Data contributed to the Project LargeNet within the European Network of Excellence Marine Biodiversity it and Ecosystem Functioning i (MarBEF).
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1 By Norbert Wasmund, Leibniz Institute for Baltic Sea Research, D Warnemünde, Germany Jarno Tuimala, Finnish Red Cross Blood Service, FI Helsinki, Finland Leen Vandepitte, Flanders Marine Institutem, InnovOcean site, B 8400 Oostende, Belgium Alexandra Kraberg, Biologische i Anstalt Helgoland, l Alfred Wegener Institute t for Polar and Marine Research, D Helgoland, Germany Data from the Monitoring Programme COMBINE of HELCOM and various research projects of the IOW. HELCOM data are free, but latest data, not available from the data bank, were contributed by Susanna Hajdu and Svante Nyberg (Stockholm University), Slawomira Gromisz and Janina M. Kownacka (MIR Gdynia, PL), Henrik Jespersen (Bornholms Regionskommune, DK) and Bente Brix Madsen (Orbicon, DK), Irina Olenina (Centre of Marine Research Klaipeda, LT). Data contributed to the Project LargeNet within the European Network of Excellence Marine Biodiversity it and Ecosystem Functioning i (MarBEF). 1
2 Introduction What is HELCOM? Baltic Marine Environment Protection Commission (Helsinki Commission) 1974: Helsinki Convention signed by the Baltic coastal states. 1979: Start of the joint Baltic Monitoring Programme. Data go to national data banks and finally to ICES. 58 I1 Kattegat and Belt Sea La atitude [deg N] 57 Kiel Bight Mecklenburg Bight Various components of the 56 Arkona Sea ecosystem K1 only Phytoplankton Whole Baltic Sea only Kiel and Mecklenburg Bight, Central and southern Baltic Proper N3 N1 M2 M1 K8 K7 K4 K5 K2 J1 Eastern Gotland Sea Longitude [deg E] Bornholm Sea 2
3 Data Source Which data are available? How are the data distributed? Station Period of Number of name data series samples (n) BMP I BMP J BMP K BMP K BMP K BMP K * BMP K BMP K BMP M BMP M ** BMP N BMP N * Station K4, K5 and K7 combined ** Station M1 and M2 combined Definition of seasons according to the HELCOM Season Belt Sea Baltic Proper Spring February April March May May Summer May August June September Autumn September November October December Data set was split into 4 parts according to the seasons. Winter was not analysed. 3
4 Revision of taxa names, examples: Data validation Microcystis reinboldii or Microcystis sp. or Chroococcales unid. Aphanothece sp Chroococcus microscopicus Coelosphaerium Lemmermanniella Cyanodictyon minutissimum parva planctonicum Aphanocapsa sp. Woronichinia oo compacta Gomphosphaeria Chaetoceros danicus Chaetoceros pusilla danicus Rhodomonas minuta Hemiselmis sp. (K. Jensen) Gomphosphaeria salina Plagioselmis prolonga (D. Hill) Teleaulax acuta Snowella lacustris Chaetoceros impressus 4
5 Statistical analyses Only taxa that contain at least 10 observations per station and season were used for the statistical analyses Mann Kendall test used for detecting monotonous linear trends: non parametric, no assumptions on the shape of distribution. If the phytoplankton biomass showed both growing and decreasing phases: special trend break analysis: 1. detecting the break point (for every taxon/station/season) 2. for further analyses, the breakpoints were averaged on the station/season level 3. testing for a linear trend in the parts before and after the break point 5
6 station season TOTAL A M BIOMASS Nostocophyceae Aphanizomenon Nodularia spumigena Chroococcales Oscillatoriales Dinophyceae A+M Prorocentrum micans Prorocentrum minimum Dinophysis acuminata Dinophysis norvegica Gymnodinium Heterocapsa triquetra Peridiniella catenata Ceratium tripos Diatomophyceae total Chaetoceros Thalassiosira Actinocyclus Leptocylindrus danicus Skeletonema costatum Rhizosolenia pungens Coscinodiscus Cerataulina pelagica Pauliella taeniata Pseudo nitzschia Dictyochophyceae Cryptophyceae Chrysophyceae Dinobryon Chlorophyceae Euglenophyceae Prasinophyceae Ciliophora TOTAL H BIOMASS Ebriidea Choanoflagellidea Dinophyceae H total Protoperidinium bipes I1 sp J1 sp +/ K1 sp +/ +/ +/ K2 sp +/ +/ /+ +/ K457 sp +/+ +/+ +/ +/ /+ +/ K8 sp +/ +/ +/ M12 sp +/ /+ +/+ +/ N1 sp N3 sp I1 su J1 su K1 su K2 su / / / / K457 su / / / / K8 su +/ M12 su / / / / +/ N1 su N3 su I1 au J1 au +/ +/ +/ K1 au +/+ K2 au K457 au / K8 au M12 au +/ +/ +/ N1 au N3 au growing trend, p<0 001 growing trend, p<0 01 growing trend, p<0 05 diminishing trend, p<0 05 diminishing trend, p<0 01 diminishing trend, p<
