Report of the Workshop on guidance for the review of MSFD decision descriptor 4 foodwebs II (WKGMSFDD4-II)

Transcription

1 ICES WKGMSFDD4-II REPORT 2015 ICES ACOM COMMITTEE ICES CM 2015\ACOM:49 Report of the Workshop on guidance for the review of MSFD decision descriptor 4 foodwebs II (WKGMSFDD4-II) February 2015 ICES Headquarters, Denmark

2 International Council for the Exploration of the Sea Conseil International pour l Exploration de la Mer H. C. Andersens Boulevard DK 1553 Copenhagen V Denmark Telephone (+45) Telefax (+45) info@ices.dk Recommended format for purposes of citation: ICES Report of the Workshop on guidance for the review of MSFD decision descriptor 4 foodwebs II (WKGMSFDD4 II), February 2015, ICES Headquarters, Denmark. ICES CM 2015\ACOM: pp. For permission to reproduce material from this publication, please apply to the General Secretary. The document is a report of an Expert Group under the auspices of the International Council for the Exploration of the Sea and does not necessarily represent the views of the Council International Council for the Exploration of the Sea

3 ICES WKGMSFDD4-II REPORT 2015 i Contents Executive Summary Introduction Background Approach of the workshop Participation Structure of the workshop Issues relevant to a potential revision Definition of terms Suggested Revision of the foodweb criteria Technical Guidance on the setting of indicator targets and limits Methods to derive limits for indicators Data and knowledge available is very limited Data exists, no undesirable effects have been observed but knowledge is limited Data are available and good knowledge exists about the indicator and its relation to other ecosystem characteristics Defining limits where ecosystem trends or changes occur Determining if the indicator is within limits or not Technical guidance on aggregating indicator assessments for determining if GES has been achieved under D The specific nature of the foodweb indicator Consideration of pressure state relationships Surveillance indicatorsvs.state indicators Aggregation of indicator assessments Aggregation across criteria Response to reviewers comments in relation to GES aggregation Potential for gaps and overlaps in relation to Descriptor Comments on the previous version of the D4 manual Suggestion to modify the three criteria to two criteria The issue of trophic guilds Methods and clarification of the concepts associated with boundaries, GES definition, and use of surveillance indicators Approaches to aggregation of GES decisions (e.g. OOAO) Future potential indicators and the knowledge base Roadmap for future development of science for implementation and evaluation Regional and Cross regional scientific advice process Information flow between descriptors: Gaps and overlaps... 28

4 ii ICES WKGMSFDD4-II REPORT Uncertainty and GES Indicator development Aggregation for GES assessment within D4, including spatial integration Conclusions References Annex 1. List of participants Annex 2. Agenda Annex 3. Compilation of national, scientist and NGO comments on previous version of the manual Annex 4. Review Group Technical Minutes... 46

5 ICES WKGMSFDD4-II REPORT Executive Summary The second Workshop to review the 2010 Commission Decision on criteria and methodological standards on good environmental status (GES) of marine waters (2010/477/EC); Descriptor 4 Foodwebs met in Copenhagen, February It met to provide input to the review of the possible approach to amend Decision 2010/477/EC and respond to comments from Member States, scientists and other stakeholders on the previous version of the D4 Manual. The workshop participants were experts in MSFD implementation and/or scientists specialising in assessing foodwebs. The submitted comments and this workshop supported the proposal of WKMSFDD4 (2014) to replace the three criteria in the Commission Decision under Descriptor 4 with two criteria: 4.1 Foodweb Structure; 4.2 Foodweb Function. For GES under Descriptor 4 to be achieved, both the structure and function of foodwebs need to be at appropriate levels. Many foodweb indicators show substantial variation due to factors not related to anthropogenic pressures (weak or indirect links to human pressure). Indicators often reflect the desire to achieve a balanced ecosystem, and hence having very high or very low indicator values could be considered equally undesirable. The desired level of a specific indicator may be related to avoiding undesirable effects on other ecosystem components and hence requires information and knowledge of the relationship between different foodweb components. Given these special considerations, a foodweb indicator may be associated with different combinations of available data (monitoring time series) and knowledge (about the relationship of the indicator with foodweb components). Guidelines were developed to derive indicator limits, according to the availability of data and knowledge. Common to these guidelines are the principles that: i. indicators should be reported together with estimates of their precision; ii. highly variable indicator estimates should not lead to changes in limits; iii. a lack of knowledge of limits and effects of falling outside limits should not be used as an excuse for lack of action. This would allow the use of precautionary principles when deciding on management actions. Such use of surveillance indicators, based on monitoring, triggers action when indicators move beyond the limits. This action should determine whether anthropogenic pressures are causing changes to the foodweb. Indicator limits should relate to current conditions of the ecosystem, implying that limits will need to be reviewed regularly and updated where necessary in response to natural variability of the ecosystem. For the GES assessment of foodwebs at the criterion level, the application of simple aggregation or averaging rules is not considered suitable. Assessments should follow a decision tree that takes into account different pressure state relationships; varying levels of uncertainty in the indicators; their interrelationships and whether indicators are surveillance indicators. State indicators that have clear links to pressures require both pressure and state indicators to be within limits, lag periods should be taken into account. Surveillance and non surveillance indicators can either be assessed and reported separately, due to their different response requirements (i.e. further investigation or management action) or combined by applying different weightings. Although methods to aggregate indicators can differ across the two D4 criteria, both structure and function need to be at GES for overall GES to be achieved.

