VLIZ
VLAAMS INSTITUUT VOOR DE ZEE
MARIEN EN KUSTGEBONDEN ONDERZOEK & BELEID IN VLAANDEREN
   
© VLIZ © VLIZ © VLIZ © VLIZ © VLIZ
 
 
  English  Sitemap  Print
U bent hier: VLIZ > datacentrum
menu1 Over het VLIZ menu2 Infoloket menu3 Zeebibliotheek menu4 Cijfers&Beleid menu5 Faciliteiten menu6 Datacentrum
   
Datacentrum
  - IMIS: Integrated Marine Information System -
log in

Personen | Instituten | Publicaties | Projecten | Datasets | Kaarten
meld een fout in dit recordmandje (1): toevoegen | tonen Print-vriendelijke versie

one publication added to basket [124196]
Where is the worm? Predictive modelling of the habitat preferences of the tube-building polychaete Lanice conchilegaPeer reviewed article
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. dx.doi.org/10.1016/j.ecolmodel.2007.10.017
In: Ecological Modelling. Elsevier: Amsterdam. ISSN 0304-3800, meer

Beschikbaar in Auteurs 

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

Auteurs  Top 

Abstract
    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.

 Top | Auteurs 
 

 

Vlaams Instituut voor de Zee
InnovOcean site
Wandelaarkaai 7
B-8400 OOSTENDE, België
Tel: +32 [0]59/34 21 30
Fax: +32 [0]59/34 21 31
Email: info@vliz.be
   

 

Vlaamse Gemeenschap Provincie West-Vlaanderen