Interim report of the HELCOM CORESET project

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192 Step 3: Prepare a list of species/groups routinely identifi ed in zooplankton samples (if unavoidable, use groups that are pooled in a consistent manner) to calculate ZSD values as a ratio between the number of species observed in a given year (or season if the assessment is done on a seasonal basis) and the total number of species ever observed in routine zooplankton monitoring in this area. This is a proxy for species richness. Step 4: Prepare available time series or data published in various reports for each of the main indicators (ZSD, CB, CB%, MMB and MMB%) and additional indicators (MeanSize, Zooplankton/Phytoplankton ratio): a) Pay particular attention to the data availability for the periods of the high fi sh growth conditions and acceptable eutrophication levels indentifi ed from the time series for these background data; b) Note that both absolute biomass (mg/m3; CB and MMB) and relative (proportion or percentage of the indicator group in the total mesozooplankton biomass; CB% and MMB%) will be needed; c) When calculating CB and CB%, include all copepodites stages of copepods; d) When calculating MMB and MMB%, include only holoplankton (i.e., no benthic larvae); most relevant being the following species/groups: i rotifers, ii appendicularians, iii small (
Casini M, Cardinale M, Hjelm J (2006) Inter-annual variation in herring, Clupea harengus, and sprat,Sprattus sprattus, condition in the central Baltic Sea: what gives the tune?. Oikos 112: 638- 650. Casini M, Hjelm J, Molinero JC, Lövgren J, Cardinale M, Bartolino V, Belgrano A, Kornilovs G (2009) Trophic cascades promote threshold-like shifts in pelagic marine ecosystems. Proc. Natl. Acad. Sci. USA 106: 197-202. Guthrie B, Love T, Fahey T, Morris A, Sullivan F. 2005. Control, compare and communicate: designing control charts to summarise effi ciently data from multiple quality indicators. Qual. Saf. Health Care 2005;14:450–454. Hanson, J.M., and Peters, R.H. 1984. Empirical prediction of zooplankton and profundal macrobenthos biomass in lakes. Can. J. Fish. Aquat. Sci. 41: 439-455. HELCOM (1988) Guidelines for the Baltic monitoring programme for the third stage. Part D. Biological determinants. Baltic Sea Environment Proceedings 27D: 1-161. HELCOM, 2009. Eutrophication in the Baltic Sea – An integrated thematic assessment of the effects of nutrient enrichment and eutrophication in the Baltic Sea region. Balt. Sea Environ. Proc. No. 115B. Manley, F.J.M. 2001. Statistics for environmental science and management, 1th ed. Chapman and Hall/ CRC, New York: McGraw-Hill Book Company. Pace, M.L. 1986. An empirical analysis of zooplankton community size structure across lake trophic gradients. Limnol. Oceanogr. 31: 45-55. Pace, M.L., and Orcutt, J.D., Jr. 1981. The relative importance of protozoans, rotifers and crustaceans in freshwater zooplankton community. Limnol. Oceanogr. 26: 822-830. Rahikainen M, Stephenson RL 2004. Consequences of growth variation in northern Baltic herring for assessment and management. ICES Journal of Marine Science 61: 338-350, Rönkkönen S, Ojaveer E, Raid T, Viitasalo M (2004) Long-term changes in Baltic herring (Clupea harengus membras) growth in the Gulf of Finland. Canadian Journal of Fisheries and Aquatic Sciences 61: 219-229. Schindler, D.W. 1987. Detecting ecosystem responses to anthropogenic stress. Can. J. Fish. Aquat. Sci. 44(Suppl.1): 6–25. Sládecek,V. 1983. Rotifers as indicators of water quality. Hydrobiologia, 100: 169–201. Stemberger, R.S., and Lazorchak, J.M. 1994. Zooplankton assemblage responses to disturbance gradients. Can. J. Fish. Aquat. Sci. 51: 2435–2447. 193
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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
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138 3.12. Micronucleus test Authors
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140 main nuclei were not counted as
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
Page 168 and 169: 166 7. Use of the indicator in prev
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
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Page 192 and 193: 190 Notes for the GES boundary The
Page 196 and 197: 194 4.22. Phytoplankton diversity 1
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
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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
Page 222: www.helcom.fi
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