Multi-metric index based on the seagrass Zostera noltii (ZoNI) for ecological quality assessment of coastal and estuarine systems in SW Iberian Peninsula

Marine Pollution Bulletin 68 (2013) 46–54

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Multi-metric index based on the seagrass Zostera noltii (ZoNI) for ecological quality assessment of coastal and estuarine systems in SW Iberian Peninsula Patricia García-Marín a,⇑,1, Susana Cabaço b,1, Ignacio Hernández a,1, Juan J. Vergara a,1, João Silva b,1, Rui Santos b,1 a b

Department of Biology, Division of Ecology, Faculty of Marine and Environmental Sciences, University of Cadiz, 11510 Puerto Real (Cadiz), Spain Marine Plant Ecology Research Group (ALGAE), Centre of Marine Sciences (CCMAR), University of Algarve, Campus of Gambelas, 8005-139 Faro, Portugal

a r t i c l e

i n f o

Keywords: Ecological quality assessment Iberian Peninsula Multi-metric index Seagrass Water Framework Directive Zostera noltii

a b s t r a c t The aim of this study was to develop an ecological quality index based on the seagrass Zostera noltii (ZoNI) according to the WFD requirements. Eleven Z. noltii meadows of SW Iberian Peninsula under contrasting levels of anthropogenic disturbance were considered: 5 sites in Ria Formosa (Portugal), and 6 sites in Spain (Huelva and Cadiz). Environmental quality was assessed through nutrients of the water column and seagrass variables from different organization levels; those variables were analyzed using PCA (47% of explained variance on the first component) to calculate the ecological quality ratio (that was significantly correlated to the environmental variables, R2 = 0.51, p < 0.01) and the ecological quality status of the sites. As a result, 4 sites were classified as good, 6 sites as moderate and 3 sites as poor ecological status. The developed index ZoNI showed to be suitable to assess the ecological status of estuarine and coastal systems in SW Iberian Peninsula reflecting their water quality. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction The importance of seagrass ecosystems is widely acknowledged because of their contribution to enhance biodiversity, productivity, water quality, coastal protection, and their essential role in habitat engineering and nutrient cycling (Hemminga and Duarte, 2000). Progressive regression and loss of seagrass ecosystems worldwide (>30%) became an issue of special concern (Waycott et al., 2009) as the loss of seagrasses represents the shortfall of all services they provide (Costanza et al., 1997). Anthropogenic pressures such as wastewater discharge, pollutants release, land reclamation and urbanization, boating, dredging, fisheries and aquaculture activities are the main causes of seagrass decline (Duarte, 2002; Walker et al., 2006). To address the protection of coastal ecosystems, many national and international policy strategies have been promoted over the last decades (e.g. Ocean Policy in Australia – Greiner et al., 1998; Oceans Act of 2000 in United States – US Congress, 2000; Water Framework Directive in Europe – European Commission, 2000). These strategies encourage the use of biological indicators as tools for ecological quality management in aquatic environments, as organisms incorporate information on the quality of the environment they are thriving in, providing an integrated, long-term re⇑ Corresponding author. Tel.: +34 956 016138; fax: +34 956 016019. 1

E-mail address: [email protected] (P. García-Marín). International Campus of Excellence of the Sea CEIMAR.

0025-326X/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2012.12.025

sponse of the effects of human pressures (Markert et al., 1999). Specifically, the European Water Framework Directive (WFD), which aims to achieve a good qualitative and quantitative ecological status in all European water bodies by 2015 (European Commission, 2000), established different Biological Quality Elements (BQE: phytoplankton, macroalgae, angiosperms, and macrobenthic fauna) to assess the ecological status of the defined water bodies. These biological elements have been previously recognized as biological indicators and included in the routine monitoring programs established by the competent authorities of United States (Environmental Protection Agency – EPA, 2000), Europe (European Environment Agency – Smeets et al., 1999), and Australia (Commonwealth Scientific and Industrial Research Organization, CSIRO – CSIRO, 1998). Seagrasses, as the only truly marine angiosperms, are one of the BQE considered within the WFD, due to several reasons: they are sensitive to changes in coastal areas, acting as sentinels of different natural or human-induced threats (Short and Wyllie-Echeverria, 1996); and they integrate the ecological processes occurring in the sediment and the water column (Hemminga and Duarte, 2000). In Europe, the implementation of seagrasses as biological indicators have followed different approaches; the studies have considered either individual metrics or combination of metrics (indices) for the environmental status assessment, using seagrass species separately. For instance, three out of four European seagrass species have been already investigated as possible bioindicators of environmental quality: Posidonia oceanica (Romero et al.,

