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Database and data tables Responsibilities: Tamara Yankovich (SRC) Freshwater Justin Brown (NRPA) Marine CEH Terrestrial David Copplestone (EA) - On-line database Database - recap Started with ERICA databases for generic


  1. Database and data tables Responsibilities: Tamara Yankovich (SRC) – Freshwater Justin Brown (NRPA) – Marine CEH – Terrestrial David Copplestone (EA) - On-line database

  2. Database - recap • Started with ERICA databases for generic freshwater, marine and terrestrial ecosystems: • QC’ed, re-categorised to ‘IAEA’ organism and wildlife groupings and also by ICRP RAP • QC resulted in some reductions in data for marine and identified that considerable number of freshwater values originated from review publications • On-line database (http://www.wildlifetransferdatabase.org/) and excel entry form used for additional data entries

  3. Additional datasets entered • Japanese estuarine data (NIRS) – Crustaceans, macroalgae & molluscs collected in estuaries – Approx. 2000 entries; >30 elements • Helcom Mors Baltic Sea data (STUK) – Approx 250 entries – Largely Sr & Cs – Crustacean, fish, mollusc, phytoplankton

  4. • CANDU Operators Group monitoring data (COG) – Approx 4000 data entries largely freshwater organisms – >30 elements • Canadian mining industry monitoring data (CNSC) – Freshwater organisms – >20000 entries – biota:sediment CRs as well as biota:water

  5. • Russian language literature (RIARAE) – Freshwater – Marine – Terrestrial plants • Natural radionuclides – grasses (SCK·CEN) • Australian terrestrial data (ANSTO) – Natural RNs, actinides, Cs-137 – Includes marsupial, reptile, witchetty grub

  6. • Freshwater database (Yankovich) • Reptile review paper data (Liverpool) • Bird/bat/rodent Sr,Cs,Pu data Chernobyl (CEH/IRL) • Sand dune studies (Liverpool) • Post FASSET/ERICA lit. review (CEH) • Oregon Forest data (Oregon Uni.) …… more (KAERI, WSC, SENES) giving c. 60,000 data entries

  7. Discussion of entered data • What we did to it • What’s changed? • Tables currently presented at broad wildlife group level (e.g. fish, mammal) • Remaining QC issues • Changes to wildlife groups? • Are we going to accept more data before final draft?

  8. Data treatment • Yankovich et al. used to convert tissue to wholebody data (for new entries) • QC – Exclude if unclear if DW or FW, tissue or wholebody, co-location of biota and water … – Duplicate data

  9. What’s changed ?

  10. Marine fish Draft TRS ERICA 1.E+05 1.E+04 1.E+03 C R 1.E+02 V a l 1.E+01 u e 1.E+00 1.E-01 1.E-02 Ag Am C Cd Ce Cl Co Cs Eu Mn Ni P Pb Po Pu Ra Ru Sr Th U Zr Element

  11. Marine Phytoplankton 1.E+06 Draft TRS ERICA 1.E+05 C 1.E+04 R V a 1.E+03 l u e s 1.E+02 1.E+01 1.E+00 1.E-01 Ag Am C Cd Ce Cm Co Cs I Mn Ni Np P Pb Po Pu Ra Ru S Se Sr Tc Te Th U Zr Element

  12. Terrestrial Birds 1.E+00 Draft TRS ERICA 1.E-01 C R V a 1.E-02 l u e s 1.E-03 1.E-04 Am Cs Pb Po Pu Ra Sr Tc Th U Element

  13. Grasses 1.E+02 Draft TRS ERICA 1.E+01 C 1.E+00 R V 1.E-01 a l u 1.E-02 e s 1.E-03 1.E-04 Ag Am Cd Ce Cl Cm Co Cs Eu I Ni Pb Po Pu Ra Sb Se Sr Tc Th U Element

