Tales from a thousand and one ways to integrate marine ecosystem components when assessing the environmental status

Abstract

Assessing the environmental status of marine ecosystems is useful when communicating key messages to policymakers or the society, reducing the complex information of the multiple ecosystem and biodiversity components and their important spatial and temporal variability into manageable units. Taking into account the ecosystem components to be addressed (e.g., biological, chemical, physical), the numerous biodiversity elements to be assessed (e.g., from microbes to sea mammals), the different indicators needed to be studied (e.g., in Europe, 56 indicators of status have been selected), and the different assessment scales to be undertaken (e.g., from local to regional sea scale), some criteria to define spatial scales and some guidance on aggregating and integrating information is needed. We have reviewed, from ecological and management perspectives, the approaches for aggregating and integrating currently available for marine status assessment in Europe and other regions of the world. Advantages and shortcomings of the different alternatives are highlighted. We provide some guidance on the steps toward defining rules for aggregation and integration of information at multiple levels of ecosystem organization, providing recommendations on when using specific rules in the assessment. A main conclusion is that any integration principle used should be ecologically-relevant, transparent and well documented, in order to make it comparable across different geographic regions.

Introduction

The requirement to assess the environmental status of marine waters is growing across continents (Borja et al., 2008). It is also one of the challenging tasks to be accomplished in Europe, within the Marine Strategy Framework Directive (MSFD) (European Commission, 2008). The different legislative mandates to asses status coming from the MSFD, Water Framework Directive (WFD) (2000/60/EC) and Habitats Directive (92/43/EEC) and other international initiatives have produced numerous methodologies that can be applied to different ecosystem components, such as various taxonomic or functional groups, habitats, traits, physical features, or to the whole ecosystem (Birk et al., 2012; Halpern et al., 2012). Despite this wealth of methods, determining environmental status and assessing marine ecosystems health in an integrative way is still one of the grand challenges in marine ecosystems ecology research and management (Borja, 2014).

Different attempts to understand, define and assess ecosystem health have been made in recent years (Costanza and Mageau, 1999; Ulanowicz, 2000; Mee et al., 2008; Ojaveer and Eero, 2011; Borja et al., 2013; Tett et al., 2013). The concept of “good environmental status” (GEnS) integrates physical, chemical and biological aspects, together with the services provided by ecosystems, including a sustainable use of the marine resources by society (Borja, 2014). However, synthesizing these aspects into a single value will never appropriately reflect all aspects considered to derive the value (Purvis and Hector, 2000; Derous et al., 2007). Still, this step is useful when communicating key messages to policymakers or the society, reducing the complex information of the multiple ecosystem components and their important spatial and temporal variability into manageable units, which can be used in ecosystem management. Following the recommendation from Mee et al. (2008), we use the GEnS acronym because the meaning of “environmental,” within the MSFD, and “ecological” (good ecological status), within the WFD, is different (see Borja et al., 2010, for differences between both concepts), implying a different emphasis between these two major pieces of legislation.

In the case of the MSFD, an appropriate integration process might be even more complex, since the assessment of the status is based upon 11 qualitative descriptors (i.e., D1: biological diversity; D2: non-indigenous species; D3: exploited fish and shellfish; D4: food webs; D5: human-induced eutrophication; D6: seafloor integrity; D7: hydrographical condition; D8: contaminants; D9: contaminants in fish and seafood; D10: litter; and D11: energy and noise), which are further divided into 29 criteria and 56 indicators of health (European Commission, 2010). An overview of MSFD descriptors, criteria and indicators is shown in Table 1.

The aim of this work is to present an overview of the different methods currently available to synthesize the ecosystem complexity, by aggregating and integrating information when assessing the status, focusing mostly on the descriptors related to biodiversity, namely D1, D2, D4, D6 (Cardoso et al., 2010; Prins et al., 2014). This overview would assist managers, through the guidelines provided, in taking decisions for a better management of the marine ecosystems.

Ecosystem components combination requirements in assessing the status

There are different methods that can be applied to combine indicators and criteria within descriptors and across descriptors to eventually result in an assessment of GEnS for a specific geographic area. This combination both involves aggregation and integration. The term aggregation is here used for the combination of comparable elements across temporal and spatial scales, indicators and criteria, within a descriptor. The term integration is used for the combination of different elements (e.g., across descriptors). Both combination methods (aggregation and integration) may involve numeric calculations.