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2007; Gobert et al., 2009; Lopez y Royo et al., 2010), Cymodocea nodosa (Orfanidis et al., 2007; Oliva et al., 2012), and Zostera marina (Krause-Jensen et al., 2005; Foden and Brazier, 2007). Some of these studies include the development of ecological quality indices to assess the ecological status of coastal waters: PoSte in Italy (Buia et al., 2005), POMI in Spain (Romero et al., 2007), PREI in France (Gobert et al., 2009), and BiPo in France (Lopez y Royo et al., 2010) for P. oceanica; and CYMOX in Spain (Oliva et al., 2012) for C. nodosa. Similarly, in other regions (e.g. Australia) the multimetric approach has also been implemented for the ecosystem health assessment (Wood and Lavery, 2000), using seagrass species separately. To our best knowledge, no index based on the seagrass Zostera noltii was developed so far. Z. noltii has a wide geographical distribution, from southern coasts of Norway to the Mediterranean Sea, the Black Sea, the Canary Islands, and the Mauritanian coast (Borum and Greve, 2004), encompassing the European coastal areas considered within the WFD. This species thrives mainly on intertidal areas of coastal lagoons and estuaries (den Hartog, 1970); and constitutes habitats included in the European Directive Habitats for protection (e.g., Habitat code 1140 – European Commission, 1992). Z. noltii is particularly vulnerable to human activities causing changes of substrate (Cunha et al., 2005; Cabaço and Santos, 2007; Han et al., 2012), changes in currents and wave exposure (Bouma et al., 2005; Widdows et al., 2008), introduction of non-native species (Tyler-Walters and Hiscock, 2005), clam exploitation (Alexandre et al., 2005; Cabaço et al., 2005; Guimarães et al., 2012) or nutrient enrichment (Brun et al., 2002; Cabaço et al., 2007, 2008). Because of its wide European distribution, its intertidal and shallow waters location and its sensitivity to disturbances, the species has a good potential for assessing the ecological quality status of coastal systems, which is of particular relevance for the implementation of the WFD. The purpose of this study was to develop an ecological quality index based on the seagrass Z. noltii (ZoNI) using multivariate analysis of plant variables (metrics) from physiological to population level in meadows of SW Iberian Peninsula under contrasting levels of disturbance. Nutrient loading was identified as a relevant factor leading to Z. noltii decline in South Iberian Peninsula (e.g. Brun et al., 2002; Cabaço et al., 2007) and elsewhere (e.g. Plus et al., 2001) and thus it was related to seagrass metrics measured in 5 systems of the Portuguese and Spanish coasts. Urban wastewater and fishfarm effluents were considered the main sources of nutri-

ents according to previous observations in the sites (Barragán, 1996; Tovar et al., 2000; Sánchez de Lamadrid Rey et al., 2002; Cabaço et al., 2008; Morris et al., 2009). This work will provide the use of a new seagrass species (Z. noltii) covering a different geographical region (Atlantic Ocean), complementing the application of ecological quality indexes based on the European seagrass species (P. oceanica and C. nodosa).

