A habitat-specific fish-based approach to assess the ecological status of Mediterranean coastal lagoons

Marine Pollution Bulletin 58 (2009) 1704–1717

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A habitat-specific fish-based approach to assess the ecological status of Mediterranean coastal lagoons Anita Franco *, Patrizia Torricelli, Piero Franzoi Dep. Environmental Sciences, University of Venice, Castello 2737/b, 30122 Venice, Italy

a r t i c l e

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Keywords: Fish Multimetric indices HFI Ecological status Habitat Mediterranean coastal lagoon

a b s t r a c t A habitat approach was promoted in the framework of ecological status assessment of transitional waters, assuming the importance of habitat heterogeneity to the overall system status. The approach was applied to the use of fish-based multimetric indices by adapting them to seagrass and marsh habitats in the Venice lagoon, Italy, through selection of appropriate metrics and reference conditions. While for marsh habitats, no clear patterns resulted, the index response for seagrass was consistent with the habitat degradation and loss recorded in the lagoon between 2002 and 2005 and with the higher habitat disturbance in southern and central lagoon sub-basins. The assessment of individual habitats is presented as a first step in the process of evaluating the overall condition of a Mediterranean lagoon environment, which should also take account of the diversity of habitats and their availability within the system to properly define an overall index of ecological status. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Estuaries, and transitional environments in general, are very important ecosystems in terms of ecological services provided worldwide (Costanza et al., 1997). Together with services like gas regulation and nutrient cycling, they function as essential habitats for many organisms, providing important nursery grounds for juveniles of many marine, estuarine and freshwater fishes, conduits for species that move between the sea and rivers, and feeding and staging sites for significant populations of migratory species (Elliott and Hemingway, 2002; Turpie et al., 2002). These services and functions are often threatened by the increasing human pressure in these areas (Dennison et al., 1993; Simenstad and Cordell, 2000), with consequent effects including water quality impairment, habitat loss and disturbance. This has led to an increasing interest in the assessment of the water quality and ecological condition of these systems, which is commonly performed through the measurement of their physical, chemical and biological characteristics. Bioassessment methods, in particular, have been recently preferred as they integrate the complexity of the other responses, providing useful tools for determining use attainment and detecting impairment in water systems (Seegert, 2000; Whitfield and Elliott, 2002). Among biological indicators, fishes have been successfully used to detect environmental quality changes in a wide variety of aquatic habitats (Whitfield, 1996; Soto-Galera et al., 1998) due to sev* Corresponding author. Tel.: +39 0412347732; fax: +39 0415281494. E-mail address: [email protected] (A. Franco). 0025-326X/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpolbul.2009.06.016

eral advantages as indicator organisms (Whitfield and Elliott, 2002; Harrison and Whitfield, 2004). As such, they are recommended by the European Commission as a biological quality element to be used in the assessment of the ecological status of transitional water bodies (Water Framework Directive, WFD; Directive 2000/60/EC). Fish community attributes, in particular, have been widely used to monitor the ecological functioning and health of estuarine ecosystems (Moore et al., 1995; Whitfield and Elliott, 2002; Uriarte and Borja, 2009), proving to be more successful than single species bioassays at predicting the effects of multiple stresses on biological systems (Deegan et al., 1997; Whitfield and Elliott, 2002). Most of the methods used to assess the ecological status, based upon fish, apply the multimetric approach first used by Karr (1981) to assess the ‘Biotic Integrity’ of North American freshwater fish communities. This approach relies on an array of measures, or metrics, that incorporate information at different levels into a single ecologically-based index of water resource quality (Karr, 1981; Karr et al., 1986; Fausch et al., 1990; Belpaire et al., 2000). Multimetric evaluation has been recognized as a more reliable and flexible tool than individual metrics (Harrison and Whitfield, 2004; Reiss and Kröncke, 2005; Hering et al., 2006), with the additional advantage of allowing easier communication between researchers, managers, stakeholders and policymakers (Ramm, 1988; USEPA, 2000a; Breine et al., 2007; Henriques et al., 2008). Detecting environmental and ecological changes resulting from anthropogenic impacts may be a particularly difficult task in transitional waters, due primarily to the high background variability that naturally characterizes these environments (Elliott and

A. Franco et al. / Marine Pollution Bulletin 58 (2009) 1704–1717

Quintino, 2007). Biological indicators must be able to measure the ‘signal’ of anthropogenic effects over the ‘noise’ of this natural variability (Whitfield and Elliott, 2002). The latter is ascribed not only to the marked temporal fluctuations of the transitional water environment and its biota, but also to the spatial heterogeneity within the system (Dauvin and Ruellet, 2009). Coastal lagoons, in particular, are dynamic systems characterized by the existence of intense gradients, providing a collection of habitat types (Gamito et al., 2005). Species and communities are then forced to adapt to such heterogeneous conditions (Dauvin and Ruellet, 2009), leading to a natural spatial variability of biological assemblages regarding both the structure (species composition and abundance) and function (productivity, trophic webs and fluxes) of the lagoon ecosystem (Gamito et al., 2005). This habitat variability should be considered when using biological assemblages as indicators of the overall ecological status of a transitional water ecosystem. The main aim of the present paper is to promote a habitat approach in the use of fish-based multimetric methods for the assessment of the ecological status of a lagoon water body. In support of this, an index (Habitat Fish Index, HFI) was calibrated and applied separately for two different habitat types in the Venice lagoon. This environment consists of a mosaic of shallow habitats (average depth 1 m) available for fishes, such as intertidal marshes and mudflats, subtidal mudflats and sand flats, and seagrass beds (Ravera, 2000; Franco et al., 2006a; Molinaroli et al., 2009). This habitat diversity is favored by the wide area of this basin (about 550 km2) (Franco et al., 2008b), being the largest lagoon in the Mediterranean region, hence representing an optimal case study for the application of such an approach. Clear effects of this environmental variability in the Venice lagoon have been recognized with regard to the distribution of fishes, with different assemblages associated with different habitats, distinguished by specific taxonomical and functional characteristics (Malavasi et al., 2004; Franco et al., 2006a,b,c). Hence, the structure and functioning of the overall lagoon fish assemblage are highly dependent on the contribution provided by the different habitats present in the system and influenced by their availability and diversity in the lagoon environment, as demonstrated for Mediterranean coastal lagoons in general (Franco et al., 2008b). The approach of this paper relies heavily on this assumption, hence promoting the assessment of the ecological status of individual lagoon habitats as an essential step in the evaluation of the condition of the whole system, which is eventually derived from the combination of the habitat-specific results. 2. Materials and methods 2.1. The calibration dataset Several shallow aquatic habitats are present in the Venice lagoon, including seagrass beds (mainly Cymodocea nodosa, Zostera marina and Nanozostera noltii), bare mudflats, sand flats and marsh habitats (Franco et al., 2006a). As a first application, the evaluation of the ecological status was focused, in particular, on seagrass beds and shallow subtidal habitats associated with salt marshes (marsh habitats, hereafter), which present clearly associated and structured fish assemblages as shown in previous studies (Mainardi et al., 2002, 2004; Malavasi et al., 2004; Franco et al., 2006a,b,c). Fish assemblages were sampled in April–May, July–August and October–November 2002 in the shallow lagoon waters by means of a seine net (see Franco et al., 2006a for details on sampling). Sampling sites (16 in the seagrass habitats and 26 in the marsh habitats, Fig. 1) were evenly distributed between the three sub-basins the lagoon is divided into from a hydrological point of view (Avanzi et al., 1979) (Fig. 1). Captured fish were identified by species and allocated to main estuarine use and feeding mode functional groups according to the classification of estuarine fishes

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provided in Franco et al. (2008a) (Table 1). Species abundance in each site was standardized over a common area (100 m2) in order to obtain comparable density data. 2.2. Index calculation The steps for the HFI calibration (metrics choice, definition of reference conditions and scoring criteria, metrics rating and combination in the final index) were undertaken separately for the two habitats such that a habitat-specific index could be derived. Help in this process came from the review of previously developed fish-based classification schemes (Deegan et al., 1997; Meng et al., 2002; Whitfield and Elliott, 2002; Harrison and Whitfield, 2004, 2006; Breine et al., 2007; Coates et al., 2007) after adjustment with the knowledge of fish assemblage peculiarities in the different Mediterranean lagoon habitats (Franco et al., 2006a,b; Pérez-Ruzafa et al., 2006). According to previous studies, the time of the year is a factor that strongly influences the occurrence and diversity of fishes in the lagoon habitats due to the seasonality of recruitment, population dynamics and migration patterns (Gordo and Cabral, 2001; Mainardi et al., 2004; Franco et al., 2006a). Hence, HFI was derived separately for the different seasons and in the present paper, results regarding only the summer season (July–August) are presented. This choice was made to provide an easier explanation of the results and was allowed by the consistency among seasons. A total of 14 metrics were selected, representing broad community attributes such as species diversity and composition (metrics 1–4), species abundance (metrics 5–6), and community functioning (metrics 7–14; Table 2). The selected metrics were calculated as follows. Species diversity (metric 1) was measured as the total number of taxa in the assemblage. As for the other metrics (except for metric 3), all possible alien taxa were removed from the dataset before the metric calculation. The presence of indicator species (metric 2) provided a measure of ‘disturbance-sensitive species’, termed ‘indicator taxa’ by the WFD (Directive 2000/60/EC), giving an additive conservation value to the habitat (Harrison and Whitfield, 2004). These species were selected on the basis of their strong association with the specific habitat (hence being highly susceptible to habitat degradation) and by also considering their conservation status and protection under European legislation (Directive 92/43/EC; IUCN, 2008). The presence of alien species (metric 3) in the assemblage was also recorded as an indicator of habitat disturbance, being considered a potential threat to naturally-occurring taxa through competitive exclusion and predation as well as a direct measure of human interference (Harrison and Whitfield, 2004). Alien species were identified by reference to the Mediterranean area (Mavruk and Avsar, 2008) and to the Venice lagoon in particular (Tortonese, 1975; zoological archives of the Venice Museum of Natural History, http://www.msn.ve.it). Species composition (metric 4) was evaluated in the assemblage and, as a quantitative complement to this metric, the species relative abundance (%, metric 5) was also considered. Their similarity with a reference assemblage was calculated by using the Bray–Curtis index based on presence–absence and relative abundance data, respectively. The species dominance in the assemblage (metric 6) was measured as the number of taxa required to make up 90% of the total abundance. The functional aspects of the habitat-associated fish assemblages were also taken into account by measuring the number of estuarine residents (metric 7) and marine migrant taxa (including both marine estuarine dependent and marine estuarine opportunistic taxa, metric 8). The relative abundance of these two functional groups (residents, metric 9; marine migrants, metric 10) was also assessed as the respective complementary quantitative measure. Functional aspects were also accounted for by considering the trophic groups characterizing the habitat-

