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  • AGU (American Geophysical Union)  (1)
  • AMER SOC LIMNOLOGY OCEANOGRAPHY  (1)
  • 2010-2014  (2)
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  • 2010-2014  (2)
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  • 1
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    Impact of sample pretreatment on the measured element concentrations in the bivalveArctica islandica (2011)
    AGU (American Geophysical Union)
    In:  Geochemistry, Geophysics, Geosystems, 12 (7).
    Details
    Publication Date: 2014-05-05
    Description: Correlating metal to calcium (Me/Ca) ratios of marine biogenic carbonates, such as bivalve shells, to environmental parameters has led to contradictory results. Biogenic carbonates represent complex composites of organic and inorganic phases. Some elements are incorporated preferentially into organic phases, and others are incorporated into inorganic phases. Chemical sample pretreatment to remove the organic matrix prior to trace element analysis may increase the applicability of the investigated proxy relationship, though its efficiency and side effects remain questionable. We treated inorganic calcium carbonate and bivalve shell powder (Arctica islandica) with eight different chemical treatments including H2O2, NaOH, NaOCl, and acetone and analyzed the effects on (1) Me/Ca ratios (Sr/Ca, Mg/Ca, Ba/Ca, and Mn/Ca), (2) organic matter (≈N) content, and (3) mineralogical composition of the calcium carbonate. The different treatments (1) cause element and treatment specific changes of Me/Ca ratios, (2) vary in their efficiency to remove organic matter, and (3) can even alter the phase composition of the calcium carbonate (e.g., formation of Ca(OH)2 during NaOH treatment). Among all examined treatments there were none without any side effects. In addition, certain Me/Ca changes we observed upon chemical treatment contradict our expectations that lattice-bound elements (Sr and Ba) should not be affected, whereas non-lattice-bound elements (Mg and Mn) should decrease upon removal of the organic matrix. For instance, we observe that NaOCl treatment did not alter Sr/Ca ratios but caused unexpected changes of the Mg/Ca ratios. The latter demonstrates that the buildup of complex biogenic composites like the shell of Arctica islandica are still poorly understood.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1029/2011GC003630
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 2
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    A multi-parameter artificial neural network model to estimate macrobenthic invertebrate productivity and production (2012)
    AMER SOC LIMNOLOGY OCEANOGRAPHY
    In:  EPIC3Limnology and Oceanography-Methods, AMER SOC LIMNOLOGY OCEANOGRAPHY, ISSN: 1541-5856
    Details
    Publication Date: 2019-07-16
    Description: I developed a new model for estimating annual production-to-biomass ratio P/B and production P of macrobenthic populations in marine and freshwater habitats. Self-learning artificial neural networks (ANN) were used to model the relationships between P/B and twenty easy-to-measure abiotic and biotic parameters in 1252 data sets of population production. Based on log-transformed data, the final predictive model estimates log(P/B) with reasonable accuracy and precision (r2 = 0.801; residual mean square RMS = 0.083). Body mass and water temperature contributed most to the explanatory power of the model. However, as with all least squares models using nonlinearly transformed data, back-transformation to natural scale introduces a bias in the model predictions, i.e. an underestimation of P/B (and P). When estimating production of assemblages of populations by adding up population estimates, accuracy decreases but precision increases with the number of populations in the assemblage.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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    PAPER (OA)
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