Arctic vegetation, snow and the global change - Niittynen, Pekka (Helsingin yliopisto, 2020)
The Arctic is warming two to three times faster than the global average. However, climate change is proceeding at different pace between seasons and the warming has been most prominent in winter. For most of the year, majority of the arctic organisms are covered by insulating snowpack. Snow protects arctic plants, bryophytes and lichens from weather events in the free atmosphere and may provide relatively warm and stable overwintering conditions. The importance of snow has been widely acknowledged, but snow information is rather rarely utilized in climate change impact models that predict the future state of the arctic vegetation. This is largely due to missing wintertime datasets and harsh winter conditions that limit field work efforts in the Arctic. Therefore, there has remained a largely unanswered question: what is the role of snow conditions in spatial redistribution of arctic species and vegetation under rapidly warming climate? In this thesis, I address these gaps in knowledge and methodology. I utilise extensive plot-scale vegetation datasets and link these data to detailed microclimatic measurements covering both summer and winter conditions and to satellite-born snow information at fine spatial scales. I use a suite of statistical modelling methods to explore the snow-vegetation relationships in species pools consisting several hundreds of arctic, alpine and boreal vascular plant, bryophyte and lichen species in northern Fennoscandia, Svalbard and western Greenland. These models are further used to predict patterns in species distributions, community and functional trait compositions and biodiversity in space and time, to test the sensitivity of these vegetation properties to concurrent and separate changes in snow conditions and temperatures. I found that snow and winter conditions have a fundamental role in arctic ecosystems by mediating the effects of climate change at local and regional scales. Snow information improves the accuracy of the models of arctic vegetation and reveals possible future ...