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Fig. 1. Sampling sites in the Venice lagoon, divided by habitat (seagrass and marsh) and year (2002 = calibration dataset; 2003–2005 = application dataset). Dashed lines are the watersheds dividing the lagoon into three sub-basins.

associated fish assemblages. According to Franco et al. (2008a), trophic groups were assessed on the resident and migrant components (including marine migrant and diadromous species) of the fish assemblage, these species being highly dependent on the lagoon system for feeding, contrary to stragglers. The number of benthic invertebrate feeding species (metric 11) was calculated by combining the microbenthivorous and macrobenthivorous categories defined in Franco et al. (2008a). In marsh habitats, the number of detritus feeder species (metric 12-marsh) was considered as

well, while in seagrass habitats, the number of fish species feeding on demersal-pelagic prey (metric 12-seagrass) was calculated by combining the hyperbenthos, zooplankton and fish feeder categories defined in Franco et al. (2008a). The relative abundance of benthic invertebrate feeding species (metric 13), of detritivores (metric 14-marsh) and of fish species feeding on demersal-pelagic prey (metric 14-seagrass) was also calculated as quantitative, complementary measure of metrics 11, 12-marsh and 12-seagrass, respectively.

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Table 1 Fish species in marsh and seagrass habitats of the Venice lagoon. Species are allocated to ecological and feeding guilds (EUFG and FMFG, respectively) according to Franco et al. (2008a), and their status as either indicator species for the habitat or alloctonous taxa is shown. Species

EUFG

FMFG

Status

Species

EUFG

FMFG

Aphanius fasciatus Arnoglossus laterna Atherina boyeri Belone belone Boops boops Carassius carassius Chelidonichthys lucernus Chelon labrosus Dicentrarchus labrax Diplodus annularis Diplodus puntazzo Diplodus sargus Diplodus vulgaris Engraulis encrasicolus Gambusia affinis Gobius niger Hippocampus guttulatus* Knipowitschia panizzae Labrus viridis Lepomis gibbosus Lithognathus mormyrus Liza aurata Liza ramado Liza saliens Mugil cephalus Mullus surmuletus Nerophis maculatus

ES MS ES MM MS F MM MM MM MM MS MS MS MM ES ES ES ES MS F MM MM D MM D MM MS

OV

Indicator (marsh)

Nerophis ophidion Oblada melanura Parablennius sanguinolentus Parablennius tentacularis Platichthys flesus Pomatoschistus canestrinii Pomatoschistus marmoratus Pomatoschistus minutus Pseudorasbora parva Rhodeus amarus Salaria pavo Sardina pilchardus Sciaena umbra Scophthalmus rhombus Scorpaena porcus Solea solea Sparus aurata Sphyraena sphyraena Sprattus sprattus Symphodus roissali Syngnathus abaster Syngnathus taenionotus Syngnathus tenuirostris Syngnathus typhle Trachurus trachurus Zosterisessor ophiocephalus

ES MS MS MS MM ES ES MM F F ES MM MS MS ES MM MM MS MM ES ES ES MM ES MS ES

Bmi,HZ

HZ HP

Bmi,BMa,HP DV HZ,HP OV

PL OV Bmi,HP Bmi Bmi

Alloctonous Indicator (seagrass) Indicator (marsh) Alloctonous

Bmi,BMa DV DV DV DV Bmi,BMa

Bmi,BMa Bmi Bmi Bmi

Status

Indicator (marsh)

Alloctonous OV PL

BMa,HP Bmi,BMa Bmi,BMa PL Bmi,BMa Bmi HZ Bmi HZ Bmi,BMa

Indicator (seagrass)

Indicator (seagrass)

EUFG: ES = estuarine species; MM = marine migrants; MS = marine stragglers; F = freshwater species; D = diadromous species. FMFG: Bmi = microbenthivores; BMa = macrobenthivores; PL = planktivores; HZ = hyperbenthivores–zooplanktivores; HP = hyperbenthivores–piscivores; DV = detritivores; OV = homnivores (feeding guilds indicated for resident and migrant species only; multiple allocation reflects the ontogenetic diet shift of the species). * Hippocampus hippocampus was considered an indicator species for seagrass habitat as well, though not present in the analysed dataset.

Table 2 Selected metrics used for index calculation, and their expected response to environmental stress (0 = absence; 1 = presence; – = decrease; + = increase; ++/– – = too high or too low values). When no indication is present, the metric is used for index calculation in both seagrass and marsh habitats. Metrics Biodiversity M1 M2 M3 M4

Response Species diversity Presence of indicator species (of naturalistic interest) Presence of alien species Species composition (similarity with reference assemblage)

Species abundance M5 Relative species abundance (similarity with reference assemblage) M6 Dominance Nursery function M7 Number of resident taxa M8 Number of marine migrant taxa M9 Relative abundance of resident taxa M10 Relative abundance of marine migrant taxa Trophic integrity M11 Number of benthivorous taxa M12-marsh Number of detritivorous taxa M12Number of species feeding on demersal-pelagic prey seagrass M13 Relative abundance of benthivorous taxa M14-marsh Relative abundance of detritivorous taxa Relative abundance of species feeding on demersal-pelagic M14seagrass prey

0 1 –

– + – – ++/– – ++/– – – – – ++/– – ++/– – ++/– –

According to Simon (2000), an a priori hypothesis on the effect of a disturbance on each metric was formulated based on previous literature. A linear relationship with a monotonic increase or decrease with habitat disturbance was assumed for most metrics

(Table 2). A reduction in the species diversity, either total or within functional groups, and in the similarity with the reference condition were expected as signals of an impairment of the status of the fish assemblage (Odum, 1983; Fausch et al., 1990; Whitfield and Elliott, 2002). A positive linear relationship with the degree of habitat degradation, in turn, was expected for the species dominance in the assemblage as a result of the shift from ‘diverse’ to ‘simple’ assemblages dominated by few species (Odum, 1983; Fausch et al., 1990). On the other hand, the metrics accounting for the relative abundance of functional guilds in the assemblage were assumed as non-linear relative to the disturbance level (Table 2). A functional imbalance in the assemblage, as indicated by both very low and very high abundances of the considered guilds, was assumed, in fact, as a signal of disturbed conditions (Harrison and Whitfield, 2004; Franco et al., 2008a). Reference conditions were identified for each metric by using a data-driven method (Harris and Silveira, 1999; USEPA, 2000b; Harrison and Whitfield, 2004, 2006; Breine et al., 2007). According to this method, the ‘best’ values observed in the calibration dataset were used to establish the expectations for each metric in the two habitats independently from where these best values were measured. The metric values observed in each site were then compared to the respective reference conditions in order to assess the degree of their deviation from the reference and to consequently rate them for the purpose of classification of ecological status. The comparison between observed and reference values relied on the calculation of Ecological Quality Ratios (EQR), whose distribution was analyzed in order to establish appropriate scoring thresholds. For most of the metrics, EQR was calculated separately for each habitat as the ratio xij/xiref, with xij being the observed value for the metric i in the site j and xiref the reference value for the metric i. The EQR was then expressed as a percentage of the reference value. For

