Response curves for ecological class boundary definition–the Gulf of Riga case study

HELSINKI COMMISSION HELCOM EUTRO-PRO 3/2006Workshop on eutrophication indicators and the Core Group Meeting Third Meeting Tisvildeleje, Denmark, 12-14 September 2006

Note by Secretariat: FOR REASONS OF ECONOMY, THE DELEGATES ARE KINDLY REQUESTED TO BRING THEIR OWN COPIES OF THE DOCUMENTS TO THE MEETING of total 21

Agenda Item 3 HELCOM Eutrophication Indicators

Document code: 3/1

Date: 05.09.2006

Submitted by: BSRP

RESPONSE CURVES FOR ECOLOGICAL CLASS BOUNDARY DEFINITION - THE GULF OF RIGA CASE STUDY The Meeting is invited to consider the attached discussion paper.

Discussion paper: Response curves for ecological class boundary definition – the Gulf of Riga case study HELCOM EUTRO-PRO 3, Denmark, 12-14 September 2006

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Response curves for ecological class boundary definition – the Gulf of Riga case study Juris Aigars1, Bärbel Müller-Karulis1, Georg Martin2, Vadims Jermakovs1 1Latvian Institute of Aquatic Ecology, Daugavgrivas 8, LV-1048 Riga, Latvia 2Estonian Marine Institute, Viljandi Rd. 18b, EE-11216 Tallinn, Estonia This discussion paper summarizes the results of a case study funded by the Baltic Sea Regional Project (BSRP) to test different approaches in setting ecological class boundaries. Substantial parts of this discussion paper have been submitted as a manuscript to the Journal of Marine Systems. Key message: • Response curves derive class boundaries from observed changes in biological

ecosystem components (quality elements) along environmental pressure gradients. They reduce, but do not eliminate the need for expert judgment in ecological classification.

• Many response curves lack clear breakpoints, which would provide obvious class boundaries. In these cases, expert judgment has to be applied to divide the response gradient into classes.

• In the Gulf of Riga, good-moderate status boundaries based on response curves almost always permitted a larger deviation from reference conditions than 50 %

• We suggest basing ecological class boundaries on response curves, supplemented by expert judgment of boundaries where response curves lack clear breakpoints.

Approaches to ecological classification Status assessment in European marine waters is currently evolving from a descriptive approach focused on time trends in ecological indicators, to a classification system. Basis for classification are status classes defined for ecological quality elements, which cover the gradient from “good” to “bad” ecological quality. Classification results are easily understandable to water quality mangers and the general public and provide both positive and negative feedback to policy makers. Failure to meet a – politically defined – water quality target is then a signal, or even a legal requirement, to improve the ecological status of a water body. Ecological status classification in European marine waters has started with the design of the OSPAR Common Procedure for the Identification of the Eutrophication Status of the Maritime Area in 1997, amended in 2005 (OSPAR 1997-11 and 2005-3). The amended OSPAR Comprehensive Procedure allows a 50 % deviation from area specific reference levels, which can be replaced by more stringent requirements, if scientifically justified. This simplified mathematical boundary setting protocol has been applied to assess eutrophication in Danish marine waters (Ærtebjerg et al. 2003) and in several open and coastal areas of the Baltic Sea (HELCOM 2006).


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Instead of allowing a fixed deviation from reference conditions, the EU Water Framework Directive (WFD) provides a normative definition of its class boundaries, which are to be expressed as an Ecological Quality Ratio, ranging from 1 (reference value) to 0 (bad ecological status) with equal intervals corresponding to high, good, moderate, poor and bad status. These classes, as qualitatively described in the WFD, correspond to undisturbed, slightly disturbed, and moderately disturbed conditions, but the WFD leaves it to implementing scientists to translate these definitions into area-specific, quantitative class boundaries. Subsequently, the Common Implementation Strategy guidance group (CIS ECOSTAT, Anon. 2005) proposed a template for the development of a boundary setting protocol, based on the concept of response curves. Response curves document the changes in a biological quality element with respect to its supporting physico-chemical quality elements. Response curves show magnitude and quality of ecosystem change, while application of simplified mathematical class boundaries still requires proof that the defined deviations represent a) significant change of ecosystem quality and b) do not lead to unacceptable ecosystem deterioration.

