORT-3 was in the easternmost, confined area of the Stagno. Main pressures (sensu EC, 2002) on the 4 transitional systems considered are summarized as: a) >25% agricultural land use according to first level CORINE (COoRdination of INformation on Environment; http://reports.eea.europa.eu/COR0landcover/en/tab_content_RLR) categories (Orbetello, S. Teodoro); b) fishing and aquaculture operations (Orbetello, Padrongiano, S. Teodoro, Tortolì); c) discharge of eutrophicated waters from the catchment basin (Orbetello, Padrongiano, S. Teodoro); d) wide water level variations (S. Teodoro); e) boating (Orbetello, Padrongiano); and f) introduction of alien species (Padrongiano).

  (2004), reviewing the peer-reviewed literature, summarized the structure of 64 commonly used benthic habitat quality indices (Table 2, op. cit.), and reproached the scientific community for a "tautological development of new indices". The WFD bears a part of the responsibility for this phenomenon, since many of the new indices (e.g. AMBI / M-AMBI, BENTIX and BQI) were developed specifically with the WFD in mind and yet all are based on the Pearson and Rosenberg (1978) model for organic enrichment. Moreover, all the

Figure legends Figure 1 .

 legends1 Figure legends

Figure 2 .

 2 Figure 2. Second-stage MDS ordination of inter-matrix rank correlations. Underlying

Table 1 .

 1 Environmental characteristics at the study sites (DO: dissolved oxygen; Sal: salinity; OM: organic matter). Station name based on lagoon name, location within lagoon and month of sampling (e.g. ORBE-CJul: Orbetello lagoon, site C, July).

	Station	Depth	Temp	DO	Sal	OM
		(m)	(°C)	(mg L -1 )	psu	%
	ORBE-CJul	0.8	29	8.5	33	4.8
	ORBE-FJul	0.7	28	7.6	28	5.3
	ORBE-NJul	1.1	29	8.1	30	4
	ORBE-CMar	0.9	11	7.7	28	2.8
	ORBE-FMar	0.8	11	9.5	27	2.1
	ORBE-NMar	1.2	12	10.1	28	2
	PADR-LMar	0.7	13	10.2	42	5.3
	PADR-SMar	0.8	12	11.3	42	4.5
	PADR-CMar	0.9	13	12	42	2.3
	PADR-LJul	0.8	21	9.9	42	5
	PADR-SJul	0.9	20	8.5	43	4.9
	PADR-CJul	1	21	9.5	42	1.3
	PADR-LOct	0.7	12	10.1	28	5.1
	PADR-SOct	0.7	12	9.6	39	5.1
	PADR-COct	0.9	11	11.2	37	2
	TORT-1Jun	0.9	27	9.4	38	2
	TORT-2Jun	1.2	27	10.7	38	3.2
	TORT-3Jun	2	27	6.8	33	4.9
	TORT-4Jun	0.9	28	9.8	38	4.1
	TORT-5Jun	0.8	28	9.9	38	3.3
	TORT-6Jun	1.9	29	5.9	34	3.9
	TORT1-Jan	1.3	13	11.8	31	2.6
	TORT-2Jan	1.6	13	13.7	32	1.6
	TORT-3Jan	2.2	12	11.1	35	4
	TORT-4Jan	1.3	13	12.3	32	3.9
	TORT-5Jan	1.2	13	13.3	33	3.2
	TORT-6Jan	2.6	14	10.9	35	4.5
	STEO-CJun	0.7	25	8.4	34	2.9
	STEO-BMJun	1.2	24	12.9	23	6.8
	STEO-DJun	1.5	24	9.0	25	7.7

Table 2

 2 Biotic data. S: number of macrobenthic species; H': benthic alpha-diversity; H'f: benthic functional diversity; BB: biomass of benthic fauna; SwB: biomass of seaweeds; SgW: biomass of seagrass.

