Current developments on fish-based indices to assess ecological-quality status of estuaries and lagoons

Ecological Indicators 23 (2012) 34–45

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Reviews

Current developments on fish-based indices to assess ecological-quality status of estuaries and lagoons Rafael Pérez-Domínguez a,∗ , Stefano Maci a,b , Anne Courrat c , Mario Lepage c , Angel Borja d , Ainhize Uriarte d , Joao M. Neto e , Henrique Cabral f , Violin St.Raykov g , Anita Franco a , María C. Alvarez a , Mike Elliott a a

Institute of Estuarine and Coastal Studies (IECS), University of Hull, Hull HU6 7RX, United Kingdom Department of Biological and Environmental Sciences and Technologies, University of Salento, Prov.le Lecce-Monteroni 73100 Lecce, Italy c CEMAGREF, Groupement de Bordeaux, Unité EPBX, Ecosystèmes estuariens et Poissons migrateurs amphihalins, 50 av. de Verdun, Gazinet 33612, CESTAS Cedex, France d AZTI – Tecnalia/Marine Research Division, Herrera kaia portualdea z/g, 20110 Pasaia, Spain e IMAR – Institute of Marine Research (C.M.A.), Department of Life Sciences, Faculty of Science and Technology, University of Coimbra 3004-517 Coimbra, Portugal f Centro de Oceanografia, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal g Institute of Oceanology – BAS, Parvi Mai str.40, P.O. Box 152, Varna 9000, Bulgaria b

a r t i c l e

i n f o

Article history: Received 26 May 2011 Received in revised form 20 October 2011 Accepted 4 March 2012 Keywords: Fish indices Transitional waters Estuaries Water quality Ecological integrity Human pressures

a b s t r a c t Estuaries and lagoons are especially affected by anthropogenic pressures. This has resulted in symptoms of degradation including water quality impairment and loss of aquatic biota. Protection of aquatic biodiversity and management of these coastal systems require robust tools to assess habitat integrity. Fish populations have been extensively used to define habitat integrity in freshwater systems. Comparatively much less has been achieved in estuarine, lagoonal and related coastal systems classified as transitional systems under the European Water Framework Directive (WFD). The implementation of the WFD has prompted the rapid development of estuarine fish indices across Europe. In this context, this paper reviews seventeen published fish-based indices applied to estuarine systems worldwide and summarises common development strategies. Most indices are computed from a number of independent metrics and are based on assemblage composition or functional attributes of fish species (guilds). Among metric groups, species richness–composition metrics are the most widely used in current indices, followed by habitat guild, trophic guild, abundance and condition, and finally nursery function metrics. Within these, indicator species or guilds associated with estuarine quality features often dominate the indices. Development strategies vary but generally include (1) selection and calibration of metrics to anthropogenic pressure; (2) development of reference conditions; (3) comparison of metric values to reference ones; and (4) designation of thresholds for ecological status class. All index developers invest a large amount of effort on the definition and formulation of the reference values. Comparatively less effort is invested in the evaluation of the relevance and precision of the assessment. Only about half of the indices reviewed attempt any validation of the index outcomes and these are limited to simple correlation analysis and misclassification rate analysis by comparing index value with anthropogenic pressure proxies. Currently there are no European-wide consistent fish indices for transitional waters. Widening of the geographical relevance will require better precision in the formulation of reference conditions and greater inclusion of functional attributes in the indices. More recent transitional fish indices have paid increased attention to sampling method and effort, as well as metric sensitivity and robustness. This trend has continued parallel to the implementation of WFD-monitoring programmes across Europe. Further improvements are still needed to link pressures with index response and the characterisation of uncertainty levels in the index outcomes. © 2012 Elsevier Ltd. All rights reserved.

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fish-based ecological monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

∗ Corresponding author. Tel.: +44 01482 463497; fax: +44 01482 464130. E-mail address: [email protected] (R. Pérez-Domínguez). 1470-160X/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2012.03.006

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3.

4.

5. 6.

Development of fish indices for TW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Review of pressures and index requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Selection of sampling methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Metrics selection and evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1. Selection of metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2. Evaluation of metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3. Reference conditions and metric scoring system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Index scoring method and ecological quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Index calibration and appraisal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structure and development strategies of indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Metric composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Development methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial relevance and intercalibration of Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion and future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Given that many economic activities and urban areas are concentrated along the coast (Costanza et al., 1997; FAO, 2006; Halpern et al., 2008), estuaries and other coastal systems such as coastal lagoons are especially affected by anthropogenic pressures resulting in ecosystem degradation. Although environmental protection of aquatic ecosystems is openly acknowledged as a worldwide priority, the precise actions to ensure its conservation are still controversial. Initiatives and legislation in the USA (Water Quality Act, US Congress, Pub. L. 100-4, 1987), the European Union (OSPAR Convention, Water Framework Directive (WFD; 2000/60/EC), Marine Strategy Framework Directive (MSFD; 2008/56/EC), and Habitats and Species Directive (HD; 1992/43/EC)), the United Nations (UN Convention on Law of the Sea (UNCLOS, 1982) or Convention of the Biological Diversity (UNESCO, 2000)), among others, have come into force to ensure protection of aquatic biodiversity and a sustainable use of the derived ecosystem goods and services to society (Borja and Dauer, 2008). Indeed the most important aim of the management of transitional (estuaries, fjords, fjards, river mouths, deltas, rias, limans and lagoons are collectively grouped as transitional waters under the WFD 2000/60/EC terminology, TW hereafter) is to provide economic goods and services while at the same time protecting and maintaining (and where necessary restoring) the ecological functioning of these systems. However, to achieve fair and effective management plans as envisaged in these agreements, it is essential to measure with adequate precision the conservation and ecological status of aquatic ecosystems (Dale and Beyeler, 2001; USEPA, 2000). Similarly, these assessments are necessary to direct remedial actions in systems greatly affected by human activities such as coastal and TW (McLusky and Elliott, 2004). In practice, assessments of ecological integrity are normally based on quality measures that are known to correlate with anthropogenic pressures. It is therefore assumed that pressure will lead to stress and impacts on the biological community in a dose response manner (Gray and Elliott, 2009; Jordan and Vaas, 2000). A priori, any physical, chemical and biological variables (i.e., metrics thereafter) can be used to produce the experimental evidence necessary to assess ecological integrity (Gray and Elliott, 2009; Niemi et al., 2004; USEPA, 2000; Weisberg et al., 1997). Unfortunately, not all ecological responses to anthropogenic pressures are known and can be accounted for when categorising the physical and chemical factors leading to the loss of ecological integrity (Fausch et al., 1990; Karr and Chu, 1997; Karr and Dudley, 1981; Oberdorff and Hughes, 1992). Therefore, measures of biotic integrity, typically community structure, are increasingly used (Niemi et al., 2004; Noges et al., 2009). The WFD (in defining Good Ecological Status—GES)