Case study setup We aim to derive a consistent set of ecological class boundaries for the marine ecosystem components primarily affected by eutrophication. Because of its legal significance for European coastal waters, components and indicators discussed are consistent with the mandatory quality elements of the WFD (nutrients, phytoplankton, phytobenthos, and macrozoobenthos). Further, our case study focuses on the threshold between acceptable status and conditions requiring remedial actions, corresponding to the good – moderate status boundary defined by the WFD for coastal and transitional waters. Because with the perspective introduction of the EU Marine Strategy classification approaches are currently also discussed for open waters, we have included the open waters of the Gulf of Riga, using the same qualitative definitions for status classes as provided by the WFD. Construction of response curves, which describe the reaction of the biological quality elements to changing supporting physico-chemical parameters, relies on the identification of relevant supporting elements. We used a conceptual model of eutrophication in the Gulf of Riga to link primary and secondary ecosystem effects with their potential driving elements as guidance for the selection of relevant pressures. Reference conditions were included into the response curves, when the status of their supporting physico-chemical parameters was known. Reference conditions, which are thought to characterize the ecological state of a water body under no or minor anthropogenic pressure, are the sole anchor point for class boundaries based on a simplified mathematical boundary setting protocol. There are several different methods to establish reference conditions in aquatic systems described in the legislation for the WFD and discussed in number of scientific articles (e.g. Nielsen et al 2003, Andersen et al 2004). Reference conditions can be established based on the ecological characteristics of pristine regions or using historical data and model results. However, it is commonly agreed that pristine areas are lacking in the Baltic Sea due to high eutrophication and the interconnection of all Baltic subbasins. Therefore all reference conditions used in this work are based on either historical values or model results.


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Site description

Figure 1. Map of the areas used in test case study: I – transitional waters in the Southern Gulf of Riga, II – central Gulf of Riga, III – coastal waters in the Northern Gulf of Riga. Circles represent hydrobiological and hydrochemical monitoring stations, squares represent phytobenthos stations used in this study. The Gulf of Riga is a relatively shallow water body, maximum and mean depth 62 and 20 m, respectively (Carman et al 1996), separated from the Baltic proper by the islands of the Moonsound archipelago. Water exchange is limited to Muhu Sound (sill depth 5 m) and Irbe Strait (sill depth 25 m, mean width 30 km). Combined with high riverine input (18-56 km3 per


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year, HELCOM 2002) the Gulf of Riga with a volume of 424 km3 is highly influenced by freshwater runoff. Consequently, the Gulf of Riga is considered to be one of most eutrophic regions of Baltic Sea (Wassmann and Andrushaitis 1993, Wasmund et al 2001). All marine water types defined by the WFD and in perspective by the EU Marine Strategy were included in the study area (see Fig. 1), consisting of transitional waters in the Southern Gulf of Riga (area I), the central open Gulf (area II), and the coastal waters of the Northern Gulf of Riga (area III). Three major rivers, Daugava, Gauja and Lielupe, enter the Gulf of Riga in area I, and their combined river plumes form a transitional water body in the Southern Gulf, which is up to 30 m deep with a pronounced salinity gradient during most of the year. Occurrence of freshwater phytoplankton species is a common feature in this area. Spatial and temporal variability of nutrient concentrations and biological elements is large. Area II represents the central part of the Gulf of Riga with water depths between 42 - 50 m. In this area the fluctuations in nutrient concentrations are small in comparison to coastal and transitional water bodies, although the spatial variability of biological quality elements can be high. Area III represents the coastal areas of the Gulf of Riga, which are furthest from the major river mouths and where direct runoff from land should have the lowest impact on the biological communities. Hard and mixed bottoms with rich benthic vegetation dominate this area. Fluctuations of nutrients and biological quality elements in this area are less pronounced than in the transitional waters but larger than in the central Gulf.