In Europe, the MSFD defines environmental status as “the overall state of the environment in marine waters, taking into account the structure, function, and processes of the constituent marine ecosystems together with natural physiographic, geographic, biological, geological and climatic factors, as well as physical, acoustic and chemical conditions, including those resulting from human activities inside or outside the area concerned.”

Taking this definition into account, Borja et al. (2013) have proposed an operational definition: “GEnS is achieved when physicochemical (including contaminants, litter and noise) and hydrographical conditions are maintained at a level where the structuring components of the ecosystem are present and functioning, enabling the system to be resistant (ability to withstand stress) and resilient (ability to recover after a stressor) to harmful effects of human pressures/activities/impacts, where they maintain and provide the ecosystem services that deliver societal benefits in a sustainable way (i.e., that pressures associated with uses cumulatively do not hinder the ecosystem components in order to retain their natural diversity, productivity and dynamic ecological processes, and where recovery is rapid and sustained if a use ceases).”

This latter definition includes all MSFD descriptors. Hence, to assess whether or not GEnS has been achieved, some aggregation within and integration across the 11 descriptors is required to move from the evaluation at the level of indicators (the 56 indicators and 29 criteria described in the Commission Decision (European Commission, 2010, see also Table 1) to a global assessment of status, as mentioned also in Cardoso et al. (2010). The problem is how to deal with the complex task of combining a high number of indicators and descriptors. To develop a common understanding on this, it is important that Member States are transparent on (i) the process of selecting the indicators to be monitored; (ii) the approaches and combination methods they have used; and (iii) the uncertainties in their indicators and methods.

General principles for combination

Based on a literature review, we identified a number of different approaches for combining a number of variables (which could be metrics, indicators, or criteria) into an overall assessment. Some of them have been used within the WFD, others within the RSCs and some others in the MSFD. An overview of the methods is given in Table 2.

When considering the aggregation of indicators, an important factor to be taken into account is the reliability of the individual indicators to be aggregated. With each indicator, it is always possible to make a type I error, i.e., to get a non-GEnS result when the system in fact is in GEnS. The probability of this false positive (FP) signal varies (i) between indicators (Murtaugh, 1996), depending on the natural variability; (ii) with the amount of data used to define the indicator value; and (iii) with the target level compared to the situation in the nature. The risk of getting a FP from each of the individual indicators should affect the aggregation rule as well: if the risk of a FP is a uniform 5% per indicator, on average 1 out of 20 indicators is expected to give a FP; a problem if all indicators should in fact show GEnS. In order to come up with an aggregated assessment in which the risk level is within reasonable bounds, this aspect cannot be overlooked.

One-out, all-out (OOAO)

The OOAO approach is used in the WFD to integrate within and across Biological Quality Elements (BQEs) (CIS, 2003), in order to reach the ecological status of a water body. This approach follows the general concept that the ecological status assigned to a water body depends on the BQE with the lowest status, and consequently, the OOAO approach results in a “worst case.”

A prerequisite for the aggregation of various indicators is that they are sensitive to the same pressure (Caroni et al., 2013). In such a case, different aggregation methods can be used to combine parameters (medians, means, etc.). Caroni et al. (2013) recommend an OOAO approach when the combination involves parameters/indicators that are sensitive to different pressures. The application of averaging rules may lead to biased results in those cases. The WFD Classification Guidance (CIS, 2003) also advises to use OOAO when combining parameters/indicators that are sensitive to different pressures.

Borja et al. (2009a) discussed the challenge of assessing ecological integrity in marine waters, and suggest that simple approaches, such as the “OOAO” principle of the WFD, may be a useful starting point, but eventually should be avoided. The ecological integrity of an aquatic system should be evaluated using all information available, including as many biological ecosystem elements as is reasonable, and using an ecosystem-based assessment approach. The OOAO rule can be considered a rigorous approach to the precautionary rule, in an ideal world where the status based on each BQE can be measured without error. It results in very conservative assessments (Ojaveer and Eero, 2011). In practice, the inevitable uncertainty associated with monitoring and assessment for each metric and BQE leads to problems of probable underestimation of the true overall status. The OOAO principle has therefore been criticized as it increases the probability of committing a false positive error, leading to an erroneous downgrading of the status of a water body as it has been observed especially within the WFD (Borja and Rodríguez, 2010; Ojaveer and Eero, 2011; Borja et al., 2013; Caroni et al., 2013). In the case of the MSFD, with such large number of descriptors, criteria and indicators, the probability of not achieving good status becomes very high and, probably, unmanageable in practical terms (Borja et al., 2013).