6 2 ICES WKGMSFDD4-II REPORT 2015 The workshop also clarified issued raised about the definition and use of trophic guilds and commented on potential gaps in coverage of ecosystem properties between described approaches for D4 and D1. It also provided a road map for further research for the implementation and development of foodweb indicators to support the MSFD. It highlighted regional and cross regional coordination, information flow between descriptors (gaps and overlaps), how to account for uncertainty when assessing GES and aggregation issues as important for further development.

7 ICES WKGMSFDD4-II REPORT Introduction This report documents the main discussions at the second ICES workshop to review the 2010 Commission Decision on criteria and methodological standards on good environmental status (GES) of marine waters (2010/477/EC); Descriptor 4 Foodwebs. The aim of the workshop was to provide a forum for scientists to provide input into the review process of the Marine Strategy Framework Directive (MSFD), especially with regards to Descriptor 4 Foodwebs. This workshop is part of the ICES led process to review the MSFD 2010 Commission Decision on fisheries, foodwebs, seafloor integrity and introduced energy (noise). The process has been instigated by the European Commission DG Environment to inform the national Marine Directors about the challenges facing the implementation of the current MFSD decision. It was carried out through the MoU between EC and ICES. 1.1 Background In accordance with the Commission Staff Working Document 2014, all Member States who have reported have defined indicators for Descriptor 4. Only two Member States were judged to have an adequate definition of GES, six were found to have a partially adequate definition whereas eight were found to be inadequate (CSWD, 2014). Four Member States have not defined GES for this descriptor. The definitions provided applied to their entire marine waters, with one exception where a Member State makes a minor differentiation between its subregions. The GES definitions vary enormously in their content and level of detail; most were qualitative and many were rather vague, lacking definitions of key terms used or specificity as to which elements of foodwebs were addressed (CSWD, 2014). Most Member States have referred to specific foodweb components in their GES definition, sometimes in addition to defining it for all foodweb components. In the Baltic region, most Member States have put an emphasis on fish communities. Most Member States referred to components such as key species or functional groups, and/or to top predators or species at the top of the foodweb. Very few Member States included in their GES definitions specific species or habitats as indicators of change. Indicator species include the harbour porpoise and the harbour seal and indicator habitats include Posidonia meadows. Only three Member States included a reference to the pressures of foodweb components, in particular fisheries. MSFD Descriptor 4 Foodwebs All elements of the marine foodwebs, to the extent that they are known, occur at normal abundance and diversity and levels capable of ensuring the long term abundance of the species and the retention of their full reproductive capacity.

8 4 ICES WKGMSFDD4-II REPORT Approach of the workshop 2.1 Participation Experts in MSFD implementation or scientific issues regarding the descriptors were invited to participate through national representatives allowing each country to nominate 1 2 participants. If nominations exceeded the meeting space available ICES reserved the right to reject participants. No timely nomination was refused for this workshop. Participants joined the workshop at national expense. Participants were not limited to those nominated by ICES Delegates and ACOM and participants from non ICES EU countries were specifically encouraged via an invitation from DGENV to the national marine directors. To conform to best practice and ICES policy, NGOs and stakeholders were permitted to attend the workshop on the understanding that policy statements were not permitted. Regional Sea Conventions were encouraged to participate through their member countries. The 19 participants present consisted of scientists, industry representatives, NGO representatives and managers. Scientists came from the NE Atlantic, Baltic and Mediterranean MSFD regions and the USA. 2.2 Structure of the workshop The workshop was planned and organized by the six chairs with expertise in foodwebs and MSFD implementation. The Chairs came from Denmark, Ireland, USA, UK, Finland, JRC and the ICES secretariat. The workshop began with an introduction and setting of the scene. The agenda was discussed and agreed. The workshop was basically structured around the following issues: Definition of terms Potential revision of the foodweb criteria Technical Guidance on the setting of indicator targets and limits Methods to derive limits for aggregate indicators at criteria level Remaining comments on the previous version of the D4 manual Road map for future development of science for implementation and evaluation. Each of the topics was addressed by parallel subgroups after a common introduction to that topic to ensure that all participants understood the task at hand. The subgroup participants were selected at random and no groups were the same between different sessions. Group work was facilitated by a designated member of the group and reported by summarizing the conclusion in plenary after each subgroup session. Generally participants were divided into four groups. This approach was used to ensure the widest possible participation in the workshop considerations and to prevent the over dominance of any individual opinions. The majority of participants contributed actively to discussions in the subgroups.

