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ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on July 4, 2009
ICES Journal of Marine Science: Journal du Conseil 2009 66(10):2106-2115; doi:10.1093/icesjms/fsp195
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© 2009 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Spatial predictive distribution modelling of the kelp species Laminaria hyperborea

Trine Bekkby1, Eli Rinde1, Lars Erikstad2 and Vegar Bakkestuen2,3

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

Correspondence to T. Bekkby: tel: +47 22 185100; fax: +47 22 185200; e-mail: trine.bekkby{at}niva.no

Bekkby, T., Rinde, E., Erikstad, L., and Bakkestuen, V. 2009. Spatial predictive distribution modelling of the kelp species Laminaria hyperborea. – ICES Journal of Marine Science, 66: 2106–2115.

The kelp species Laminaria hyperborea constitutes highly productive kelp forest systems hosting a broad diversity of species and providing the basis for commercial kelp harvesting and, through its productivity, the fishing industry. Spatial planning and management of this important habitat and resource needs to be based on distribution maps and detailed knowledge of the main factors influencing the distribution. However, in countries with a long and complex coastline, such as Norway, detailed mapping is practically and economically difficult. Consequently, alternative methods are required. Based on modelled and field-measured geophysical variables and presence/absence data of L. hyperborea, a spatial predictive probability model for kelp distribution is developed. The influence of depth, slope, terrain curvature, light exposure, wave exposure, and current speed on the distribution of L. hyperborea are modelled using a generalized additive model. Using the Akaike Information Criterion, we found that the most important geophysical factors explaining the distribution of kelp were depth, terrain curvature, and wave and light exposure. The resulting predictive model was very reliable, showing good ability to predict the presence and absence of kelp.

Keywords: geographical distribution, GIS, habitat mapping, kelp, Laminaria hyperborea, Norway, Norwegian Sea, predictive modelling

Received 24 February 2009; accepted 9 June 2009; advance access publication 4 July 2009.


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