	Station	S	Abundance	H'	H'f	BB	SwB	SgB
			(ind m -2 )			(gAFDW m -2 )	(gWW m -2 ) (gWW m -2 )
	ORBE-CJul	25	29563	2.53	1.07	173.3	0	0
	ORBE-FJul	23	75134	1.41	0.88	113.8	0	0
	ORBE-NJul	32	37592	2.25	1.29	134.1	1000	0
	ORBE-CMar	34	29797	2.57	1.19	145	0	0
	ORBE-FMar	17	13270	2.18	1.07	29.6	0	0
	ORBE-NMar	25	48901	1.85	1.08	77.7	0	0
	PADR-LMar	34	22107	0.65	0.39	158.3	0	0
	PADR-SMar	44	6752	1.76	0.86	48.9	0	0
	PADR-CMar	37	6410	2.2	1.12	4.1	0	0
	PADR-LJul	34	23060	1.79	1.19	606.5	0	0
	PADR-SJul	36	5966	2.61	1.33	110.9	0	0
	PADR-CJul	36	15466	2.61	1.43	10.7	104	0
	PADR-LOct	54	31218	2.31	1.42	205.1	200	0
	PADR-SOct	62	26732	2.69	1.48	57.3	0	0
	PADR-COct	42	10767	2.44	1.46	23.9	50	0
	TORT-1Jun	29	10754	2.12	1.33	45.8	0	320
	TORT-2Jun	32	6925	2.77	1.49	247.9	0	0
	TORT-3Jun	13	5426	1.33	0.85	12	0	0
	TORT-4Jun	22	21188	1.33	1.02	243	0	570
	TORT-5Jun	19	27984	1.52	1.11	86.2	0	890
	TORT-6Jun	22	7868	2.27	1.46	56.8	290	0
	TORT1-Jan	42	18056	2.53	1.31	345.3	0	325
	TORT-2Jan	34	8596	2.65	1.44	42.4	0	0
	TORT-3Jan	26	2503	2.63	1.25	25.3	0	0
	TORT-4Jan	14	5377	1.36	0.86	208.8	0	173
	TORT-5Jan	23	11901	1.99	1.4	97.5	0	127
	TORT-6Jan	23	2836	2.33	1.05	40.6	0	0
	STEO-CJun	26	3774	2.14	0.98	3.27	50	0
	STEO-BMJun	14	12444	1.48	1.19	3.77	15000	0
	STEO-DJun	5	2002	0.52	0.42	0.8	25000	0

Table 4

 4 Values of the different indices estimated at the sampling stations.

	Station	1-	W-stat	AMBI	BOPA
	ORBE-CJul	0.9	0.21	3.28	0.33
	ORBE-FJul	0.55	-0.08	3.83	0.63
	ORBE-NJul	0.79	0.11	2.98	0.12
	ORBE-CMar	0.89	0.17	3.24	0.28
	ORBE-FMar	0.83	0.18	3.73	0.4
	ORBE-NMar	0.77	0.07	3.24	0.1
	PADR-LMar	0.2	0.02	2.09	0.02
	PADR-SMar	0.64	0.08	4.63	0.05
	PADR-CMar	0.82	0.02	1.28	0.28
	PADR-LJul	0.69	0.13	2.68	0.07
	PADR-SJul	0.89	0.25	1.36	0.16
	PADR-CJul	0.89	0.19	1.23	0.35
	PADR-LOct	0.86	0.1	4.39	0.41
	PADR-SOct	0.88	0.12	3.71	0.53
	PADR-COct	0.85	0.14	3.14	0.3
	TORT-1Jun	0.77	0.07	3.35	0.46
	TORT-2Jun	0.92	0.37	2.88	0.21
	TORT-3Jun	0.6	-0.11	3.7	0.67
	TORT-4Jun	0.52	0.31	2.75	0.07
	TORT-5Jun	0.68	0.04	1.81	0.05
	TORT-6Jun	0.88	0.16	1.94	0.04
	TORT1-Jan	0.85	0.2	2.71	0.3
	TORT-2Jan	0.89	0.2	2.95	0.42
	TORT-3Jan	0.88	0.23	2.75	0.37
	TORT-4Jan	0.54	0.16	1.72	0.05
	TORT-5Jan	0.79	0.16	1.37	0.01
	TORT-6Jan	0.85	0.25	2.94	0.42
	STEO-CJun	0.77	0.08	4.58	0.22
	STEO-BMJun	0.68	-0.06	1.4	0.01
	STEO-DJun	0.24	-0.29	3	0

Table 5

 5 Significancy of regression ANOVAs (p-values) between different indices and environmental variables DO: dissolved oxygen; OM: organic matter).

		FINE	1-'	W-stat	AMBI	BOPA
	Salinity	0.104	0.708	0.052	0.205	0.717
	DO	0.276	0.803	0.325	0.108	0.237
	OM	0.001	0.0015	0.006	0.759	0.29

Acknowledgements

W. Silvert, J. Widdows and an anonymous reviewer are gratefully acknowledged for constructive criticism.