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multivariate metrics (metrics 4 and 5), EQR was calculated as the percentage similarity value between the observed and the habitat reference assemblage. In turn, for metrics 2 and 3 (based on presence–absence of certain species) and for metrics 9, 10, 13 and 14 (assumed as non-linear relative to the habitat disturbance), alternative methods, based on a combination of the spread of the data and expert judgment, were adopted to assess the degree of deviation from reference conditions (see results for details). Based on the distribution of the values within the dataset for each metric and on the derived thresholds, metrics were then rated good (scored 5) when similar to the reference, fair (scored 3) when different from the reference, or poor (scored 1) when substantially different from the reference, following Harrison and Whitfield (2004, 2006). The scoring of the metrics allowed them to be transformed into standard, comparable scores for their integration in the final index. HFI was calculated for each habitat as the sum of these scores, giving equal weight to each metric in terms of their contribution to the overall index. To assist the interpretation of the index results, the final HFI values were also scored 1, 2, 3, 4 or 5 in order to reflect the five ecological status conditions indicated in the WFD: ‘bad’, ‘poor’, ‘moderate’, ‘good’, and ‘high’, respectively (Directive 2000/ 60/EC). The scoring criterion was defined according to the range of variation of the index and to the distribution of its values. The contribution of the different metric scores to the final ecological status classification was analyzed by means of histograms for the two habitats. 2.3. Index application The calibrated HFIs (one for each habitat type) were applied to an independent dataset of fish assemblages in the two shallow water habitats of the Venice lagoon sampled from 2003 to 2005 by seine netting. In particular, data on fish assemblages sampled during summer surveys in the seagrass habitat (four stations in 2004 and five in 2005) and in the marsh habitat (four stations in 2003, five in 2004 and five in 2005) were used (Fig. 1). In 2003 and 2004, sampling sites were located in the northern lagoon sub-basin only, while in 2005, samples from the three sub-basins were collected (Fig. 1). The index values were then compared between years and between lagoon sub-basins (northern, central and southern) for each habitat in order to allow the interpretation of possible temporal and spatial differences of habitat conditions in the lagoon. Kruskal–Wallis analysis was performed to test the significance of observed differences. 3. Results 3.1. Index calibration The reference conditions for species diversity (metric 1) were defined separately for each habitat by ranking the data on total number of taxa and averaging the values falling in the upper quartile. For marsh habitats, the reference species richness was 11 species, while for seagrass habitats, it was 13 species. A score of 5 was then assigned to a species richness of P90% of the reference value. A score of 3 was given to a species richness between 50% and 90% of the reference value, and a score of 1 was assigned to a species richness of <50% of the reference value (Table 3). According to this scoring, 10, 13 and 3 marsh sites, and 3, 5 and 8 seagrass sites were assigned a score of 5, 3 and 1, respectively. Regarding metric 2, Hippocampus hippocampus, Hippocampus guttulatus, Syngnathus abaster and Zosterisessor ophiocephalus were selected as indicator species for seagrass habitats, while Aphanius fasciatus, Pomatoschistus canestrinii and Knipowitschia panizzae were chosen for marsh habitats. The reference condition was

assumed to be the presence of any of these species in the fish assemblage and, when fulfilled, a score of 5 was assigned. In turn, a score of 3 was given when these taxa were not recorded, as their absence does not necessarily indicate degradation (Table 3). According to this scoring, 24 and 2 marsh sites were assigned a score of 5 and 3, respectively, while all of the seagrass sites were scored 5. Alloctonous fish species in the Venice lagoon are mostly of freshwater origin, e.g., Gambusia affinis, Lepomis gibbosus, and Pseudorasbora parva. The reference condition for metric 3 was assumed to be the absence of any of these species in the habitat fish assemblage and, when fulfilled, a score of 3 was assigned. In turn, a score of 1 was given if any of the alien species were recorded (independent of their abundance; Table 3). All the lagoon sites were scored 3 according to this metric, except for one marsh site in which G. affinis was found in the fish assemblage. A reference fish assemblage composition (metric 4) was established for each habitat by calculating the frequency of occurrence of each species in the calibration dataset and by selecting the most frequently occurring taxa in a number corresponding to the habitat-specific reference richness value. After calculating Bray–Curtis similarity between each observed assemblage and the reference one, similarity values of P80% were assigned a score of 5, values between 50% and 80% were given a score of 3 and a score of 1 was given to lower similarity values (Table 3). According to this criterion, 9, 15 and 2 marsh sites, and 0, 11 and 5 seagrass sites were assigned a score of 5, 3 and 1, respectively. Regarding metric 5, the mean relative abundance of each species was calculated for the group of sites within each habitat, and the most abundant taxa, in a number corresponding to the reference richness value, were selected as the reference species assemblage. After calculating Bray–Curtis similarity between each observed assemblage and the reference one, similarity values of P60% were assigned a score of 5, values between 40% and 60% were given a score of 3, and a score of 1 was given to similarity values of <40% (Table 3). According to this scoring, 7, 16 and 3 marsh sites, and 12, 4 and 0 seagrass sites were assigned a score of 5, 3 and 1, respectively. The reference conditions for species dominance (metric 6) were defined by ranking the data and averaging the values falling in the upper quartile separately for each habitat. For marsh habitats, the reference dominance was 4 species, while for seagrass habitats, it was 5 species. The metric scoring was carried out according to the criteria described for metric 1 (Table 3), leading to 11 and 15 marsh sites, and 8 and 8 seagrass sites assigned a score of 5 and 3, respectively. The reference conditions for metric 7 were defined by ranking the data on the number of estuarine resident species and averaging the values falling in the upper quartile separately for each habitat. For both habitats, the reference value was 8 resident species. The metric scoring was carried out according to the criteria described for metric 1 (Table 3), leading to 12, 12 and 2 marsh sites, and 6, 9 and 1 seagrass sites assigned a score of 5, 3 and 1, respectively. Reference conditions and scoring for metric 8 were defined as for metric 7, starting from the number of marine migrant taxa in the samples (Table 3). For both habitats, the reference value was 3 marine migrant species. The scores of 5, 3 and 1 were assigned to 9, 9 and 8 marsh sites, and 2, 2 and 12 seagrass sites, respectively. The reference conditions, thresholds and scoring criteria for metrics 9 and 10 were defined based on the knowledge of the functional structure of fish assemblages in transitional waters and in the two studied habitats in particular. In marsh habitats, the reference condition for these metrics was assumed as a total relative abundance of either resident or marine migrant species comprising between 25% and 75% of the total fish abundance. In turn, in seagrass habitats, the reference assemblage included 75–95% estuarine resident fishes and 5–25% marine migrant fishes. A score of 5 was given when the metric values fell within the respective reference range. A score of 3 was allocated

Table 3 Metric scoring thresholds for seagrass and marsh lagoon habitats in summer. Marsh habitat

Seagrass habitat 3 (fair)

1 (poor)

5 (good)

3 (fair)

1 (poor)

P10 Presence – P80

5–9 Absence Absence P50 and <80

<5 – Presence <50

P12 Presence – P80

7–11 Absence Absence P50 and <80

<7 – Presence <50

Species abundance M5 Relative species abundance (% similarity with reference assemblage) M6 Dominance

P60 P4

P40 and <60 2–3

<40 <2

P60 P4

P40 and <60 2–3

<40 <2

Nursery function M7 M8 M9 M10

Number of resident taxa Number of marine migrant taxa Relative abundance of resident taxa (%) Relative abundance of marine migrant taxa (%)

P7 P3 P25 and 675 P25 and 675

4–6 2 P10 and <25, or >75 and 690 P10 and <25, or >75 and 690

<4 <2 <10 or >90 <10 or >90

P7 P3 P75 and 695 P5 and 625

4–6 2 P25 and <75, or >95 <5, or >25 and 675

<4 <2 <25 >75

Trophic integrity M11 M12-marsh M12-seagrass M13 M14-marsh M14-seagrass

Number of benthivorous taxa Number of detritivorous taxa Number of species feeding on demersal-pelagic prey Relative abundance of benthivorous taxa (%) Relative abundance of detritivorous taxa (%) Relative abundance of species feeding on demersal-pelagic prey (%)

P6 >1 – P50 and 690 P1 and 610 –

3–5 1 – P10 and <50, or >90 <1 or >10 and 675 –

<3 0 – <10 >75 –

P4 – P4 P25 and 675 – P25 and 675

2–3 – 2–3 P10 and <25, or >75 and 690 – P10 and <25, or >75 and 690

<2 – <2 <10 or >90 – <10 or >90

Biodiversity M1 M2 M3 M4

Species diversity Presence of indicator species (of naturalistic interest) Presence of alien species Species composition (% similarity with reference assemblage)

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5 (good)

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when both metric values were comprised of between 10% and 25% or between 75% and 90% in marsh habitats. In seagrass habitats, sites were scored as 3 when the relative abundance of resident species fell between 25% and 75% or it was >95% and when the relative abundance of marine migrant species fell between 25% and 75% or it was <5%. A score of 1 was allocated to those sites where resident or marine migrant species comprised either <10% or >90% of the total abundance in marsh habitats or when resident abundance was <25% and marine migrant abundance was >75% of the total fish abundance in seagrass habitats (Table 3). According to these criteria, 4, 4 and 18 marsh sites, and 2, 14 and 0 seagrass sites were assigned a score of 5, 3 and 1, respectively, for both metrics. The reference conditions for metric 11 were defined separately for each habitat by ranking the data on the number of benthic invertebrate feeding fish species and averaging the values falling in the upper quartile. The resulting reference values were 6 and 5 for marsh and seagrass habitats, respectively. The metric scoring was carried out according to the criteria described for metric 1 (Table 3), leading to 10, 14 and 2 marsh sites, and 5, 10 and 1 seagrass sites assigned a score of 5, 3 and 1, respectively. With regard to metric 12, reference conditions and scoring were calculated as for metric 11, starting from the number of detritivorous taxa in marsh habitats and from the number of taxa feeding on demersal-pelagic prey in seagrass habitats (Table 3). The reference condition for marsh habitats assumed the presence of at least 1 detritivorous species, with 1, 12 and 13 marsh sites being assigned a score of 5, 3 and 1, respectively. In turn, the reference condition for seagrass habitat was 5 species feeding on demersal-pelagic prey in the fish assemblage, and 6, 9 and 1 seagrass sites were assigned a score of 5, 3 and 1, respectively. As with metrics 9 and 10, the reference conditions, thresholds and scoring criteria for metrics 13 and 14 were defined based on the knowledge of the functional structure of fish assemblages in transitional waters and in the two studied habitats in particular. In marsh habitats, the reference condition was assumed as a total relative abundance of benthivorous taxa comprising between 50% and 90% and a relative abundance of detritivorous taxa comprising between 1% and 10% of the total fish abundance. In seagrass habitats, the reference assemblage was defined when the relative abundance of either benthivorous

