Seagrass Quality Index (SQI), a Water Framework Directive compliant tool for the assessment of transitional and coastal intertidal areas

Ecological Indicators 30 (2013) 130–137

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Seagrass Quality Index (SQI), a Water Framework Directive compliant tool for the assessment of transitional and coastal intertidal areas João M. Neto ∗ , Dimitri V. Barroso, Pablo Barría IMAR – Institute of Marine Research, Department of Life Sciences, Faculty of Sciences and Technology, University of Coimbra, Largo Marquês de Pombal, 3004-517 Coimbra, Portugal

a r t i c l e

i n f o

Article history: Received 21 November 2012 Received in revised form 4 February 2013 Accepted 8 February 2013 Keywords: Zostera Environmental quality Response to pressure Assessment uncertainty Abundance Shoot density

a b s t r a c t This work describes a new method for assessing the ecological quality of intertidal seagrass in estuaries and coastal systems, the Seagrass Quality Index (SQI). The design of the SQI aims to fulfil the Water Framework Directive requirements in terms of compliance (e.g., metrics, sampling procedure, pressure relationship, uncertainty of misclassification and comparability to other methodologies in terms of concept). The index includes three common and easy-to-measure structural parameters of seagrass (i.e., the no. of taxa, bed extent and shoot density) combined in a calculation rule that allows the index to report all five of the quality classes (i.e., high, good, moderate, poor and bad). The present study contains analyses of the relationships between the ecosystem-quality results produced by the index and the pressures measured in the system as well as the relationships between the SQI and the seagrass parameters composing it (both the correlation between the SQI and metrics and the SQI sensitivity to the individual variation of each metric). These relationships were tested using a Spearman rank-correlation analysis, producing significant correlations between the biological metrics and the index results as well as between the index results and the environmental quality-pressure category (i.e., the concentration of winter DIN and turbidity). In terms of management, it is possible to apply the methodology on a broad geographical scale in systems where the reference condition for the number of taxa is even higher than one (for the Mondego studied here, the reference value was one species). The tool fulfilled the WFD requirements, had a robust sampling design and proved to be able to track the inertia that usually exists from the moment the pressure is alleviated as well as the biological response that characterises the recovery phase in systems under restoration. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction The implementation of the European Water Framework Directive (WFD; Directive 2000/60/EC – European Council 2000) has inspired Member States (MSs) to develop ecological indices able to assess the quality (EcoQ) of water bodies. In addition to the hydromorphological and physicochemical elements, the assessment must primarily focus on biological quality elements (BQE; e.g., phytoplankton, macroalgae, angiosperms, fish fauna and macrobenthic fauna), the individual contributions of which are considered in the final assessment result. For the BQEs, type-specific reference conditions (undisturbed) must also be defined, and the quality status of each element is measured as the divergence from undisturbed conditions. This measurement is the Ecological Quality Ratio (EQR), a zero-to-one continuous numerical scale that corresponds to the standardised Ecological Quality Status (EQS) scale, a classification system comprising five quality classes (i.e., bad, poor, moderate, good and high) (for further details, consult: WFD CIS, 2003a,b,c,d;

∗ Corresponding author. Tel.: +351 239 855 760x415; fax: +351 239 823603. E-mail address: [email protected] (J.M. Neto). 1470-160X/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2013.02.015

http://ec.europa.eu/environment/water/water-framework/index en.html; http://www.euwfd.com/; http://www.epa.ie/whatwedo/ wfd/; http://circa.europa.eu/Public/irc/env/wfd/library?l=/framework directive/). In Europe, several assessment tools were developed for transitional waters (TW) and coastal waters (CW) following these criteria when considering measures of ‘composition’ and ‘abundance’: (1) the taxonomic composition corresponds totally or nearly totally to undisturbed conditions (where all sensitive taxa should be present), and (2) there are no detectable changes in the abundances due to anthropogenic activities. For seagrasses, a sub-element of the angiosperms BQE (a combination of seagrass and saltmarsh plants for TW and those plants combined with macroalgae for CW), the monitoring requirements are met by assessing the taxonomic composition and abundance as the presence of disturbance-sensitive taxa and as the shoot density or bed spatial extent, respectively (Foden and Brazier, 2007). Moreover, other metrics, such as “seagrass health”, can still be used to complement the basic assessment (Foden and de Jong, 2007). Seagrasses constitute important key elements present in TW and CW and are considered good biological indicators of environmental quality because they are sessile organisms and respond

