Interim report of the HELCOM CORESET project

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188 indirectly impacted by eutrophication (via changes in primary productivity and phytoplankton composition), whereas direct impacts are expected from climatic changes, predation, introduction of synthetic compounds (at point sources) and invasive species (via predation). Both positive and negative responses can result from changes in thermal regime and salinity. Microphagous mesozooplankton biomass (MMB; mg/m 3 ) – eutrophication indicator; calculated using abundance of microphagous feeders present in mesozooplankton community and their individual weights. Alternatively (or in addition), contribution of microphagous biomass to total mesozooplankton biomass (MMB%) can be used. The indicator refl ects composition of zooplankton community and availability of small-sized phytoplankton and bacterioplankton. The increase in the latter is commonly observed with increasing eutrophication that favors growth of smallest primary producers. This provides a high food supply for grazers capable of effi cient fi ltration of small particles; these grazers are usually also small. MMB and MMB% are also negatively related to food availability for zooplanktivorous fi sh as increased proportion of microphagous species, at least to some extent, implies a reciprocal trend in large copepods that have low effi ciency for fi ltration of small algae. This indicator is directly impacted by eutrophication (through changes in food abundance and size spectra), changes in thermal regime and salinity, introduction of synthetic compounds (at point sources) and invasive species (via predation). Impacted by commercial fi sheries, both indirectly (through changes in pelagic food webs) and directly (via predation by fi sh larvae). Policy relevance for zooplankton indicators MSFD Descriptor 1 (Biodiversity): – Criterion 1.2 Population size; Indicator 1.2.1 Population abundance and/or biomass, as appropriate; – Criterion 1.6 Habitat condition; Indicator 1.6.2. Relative abundance and/or biomass; – Criterion 1.7 Ecosystem structure; Indicator 1.7.1 Composition and relative proportions of ecosystem components (habitats and species) MSFD Descriptor 4 (Food webs): – Criterion 4.3 Abundance/distribution of key trophic groups/species; Indicator 4.3.1 Abundance trends of functionally important selected groups/species; BSAP Ecological Objective: Viable population of species. Methods used to test the indicators See provisional guidelines for a specifi c description of zooplankton indicator testing procedure. The approach is based on examining zooplankton time series in relation to: – the time series on zooplanktivorous fi sh growth (WAA, or condition) and fi sh stocks from relevant ICES subdivision(s) to identify time periods when fi sh growth and fi sh stocks were relatively high (e.g., Rahikainen and Stephenson 2004) when setting GES boundary for CB (CB%); – the existing GES values for water transparency or chlorophyll values (e.g., fi gs. 2.13 and 2.20 in HELCOM, 2009) when setting GES boundary for MMB (MMB%). In the example below, the testing was done using coastal zooplankton data from the northern Baltic proper (ICES subdivision 29); the data are from Swedish national monitoring conducted by Stockholm University collected and analyzed according to HELCOM guidelines (HELCOM 1988). Approach for defi ning GES Data analysis and defi nition of GES boundary To establish baselines and acceptable variability for specifi c indicators, trends and reference periods were evaluated using indicator Control Chart (Manley 2001; Guthrie et al. 2005). For CB% and MMB%, mean values and 95% confi dence intervals (CI) were used. As these variables are non-normally distributed, stand-
ard deviation (SD) was calculated using transformed values and then back-transformed to arrive to the upper and lower 95% limits of CI. In the context of GES for CB (CB%), upper limit is not relevant, whereas lower limit is not relevant for MMB (MMB%). Thus, GES boundary for CB (CB%) is the lower 95% CI limit, and for MMB (MMB%), it is the upper 95% CI limit. These values are set as threshold values which mark the boundary between acceptable and unacceptable conditions, i.e. CB (CB%) should not decline below its GES boundary, whereas MMB (MMB%) should not increase above its GES boundary. GES boundary for CB% For the data set analyzed, the most easy interpretable results were obtained with CB%. The GES boundary was estimated to be 73% based on the reference period 1983-1990, when herring and sprat stocks were relatively stable and had high WAA and body condition. This reference period was selected based on the data presented by Rahikainen & Stephenson (2004) and Casini et al. (2006). See Guidelines below for details. CB% 100 80 60 40 reference period CB% mean GES boundary: lower 95% CI 1980 1990 2000 2010 Year Figure 4.4. Long-term changes in copepod contribution to the total mesozooplankton biomass (CB%) in the Askö area, northern Baltic proper (1976-2010; average summer values, n=6). According to the trend observed, CB% >73% indicate good feeding conditions for zooplanktivorous fi sh (green area). GES boundary for MMB% For the data set analyzed, the most easy interpretable results were obtained with MMB%. The GES boundary estimated was 22% based on the reference period 1976-1990, when water transparency complied with GES levels (various HELCOM documents; this period might need refi ning). MMB% 40 30 20 10 reference period MMB% GES boundary: upper 95% CI mean 0 1980 1990 2000 2010 Year Figure 4.5. Long-term changes in microphagous zooplankton contribution to the total mesozooplankton biomass (MMB%) in the Askö area, northern Baltic proper (1976-2010; average summer values, n=6). According to the trend observed, MMB%
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Baltic Sea Environment Proceedings
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2 Helsinki Commission Katajanokanla
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4 4.16. Cumulative impact on benthi
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6 descriptions of the indicators. T
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8 The CORESET expert group on biodi
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10 fi sh stocks and change in diet,
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12 Figure 2.3. The prevalence of pr
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14 Seasonal changes in blubber thic
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16 Blubber thickness suggestions Bl
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18 Weaknesses/gaps Monitoring of th
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20 2.3. Population growth rate of m
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22 Annual adult survival 1 0,95 0,9
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24 Existing monitoring data Informa
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26 References Bäcklin, B. M. 2011.