2. Materials and methods 2.1. Selection of seagrass metrics To select appropriate variables having the potential to respond to changes of environmental quality, a revision of the studies defining ecological quality indices for seagrasses was carried out (Pergent-Martini et al., 2005; Romero et al., 2007; Martínez-Crego et al., 2008; Lopez y Royo et al., 2010; Oliva et al., 2012). Because these studies were based on other seagrass species (P. oceanica and C. nodosa), specific studies on Z. noltii were also considered to select sensitive variables (e.g. Peralta et al., 2005; Brun et al., 2007; Cabaço et al., 2008). From this survey, a reduced list of 11 seagrass variables, with a high response potential to disturbances and representative of different organizational levels, i.e. population-, plant-, and physiological-level, were selected for Z. noltii, and their expected response to increasing environmental disturbance was defined (Table 1). 2.2. Study sites Eleven Z. noltii meadows along the Atlantic coast of south Iberian Peninsula were selected (Fig. 1), including five sites (R1, R2, R3, I1 and I2) in Ria Formosa lagoon (Portugal), two sites (H1 and H2) in the Guadiana estuary (Huelva, Spain), one site (H3) at the Rio Piedras river mouth (Huelva, Spain), two sites (C1 and C2) in the Cadiz Bay and one site (C3) at the Cachon river mouth in Zahara de los Atunes (Cadiz, Spain). All these systems are mesotidal with semi-diurnal tides of 2.5 m of average amplitude, mainly characterized by intertidal mudflats, where Z. noltii forms homogenous monospecific stands. The study sites were selected to encompass a gradient of anthropogenic pressures related to water quality from nearly undisturbed sites to highly impacted ones. Anthropogenic nutrient enrichment, specifically urban wastewater and fish-farm

Table 1 Zostera noltii seagrass variables (metrics) analysed for the development of the ecological quality index, methods used for their quantification and expected response to increasing environmental degradation. Organization level

Metric (units)

Standard method used (reference)

Response to degradation

Population

Cover (%)

Decrease (Guimarães et al., 2012)

Above/below ratio

Visual estimation of the area covered by seagrasses within 50  50 cm quadrates (Duarte and Kirkman, 2001). Count of the number of shoots present in a 12 cm diameter core (Duarte and Kirkman, 2001). Weight of seagrass material within a 12 cm diameter core after drying at 60 °C for 48 h (Duarte and Kirkman, 2001). Weight of aboveground fraction after drying at 60 °C for 48 h (Duarte and Kirkman, 2001). Weight of belowground fraction after drying at 60 °C for 48 h (Duarte and Kirkman, 2001). Ratio of above and belowground biomass.

Individual

Leaf length (cm)

Average of maximum leaf length of five shoots within the core.

Decrease (Cabaço et al., 2008)

Physiological

Total non-structural carbohydrates (TNC) in leaves/rhizomes (mg g DW1)

Extracted in hot ethanol from dried plant tissue, analyzed spectrophotometrically, using anthrone assay standardized to sucrose (Yemm and Willis, 1954). Flash EA1112 elemental analyser. Flash EA1112 elemental analyser, DPIRMS of stable isotopes, standard: acetanilide (Preston, 1992).

Decrease (Brun et al., 2008)

Shoot density (no. shoots m2) Total biomass (g DW m2) 2

Aboveground biomass (g DW m

)

Belowground biomass (g DW m2)

N content in leaves (% DW) N stable isotopes in leaves (‰)

15

Decrease (Cabaço et al., 2008) Decrease (Cabaço et al., 2008) Decrease (Leston et al., 2008) Decrease (Leston et al., 2008) Increase (Plus et al., 2001)

Increase (Cabaço et al., 2008) Increase (Machás, 2007)

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Legend

Sampling sites

Ria Formosa Huelva Cadiz R1 R2 R3 I1 I2

H1 H2 H3

C1 C2 C3

SPAIN 8°0'0"W

7°50'0"W

7°40'0"W

PORTUGAL

I1

I2

R1

37°0'0"N

37°0'0"N

R3

R2

MEDITERRANEAN SEA ATLANTIC OCEAN

0

62.5

125

250

375

Ria Formosa 8°0'0"W

7°50'0"W

7°20'0"W

500 Kilometers

7°40'0"W

6°30'0"W

7°10'0"W

6°20'0"W

6°10'0"W

6°0'0"W

5°50'0"W

C1 C2

36°30'0"N

H2 H1

36°30'0"N

H3 36°20'0"N

37°10'0"N

36°20'0"N

37°10'0"N

C3

36°10'0"N

Huelva

36°10'0"N

Cadiz 7°20'0"W

7°10'0"W 6°30'0"W

6°20'0"W

6°10'0"W

6°0'0"W

5°50'0"W

Fig. 1. Map showing the location of sampling sites along SW Iberian Peninsula.