Alternative methods for integrating multiple BQEs in the WFD are currently being considered (Caroni et al., 2013).

Averaging approach

The averaging approach is the most commonly used method to aggregate indicators (Shin et al., 2012) and consists of simple calculations, using methods such as arithmetic average, hierarchical average, weighted average, median, sum, product or combinations of those rules, to come up with an overall assessment value.

Ojaveer and Eero (2011) showed that in cases where a large number of indicators is available, the choice of e.g., either medians or averages in aggregating indicators did not substantially influence the assessment results. However, this might not necessarily be the case when only a few indicators are available. In such a situation, the result will depend to a larger degree on the distribution of the values involved. A skewed distribution reflecting some major factors and a few ones with very different values will result in very different assessment results for the median compared to assessments based on means. Apart from the mathematical applicability of either method based on the underlying data (e.g., homoscedasticity), the choice of the actual averaging method may be driven by policy decisions focusing on either central trends without much attention to extreme values (median) or focusing on weighting the individual values by their magnitude (arithmetic mean).

The way the indicators are hierarchically arranged influences the assessment results as well, but Ojaveer and Eero (2011) found that these effects were considerably less important than the effects of applying different aggregation rules.

Differential weighting applied to the various indicators can be used when calculating means or medians. An adequate basis for assigning weights is not always available and in such cases an equal weight is recommended by Ojaveer and Eero (2011). Assigning weights often involves expert judgment, and Aubry and Elliott (2006) point out that in some cases, expert opinions on weights can show important divergence.

Conditional rules

Conditional rules (a specific proportion of the variables have to achieve good status) are an approach where indicators can be combined in different ways for an overall assessment, depending on certain criteria. This provides an opportunity to use expert judgment when combining indicators, in a transparent way. An example of this approach is the application of a conditional rule of at least two out of three indicators (one biotic index and two structural or diversity indices) should pass the threshold in order to achieve GEnS for benthic community condition under D6 in Hellenic waters (Simboura et al., 2012). Tueros et al. (2009) present another example of the conditional rule in which when integrating water and sediment variables into an overall assessment of the chemical status and only one sediment or water variable does not meet the objective, while the rest of the variables meet, the final chemical status achieves the objective. This work was also mentioned under the “two out, all out” approach considering the case when two variables do not meet the objective and the final status fails.

Breen et al. (2012) used several risk criteria rules and worst-case or integrated approaches when combining evidence before a final assessment. Following Cardoso et al. (2010) the integrated approach was applied to Biodiversity, Non-indigenous species, Eutrophication and Seafloor Integrity descriptors, while all other descriptors used a worst case approach following the OOAO principle whereby if one set of evidence suggested that the risk was “high” then “high” was automatically assessed for the entire descriptor.

Scoring or rating

In this method different scores are assigned to a status level (for example, ranging from 1 to 5), for a number of different elements. The scores are summed up to derive a total score which is then rated according to the number of elements taken into account. Different weights can be assigned to the various elements. This method was proposed by Borja et al. (2004) to calculate an integrative index of quality and is the basis of many multimetric indices used within the WFD and the MSFD combining different parameters or metrics using the weighted scoring or rating rule into one integrative multimetric index (Birk et al., 2012). It must be recognized here that this approach implies the score values being on a cardinal scale and acting as weighting factors. Otherwise, using an ordinal scale for the scores, summing up the individual elements is mathematically not defined.

Another example is the method developed by Borja et al. (2010, 2011b) for a cross-descriptor integration, combining the 11 descriptors of MSFD based on the WFD, HELCOM (2009a,b, 2010) and OSPAR (2010, 2012) experiences. An Ecological Quality Ratio (EQR) was calculated for each indicator of the various MSFD descriptors, with the EQR for the whole descriptor being the average value of the EQR of the indicators. Then, by multiplying the EQR with the percent weight assigned to each descriptor (and summing up to 100), an overall environmental status value was derived.

Multimetric indices to combine indicators

Within the WFD there are many examples of multimetric indices developed for different biological elements, driven by the need to fulfill the detailed requirements of the WFD (see Birk et al., 2012 for a complete synthesis).