Reference conditions Ecological studies of the Gulf of Riga date back to beginning of 20th century (Ojaveer and Andrushaitis 2004). Early studies were mostly descriptive and performed using techniques which are not comparable with modern methods. Therefore, we used only those historical studies, which are reliable according to modern standards, e.g. water chemistry studies performed during 1957-1962 (Aunins 1965a, 1965b) and water transparency measurements during 1949-1960 (Zaharchenko 1962). Subsequently we evaluated these historical values against available data from regular observations started in 1973 and model calculations by Schernewski and Neumann (2005). The reference conditions for the maximum depth distribution of macroalgae in the northern part of Gulf of Riga were developed using combination of different methods including analyses of historical records and expert judgment (Martin 1999, Krause-Jensen et al. 2004, Lips 2005). The final estimated reference conditions for all indicators considered here are summarized in table 1.

Response curve components – conceptual model Our conceptual model of eutrophication in the Gulf of Riga assumes that increasing supply of the growth limiting nutrient enhances phytoplankton production and therefore enlarges also the standing stock of phytoplankton biomass. Secondary effects of eutrophication impact a) macrozoobenthos communities, because of higher sedimentation, primarily of spring


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phytoplankton, and b) phytobenthos, as larger phytoplankton concentration, especially during summer, reduce water transparency and exacerbate phytobenthos light limitation. Similar primary and secondary eutrophication effects have been described in several Baltic coastal ecosystems (e.g. Rönnberg 2001, Bernes 1988, Bonsdorff et al. 1997).

Spring In the central Gulf, distant from freshwater inputs, the phytoplankton spring bloom terminates, when the growth limiting nutrient is depleted above the forming thermocline. Winter nutrient concentrations in the surface layer (0 – 10 m), which are typically measured in January/February shortly before the start of the growth season, were therefore assumed to determine the intensity of the spring bloom. In the 30 year period covered by monitoring observations, winter nutrient ratios have mostly been close to the Redfield ratio, with periods of potential P-limitation of the spring bloom at the end of the 1980ies, which were replaced by potential N-limitation in the 1990ies (Yurkovskis, 2004). Since, as a consequence of eutrophication, the internal release of phosphorus from the sediments formed an excess winter phosphorus pool in the 1990ies and the periods of potential spring P-limitation were relatively brief, DIN was considered the major limiting nutrient for the phytoplankton spring bloom in the central Gulf of Riga. Because the observation frequency is insufficient to assess the magnitude of the spring bloom directly, winter NO23 concentrations were used as a proxy for the causes of secondary eutrophication effects observed in the zoobenthos communities in the central Gulf. The transitional waters in the Southern Gulf of Riga on the other hand are P limited in spring due to the high nitrogen surplus in the riverine inputs (Maestrini et al., 1997, Maestrini et al., 1999, Põder et al., 1999, Tamminen and Seppälä, 1999) and we therefore connected secondary effects of eutrophication on zoobenthos in the transitional waters to the riverine phosphorus input.

Summer Contrary to the Baltic Proper, where a phosphate surplus is often observed after the spring bloom, in the central Golf of Riga both DIN and DIP concentrations generally are depleted to analytical zero. Bioassays and modeling studies have described the summer phytoplankton communities in the central part of the Gulf of Riga as N limited (Maestrini et al., 1997, Maestrini et al., 1999, Savchuk, 2002) or N and P co-limited (Seppälä et al., 1999). Also entrainment of relatively DIN rich bottom water plays a role in regulating summer phytoplankton growth (Põder et al., 1999, Tamminen and Seppälä, 1999). However, since in large parts of the Baltic Sea nitrogen limitation in summer can ultimately be overcome by cyanobacteria growth (e.g. Sellner 1997, Wasmund 1997), the entrainment of phosphate from the bottom waters was thought to ultimately limit summer phytoplankton growth in the central Gulf of Riga. Because summer phosphate concentrations right below the thermocline (30 – 35 m depth) correlate well with winter phosphate concentrations (R2 = 0.54, α < 0.001), winter phosphate concentrations were used as a proxy to construct response curves for summer chlorophyll a and for the phytoplankton proxy Secchi depth. The northern Gulf on the other hand forms the transition area to the N-limited Baltic Proper. Studies on summer phytoplankton communities in the Gulf of Riga have therefore found a gradient from N-limitation in the northern region to N/P co-limitation in the central area


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(Seppälä et al. 1999). However, for the northern Gulf of Riga we lacked consistent information on nutrient concentrations in the water column. We therefore tested the response of phytobenthos to changes in the riverine nutrient load, but we were not able to follow the causal chain – increase in limiting nutrient concentration and phytoplankton growth – that drive secondary effects of eutrophication on phytobenthos.