35

36 37 37 38 38 39 39 40 40 40 40 41 41 42 43 43

and the HD (in measuring Favourable Conservation Status), require the use of structural and functional (i.e. quantifiable) traits or metrics for the assessment of aquatic ecosystem (WFD; 2000/60/EC, Art.2-21). In some cases it is assumed that structural metrics are a surrogate for functional metrics, but this may not always be the case, resulting in incorrect interpretations (de Jonge et al., 2006; Seegert, 2000). More holistic assessments are possible when different metrics covering a variety of responsive ecological and community features are combined (Borja and Dauer, 2008; Hering et al., 2006; Karr, 1981; Niemi et al., 2004). Despite the predicted advantage of using indices based on several elements to convey multiple quality measures in one single score, their formulation and use is not simple (Borja et al., 2010; Hering et al., 2010). In the context of the WFD, fish is one of the four Biological Quality Elements (BQEs) included in the quality analysis of Europe’s freshwater and transitional water systems (Borja, 2005). The need for robust fish-based assessments in TW has resulted in the development of new fish indices specifically tailored to estuarine and lagoon systems. This paper summarises these developments together with previous initiatives in fish-based quality assessments and outlines common approaches in the development of fish indices of biotic integrity for TW worldwide. It reviews the relevance of available indices and metrics for management needs and proposes future avenues of research to improve assessment of ecological quality status using fish communities in TW ecosystems. 2. Fish-based ecological monitoring When first proposed by Karr (1981), multimetric fish indices pioneered a change in environmental quality assessment from traditional indicators based on water quality disturbance (i.e. physico-chemical variables and toxic substances) to Biological Quality Elements (BQEs) based on community parameters. Since this early work, fish communities have been used effectively to convey information on the conservation and ecological-quality status of aquatic ecosystems (Roset et al., 2007). The advantages and disadvantages of fish as a BQE have been extensively discussed in the literature (Breine et al., 2010, 2007; Elliott and Hemingway, 2002; Harrison and Whitfield, 2004; Karr, 1981; Karr and Dudley, 1981; Whitfield and Elliott, 2002). Advantages of using fish as BQEs include: proven sensitivity to habitat quality loss; occurrence in all aquatic systems and areas; high integrative level of ecosystem functioning; cost effective means for their assessment including training on taxonomical competence; direct economic value; and intuitive appreciation of quality condition changes by all audiences. Disadvantages include the bias associated with

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Table 1 List of fish indices for transitional waters quality assessment by year of publication (earliest appearance in the literature). The number of metrics in the index is given in parentheses. Note: When indices have been presented in different publications only the more relevant references to the development of the index is presented. Tool name

Abbreviation

Area of use

Type

WFD

References

Index of biotic integrity

IBI a

Transitional (Louisiana, USA)

NO

Thompson and Fitzhugh (1986)

Community degradation index Index of biotic integrity

CDI IBI b

Transitional (South Africa) Transitional (Maryland, USA)

Multimetric (13) Single metric Multimetric (9)

NO NO

Biological health index Estuarine Biotic Integrity Index

BHI EBI a

Transitional (South Africa) Transitional (Massachusetts, USA)

Recruitment Index Index of biotic integrity AZTI’s Fish Index

RI IBI c AFI

South Africa Transitional (Nagarranset bay, USA) Transitional (Basque Country, Spain)

Estuarine fish community index

EFCI

Transitional (South Africa)

WFD Fish Index for Transitional waters Fish-based Estuarine Biotic Index Transitional fish classification index MJ nursery index

FITW

Transitional (Holland)

YES

Ramm (1988) Jordan and Vaas (2000), Vaas and Jordan (1991) Cooper et al. (1994) Chun et al. (1996), Deegan et al. (1997) Quinn et al. (1999) Meng et al. (2002) Borja et al. (2004), Uriarte and Borja (2009) Harrison and Whitfield (2004, 2006) Jager and Kranenbarg (2004)

EBI b

Transitional (Brackish Scheldt, Belgium)

YES

Breine et al. (2007)

TFCI

Transitional (United Kingdom)

YES

Coates et al. (2007)

MJNI

Transitional (France)

NO

Courrat et al. (2009)

Habitat Fish Index

HFI

YES

Franco et al. (2009)

Zone-specific Fish-based Estuarine Biotic Index French Multimetric Fish Index

Z-EBI

Transitional and coastal (Venice Lagoon, Italy) Transitional (Brackish and freshwater Scheldt, Belgium) Transitional (Atlantic and Channel coast (France) Transitional (Portugal)

YES

Breine et al. (2010)

Multimetric (4)

YES

Delpech et al. (2010)

Multimetric (7)

YES

Cabral et al. (2011)

Estuarine Fish Assessment Index a b

f-MFI (ELFI, Estuarine and Lagoon Fish Index) EFAI

Single metric Multimetric (12) Single metrica Multimetric (6) Multimetric (9) Multimetric (14) Multimetric (10) Multimetric (5) Multimetric (10) Non aggregating multimetric (3)a Multimetric (16) Multimetric (6)b

NO NO NO NO YES NO

Restricted to the ecological quality assessment of estuarine nursery grounds. Independent indices for each zone. WFD—Water Framework Directive; AZTI—Arrantzatzuarekiko Zientzi eta Teknoloji Ikerketa; MJ—Marine Juvenile.

sampling gear; the often variable association with pressures and more importantly high mobility and marked seasonal variation (Fausch et al., 1990; Harrison and Whitfield, 2004). In the case of TW, fish assemblages are dependent on conditions and pressures within the riverine catchment and adjacent marine areas, as well as those affecting the connectivity in the system, leading to complex interactions both in space and time (Elliott and Hemingway, 2002). To integrate all these factors and interactions, almost all indices now available are based on an aggregated set of metrics and are generally referred to as multimetric fish indices (Table 1). Multimetric indices are constructed from an array of fish ecological attributes and are therefore, considered to be superior to single-metric assessments. In particular, multimetric indices are regarded to have wider sensitivity to complex and cumulative pressures and greater relevance to different ecological regions (Fausch et al., 1990; Karr, 1981). Despite this, single-metric tools have been proposed for estuaries (Cooper et al., 1994; Ramm, 1988). These stand alone metrics give a comparative score based on an analysis of similarity between the control community and the actual community. Alternative multivariate techniques such as ordination or correlation analysis have been also proposed (Fausch et al., 1990). These are simple to compute using available statistical packages, are data driven, can integrate fish functional information and are effective at condensing taxonomic information to a few main ordination axes, or even down to a numerical value (Boyle et al., 1984; Fausch et al., 1990; Ramm, 1990; Whitfield and Elliott, 2002). However, the interpretation of outputs may be difficult and most importantly the outputs may indicate meaningful relationships from random variation. Therefore, it has been recommended to use these techniques as exploratory tools during the evaluation of more complex approaches rather than as final assessment tools (Fausch et al., 1990).

