| Summary: | The Red List of threatened species is among the best tools available for management and conservation of species. In this thesis, I study nationally red-listed species at various geographical scales from fine-scale forest areas in Norway, to the region of Fennoscandia. I focus on the use of national Red Lists as a tool for assisting conservation priorities, for identification of important habitats for red-listed species, and for selection of sites for conservation. For national Red List assessments, most species are assessed at a scale smaller than their distribution range, and the national status is therefore often based on assessment of parts of the total population. This might pose challenges to conservation, as the species can have a different status at broader scales. In the first paper of this thesis, I investigate the effects of geographic scale on nationally red-listed species in Fennoscandia. The national Red Lists of Finland, Norway and Sweden was used to create a dataset of 4830 nationally red-listed forest species from the three countries. From this dataset, a subset called “Candidates for a Fennoscandian Red List” was extracted, and for each country this set of candidates, representing the regional level, was compared with the nationally red-listed species not chosen as candidates. Our results showed that the set of “Candidates” from each country represented a similar composition of organism groups and species of similar forest associations, despite including a lower number of species. In the second paper, the aim was to investigate if ecological documentation in national Red Lists could be sufficient to characterize general habitat associations and important ecological variables for red-listed species in Fennoscandia. The same Fennoscandian Red List dataset was used, and ecological information extracted for each species and used for analyses. Results showed that criteria documentation in national Red Lists can be used to identify habitat associations and important ecological variables for larger ...
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