Response curves Data coverage was sufficient to construct response curves for summer chlorophyll a, transparency, macrozoobenthos and phytobenthos. Because the biological parameters that cause secondary eutrophication effects are mostly difficult to measure precisely, all response curves were constructed based on physico-chemical parameters. A response curve for winter nutrient concentrations in relation to the river loads was included, because winter nutrient concentrations serve both as driver for changes in the conditions of biological quality elements, as well as eutrophication status indicator.

Winter nutrient concentrations The Gulf of Riga underwent large changes in external nutrient loads and winter nutrient concentrations during the 30-year period covered by monitoring observations. Riverine loads of nitrogen and phosphorus increased up to 1990, followed by declining DIN concentrations, while the winter DIP pool remained on a high level (Fig. 2). Correlation between load in the preceding year and the winter nutrient pool was significant for DIN (R2 = 0.34, α = 0.05), but not for DIP. Long residence time and consequent internal loading from the bottom sediments cause the slow response of the phosphorus pool to the decrease in load (Savchuk, 2002), whereas the winter DIN pool is largely determined by the riverine load (Yurkovskis, 2004).

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Figure 2. Time series of winter nutrient concentrations and the riverine nutrient loads to the Gulf of Riga in the preceding year.

Macrozoobenthos According to our conceptual model of eutrophication in the Gulf of Riga, macrozoobenthos communities are most likely affected by the magnitude of the sedimenting spring bloom, which is charactterized by the proxies riverine DIP load and winter NO23 concentrations in the Southern and central Gulf, respectively. State of the macrozoobenthos communities at 10 – 20 m depth was expressed by a biotic

coefficient (BC), which was calculated as a weighted average ∑∑ ⋅

=

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abundance in 5 species groups, weighted with sensitivity factors that describe sensitivity to eutrophication. Numerical values for the biotic coefficient range from 0 (unpolluted) to 6 (heavily polluted), and a value of 7 is assigned when the sediment is azoic. This method has originally been developed in Spain (Borja et al. 2000, Borja et al. 2003), based on earlier investigations, e.g. Glemarec and Hily (1981).

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Figure 3. Biotic Coefficient for macrozoobenthic communities at 10 – 20 m depth in the transitional waters of the Gulf of Riga (box and whiskers represent median, quartiles and data range, respectively) compared to riverine DIP input in the previous year (rhombs and stippled line) The biotic coefficient increased between 1978 and 1987, and decreased from 1991 to present (Fig. 3). This overall pattern agrees very well with the temporal trend of the riverine DIP load in the year prior to macrozoobenthos observations. The rapid decrease of the Biotic Coefficient around 1989 was caused by a drastic restructuring of the benthic communities, with a breakdown of both pollution sensitive species Macoma balthica and Monoporeia affinis populations (Gaumiga and Lagzdinsh, 1995). Simultaneously, invasion of the more pollution tolerant non-indigenous species Marenzelleria neglecta was observed (Lagzdins and Pallo 1994, Cederwall et al. 1999). Recovery of the Macoma and Monoporeia populations drove the improvement of the Biotic Coefficient in the 1990ies. The restructuring of the benthic communities in the Southern Gulf was considered a significant ecosystem disturbance and we associated the large BC values (median BC ≈ 3.5) in the restructuring phase with the moderate – poor status boundary. In the central part of the Gulf of Riga at 40 – 50 m water depth benthic communities are naturally species poor. Therefore, instead of the BC, which requires high species diversity, we used macrozoobenthos abundance as an indicator of the ecological state of benthic communities. Winter NO23 concentrations, most likely the limiting nutrient of the spring bloom, were used as a proxy for the sedimenting organic matter. Although limited, the data demonstrates a sudden decline in macrozoobenthos abundance when a threshold winter NO23 concentration was exceeded (Fig. 4). We interpreted this threshold (winter NO23 ≈ 17 mmol


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m-3, see Fig. 4) as the moderate-poor boundary, because benthic communities were severely impacted, but the sediment was not yet azoic. The system started to restore only several years after the concentration of winter nitrate + nitrite had significantly dropped, but the sensitiveness of macrozoobenthos abundance to ecosystem degradation and recovery was too low to define also a threshold for the good-moderate boundary of winter NO23 concentration, which was instead derived by dividing the range between reference value and moderate – poor boundary into equal intervals.