3. Development of fish indices for TW In general, the procedure to develop fish-based indices of ecological integrity follows a more or less complex sequence (Hughes et al., 1998) that starts with an initial appraisal of anthropogenic pressures (Table 2). Most TW indices reviewed have been developed using a five step procedure: (i) assessment of the pressure; (ii) fish sampling strategy; (iii) selection of metrics; (iv)

Table 2 Type of common human pressures proxies affecting biological integrity in estuarine system. The table highlights those pressure types with proxies used for preclassification and scoring of fish metrics. Pressure Class

Pressure type

Included

Hydromorphological

channelling and dredging land-reclamation and coastal defence port and navigation infrastructure flow manipulations (dams, weirs, sluices) underwater structures (wrecks, piers, armouring) nutrient discharge and concentration waste disposal water pollution sediment pollution dissolved oxygen noise sediment load heat exchange

YES YES YES YES NO

behavioural interference and disturbance abundance of macroalgae and eelgrass condition of benthic organisms resource gathering (fishing, game, natural crops) invasive species

YES YES YES NO

Chemical and Physical

Biological

a

Invasive species are included as metrics in some indices.

YES YES YES YES YES NO NO NO

NOa

R. Pérez-Domínguez et al. / Ecological Indicators 23 (2012) 34–45 Table 3 Steps and tasks included in the development of multimetric fish indices as discussed in this review. Developmental sequence (Tasks) (1) Review of pressures and index requirements Determine pressure field, index scope and quality targets Selection of ecologically relevant metric types according to relevant pressures Classify habitat typology and fish functional guilds (2) Selection of sampling methods Sampling tools, sampling standardisation and sample analysis Indexing period and sampling sites Effort level. Precision and accuracy in the assessment (3) Metrics selection and evaluation Determine responsiveness to pressures Metric redundancy assessment Define metric thresholds and scoring system Development of reference conditions Optimization of sampling methods (4) Index scoring method and ecological status class Metric combination rules Define ecological quality ratio and thresholds Assignment to ecological status class (5) Index calibration and appraisal Misclassification rate, sensitivity analysis Global uncertainty assessment Presentation format and value to end-user

formulation of indices, and (v) final appraisal (Table 3). Although not always considered initially, it is essential in practice to define constraints, requirements and performance goals for the final index. For example in Europe, indices in development for the WFD must fulfil certain requirements and assessments must include fish composition and abundance information, be based on current ecological understanding, and be biologically meaningful (Borja et al., 2009a; Noges et al., 2009). Other indices may focus on a particular quality feature such as nursery habitat quality (Quinn et al., 1999; Courrat et al., 2009). In a similar way an index could be designed to be sensitive to a specific stressor if intended for remediation programmes and to evaluate the course of recovery. In all cases indices need to be robust (i.e. minimise the uncertainty associated with the classification of ecological quality), and be readily understandable by biologists, stakeholders, water resource managers and the public (Noges et al., 2009; Niemi et al., 2004).

3.1. Review of pressures and index requirements All estuarine fish indices are built on the assumption of a range of anthropogenic pressures acting upon a normal background of natural variability. The effects on fish populations should then scale according to the intensity of the disturbance in an approximate dose–response manner and be specific for the pressure type (USEPA, 2000). In the reviewed literature there are two basic approaches to the definition of ecological quality in this context of variable human pressures. The first and simplest is to classify as reference sites undisturbed or, more commonly, least disturbed sites according to the size and diversity of the fish community present across the area being assessed (Coates et al., 2007; Harrison and Whitfield, 2006; USEPA, 2000). This method does not require any previous knowledge of the expected reference community but needs good knowledge of the estuarine typology (i.e. the natural make up of the systems) and relatively large datasets (Borja et al., 2004; Coates et al., 2007; Deegan et al., 1997; Franco et al., 2009). The strength of the approach is its simplicity. It relies on the assumptions that sites are exposed to a varied degree of human pressures (including low or no pressure) and that all sites respond equally to disturbance. However, when there is an insufficient

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representation of reference sites (truly pristine or defined by conservation goals) an alternative approach is needed. The second common approach uses a preclassification of sites according to hydromorphological, chemical and physical disturbances using a suite of proxies for these anthropogenic pressures (Breine et al., 2010, 2007; Deegan et al., 1997; Delpech et al., 2010; Harrison and Whitfield, 2004; Jordan and Vaas, 2000; Vasconcelos et al., 2007). The site preclassification to define expected quality thresholds is done on a simple rating scale using expert judgment or based in pseudo-quantitative pressure data. However, this preclassification is solely intended to provide a reference to evaluate relevant fish metrics and not a complete assessment of the site (Breine et al., 2010; Delpech et al., 2010). Water quality, dissolved oxygen, pollutants, channelling, dredging intensity, shore stabilization, intertidal area integrity, land claim, benthic integrity, human population density, industrial development are all used (Table 2). This approach allows the sensitivity evaluation of the metrics to human pressures and the elimination of redundant metrics in the indices. However, the choice of anthropogenic pressure proxies and its combination into quality scores can be very subjective, relying heavily on expert judgment and being highly dependent on data availability (Borja et al., 2004, 2009b; Breine et al., 2010; Chun et al., 1996; Deegan et al., 1997; Franco et al., 2009). Regardless of the approach used, an early identification of the best and more sensitive metric types according to the pressures acting upon the system is required. Hering et al. (2006) described the procedure for the selection of the core metrics for a multimetric index as follows; (1) establishment of a list of candidate metrics, (2) metric calculation and elimination of unreliable metrics (those with too narrow range of values, non-monotonic responses, or too many outliers), (3) testing of correlation between metrics and some stressor gradient, and (4) removal of redundant metrics. Although precise evaluation of the metrics (steps 2–4) is normally done later in the index development (see Section 3.3), the review of pressure gradients early in the planning of the index is useful to select a pool of fish metrics whose behaviour in such pressure gradients can be predicted (Breine et al., 2010; Delpech et al., 2010; Hering et al., 2006). 3.2. Selection of sampling methods There are important logistic and cost considerations that affect the method of sampling and the degree of effort in ecological assessments. The fact that any assessment will only be as good as the data used to derive the metrics is recognised explicitly or implicitly in all the indices reviewed. Sample sizes from as little as 36 samples (Meng et al., 2002) to large long-term datasets containing over a 1000 sampling events (Breine et al., 2010; Coates et al., 2007) have been used. The level of effort that is “sufficient” is not formally analysed in current indices although this certainly has important consequences for the selection of robust metrics and the assessment of index reliability and uncertainty. A suite of gears such as seine nets (Coates et al., 2007; Franco et al., 2009; Harrison and Whitfield, 2004, 2006; Meng et al., 2002; Thompson and Fitzhugh, 1986), beam trawls (Borja et al., 2004; Cabral et al., 2011; Coates et al., 2007; Courrat et al., 2009; Delpech et al., 2010; Uriarte and Borja, 2009), otter trawls (Chun et al., 1996; Deegan et al., 1997; Thompson and Fitzhugh, 1986), gillnets (Coates et al., 2007; Harrison and Whitfield, 2004; Thompson and Fitzhugh, 1986), fyke nets (Breine et al., 2010, 2007; Coates et al., 2007), and visual diver censuses (A. Perez-Ruzafa,1 personal communication) have been used to generate catch data. All these gears have their

