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Diatom Intercalibration Workshops

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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. - PowerPoint PPT Presentation

Text of Diatom Intercalibration Workshops

  • Diatom Intercalibration Workshops4 meetings: attended by Annie, Elinor, KaarinaDave Ryves guest appearance

    Issues covered:ID notes for 100 common taxaClumping of problem species complexesExcluded taxaCross counting exercise

  • Clumping of problem species complexesPlanothidium 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

  • 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. majorComplexes continued

  • Excluded taxaChaetoceros 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 werent after the first attempt!)Still To DoAuthorities and references for MOLTEN taxaAgreement on data-base images

  • Transfer Function RequirementsUnimodal modelsThe under-lying taxa-response should predominantly be unimodalUse 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.

  • Dutch training set30 sites within countrySylt/Seine samples 308 taxa in total53 taxa with 1% abundance in 2 or more samplesThalassiosira pseudonana (42%), Delphineis surirella (29%) and Cymatosira belgica (20%) were among the most widespread and abundant taxa.

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

  • Dutch diatom data All sitesSOELKK removedDCADCAGradient length 2.5Gradient length 1.8

  • Dutch data set RDA (SOELKK removed)Linear version of CCAInterpret in same way

    Significance testing with Monte Carlo permutation tests indicated that only NH4 had a significant relationship to distribution of diatom taxa.

  • C1C2C3RMSE0.080.050.03R20.670.870.93RMSEP0.120.170.18Boot_R20.380.150.08

    PLS on NH4 (DCCA gradient length 1.3)Dutch training set Performance

  • Danish training set91 sites508 taxa in total152 taxa with 1% abundance or more in 2 or more samplesOpephora mutabilis (22%), Fragilaria elliptica agg. (26%) and Thalassiosira sp. 1A (55%) were among the most frequent and abundant taxa.

  • Danish site DCAShallow, sandy, salineDeepShallow, brackishGradient length 3.1

  • DCA gradient length 3.1DCCA on TN gradient 1.7Variables in BLUE are significant in forward selectionDanish data set CCA (91 sites & 152 taxa)

  • Danish Diatom CCANutrient variables (with exception of PO4) are colinear.

  • C1C2C3RMSE0.140.110.089R20.690.810.88RMSEP0.180.160.17Boot_R20.560.650.66Danish predicted vs obs TN

  • DCA Gradient length 3.7DCCA on TN gradient 2.5 CCASwedish Data Set (30 sites & 75 taxa)

  • C1C2C3R20.760.860.94 RMSE0.0850.0650.044Boot_R20.600.630.65RMSEP0.120.120.12 Swedish predicted vs Obs TN

  • DCA gradient length 2.8DCCA on TN gradient 2.2DCCA on TP gradient 1.9CCAFinnish Data Set (55 samples & 73 taxa)

  • Finnish predicted vs obs TNModel performanceC1C2C3RMSE0.0940.0760.063R20.690.790.86RMSEP0.130.130.14 Boot_R20.480.510.48Model performance (fi_46 removed)C1C2C3RMSE0.0840.0660.055R20.750.850.89RMSEP0.130.120.12 Boot_R20.560.600.62

  • Finnish predicted vs obs TPModel performanceC1C2C3RMSE0.0830.0730.066R20.670.740.79RMSEP0.110.100.11 Boot_R20.530.590.61

  • All Sites DCAOpen shallow sandyOpen deep muddyBaltic

  • CCADCA gradient length 4.13DCCA on TN gradient 1.9DCCA on TP gradient 2.1DCCA on salinity gradient 3.0CCA with forward selection indicated ALL variables to be significantMOLTEN Data Set CCA (202 sites & 234 taxa)

  • CCADCA gradient length 3.3DCCA on TN gradient 2.5DCCA on TP gradient 2.0Forward selection indicated ALL variables to be significant, those in BLUE explained highest total varianceEast coast Sweden and FinlandBaltic Data Set CCA (75 sites & 86 taxa)

  • Model performanceC1C2C3RMSE0.100.080.07R20.700.790.84RMSEP0.130.130.14 Boot_R20.550.570.54Baltic Predicted vs obs TN

  • Baltic predicted vs obs TPModel performanceC1C2C3RMSE0.100.080.07R20.610.730.77RMSEP0.120.120.13Boot_R20.490.510.52

  • CCADCA gradient length 3.1DCCA on TN gradient 1.6DCCA on TP gradient 1.4DCCA on salinity 2.1Danish with Swedish west coast (101sites & 163 taxa)

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

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