species or taxa feeding on demersal-pelagic prey fell between 25% and 75% of the total. A score of 5 was given when the metric values fell within the respective reference ranges. A score of 3 was allocated to marsh sites where the abundance of benthivorous species fell between 10% and 50% or >90% and where the detritivorous fishes accounted for 10–75% or <1% of the total abundance. In seagrass sites, a score of 3 was assigned when both metrics assumed values between 10% and 25% or between 75% and 90%. Where benthivorous species were <10% or detritivorous fishes were >75% of the total abundance, a score of 1 was allocated to marsh sites. In turn, seagrass sites were scored as 1 when the values for both metrics were <10% or >90% (Table 3). According to these criteria, 15 and 11 marsh sites, and 13 and 3 seagrass sites were assigned a score of 5 and 3, respectively, with regard to metric 13. Regarding metric 14, 7 and 19 marsh sites, and 13 and 3 seagrass sites were assigned a score of 5 and 3, respectively. The final HFI, calculated as the sum of the metric scores, potentially ranges between 16 and 68, with a central value of 42 resulting when all the metrics are rated fair (score 3). According to this range, thresholds were derived for the classification of the ecological status in the five classes indicated in the WFD (Directive 2000/ 60/EC) (Fig. 2). Index scores falling around the central value within the interval 35–49 (corresponding to 30% of the total range) were rated as ‘moderate’. Those comprising between 24 and 34 and between 50 and 60 (each interval corresponding to 20% of the total range) were rated as ‘poor’ and ‘good’, respectively. Index scores falling within the intervals 16–23 and 61–68 (corresponding to 15% of the total range each) were rated as ‘bad’ and ‘high’, respectively. The values of HFI observed for marsh habitats in the calibration dataset ranged from 28 to 58, with a mean value of 46 (moderate status), while those for the seagrass habitats ranged from 40 to 64, with a mean value of 50 (good–moderate status; Fig. 2). The metric that contributed with higher scores to the ecological status for the marsh-HFI was the presence of indicator species (M2 in Fig. 3), any of them occurring in 24 out of 26 marsh sites. A high contribution was also given by the relative abundance of benthivorous taxa (M13 in Fig. 3), which showed values similar to the reference in 15 out of 26 cases. In turn, the relative abundance of either estuarine species or marine migrants in the fish assemblages of marsh habitat (M9 and M10 in Fig. 3) showed, on

30

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25

rank

20

15

10

5

0 16

20

24

28

32

36

40

44

48

52

56

60

64

68

index Fig. 2. Ranked values of the index data for marsh (―) and seagrass (e) habitats (calibration dataset). The vertical lines represent the threshold values for ecological status classification.

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6

metric score

5

4

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2

1

0 M1

M2

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M4

M5

M6

M7

M8

M9

M10

M11

M12

M13

M14

Fig. 3. Average scores (±S.D.) obtained for each metric of the index for marsh (light grey) and seagrass (dark grey) habitats (calibration dataset).

average, values substantially different from the reference, mainly due to an excess of estuarine fishes and low numbers of marine migrants with respect to the expected reference. Another metric that contributed with low scores, on average, to the final marsh-HFI was the number of detritivorous species (M12 in Fig. 3), which were absent in 13 out of 26 sites. The metrics that most contributed with higher scores to the ecological status for the seagrassHFI were the presence of indicator species (M2 in Fig. 3), any of them occurring in all of the seagrass sites, and the relative abundance of benthivorous taxa and species feeding on demersal-pelagic prey (M13 and M14, respectively, in Fig. 3), both showing values similar to the reference in 13 out of 16 cases. The fish assemblage taxonomical structure (species relative abundance, M5 in Fig. 3) was also highly scored, showing values similar to the reference in 12 out of 16 seagrass sites. In turn, the metric that contributed less to the index value was the diversity of the marine migrants group (M8 in Fig. 3), showing values substantially different from the reference in 12 out of 16 cases, always due to the very low number of marine migrant species observed in seagrass assemblages.

3.2. Index application The observed values of HFI calculated on the application dataset ranged from 26 to 58 for the marsh habitats, with a mean value of 44 (moderate status), while those for the seagrass habitats ranged from 40 to 58, with a mean value of 50 (moderate–good status; Fig. 4). The status of most marsh sites was ranked as moderate (seven sites) in addition to five sites ranked as good and only two sites with a poor status (Table 4). In contrast, no poor conditions were detected for seagrass habitats in the application dataset, with most of the sites (6 out of 9) having a good status (Table 4). The presence of indicator species (M2) was the metric contributing the highest score, on average, to the ecological status of marsh habitats, with the occurrence of any of them in 13 out of 14 sites. The number of estuarine resident (M7) and marine migrant species (M8) also contributed, showing values similar to the reference in 9 and 8 cases out of 14, respectively. In turn, the relative abundance of these two guilds (M9 and M10) showed values substantially different from the reference, both in 11 out of 14 sites, always due to an excess of estuarine fishes and low numbers of marine migrants

16

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high

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rank

10 8 6 4 2 0 16

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68

index Fig. 4. Ranked values of the index data for marsh (―) and seagrass (e) habitats (application dataset). The vertical lines represent the threshold values for ecological status classification.

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Table 4 Distribution of the sampling sites (application dataset) across years, sub-basins (N, northern; C, central; S, southern) and ecological status classes in the two lagoon habitats. Ecological status Year Marsh 2003 2004 2005

Seagrass 2004 2005

Sub-basin

Poor

Moderate

Good

N N N C S

2 – – – –

2 2 1 2 –

– 3 1 – 1

N N C S

– – – –

– 2 1 –

4 1 – 1

with respect to what was expected. The presence of indicator species (M2) was the metric contributing with the highest score to the ecological status of seagrass habitats as well, with the occurrence of any of them in all of the seagrass sites. On average, seagrass fish assemblages showed highly similar features to the reference assemblage with regard to the species richness of estuarine residents (M7) and the benthivorous group (M11) as well as with regard to the relative abundance of demersal-pelagic prey (M14). In turn, a lower contribution to the ecological status of seagrass habitats was given by the total species richness (M1) and the species assemblage composition (M4). Comparisons between years and sub-basins were carried out by gathering the calibration (2002) and the application (2003–2005) datasets. According to the results, the two habitats showed a moderate to good ecological status on the whole, with an overall mean HFI value of 50 (±7 S.D.) and 45 (±9) in seagrass and marsh habitats, respectively. Due to the unequal distribution of sampling sites between sub-basins in the different years (Fig. 1, Table 4), comparisons between sampling years was carried out for both habitats by considering the northern sub-basin only, in which data were available for all years. Different temporal patterns characterized the ecological status of the two habitats (Fig. 5). While a monotonic decrease was observed for seagrass habitats from a good status in 2002 and 2004 to a moderate one in 2005, a more variable pattern

was detected for marsh habitats, with the index ranging between values characterizing poor (in 2003) and good (in 2004) ecological status (Fig. 5). However, these temporal patterns were not significant according to the Kruskal–Wallis test (seagrass: H2,12 = 3.22, p > 0.05; marsh: H3,20 = 7.24, p > 0.05). The comparison between sub-basins was carried out by considering 2002 data only, in which data from all three sub-basins were available. Marsh-HFI values were comprised within the moderate status class (Fig. 6), with no significant differences between sub-basins (Kruskal–Wallis test: H2,26 = 0.85, p>0.05). In turn, a significant sub-basin effect was detected for seagrass habitats (Kruskal–Wallis test: H2,16 = 8.46, p < 0.05), with an increase from moderate to good status northward (Fig. 6). 4. Discussion Transitional water systems can be regarded as a complex mixture of distinctive aquatic habitats, as described in Pihl et al. (2002). Habitat diversity and availability highly affect the structure and functioning of the overall fish community in these systems (Pihl et al., 2002; Gamito et al., 2005; Franco et al., 2008b) by virtue of the habitat-species associations. The different preferences of species for different habitat types result, in fact, in natural changes of the fish assemblage characteristics across habitats (Ayvazian et al., 1992; Baltz et al., 1993; Szedlmayer and Able, 1996; Meng and Powell, 1999). The Venice lagoon is a main example of this condition, also being the largest lagoonal system in the Mediterranean basin, with multi-directional environmental gradients leading to a high spatial complexity resulting in a mosaic of different habitats and associated fish assemblages (Ravera, 2000; Mainardi et al., 2002, 2004; Malavasi et al., 2004; Franco et al., 2006a,b,c). In this context of high natural variability within the transitional water system, the use of biological element methodologies, such as fish assemblages, to assess its ecological status and the response to anthropogenic pressures would be a difficult task (Elliott and Quintino, 2007; Uriarte and Borja, 2009). Therefore, it appears essential to take the natural habitat-community relationship into account in order to properly assess the final ecological status of such systems. The approach proposed in this paper promotes the evaluation of individual fish habitats as a preliminary essential requirement for the final assessment of the ecological status of the whole water body. Further steps to reach this final aim would

High

68 64 60

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56 52

HFI

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48 44 40 36

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32 28 24

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20 16 2002

2003

2004

2005

Fig. 5. Index values (±S.D.) in the sampling years for marsh (light grey) and seagrass (dark grey) habitats in the northern lagoon sub-basin. The horizontal lines represent the threshold values for ecological status classification.