1 Department of Ecology and Hydrology, University of Murcia, Campus Universitario de Espinardo, 30100 Murcia, Spain.

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own selectivity; therefore, reference conditions are determined in agreement with the selected sampling gears. The combined use of several sampling methods might seem advantageous to avoid extreme sampling bias on field data; however, it makes the definition of reference condition more complicated as the sampling effort is often not comparable from one gear to another (Borja et al., 2007; Coates et al., 2007; Hayes, 1989; Jurvelius et al., 2011; Rozas and Minello, 1997). Generally, sampling is done in a stratified way (i.e. replicate samples taken within salinity classes) within the season of maximum expected fish diversity and density. Effort is also put on finding representative sampling locations based on estuarine typology and expert knowledge (Breine et al., 2010, 2007; Meng et al., 2002) but information is rarely given on the randomisation of sampling sites. 3.3. Metrics selection and evaluation The US Environmental Protection Agency (USEPA, 2000) defines a metric as ‘a measurable factor that represents various aspects of biological assemblage, structure, function or other community component’. Metrics and indices based on biological elements such as indicator species, species richness or guild composition are currently favoured among ecological indicators as they provide a direct assessment of ecological integrity as a whole (Bain et al., 2000; McLusky and Elliott, 2004). Better assessments are possible when the metrics and indices reflect functional attributes of the ecosystems (Karr, 1981). The latter is achievable in fish-based assessment when there is information on the species functional requirement or more commonly termed functional guilds (Elliott et al., 2007; Franco et al., 2008). Essential functional requirements for estuarine fish are grouped at three main guild levels: trophic, habitat and reproduction (Elliott and Dewailly, 1995; Elliott et al., 2007; Franco et al., 2008). The guild approach is followed by all current multimetric indices. By grouping fish in common guilds, taxonomic information is directly translated into proxies of ecosystem functional features. Under the guild approach, indices could be relevant on a large geographic scale, as long as the guilds are represented and relevant for the quality assessment. Within each guild, metrics are either formulated based on number of individuals, number of species or relative abundance. In addition to guild-based metrics, fish indices have included assemblage diversity or dominance metrics, health metrics (e.g. diseased or parasite loads), indicator or disturbance-tolerant species presence, and presence of exotic species. Hence there is a large pool of candidate metrics for inclusion in multimetric indices (Noble et al., 2007). 3.3.1. Selection of metrics Most metrics in use are measures of Species Richness and Diversity (Table 4). Increased diversity is generally assumed to indicate higher quality (Gray, 1989). The largest family of metrics in this group (20 metrics) involves the number and proportion of indicator species based on their requirement of precise estuarine quality features (e.g. European smelt, Osmerus eperlanus requires welloxygenated waters thus its presence indicates the realisation of this estuarine quality feature) (Breine et al., 2007; Jager and Kranenbarg, 2004; Maes et al., 2007). However, when using specific taxa the metric will be relevant only to the geographical range distribution of the specific indicator species. For example O. eperlanus is profusely used in North Sea estuaries, but not used in southern or Mediterranean estuaries. This indicates a spatially restricted relevance built into some current indices, and the prevalence of indices developed within a single estuary or uniform biogeographical zone. The next group of metrics is the Habitat Use guild (Table 4). The most common metric is the estuarine resident guild, followed by metrics providing information on habitat requirements which are equivalent to indicator species but grouped into a functional

Table 4 Families of metrics used in the different multimetric fish indices evaluated. Metric type Species Richness-Composition Number or Proportion of indicator speciesa Total number of species Number of species that make up 90% of abundance (dominance) Species composition (Similarity or dissimilarity index) Number of species without freshwater species Seasonal overlap of fish community Shannon diversity H Habitat use Number or proportion of estuarine species or individualsa Number or proportion of diadromous species or individuals Number or proportion of benthic species or individualsa Number or proportion of habitat sensitive speciesa Proportion of estuarine-dependent marine species Number and identity of marine species Number and identity of freshwater species Functional guild complexity (number of habitat guilds) Trophic Guild Number or proportion of piscivores and carnivores species or individualsa Number or proportion of benthic-feeding species or individualsa Number or proportion of demersal and pelagic-prey feeding species Number or proportion of individuals as omnivorous or detrivorous Feeding guild complexity (number of trophic guilds) Abundance and Condition Number or Proportion of indicator individualsa Proportion of disease or abnormal individualsa Number or density of individuals Species relative abundance (BC similarity) Presence and status of introduced species Nursery function Number or proportion of juvenile resident species or individualsa a

Number of metrics 20 9 6 4 1 1 1 13 5 5 3 2 2 1 1 15 8 2 2 1 6 4 4 3 1 15

A metric family.

guild such as benthic species or specialist spawners. Some of these habitat metrics are expected to decrease together with decreasing estuarine habitat quality, although some may have a reverse trend (i.e. benthic habitat destruction may lead to a decrease in benthic metrics but to a relative increases in pelagic metrics). Also, as the diversity of the system is related to the number of niches available, then more complex or larger TW should give higher values for these habitat guilds (Nicolas et al., 2010; Wootton, 2001). In the fish Trophic Guild group (Table 4), the most profusely used metrics are centred on the number and diversity of predatory fish (carnivorous and/or piscivorous) followed by benthic feeding fish. Here too, the impact of pressures is metric-specific. Generally speaking, specialised-feeder’s metrics decrease while omnivore’s metrics increase with increasing level of disturbance; this is providing that the overall disturbance does not result in the complete collapse of the food resource. Total or relative Abundance and Condition (i.e. health status) are grouped together as both measures provide a quantitative measure of fitness at the level of the individual (Table 4). Abundance of indicator individuals have the largest number of metrics in the group. Abundance is often negatively correlated with impacted systems which have lower abundances overall, whereas the number of individuals in poor condition due to disease or malformations is assumed to be correlated with increased disturbance. Finally, the Nursery Function group includes metrics associated with the use of estuarine habitats by juvenile fish (Table 4). These are usually marine species that use estuarine habitats when young

Percentage total metrics (%)

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50 40 30 20 10 0

Species RichnessComposition

Habitat Guild

Trophic Guild

Abundance & Condition

Nursery function

Fig. 1. Relative importance of metric distribution across ecological attributes in multimetric indices for transitional waters. Mean (Standard deviation above bars). The marine Juvenile Nursery Index (MJNI) and the Recruitment Index (RI) are excluded from the analysis as they are exclusively nursery quality indices. Nursery condition is often identified by Habitat use guild but it is presented separately due to the recognized value of estuaries as fish nurseries.