effluents, was considered the main source of disturbance (Cabaço et al., 2008; Morris et al., 2009). 2.3. Sampling design Seagrass sampling was conducted at low tide in summer 2010 in Ria Formosa (Portugal), and in summer 2009–2011 in Huelva and Cadiz (Spain). Over a 25 m transect settled at the mid intertidal area, six quadrats of 50  50 cm were randomly placed to estimate percentage of cover, and six cores (12 cm diameter) of Z. noltii were collected near each quadrate for density and biomass (above- and below-ground) estimations. Three additional samples of plant material (leaves and rhizomes) were collected for biochemical analyses. The methods used to measure the seagrass metrics are presented in Table 1. To assess nutrient loading, three water samples were collected in each site at low tide (matching with plant sampling), filtered (Whatman cellulose acetate filters, 0.45 lm pore size) and frozen for later nutrient analysis (ammonium, nitrate and phosphate). Nutrients from Ria Formosa (Portugal) were determined in a loop-flow analyzer (lMac-1000, Systea), and nutrients from Huelva and Cadiz (Spain) were analyzed with a continuous-flow auto-analyzer (Skalar SAN++). Even though different equipment was used to analyse nutrients from Portuguese and Spanish sites, the same standard protocols were applied: the modified Berthelot reaction (hypochlorite method) for ammonium (Krom, 1980), the Cd–Cu column reduction method for nitrate (Wood et al., 1967), and the ammonium-molybdate and ascorbic acid colorimetric method for phosphate (Boltz and Mellon, 1948).

2.4. Calculation of the ZoNI index The environmental variables (i.e. ammonium, nitrate and phosphate concentration in the water column) were independently analysed from the seagrass variables, using a Principal Component Analysis (PCA), to extract a common source of variability among sites related to nutrient enrichment (Townend, 2002). Next, to assess which seagrass metrics are significantly affected by nutrient loading, a multiple regression analysis was performed using the site-specific Z. noltii metrics as independent variables and the site scores of the first component (CI) of the PCA that was performed using the nutrient variables of each site. Then, a PCA based on a correlation matrix was performed using the mean values of the selected seagrass metrics in each site to calculate the linear combinations of seagrass metrics that explain most of the data variability. The PCAs were performed using CANOCO v. 4.5 software package (ter Braak and Smilauer, 1998) without data transformation. The first component scores (CI) of the PCA performed with the seagrass metrics were used to calculate the ZoNI index of ecological quality. As required by the European WFD, the ZoNI index defined the Ecological Quality Ratio (EQR), a numerical value between 0 and 1 that integrates in a single scale the measurements of environmental quality defined by the seagrass Z. noltii. To set two potential undisturbed (no, or minor evidence of anthropogenic disturbance as required by the WFD – European Commission, 2000) and heavily disturbed extreme conditions related to nutrients, among which all studied sites will be positioned (following the approach of Romero et al. (2007); Oliva et al. (2012)), optimal and worst environmental sites were artificially created by averaging the three best and the three worst values of

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P. García-Marín et al. / Marine Pollution Bulletin 68 (2013) 46–54 Table 2 Quantitative boundaries of Ecological Quality Ratio (EQR) levels and respective categories of Ecological Quality Status (EQS). EQR

