Interim report of the HELCOM CORESET project

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142 ing national data sets. Note: the values given here are provisional and require further validation when new data becomes available. The 90% percentile (P90) separates the upper 10% of all values in the group from the lower 90%. The rationale for this decision was that elevated MN frequency would lie above the P90 percentile, whereas the majority of values below P90 belong to unexposed, weakly-medium exposed or non-responding adapted individuals. P90 values were calculated for those stations/areas which were considered being reference stations (i.e. no known local sources of contamination or those areas which were not considered unequivocally as reference sites but as those less infl uenced from human and industrial activity). ACs in bivalves Mytilus edulis, Mytilus trossulus, Macoma balthica (data from MN analysis in 2370 specimens), in fi sh Limanda limanda, Zoarces viviparus, Platichthys fl esus, Gadus morhua and Clupea harengus (data from MN analysis in 3239 specimens) from Baltic Sea have been calculated using NRC (Lithuania) databases (Table 3.22). Table 3.22. Assessment criteria of MN frequency levels in bivalve mollusc and fi sh. BR =Background response; ER = Elevated response; n = number of specimens analysed. Species Size (cm) T (ºC) Region Tissue BR ER n Mytilus edulis 1.5-3 8-18 Baltic Sea Gills 2.50 1810 Mytilus trossulus 2-3 3-15 Baltic Sea Gills 4.50 230 Macoma balthica 1-3 13-18 Baltic Sea Gills 2.90 330 Zoarces viviparus 15-32 7-17 Baltic Sea Erythrocytes 0.38 824 Limanda limanda 18-25 8-17 Baltic Sea Erythrocytes 0.49 117 Platichthys fl esus 17-39 10-17 Baltic Sea coastal Erythrocytes 0.29 828 Platichthys fl esus 18-40 6-18 Baltic Sea offshore Erythrocytes 0.23 970 Gadus morhua 20-48 13-15 Baltic Sea Erythrocytes 0.38 50 Clupea harengus 16-29 6-18 Baltic Sea Erythrocytes 0.39 450 Distribution of indicator species in Baltic Sea subregions MN test has generally been applied to organisms where other biological-effects techniques and contaminant levels are well documented. That is the case for mussels and for certain demersal fi sh species (as European fl ounder, dab or Atlantic cod), which are routinely used in biomonitoring programs and assess contamination along western European marine. However, the MN assay may be adapted for alternative sentinel species using site-specifi c monitoring criteria. When selecting an indicator fi sh species, consideration must be given to its karyotype as many teleosts are characterised by an elevated number of small chromosomes (Udroiu et al. 2006). Thus, in certain cases MN formed after exposure to clastogenic contaminants will be very small and hard to detect by light microscopy. This can be addressed to a certain extent by using fl uorescent staining. After selecting target/suitable species, researchers should also ensure that other factors including age, sex, temperature and diet are similar between the sample groups. If conducting transplantation studies, consideration needs to be given to the cellular turnover rate of the tissue being examined to ensure suffi cient cells have gone through cell division. For example, if using blood the regularities of erythropoiesis should be known prior to sampling. Large-scale and long-term studies took place from 2001 to 2010 at the Nature Research Centre (NRC, Lithuania) on MN and other abnormal nuclear formations in different fi sh and bivalve species inhabiting various sites of the Baltic Sea. These studies revealed the relevance of environmental genotoxicity levels
in ecosystem assessments. NRC established a large database on MN and other nuclear abnormalities in 8 fi sh species and in mussels and clams from the Baltic Sea. Fish and bivalve species were collected from 117 coastal and offshore sites. The following organisms have been tested as the target species for MN test in the different regions of the Baltic Sea: Fish fl ounder (Platichthys fl esus), dab (Limanda limanda, herring (Clupea harengus), eelpout (Zoarces viviparus) plaice (Pleuronectes platessa) Atlantic cod (Gadus morhua) perch (Perca fl uviatilis) turbot (Scophthalmus maximus, Psetta maxima) Bivalves blue mussels (Mytilus edulis, Mytilus trossulus Baltic clam (Macoma baltica) Amphipods Gammarids Acknowledgements Research on the application of the MN test as a biomarker for the monitoring and assessment of environmental genotoxicity in different Baltic Sea regions and indicator organisms has been funded by the EU Project BEEP, Contract EVK3-CT-2000-00025, BONUS+ Project BEAST and Lithuanian Science Council GENCITOX project MIP-10123. References Al-Sabati K, Metcalfe CD. Fish micronuclei for assessing genotoxicity in water. Mutation Research 1995; 323: 121-35. Andreikėnaitė L. 2010. Genotoxic and cytotoxic effects of contaminants discharged from the oil platforms in fi sh and mussels. Summary