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dc.contributor.authorSimensen, Trond
dc.contributor.authorHorvath, Peter
dc.contributor.authorVollering, Julien
dc.contributor.authorErikstad, Lars
dc.contributor.authorHalvorsen, Rune
dc.contributor.authorBryn, Anders
dc.date.accessioned2021-02-04T17:58:24Z
dc.date.available2021-02-04T17:58:24Z
dc.date.created2020-06-10T13:55:47Z
dc.date.issued2020-06-10
dc.identifier.citationSimensen, T, Horvath, P, Vollering, J, Erikstad, L, Halvorsen, R, Bryn, A. Composite landscape predictors improve distribution models of ecosystem types. Divers Distrib. 2020; 26: 928– 943.en_US
dc.identifier.issn1366-9516
dc.identifier.urihttps://hdl.handle.net/11250/2726281
dc.description.abstractAim: Distribution modelling is a useful approach to obtain knowledge about the spatial distribution of biodiversity, required for, for example, red-list assessments. While distribution modelling methods have been applied mostly to single species, modelling of communities and ecosystems (EDM; ecosystem-level distribution modelling) produces results that are more directly relevant for management and decision-making. Although the choice of predictors is a pivotal part of the modelling process, few studies have compared the suitability of different sets of predictors for EDM. In this study, we compare the performance of 50 single environmental variables with that of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The CLGs represent gradients in landscape element composition derived from multivariate analyses, for example “inner-outer coast” and “land use intensity.” Location: Norway. Methods: We used data from field-based ecosystem-type mapping of nine ecosystem types, and environmental variables with a resolution of 100 × 100 m. We built nine models for each ecosystem type with variables from different predictor sets. Logistic regression with forward selection of variables was used for EDM. Models were evaluated with independently collected data. Results: Most ecosystem types could be predicted reliably, although model performance differed among ecosystem types. We identified significant differences in predictive power and model parsimony across models built from different predictor sets. Climatic variables alone performed poorly, indicating that the current climate alone is not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs resulted in parsimonious models with relatively high predictive power. Used together with other variables, they consistently improved the models. Main conclusions: Our study highlights the importance of variable selection in EDM. We argue that the use of composite variables as proxies for complex environmental gradients has the potential to improve predictions from EDMs and thus to inform conservation planning as well as improve the precision and credibility of red lists and global change assessments.conservation planning, distribution modelling, ecosystem classification, ecosystem types, IUCN Red List of Ecosystems, landscape gradients, spatial prediction, species response curvesen_US
dc.language.isoengen_US
dc.publisherJohn Wiley & Sons Ltd.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleComposite landscape predictors improve distribution models of ecosystem typesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2020 The Authorsen_US
dc.subject.nsiVDP::Økologi: 488en_US
dc.subject.nsiVDP::Ecology: 488en_US
dc.source.pagenumber928-943en_US
dc.source.volume26en_US
dc.source.journalDiversity and Distributions: A journal of biological invasions and biodiversityen_US
dc.source.issue8en_US
dc.identifier.doi10.1111/ddi.13060
dc.identifier.cristin1814808
dc.relation.projectNorges forskningsråd: xxxxxxxxen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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