for shelter and food (Able, 2005; Able et al., 1999; Beck et al., 2001; Blaber et al., 1989; Dahlgren et al., 2006). Habitat degradation (in quality and quantity) in estuaries is considered to have a direct effect on the nursery function central for the development of marine and resident fish (Costa and Cabral, 1999; Courrat et al., 2009; Vasconcelos et al., 2007). The relative large number of nursery function metrics found among the indices confirms the overall importance given to this functional guild in most indices (Fig. 1). 3.3.2. Evaluation of metrics Borja and Dauer (2008) assume that multimetric indices perform better than the individual metrics alone. However, this will only be true when the index contains a balanced and complementary set of metrics that respond to a range of habitat quality degradation parameters (Hering et al., 2006). In addition, best metrics are those not, or minimally affected by natural variability, i.e. they are specifically sensitive to anthropogenic disturbance (Rice, 2003). The simplest and more common approach chooses metrics based on previous successful indices and expected ecological responses to degradation. This has proved successful and it is the only option when human pressure data are not available to assess the relevance of the metrics (Borja et al., 2004; Coates et al., 2007; Franco et al., 2009; Harrison and Whitfield, 2004; Jager and Kranenbarg, 2004; Meng et al., 2002; Thompson and Fitzhugh, 1986). When pressure data are available the sensitivity of the metrics has been assessed by box and whisker plots (Jordan and Vaas, 2000), correlation analysis (Cabral et al., 2011), regression analysis (Breine et al., 2010, 2007; Delpech et al., 2010), discriminant analysis (Breine et al., 2007; Meng et al., 2002), ANOVA (Deegan et al., 1997), Principal Component Analysis (Breine et al., 2010), or a combination of methods. Stepwise discriminant analysis is used to assess metric relevance and identifies the best combination of metrics eliminating redundancies and metrics with large uncertainty or without monotonic response between metrics and pressure (Breine et al., 2007; Jordan and Vaas, 2000; Meng et al., 2002; Vaas and Jordan, 1991). Furthermore, redundancy screening of metrics is done in some indices after the sensitivity assessment by calculating correlation coefficients between responsive metrics and rejecting one metric from each correlated pair (Breine et al., 2010; Delpech et al., 2010). Methods using formal statistical approaches in the selection process often resulted in more stringent conditions for metric inclusion and some authors use expert judgment to decide on the inclusion of metrics that otherwise will not meet the requirements (Breine et al., 2010; Chun et al., 1996; Deegan et al., 1997).

39

Likewise, the initial pool of metrics included for statistical screening is entirely decided on expert knowledge which requires a sound ecological knowledge of the expected responses. The number and choice of metrics varies with most multimetric indices built around a pool of 9–10 metrics with a maximum of 16 (Franco et al., 2009) and a minimum of 4 (Delpech et al., 2010). A high number of metrics could lead to misclassification due to over-fitting problems when adjusting dose–response models (Hawkins, 2003). Hence, there is a tendency for fewer metrics in those indices where metric sensitivity and redundancy has been formally assessed against pressure scores. To overcome some modelling problems in the linkage between pressures and habitat quality, the use of fuzzy-logic based methods has been trialled in freshwater systems (Ocampo-Duque et al., 2006) as a means to reduce uncertainty in the decisionmaking process, adding rigour to any decision process based on expert knowledge. These methods are yet to be used in TW indices.

3.3.3. Reference conditions and metric scoring system In TW there is an elevated natural fluctuation and a high inherent variability which introduces natural sources of noise into the assessment. Therefore, the reference conditions, and the thresholds between ecological quality statuses needs to be defined using confidence intervals (Borja et al., 2009b; Courrat et al., 2009; Delpech et al., 2010). Increased precision in the assessment will therefore be dependent upon the robustness of the estimations of the reference condition among others. To reduce the natural noise of spatial and temporal variability, specific reference conditions have been chosen according to environmental factors and protocol specificity such as estuarine system, estuarine topology, season, habitat, salinity regime and gear types. Therefore, the literature distinguishes between metric-, habitat-, season-, gear-, salinity class-, estuaryand ecotype-specific reference conditions as relevant to the data structure and analysis. In practice, the reference community (whether pristine or defined at any other level) is derived by either using pressureresponse models (Borja et al., 2004; Breine et al., 2010, 2007; Chun et al., 1996; Deegan et al., 1997; Delpech et al., 2010; Jordan and Vaas, 2000; Meng et al., 2002; Uriarte and Borja, 2009) or by directly ranking sites according to metric values and assuming that less impacted sites are present (Coates et al., 2007; Franco et al., 2009; Harrison and Whitfield, 2004) (see Section 3.1 for more detail). In both cases the reference community is then derived from the top scoring samples (e.g. upper quintile) assuming that an increased ecological integrity correlates with the distribution of the top scores. Although both methods provide the necessary reference, it is increasingly accepted that there is the need to incorporate some degree of historical information (before anthropogenic impacts) or expert knowledge of the systems (i.e. indicator or conservation species) to account for shifting baselines and particular quality features of special conservation interest, respectively (Coates et al., 2007). Once the reference values are set, each sample is scored independently depending on where its metric value lies with respect to the reference. This is done as a relative score in the form of a ratio or directly by setting threshold values that define the quality classes (Meng et al., 2002). Several scoring systems are available. In the simplest case a sliding scale is used to rate sites using discrete scores (1–5) depending on whether their raw value deviates greatly from (score 1), deviates somewhat from (score 3) or is comparable to the reference value (score 5) (Cabral et al., 2011; Coates et al., 2007; Deegan et al., 1997; Franco et al., 2009; Harrison and Whitfield, 2006; Jager and Kranenbarg, 2004; Jordan and Vaas, 2000; Thompson and Fitzhugh, 1986; Uriarte and Borja, 2009). Semi-quantitative scales with intervening scores (2 and 4) have also been used (Coates et al., 2007; Delpech et al., 2010). The