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68

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64 60

Good

56 52

HFI

Moderate

48 44 40 36

Poor

32 28 24

Bad

20 16 Northernsub-basin

Centralsub-basin

Southernsub-basin

Fig. 6. Index values (±S.D.) in the lagoon sub-basins for marsh (light grey) and seagrass (dark grey) habitats in 2002. The horizontal lines represent the threshold values for ecological status classification.

be based on the linear combination of these habitat-specific evaluations, relying on the assumption that the ecological status of the whole water body is the result of the contribution of the different habitats composing it (de Paz et al., 2008). This combination would account for the fish habitat diversity within the system, a proxy for the available niches, an important factor affecting the structural and functional characteristics of the whole lagoon fish assemblage (Pihl et al., 2002; Franco et al., 2008b). In addition, the availability of such habitats, as measured by their relative area with respect to the whole lagoon, should also be taken into account, therefore providing different weights to their contribution (de Paz et al., 2008). In fact, the area of available habitat is another factor that likely determines the biodiversity of the fish community of the system (Wootton, 1990; Pihl et al., 2002), thus affecting its final status. In order to promote the habitat approach in the assessment of the ecological status of Mediterranean lagoon systems, multimetric tools were used by adapting them to account for the peculiarity of single lagoon habitats through the associated fish assemblage. Fish-based multimetric indices have been widely employed in the evaluation of the status of transitional water systems (Harrison and Whitfield, 2006; Breine et al., 2007; Coates et al., 2007; Martinho et al., 2008). Such tools, in fact, have the advantage of providing a reliable and flexible evaluation, allowing easier communication of results to managers, stakeholders and policymakers at the same time (Henriques et al., 2008). In addition, the use of fish assemblages as biotic indicators allows the integration of the effects of a range of environmental impacts (such as water quality and habitat degradation), as demonstrated by Uriarte and Borja (2009). However, although the importance of habitat heterogeneity in transitional waters to biological assemblages has been highlighted in several studies (e.g. Pihl et al., 2002; Franco et al., 2008b; Pérez-Ruzafa et al., 2008), the application of fish-based multimetric tools for the assessment of the ecological status of these systems rarely took this factor into account. Many studies, in fact, developed an index for the whole estuarine basin, accounting for just the natural variability among systems (estuarine types), but not within them (Ramm, 1990; Cooper et al., 1994; Borja et al., 2004; Harrison and Whitfield, 2004, 2006; Martinho et al., 2008). Meng et al. (2002) also developed an index of biotic integrity for Narragasett Bay, USA, but after acknowledging its failure in detecting the system ecological status, ascribed it to the environmental heterogeneity in the bay. Other studies moved a step forward in the use of fish-based indices by identifying metrics and reference

conditions separately for the different sections of the estuarine gradient (upper, middle and lower reaches), thus accounting for the assemblage variability along the salinity gradient in estuaries, mainly due to the different balance between marine and freshwater species (Breine et al., 2007; Coates et al., 2007). The importance of a habitat approach, in turn, was explicitly acknowledged by Henriques et al. (2008), although they applied it to the marine environment by adapting estuarine fish-based multimetric indices to different types of marine substrates. In addition, de Paz et al. (2008) promoted the integration of habitat heterogeneity in the global ecological status assessment of estuarine systems, but regarding benthic communities. In transitional waters, Deegan et al. (1997) were the only authors who explicitly recognized the importance of the fish habitat-community association by defining a fish-based index specifically for submerged aquatic vascular plant habitats, considered as important habitats for fishes. The premise of this approach was embraced by the present paper, assuming that the alteration of the function of an aquatic habitat to higher trophic levels may be an early signal of anthropogenic stress long before the habitat disappears from the system (Deegan et al., 1997). However, Deegan et al. (1997) did not go further, as they did not put their approach in the wider context of habitat heterogeneity within the estuary for assessing the ecological status of the whole system, as suggested by de Paz et al. (2008) and accounted for in the present paper. As a preliminary step in this process, the condition of seagrass and marsh habitats in particular was evaluated by means of an HFI and results were presented in this paper. The applied multimetric approach relies on several steps, including the selection of appropriate metrics representing the main aspects of the biological assemblage, the development of reference conditions for each metric, the establishment of metric thresholds to score their deviation from the reference condition, and the final index calculation (Harrison and Whitfield, 2004). All of these steps were undertaken by adapting them to the specific characteristics of fish assemblages in either of the two habitats, as discussed below. The suite of metrics composing the multimetric index should reflect the function of the ecosystem through various aspects of the composition and abundance of the ichthyofauna (Breine et al., 2007). This criterion guided the metrics choice, which was based on the review of the above-mentioned fish-based indices defined for transitional waters. Following Seegert’s (2000) recommendation and the habitat approach of this study, particular

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attention was paid to the ecological relevance and appropriateness of selected metrics to the specific habitat. As a result, most of the selected metrics correspond, at least in their general definition, to those used by Harrison and Whitfield (2004, 2006), accounting for the main aspects of the fish assemblage. Its taxonomical biodiversity was measured by using univariate indicators, such as the number of taxa and the presence of certain species enhancing or reducing the natural value of the assemblage, as well as by analyzing the overall species composition. Fish abundance was accounted for by the species dominance and the taxonomical structure of the fish assemblage, but relative (%) abundance was considered, rather than absolute numbers, in order to reduce the variability of this factor (Karr and Chu, 1999; Seegert, 2000; Harrison and Whitfield, 2006). The functional aspects of the fish assemblage were measured by using fish guilds, which, by gathering the species according to their life strategies in transitional waters, may give an indication of the functioning of the system (Elliott et al., 2007; Franco et al., 2008a). The resident and marine migrant species were chosen for their high susceptibility to habitat degradation, being highly dependent on estuarine/lagoon habitats and hence, being likely indicators of their function as spawning, feeding and nursery grounds for residents and as feeding and nursery areas for marine species (Harrison and Whitfield, 2004; Franco et al., 2006a, 2008a; Elliott et al., 2007). In addition, the trophic structure of the fish assemblage was accounted for as a possible indicator of habitat functioning in supporting estuarine food webs. These mainly rely on the high diversity and abundance of benthic invertebrate feeding fish species in European transitional waters (Franco et al., 2006a, 2008a,b). In addition, this trophic guild was also assumed as an indirect indicator of the condition of the benthic invertebrate fauna in the lagoon habitats (Harrison and Whitfield, 2004). Other trophic groups have been taken into account elsewhere, such as strictly piscivorous taxa as indicators of the broader trophic network within estuaries (USEPA, 2000b). However, this metric was not considered suitable for the ecological assessment of Mediterranean lagoon habitats. Large fish predators, in fact, are less represented in shallow water lagoon habitats where fish are generally small and have demersal habits (Desmond et al., 2000; Mathieson et al., 2000; Franco et al., 2008a). In addition, the seine net selectivity toward this kind of fish might lead to an underestimation of larger pelagic predators in our study. Hence, other trophic guilds were taken into account to characterize the fish assemblages of the two studied habitats. Detritivorous fishes were considered indirect indicators of the role of marsh habitats in supporting the food web at its basis. Decomposed organic matter and the associated benthic microalgae and microfauna, in fact, are important sources of nutrition for detritivorous species, either fish or small epibenthic and benthic invertebrates, hence being the link between the marsh vegetation production and the aquatic estuarine food web (Kneib, 2000; Franco et al., 2008a). In turn, fish species feeding on demersal-pelagic prey were assumed as a proxy for the condition of the small pelagic-demersal fauna, giving insight into the value of the seagrass habitat as a refuge for these organisms (Baden and Boström, 2001; Heck and Orth, 2006). Reference conditions were separately derived for each of these metrics for the two habitats. It would be, in fact, unrealistic to develop a uniform set of reference limits that could be applied to all the habitats in a lagoon, particularly given that this is a very sensitive issue, since the obtained ecological status depends highly on these values (Henriques et al., 2008). According to the WFD indications, reference or baseline conditions, against which the data are to be compared, can be defined in several ways: by using historical data (hindcasting), expert opinion, predictive models or by selecting a physical control area assumed as minimally disturbed that represents the most natural ambient conditions present (WFD, Directive 2000/60/EC). However, each of these approaches may