  14. Terrestrial Mammals 1.E+01 Draft TRS ERICA 1.E+00 C R 1.E-01 V a 1.E-02 l u e 1.E-03 s 1.E-04 Am Cd Co Cs Mn Ni Pb Po Pu Ra Se Sr Th U Element

  15. Freshwater fish Draft TRS ERICA Benthic fish 1.E+06 1.E+05 1.E+04 C R 1.E+03 V a 1.E+02 l u 1.E+01 e s 1.E+00 1.E-01 Am C Cd Ce Cl Cm Co Cs Eu I Mn Ni P Pb Po Pu Ra Ru Sb Se Sr Tc Te Th U Zr С Element

  16. Freshwater - mollusc 1.E+06 Draft TRS ERICA Bivalve molluscs ERICA Gastropod 1.E+05 C 1.E+04 R V 1.E+03 a l u 1.E+02 e s 1.E+01 1.E+00 Am Ce Cm Cs I Pb Po Pu Ra Sb Se Sr Element

  17. Wildlife group? • Currently at broad wildlife group • Can we justify going to subcategory?

  18. Freshwater fish

  19. Marine fish

  20. Wildlife subgroups • Statistical justification for separating some • Others have large amounts of data e.g. grasses v’s herbs, coniferous tree v’s broadleaf tree … so why combine?

  21. Terrestrial mammals

  22. Wildlife subgroups • Statistical justification for separating some • Others have large amounts of data e.g. grasses v’s herbs, coniferous tree v’s broadleaf tree … so why combine? • ….. unless we don’t like the answer!

  23. QC issues • Summarisation of data/BMG scenarios etc. have id’ed issues with data base

  24. Cs Fish

  25. Marine Phytoplankton 1.E+05 Draft TRS ERICA 1.E+04 C R 1.E+03 V a l 1.E+02 u e s 1.E+01 1.E+00 1.E-01 Ag Am C Cd Ce Cl Cm Co Cs Eu H I K Mn Nb Ni Np P Pb Po Pu Ra Ru S Sb Se Sr Tc Th U Zr Element

  26. • There is some bias some datasets, e.g. – Lots of data from Canada for freshwaters – All terrestrial Tc data from UK sand dunes – Terrestrial bird/mammal Pu/Am signif. amount of data from Chernobyl zone • Are there obvious weaknesses in database (probably) …. which can be quickly addressed?

  27. Freshwater Fishes - Phylogeny Can evolutionary history be used to categorised transfer? For plants appear to be phylogenetic relationships for a number of relationships .. .. and marine organisms – wait until Thursday

  28. 0.12 0.6 0.07 Predicted soil-plant TF Predicted soil-plant TF 0.06 Predicted Soil-Plant TF 0.1 0.5 0.05 0.08 0.4 0.04 0.06 0.3 0.03 0.04 0.2 0.02 0.02 0.1 0.01 0 0 0 0.6 7 0.02 0.018 Predicted soil-plant TF Predicted soil-plant TF Predicted soil-plant TF 6 0.5 0.016 5 0.014 0.4 0.012 4 0.3 0.01 3 0.008 0.2 0.006 2 0.004 0.1 1 0.002 0 0 0 3 Predicted soil-plant TF 2.5 2 1.5 1 0.5 0

  29. b) 2.5 2 1.5 1 Standardised Concentration 0.5 0 0 50 100 150 200 250 -0.5 -1 Ra -1.5 Ca -2 Sr -2.5

  30. Freshwater fish • Started with Cs • Data for 12 orders

  31. Orders • Lepisosteiformes • Salmoniformes • Amiiformes • Esociformes • Clupeiformes • Gadiformes • Cypriniformes • Cyprinodontiformes • Siluriformes • Perciformes • Osmeriformes • Scorpaeniformes

  32. 4 3.5 3 2.5 2 1.5 1 0.5 0 0 10 20 30 40 50 60 70

  33. Perciformes Salmoniformes Esociformes Cypriniformes Gadiformes

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