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number and cut off point for the scores thresholds varies among indices and estuarine typology. 3.4. Index scoring method and ecological quality In all cases, fish indices produce a single value of ecological quality which is based either on a single metric or in an aggregated value. All multimetric indices use the sum, the average or the weighted sum of all individual metric scores. The weighting of the metric scores by their relevance is only reported in two indices Z-EBI and HFI (Breine et al., 2010; Franco et al., 2009) using 1 or 0 weighting depending on whether the metric is relevant or not, which results in de facto formulation of different indices with a distinct effective metric composition. Drouineau et al. (2011) recently proposed a new way to combine core fish metrics in a multimetric index using the probability of a water body to be at a certain anthropogenic pressure level using a Bayesian probability model to combine the core metrics. This allows taking into account both the sensitivity and the robustness of the fish metrics in the assessment, and objectively accounts for expert knowledge in the diagnostic. The ecological quality thresholds of the index values are defined using the same approach as to derive metrics cut off scores and are often calibrated with pressure data if available. As an example, all WFD-compliant indices use a 5-band scoring system although some indices eliminate the top quality bands when these quality status classes are not present in the dataset (Breine et al., 2010, 2007; Delpech et al., 2010). Finally some indices also report the scores of the individual metrics using radar plots (Jager and Kranenbarg, 2004; Breine et al., 2010). 3.5. Index calibration and appraisal The relevance and precision of the assessment is often obtained by similar modelling exercises as those used to determine the sensitivity of the single metrics. The intention is to gauge whether the calculated quality score explains known human pressures in estuarine habitats. At present, only about half of the indices attempt any validation and these use correlation analysis between indexcomputed values and pressure scores to estimate the behaviour of the new index. In practice, when included in the development of the index, the calibration uses site scores calculated on a different dataset to those used to define sensitivity of the assessment metrics (Breine et al., 2010, 2007; Cooper et al., 1994; Meng et al., 2002), catch data from a different estuary that is then compared with pressure scores (Coates et al., 2007; Deegan et al., 1997) or pollution proxies (Delpech et al., 2010). The exercise can be as simple as the calculation of a correlation coefficient (Borja et al., 2004; Uriarte and Borja, 2009), linear regression (Delpech et al., 2010), or box plots and linear regression (Breine et al., 2010, 2007; Cooper et al., 1994; Harrison and Whitfield, 2004; Ramm, 1988). In some cases, these investigations concentrate on the responses of the indices to human pressures increases (i.e. dredging, engineering works, wastewater discharge), but also on actions taken to remove such pressures (i.e. wastewater treatment) (Uriarte and Borja, 2009). As for any quality assessment tool, it is important to further evaluate the precision and accuracy of the indices and the uncertainty or potential errors in the diagnostic they provide (Carstensen, 2007; Clarke et al., 2006; Clarke and Hering, 2006; Kurtz et al., 2001). Only few examples are given in the literature about fish indices in TW and they most commonly consist of the calculation of the misclassification rate after comparison with a preclassification exercise using habitat quality scores (Breine et al., 2010, 2007; Harrison and Whitfield, 2004). In some cases, discriminant analysis and residual analysis has also been employed (Meng et al., 2002). Also, and although not a formal analysis, visual evaluation of the degree of

Fig. 2. Hierarchical cluster analysis of fish-based indices according to similarities (Bray–Curtis similarity) in component metrics. To calculate the similarities the various indices were arranged in a matrix against the possible different metrics. The metrics were organised in the 5 families described in Table 4 (see Section 3) and scores of 1 or 0 were given if present or absent, respectively. Three groups were identified using Similarity Profile Routine—SIMPROF (5%, 999 random permutations) and carried forward to the SIMPER analysis presented in Table 5. For indices acronyms see Table 1.

scatter around the average scores have also been used as an indication of index precision (Cooper et al., 1994). However, rigorous uncertainty analysis providing probability estimates of the robustness and confidence in the ecological status class assignment are notoriously lacking. Drouineau et al. (2011) have used a probabilitybased method to provide an uncertainty assessments approach that still needs testing on other fish indices. 4. Structure and development strategies of indices To summarise common trends in the formulation of current TW fish indices, we conducted a classification analysis using similarities between indices development strategies and metric selection. For each of the 17 indices, the inclusion of a developmental step or metric was scored as 1 if present or 0 if absent resulting in two separate binary (presence–absence) data matrices. The former matrix was used to assess common strategies in the development of the indices and the second matrix was used to assess the similarity between indices with respect to metric composition. Similarities between indices were calculated using similarity coefficients (Clarke, 1993) and later visualised using cluster analysis. A similarity profile analysis (SIMPROF, with p = 5% and 999 permutations) was also conducted in order to reveal the presence of common patterns in index development method or constituent metrics. Further similarity percentage (SIMPER) analyses were performed to assess the contribution of each of the variables included in the analysis (i.e. design strategy or metric composition) to the observed clusters. This allowed the identification of features responsible for the group structure and the variables accounting for the differences between indices. All multivariate analyses were conducted in PRIMER V6.1.10 (Plymouth Marine Laboratories, UK). 4.1. Metric composition Current fish indices follow more of less the rationale given in Section 3.3 for the selection of candidate metrics for further evaluation. When the indices are grouped by their similarity with respect to their final selection of constituent metrics, they segregate across three distinct groups (SIMPROF, p < 0.05; Fig. 2). In clear contrast,

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41

Table 5 Similarity percentage (SIMPER) analysis of the different fish-based indices by the types of metrics used. Only metrics accounting for 90% of the similarity observed are shown here. For index abbreviation see Table 1. Metrics Species Richness-Composition Number or Proportion of indicator species Total number of species Number of species that make up 90% of abundance Species composition (Similarity or dissimilarity index) Habitat use Guild Number or proportion of estuarine species or individuals Number or proportion of benthic species or individuals Nursery function Number or proportion of juvenile resident species or individuals Trophic Guild Number or proportion of piscivores/carnivores species or individuals Number of bentivorous species Number of detritivorous species Number of demersal-pelagic prey feeding species Abundance and Condition Number or density of individuals

Group 1 (49.6)

Group 2 (33.3)

Group 3 (32.4)

23.7 14.3 5.5 100

19.2 10.3

8.4

70.1

15.3

3.7

10.3

Number in brackets indicates the group similarity percentage. Group 1: HFI, IBIb, EFCI, TFCI, AFI, FITW, IBI a, EBI a, Z-EBI and EFAI. Group 2: RI, CDI and BHI. Group 3: MJNI, EBI b, IBI c and f-MFI

single fish metric indices (CDI, BHI, and RI; Group 2, Fig. 2) rely solely on metrics of species richness and composition (Table 5). While in multimetric indices (Group 1 and 3) the simple taxonomybased analysis is combined with a wider guild-based approach (Group 1, Table 5) or is based exclusively on functional guilds (Group 3, Table 5). However, for indices in Group 1 (IBI a and b, EBI a, AFI, EFCI, FITW, TFCI, HFI, Z-EBI, and EFAI) most of the similarity is accounted for by metrics of Species Richness-Composition, 43.5% (SIMPER analysis Table 5). Group 3 (Table 5), includes both general fish indices (EBI b, IBI c and f-MFI) and a dedicated nursery and recruitment index (MJNI). This group includes mostly guild-based metrics (80.4%, Table 5). Under this selection of metrics the assessment is done at the functional level rather than at the structural (i.e. species composition) level (Karr, 1981). 4.2. Development methodology The comparison of indices based on the steps taken in the development of the index produced three distinct groups and a single index (MJNI; Fig. 3). SIMPER analysis indicated that the similarity within indices in Group 1 (CDI, BHI, and RI) was accounted for mainly by the selection of a reference by expert judgement (43.8%) and, with approximately equal weight, use of a single metric, type-specific reference, metric sensitivity evaluated by expert judgement, and index calibration (Table 6). Group 2 (IBI b, AFI, EFCI, FITW, TFCI, and HFI) included indices that were similar mainly due to the use of mobile netting, expert judgment to determine metric sensitivity, inclusion of 6 or more metrics, the use of a type-specific reference, and metric combination rules (Table 6). Group 3 had indices that were developed in a very similar way (IBI a, EBI b, Z-EBI, IBI c, f-MFI, EFAI and EBI a), which were multimetric indices based on aggregated metric scores, generally employ regression analysis and correlation analysis to evaluate metric sensitivity and to eliminate redundant pairs, respectively. Most indices in this group have less than six metrics and use of mobile netting (Table 6). Indices

