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Diatom Intercalibration Workshops

Diatom Intercalibration Workshops. 4 meetings: attended by Annie, Elinor, Kaarina Dave Ryves guest appearance Issues covered: ID notes for 100 common taxa Clumping of ‘problem’ species complexes Excluded taxa Cross counting exercise. Clumping of ‘problem’ species complexes.

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Diatom Intercalibration Workshops

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  1. Diatom Intercalibration Workshops 4 meetings: attended by Annie, Elinor, Kaarina Dave Ryves guest appearance Issues covered: ID notes for 100 common taxa Clumping of ‘problem’ species complexes Excluded taxa Cross counting exercise

  2. Clumping of ‘problem’ species complexes Planothidium delicatulum agg.:P. delicatulum, P. haukianum, P. septentrionalis, P. engelbrechtii Fragilaria elliptica agg.:Staurosira elliptica (1&2), S. punctiformis, Staurosirella sopotensis, Pseudostaurosira perminuta, P. zeillerii, Opephora krumbeinii, Fragilaria neoelliptica, F. sopotensis, F. guenter-grassii

  3. Complexes continued Tabularia fasciculata agg.:T. affine, T. laevis, T. fasiculata, T. tabulata, T. waerneii Nitzschia frustulum agg.: N. amphibia, N. amphibia f. rostrata, N. frustulum, N. inconspicua, N. liebtruthii, N. liebtruthii v. major

  4. Excluded taxa Chaetoceros spp. resting spores as identification to species level not possible. Chaeotoceros spp. vegetative cells, Skeletonema costatum and Rhizosolenia spp. spines as preservation in sediments is uneven. Cross Counting Exercise All 3 diatomists within 5% of each others counts after the exercise finished. (We weren’t after the first attempt!) Still To Do Authorities and references for MOLTEN taxa Agreement on data-base images

  5. Transfer Function Requirements Unimodal models The under-lying taxa-response should predominantly be unimodal Use Detrended Canonical Correspondence Analysis:gradient lengths greater than 2 have many taxa showing a unimodal response,less than 1 and no taxa will have a unimodal reponse Variables to be reconstructed should explain a significant (and unique) portion of the variance in the diatom data: CCA (and partial CCA) tested by Monte Carlo permutation tests are used to determine this.

  6. Dutch training set 30 sites within country Sylt/Seine samples 308 taxa in total 53 taxa with 1% abundance in 2 or more samples Thalassiosira pseudonana (42%), Delphineis surirella (29%) and Cymatosira belgica (20%)were among the most widespread and abundant taxa.

  7. Dutch data set DCA (26 sites & 54 taxa) DCA Adding Western Danish samples gave only small improvement: DCA gradient 2.3 DCCA on TN gradient 0.8 DCA Gradient length 2.5 (without SOELKK 1.8) DCCA on TN gradient 0.7

  8. Dutch diatom data All sites SOELKK removed DCA DCA Gradient length 2.5 Gradient length 1.8

  9. Dutch data set RDA (SOELKK removed) Linear version of CCA Interpret in same way Significance testing with Monte Carlo permutation tests indicated that only NH4 had a significant relationship to distribution of diatom taxa.

  10. Dutch training set Performance PLS on NH4 (DCCA gradient length 1.3) C1 C2 C3 RMSE 0.08 0.05 0.03 R2 0.67 0.87 0.93 RMSEP 0.12 0.17 0.18 Boot_R2 0.38 0.15 0.08

  11. Danish training set 91 sites 508 taxa in total 152 taxa with 1% abundance or more in 2 or more samples Opephora mutabilis (22%), Fragilaria elliptica agg. (26%) and Thalassiosira sp. 1A (55%) were among the most frequent and abundant taxa.

  12. Danish site DCA Shallow, sandy, saline Deep Shallow, brackish Gradient length 3.1

  13. Danish data set CCA (91 sites & 152 taxa) CCA 1.5 RKB23 STR13005 ROS55 RKB2 TN VSJ44011 TP chla NH4_N PO4 RIB16103 FYN63044 depth RIB16212 salinity VIB37021 -1.5 -1.5 2.5 DCA gradient length 3.1 DCCA on TN gradient 1.7 Variables in BLUE are significant in forward selection

  14. Danish Diatom CCA Nutrient variables (with exception of PO4) are colinear.

  15. Danish predicted vs obs TN C1 C2 C3 RMSE 0.14 0.11 0.089 R2 0.69 0.81 0.88 RMSEP 0.18 0.16 0.17 Boot_R2 0.56 0.65 0.66

  16. Swedish Data Set (30 sites & 75 taxa) DCA Gradient length 3.7 DCCA on TN gradient 2.5 CCA

  17. Swedish predicted vs Obs TN C1 C2 C3 R2 0.76 0.86 0.94 RMSE 0.085 0.065 0.044 Boot_R2 0.60 0.63 0.65 RMSEP 0.12 0.12 0.12

  18. Finnish Data Set (55 samples & 73 taxa) DCA gradient length 2.8 DCCA on TN gradient 2.2 DCCA on TP gradient 1.9 CCA

  19. Finnish predicted vs obs TN Model performance C1 C2 C3 RMSE 0.094 0.076 0.063 R2 0.69 0.79 0.86 RMSEP 0.13 0.13 0.14 Boot_R2 0.48 0.51 0.48 Model performance (fi_46 removed) C1 C2 C3 RMSE 0.084 0.066 0.055 R2 0.75 0.85 0.89 RMSEP 0.13 0.12 0.12 Boot_R2 0.56 0.60 0.62

  20. Finnish predicted vs obs TP Model performance C1 C2 C3 RMSE 0.083 0.073 0.066 R2 0.67 0.74 0.79 RMSEP 0.11 0.10 0.11 Boot_R2 0.53 0.59 0.61

  21. All Sites DCA Finnish Danish Dutch Swedish Open deep muddy Open shallow sandy Baltic

  22. MOLTEN Data Set CCA (202 sites & 234 taxa) DCA gradient length 4.13 DCCA on TN gradient 1.9 DCCA on TP gradient 2.1 DCCA on salinity gradient 3.0 CCA CCA with forward selection indicated ALL variables to be significant

  23. Baltic Data SetCCA (75 sites & 86 taxa) East coast Sweden and Finland DCA gradient length 3.3 DCCA on TN gradient 2.5 DCCA on TP gradient 2.0 CCA Forward selection indicated ALL variables to be significant, those in BLUE explained highest total variance

  24. Baltic Predicted vs obs TN Model performance C1 C2 C3 RMSE 0.10 0.08 0.07 R2 0.70 0.79 0.84 RMSEP 0.13 0.13 0.14 Boot_R2 0.55 0.57 0.54

  25. Baltic predicted vs obs TP Model performance C1 C2 C3 RMSE 0.10 0.08 0.07 R2 0.61 0.73 0.77 RMSEP 0.12 0.12 0.13 Boot_R2 0.49 0.51 0.52

  26. Danish with Swedish west coast (101sites & 163 taxa) DCA gradient length 3.1 DCCA on TN gradient 1.6 DCCA on TP gradient 1.4 DCCA on salinity 2.1 CCA

  27. Danish and Swedish West Coast > 10m CCA (56 sites & 121 taxa) CCA DCA gradient length 2.9 DCCA on TN gradient 1.7 DCCA on TP gradient 1.6 DCCA on salinity gradient 2.0

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