Open Marien Archief | Vlaams Instituut voor de Zee

Vlaams Instituut voor de Zee

Platform voor marien onderzoek


Open Marien Archief

Het Open Marien Archief van België (OMA) biedt vrije toegang tot de digitale publicaties over de Vlaamse kust en het Belgisch deel van de Noordzee, en alle andere mariene, estuariene en kustgebonden publicaties van Belgische auteurs en wetenschappers en van buitenlandse wetenschappers geaffilieerd aan een Belgische instelling.

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Where is the worm? Predictive modelling of the habitat preferences of the tube-building polychaete Lanice conchilega
Willems, W.; Goethals, P.; Van den Eynde, D.; Van Hoey, G.; Van Lancker, V.; Verfaillie, E.; Vincx, M.; Degraer, S. (2008). Where is the worm? Predictive modelling of the habitat preferences of the tube-building polychaete Lanice conchilega. Ecol. Model. 212(1-2): 74-79.
In: Ecological Modelling. Elsevier: Amsterdam; Lausanne; New York; Oxford; Shannon; Tokyo. ISSN 0304-3800, meer
Peer reviewed article

Beschikbaar in Auteurs 

    Habitatselectie; Lanice conchilega (Pallas, 1766) [WoRMS]; Lanice conchilega (Pallas, 1766) [WoRMS]; Polychaeta [WoRMS]; Marien
Author keywords
    Lanice conchilega; Polychaeta; Habitat preference; Generalized linear models (GLM); Artificial neural networks (ANN)

Auteurs  Top 
  • Willems, W., meer
  • Goethals, P., meer
  • Van den Eynde, D., meer
  • Van Hoey, G., meer

    Grab samples to monitor the distribution of marine macrobenthic species (animals >1 mm, living in the sand) are time consuming and give only point based information. If the habitat preference of a species can be modelled, the spatial distribution can be predicted on a full coverage scale from the environmental variables. The modelling techniques Generalized Linear Models (GLM) and Artificial Neural Networks (ANN) were compared in their ability to predict the occurrence of Lanice conchilega, a common tube-building polychaete along the North-western European coastline. Although several types of environmental variables were in the data set (granulometric, currents, nutrients) only three granulometric variables were used in the final models (median grain-size, % mud and % coarse fraction). ANN slightly outperformed GLM for a number of performance indicators (% correct predictions, specificity and sensitivity), but the GLM were more robust in the crossvalidation procedure.

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