EQS

1–0.775 0.774–0.550 0.549–0.325 0.324–0.1 0.1–0

High Good Moderate Poor Bad

1.0

LL Optimal

ABIOM

Ammonium

Nitrate

Phosphate

CI

1.76 ± 0.81 1.54 ± 0.45 0.50 ± 0.24 10.15 ± 0.14 4.25 ± 0.14 5.94 ± 0.37 6.10 ± 0.27 6.10 ± 0.27 4.85 ± 0.13 2.69 ± 0.22 1.33 ± 0.01 2.57 ± 0.19 3.98 ± 0.04

0.14 ± 0.14 1.25 ± 0.24 0.00 ± 0.00 0.38 ± 0.38 0.00 ± 0.00 0.15 ± 0.02 1.43 ± 0.18 1.43 ± 0.18 0.80 ± 0.10 0.07 ± 0.01 0.06 ± 0.01 0.06 ± 0.01 0.05 ± 0.01

0.16 ± 0.02 0.21 ± 0.04 0.34 ± 0.02 0.57 ± 0.05 0.59 ± 0.03 0.11 ± 0.00 0.13 ± 0.00 0.13 ± 0.00 0.41 ± 0.01 0.07 ± 0.00 0.03 ± 0.00 0.06 ± 0.00 0.03 ± 0.00

0.841 0.074 0.787 2.223 0.673 0.005 0.893 0.893 0.876 0.906 1.301 0.946 0.709

Table 4 Summary of statistics of multiple regression of seagrass metrics (independent variables) on site scores of the first component of nutrient PCA.

Regression Residual Total

10 2 12

12.958 1.296 61.176 0.016 0.042 0.021 13.000

Adjusted R2 0.980

MS

F

Significance F Metrics

C1.11

COVER

R1.10 R2.10 R3.10 I1.10 I2.10 H1.09 H1.11 H2.11 H3.11 C1.11 C2.10 C2.11 C3.10

SS

H2.11 C3.10

Site

df

15N H3.11

p-Value

COVER DENS TBIOM

0.071 0.031 0.043

ABIOM A/BBIOM LL TNC-L TNC-R N 15 N

0.005 0.006 0.058 0.006 0.005 0.017 0.105

Table 5 Percentage of variance explained and PCA loadings of the Zostera noltii metrics used to calculate the ZoNI index. CI, CII and CIII are the three main components. High contributions of each variable in each component are in grey.

all samples for each variable based on their expected response to increasing degradation conditions (Table 1). These artificial sites were added as supplementary objects to the PCA (e.g. Romero et al., 2007).

H1.11

CII (22%)

Table 3 Environmental characterization of sites based on the measured average nutrient concentrations (lM ± SE) and site scores of the first PCA component (CI), used in multiple regression analysis.

ANOVA

A/B

N R3.10 C2.10

DENS TBIOM

R1.10 C2.11

TNC-R

Worst

H1.09 I1.10

I2.10

R2.10

TNC-L

-1.0 -1.0

CI (47%)

1.0

Fig. 2. Factor loadings in the PCA analysis of the selected metrics for Zostera noltii of SW Iberian Peninsula, and relative position of each study site, including the artificially created optimal and worst sites.

Table 6 Scores of the first component (CI) obtained from PCA, EQR (ZoNI) values calculated for each site (see Fig. 1) of SW Iberian Peninsula based on the Zostera noltii metrics, and the EQS obtained according to Table 2. Site

CI

EQR

EQS

R1.10 R2.10 R3.10 I1.10 I2.10 H1.09 H1.11 H2.11 H3.11 C1.11 C2.10 C2.11 C3.10 Optimal Worst ORIGIN

0.3469 0.4294 0.9095 1.1033 1.4036 0.4513 1.9809 0.0633 0.1059 0.4394 1.544 0.9633 1.0163 4.1221 2.3428 1.0144

0.48 0.49 0.56 0.27 0.23 0.37 0.15 0.44 0.44 0.37 0.65 0.56 0.57

Moderate Moderate Good Poor Poor Moderate Poor Moderate Moderate Moderate Good Good Good