Fig. 3. Hierarchical cluster analysis of fish-based indices according to similarities (Jaccard similarity index) in their developmental methodology. The development components included in the analysis were chosen from 6 groups (each containing different categories) that roughly follow the developmental tasks presented in Table 3. These were: (1) sampling methods; (2) metric sensitivity and selection methods; (3) selection of the scoring system method; (4) calculation of reference conditions method; (5) metrics combination rules; and (6) index appraisal method. The similarity threshold to define the clusters was set arbitrarily to 20%. Four groups were identified using Similarity Profile Routine-SIMPROF (5%, 999 random permutations) and carried forward to the SIMPER analysis presented in Table 6. For indices acronyms see Table 1.

in Group 3 have more stringent condition for the inclusion of metrics, possibly a reflection of their increased reliance on statistical procedures in their selection. Finally the use of expert judgment to derive reference values (expected fish assemblage or anthropogenic proxies of pressure) was included in all groups suggesting a general dependence on qualitative appraisals in current estuarine fish indices. 5. Spatial relevance and intercalibration of Indices The indices reviewed here have been developed with specific geographic boundaries in mind which usually do not extend further than a single ecoregion and often focus on one system. When focusing on individual systems or smaller regions, we expect a better and generally more precise assessment, but it results in Table 6 Similarity percentage (SIMPER) analysis of the common strategies included in the development of the different fish-based indices. Only developmental steps accounting for 90% of the similarity observed are shown here. For indices acronyms see Table 1. Index developmental steps Sampling by active netting (i.e. beam trawl) Single metric selection Multimetric < 6 metrics Multimetric ≥ 6 metrics Metric sensitivity by expert judgment Metric sensitivity by regression analysis Metric relevance and redundancy analysis Reference by expert judgment Reference by pressure proxy models Site-specific reference Type-specific reference Index as aggregated score Five quality status classes Index calibration

Group 1 (53.3)

Group 2 (78.4)

Group 3 (61.5)

15.7

10.0

12.5 9.1 12.5

15.7 15.7

43.8

6.4

15.6

15.7 15.7 10.1

13.8 13.5 5.5 5.7 5.4 19.5 5.4

16.6

Number in brackets indicates the group similarity percentage. Group 1: RI, CDI and BHI. Group 2: IBIb, AFI, EFCI, FITW, TFCI, and HFI. Group 3: IBI a, EBI a, IBI c, EBI b, Z-EBI, f-MFI, EFAI, and MJNI.

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greater difficulties when comparing the ecological status for wider geographical areas or with different habitat quality conditions (Borja, 2005). The difficulties arise as the reference used to score the metrics and the metrics themselves may not be as relevant to the general ecotype as they are to the local system, leading to erroneous global assessments. Therefore, the comparison and intercalibration of indices results is a critical issue in large programmes like the WFD (Borja et al., 2009a) for which European countries have to develop fish-based assessments to the range of European eco-regions (European Commission, 2008). Similar diffculties arise when multiple ecotypes are compared in ecological research. Five of the index reviewed here (EBIa, EBI b, EFCI, TFCI and an earlier development of the AFI) were adapted by Martinho et al. (2008) to provide a quality assessment of the Mondego estuary (Portugal). Despite that neither of the indices was specifically developed for this particular estuary, all gave a consistent classification throughout the studied period. Nevertheless, a high level of mismatch between indices occurred suggesting that there is still a great influence of the developmental assumptions and methods in the behaviour of the indices. Recent studies have been conducted in an attempt to cross validate different sampling methods in lagoons comparing structural and functional aspects of fish assemblages (Franco et al., in press). Borja et al. (2009b) and Borja and Elliott (2007) proposed that type-specific reference conditions should be established in the basis of ecotype classification schemes, and where possible the indices should be calibrated to account for local variability and index structure and metric composition. In the same manner, different methods to derive quality-class boundaries can readily impact the outcome of the assessment and this alone could explain index discrepancies. The implication of the reference and the quality boundary assessment method on the quality assessment and reporting of the water body status has been highlighted as important issues in the implementation of fish-based assessment in TW (Borja and Elliott, 2007; Borja et al., 2009b).

6. Discussion and future research In order to be effective, a fish index should be sensitive to anthropogenic stressors in a predictable manner but sufficiently robust to be relatively insensitive to natural variability at different spatial and temporal scales (Noges et al., 2009; Rice, 2003). All fish indices found in this review are, to a greater or lesser extent, based on premises of responsiveness (i.e. sensitivity) to human-induced stress, low metric variability and simplicity of measurement, which is in line with general design considerations of other biotic indices (Dale and Beyeler, 2001; Rice, 2003). However, in estuaries it is difficult to select any set of ecological measures responding to anthropogenic stress, and which are relatively free from natural background variability (Dale and Beyeler, 2001; Elliott and McLusky, 2002; Williams and Zedler, 1999). The Estuarine Quality Paradox (Dauvin, 2007; Dauvin and Ruellet, 2009; Elliott et al., 2007) refers to this difficulty in detecting signals of anthropogenic stress from areas of high natural variability (natural stress). Additionally, the subjectivity of anthropogenic pressure measurement is often constrained by availability of reliable pressure data or is greatly based on expert judgement. This review has indicated the reduced number of metrics included in the indices developed using more rigorous statistical procedures. However, the question remains as to whether the metrics that do not appear to respond to a pressure gradient are really unresponsive or the data we use to evaluate them is too variable or unreliable (Teixeira et al., 2010). Large datasets may appear as a robust option, however, it is important to know the behaviour of a particular metric in relation to the statistical confidence or power associated to any analysis. The risk of failing