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Fig. 1. The Mondego estuary (40◦ 08 N, 8◦ 50 W). Sampling area in the South Arm (circle), and Zostera noltei bed extent and biomass throughout the study period. C = the upstream connection area between the two estuarine arms.

to toxic substances and to changes in nutrient concentrations, light intensity and hydromorphology (Benedetti-Cecchi et al., 2001; Soltan et al., 2001; Panayotidis et al., 2004; Melville and Pulkownik, 2006; Yuksek et al., 2006; Arévalo et al., 2007; Scanlan et al., 2007; Krause-Jensen et al., 2008). Under oligotrophic conditions, seagrasses dominate the primary production in shallow temperate coastal systems, but they are usually replaced by proliferations of macroalgae as eutrophication progresses (Duarte, 1995; Valiela et al., 1997; McClelland and Valiela, 1998; Moore and Wetzel, 2000). Based on seagrass data from a system that was under several levels of human pressure over time, this work aimed in general to validate the quality assessment tool [the Seagrass Quality Index SQI)] within the WFD requirements. Specifically, the objectives were (1) to verify the response of the tool (SQI) against different anthropogenic pressure levels and (2) to identify the sensitivity of the tool to variations in each of its composing metrics (i.e., taxonomic composition, bed extent and shoot density).

2. Material and methods 2.1. Study site The study area is located in an Atlantic estuary in southern Europe along the western coast of Portugal (Fig. 1). The Mondego estuary (40◦ 08 N, 8◦ 50 W) is a shallow TW classified as a mesotidal well-mixed estuary with irregular river discharges and is included in the European type NEA 11 (North East Atlantic ecoregion) of the WFD (2000/60/EC) classification system. The South Arm of the estuary, where seagrass meadows are present, constitutes a subsystem with approximately 7 km length, 0.5 km of maximum width, 2–4 m depth and an area of 2.57 km2 . The marine influence is strong in the lower estuary area, and the average tidal amplitude of 1–3 m allows up to 75% of this subsystem’s area to be exposed to air during low tide (Neto et al., 2010).

2.2. Hydromorphology and environmental disturbances Due to regional economic interests, the Mondego River catchment basin suffered several physical modifications over the years (Neto et al., 2010). The estuary supports industrial activities, salt works, mercantile systems and fishing harbours as well as agriculture (11,000 ha of cultivated land) and the urban pressures (∼28,000 permanent inhabitants) from Figueira da Foz, a centre of seasonal tourism activity (Marques et al., 2003; Lillebø et al., 2005; Neto et al., 2008). Two distinct time intervals could be observed throughout the study period, from 1986 to 1997 and from 1998 to 2009. The first phase was characterised by a general environmental degradation of the study area. Intense anthropogenic disturbance occurred in the last years of this period (e.g., the alignment of the margins with stones and deepening of the bottom in the North Arm conducted between 1990 and 1992) and culminated in a complete interruption of the upstream communication between the north and south arms of the estuary. Eutrophication symptoms were then visible in the South Arm, both as the proliferation of opportunistic green macroalgae (Martins et al., 2001; Marques et al., 2003) and as a massive reduction of the extent of seagrass beds. The second period started with the implementation of experimental mitigation measures in the South Arm in 1997 and 1998. The upstream communication between the two arms was re-established, and the water discharged through the Pranto River (a small tributary draining 2300 ha of agricultural fields and flowing directly into the South Arm through a sluice) was partially diverged from the South Arm to an alternative sluice placed upstream in the North Arm. The upstream connection between the northern and southern arms (Fig. 1) was re-established through a 1 m2 wall break that allowed the water to flow between the arms only during high tide (Neto et al., 2010). After positive results were obtained with the experimental reestablishment, this upstream connection was enlarged to a 30 m wide channel in 2006 by the

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Table 1 Categories, indicators/stressors and criteria used to assess anthropogenic pressures in the Mondego estuary. Pressure

Scores Indicator/stressor

Criteria

No change (0)

Very low (1)

Low (3)

Medium (5)

High (7)

Very high (9)

Hydromorphological changes

Land claim (ha)

Consider both: mudflats and tidal marshes. This indicator includes both anthropogenically induced changes (land claim) and natural variations, since the 1900–1950s or before big morphological changes occurred (since when trustful maps are available) Percentage of the shoreline or estuarine margin that suffered re-enforcement work