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Figure 4. Response of macrozoobenthos abundance of the central part of the Gulf of Riga to increase and subsequent decrease of winter NO23 concentration. Squares represent the degradation pathway from 1983 to 1991, circles show the effect of recovery from 1992 to 2004.

Summer chlorophyll a In the open part of the Gulf of Riga, summer (June – August) chlorophyll a differed significantly between the individual months, with concentrations decreasing in the order June > July > August (ANOVA, p < 0.05). Therefore response curves for monthly average concentrations were constructed taking into account the systematic differences between months. Best fit was achieved by a general linear model using variable slopes to describe the response of log chlorophyll a to winter DIP, depending on month i:

bWinterDIPaChla ii +⋅=log p<0.05, R2adj = 0.25


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However, scatter around the response curves was large, and the model explained only 25 % of the data variance. At the same time, standard deviation of summer chlorophyll a values tended to increase strongly with growing winter DIP concentrations (Fig. 5), with most pronounced increase after a DIP concentration of 1 mmol m-3 was exceeded. High variability of chlorophyll a concentrations suggests an increasing tendency of summer algal blooms, which are temporally and spatially limited events. Pronounced summer blooms should not occur at good ecological state of the open Gulf of Riga and therefore the clear change in standard deviation of summer chlorophyll a was thought to mark the moderate-poor status boundary. Limited data corresponding to low winter phosphorus and low summer chlorophyll a concentrations prevented construction of a good-moderate threshold value for this response curve.

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Figure 5. Response of summer chlorophyll a variability to winter DIP concentration in the central Gulf of Riga.

Summer transparency Summer transparency (Secchi depth) was treated as a proxy for phytoplankton concentrations in the open Gulf of Riga and a response curve was constructed with respect to the winter DIP pool, which is most likely ultimately growth limiting (Fig. 6). The resulting response curve was linear, without obvious break points to anchor class boundary definitions. However, Secchi depth is a robust indicator with a simple measurement protocol and well defined reference conditions. Using expert judgment to set the poor – bad class boundary at a transparency of 2 m, linear division of the intermediate transparency range suggests class boundaries that correspond well to the good – moderate boundary derived from the variability of summer chlorophyll a concentrations, as both approaches estimate the moderate – poor boundary at similar winter DIP concentrations.


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R2 = 0.42

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Figure 6. Response of summer transparency in the central Gulf of Riga to winter DIP concentration. Triangle represents reference conditions. Since, in addition to the high uncertainty (correlation coefficient R2 = 0.25), the exponential chlorophyll a – winter DIP response curve provided no clear breakpoints, we used the more robust (R2 = 0.36) Secchi depth – winter DIP relationship instead to translate the transparency quality classes into their corresponding chlorophyll a boundary concentrations (Fig. 7). The same relationships were also used to derive chlorophyll a and winter DIP boundary concentrations for the Southern Gulf, assuming that the corresponding good – moderate boundary for Secchi depth is by one meter lower in the transitional waters.


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y = 12.26e-0.39x

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Figure 7. Definition of ecological quality status boundaries for summer chlorophyll a from correlation with water transparency in the central Gulf of Riga. Triangle represents reference conditions.

Phytobenthos Macroalage and aquatic angiosperms are biological quality elements to be used for classification of ecological state of coastal waters according to WFD. Although normative definitions require the assessment of taxonomical structure of the communities as well as macroalagal cover and abundance of angiosperms as quantitative parameters, in many cases depth distribution of total aquatic vegetation or certain species is proven to be the most suitable indicator responding to the human impact. Depth distribution of phytobenthos is largely determined by light (e.g. Duarte 1991, Nielsen et al. 2002a) and therefore also by parameters affecting the light climate. Increased nutrient concentrations stimulate the production of phytoplankton and epiphytes, which reduce water clarity and thereby reduce the depth penetration of phytobenthos (Nielsen et al. 2002a,b). Depth distribution of phytobenthos should therefore respond predictably to eutrophication.