present some problems, due either to possible incomparability/ unavailability of historical information, discrepancies in expert opinions, untested hypotheses at the basis of mathematical models or the impossibility of a prior definition of least disturbed sites (Harrison and Whitfield, 2004). An alternative method was used in this paper by deriving reference conditions from the biological dataset under the assumption that some sites that are minimally disturbed are included but without a pre-selection of such sites (Harris and Silveira, 1999; USEPA, 2000b; Harrison and Whitfield, 2004, 2006; Breine et al., 2007). Even this method is not free from criticism due to the setting of the baseline condition in recent years when it is known that estuarine areas are already impacted greatly by human activities. This could lead to an over-classification of the water body with respect to the possible response resulting from using a pristine reference condition. However, for many water bodies, data on such pristine conditions are not available as the information on fish assemblages were collected only after significant changes in hydrology and morphology (Coates et al., 2007). Furthermore, pristine conditions would be very difficult to identify for the Venice lagoon, even if data were available, as the existence and natural evolution of this system was highly linked to human interventions in historical times (e.g., the diversion of tributaries and major engineering works in the 15th and 19th centuries, which prevented the lagoon disappearance due to the filling by river sediments). The EQR was used to compare most of the observed metric values to the reference conditions, following the WFD (Directive 2000/60/EC), and thresholds for the metric scoring were derived accordingly. The EQR was calculated not only as the simple ratio of observed and reference values, but also as a measurement of the broader ‘relationship between the values of the biological parameters’, as highlighted by Borja et al. (2004), hence, being also represented by the similarity values between the observed and reference fish assemblages. The general criteria adopted to define reference conditions and the scoring thresholds were those proposed by Harrison and Whitfield (2004), although final results were different. In fact, these criteria mainly rely on the analysis of the empirical distribution of the metrics, here undertaken separately for the two habitats. With regard to those metrics accounting for the relative abundance of functional guilds, in particular, the definition of reference conditions and thresholds was based on results reported by Franco et al. (2006a) in combination with expert inputs on functional groups in European transitional waters (Elliott et al., 2007; Franco et al., 2008a). The obtained index, HFI, was applied to evaluate the ecological status of seagrass and marsh habitats in the lagoon by investigating both spatial and temporal variations. To be effective, the index should respond to environmental stress, thus allowing the detection of the condition of the studied system (Harris and Silveira, 1999; USEPA, 2000b). Several studies give an a priori classification of sites based on independent indicators of habitat quality (e.g., dissolved oxygen, nitrogen concentration and pressure index) to test the index effectiveness (Deegan et al., 1997; Meng et al., 2002; Breine et al., 2007; Uriarte and Borja, 2009). In contrast, following Harrison and Whitfield (2004) and Coates et al. (2007), in the present paper, general hypotheses on the condition of lagoon habitats and the relative spatial and temporal patterns were derived from available local knowledge, with particular regard to differences between sub-basins and across years (Day et al., 1999; Rismondo et al., 2003, 2006; Apitz et al., 2007; Sfriso and Facca, 2007; Rismondo and Mion, 2008; Molinaroli et al., 2009). According to the HFI results, marsh habitats in the Venice lagoon show an average moderate ecological status (mean index value 45) with no significant variations between years or sub-basins. A marked negative trend has been highlighted in the 20th century in the Venice lagoon, with a relevant loss of marsh areas (from 120 km2 at the beginning of the century to 40 km2 at its end),

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mainly due to rising water levels, lower sediment input, increasing hydrodynamic energy enhancing erosion processes, natural and human-induced subsidence, reclamation and pollution (Day et al., 1999; Apitz et al., 2007; Molinaroli et al., 2009). An effect of such hydromorphological pressures on fish assemblages was then expected, passing through habitat losses and disturbances in the food webs (Madon, 2008) with possible consequences, for example, on the nursery value of the habitat for marine fishes (Franco et al., unpublished data). In contrast, this habitat degradation trend was not detected by the fish-based index for marsh habitats in the northern lagoon, possibly due to the smaller temporal scale of this study (2002–2005), as well as to the works undertaken by the local Water Authority in recent years to contrast the negative trend, e.g., by protecting marshes from erosion. In addition, marsh areas and their fish assemblages are expected to be less affected by this impairment in the northern lagoon as this sub-basin is reported as the most ‘‘pristine” with respect to the others, being characterized by natural and stable conditions from a morphological point of view (Molinaroli et al., 2009). Nevertheless, no significant differences between sub-basins were detected by the index. The positive effects of a higher stability and naturality of northern lagoon marsh areas, in contrast to stronger erosion processes in central and southern sub-basins, in fact, could have been contrasted by the relatively higher contaminant load in the northern basin marsh areas (Apitz et al., 2007), which may negatively affect the ecological status of marsh fish assemblages. Seagrass habitats of the Venice lagoon showed an average good–moderate ecological status, with a mean HFI value slightly higher than in marsh habitats (50). Though not significant, a decreasing trend in seagrass habitat condition, from good to moderate, was detected in the northern lagoon sub-basin between 2002 and 2005. This habitat degradation affecting fish fauna is consistent with the marked regression of seagrass beds observed in the period in this area (Rismondo et al., 2006), in continuity with the trend recorded in the previous decade as a result of the progressive deepening of lagoon bottoms induced by erosive processes (Rismondo et al., 2003; Sfriso and Facca, 2007; Rismondo and Mion, 2008). A high contribution to the decrease of the status of seagrass fish assemblages, in fact, is given by a reduction in the species diversity, leading to an increased deviation of the species composition from reference conditions, which are highly affected by the available habitat area (Wootton, 1990). The index analysis also highlighted a significant difference in the ecological status of seagrass habitats between sub-basins, with a decreasing pattern from good to moderate status southward. This result is consistent with the higher bottom stability in the northern lagoon (Molinaroli et al., 2009), as well as with the higher chemical quality of sediments in the seagrass area there (Apitz et al., 2007). The lower ecological status of the other sub-basins, particularly the southern one, in turn, might be the response of fish assemblages to the high habitat disturbance induced in these areas by the intense activity connected to clam harvesting and collection (Rismondo et al., 2003; Sfriso and Facca, 2007; Rismondo and Mion, 2008). Using hydraulic and mechanical gear, it has disrupting effects on the surface sediment texture and enhances turbidity with consequent negative effects on seagrass habitats and their fauna (Sfriso and Facca, 2007). In addition, a common behavior of clam collectors is to eliminate the leaf canopy and the root compartment, thereby enhancing the local seagrass habitat loss (Rismondo and Mion, 2008). Habitat loss is reported as the most impacting pressure on fish, leading, for example, to the impairment of the potential nursery role of lagoon habitats to marine fishes (Vasconcelos et al., 2007), as partly suggested by the observed shortage of marine migrant taxa with respect to the expected reference condition in the Venice lagoon.

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Multimetric biotic indices are reported as excellent tools for detecting signals of impairment, but they are not nearly as effective at determining the cause of this impairment, especially when multiple discharges are present and/or the habitat has been disturbed (Seegert, 2000; Harrison and Whitfield, 2006). In addition, indices such as those presented in this paper may present other limitations that need to be taken into account in order to properly apply these tools. The index definition, and therefore its application, highly depends on the sampling methods used to collect the data, as well as on the sampling effort, factors that heavily affect species richness and fish abundance metrics (Seegert, 2000; Harrison and Whitfield, 2004; Franco et al., 2008b; Martinho et al., 2008; Uriarte and Borja, 2009). For example, the pelagic component present in the seine net catches would be underestimated in beam trawl samples, mainly composed of demersal/benthic fishes (Elliott and Hemingway, 2002), with possible effects on the overall species richness and the balance of fish guilds in the assemblage. Otter trawl samples, in turn, would show lower species richness than seine nets, hence scoring lower this metric due to the operating restrictions of otter trawls in such low depths as those of the analyzed lagoon habitats (Martinho et al., 2008). Hence, reference values and the resulting overall index are to be regarded as method-specific, and the importance of standardizing gear and protocols is highlighted by several authors (Deegan et al., 1997; Seegert, 2000; Coates et al., 2007; Martinho et al., 2008). Fish assemblage characteristics, either structural or functional, may also show a marked temporal variability in transitional waters, mainly due to the seasonality of both recruitment of estuarine residents and colonization of lagoon habitats by marine migrant species (McErlean et al., 1973; Potter et al., 1986; Loneragan and Potter, 1990; Franco et al., 2006a). This variability has an effect on the index results as well, which can be controlled for by defining indices separately for different periods, as in the present paper and in others (Seegert, 2000; Deegan et al., 1997; Coates et al., 2007). This has the aim of reducing the noise of natural temporal variability in order to enhance the ability of the index to respond to the signal of anthropogenic degradation. 5. Conclusions Biological criteria, such as fish-based multimetric indices, are a practical approach to provide information to support management decisions (Harrison and Whitfield, 2004). In Europe, such indices are becoming important bioassessment tools, since the WFD recommends fish assemblages as a biological quality element to be used to manage a transitional water body when its status needs to be brought to a good level (WFD, Directive 2000/60/EC). However, in addition to the taxonomical composition and abundance of fish fauna indicated by the Directive, other aspects taking account of habitat uses and fish movements between transitional and marine systems have been highlighted as relevant factors for the proper evaluation of ecological status (Harrison and Whitfield, 2004; Coates et al., 2007; Breine et al., 2007) and were considered as such in this paper. In order to be effective, an index should be robust enough to account for natural variability, either spatial or temporal, and to respond to anthropogenic impacts only, although this could be particularly difficult in transitional water systems, which are characterized by highly variable natural conditions, leading to the estuarine quality paradox highlighted by Elliott and Quintino (2007). However, the adjustment of the metric criteria for habitat types and season, as in the present paper, may allow the reduction of the influence of such natural variability, as also suggested by Breine et al. (2007). The HFI proposed in the present paper provides information on the ecological status of lagoon seagrass and marsh habitats (in summer), with an apparent clearer response of fish assemblages to the degradation of seagrass than of