No change

<0.5% lost

<1% lost

<5% lost

<10%lost

≥10% lost

No change

<5%

<30%

<60%

<90%

≥90%

The annually subtidal dredged area in relation to total area of estuaries (or WB) The amount of material dredged annually from estuaries (1 m3 of sand dredged is equivalent to 2 tonnes) The area designated for disposal in estuaries (or WB) or length affected by disposal (for tidal rivers) as suggested within the Water Framework Directive for the designation of Heavily Modified Water Bodies (HMWB) Represented by the total tonnage annually disposed in estuaries Percentage of the length of coast or estuarine (or WB) area affected by fishery The intensity of marina development is measured by the number berths/km2 of the WB Percentage of the length of coast (riverbank) or estuarine (or WB) area affected by tourism and recreation activity

No dredging

<1%

<10%

<30%

<50%

≥50%

No dredging

<5000 tonnes

<100,000 tonnes

<1 million tonnes

<4 million tonnes

≥4 million tonnes

No disposal

<1%

<10%

<30%

<50%

≥50%

No disposal

<5000 tonnes

<100,000 tonnes

<1 million tonnes

<4 million tonnes

≥4 million tonnes

No fishery activities

<10%

<30%

<60%

<90%

≥90%

No marina

<100 berths/km2 WB

<150 berths/km2 WB

<300 berths/km2 WB

<500 berths/km2 WB

≥500 berths/km2 WB

None

<10%

<30%

<60%

<90%

≥90%

<6.5

<10

<30

<60

<90

≥90

<0.5

<1

<1.5

<2

<2.5

≥2.5

Shoreline re-enforcement (%)

Resource use change

Maintenance dredging area (ha) Maintenance dredging volume (tonnes)

Maintenance disposal area (ha)

Maintenance disposal volume (tonnes) Other fisheries near shore disturbance Marina development

Tourism and recreation

Environmental quality and its perception

Nutrients (␮mol/L)

Natural turbidity

Quantified as the DIN winter median concentration (␮mol/L) Measured as the mean secchi disc transparency (m) during growing season (May to September)

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Table 2 Metrics used in the Seagrass Quality Index (SQI), criteria used to define the reference conditions for each metric and the weight that the metrics were given in the combination rule to produce the final EQR result. The values that were considered as references for the Mondego estuary are shown between brackets. Metric

Reference condition (for the Mondego)

Weight into combination rule

Taxonomic composition Bed extent Shoot density

Maximum no. of seagrass taxa ever registered in site (1) Higher measured value or 5% of the available intertidal area (15 ha)a 90th percentile of shoot densities measured at meadow (12,000)b

0.2 0.3 0.5

a b

The available intertidal is considered as the suitable area for seagrass to grow and does not include occupations of several orders or the saltmarsh area. The shoot density’s percentile 0.90 was calculated from samples collected randomly inside healthy meadows.

National Institute of Water (INAG – Instituto da Água). The water could then flow easily between the two arms during the full tidal cycle.

APHA-recommended (Greenberg et al., 1980) techniques. The ‘natural turbidity’ was determined as the water transparency measured using a Secchi disc.