9 ICES WKGMSFDD4-II REPORT Issues relevant to a potential revision 3.1 Definition of terms The workshop participants came from different backgrounds and interpreted certain terms very differently from each other. For example, the term reference level was perceived by some as describing the normal or baseline variability of an indicator; yet OSPAR defined Reference state or Reference condition as The value or range of values of state at which impacts from anthropogenic pressures are absent or negligible. (OSPARʹs MSFD Advice Manual on Biodiversity v.3 [5/12/2011] see meeting paper ICG MSFD(4) 11/2/3 E). As a result of this, the following common terminology is used in this document: Foodweb Surveillance Indicators: The aim of surveillance indicators is to monitor key aspects of the foodweb structure and function and, by doing so, gain evidence to better understand the relationship between the monitored aspect and other ecosystem components as well as pressure/state relationship for these indicators. Surveillance indicators are defined as indicators of aspects of the structure or function of the foodweb, for which it is either not possible (through lack of evidence) to define limits based on knowledge of the system or where the link to anthropogenic pressures a weak or unclear, so direct management actions cannot be prescribed. Limit: a limit defines the indicator value(s) at which a Foodweb Indicator changes between a desirable and undesirable state. Within limits: Foodweb Indicators are defined as within limits when they are in the desirable state. Target: The target equates to the values or range of values that are within limits and represent a desirable state. GES of criteria: GES is measured for each Foodweb criterion. The assessment of GES is based on indicators under each criterion The exact link between the number, level and other aspects of indicator that need to be within limits in order to achieve GES depends on the specific aggregation methods that are used to combine indicator assessments and the methods used to set GES boundaries (sec. 3.4). GES boundaries: GES boundaries define the difference between GES and sub GES in assessments of Criteria and Descriptors. GES boundaries are defined according to the assessments of an agreed set of indicators and according to agreed methods of aggregating these indictor level assessments (sec. 3.3). GES of Descriptor 4: Descriptor 4 should be assessed as achieving GES when both D4 criteria are assessed as being at GES. 3.2 Suggested Revision of the foodweb criteria WKGMSFDD4 (ICES 2014) proposed the revision of the three criteria in the Commission Decision under Descriptor 4 into two criteria: 4.1 Foodweb Structure; 4.2 Foodweb Function (see Figure 3.1). The workshop supported this proposal, as did the comments received on the WKGMSFDD4 Report (ICES 2014). Foodweb structure and function each represent different ambitions of the MSFD in achieving GES. The MSFD defines GES as follows: good environmental status means the environmental status of marine waters where these provide ecologically diverse and dynamic

10 6 ICES WKGMSFDD4-II REPORT 2015 oceans and seas which are clean, healthy and productive within their intrinsic conditions, and the use of the marine environment is at a level that is sustainable, thus safeguarding the potential for uses and activities by current and future generations [...]. As Rossberg et al., (2015) point out, this definition of GES makes reference to potential for uses and activities by [...] future generations. In doing so, the MSFD recognizes the needs of future generations might be different from those of the present generation. The structure of foodwebs could be relevant to identifying ecosystem characteristics that we, as a society, would want to preserve for potential use by future generations. This aspect of GES relates to the concept of strong sustainability, as used in environmental economics (e.g. Figge, 2005), where ecological capital is preserved, irrespective of current needs or uses. Alternatively, weak sustainable use aims to maintain current patterns of use into the future. In order to achieve GES, as defined above, strong sustainable use would be required. The proposed criterion 4.1 on foodweb structure will provide the framework within which to assess whether the ecosystem is subjected to sufficiently strong sustainable use. GES also requires that ecosystems effectively support current uses by being sufficiently productive. A productive ecosystem requires fully functioning foodwebs. The proposed criterion 4.2 on foodweb function will provide the framework within which the following can be assessed: a) functioning of foodweb, b) negative human impacts of overexploitation can be identified, and c) responses to management. WKGMSFDD4 II warned that the two proposed foodweb criteria structure and function, should not be assessed in isolation. Structure is sensitive to overexploitation, but could recover with appropriate management. However, if foodweb functioning in significantly affected, recovery of the foodweb may be much more difficult to achieve. Hence, for GES to be achieved, both the structure and function of foodwebs need to be at appropriate levels. The aggregation of assessments of the criteria for structure and function are considered in section 3.3. The relationship between foodweb structure and function in examples of benthic macro invertebrate communities and associated benthic feeding fish in the Baltic Sea was discussed. Direct anthropogenic pressure alters structural properties of foodwebs, for example seen as reduced diversity and complexity of trophic networks for softsediment macrofauna along a gradient of increasing organic enrichment and decreasing oxygen levels (Nordström and Bonsdorff, in prep.). There are non random patterns of functional diversity in benthic foodwebs, structured by trophic interactions and biological traits such as body size. Degradation of interaction networks is thereby likely to affect the functioning of the foodweb by reducing the range of interacting components/nodes that maintain processes of consumption and energy flow in the community (Nordström et al., in review). However, even drastic changes in community composition do not always imply immediate alteration of trophic processes. Despite the regime shift seen for Baltic Sea pelagic biota in the late 1980 s (Österholm et al., 2007), the main processes remain dominant in the foodweb, as shown by a network motif approach examining foodweb processes before and after the regime shift in Baltic Sea offshore and coastal areas (Yletyinen et al., in prep.).

