Marine Biodiversity and Ecosystem Functioning
EU Network of Excellence

 
Main Menu

· Home
· Contacts
· Data Systems
· Documents
· FAQ
· Links
· MarBEF Open Archive
· Network Description
· Outreach
· Photo Gallery
· Quality Assurance
· Register of Resources
· Research Projects
· Rules and Guidelines
· Training
· Wiki
· Worldconference

 

Register of Resources (RoR)

 People  |  Datasets  |  Literature  |  Institutes  |  Projects 

[ report an error in this record ]basket (1): add | show Print this page

one publication added to basket [134327]
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. dx.doi.org/10.1016/j.ecolmodel.2009.03.016
In: Ecological Modelling. Elsevier: Amsterdam; Lausanne; New York; Oxford; Shannon; Tokyo. ISSN 0304-3800; e-ISSN 1872-7026
Peer reviewed article  

Available in  Authors 

Keywords
    Analysis > Mathematical analysis > Statistical analysis > Correlation analysis > Autocorrelation
    Artificial neural networks
    Biodiversity
    Marine
    Nematoda [WoRMS]
    Marine/Coastal
Author keywords
    Biodiversity; Marine; Nematoda; Spatial autocorrelation; Artificialneural networks

Authors  Top 
  • Merckx, B., more
  • Goethals, P.
  • Steyaert, M., more

Abstract
    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.

All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors 


If any information here appears to be incorrect, please contact us
Back to Register of Resources
 
Quick links

MarBEF WIKI

Erasmus Mundus Master of Science in Marine Biodiversity and Conservation (EMBC)
Outreach

Science
Responsive Mode Programme (RMP) - Marie Nordstrom, copyright Aspden Rebecca

WoRMS
part of WoRMS logo

ERMS 2.0
Epinephelus marginatus Picture: JG Harmelin

EurOBIS

Geographic System

Datasets

 


Web site hosted and maintained by Flanders Marine Institute (VLIZ) - Contact data-at-marbef.org