2.3. Biological data

2.5. Seagrass Quality Index (SQI) methodology and reference conditions

A long-term data series from the South Arm of the Mondego estuary (1986–2009) was used to provide information on the basic structural parameters of Zostera noltei Hornemann 1832, including the ‘bed extent’, ‘biomass’ and ‘shoot density’. Although limited to the parameter ‘bed extent’, the information from 1986 was useful to infer the meadows’ conditions before the severe interventions that occurred in the system during the 1990s. Sampling was performed at intertidal areas on the South Arm of the Mondego estuary during low tide and using a manual corer (13.5 cm inner diameter, ∼3 L). Samples were randomly collected inside the Z. noltei meadow to provide data on the biomass and shoot density. The samples were collected at rates ranging from monthly to a lower frequency of only one to three sampling events per year concentrated in the growing season, depending on the survey’s purpose at the time. The samples were sorted in the laboratory, the plants were identified, the number of shoots was quantified and the biomass was determined as the dry weight (g DW m−2 ) after weight stabilisation at 70 ◦ C in a drying oven. The mapping of the bed extent was based on field observations (using a portable GPS to register the perimeter of meadows) and vertical photographs and assisted by GIS methodology (ArcView GIS version 8.3). 2.4. Assessment of anthropogenic pressures Following the proposal of Aubry and Elliott (2006), three categories of pressure, with a total of 11 pressure indicators/stressors (Table 1), were considered potentially significant to assess the impact of anthropogenic pressures on seagrasses (based on the expert opinion of the working group on marine macroalgae and angiosperms). The anthropogenic pressures were quantified and translated into scores following the criteria shown in Table 1. After translation into standardised scores, the individual pressure indicator/stressor and the three categories of pressures were correlated with the ecological metrics (e.g., shoot density) and the EQR to check for significant responses of the biology to anthropogenic pressure. The quantification of the pressure indicators ‘land claim’, ‘shore line re-enforcement’, ‘other fisheries near shore disturbance’, ‘marina development’ and ‘tourism and recreation’ was based on GIS work [e.g., using aerial photos, old (1948) and recent maps and field surveys]. The dredging data (‘maintenance dredging area’, ‘maintenance dredging volume’, ‘maintenance disposal area’ and ‘maintenance disposal volume’) were supplied by IPTM (Instituto Portuário e dos Transportes Marítimos; www.imarpor.pt/). Data on ‘nutrients concentration’ were determined from water samples that were collected during field surveys (winter data), filtered (through a GF/C glass-fibre filter) and analysed for dissolved inorganic nitrogen (DIN = NH4 + + NO3 − + NO2 − ) following

The Seagrass Quality Index (SQI) is a multimetric tool that combines three different seagrass metrics: (1) the taxonomic composition, defined as the number of taxa identified in surveys; (2) the bed extent, defined as the areal cover of the meadows (with a coverage density greater than 5%) mapped based on field observations (GPS measurements collected on foot) and vertical photographs; and (3) the shoot density, defined as the number of shoots per square metre quantified from representative parts (with different densities) of the meadow (min. 3 replicates) (Table 2). These metrics are common structural indicators widely used in studies concerning seagrasses (Marbà et al., 2013) that are in compliance with the WFD recommendations in terms of parameters to include in the assessment tools. The reference conditions (Table 2) basically correspond to the Best Attainable Condition (BAC), which “...is equivalent to the expected ecological condition of least disturbed sites if the best possible management practices were in use for some period of time” (Stoddard et al., 2006), and are defined as follows: (1) Taxonomic composition: the maximum no. of taxa ever recorded in the system; (2) Bed extent: the area occupied by the meadows with coverage densities greater than 5%; and (3) Shoot density: the 90th percentile of the no. of shoots per square metre registered in samples collected randomly inside healthy meadows. Although a set of reference conditions was defined at the moment (Table 2), BAC is not invariant, and the temporal variation of influencing factors (e.g., available technology and public commitment) (Stoddard et al., 2006) may always stimulate future improvement of these standards. The deviation from the reference condition was calculated for each metric in a continuous way inside the range 0–1 by dividing the measured value of the metric (i.e., no. of taxa, bed extent or shoot density) by the corresponding reference condition. The EQR values were obtained using the combination rule expressed in Eq. (1). EQR =

 T  T ref

∗ 0.2 +

 E  E ref

∗ 0.3 +

 D  D ref

∗ 0.5

(1)

where T = taxonomic composition, E = bed extent, D = shoot density and ref = reference condition. Equidistant boundaries with a 0.2 difference in value were used to translate the EQR scale into EQS classes (Table 3). 2.6. Data analysis The structural parameters of the seagrass (bed extent and biomass) were analysed throughout the study period. The responses of the bed extent, biomass, shoot density and EQR values to the different levels of pressure were analysed yearly for the summer period (July to September).

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J.M. Neto et al. / Ecological Indicators 30 (2013) 130–137 Table 3 Ecological Quality Ratios (EQR) defining the boundaries used to set the different Ecological Quality Statuses (EQS) in the Seagrass Quality Index (SQI).