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28 Reproduction in the Baltic eagle
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30 Breeding success The proportion
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32 of the real number of nestlings
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34 Gaps and weaknesses Minimum dete
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36 2.5. Abundance of wintering popu
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38 Table 2.10. Pressures affecting
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40 Refrences Skov et al. (2000) Bir
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42 termined by application of the 7
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44 Approach for defi ning GES All s
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46 Sampling It is suggested to incl
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48 2.7. Fish population abundance 2
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50 Coastal Fish - Community Abundan
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52 When a reference data set cannot
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54 well, but were not included in t
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56 Weighting For Nordic Costal mult
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58 12. Current monitoring Monitored
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60 Błeńska M., Osowiecki A., Kra
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62 10. Spatial considerations Fucus
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64 2.15. Trend in the arrival of ne
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66 jectives is to reach “No intro
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68 Sweden Figure 2.23 shows the rat
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70 Strengths and weaknesses of data
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72 need to be revisited in light of
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74 Considering the data set availab
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76 In Denmark, the highest contamin
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78 Status of a compound on internat
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80 A temporal analysis (EU-RAR 2006
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82 Pathways of PFOS to the Baltic e
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84 In regions less affected by anth
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86 3.4. Polychlorinated biphenyls a
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88 actions related to the environme
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90 The CORESET expert group decided
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92 pheric deposition pattern (lowes
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94 3.5. Polyaromatic hydrocarbons (
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96 GES boundaries and matrix Existi
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98 Quality Assurance of PAH metabol
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100 Ariese F, Burgers I, Oudhoff K,
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102 GES boundaries and matrices Exi
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104 3.7. Cesium-137 Authors: Jürge
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106 Figure 3.5. Average surface lev
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108 Temporal trends in concentratio
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110 Other important sources are glo
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112 Conceptual model of the sources
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114 3.8 Tributyltin (TBT) and the i
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116 GES boundaries and matrix Exist
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118 Monitoring the compound Status
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120 Recommendation TBT measurements
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122 3. Frogs have been shown to app
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124 not fi sh (Köhler et al. 2002;
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126 The use of different fi sh spec
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128 Kirchin, M.A. Moore, M.N. Dean,
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130 3.11. Fish Disease Index: Exter
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132 Confounding factors The multifa
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134 analyses ICES data from various
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136 An EAC is the threshold beyond
Page 140 and 141: 138 3.12. Micronucleus test Authors
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Page 144 and 145: 142 ing national data sets. Note: t
Page 146 and 147: 144 Baršienė J. Rybakovas A. 2006
Page 148 and 149: 146 3.13 A. Reproductive disorders
Page 150 and 151: 148 dead larvae can be induced by s
Page 152 and 153: 150 Strand, J, Dahllöf, I. (2005).
Page 154 and 155: 152 of contaminants in sediments wi
Page 156 and 157: 154 Eriksson Wiklund, A-K., and Sun
Page 158 and 159: 156 4. Candidate indicators for bio
Page 160 and 161: 158 Zooplankton-phytoplankton bioma
Page 162 and 163: 160 4.2. By-catch of marine mammals
Page 164 and 165: 162 4.3. Impacts of anthropogenic u
Page 166 and 167: 164 4.4. Fatty-acid composition of
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Page 170 and 171: 168 10. Spatial considerations Balt
Page 172 and 173: 170 4.10. Large fi sh individuals f
Page 174 and 175: 172 11. Temporal considerations Sam
Page 180 and 181: 178 A proposal for an approach to a
Page 182 and 183: 180 Technical data Metadata This is
Page 184 and 185: 182 F igure 4.1. Map showing the cu
Page 186 and 187: 184 4.17. Biomass of copepods 1. Wo
Page 188 and 189: 186 4.19. Zooplankton species diver
Page 192 and 193: 190 Notes for the GES boundary The
Page 194 and 195: 192 Step 3: Prepare a list of speci
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Page 198 and 199: 196 4. Policy relevance Nonylphenol
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Page 202 and 203: 200 4.27. EROD/CYP1A induction The
Page 204 and 205: 202 5.1. Summary of all supplementa
Page 206 and 207: 204 Table 5.1. (continues) Suppleme
Page 208 and 209: 206 5.5. Biopollution level index 1
Page 210 and 211: 208 NIS for aquaculture purposes. O
Page 212 and 213: 210 Balanus improvisus Baltic Katte
Page 214 and 215: 212 Figure 5.3. The scheme for asse
Page 216 and 217: 214 5.6. Organochlorine compounds G
Page 218 and 219: 216 EU Directive 2008/105/ EC Daugh
Page 220 and 221: 218 Table 5.4. Monitoring of HCB, H
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