In addition, within the MSFD, the European Commission established a number of Task Groups consisting of technical experts to help inform the discussions on how to reach a common understanding of the 11 descriptors. Hence, Task Group 6 report on seafloor integrity (Rice et al., 2010) recommends the use of multimetric indices or multivariate techniques for integrating indicators of species composition attributes of this descriptor, such as diversity, distinctness, complementarity/(dis)similarity, or species-area relationships.

There are various other examples of multi-metric indices used to assess the status of the macrobenthos (see Borja et al., 2011a for an overview). Multimetric methods to combine multiple parameters in one assessment may result in more robust indicators, compared to indicators based on single parameters. However, scaling of a multimetric index may be less straightforward, and ideally the various parameters should not be inter-correlated (e.g., the discussion on the TRIX index in Primpas and Karydis, 2011).

Multidimensional approaches

Multivariate methods, such as Discriminant Analysis or Factor Analysis combine parameters in a multi-dimensional space. For assessment purposes, areas need to be classified into groups of GEnS and non-GEnS.

Multivariate methods have the advantage of being more robust and less sensitive to correlation between indicators. However, interpretation is less intuitive than other methods, as information on individual indicators in each ecosystem is lost (Shin et al., 2012) and links to management options are less obvious.

Decision tree

Decision trees provide the opportunity to apply different, specific, rules to combine individual assessments into an overall assessment. A decision tree allows implementing individual rules at each of its nodes and thus incorporates arbitrary decisions at each step within the decision tree. The decision rules can be quantitative or qualitative as well as based on expert judgment. This gives room for a high degree of flexibility in reaching the final assessment and can thus be used where the other principles fail to represent the intricate interactions, feedback loops and dependencies involved in ecosystem functioning between the ecosystem components.

A simple version of a decision tree involves only having a few conditional rules where a specific proportion or certain individually specified indicators have to achieve good status in order to achieve GEnS. Borja et al. (2013) implicitly propose using this kind of decision tree when they take the view that for biodiversity (D1) to be in good status, all other descriptors must be in good status and if one of the pressure descriptors fails, then D1 also fails.

Borja et al. (2004, 2009b) describe a methodology that integrates several biological elements (phytoplankton, benthos, algae, phanerogams, and fishes), together with physicochemical elements (including pollutants) into a quality assessment. The proposed methodologies accommodate both WFD and the MSFD. They suggest that the decision tree should give more weight to individual elements taking into account the spatial and temporal variability and the availability of accurate methodologies for some of them (i.e., benthos) and to individual assessment methods which have been used broadly by authors other than the proposers of the method, tested for several different human pressures, and/or intercalibrated with other methods.

Probabilistic approach

Each of the indicator results are uncertain, due to several factors e.g., natural variation in the sampling sites, random variation in the samples, insufficient scientific understanding about what should be the reference value for good status, etc. Some indicators are bound to include more uncertainty that others, due to differences in the amount of data used, the extent of scientific understanding regarding the issue, and the amplitude of natural variation. If these uncertainties can be approximated, this gives rise to the possibility of taking this information into account when integrating the indicators. The more uncertain indicators will get less weight in the integrated assessment, while the more certain ones will be more reliable and hence get more weight. The calculus of the integrated assessment can be based on Bayesian statistics, giving transparent and coherent rules by which the final score is calculated.

This approach can be combined to one or several of the above-mentioned approaches: for example, conditional rules can be set in addition to the probabilistic integration rule to include expert judgment; and the principles outlined in the decision tree approach can be applied as well.

Barton et al. (2012) demonstrate how to use the probabilistic approach in the DPSIR framework in the case of eutrophication management. There are several other examples in the recent literature about how to evaluate various management measures under uncertainty to optimize one target, such as eutrophication (Barton et al., 2008; Lehikoinen et al., 2014) and oil spill severity (Lehikoinen et al., 2013). This approach could be expanded to include several descriptors or indicators.

Probabilistic combination of uncertain indicators would naturally lead to a probability estimate of how likely it is that a marine area is in GEnS; we would, for example, end up with an estimate that the sea area is in GEnS with 70% probability. The managers would then have to decide how much uncertainty they are willing to tolerate; i.e., are they happy if the probability of GEnS is above 50%, or whether they want a higher certainty?