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marsh habitats. This could be ascribed to a stronger association in the farther between the fish assemblage and the habitat structure, due to the high specialization of resident fish species resulting from strong morphological and behavioral adaptations to the tridimensional structure and complexity of seagrass vegetation. However, these results should not be taken as definitive. The present paper, in fact, represents an application of the proposed habitat approach by using fish-based indices that need further effort in order to make them more effective. As suggested by Seegert (2000), in fact, individual selected metrics need to be tested in order to ensure an adequate measure of ecological conditions, as well as to evaluate the shape of their response to human pressures (Harrison and Whitfield, 2004; Uriarte and Borja, 2009). The relationship between metrics must also be further tested and reviewed in order to eliminate possible redundant metrics (Seegert, 2000), to include only metrics that characterize typical communities of the various habitats (Henriques et al., 2008), and to evaluate the index performance in relation to its structure. Moreover, the correlation of the index with habitat degradation needs to be further investigated (Deegan et al., 1997). In addition, as highlighted above, the proposed index represents just an initial step in the evaluation of the ecological status of a whole lagoon water body, a process that passes through the assessment of the other lagoon habitats (e.g., intertidal and subtidal mudflats) and the final combination of such habitat-specific results. However, although the multimetric methods are still under improvement, the habitat approach proposed here gives rise to deepening the knowledge of Mediterranean lagoon systems and their habitats, as well as their harmonization. In fact, a prerequisite for the application of the proposed approach for Mediterranean lagoons is to find a consensus on the fish habitat classification to allow the measure of their diversity and availability within these water bodies. Acknowledgements The results presented in the paper were partly produced within Study B.12.3/III on the functioning of the Venice lagoon environment, funded by the local water authority Magistrato alle Acque through its agent Consorzio Venezia Nuova in the sphere of the activities for the safeguard of the Venice lagoon (Italian special law n. 798/84). References Apitz, S., Barbanti, A., Bernstein, G.A., Bocci, M., Delaney, E., Montobbio, L., 2007. The assessment of sediment screening risk in Venice lagoon and other coastal areas using international sediment quality guidelines. Journal of Soils and Sediments 7, 326–341. Avanzi, C., Fossato, V., Gatto, P., Rabagliati, R., Rosa Salva, P., Zitelli, A., 1979. Ripristino, conservazione ed uso dell’ecosistema lagunare veneziano. Comune di Venezia, Venezia, 197. Ayvazian, S.G., Deegan, L.A., Finn, J.T., 1992. Comparison of habitat use by estuarine fish assemblages in the Acadian and Virginian zoogeographic provinces. Estuaries 15, 368–383. Baden, S.P., Boström, C., 2001. The leaf canopy of seagrass beds: faunal community structure and function in a salinity gradient along the Swedish Coast. Ecological Studies 151, 213–236. Baltz, D.M., Rakoncinski, C., Fleeger, J.W., 1993. Microhabitat use by marsh-edge fishes in a Lousiana estuary. Environmental Biology of Fishes 36, 109–126. Belpaire, C., Smolders, R., Auweele, I.V., Ercken, D., Breine, J., Van Thuyne, G., Ollevier, F., 2000. An index of biotic integrity characterizing fish populations and the ecological quality of Flandrian water bodies. Hydrobiologia 434, 17–33. Borja, A., Franco, J., Valencia, V., Bald, J., Muxika, I., Belzunce, J.M., Solaun, O., 2004. Implementation of the European water framework directive from the Basque country (northern Spain): a methodological approach. Marine Pollution Bulletin 48, 209–218. Breine, J., Maes, J., Quattaert, P., Van den Bergh, E., Simoens, I., Van Thuyne, G., Belpaire, C., 2007. A fish-based assessment tool for the ecological quality of the brackish Schelde estuary in Flanders (Belgium). Hydrobiologia 575, 141–159. Coates, S., Waugh, A., Anwar, A., Robson, M., 2007. Efficacy of a multi-metric fish index as an analysis tool for the transitional fish component of the water framework directive. Marine Pollution Bulletin 55, 225–240.

Cooper, J.A.G., Ramm, A.E.L., Harrison, T.D., 1994. The estuarine health index: a new approach to scientific information transfer. Ocean and Coastal Management 25, 103–141. Costanza, R., d’Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, V., Limburg, K., Naeem, S., O’Neill, R.V., Paruelo, J., Raskin, R.G., Sutton, P., van den Belt, M., 1997. The value of the world’s ecosystem services and natural capital. Nature 287, 253–260. Dauvin, J.C., Ruellet, T., 2009. The estuarine quality paradox: is it possible to define an ecological quality status for specific modified and naturally stressed estuarine ecosystems? Marine Pollution Bulletin 59, 38–47. Day Jr, J.W., Rybczyk, J., Scarton, F., Rismondo, A., Are, D., Cecconi, G., 1999. Soil accretionary dynamics, sea-level rise and the survival of wetlands in Venice lagoon: a field and modelling approach. Estuarine, Coastal and Shelf Science 49, 607–628. de Paz, L., Patricio, J., Marques, J.C., Borja, A., Laborda, A.J., 2008. Ecological status assessment in the lower Eo estuary (Spain). The challenge of habitat heterogeneity integration: a benthic perspective. Marine Pollution Bulletin 56, 1238–1257. Deegan, L.A., Finn, J.T., Ayvazian, S.G., Ryder-Kieffer, C.A., Buonaccorsi, J., 1997. Development and validation of an estuarine biotic integrity index. Estuaries 20, 601–617. Dennison, W.C., Orth, R.J., Moore, K.A., Stevenson, J.C., Catrer, V., Kollar, S., Bergstrom, P.W., Batiuk, R.A., 1993. Assessing water quality with submersed aquatic vegetation. BioScience 43, 8–94. Desmond, J.S., Zedler, J.B., Williams, G.D., 2000. Fish use of tidal creek habitats in two southern California salt marshes. Ecological Engineering 14, 233–252. Elliott, M., Hemingway, K.L., 2002. Fishes in Estuaries. Blackwell Science, Oxford. p. 636. Elliott, M., Quintino, V., 2007. The estuarine quality paradox, environmental homeostasis and the difficulty of detecting anthropogenic stress in naturally stressed areas. Marine Pollution Bulletin 54, 640–645. Elliott, M., Whitfield, A.K., Potter, I.C., Blaber, S.J.M., Cyrus, D.P., Nordlie, F.G., Harrison, T.D., 2007. The guild approach to categorizing estuarine fish assemblages: a global review. Fish and Fisheries 8, 241–268. Fausch, K.D., Lyons, J., Karr, J.R., Angermeir, P.L., 1990. Fish communities as indicators of environmental degradation. In: Marshall Adams, S. (Ed.), Biological Indicators of Stress in Fish. American Fisheries Society, Bethesda, MD, pp. 123– 144. Franco, A., Franzoi, P., Malavasi, S., Riccato, F., Torricelli, P., 2006a. Use of shallow water habitats by fish assemblages in a Mediterranean coastal lagoon. Estuarine, Coastal and Shelf Science 66, 67–83. Franco, A., Franzoi, P., Malavasi, S., Riccato, F., Torricelli, P., 2006b. Fish assemblages in different shallow water habitats of the Venice lagoon. Hydrobiologia 555, 159–174. Franco, A., Malavasi, S., Zucchetta, M., Franzoi, P., Torricelli, P., 2006c. Environmental influences on the fish assemblage of the Venice lagoon, Italy. Chemistry and Ecology 22, 105–118. Franco, A., Elliott, M., Franzoi, P., Torricelli, P., 2008a. Life strategies of fishes in European estuaries: the functional guild approach. Marine Ecology Progress Series 354, 219–228. Franco, A., Franzoi, P., Torricelli, P., 2008b. Structure and functioning of Mediterranean lagoon fish assemblages: a key for the identification of water body types. Estuarine, Coastal and Shelf Science 79, 549–558. Gamito, S., Gilabert, J., Marcos Diego, C., Pérez-Ruzafa, A., 2005. Effects of changing environmental conditions on lagoon ecology. In: Gonenc, I., Wolflin, J. (Eds.), Coastal Lagoons – Ecosystem Processes and Modeling for Sustainable Use and Development. CRC Press, pp. 193–229. Gordo, L.S., Cabral, H.N., 2001. The fish assemblage structure of a hydrologically altered coastal lagoon: the Obidos lagoon (Portugal). Hydrobiologia 459, 125–133. Harris, J.H., Silveira, R., 1999. Large scale assessments of river health using an index of biotic integrity with low-diversity fish communities. Freshwater Biology 41, 235–252. Harrison, T.D., Whitfield, A.K., 2004. A multi-metric fish index to assess the environmental condition of estuaries. Journal of Fish Biology 65, 683–710. Harrison, T.D., Whitfield, A.K., 2006. Application of a multimetric fish index to assess the environmental condition of South African estuaries. Estuaries and Coasts 29, 1108–1120. Heck Jr, K.L., Orth, R.J., 2006. Predation in seagrass beds. In: Larkum, A.W.D., Orth, R.J., Duarte, M. (Eds.), Seagrasses: Biology Ecology and Conservation. Springer, Dordrecht, The Netherlands, pp. 537–550. Henriques, S., Pais, M.P., Costa, M.J., Cabral, H., 2008. Efficacy of adapted estuarine fish-based multimetric indices as tools for evaluating ecological status of the marine environment. Marine Pollution Bulletin 56, 1696–1713. Hering, D., Feld, C., Moog, O., Ofenböck, T., 2006. Cook book for the development of a multimetric index for biological condition of aquatic ecosystems: experiences from the European AQEM and STAR projects and related initiatives. Hydrobiologia 566, 311–324. IUCN, 2008. 2008 IUCN Red List of Threatened Species. . Downloaded on 18 March 2009. Karr, J.R., 1981. Assessment of biotic integrity using fish communities. Fisheries 6, 21–27. Karr, J.R., Chu, E.W., 1999. Restoring Life in Running Waters – Better Biological Monitoring. Island Press, Washington DC, USA. Karr, J.R., Fausch, K.D., Angermeier, P.L., Yant, P.R., Schlosser, I.J., 1986. Assessing biological integrity in running waters: a method and its rationale. Illinois Natural Hystory Survey Special Publication 5, 1–28.