EQR 0.00 – 0.20 0.21 – 0.39 0.40 – 0.59 0.60 – 0.79 0.80 – 1.00

EQS Bad Poor Moderate Good High

The ability of the SQI tool to distinguish EQR values into the five ecological quality classes (i.e., bad, poor, moderate, good and high) was also examined and correlated with the pressure level acting at each moment on the system. The chance that the SQI methodology would succeed when assessing quality in systems that have more than one seagrass species was also studied. Different taxonomic compositions (the presence of 0, 1, 2 or 3 species) were tested for systems that hypothetically had different numbers of species as the reference condition (1, 2 or 3 species). Several combinations for the other two metrics (i.e., bed extent and bed density) were used for each of the different possible cases of the number of species. For the cases with a number of taxa higher than one, a weighted average (based on the percentage of the bed occupied by each taxa) was used to estimate the reference values for the shoot density. To test the sensitivity of the SQI to variations in each of the composing metrics, an interval variation of 10% (in relation to the reference condition) was successively used to simulate the effects of different conditions on the status of each metric along the highbad gradient. The accumulated effect of a simultaneous variation of all of the metrics was also tested using equal interval values of 10% variation for all of the metrics in successive possible cases (i.e., 10%, 20%, 30%, etc.). This procedure was performed for systems having only one species as the reference condition for the taxonomic composition and for systems having two species as the reference condition. The normality of the data distribution was tested using the Shapiro–Wilk test (Shapiro and Wilk, 1965). Due to the low number of data points, possibly countering the assumptions of the underlying principles of continuity and normal distribution, correlations were analysed using Spearman rank-correlation tests for a significance level p < 0.05. Statistical tests were performed with the StatSoft, Inc. (2004) STATISTICA data analysis software system, version 7. 3. Results The variations of the bed extent and biomass in the estuary can be observed in Fig. 1. These variables decreased during the first period (degradation), from 1986 to 1997, but showed a reversed pattern after 1998, when the mitigation measures were implemented in the South Arm. Starting in 1992, the bed extent registered a drop of nearly 400-fold during the following 4-year period. In the same period, the biomass decreased approximately eight-fold below the value registered in 1992. After implementing the mitigation measures, the bed extent improved in a rhythm of 3- to 4-fold in each subsequent period of 4 years. The biomass evolution was not as constant as that of the bed-extent parameter, although an increase close to 4-fold was observed during most of the 4-year periods that followed the mitigation (Fig. 1). After testing for the normal distribution of data (only the SQI and total pressure were normally distributed, with W = 0.884, p = 0.098 and W = 0.920, p = 0.288, respectively), correlation analyses were performed between the simple metrics and the pressure

indicators/stressors using the Spearman rank-correlation test (Table 4). All of the biological parameters (single structural metrics) were significantly correlated with each other and showed a significant correlation with the EQR values provided by the SQI tool. The total pressure (the sum of all of the single pressure indicators/stressors) was not significantly correlated to any of the seagrass biological parameters. Alone, the turbidity showed a significant correlation only with the biological parameter of the bed extent, but together with winter DIN, the two factors showed a significant correlation with all of the biological parameters and the EQR values (Table 4). These two pressure indicators/stressors together corresponded to the environmental quality-pressure category. The responses of the SQI (i.e., EQR values and corresponding EQS classifications) and the biological parameters (i.e., bed extent, shoot density and biomass) in relation to the environmental quality pressure were all significantly correlated (Fig. 2). Although the number of biological samples considered in this study was not very high (n = 11), there was an apparent difference between the degradation and recovery processes. This difference is more evident for the parameters shoot density and biomass (Fig. 2C and D), where, for the same intermediate pressure level of eight, the degradation values were always higher than the recovery ones. The EQR values achieved using the SQI when the comprised metrics vary alone or in combination with each other are shown in Fig. 3. The results report four distinct situations: (a) when the reference condition for the number of taxa is one and the bed extent varies in 10% intervals (E); (b) when the reference condition for the number of taxa is one and the shoot density varies in 10% intervals (D); (c) when the reference condition for the number of taxa is one and both of the other parameters (E + D) vary simultaneously in 10% intervals (T = 1); and (d) when the reference condition for the number of taxa is two and all of the parameters (E + D + T) vary simultaneously in 10% intervals (T = 2). In this last case, the number of taxa can be 1 or 2, the reference condition for E is estimated as the highest historical bed extent registered and the reference for D is estimated as a weighted average, taking into account the percentage of the bed extent occupied by each of the species present. Many systems have Z. noltei and Z. marina present in the intertidal zone, with 70% and 30% for the bed extent and 12,000 and 2000 as the shoot densities as the usual reference values, respectively, for each of these species, so the reference condition for the shoot density can be calculated as (0.7 * 12,000) + (0.3 * 2000) = 9000 shoots/m2 . From the results shown in Figs. 2 and 3A, it is possible to check the ability of the SQI to report regarding all of the quality classes, both when the reference condition for the number of taxa is one and when it is higher. The sensitivity of the SQI to the different metrics is directly related to the numerical coefficient operating on each of the metrics used in the SQI formula (Eq. (1)).