High-level integration

An example of a high-level integration, where assessments for several ecosystem components are merged into a final assessment, is the HELCOM-HOLAS project (HELCOM, 2010). The report presents an indicator-based assessment tool termed HOLAS (“Holistic Assessment of Ecosystem Health Status”). The indicators used in the thematic assessments for eutrophication (HEAT), hazardous substances (CHASE) and biodiversity (BEAT) were integrated into a Holistic Assessment of “ecosystem health.” The HOLAS tool presented assessment results for three groups: biological indicators, hazardous substances indicators and supporting indicators, and then applied the OOAO principle on the assessment results of those three groups for the final assessment (Figure 1).

Figure 1

This approach, which includes the selection of an agreed reduced set of indicators and agreed weighting rules, could be considered a pragmatic compromise, reducing the risks associated with OOAO while still giving an overall assessment.

An example of such a high level aggregation is the integrative method of Borja et al. (2010, 2011b), which includes a weighted scoring or rating method proposed for the MSFD in the southern Bay of Biscay. After aggregating the indicators within each descriptor, each descriptor was weighted according to the human pressure supported by the area. Then the value of each descriptor (i.e., an EQR) was multiplied by the weighting and added to obtain a final value between 0 and 1, being 0 the worst environmental status and 1 the best. This high-level integration was done at spatial and temporal scale. Although these authors combine values across descriptors, leading to a single value of environmental status, it could also be reported as “x out of 11 descriptors” having reached GEnS. In both cases, this allows to take management measures on those human activities impacting more in some of the descriptors or indicators not achieving good status, as shown in Borja et al. (2011b).

Halpern et al. (2012) developed another method, based more upon human activities and pressures, which presents a high-level integration at country level, using internationally available datasets (Ocean Health Index http://www.oceanhealthindex.org). Similarly, Micheli et al. (2013) looked at cumulative impacts to the marine ecosystems of the Mediterranean and the Black Sea as a whole, while producing impact scores and maps for seven ecoregions and the territorial waters of EU Member states.

A Baltic Sea Health Index (BSHI) will be developed based on: (i) the existing HELCOM toolbox (HEAT, BEAT, CHASE and HOLAS), the MSFD (European Commission, 2008, 2010), and (ii) the Ocean Health Index (Halpern et al., 2012).

Finally, there is a recent high-level integration example in Tett et al. (2013), for the North Sea, which includes five steps in the calculation: (i) identify (spatial extent) of ecosystem; (ii) identify spatial granularity and extent of repetitive temporal variability, and decide how to average or integrate over these; (iii) select state variables; (iv) plot trajectory in state space and calculate Euclidian (scalar) distance from (arbitrary) reference condition; and (v) calculate medium-term variability about trend in state space, and use this variability as proxy for (inverse) resilience.

Considerations and recommendations when using specific rules

Application of combination rules in assessments

As shown above, the WFD focuses on the structure of the ecosystem using a limited number of biodiversity components (the BQEs), that are combined through the precautionary OOAO approach (Borja et al., 2010). In contrast, the MSFD can be considered to follow a “holistic functional approach,” as it takes into account not only structure (biodiversity components, habitats), but also function (e.g., food webs, seafloor integrity) and processes (e.g., biogeochemical cycles) of the marine ecosystems. The MSFD also uses descriptors that not only relate to biological and physicochemical state indicators but also to pressure indicators (Borja et al., 2010, 2013). The MSFD requires the determination of GEnS on the basis of the qualitative descriptors in Annex I, but does not specifically require one single GEnS assessment, in contrast to the WFD.

There are many methodological challenges and uncertainties involved in establishing a holistic ecosystem assessment, when it is based on the large number of descriptors, associated criteria and indicators defined under the MSFD. The choice of indicator aggregation rules is essential, as the final outcome of the assessment may be very sensitive to those indicator aggregation rules (Ojaveer and Eero, 2011; Borja et al., 2013; Caroni et al., 2013). As shown in the previous section, different methodologies can be applied for aggregating indicators, which vary, amongst others, in the way the outliers influence the aggregate value.

When aggregating indicators most researchers agree that multiple accounting should be avoided. For example, phytoplankton indicators under D1 should be indicative of biodiversity state while under D5 it should be an estimator of the level of eutrophication. Similarly, macroinvertebrates under D1 should represent biodiversity state and under D6 also the state change from pressures on the seafloor. In these cases, although the datasets used could be the same, the main characteristics of the indicators to be used within each descriptor should be different, e.g., the value of macroinvertebrates indicators under D1 (rarity of species, endangered species, engineer species presence, etc.) and the condition of benthic community under D6 (ratio of opportunistic/sensitive, multimetric methods to assess the status, etc.). Of course, for aggregating indicators within the same criterion it is important that all indicators have the same level of maturity and that sufficient data are available.