A. Franco et al. / Marine Pollution Bulletin 58 (2009) 1704–1717 Kneib, R.T., 2000. Salt marsh ecoscapes and production transfers by estuarine nekton in the Southeastern United States. In: Weinstein, M.P., Kreeger, D.A. (Eds.), Concepts and Controversies in Tidal Marsh Ecology. Kluwer Academic Press, Dordrecht, The Netherlands, pp. 267–291. Loneragan, R.J., Potter, I.C., 1990. Factors influencing community structure and distribution of different life-cycle categories of fishes in shallow waters of a large Australian estuary. Marine Biology 106, 25–37. Madon, S., 2008. Fish community responses to ecosystem stressors in coastal estuarine wetlands: a functional basis for wetlands management and restoration. Wetlands Ecology and Management 16, 219–236. Mainardi, D., Fiorin, R., Franco, A., Franzoi, P., Giovanardi, O., Granzotto, A., Libertini, A., Malavasi, S., Pranovi, F., Riccato, F., Torricelli, P., 2002. Fish diversity in the Venice lagoon: preliminary report. In: Campostrini, P. (Ed.), Scientific Research and Safeguarding of Venice, Corila Research: Program 2001 Results. La Garangola, Padova, pp. 583–594. Mainardi, D., Fiorin, R., Franco, A., Franzoi, P., Granzotto, A., Malavasi, S., Pranovi, F., Riccato, F., Zucchetta, M., Torricelli, P., 2004. Seasonal distribution of fish fauna in the Venice lagoon shallow waters: preliminary results. In: Campostrini, P. (Ed.), Scientific Research and Safeguarding of Venice, Corila Research: Program 2001 Results. Multigraf, Venezia, pp. 437–447. Malavasi, S., Fiorin, R., Franco, A., Franzoi, P., Granzotto, A., Riccato, F., Mainardi, D., 2004. Fish assemblages of Venice lagoon shallow waters: an analysis based on species, families and functional guilds. Journal of Marine Systems 51, 19–31. Martinho, F., Viegas, I., Dolbeth, M., Leitão, R., Cabral, H.N., Pardal, M.A., 2008. Assessing estuarine environmental quality using fish-based indices: performance evaluation under climatic instability. Marine Pollution Bulletin 56, 1834–1843. Mathieson, S., Cattrijsse, A., Costa, M.J., Drake, P., Elliott, M., Gardner, J., Marchand, J., 2000. Fish assemblages of European tidal marshes: a comparison based on species, families and functional guilds. Marine Ecology Progress Series 204, 225–242. Mavruk, S., Avsar, D., 2008. Non-native fishes in the Mediterranean from the Red Sea, by way of the Suez Canal. Reviews in Fish Biology and Fisheries 18, 251– 262. McErlean, A.J., O’Connor, S.G., Mihursky, J.A., Gibson, C.I., 1973. Abundance, diversity and seasonal patterns of estuarine fish populations. Estuarine, Coastal and Marine Science 1, 19–36. Meng, L., Powell, C.J., 1999. Linking juvenile fish and their habitats: an example from Narragansett Bay, Rhode Island. Estuaries 22, 905–916. Meng, L., Orphanides, C.D., Powell, C.J., 2002. Use of a fish index to assess habitat quality in Narragansett Bay, Rhode Island. Transactions of the American Fisheries Society 131, 731–742. Molinaroli, E., Guerzoni, S., Sarretta, A., Masiol, M., Pistolato, M., 2009. Thirty-year changes (1970–2000) in bathymetry and sediment texture recorded in the Lagoon of Venice sub-basins, Italy. Marine Geology 258, 115–125. Moore, J.A., Lew, A.A., Cunningham, J.A., Kachuba, M., 1995. Fish communities as indicators of environmental quality in the West River watershed. Yale Forestry and Environmental Studies Bulletin 100, 178–196. Odum, E.P., 1983. Basic Ecology. Saunders College Publishing, Philadelphia, PA. Pérez-Ruzafa, A., Garcia-Charton, J.A., Barcala, E., Marcos, C., 2006. Changes in benthic fish assemblages as a consequence of coastal works in a coastal lagoon: the Mar Menor (Spain, Western Mediterranean). Marine Pollution Bulletin 53, 107–120. Pérez-Ruzafa, A., Hegazi, M.I., Pérez-Ruzafa, I.M., Marcos, C., 2008. Differences in spatial and seasonal patterns of macrophyte assemblages between a coastal lagoon and the open sea. Marine Environmental Research 65, 291–314. Pihl, L., Cattrijsse, A., Codling, I., Mathieson, D.S., McLusky, D.S., Roberts, C., 2002. Habitat use by fishes in estuaries and other brackish areas. In: Elliott, M., Hemingway, K. (Eds.), Fishes in Estuaries. Blackwell Science, Oxford, pp. 10–53. Potter, I.C., Claridge, P.N., Warwick, R.M., 1986. Consistency of seasonal changes in an estuarine fish assemblage. Marine Ecology Progress Series 32, 217–228.

1717

Ramm, A.E.L., 1988. The community degradation index: a new method for assessing the deterioration of aquatic habitats. Water Research 22, 293–301. Ramm, A.E.L., 1990. Application of the community degradation index to South African estuaries. Water Research 24, 383–389. Ravera, O., 2000. The lagoon of Venice. The results of both natural factors and human influence. Journal of Limnology 59, 19–30. Reiss, H., Kröncke, I., 2005. Seasonal variability of benthic indices: an approach to test the applicability of different indices for ecosystem quality assessment. Marine Pollution Bulletin 50, 1490–1499. Rismondo, A., Mion, D., 2008. Variability in Venice lagoon marine seagrasses populations: natural dynamics or induced modifications? Biologia Marina Mediterranea 15, 130–133. Rismondo, A., Curiel, D., Scarton, F., Mion, D., Caniglia, G., 2003. A new seagrass map for the Venice lagoon. In: Ozhan, E. (Ed.), Proceedings of the 6th International Conference on the Mediterranean Coastal Environment, MEDCOAST 03, 7–11 October 2003, Ravenna, Italy, pp. 844–852. Rismondo, A., Curiel, D., Mion, D., 2006. Short term spatial-temporal dynamics of marine seagrasses in Venice lagoon. Biologia Marina Mediterranea 13, 98– 99. Seegert, G., 2000. The development, use, and misuse of biocriteria with an emphasis on the index of biotic integrity. Environmental Science and Policy 3, S51– S58. Sfriso, A., Facca, C., 2007. Distribution and production of macrophytes and phytoplankton in the lagoon of Venice. comparison of actual and past situation. Hydrobiologia 577, 71–85. Simenstad, C.A., Cordell, J.R., 2000. Ecological assessment criteria for restoring anadromous salmonid habitat in Pacific Northwest estuaries. Ecological Engineering 15, 283–302. Simon, T.P., 2000. The use of biological criteria as a tool for water resource management. Environmental Science and Policy 3, 43–49. Soto-Galera, E., Diaz-Pardo, E., Lopez-Lopez, E., Lyons, J., 1998. Fish as indicators of environmental quality in the Rio Lerma Basin, Mexico. Aquatic Ecosystem Health and Management 1, 267–276. Szedlmayer, S.T., Able, K.W., 1996. Patterns of seasonal availability and habitat use by fishes and decapod crustaceans in a southern New Jersey estuary. Estuaries 19, 697–709. Tortonese, E., 1975. Fauna d’Italia – Osteichthyes (Pesci ossei). Ed. Calderini, Bologna. Turpie, J.K., Adams, J.B., Joubert, A., Harrison, T.D., Colloty, B.M., Maree, R.C., Whitfield, A.K., Wooldridge, T.H., Lamberth, S.J., Taljaard, S., Van Niekerk, L., 2002. Assessment of the conservation priority status of South African estuaries for use in management and water allocation. Water SA 28, 191–206. Uriarte, A., Borja, A., 2009. Assessing fish quality status in transitional waters, within the European water framework directive: setting boundary classes and responding to anthropogenic pressures. Estuarine, Coastal and Shelf Science 82, 214–224. USEPA, 2000a. Evaluation Guidlines for Ecological Indicators. United States Environmental Protection Agency, EPA-620-R-99-005, Washington. USEPA, 2000b. Estuarine and Coastal Marine Waters: Bioassessment and Biociteria Technical Guidance. United States Environmental Protection Agency, EPA-822B-00-024, Washington. Vasconcelos, R.P., Reis-Santos, P., Fonseca, V., Maia, A., Ruano, M., França, S., Vinagre, C., Costa, M.J., Cabral, H., 2007. Assessing anthropogenic pressures on estuarine fish nurseries along the Portuguese coast: a multi-metric index and conceptual approach. Science of the Total Environment 374, 199–215. Whitfield, A.K., 1996. Fishes and the environmental status of South African estuaries. Fisheries Management and Ecology 3, 45–57. Whitfield, A.K., Elliott, M., 2002. Fishes as indicators of environmental and ecological changes within estuaries: a review of progress and some suggestions for the future. Journal of Fish Biology 61 (Suppl. A), 229–250. Wootton, R.J., 1990. Ecology of Teleost Fishes. Chapman and Hall, London.