4. Discussion The SQI was developed in the scope of the WFD to assess the ecological quality of TW and CW. For ECOSTAT (Working Group A on Ecological Status) to accept it as a valid method, the SQI must succeed in the compliance and feasibility-checking processes. In this sense, the validation of the method against the WFD precondition requirements was positively performed (Table 5) and the tool’s ability to express the correct ecological quality level of the water body was also analysed to confirm the tool’s feasibility. The methodology followed all of the requirements in terms of the sampling, parameters comprised, combination rule and output of results. The reference conditions were also defined in accordance to WFD requirements and selected from the best attainable conditions using a long-term data series collected in a

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Table 4 The Spearman rank-correlation () results for significance level p < 0.05. Significant values are in bold (parameters that were not significantly correlated to any other are not shown). Complete series (n = 11)

Shoot density

Bed extent Shoot density Biomass SQI Total pressure Env. qual. pressure Winter DIN

0.930

Biomass 0.825 0.909

SQI 0.958 0.965 0.860

Total pressure

Env. qual. pressure

Winter DIN

Turbidity

−0.260 −0.293 −0.443 −0.337

−0.634 −0.592 −0.596 −0.713 0.751

0.031 −0.130 −0.298 −0.153 0.675 0.501

−0.642 −0.428 −0.257 −0.514 0.008 0.420 −0.518

Fig. 2. The response of the SQI (EQR) (A), bed extent (B), shoot density (C) and biomass (D) to the environmental quality pressure. For the corresponding SQI quality status (A), see Table 3 for the meaning of the colours. The trend line and equation, the Spearman rank-correlation result () and the number of data points (n) are also shown. The red symbols correspond to the degradation period, and the blue symbols correspond to the recovery period.

common intercalibration-type system. Following the recommendations expressed in the Guidance document of the Intercalibration Process 2008–2011 (WFD CIS, 2011), the response of the tool (EQR values) to anthropogenic pressure was also studied by checking the consistency of the tool output, the EQR values, in relation to the pressure variation quantified in the system (a temporal pressure gradient for the Mondego estuary). Although the case study here presented is limited to the Mondego, the data here used and the SQI method have participated in the European intercalibration (IC2) exercise for transitional waters. This IC2 exercise involved France, United Kingdom, Ireland, Germany and The Netherlands, and all the methods showed a common assessment perspective and a comparable response against the used pressure index (NEA GIG, 2011). Concerning the SQI results, it was possible to confirm the tool’s consistency based on the significant correlation found between the EQR values and the anthropogenic pressure ( = −0.713; p < 0.05; n = 11) quantified in the system (Table 4 and Fig. 2). The environmental quality-pressure category, comprising winter DIN and turbidity (based on Secchi disc readings), was the pressure group that had the highest correlation with the seagrass biological metrics and EQR values (Table 4). For this reason, that pressure group was adopted here to express the anthropogenic pressure in the system.

The responses of the bed extent, biomass and shoot density were also significantly correlated with the pressure levels registered in the study site (Table 4 and Fig. 2). Moreover, the structural parameters shoot density and biomass showed different trends for the degradation and recovery periods (Fig. 2) (these factors were not

Fig. 3. Sensitivity of the SQI (EQR) to the different metrics composing the index. The results are presented for a variation (in 10% intervals) of the bed extent (E) and shoot density (D) metrics alone and for the simultaneous variation of the composing metrics (i.e., bed extent, shoot density and no. of taxa), considering the reference condition for the no. of taxa as one (T = 1) and as two (T = 2).

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Table 5 Compliance criteria required by the WFD and results of the checking procedure achieved in the validation process of the SQI. Compliance criteria

Compliance checking

Ecological status is classified by one of five classes (high, good, moderate, poor and bad)

The SQI is able to provide classification results for all the quality classes (Figs. 2 and 3)

High, good and moderate ecological status are set in line with the WFD’s normative definitions (Boundary setting procedure)

Yes. The SQI sliding results are in agreement with the magnitude’s variation of the metrics that compose it (Fig. 3)

All relevant parameters indicative of the biological quality element are covered (“taxonomic composition” and “abundance” for TW or “abundance” and “disturbance sensitive taxa” for CW)

The taxonomic composition (no. of taxa) and abundance (bed extent and shoot density) are biological parameters included in the tool