There are at least four levels of combination required to move from evaluation of the individual metrics or indicators identified by the Task Groups to an assessment of GEnS (Cardoso et al., 2010). As an example, using D6 (Seafloor integrity), Figure 2 shows: (i) aggregation of metrics/indices within indicators (see names of indicators in Table 1); (ii) aggregation of indicators within the criteria of a descriptor (for complex descriptors), e.g., criteria 6.1 (physical damage) and 6.2 (condition of benthic community); (iii) status across all the criteria of a descriptor; and (iv) integration of status across all descriptors.

Figure 2

As one moves up the scale from metric/indicator level to overall GEnS, the diversity of features that have to be combined increases rapidly (Figure 2). This poses several challenges arising from the diversity of metrics, scales, performance features (sensitivity, specificity, etc.) and inherent nature (state indicators, pressure indicators, impact indicators) of the metrics that must be integrated.

Aggregation of indicators and criteria (combination within a descriptor)

Cardoso et al. (2010) summarize the methods for an integration within a MSFD descriptor, categorizing them into two wider categories: (i) integrative assessments combining indicators and/or attributes appropriate to local conditions; and (ii) assessment by worst case (in this context, “worst case” means that GEnS will be set at the environmental status of the indicator and/or attribute assessed at the worst state for the area of concern).

Table 3 summarizes the approaches to aggregate attributes within each descriptor. In some cases the MSFD Task Groups propose deconstructing the ecosystem into “descriptor indicators” and then recombining them again to give a pass/fail for the GEnS, using (in four cases) the OOAO principle (Table 3). Borja et al. (2013) emphasize that such a “deconstructive structural approach” makes large assumptions about the functioning of the system and does not consider the weighting of the different indicators and descriptors. It implies that recombining a set of structural attributes gives an accurate representation of the ecosystem functioning.

An example of this accurate representation is shown by Tett et al. (2013), who assess the ecosystem health of the North Sea, using different attributes and components of the ecosystem. These components include structure or organization, vigor, resilience, hierarchy and trajectory in state space. All the information from the different components are combined and synthesized for a holistic approach to assess the ecosystem health.

Other approaches have been used in aggregating indicators within each descriptor. For example, Borja et al. (2011b) use the biodiversity valuation approach, in assessing biodiversity within the MSFD, integrating several biodiversity components (zooplankton, macroalgae, macroinvertebrates, fishes, cetaceans and seabirds). Biodiversity valuation maps aim at the compilation of all available biological and ecological information for a selected study area and allocate an integrated intrinsic biological value to the subzones (Derous et al., 2007). Details on valuation methodology can be consulted in Pascual et al. (2011) (see Figure 4 in that paper). This methodology provides information for each of the components and their integrative valuation, together with the reliability of the result, taking into account spatial and temporal data availability (Derous et al., 2007). The advantage of this method is that the current information used to valuate biodiversity can be adapted to the requirements of the MSFD indicators. Moreover, this method can avoid duplication of indicators in two descriptors (e.g., D1 and D6), since the metrics used could be different. This information can be converted into environmental status values, as shown in Borja et al. (2011b).

Integration of descriptors (combination across descriptors)

Discussion on how to integrate the results of each descriptor into an overall assessment of GEnS for regions or subregions was not part of the Terms of Reference for the Task Groups. However, work within Task Group 6 (Sea floor integrity) identified a method for integration and assessment that might also be appropriate, if applied across all descriptors, at a regional scale (Cardoso et al., 2010). As these authors pointed out, cross-descriptor integration at the scale of (sub)regional seas runs the risk of blending and obscuring the information that is necessary to follow progress toward GEnS and to inform decision-makers about the effects and the efficiency of policies and management. It may lead to masking of problems within specific descriptors.

Borja et al. (2013) describe at least 8 options to determine GEnS in a regional sea context (Table 4). These authors detail the concept behind these options, and propose the decision rule more adequate for the assessment method to be used, depending on the circumstances i.e., data availability, lack of monitoring, etc. In addition, these authors consider what type and amount of data are required, and then discuss the pros and cons of the different options. The implementation of a complex directive, such as the MSFD, requires a high amount of data to assess the environmental status in a robust way. Hence, the options from 1 to 8 proposed in Table 4 are sequentially less demanding of new data, and the degree of detailed environmental assessment is also decreasing.