to identify a balanced set of metrics exclusively through modelling can be minimised by using a supervised analysis where metrics are finally included based on general ecological theory (Breine et al., 2010). For other indices developed mainly on expert knowledge there is the tendency to include a large number of metrics in the hope that all a priori relevant quality features of the estuary are represented in the composite index. This increases the risk of including irrelevant or correlated metrics that may introduce undesirable noise or might artificially give a higher weight to a particular pressure type. However, some degree of metric redundancy is desirable because some metrics may have overlapping sensitivity to multi-pressures acting in TW. This is as a result of variable response lag times, response thresholds, and changes in relative contribution of the metrics across different ecoregions or sampling periods (Fausch et al., 1990; Noges et al., 2009). For example, the richness of sensitive species is likely to be an unresponsive metric in highly degraded areas, and conversely the incidence of diseased or abnormal individuals would only be apparent after substantial degradation, consequently being unresponsive in good quality areas. In an ideal index, the complement of metrics would have to include a balanced combination of sensitive metrics with a good combined predictive power for all expected quality conditions possible in the whole area under assessment. This necessary validation step and appraisal according to ecological theory of the final index is seldom present in the reviewed literature and this is an area where further research is necessary. Given historical human pressures within most estuaries worldwide, a baseline computed only on current data would be set at a somewhat reduced quality status compared to the pristine system (see Hering et al., 2010). The adjustment of the reference community to reflect extant good or high status communities and functioning is therefore extremely important and until now, this is still largely based on expert judgment. Even in the current situation where pristine systems are generally lacking, predicting the expected community before human intervention may be a possible method to correct the changed baselines where historical data are available (Breine et al., 2010). In addition, data-driven logistic regression models where metric outputs are correlated to environmental and biological factors could provide the necessary predictive power to derive statistically-significant models of original reference communities (Delpech et al., 2010; Maes et al., 2007). It is emphasised here that each of these methods has its disadvantages in that it is difficult to agree on a year of reference and even if data are available for that time, or it is unrealistic to think that conditions could be returned to that pristine status due to economic arguments. Similarly, predictive models including Bayesian probability models (Drouineau et al., 2011) are currently at an academic level but are not sufficiently developed as management tools. Hence, the current situation rely heavily on expert judgement as being a more pragmatic and cost-effective manner. This scenario not only applies to fish indices but it is also apparent in macroinvertebrate assessment tools (Teixeira et al., 2010). Most of the present indices rely heavily on indicator species or community composition metrics which are structural attributes of ecosystems. However, a functional analysis of the ecosystem is widely acknowledged among experts in the field of TW fish and in other bioassessments alike to provide a better understanding and more direct insight into processes (de Jonge et al., 2006). It is of note that all multimetric indices use functional guilds to the assessment of ecological quality. Likewise current European marine legislation (MSFD) is adopting a more functional-based approach (Borja, 2006; Borja et al., 2010; Mee et al., 2008). The guild approach is known to reduce the complexity of aquatic systems (Elliott and Dewailly, 1995; Elliott et al., 2007), allowing insight on the functional aspects affected by stress (Jager and Kranenbarg, 2004) and extending the

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geographical application of indices beyond a single species normal range (Elliott and Dewailly, 1995; Karr and Chu, 1997). Therefore functional guilds could increase the geographical breadth of fish indices which in turn could reduce the number of indices to only a few general ones. A serious limitation of the approach is the uncertain ecology of some fish species in estuarine systems which forces their exclusion from these guild analyses (Breine et al., 2010). The need for indicators which give detailed information on the cause of change together with the effects of the change observed has been recognised (McLusky and Elliott, 2004). Since the success of mitigation and restoration plans depends on our ability to minimise the effects of stress, any assessment tool that can both determine conservation status and diagnose damaging pressures can potentially provide cost and time savings for resource managers. Noteworthy is the use of radar plots as a visual representation of metric scores included in the final index, where independent metrics scores are represented in a relative scale (Breine et al., 2010; Jager and Kranenbarg, 2004). It is widely recognised that gear efficiency varies with habitat type and species behaviour (Elliott and Hemingway, 2002; Franco et al., 2008) therefore, all indices reviewed, are built around a more or less biased view of the fish assemblage. This limits the applicability of the indices to datasets obtained with sampling methods that differ to those used for the index development and calibration. Similarly the level of effort is crucial to ensure comparability, reproducibility and relevance of reference communities especially to those metrics based on diversity estimates (i.e. number of species) (Breine et al., 2010; Karr, 1981). Clearly sampling bias can easily compromise the use of the functional approach based on habitat guilds, as fishing devices are often specific for a single type of habitat. In order to increase the confidence on the assessments, local effects will need to be taken into account at appropriate scales and the variances and power associated with the metrics or indices assessed (Courrat et al., 2009; Hughes et al., 1998). Although the standardisation of sampling protocols helps to improve dataset comparability, it is suggested that the use of standard analysis to test sensitivity and behaviour of metrics, natural variability of reference communities, relevance of outcomes, and overall validation of indices has a higher significance and should facilitate the identification of robust and generally aplicable fish metrics for use in TWs (Fairweather, 1999; Harrison and Whitfield, 2004; Hering et al., 2010; Karr and Chu, 1997; Niemi et al., 2004; Noges et al., 2009). Despite its lack of uniformity, there is a general agreement in the formulation and structure of the different indices as suggested by the identification of groups in the multivariate analysis (Section 3.6 and Figs. 2 and 3). As yet, there are no general estuarine fish indices but rather locally applicable indices. The widening of the geographical relevance of estuarine fish indices will require better precision in the formulation of reference conditions and greater inclusion of functional metrics and similar sampling gear or gear intercalibration. It can also be noted that most of the published indices refer to estuaries and very few to lagoons. Measures of uncertainty of the indices are often lacking and the effects of the gear type and season on the output of the indices have yet to be rigorously interrogated. Signals from human pressures may be confounded not only by natural environmental variability (i.e. noise) but also by sampling bias and unsatisfactory sampling effort level resulting in low power assessments. Further research on sources of uncertainty in fishbased ecological quality assessments in estuaries and lagoons is currently being undertaken by the European-funded project WISER (http://www.wiser.eu/). Improvements in fish-based estuarine indices of habitat integrity are more urgently needed in four main areas that include: (1) improving the mechanisms of linking anthropogenic pressures and ecological responses; (2) deriving reference conditions, (3) disentangling the effect of natural and anthropogenic stress, and (4)

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testing the effect of sampling effort and design on indicator outcomes and assessing the uncertainty of outcomes. Acknowledgments This study is a result of the project WISER (Water bodies in Europe: Integrative Systems to assess Ecological status and Recovery) funded by the European Union under the 7th Framework Programme, Theme 6 (Environment including Climate Change) (contract no. 226273), www.wiser.eu. References Able, K.W., 2005. A re-examination of fish estuarine dependence: evidence for connectivity between estuarine and ocean habitats. Estuarine, Coastal and Shelf Science 64, 5–17. Able, K.W., Manderson, J.P., Studholme, A.L., 1999. Habitat quality for shallow water fishes in an urban estuary: the effects of man-made structures on growth. Marine Ecology Progress Series 187, 227–235. Bain, M.B., Harig, A.L., Loucks, D.P., Goforth, R.R., Mills, K.E., 2000. Aquatic ecosystem protection and restoration: advances in methods for assessment and evaluation. 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