A combination rule to combine parameter assessment into BQE assessment has to be defined

The combination rule to articulate the metrics is clearly defined (Eq. (1))

Assessment is adapted to intercalibration common types that are defined in line with the typological requirements of the WFD Annex II and approved by WG ECOSTAT

For the country of origin of SQI, the typologies approved by the ECOSTAT during the intercalibration phase 1 were NEA 11 (TW) and NEA 1/26 (CW). The samples used in this study were collected in a NEA 11 system

The water body is assessed against type-specific near-natural reference conditions

The setting of reference conditions was based on Best Attainable Conditions (BAC) (expert judgement and historical data) and the quality assessment is made in comparison against those conditions

Assessment results are expressed as EQRs

Yes, the EQR is expressed in a 0–1 scale.

Sampling procedure allows for representative information about water body quality/ecological status in space and time

Yes. The life cycle of this BQE is annual, so one sampling event per year (growing season, June to September) is enough to capture the environmental variations and to provide assessment results for the water body

All data relevant for assessing the biological parameters specified in the WFD’s normative definitions are covered by the sampling procedure

Yes, the taxonomic composition and abundance (and presence of sensitive species) are biological parameters assessed during the sampling events

Selected taxonomic level achieves adequate confidence and precision in classification

Yes, the taxomonic level used is the species (when ever it is possible)

significantly correlated to degradation due to the low number of existing points). Apparently, when disturbance is alleviated after surpassing a considerably elevated pressure level, a hysteresis situation may occur before the community’s stabilisation is reached again. The disruption of the habitat’s optimal conditions leads the community’s recovery in an uncertain trajectory (Simenstad and Cordell, 2000). Since the sediment conditions (i.e., grain size and organic matter content) were altered with the near banishment of seagrass from the system, a non-linear recovery trajectory of this intertidal community was observed due to the time lag until the adequate conditions could be recreated in the system. Marques et al. (2003) showed a similar hysteresis situation for the macroinvertebrate assemblages in the Mondego estuary when unexpected values were registered for the biological parameters in a previously disturbed area. The hysteresis situation was evidenced in the recovery period

in this study by the higher dispersion of the EQR values around the same intermediate pressure level. The oscillation observed during the recovery period was the expression of the ecological instability that the system experienced, meaning that the habitat “endpoint” conditions, under which a mature community might prevail, were not present at that moment in the system (Simenstad and Cordell, 2000). The dynamic estuarine conditions usually have great influence distorting the continuous linear recovery expected for the biological communities thriving there, and a new equilibrium stage, considerably different from the initial situation, might be achieved in the future by these intertidal assemblages. These findings are important when defining restoration plans. It is important to consider both the degradation stage of the system at the moment the recovery is launched and the difficulty that each biological component may face before attaining any substantial improvement due to habitat modifications and biological interactions. In the present study, the recuperation of the Z. noltei meadows’ bed extent was evident, showing a value in 2009 close to the 15 ha recorded in the 1980s; the same was not true for the shoot density and biomass parameters, which registered a considerable inertia on the recovery response (Fig. 2C and D). As mentioned above, the SQI was able to cover all of the quality classes present in the classification scale (Fig. 2A). This finding was true both when considering the reference condition as one species for the metric no. of taxa (for systems similar to the Mondego) and when considering a reference condition different from one species for that metric (e.g., reference condition for no. of taxa = 2; Fig. 3). All of the quality classes were attainable when the three metrics varied along their hypothetical degradation gradient (Fig. 3), confirming the possible use of the SQI methodology in systems that have a reference condition for the no. of taxa that is not one intertidal species. From this exercise, it was also possible to verify that when the bed extent and shoot density metrics varied in the same percentage (cases E and D in Fig. 3), the SQI was more sensitive to the shoot density parameter (case D) than to the bed extent (case E). The reason for this pattern is that the shoot density parameter is multiplied by the factor 0.5 instead of 0.3, as occurs for the bed extent. The different factors coupled to the different metrics in the SQI combination formula (Eq. (1)) were selected to allow the method to be applied to systems with only one species as the reference condition for the no. of taxa. With this strategy, the weight of the final classification was more dependent on the shoot density metric, which is a parameter focused on the general health of the meadow, thus allowing the five quality classes to be achieved when severe degradation characterises the water body. The uncertainty associated to the SQI methodology was analysed by Mascaró et al. (2013), identifying the strengths and fragilities of the index and providing evolution clues for its general improvement and reinforcement of the confidence in the environmental results provided. The factors “sample”, “site” and “zone” were well accommodated by the proposed methodology and the spatial heterogeneity displayed by seagrass biological communities at this level was properly captured using the SQI sampling design (Mascaró et al., 2013). Due to the reduced number of years used in that analysis, it was not possible to isolate the uncertainty associated with the factor “year”, which is very important when planning a monitoring action. The uncertainty associated to other factors not included in the study (including the factor “year”) reached levels between 45% and 57% near the boundaries between different quality classes, representing a high probability of misclassification when the quality of the water body approaches the class boundary values (Mascaró et al., 2013). The precautionary principle should be followed and the seagrass meadows surveyed every year until further analyses can confirm the uncertainty associated with this factor. The presence of some highly dynamic taxa at the intertidal

