| Summary: | Tundra ecosystems are globally under a threat due to climate warming that has been projected to be up to four times stronger in the high-latitude regions than the global average, even though direct human impacts are lesser in these remote areas than elsewhere. Additionally, climate at high latitudes is predicted to shift from snow- towards rain-dominated. Changes in cryospheric, that is, snow- and frost-related, conditions might be particularly important as they would alter freeze and thaw cycling and the related processes posing a great extinction threat to snow- dependent and stress-tolerant arctic-alpine species. Decline in species´ ranges as well as changes in community composition and diversity are expected, which will reflect to ecosystem processes. In this thesis, I study drivers of species distributions, occupancy, community functional and phylogenetic composition and diversity of vascular plants in the high-latitude areas of the northern hemisphere focusing on the arctic-alpine region of Fennoscandia, northern Europe. The relationships between vegetation patterns and their drivers are explored at multiple spatial scales using various statistical modelling methods widely applied in biogeography and ecology but expanding the former approaches by considering cryogenic and geomorphologic predictor variables to look beyond the effects of commonly examined climate and soil variables on vegetation. I found that snow information is required when modelling vegetation patterns in cold ecosystems regardless of the study scale and the studied response variable as snow persistence was a highly important predictor for species distributions, community functional composition and diversity. However, contrasting responses may arise depending on the studied taxa, functional property, and diversity facet. Furthermore, study scale can affect the relationship between environment and plant occupancy and community functional composition. Altogether, investigating vegetation patterns at several spatial scales using different ...
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