The qualitative Ecological Quality Status (EQS), which is divided into high, good, moderate, poor and bad categories, as required for the implementation of the WFD (European Commission, 2000), was defined using the quantitative EQR divided into the same five categories, from 1 to 0. The bad status category (0–0.1) was considered for meadows with less than 5% cover or where Z. noltii meadows have recently disappeared, following the recommendations of WFD for this category: ‘‘Waters showing evidence of severe alterations to the values of the biological quality elements in which large portions of the relevant biological communities are absent’’, and the consideration of the OSPAR Commission that ‘‘Zostera sp. cover should be at least 5% to be considered a meadow’’ (Tullrot, 2009). The remaining boundaries were equally distributed through the other four categories, from 0.1 to 1 (see Table 2). For each site, the EQR (EQR0site ) was calculated as EQR0site = (CIsite  CIworst)/(CIoptimal  CIworst), using the PCA scores of the first component (CI), which represented an overall description of the relationships among Z. noltii metrics that are mostly af-

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Fig. 3. Zostera noltii population- and individual-level metrics in the study sites along SW Iberian Peninsula (see Fig. 1). (a) Cover, (b) shoot density, (c) total biomass, (d) above biomass, (e) A/B biomass ratio and (f) leaf length. Bars represent mean + SE (n = 6). White, grey and black bars represent good, moderate and poor EQS, respectively (according to Table 6).

fected by environmental disturbances and thus an estimator of site-specific environmental quality (as in Romero et al. (2007) and Oliva et al. (2012)). The EQR values calculated for each site of Z. noltii cover higher than 5%, were corrected as EQR = (EQR0 + 0.11)/(1 + 0.10) to force them to be included in the categories from poor to high (see Table 2). Besides, to verify the relationship between the EQR calculated based on the seagrass variables and the environmental quality gradient established using the nutrient concentrations, a Pearson correlation was conducted with a significance level of p < 0.05 (Sokal and Rohlf, 1995).

3. Results The characterization of the sampling sites based on nutrient loading is presented in Table 3. A higher CI score indicates higher nutrient load, whereas lower CI score (negative values) correspond to sites with lower nutrient enrichment. These results were in agreement with subjective knowledge of the disturbance level of sites (e.g. proximity to urban wastewater or fish farm effluents). Ammonium concentrations ranged from 1 to 10 lM, being always higher than nitrate concentrations. Nitrate and phosphate concentrations were always low (<1.5 lM). The site scores of the first

P. García-Marín et al. / Marine Pollution Bulletin 68 (2013) 46–54

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Fig. 4. Zostera noltii physiological-level metrics in the study sites along SW Iberian Peninsula (see Fig. 1): (a) total non-structural carbohydrates in leaves, (b) total nonstructural carbohydrates in rhizomes, (c) N content in leaves and (d) 15N stable isotopes in leaves. Bars represent mean + SE (n = 3). White, grey and black bars represent good, moderate and poor EQS, respectively (according to Table 6).

component (CI) of PCA analysis of the nutrient concentrations (Table 3) were used for multiple regression analysis. Ten Z. noltii metrics out of the initial 11 were used for multiple regression analysis (Table 4). Below ground biomass data were not considered as this variable was highly correlated with total biomass (R2 = 0.89). The multiple regression analysis of seagrass metrics (independent variables) on site scores of the first component of the nutrient PCA was significant at p < 0.02. All individual Z. noltii metrics were significantly affected by nutrient loading, except cover and o15N, with showed marginal significant differences (p = 0.07 and p = 0.10, respectively) and thus were also included in the calculation of the ZoNI index. Furthermore, their inclusion may increase the capacity of the ZoNI index to react to disturbances other than nutrient loading, such as physical disturbances or extreme nutrient discharges of urban effluents. The first component (CI) of the PCA based on Z. noltii metrics explained 47% of the data variability (Table 5). In general, most variables showed high loadings in this component, which indicated that the CI described the general interrelationship among Z. noltii metrics. The population level metrics, such as cover, density, total biomass and above ground biomass, were all positioned on the same side of the CI, and evidenced the higher loadings; whereas the N and o15N content in leaves, varied with the opposite sign than population parameters, i.e. they decrease when the former increase. The second (CII) and third (CIII) components explained 22% and 16% of variability, respectively. The CII represented the contrast between the variability of above/below biomass ratio, leaf length and 15N isotope ratio with total sugar in leaves, which was the opposite pattern of CI (i.e. positive loadings for the first variables and negative for total sugar in leaves). The CIII repre-