11 ICES WKGMSFDD4-II REPORT Figure 3.1 Suggested revision of criteria under D Technical Guidance on the setting of indicator targets and limits Foodweb indicators differ from indicators of other descriptors in a number of ways. First, many foodweb indicators have weak or indirect links to human pressure and may show substantial variation due to factors not related to anthropogenic activities. With such indicators, it is difficult or impossible to identify values of the indictor that are desirable or undesirable in relation to human impacts. Further, indicators often reflect the desire to achieve a balanced ecosystem, and hence having very high or very low indicator values can be equally undesirable. This is in contrast to, for example, indicators for environmental contaminants, where an upper limit alone constraints the desired range of values. Finally, the desired level of a specific indicator may be related to avoiding undesirable effects on other ecosystem components and hence requires information and knowledge of the relationship between different foodweb components. Given these special considerations, a foodweb indicator may be associated with different combinations of available data with which to construct the indicator and knowledge of the relationship of the indicator with the foodweb components in the specific ecosystem, as illustrated in Figure 3.2. Each combination of knowledge and data availability requires associated guidance for setting indicator limits. Below, in sections , we have provided examples of existing indicators that have set limits foodweb indicator application within each knowledge/data scenario.

12 8 ICES WKGMSFDD4-II REPORT 2015 Figure 3.2. Illustration of the scenarios explored for setting of indicator limits and ranges, considering data (time series) and the available knowledge of the relationship of the indicator with the foodweb components in the specific ecosystem. Low data and knowledge, higher date and low knowledge, high data and knowledge Methods to derive limits for indicators Limits for indicators can be determined by several methods. For many foodweb indicators, there is little knowledge of what values of the indicator should be considered desirable or undesirable. In such cases, limits can be derived from the range of variation in the indicator, which is known from past time series or from historical knowledge, where limits could be set at, for example, ± 1 SD of the mean of the previous time series (e.g. Gaichas et al., 2014). Other options are available when more knowledge exists, including expert elicitation, empirical analysis, and modelling (also reviewed by WKFooWI 2014). Expert elicitation allows for synthesis across a range of understandings between indicators and associated pressures. Link (2005) and Shin et al., (2010) summarize candidate limit reference points from literature (Link 2005) and a team of experts from many marine systems (Shin et al., 2010, Figure 3.3). Formal stakeholder participation processes that form part of a risk assessment procedure to identify and test candidate reference points for indicators can also be used (e.g. Levin et al., 2009, Samhouri et al., 2011, Smith et al., 2007). Empirical analyses to determine limits have focused on identifying critical points (with or without relation to a specific pressure response relationship) associated with large changes in the value for indicators. Time series methods have included CUSUM control charts (Hinkley, 1970) and sequential t tests (e.g. STARS, Rodionov, 2004). Vert Pre et al., (2013) used the STARS approach to define change points in productivity (surplus production) of fish stocks over time, and Szuwalski et al., (in press) related estimated changes to possible shifts at ecosystem level. Large et al., (2013) used GAMs to estimate thresholds in foodweb indicators in pressure state relationships, identifying empirical thresholds in indicator responses to both fishing pressure and environmental drivers (Figure 3.4). These statistical approaches have also been applied to identify threshold levels in response to multiple pressures concurrently, allowing for change in targets and limits with changing environmental conditions (Large et al., 2015, Figure 3.5. Non parametric methods (e.g. gradient forests, Baker et al., 2014, Large et al., in

13 ICES WKGMSFDD4-II REPORT press) have also been used to evaluate points for which changes occur across a suite of indicators and also investigate the influence of a range of pressures. Simulation modelling exercises offer the opportunity to investigate expected indicator behaviour across a range of scenarios for system dynamics and management strategies (e.g. Fay et al., 2013). Models can therefore be used to identify limit levels for indicators but also to evaluate expectations for the frequency of indicators falling outside limits, useful for determining decision rules for when indicators should be considered falling outside targets for determination of status. Samhouri et al., (2010) combined the results of ecosystem models with piece wise regressions to identify breakpoints in indicators related to fishing pressures. The desirable range can also be determined from the principle of strong sustainable use. For example, the DEVOTES project (Rossberg et al., 2015) proposed that the desirable range is that from which the indicator could revert to its natural (in the sense of pressure free) range of variation within a fixed, given time interval. Model testing of indicators, for example using a Management Strategy Evaluation (MSE) framework, can be used to identify the suitability of limit levels for indicators for achieving objectives (Fay et al., in press), and to test robustness of alternatives for deciding when indicators are outside limits (e.g. probabilities of indicators having crossed limits given a mean of recent values for the indicator). The suitability of alternative approaches depends on the feasibility of measuring the indicators, the availability of data, and on the pressure pressures affecting on the indicator, and on our ability to detect these impacts. Figure 3.3Density distributions of Indicator Limit Reference Levels (LRL) from surveys of scientific experts (from Shin et al., 2010)

