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Predictability of marine nematode biodiversity
Merckx, B.; Goethals, P.; Steyaert, M.; Vanreusel, A.; Vincx, M.; Vanaverbeke, J. (2009). Predictability of marine nematode biodiversity. Ecol. Model. 220(11): 1449-1458.
In: Ecological Modelling. Elsevier: Amsterdam; Lausanne; New York; Oxford; Shannon; Tokyo. ISSN 0304-3800
Peer reviewed article  

Available in  Authors 
    Vlaams Instituut voor de Zee: Open Repository 147633 [ download pdf ]

    Artificial neural networks; Autocorrelation; Biodiversity; Marine; Nematoda [WoRMS]; Marine
Author keywords
    Biodiversity; Marine; Nematoda; Spatial autocorrelation; Artificialneural networks

Authors  Top 
  • Merckx, B.
  • Goethals, P.
  • Steyaert, M.
  • Vanreusel, A.
  • Vincx, M.
  • Vanaverbeke, J.

    In this paper, we investigated: (1) the predictability of different aspects of biodiversity, (2) the effect of spatial autocorrelation on the predictability and (3) the environmental variables affecting the biodiversity of free-living marine nematodes on the Belgian Continental Shelf. An extensive historical database of free-living marine nematodes was employed to model different aspects of biodiversity: species richness, evenness, and taxonomic diversity. Artificial neural networks (ANNs), often considered as “black boxes”, were applied as a modeling tool. Three methods were used to reveal these “black boxes” and to identify the contributions of each environmental variable to the diversity indices. Since spatial autocorrelation is known to introduce bias in spatial analyses, Moran's I was used to test the spatial dependency of the diversity indices and the residuals of the model. The best predictions were made for evenness. Although species richness was quite accurately predicted as well, the residuals indicated a lack of performance of the model. Pure taxonomic diversity shows high spatial variability and is difficult to model. The biodiversity indices show a strong spatial dependency, opposed to the residuals of the models, indicating that the environmental variables explain the spatial variability of the diversity indices adequately. The most important environmental variables structuring evenness are clay and sand fraction, and the minimum annual total suspended matter. Species richness is also affected by the intensity of sand extraction and the amount of gravel of the sea bed.

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