Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2020
Authors
Lars Rød-Eriksen Johanna Skrutvold Ivar Herfindal Henrik Jensen Nina Elisabeth EideAbstract
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Aim: 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 curves
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Editors
Ragnar Våga PedersenAbstract
Brochure in English about NIBIO. NIBIO contributes to food security and safety, sustainable resource management, innovation and value creation through research and knowledge production. Multi-disciplinary and integrated activities Science-policy-stakeholder interactions
Authors
Lorène Julia Marchand Inge Dox Jožica Gričar Peter Prislan Sebastien Leys Jan Van den Bulcke Patrick Fonti Holger Lange Erik Matthysen Josep Peñuelas Paolo Zuccarini Matteo CampioliAbstract
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