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(commonly used to assess the ecological quality of European systems), which also promote a high inter-annual variability of the metrics used in the SQI, may increase the risk of misclassifying the ecological quality and negatively affect the assessment results required in the WFD reporting scheme (once every three years) if the survey frequency is reduced. 5. Conclusions The SQI is a WFD-compliant tool because it fulfils all of the WFD requirements (e.g., pertaining to the metrics, combination rule, EQR output results, five EQS classes, sampling efficiency, typological representativeness, reference conditions’ adequacy, response against pressure and broad geographical applicability). The SQI results revealed a strong relationship with the seagrass structural biological metrics of bed extent, biomass and shoot density, and a significant correlation was also demonstrated between the EQR values calculated using the SQI and the anthropogenic pressure quantified in the water body. All of these findings, together with the ability of the SQI to report regarding all of the quality classes and the low uncertainty associated with the method and its sampling design, allow the SQI to be considered a valid and robust assessment methodology for TW. Because all seagrass taxa are considered sensitive species (a WFD requirement for CW), this methodology would also be appropriate for monitoring the ecological quality in CW. This study, comprised in a first step of implementing the WFD in Europe, validated the SQI methodology relative to WFD requirements. The next step is the intercalibration exercise, where boundary values may be harmonised among participating countries and the validity of the index (with defined boundaries) can be officially confirmed. Acknowledgements This research was supported by WISER (Water bodies in Europe: Integrative Systems to assess Ecological status and Recovery), funded under the 7th EU Framework Programme, Theme 6 (Environment including Climate Change), Contract No.: 226273); RECONNECT – System dynamic response to an ample artificial REestablishment of the upstream CONNECTion between the two arms of the Mondego estuary (Portugal): implications for recovery, ecological quality status, and management (PTDC/MAR/64627/2006); EEMA – Avaliac¸ão do Estado Ecológico das Massas de Água Costeiras e de Transic¸ão e do Potencial Ecológico das Massas de Água Fortemente Modificadas (POVT-03-0133-FCOES-000017). References Arévalo, R., Pinedo, S., Ballesteros, E., 2007. Changes in the composition and structure of Mediterranean rocky-shore communities following a gradient of nutrient enrichment: descriptive study and test of proposed methods to assess water quality regarding macroalgae. Mar. Pollut. Bull. 55, 104–113. Aubry, A., Elliott, M., 2006. The use of environmental integrative indicators to assess seabed disturbance in estuaries and coasts: application to the Humber Estuary, UK. Mar. Pollut. Bull. 53, 175–185. Benedetti-Cecchi, L., Pannacciulli, F., Bulleri, F., Moschella, P.S., Airoldi, L., Relini, G., Cinelli, F., 2001. Predicting the consequences of anthropogenic disturbance: large-scale effects of loss of canopy algae on rocky shores. Mar. Ecol. Prog. Ser. 214, 137–150. Duarte, C.M., 1995. Submerged aquatic vegetation in relation to different nutrient regimes. Ophelia 41, 87–112. Foden, J., Brazier, D.P., 2007. Angiosperms (seagrass) within the EU Water Framework Directive: a UK perspective. Mar. Pollut. Bull. 55, 181–195. Foden, J., de Jong, D.J., 2007. Assessment metrics for littoral seagrass under the European Water Framework Directive; outcomes of UK intercalibration with the Netherlands. Hydrobiologia 579, 187–197. Greenberg, A.E., Connors, J.J., Jenkins, D., 1980. Standard Methods for the Examination of Water and Wastewater, 15th ed. American Public Health Association, Washington, DC.

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