As such, Option 1, which is most similar to the WFD approach, deconstructs GEnS into the 11 descriptors and then into the component indicators, assessing each components for each area before attempting to produce an overall assessment (Table 4). However, having a complete dataset covering all descriptors and indicators for the assessment is difficult, if not impossible to achieve in practical terms. The use of pressure maps as an estimator of the environmental status and possible impacts to marine ecosystems could be considered instead (see Table 4). This would, however, build on the substantial assumption that the level of pressure is adequately representing the current state on all different levels of ecosystem components. Option 7, in contrast, only uses published data for the activities, and then infers a static relationship between activity, pressures, state changes and impacts both on the natural and the human system. Here, the number of underlying assumptions is even larger than using pressure maps, since the method relies on predefined and static DPSIR relations. Between these extremes, there are several intermediate options to integrate and present information, each with its own requirements, pros and cons (Table 4).

One-out, all-out (OOAO)

Although the MSFD describes the GEnS individually for each of the 11 descriptors, this does not necessarily imply the ability to have GEnS at the level of all the descriptors, nor does it mean that each descriptor should necessarily be graded individually in a binary way (i.e., good or not good environmental status) (Borja et al., 2013).

It could be argued that the 11 descriptors together summarize the way in which the ecosystem functions in terms of the MSFD view. As Member States have to consider each of the descriptors to determine good environmental status, this could be interpreted as a requirement to achieve GEnS for each of these descriptors. In that case, applying OOAO is the only integration method that can be applied to arrive at an overall assessment of GEnS, leading to a high probability of not achieving GEnS.

This assumes that the 11 descriptors, and the associated indicators, can be considered a coherent and consistent framework that adequately reflects the environmental status. In that situation, state descriptors not achieving GEnS would be accompanied by pressure descriptors not achieving GEnS, if the reaction of the ecosystem components is immediate, acting on the same time scale as the pressures. If this is not the case, for example if a pressure descriptor (e.g., D5 or D8) indicates that the level of the pressure is too high to achieve GEnS, while state descriptors (e.g., D1 or D4) do not reflect this, there is clearly an inconsistency in the assumed MSFD assessment framework, indicating that it does not capture delayed responses of state indicators to changing pressure indicators. That could be interpreted as a need for further research on the nature of P-S-I relations and the consistency in environmental targets for the descriptors involved, since our current state of knowledge on quantitative causal relations between pressures, state changes and impacts is limited. In addition, nearly all ecosystem components are subject to the true cumulative effects of many simultaneous pressures related to a range of human activities (Crain et al., 2008; Stelzenmüller et al., 2010; Knights et al., 2013). This means that, for some descriptors at least, there is a large scientific uncertainty associated with the definition of environmental targets and GEnS. Uncertainties in target setting, in the performance of an action (e.g., ecosystem state post-management) or in the contribution of individual driver(s) causing state change can undermine decision making when implementing environmental policy and can limit our ability to identify what should be managed, and what the impact of management might be (Knights et al., 2014). Consequently, developing a consistent assessment framework for all descriptors and indicators is an extremely challenging task, and using the OOAO approach is not appropriate.

Alternative approaches

The usefulness of integrating descriptors to one single value (overall GEnS assessment based on combination of the 11 descriptors) is under discussion by the Member States and the European Commission groups for the implementation of the MSFD. An argument against integration across descriptors is that it may not be informative any more since it results in loss of information at a crucial level where different elements are combined that cannot be integrated without major concessions.

The abovementioned groups have suggested that an integration across the biodiversity-related descriptors (D1, D2, D4, D6) might be an option, splitting those descriptors into various groups (e.g., functional or species groups). If a species or species group is assessed under more than one descriptor different aspects should be considered (e.g., chlorophyll a under D5 and phytoplankton species composition under D1).

However, if an integration across all descriptors is decided, Borja et al. (2010) suggest that the 11 descriptors are hierarchical and do not have an equal weighting when assessing the overall GEnS. Hence, Borja et al. (2013) suggest that for biodiversity (D1) to be fulfilled requires all others to be met and similarly if one of the stressor or pressure-related descriptors (e.g., D11, energy including noise) fails then by definition the biodiversity will be adversely affected at some point. This approach addresses the conceptual drawback of the OOAO principle and allows to have delayed responses to changing pressure regimes without drawing false conclusions and still being precautionary.

