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ICES Journal of Marine Science: Journal du Conseil Advance Access published online on June 4, 2008
ICES Journal of Marine Science: Journal du Conseil, doi:10.1093/icesjms/fsn095
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© 2008 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Spatial probability modelling of eelgrass (Zostera marina) distribution on the west coast of Norway

Trine Bekkby1, Eli Rinde1, Lars Erikstad2, Vegar Bakkestuen2,3, Oddvar Longva4, Ole Christensen5, Martin Isæus6 and Pål Erik Isachsen7

1 Norwegian Institute for Water Research, Gaustadalléen 21, N-0349 Oslo, Norway
2 Norwegian Institute for Nature Research, Gaustadalléen 21, N-0349 Oslo, Norway
3 Department of Botany, NHM, University of Oslo, PO Box 1172 Blindern, N-0318 Oslo, Norway
4 Geological Survey of Norway, N-7491 Trondheim, Norway
5 Electromagnetic Geoservices (EMGS), Stiklestadveien 1, N-7041 Trondheim, Norway
6 AquaBiota Water Research, Svante Arrhenius väg 21A, SE-10405 Stockholm, Sweden
7 Norwegian Meteorological Institute, Gaustadalléen 21, N-0349 Oslo, Norway

Correspondence to T. Bekkby: tel: +47 22185100; fax: +47 22185200; e-mail: trine.bekkby{at}niva.no.

Bekkby, T., Rinde, E., Erikstad, L., Bakkestuen, V., Longva, O., Christensen, O., Isæus, M., and Isachsen, P. E. 2008. Spatial probability modelling of eelgrass (Zostera marina) distribution on the west coast of Norway. – ICES Journal of Marine Science, 65.

Based on modelled and measured geophysical variables and presence/absence data of eelgrass Zostera marina, we developed a spatial predictive probability model for Z. marina. Our analyses confirm previous reports and show that the probability of finding Z. marina is at its highest in shallow, gently sloping, and sheltered areas. We integrated the empirical knowledge from field samples in GIS and developed a model-based map of the probability of finding Z. marina using the model-selection approach Akaike Information Criterion (AIC) and the spatial probability modelling extension GRASP in S-Plus. Spatial predictive probability models contribute to a better understanding of the factors and processes structuring the distribution of marine habitats. Additionally, such models provide a useful tool for management and research, because they are quantitative and defined objectively, extrapolate knowledge from sampled to unsurveyed areas, and result in a probability map that is easy to understand and disseminate to stakeholders.

Keywords: Akaike's information criterion (AIC), eelgrass, GIS, habitat mapping, predictive modelling, seagrass, Zostera marina

Received 8 November 2007; accepted 25 April 2008.


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