14 10 ICES WKGMSFDD4-II REPORT 2015 Figure 3.4 Indicator (pelagic to demersal ratio) response to fishing pressure. (f) Dashed line represents a GAM smoother, grey polygon represents 95% bootstrap CI, points represent the raw data, black solid line indicates significant positive or negative trends, and grey solid lines indicate significant thresholds. (h) First derivative of the GAM smoother with black polygon and arrow indicating direction (positive or negative) of the trend where the 95% CI pass above or below zero ; from Large et al., Figure 3.5 Topographic surfaces representing the pressure environment state fit of 2 covariate generalized additive models. (a) Mean length and (b) pelagic to demersal ratio response to fishing pressure and environment summarized using dynamic factor analysis (DynFA) 2. From Large et al., 2015.

15 ICES WKGMSFDD4-II REPORT Data and knowledge available is very limited When the data and knowledge available is very limited, for example, when sampling has only just begun, appropriate limits can be suggested based on expert knowledge from similar ecosystems, theoretical considerations or a desired direction of change. In all cases, the estimated limits are highly uncertain and this should be reported together with the indicator. Limits should be updated regularly as more information becomes available Data exists, no undesirable effects have been observed but knowledge is limited Where data exists, no undesirable effects have been observed but knowledge of the direct relationship between the indicator and other ecosystem characteristics is limited, the indicator limits should described the observed range of known indicator values. Protocols should be in place, such that, when the indicator is not within limits, this triggers further investigation to determine the cause of the change as well as the effects on other ecosystem components. The likely impacting pressures should also be reviewed. As there is limited knowledge of the relationship between the indicator and other components, there is a possibility that undesirable effects occurred but were not recorded. This should be reflected in the reported uncertainty of assessments Structure indicator example: Biomass of demersal fish trophic guilds in the North Sea Biomass of demersal fish trophic guilds in the North Sea shows substantial historic variation followed by pronounced recent trend (Figure 3.6.). Guild membership was determined taking account of both variation in the diet between species and ontogenetic development of the diet within species. Numbers at length of each species were determined using data derived from the ICES coordinated first quarter (Q1) International Bottom Trawl Survey (IBTS) that were raised to take account of both betweenspecies and within species (length related) variation in catchability in the GOV trawl (Fraser et al., 2007). Species abundance at length were converted to estimates of biomass at length using both published and unpublished species specific weight at length power function relationships. Both the demersal benthivore and demersal piscivore fish guilds display trends that show a marked increase in biomass towards the end of the time series. However, our lack of understanding of foodweb structure in the North Sea prevents us from concluding that these recent marked increases in the biomass of both guilds is necessarily good. We cannot therefore determine boundary levels as other than the historical range. In both instances, the most recent guild biomass estimates represent the highest level observed throughout the Q1 IBTS time series. In the case of demersal benthivorous fish, biomass in 2011 was over twice that recorded at the start of the time series. Given our lack of understanding as to what exactly GES for each guild would look like, the fact that both time series are moving out of the bounds of our empirical experience is perhaps worrying. This suggest the need for additional research to determine whether such biomass levels in each guild represent the foodweb moving towards a more desirable state, or conversely whether such trends might indicate a departure away from GES.

16 12 ICES WKGMSFDD4-II REPORT 2015 Figure 3.6. Biomass of piscivore and benthivore fish trophic guilds (taking ontogenetic development in the diet into account,), derived from Q1 IBTS estimates of species biomass density at length estimates raised to take account of species and size related catchability in the GOV trawl (solid line shows total). Dashed horizontal lines show the boundaries of GES of an assessment made 6 years before the most recent data point Function indicator example: Eastern Baltic cod weight-at-age The mean body size (weight at age, length at age and condition) of the Eastern Baltic cod has declined since the early 1990s (ICES 2014a). The reasons for the decline are not fully understood, but it might be a consequence of the combination of several factors such as density dependent effects, food availability, anoxic areas and parasites. Recent changes in cod mean weight in stock are presented in Figure 3.7. A strong statistical relationship between cod body size and hypoxic areas can be explained by different mechanisms (ICES 2014a), such as increased density dependence (same or higher amount of fish concentrated in a more restricted area, resulting in increasing competition), decrease in benthic food, reallocation of cod into the pelagic water mass, and direct physiological effects. Density dependence and food limitation were also significantly correlated to cod mean body size, although these correlations were much weaker than the one with hypoxic areas. Due to increasing numbers of grey seals, an increasing cod parasite infestation (in prevalence and intensity) is recently observed (ICES 2014a). Seals act as the final host for parasites, such as cod worm (Pseudoterranova decipiens) and liver worm (Contracaecum osculatum), with crustaceans/polychaetes and cod constituting first and second transport hosts, respectively.

