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    Scientific background document in support of the development of a CCAMLR MPA in the Weddell Sea (Antarctica) – Version 2014 (2014)
    CCAMLR
    In:  EPIC3CCAMLR Scientific Committee / sc-xxxiii-bg-02, CCAMLR
    Details
    Publication Date: 2018-08-10
    Description: Germany intends to present the Scientific Committee the background document that provides the scientific basis for the evaluation of marine protected areas (MPAs) in the Weddell Sea. Please note, that the current state of the background document presents a comprehensive yet incomplete first version concerning chapters that have to be (further) developed or revised. The contents and structure of the document reflect also its main objectives, i.e. (i) to set out the general background and context of the establishment of MPAs, (ii) to describe the boundaries of the Weddell Sea MPA Planning Area, (iii) to inform on the data retrieval process, (iv) to provide - for the first time- a comprehensive, yet succinct, general description of the Weddell Sea ecosystem to reflect the state of the science, and additionally to present the results of the various preliminary scientific analyses that were carried out so far within the framework of the MPA Weddell Sea project, and finally (v) to describe future work beyond the development of the scientific basis for the evaluation of a Weddell Sea MPA.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Miscellaneous , notRev
    Format: application/pdf
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  • 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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