7 station season TOTAL A M BIOMASS Nostocophyceae Aphanizomenon Nodularia spumigena Chroococcales Oscillatoriales Dinophyceae A+M Prorocentrum micans Prorocentrum minimum Dinophysis acuminata Dinophysis norvegica Gymnodinium Heterocapsa triquetra Peridiniella catenata Ceratium tripos Diatomophyceae total Chaetoceros Thalassiosira Actinocyclus Leptocylindrus danicus Skeletonema costatum Rhizosolenia pungens Coscinodiscus Cerataulina pelagica Pauliella taeniata Pseudo nitzschia Dictyochophyceae Cryptophyceae Chrysophyceae Dinobryon Chlorophyceae Euglenophyceae Prasinophyceae Ciliophora TOTAL H BIOMASS Ebriidea Choanoflagellidea Dinophyceae H total Protoperidinium bipes I1 sp J1 sp +/ K1 sp +/ +/ +/ K2 sp +/ +/ /+ +/ K457 sp +/+ +/+ +/ +/ /+ +/ K8 sp +/ +/ +/ M12 sp +/ /+ +/+ +/ N1 sp N3 sp I1 su J1 su K1 su K2 su / / / / K457 su / / / / K8 su +/ M12 su / / / / +/ N1 su N3 su I1 au J1 au +/ +/ +/ K1 au +/+ K2 au K457 au / K8 au M12 au +/ +/ +/ N1 au N3 au growing trend, p<0 001 growing trend, p<0 01 growing trend, p<0 05 diminishing trend, p<0 05 diminishing trend, p<0 01 diminishing trend, p<
8 station season TOTAL A M BIOMASS Nostocophyceae Aphanizomenon Nodularia spumigena Chroococcales Oscillatoriales Dinophyceae A+M Prorocentrum micans Prorocentrum minimum Dinophysis acuminata Dinophysis norvegica Gymnodinium Heterocapsa triquetra Peridiniella catenata Ceratium tripos Diatomophyceae total Chaetoceros Thalassiosira Actinocyclus Leptocylindrus danicus Skeletonema costatum Rhizosolenia pungens Coscinodiscus Cerataulina pelagica Pauliella taeniata Pseudo nitzschia Dictyochophyceae Cryptophyceae Chrysophyceae Dinobryon Chlorophyceae Euglenophyceae Prasinophyceae Ciliophora TOTAL H BIOMASS Ebriidea Choanoflagellidea Dinophyceae H total Protoperidinium bipes I1 sp J1 sp +/ K1 sp +/ +/ +/ K2 sp +/ +/ /+ +/ K457 sp +/+ +/+ +/ +/ /+ +/ K8 sp +/ +/ +/ M12 sp +/ /+ +/+ +/ N1 sp N3 sp I1 su J1 su K1 su K2 su / / / / K457 su / / / / K8 su +/ M12 su / / / / +/ N1 su N3 su I1 au J1 au +/ +/ +/ K1 au +/+ K2 au K457 au / K8 au M12 au +/ +/ +/ N1 au N3 au growing trend, p<0 001 growing trend, p<0 01 growing trend, p<0 05 diminishing trend, p<0 05 diminishing trend, p<0 01 diminishing trend, p<
9 station season TOTAL A M BIOMASS Nostocophyceae Aphanizomenon Nodularia spumigena Chroococcales Oscillatoriales Dinophyceae A+M Prorocentrum micans Prorocentrum minimum Dinophysis acuminata Dinophysis norvegica Gymnodinium Heterocapsa triquetra Peridiniella catenata Ceratium tripos Diatomophyceae total Chaetoceros Thalassiosira Actinocyclus Leptocylindrus danicus Skeletonema costatum Rhizosolenia pungens Coscinodiscus Cerataulina pelagica Pauliella taeniata Pseudo nitzschia Dictyochophyceae Cryptophyceae Chrysophyceae Dinobryon Chlorophyceae Euglenophyceae Prasinophyceae Ciliophora TOTAL H BIOMASS Ebriidea Choanoflagellidea Dinophyceae H total Protoperidinium bipes I1 sp J1 sp +/ K1 sp +/ +/ +/ K2 sp +/ +/ /+ +/ K457 sp +/+ +/+ +/ +/ /+ +/ K8 sp +/ +/ +/ M12 sp +/ /+ +/+ +/ N1 sp N3 sp I1 su J1 su K1 su K2 su / / / / K457 su / / / / K8 su +/ M12 su / / / / +/ N1 su N3 su I1 au J1 au +/ +/ +/ K1 au +/+ K2 au K457 au / K8 au M12 au +/ +/ +/ N1 au N3 au growing trend, p<0 001 growing trend, p<0 01 growing trend, p<0 05 diminishing trend, p<0 05 diminishing trend, p<0 01 diminishing trend, p<
10 Conclusions: Phytoplankton revealseals trends in total biomass and the biomass of different important taxa. Analyses for linear trends are useful tools if changes in phytoplankton occur more or less continuosly, e.g. due to eutrophication which is the main threat in the Baltic. However, non monotonous trends occur which might require other statistical methods. The linear trend analyses do not detect sudden jumps in the ecosystem (regime shifts) Data basis has to be improved (better coverage of blooms, quality assurance, data validation).
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