17 ICES WKGMSFDD4-II REPORT WEST: Mean weight in Stock (kg) Age 2 Age 3 Age 4 Age 5 Age 6 Age 7 Age Figure 3.7 COD in SD WEST: Mean weight in stock (kg). Based on ICES WGBFAS 2014b report (Table ). Dashed horizontal lines show the potential boundaries of GES of an assessment of age 8+ made 6 years before the most recent data point Data are available and good knowledge exists about the indicator and its relation to other ecosystem characteristics Where data exists and undesirable effects on other ecosystem components has been observed or is predicted based on solid knowledge of the direct relationship between the indicator and other ecosystem aspects, the range of indicator values associated with no (substantial) undesirable effects on other components should be used to set limits that denote the desirable range of indicator values. If the indicator is not within limits, means action should be triggered. This action would involve further investigation to determine the cause of the deviation from the desired range, as well as the effect on other ecosystem components. The presence of (substantial) undesirable effects on ecosystem components can be determined based on a variety of measures. Ideally, the evaluation includes both expert judgment, analyses of historic data and investigation of model results Structure indicator example: Baltic Sea zooplankton community The dominating role in the Central Baltic Sea zooplankton community is played by the copepod species Acartia spp., Temora longicornis and Pseudocalanus acuspes dominate in the Central Baltic Sea zooplankton community (Figure 3.8). During spring, a clear shift has occurred from a dominance of P. acuspes until the end of the 1980s to Acartia spp. and T. longicornis afterwards. This shift in taxonomic composition might be explained by decreased salinity and high sprat predation pressure (P. acuspes) and increased temperature (Acartia spp., T. longicornis) (Möllmann and Köster, 2002; Möllmann et al., 2003). Despite much higher variability during summer, the shift can still visible (ICES 2007). Replacement of big copepods (i.e. Pseudocalanus) by smaller species had pronounced consequences for feeding conditions of larval cod and adult sprat and herring. This shift in species composition is considered to be a reason for a decrease in the growth

18 14 ICES WKGMSFDD4-II REPORT 2015 rate of herring since the early 1980s and of sprat since the early 1990s (Möllmann et al., 2000). Figure 3.8. Changes in zooplankton species composition in the Central Baltic Sea: Anomalies of Pseudocalanus acuspes in spring and summer (ICES 2007). Dashed horizontal lines show the possible limit of copepod biomass, below which six there are undesirable effects on the growth of dependent species (clupeids) Structure indicator example: LFI in the North Sea The Large Fish Indicator (LFI) is an example of a situation where the historical indicator trajectory is considered to be at least partly outside the limits, and the appropriate limit assumed to be above the range of the historical data. The indicator is defined as the proportion by biomass of large fish in demersal trawl surveys, where fish are considered as large if they exceed a length threshold, for example, 40 cm (Greenstreet et al., 2011). The pressure state relation for the LFI is well understood (Fung et al., 2013) and historical time series can be reproduced by models (Figure 3.9). The indicator is sensitive and specific to fishing pressure (Houle et al., 2012) that truncates the upper end of the fish size spectrum (Sheldon et al., 1972). Exhibiting recovery times on the order of magnitude of decades (Greenstreet 2011, Fung et al., 2012, 2013), the indicator quantifies a characteristic of marine foodwebs that are slow to recover and often under intense pressure. In many EU waters, indicator values are considered outside appropriate limits over most of the documented time intervals (Greenstreet et al., 2011, Shephard et al., 2011, Modica et al., 2014); the observed low indicator values have been found to be inconsistent with sustainable fishing (Greenstreet et al., 2011), fast and secure indicator recovery (Shephard et al., 2013), and conservation of biodiversity (Fung et al., 2013). Proposed indicator limits are therefore larger than the values observed. Reference levels for the LFI have been set using historic time series of the LFI and combined fishing pressure (Greenstreet et al., 2011). Fish stocks were thought to be exploited at a sustainable rate in the early 1980s, so in a process echoing the precautionary approach to fish stock management, this was considered the reference period for the LFI, suggesting a value of 0.3 as appropriate.