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R2 = 0.64

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Figure 8. Relationship between depth limit of phytobenthos in the Northern part of Gulf of Riga and the total load of phosphorus during the springtime of the same year (sum of loads for period January – May). The lack of significant correlation in the Northern Gulf of Riga between the maximum depth limit of macroalgae and parameters as Secchi depth and concentration of chlorophyll a, which characterize the changes in light climate directly, can be explained by very low frequency of pelagic measurements in the area. Instead, in the available dataset the maximum depth distribution of macroalgae proved to respond directly to nutrient addition to the Gulf of Riga ecosystem. High correlation (R2 = 0.64) between spring (January to May) load of total phosphorus and late summer values of the maximum depth distribution of macroalgae support a linear response curve (Fig. 8). However, as the response curve lacks break points, class boundaries were again constructed by using expert judgment to set the poor – bad status boundary and linear division of the remaining interval.

Response curves as a tool to derive class boundaries Construction of response curves requires more data than the application of a simplified mathematical boundary setting protocol, because for the construction of response curves a gradient in ecological status has to be covered. Even in the case of the Gulf of Riga, a well monitored water body with regular observations starting in the 1970ies, data was not always sufficient. In the transitional waters of the Southern Gulf of Riga and in the Northern Gulf chlorophyll a and nutrient observations for the derivation of response curves were lacking. Even for parameters with well documented response to environmental pressures, the definition of class boundaries from response curves still might require expert judgment. Only three of the investigated parameters – macrozoobenthos abundance in the central Gulf, the biotic coefficient for macrozoobenthic communities in the transitional waters, and the variability of summer chlorophyll a concentrations in the open Gulf showed breakpoints in their response to environmental pressures. On the other hand, summer Secchi depth and the


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average summer chlorophyll a concentrations in the open Gulf, as well as the maximum depth of macroalgae in the Northern Gulf, resulted in smooth – either linear or exponential – response curves. Breakpoints in response curves for the Gulf of Riga always corresponded to the moderate – poor or poor – bad class boundaries, while changes in the crucial good – moderate range were linear and did therefore not provide a clear boundary definition. Partially this might be caused by limited data from high and good status within the monitoring time series. But also the choice of indicators for the biological quality elements might have masked ecological changes, with the summative parameters chlorophyll a, zoobenthos abundance and biotic coefficient reacting only gradually to minor ecosystem disturbances. Ecological studies in the Gulf of Finland for example found distinct community changes in phytoplankton assemblages along water quality gradients (Kauppila et al. 2005, Kauppila et al. 1995). Still, the constructed response curves supported a system of consistent class boundaries that minimized the use of expert judgment. Purely linear class divisions had only to be used for robust, reliable parameters with well defined reference conditions like Secchi depth in the Central and phytobenthos depth limit in the Northern Gulf, respectively. In the Central Gulf, response curves can be checked for consistency by their relationship with winter DIP concentrations. For example, the moderate – poor boundary extracted from the linear Secchi depth – DIP response curve corresponds well to the breakpoint in the summer chlorophyll a variability – DIP relationship. Both boundaries are reached at almost equal winter DIP concentrations (1.00 mmol m-3 and 1.05 mmol m-3, respectively, see Figs. 5 and 6). Thus, response curves also provide a tool to check derived class boundaries for internal consistency. On the other hand, allowing a 50 % deviation from reference conditions for all indicators does not necessarily lead to consistent class boundaries. For example, a 50 % deviation in winter DIP results in only negligible increase in summer chlorophyll a, because of the non-linear response of summer chlorophyll a to winter DIP concentrations. If biological class boundaries correspond to different values of inorganic supporting parameters, discrepancies must be well justified to maintain water manager and stakeholder confidence in the classification system, on which costly actions to improve water quality will be based. In the Gulf of Riga, response curves almost always permitted a larger deviation than 50 % from reference conditions, before conditions for good status are exceeded (Table 1). Since we tested the response curve method only with data from the Gulf of Riga, which is relatively shallow and highly influenced by river runoff, there is no sufficient confidence to assume that also in other areas of the Baltic Sea ecological class boundaries derived by response curves will exceed the 50 % deviation. However, since failure to meet good ecological status will require costly management actions, class boundary definitions should take into account the sensitivity of the target water body. The driving force of eutrophication in the Gulf of Riga – high riverine nutrient inputs – can only be partially addressed with the tools of the boundary setting protocol. The weak correlation between riverine DIN inputs and winter NO23 concentrations, as well as the impact of phosphate loads macrozoobenthic communities in the Southern Gulf of Riga suggest a corridor of permissible N and P inputs. On the other hand, the phytoplankton related response curves found together with our conceptual model of eutrophication in the Gulf of Riga