sented the joint variation of total sugar in rhizomes and in leaves, not represented in the other components, which varied with the same sign of N content in leaves. The metrics with higher loadings in CII and CIII, above/below biomass ratio and total sugars in rhizomes respectively, were not significantly related to nutrient loading (CI site scores of nutrient PCA, Table 3). The first component was thus most appropriate to use as an integrative indicator of Z. noltii variability to nutrient loading. Moreover, the variation between the artificially created optimal and worst sites mostly developed (major stretched) along CI. The distribution of sites along CI from the optimal to the worst artificial sites (Fig. 2) represented their environmental quality alignment, from the best quality sites at Cadiz (C2 and C3) and Ria Formosa (R3), to the worst quality sites at Ria Formosa (I1 and I2) and Huelva (H1.11). The CI scores (ZoNI index) for each site are presented in Table 6 as well as their qualitative EQS based on the classification presented in Table 2. None of the studied sites was classified as having a bad EQS. Four sites were classified as good, six as moderate and three as poor. The status of site H1 changed from moderate to poor from 2009 to 2011, suggesting a deterioration of its environmental quality. In general, both population and plant level metrics (e.g. percentage of cover, shoot density, total biomass, above ground biomass, above/below biomass and leaf length; Fig. 3) presented lower values in sites of poor EQS than in moderate or good status sites, revealing the negative effects of disturbances. On the other hand, the metrics at the physiological level (tissue N content and 15 N stable isotopic ratio in leaves) showed higher values in sites of poor and moderate EQS than in sites of good status (Fig. 4). This suggests the effects of the environmental N loading and the urban/ animal effluent of heavy d15N, respectively. Non-structural carbo-

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Fig. 5. Relationship between the EQR obtained using Zostera noltii variables (ZoNI, from Table 6) and the CI scores of the environmental variables for each site of SW Iberian Peninsula (from Table 3).

hydrates of rhizomes are lower in poor EQS sites, suggesting higher energy demand to thrive in disturbed sites; in contrast, non-structural carbohydrates of leaves did not show a clear trend (Fig. 4).

4. Discussion The results presented here showed that the ecological quality index ZoNI, based on the PCA analysis of Z. noltii metrics is appropriated for the ecological quality assessment of transitional and coastal waters dominated by this BQE. The index suitability was supported by: (1) the considerable variance explained by the CI of the PCA analysis (47%), which was indicative of a major and common source of variability (Legendre and Legendre, 1998); (2) the partial redundancy among metrics indicated by the clustering of metrics on each side of the first axis, thus contributing to the index robustness (Borja et al., 2004; Puente et al., 2008); (3) the significant correlation between the index (calculated from CI site scores of the seagrass metrics PCA, Table 6) and the environmental quality of sites (given by CI site scores of the nutrient PCA, Table 3), which accounted for 51% of the disturbance variability related to nutrients (Fig. 5). Furthermore, the reliability of ZoNI is also based on the species well-known response of the metrics to the anthropogenic disturbances related to water quality (e.g. Brun et al., 2002, 2008; Plus et al., 2003; Cabaço et al., 2007, 2008), which has also been verified in this study. However, the relationships investigated here explain overall ca. 50% of the data variability. This is both an indication that other sources of disturbance than nutrients are also playing a role and that the final Ecological Quality Status of transitional and coastal waters have to rely also on biological elements other than Z. noltii. Even though seagrasses may be affected by different types of disturbances, the impacts related to water quality (e.g. nutrient enrichment) are amongst the major causes of decline worldwide (Waycott et al., 2009). The negative effects of such disturbances on Z. noltii have been reported elsewhere (e.g. Plus et al., 2001; Brun et al., 2002; Cardoso et al., 2004; Cabaço et al., 2007, 2008). In particular, changes related to urban wastewater and fish farm effluents induce the increase of seagrass N content (e.g. Lapointe et al., 1994; Romero et al., 2006; Cabaço et al., 2008; Pérez et al., 2008) and of 15N stable isotope signal (e.g. Fourqurean et al., 1997; Lepoint et al., 2004; Pérez et al., 2008). Such responses of Z. noltii were evident in the disturbed sites studied here. The ammonia d15N isotopic signal measured in the summer at the site