of doctoral dissertation, 38p.Vilnius. Arkhipchuk V.V. Garanko N.N. 2005. Using the nucleolar biomarker and the micronucleus test on in vivo fi sh fi n cells. Ecotoxicology and Environmental Safety. Vol. 62. P. 42-52. Bagni G. Baussant T. Jonsson G. Baršienė J. Mascini M. 2005. Electrochemical device for the rapid detection of genotoxic compounds in fi sh bile samples. Analytical Letters, 38: 2639-2652. Baršienė J. Andreikėnaitė L. 2007. Induction of micronuclei and other nuclear abnormalities in blue mussels exposed to crude oil from the North Sea. Ekologija, 53, No 3: 9-15. Baršienė J. Andreikėnaitė L. Garnaga G. Rybakovas A. 2008b. Genotoxic and cytotoxic effects in bivalve mollusks Macoma balthica and Mytilus edulis from the Baltic Sea. Ekologija, 54: 44-50. Baršienė J. Bjornstad A. Rybakovas A. Šyvokienė J. Andreikėnaitė L. 2010. Environmental genotoxicity and cytotoxicity studies in mussels and fi sh inhabiting northern Atlantic zones impacted by aluminum industry. Ekologija, 56, No 3-4: 124-131. Baršienė J. Dedonytė V. Rybakovas A. Andreikėnaitė L. Andersen O.K. 2006a. Investigation of micronuclei and other nuclear abnormalities in peripheral blood and kidney of marine fi sh treated with crude oil. Aquatic Toxicology,78, Supplement 1: S99-104. Baršienė J. Dedonytė V. Rybakovas A. Broeg K. Forlin L. , Gercken J. Kopecka J. Balk L. 2005a. Environmental mutagenesis in different zones of the Baltic Sea. Acta Zoologica Lituanica, 15: 90-95. Baršienė J. Lazutka J. Šyvokienė J. Dedonytė V. Rybakovas A. Bjornstad A. Andersen O.K. 2004. Analysis of micronuclei in blue mussels and fi sh from the Baltic and the North Seas. Environmental Toxicology, 19: 365-371. Baršienė J. Lehtonen K. Koehler A. Broeg K. Vuorinen P.J. Lang T. Pempkowiak J. Šyvokienė J. Dedonytė V. Rybakovas A. Repečka R. Vuontisjarvi H. Kopecka J. 2006c. Biomarker responses in fl ounder (Platichthys fl esus) and mussel (Mytilus edulis) in the Klaipėda-Būtingė area (Baltic Sea). Marine Pollution Bulletin, 53: 422- 436. 143
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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
Page 94 and 95: 92 pheric deposition pattern (lowes
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Page 98 and 99: 96 GES boundaries and matrix Existi
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Page 104 and 105: 102 GES boundaries and matrices Exi
Page 106 and 107: 104 3.7. Cesium-137 Authors: Jürge
Page 108 and 109: 106 Figure 3.5. Average surface lev
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Page 114 and 115: 112 Conceptual model of the sources
Page 116 and 117: 114 3.8 Tributyltin (TBT) and the i
Page 118 and 119: 116 GES boundaries and matrix Exist
Page 120 and 121: 118 Monitoring the compound Status
Page 122 and 123: 120 Recommendation TBT measurements
Page 124 and 125: 122 3. Frogs have been shown to app
Page 126 and 127: 124 not fi sh (Köhler et al. 2002;
Page 128 and 129: 126 The use of different fi sh spec
Page 130 and 131: 128 Kirchin, M.A. Moore, M.N. Dean,
Page 132 and 133: 130 3.11. Fish Disease Index: Exter
Page 134 and 135: 132 Confounding factors The multifa
Page 136 and 137: 134 analyses ICES data from various
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Page 140 and 141: 138 3.12. Micronucleus test Authors
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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
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Page 166 and 167: 164 4.4. Fatty-acid composition of
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Page 172 and 173: 170 4.10. Large fi sh individuals f
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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
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192 Step 3: Prepare a list of speci
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194 4.22. Phytoplankton diversity 1
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198 among different studies/measure
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200 4.27. EROD/CYP1A induction The
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202 5.1. Summary of all supplementa
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204 Table 5.1. (continues) Suppleme
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206 5.5. Biopollution level index 1
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208 NIS for aquaculture purposes. O
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210 Balanus improvisus Baltic Katte
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212 Figure 5.3. The scheme for asse
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214 5.6. Organochlorine compounds G
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216 EU Directive 2008/105/ EC Daugh
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218 Table 5.4. Monitoring of HCB, H
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