Discussion paper: Response curvesdefinition – the Gulf of Riga case study HELCOM EUTRO-PRO 3, Denmark, 12-14 September 2006

for ecological class boundary

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suggest, that phosphate pools are more important than nitrogen for the summer planktonic ecosystem. Phosphate pools are in turn driven by internal loading (Savchuk 2002), which is the long-term consequence of a positive phosphorus budget for the Gulf of Riga. The resulting time-lag between changes in riverine load and the response of the DIP pool cannot be captured by the response curve concept. Therefore the design of nutrient load reductions should not rely on direct load-response relationships alone.

Conclusions Response curves provide status class definitions with proven ecological significance. Even though data availability in most marine systems will not permit constructing a system of class boundaries based on response curves alone, they reduce the need for expert judgment and “educated guessing” in ecological classification. Where various ecological quality elements react to the same forcing factor, response curves are a tool to check class boundaries for consistency. We therefore suggest a stepwise approach in designing ecological class boundaries that identifies first class boundaries corresponding to breakpoints in ecological response curves, then uses expert judgment to assign boundaries to smooth parts or entirely smooth response curves, and last uses a simplified mathematical boundary setting protocol in cases where only reference conditions are available. Forcing factors underlying the response curves have to be carefully selected based on scientific understanding of processes leading to ecosystem change. Continuous refinement of the classification system should further aim to maximize the proportion of class boundaries based on breakpoints in response curves. The Gulf of Riga case study has clearly proven the importance of basing ecological class boundaries on the specific response of ecological quality elements in marine water bodies to environmental pressures, because class boundaries based on response curves deviated significantly from those relying on a uniform permissible deviation from reference conditions. However, because observations rarely covered the entire status class spectrum between bad and high status and were often rare within the critical good – moderate status range, it was not possible to construct a classification system relying on response curves alone. Furthermore, breakpoints in response curves corresponded only to the moderate – poor or poor – bad class boundaries. Therefore expert judgment was still necessary to identify missing class boundaries. In addition, for some quality elements only reference conditions were known. The classification system in the Gulf of Riga should therefore be successively refined, giving attention to indicators that specifically describe the changes moving from good to moderate ecological conditions.

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Area I (Southern Gulf) Area II (Central Gulf) Area III (Northern Gulf) Reference condition

good – moderate boundary

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Winter NO23 (mmol m-3) 6.5 9.8 4 6 11 Winter Ntot (mmol m-3) Winter DIP (mmol m-3) 0.4 0.6 0.9 0.25 0.38 0.75 Winter Ptot (mmol m-3) 0.25 0.38 Summer chlorophyll a (mg m-3) 2 3 3.8 1.1 1.7 2.6 2.1 3.2 Summer Secchi depth (m) 5 4.3 3 6 4.5 4 5 3.8 Macrozoobenthos Biotic Coefficient 0.5 0.75 2.5

Macrozoobenthos total abundance (ind m-3)

Phytobenthos depth limit (m) 10 7.5 8 Fucus depth limit (m) 6 4.5 Table 1. Reference values, estimated good-moderate boundaries calculated by a simplified mathematical protocol and based on response curves transitional waters (Area I), central part (Area II) and coastal waters (Area III) of the Gulf of Riga. 50 % deviation from reference conditions was used in the simplified boundary setting protocol for all parameters except Secchi depth and phytobenthos indicators, for which 25 % deviation was considered acceptable.

Discussion paper: Response curvesdefinition – the Gulf of Riga case study HELCOM EUTRO-PRO 3, Denmark, 12-14 September 2006


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Response curves for ecological class boundary definition–the Gulf of Riga case study

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