19 ICES WKGMSFDD4-II REPORT Figure 3.9 Variation in the redefined proportion of LFI calculated for both the Q1 IBTS and the SAGFS datasets. The current LFI value is indicated, as is the EcoQO level for the indicator of 0.3 for the North Sea demersal fish community (Greenstreet et al., 2011) Defining limits where ecosystem trends or changes occur Foodweb indicators are often influenced by a combination of several factors including climatic conditions, changes in other ecosystem components and anthropogenic pressures. To ensure that the limits continue to be relevant, the limits should relate to current conditions of the ecosystem. Hence, if the foodweb has exhibited pronounced regime shifts, the limit level should reflect the current regime rather than historic regimes. This conclusion also applies to the case where the regime shift is caused by excessive human pressure at an earlier time, for example, excessive removal of top predators have led to an increase in forage fish and a subsequent decrease of zooplankton (trophic cascade), but the system appears stable in the present regime. In this case, the current limit level for zooplankton biomass should reflect the current regime rather

20 16 ICES WKGMSFDD4-II REPORT 2015 than a regime where predators have returned. If top predators are returning to the system, limit levels should be updated accordingly. When ecosystem trends are more gradual, such as is often the case with the effect of climate change on foodwebs, a gradual change in the limit level should be implemented. Another example is the introduction of alien species with apparent disruption of foodweb structure and/or function. Examples of this include Pacific oysters in the Wadden Sea, American jackknife clam along the southern North Sea coast, and Red Sea fish entering the eastern Mediterranean. All of these introductions are irreversible and have a likely effect on the foodweb and may limit the use of historical data for determination of assessment limits. Concluding that limit levels should reflect current conditions implies that limit levels will need to be reviewed regularly and updated where necessary. This applies even in cases where there is a known effect of, for example, temperature, as it is necessary to review and update the relationship regularly to ensure that it is still present Function indicator example: Kittiwake breeding success in the North Sea An example of an indicator of foodweb function where the limit depends on environmental conditions is kittiwake breeding success (Cook et al., 2014). This indicator is constructed from data on annual mean breeding success (number of chicks fledged per pair) of black legged kittiwakes (Rissa tridactyla) at colonies on the UK North Sea coast. The indicator is based on previous work by Frederiksen et al., (2004, 2007), which found kittiwake breeding success at seven colonies along the North Sea coast of the UK to be significantly negatively correlated with local mean sea surface temperature (SST) two winters previously (SST 1 ). The relationship is thought to be related to larval sandeel survival and the subsequent availability of 1 year class (1 group) sandeels for kittiwakes to rear their chicks on. The premise of the indicator is that any statistically significant negative deviation from the relationship of annual breeding success and SST 1, may indicate a detrimental anthropogenic impact (Figure 3.10). A statistically significant negative relationship between annual breeding success and SST 1 was found at 29 colonies (Figure 3.11). Cook et al., (2014) also found a significant effect on kittiwake breeding success from a fishing pressure factor denoted by the interaction between the annual North Sea stock size of lesser sandeels and the proportion of the stock that was harvested Determining if the indicator is within limits or not There are three types of uncertainty in determining the location of current state of the indicators relative to their limit or limits: uncertainty about the correct limit level, uncertainty about the precision of the indicator estimated from data and uncertainty about the effect of pressures on the indicator and hence about the potential effect of management measures. Ideally, the indicator relative to the limit is determined from properly determined limit levels, an accurate estimate of the indicator and a strong and well known relationship between management, pressure and indicator. In this case, pressures should be managed in accordance with the defined acceptable risk of falling outside limits.

21 ICES WKGMSFDD4-II REPORT Figure 3.10: Kittiwake breeding success indicator at Isle of May and Sumburgh Head colonies between 1986 and 2010 (Cook et al., 2014). Solid line shows level of breeding success expected at each colony given the Sea Surface Temperature in February and March of the previous year. The lower 95% confidence limit of the relationship, shown by the broken line, is the limit of the indicator, so points below the broken line are considered outside the limit. Figure 3.11: Change in kittiwake breeding success indicator in relation to the presence of the Wee Bankie sandeel fishery (From Cook et al., 2014). Following Frederiksen et al., (2004) the sandeel fishery was assessed as present from Each pie chart represents a kittiwake breeding colony, green indicates that breeding success reflected the underlying environmental conditions in the target year, red indicates that breeding success was lower than expected given the underlying environmental conditions and black/white indicates colony was not recorded in the target year. Darker segments indicate the proportion of the preceding years in which the target level of breeding success was not achieved in the pre fishery ( ), operational fishery ( ) and closed fishery ( ) periods. In the case of foodwebs, one or more of these uncertainties are often considerable. Despite this, the advice format should remain the same: the probability that the indicator is not within limits should be derived from the observed variance of the estimated indicator. If the estimated indicator is being measured with a poor precision, there is a strong likelihood that the indicator will be recorded as outside the limits. It is important that this does not lead to revised (wider) limits. Instead, the frequent occurrence of indicators outside limits, or a high probability of being outside limits, should provide

