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Reliability of N flux rates estimated from 15N enrichment and dilution experiments in aquatic systems
Elskens, M.; Baeyens, W.F.J.; Brion, N.; De Galan, S.; Goeyens, L.; de Brauwere, A. (2005). Reliability of N flux rates estimated from 15N enrichment and dilution experiments in aquatic systems. Global Biogeochem. Cycles 19(4): GB4028.
In: Global Biogeochemical Cycles. American Geophysical Union: Washington, DC. ISSN 0886-6236; e-ISSN 1944-9224, meer
Peer reviewed article  

Beschikbaar in  Auteurs 
    VLIZ: Open Repository 101127 [ OMA ]

    Marien; Brak water; Zoet water

Auteurs  Top 
  • De Galan, S., meer
  • Goeyens, L., meer
  • de Brauwere, A., meer

    This paper investigates the estimation behavior of six increasingly complex 15N models, for estimating flux rates between phytoplankton and dissolved N pools in aquatic ecosystems. The development of these models over the last 40 years reflects increasing realism in the pools and fluxes that constitute the N cycle. The purpose of this paper is to assess how the model results are influenced by the underlying assumptions. For example, with respect to uptake of 15N by phytoplankton, is anything gained by assuming that regenerated N become isotopically enriched after the introduction of the 15N label, or is it just as accurate to assume that no source other than the initial 15N label contributes to the enrichment signal in phytoplankton? To conduct an objective assessment of the models, we compared them to (1) a set of reference values generated numerically by a process oriented model, and (2) real experimental data. The results show that for a number of 15N models, properties such as accuracy and precision cannot both be optimized under the same conditions, and a compromise must be struck. Oversimplified models risk bias when their underlying assumptions are violated, but overly complex models can misinterpret part of the random noise as relevant processes. Therefore none of the 15N model solutions can a priori be rejected, but each should carefully be assessed with hypothesis testing. A backward regression strategy based on a statistical interpretation of the cost function (sum of the weighted least squares residuals) was used to select optimal solution subsets corresponding to a given data set.

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