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dc.contributor.authorvan Son, Thijs Christiaan
dc.contributor.authorNikolioudakis, Nikolaos
dc.contributor.authorSteen, Henning
dc.contributor.authorAlbretsen, Jon
dc.contributor.authorFurevik, Birgitte Rugaard
dc.contributor.authorElvenes, Sigrid
dc.contributor.authorMoy, Frithjof
dc.contributor.authorNorderhaug, Kjell Magnus
dc.date.accessioned2020-10-15T13:12:33Z
dc.date.available2020-10-15T13:12:33Z
dc.date.created2020-07-20T14:15:34Z
dc.date.issued2020
dc.identifier.citationFrontiers in Marine Science. 2020, 7 .en_US
dc.identifier.issn2296-7745
dc.identifier.urihttps://hdl.handle.net/11250/2683121
dc.description.abstractKelp forests are highly productive systems that are important ecologically and commercially as well as in a blue carbon perspective. Given their importance, there is an urgent need to achieve reliable estimates of the spatial distribution of their biomass. Species distribution modelling is a powerful tool for producing such estimates, but it requires a solid framework, including important environmental covariates that have a direct effect on their biomass, a proper sampling strategy, and an independent evaluation dataset. Using Laminaria hyperborea as a model species, we developed a modelling framework considering these requirements and necessary steps to produce reliable predictions. Our modelling framework included proportion of hard substrate and bottom wave exposure, both crucial covariates that have a direct effect on the biomass of L. hyperborea, but rarely included in modelling studies. Furthermore, we devised a sampling strategy with field observations covering the whole environmental covariate space present in the study area. Subsequently, we fitted GAMs relating the field observations of the biomass of L. hyperborea to relevant environmental covariates. The best model containing the predictors bottom wave exposure, depth, and proportion hard substrate explained most of the variance in the dataset (83.1% deviance explained). This model was then used to predict the spatial distribution of biomass across the whole study area. To assess the reliability of the biomass predictions, we used an independent dataset of L. hyperborea biomass observations from the same area. This independent dataset correlated very well with spatial predictions of biomass based on our best model (R = 0.85). In total, we predicted a biomass of 457,000 tonnes in a 1,150 km2 study area on the West coast of Norway. Our modelling framework provides the means for developing a biomass model on a broader geographical scale. Such a model will be invaluable in improving kelp management regimes as well as for estimating the contribution of kelp forests to ecosystem services such as carbon sequestration and climate budgets.en_US
dc.language.isoengen_US
dc.titleAchieving Reliable Estimates of the Spatial Distribution of Kelp Biomassen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber13en_US
dc.source.volume7en_US
dc.source.journalFrontiers in Marine Scienceen_US
dc.identifier.doi10.3389/fmars.2020.00107
dc.identifier.cristin1819878
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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