Degraer, S.

Goethals, P.

Van Lancker, V.R.M.

Verfaillie, E.

Vincx, M.

Willems, W.

Open Marine Archive

 HABITAT SUITABILITY MODELLING OF THE NORTH SEA 
MACROBENTHOS: DATA EXPLORATION 
Willems Wouter1, Peter Goethals2, Vera Van Lancker3, Els Verfaillie3, Magda Vincx1 and 
Steven Degraer1 
1 Section Marine Biology, Department of Biology, Ghent University 
Krijgslaan 281/S8, B-9000 Ghent, Belgium 
E-mail: wouter.willems@UGent.be 
2 Laboratory of Environmental Toxicology and Aquatic Ecology, Department of Applied Ecology 
and Environmental Biology, Ghent University, J. Plateaustraat 22, B-9000, Ghent, Belgium 
3 Renard Centre for Marine Geology, Department of Geology and Soil Science, Ghent 
University, Krijgslaan 281/S8, B-9000 Ghent, Belgium 
In the context of sustainable management of the North Sea it is necessary to have 
detailed knowledge of the ecosystem status. This study aims at developing distribution 
maps of the North Sea macrobenthos (i.e. bottom dwelling animals >1mm) through 
habitat suitability modelling. Macrobenthos was selected because of (1) its key function 
as a food resource for many fish species, (2) the ecological knowledge already 
available, and (3) the well known link between the distribution of macrobenthos and the 
physical habitat, indispensable for habitat suitability models. Based on the suitability of 
the physical habitat, habitat suitability models can predict the spatial distribution of a 
species. To develop the models, three datasets with varying scale and resolution are 
available. These datasets contain information on the macrobenthos and the physical 
environment (North Sea, Belgian part of the North Sea and Belgian Western Coastal 
Banks).  
As a first step in the modelling approach, classification trees (CTs) were constructed for 
a set of key species. This data mining technique uses a tree to present a habitat 
classification scheme based on a set of rules. It can help identify the important 
environmental variables (and their critical values) for a species. By answering questions 
on the physical habitat (e.g. depth > 10 m or depth < 10 m), the user is directed 
through the hierarchical tree down to the level of the actual prediction of the species 
occurrence. Opposed to traditional classification techniques (e.g. discriminant 
functions), CTs have a hierarchical structure and require fewer assumptions to be 
fulfilled. CTs are visually attractive and allow for an easy interpretation of the patterns in 
the data, also by non-experts (e.g. decision makers).  
In a later stage of the research, regression and neural network models will be 
developed. All modelling techniques will be compared and a strategy will be proposed 
for intertuning data collection, modelling and management. This will particularly 
enhance and stimulate mapping exercises of the North Sea ecosystem, necessary to 
protect marine resources in a sustainable manner in the future. 
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Habitat suitability modelling of the North Sea macrobenthos: data exploration

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