closer to the urban effluents investigated in Cabaço et al. (2008) was 34% (unpublished data), reflecting in a Z. noltii leaf signal of 8.5‰. This suggests that the poor status sites I1.10, I2.10 and H1.11 and the moderate status sites H1.09, H2.11 and C1.11 (Fig. 4d) are highly influenced by urban wastewater effluents. In contrast, the sites classified in good ecological status, particularly C2.10 and C2.11 showed very low values of 15N, indicative of a nutrient environment that was not influenced by anthropogenic disturbances (Castro et al., 2007). In fact, these sites are located far away from possible sources of water quality disturbances. In spite of the environmental quality differences among sampling sites, none of the studied Z. noltii meadows reached the level of high ecological status. This may be explained by the location of this species, in the intertidal area, where the access is easy at low tide and some direct physical impacts from visitors are frequent, such as those caused by clam harvesting or mooring (Cabaço et al., 2005; Masero et al., 2008; Guimarães et al., 2012). Physical disturbances affected negatively the metrics at the population level such as total biomass and shoot density, which, in turn, affected EQR (lower biomass and density implies a decrease in EQR), and consequently resulted in a worse EQS classification. The development of biotic multivariate indices based on seagrasses (e.g. POMI for P. oceanica – Romero et al., 2007, CYMOX for C. nodosa – Oliva et al., 2012, ZoNI for Z. noltii – this study) fulfil the WFD requirements for environmental quality assessment of coastal and transitional waters, providing essential tools for the application of the WFD and highlighting the role of seagrasses as biological quality elements. The use of multivariate indices represent further steps in the improvement of previous indices (e.g. Krause-Jensen et al., 2005; Foden and Brazier, 2007) because of: (1) the index robustness derived from the partial redundancy among metrics (e.g. Borja et al., 2004; Puente et al., 2008); (2) the different levels of biological organisation considered; and (3) the relevant information provided by the indices for the identification of different disturbance sources (Lepoint et al., 2004; Castro et al., 2007). For instance, the multimetric ZoNI index constitute an integrated approach that is able to reflect the ecological quality of coastal systems compared to the individual assessment of metrics proposed by other approaches, such as the bio-optical model developed to define habitat quality for submersed aquatic vegetation in United States (Biber et al., 2004). In summary, this work showed the suitability of Z. noltii as a biological quality element to assess the environmental quality of water bodies, and that the new multivariate index developed, ZoNI, properly reflected the ecosystem ecological status in SW Atlantic coasts of the Iberian Peninsula. The ZoNI index complies with WFD requirements, being a reliable tool for its implementation and providing a useful tool for coastal management. The main advantage of the ZoNI, in contrast with previous seagrass-based indices, is its broader potential applicability, as Z. noltii is widely distributed along the Atlantic and Mediterranean coastal and transitional waters, while other species (i.e. C. nodosa and P. oceanica) are limited to the southern European Atlantic coasts and/or the Mediterranean Sea. Acknowledgements The data base of this work was collected under the SUDOE Interreg IV B project, ECO-LAGUNES (SOE1/P2/F153) and the Spanish national projects Imachydro (CTM2008-00012) and Sea-Live (CTM2011-24482). The work was developed within the program of the COST Action ES0906 ‘‘Seagrass productivity: from genes to ecosystem management’’. P. García-Marín holds a FPU grant from the Spanish Ministry of Education, and received a short-term scientific mission grant, COST-STSM-ES0906-9279 to develop the ZoNI index at CCMAR. S.

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