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.
2016
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Grete StokstadAbstract
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Christian PedersenAbstract
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Hanna Marika Silvennoinen Teresa Gómez de la Bárcena Christophe Moni Marcin Szychowski Paulina Rajewicz Daniel RasseAbstract
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Terroir characteristics of local food products are sometimes a result of ecosystem services from special nature types as mountain semi-natural grasslands. Several environmental conditions such as climate, topography, location above sea level, geology and soil are important factors defining frames for different vegetation types and available fodder resources in mountain areas. In addition, cultural traditions and a great variety in human land use systems are important determinants for grassland biodiversity. Results from several Norwegian studies show that species rich mountain pastures improve local food quality.
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Gisela Lüscher Youssef Ammari Aljona Andriets Siyka Angelova Michaela Arndorfer Debra Bailey Katalin Balázs Marion Bogers R.G.H. Bunce Jean-Philippe Choisis Peter Dennis Mario Díaz Tetyana Dyman Sebastian Eiter Wendy FjellstadAbstract
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Abstract Questions Vegetation mapping based on field surveys is time-consuming and expensive. Distribution modelling might be used to overcome these challenges. What is the performance of distribution modelling of vegetation compared to traditional vegetation mapping when projected locally? Does the modelling performance vary among ecosystems? Does vegetation type distribution and abundance influence the modelling performance? Location Gravfjellet, Øystre Slidre commune, southern Norway. Methods Two comparable neighbouring areas, each of 4 km2, were mapped for species-defined vegetation types. One area was used for model training, the other for model projection. Maximum entropy models were run for six vegetation types, two from each of the ecosystems present in the area: forest, wetland and mountain heath- and shrublands. For each ecosystem, one locally abundant and one locally rare vegetation type were tested. AUC, the area under the receiver operating curve, was used as the model selection criterion. Environmental variables (n = 9) were selected through a backwards selection scheme, and model complexity was kept low. The models were evaluated using independent data. Results Distribution modelling of vegetation types by local projection gave high AUC values, and the results were supported by the evaluation using independent data. The modelling ability was not affected by ecosystem differences. A negative relationship between the number of points used to train the models and the AUC value before evaluation suggests that models for locally rare vegetation types had better predictive performance than the models for abundant types. This result was not significant after evaluation. Conclusion Provided that relevant explanatory variables are available at an appropriate scale, and that field-validated training points are available, distribution modelling can be used for local projection of the six tested vegetation types from the boreal–alpine ecotone.