Visualizing and communicating the status

The outlined alternative approach also shows that concerns on integration across descriptors do not necessarily have to be a problem. There are some methods which have demonstrated that integrating the information into single values (Borja et al., 2011b), maps (HELCOM, 2010) or radar schemes (Halpern et al., 2012) is still helpful and informative for ecosystem management, despite the involved loss of information that is inherent to a single number. Information can be retained when always presenting that single number together with the main underlying data, ideally visualizing the different levels of aggregation, allowing the lookup of the status at any level and relating the status with the actual pressures that lead to the synthesized value.

As an example, the Ocean Health Index (Halpern et al., 2012) provides weighted index scores for environmental health, both a global area-weighted average and scores by country (Figure 3). The outer ring of the radar scheme is the maximum possible score for each goal, and a goal's score and weight (relative contribution) are represented by the petal's length and width, respectively. This way of visualizing the integration could be adapted for the MSFD, integrating at the level of region or subregion, but also showing the values within each descriptor. This would still allow managers to extract relevant information and take actions at different levels: small (or local) scale, large (regional) scale, integrative (whole ecosystem status), or for each descriptor.

Figure 3

Another example, applied specifically for the MSFD, using all descriptors and most of the indicators, can be consulted in Borja et al. (2011b). These authors studied a system in which the main driver for the whole area is fishing, whilst at local level some pressures such as waste discharges are important. Although the overall environmental status of the area was considered good, after the integration of all indicators and descriptors, two of the descriptors (fishing and food webs) were not in good status (Table 5). Interestingly, biodiversity was close to the boundary to good status (Table 5), suggesting that the system could be unbalanced by fishing, but affecting various biological descriptors to different degrees. This means that the pressure must be managed to avoid problems in the future, especially because the descriptors already in less than good status showed a negative trend (Table 5).

Table 5

Example of an assessment of the environmental status, within the Marine Strategy Framework Directive, in the Basque Country offshore waters (Bay of Biscay) (modified from Borja et al., 2011b).

EQS, Environmental Quality Standards; EQR, Ecological Quality Ratio, both based upon the Water Framework Directive (WFD); NA, not available; Trends: red color, negative; green color, positive (in both cases can be increasing/decreasing, depending on the indicator).

Hence, from the examples above and the given reasoning, both main choices are still useful: either integrate or not integrate information across descriptors. Irrespectively of which combination proposal(s) is adopted and at which level, the precautionary principle should always be followed in absence of more robust knowledge (Borja et al., 2013). As a summary, the pros and cons of each decision are shown in Table 6.

Proposed steps for combination

Aggregation up to this level gives a detailed assessment result that suits the information needs for identifying environmental problems and needs for measures. The result of those steps at European level would be a very high number of assessment results, for each descriptor and assessment area (comparable to presenting the WFD assessments at water body level).

Figure 4

The following steps could provide information at a higher level of integration presenting the required overview of the current status of the overall environmental state and the progress toward GEnS:

• –Within a descriptor, the assessment results of all assessment areas within a subregion can be presented in a more integrated way (Step 3—spatial aggregation).

  • Generally, use of OOAO (if one assessment area fails GEnS, the whole subregion fails) is not useful, as it gives a very conservative result and is not informative. Also, if the pressure is highly localized this approach is not adequate, since the whole subregion could fail GEnS due to a single location (which, of course, will need specific management measures).

  • In some cases, for example if a pressure is more or less homogeneous across a whole subregion (fishing, shipping), it could be useful to apply OOAO.

• –Percentage of surface area achieving GEnS: This could be a more useful approach, if the extent and intensity of a pressure can be quantified. For example, if the pressure is present in 45% of the surface area of a subregion, but the surface area not achieving GEnS is only 2%, it could be concluded that the subregion does not achieve GEnS in 2% of its area, where management measures are needed.

• –Other metrics.

For some descriptors, surface area may be a good measure to express status at a subregional level: for example, D5, D8, and D10. For other descriptors, surface area is not suitable but other metrics should be considered, e.g., D1: numbers of species/habitats failing to achieve favorable conservation status; D3: number of stocks failing to meet “Maximum Sustainable Yield.”

The end result of Step 3 could present the level at which GEnS is achieved at subregional scale as a pie chart. The aggregation results of Step 3 could be integrated across descriptors in a final presentation per subregion, using methods such as radar plots, or methods similar to the Ocean Health Index (Step 4—aggregation across descriptors). In this step, weighted approaches as suggested in previous sections would be considered.

Concluding remarks

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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