22 18 ICES WKGMSFDD4-II REPORT 2015 the incentive to improve the precision of the indicator. Where the link between management, pressure and state of the indicator is poorly understood, assessments of such indicators, should include explicit advice on a) the probability of the indicator being outside the agreed limits, b) the quality and reliability of the limits, and c) the strength of the link with pressures and management. This information can be used when aggregating assessments of different in order to determine if GES has been achieved at the criterion or Descriptor level (section 3.4). 3.4 Technical guidance on aggregating indicator assessments for determining if GES has been achieved under D The specific nature of the foodweb indicator When deciding on methods for aggregation or combination of different indicators within criterion and across the two criteria structure and function, the specific nature of the foodweb descriptor must be considered. Most foodweb indicators do not have clear pressure state relationships; there are many indirect impacts and close linkages between different foodweb components exist. For instance, rebuilding predator populations may cause cascading effects through the ecosystem or cyclic behaviour, in which case not even an undisturbed and perfectly monitored ecosystem will necessarily show all indicators within limits at a specific point in time. In addition, there are indicators that are used for surveillance purposes which induce further investigation. This aspect means that the application of simple aggregation or averaging rules (e.g. one out all out, % agreed targets) are not suitable for foodweb criteria. Assessments of different indicators should be aggregated using a decision tree that takes into account the varying qualities of each indicator, in terms of their pressure state relationships, levels of uncertainty in their estimation, relationships with other foodweb indictors and whether or not they state indicators or surveillance indicators. The aggregation method for foodweb indicator assessments will depend on the suite of indicators being assessed. Not all indicators will be considered equally important. Not all indicators will be assessed with equal confidence, due to differences in precision and/or accuracy of indicator values and the degree to which the indicator s limits relate to changes between desirable and undesirable states Consideration of pressure-state relationships Foodweb indicators can show multiple pressure state relationships that may be indirect and difficult to observe. When there are known linkages between indicator state and pressures, these should be listed, even if only qualitative. State indicators that have clear links to pressures, would require associated pressure indicators to also be within desirable limits. There will likely be scenarios where pressure indicators are within limits, while state indicators are not. These mismatches may be due to lag periods (e.g. slow recovery times), other forcing, or different requirements for determining status. Relevant pressures should be listed with estimated response time required for status to reach GES to incorporate time lags Surveillance indicatorsvs.state indicators As defined in section 3.1, foodweb state indicators are constructed from attributes that can either be qualitatively described or quantitatively assessed as desirable or undesirable, where a desirable state will contribute to achieving GES at the Criterion and Descriptor level and an undesirable state will detract from GES. The distinction between desirable and undesirable is not possible for surveillance indicators. It is still

23 ICES WKGMSFDD4-II REPORT possible to set limits for surveillance indicators (see section 3.3.), but when an indicator is not within limits, it is unclear what it means for the foodweb. Aggregation of indicator assessments with respect to limits needs to consider how to combine assessments of both surveillance and state indicators. One option is to assess and report the assessment results of the two types of indicators separately. The outcome of the assessment on surveillance indicators will inform on the required response in relation to further investigation and/or monitoring. The outcome of the assessment of state indicators should provide guidance for required management actions. A second option is to apply different weightings to the two types of indicators in a combined assessment at the criterion level Aggregation of indicator assessments Some form of aggregation of individual indicator assessment results is necessary to determine whether foodwebs are at GES at the criterion level and/or to measure progress towards GES. This summary of GES should not replace the reporting of the outcome of individual indicator assessment results towards GES to ensure all the information is available to determine adequate management actions. Prior to combining foodweb indicator assessments, indicators must be assigned to criteria and a decision needs to be made on whether to weight indicator assessments and how to do so. For example, indicator assessments could be weighted according to the precision of the indicators estimation and/or the perceived importance of the indicator the assessment of foodweb structure or function. In many cases, spatial aggregation of indicators is required, for example, where different time series are used to cover one assessment area. The assessment result of each indicator can be scored as either 0 or 1 depending on whether the current value for the indicator is outside or within limits. As stated above, weighting procedures for D4 indicators should not be one out all out (OOAO). Borja et al., (2014) reviewed methods for weighting, as well as considering the pros and cons of different methods. Borja et al., (2014) is a useful to guide for choosing the most appropriate aggregation method. Various possible aggregation scenarios can be tried and compared to find the most preferable approach, given the properties of the indicators being assessed. For some indicators that receive a very high weighting (e.g. they are tightly linked to management) it may be appropriate to provoke a oneout all out response if such indicators are not within limits. The historical development of the aggregated assessment results should be evaluated. It is important not to make the assumption that indicators behave independently; many foodweb indicators are highly correlated. Assumption of independence of indicators can be tested by quantifying the covariance and modelling indicator behaviour. Projections of possible indicator behaviour should incorporate stochasticity, for example by using estimated indicator covariance Aggregation across criteria Although methods to aggregate indicators within the D4 criteria might differ, there was broad agreement that both structure and function need to be at GES for overall GES to be achieved Response to reviewers comments in relation to GES aggregation Comments 8, 14 and 29 relate to the guidance in aggregation methods at criterion and descriptor level. Comments 8 and 29 are addressed in the paragraphs on surveillancevs.non surveillance indicators and construction of composite indicators, while