| Summary: | Reindeer (Rangifer tarandus) is the dominant large herbivore affecting the vegetation of the northern Eurasian tundra. It has been observed throughout the Arctic, and especially in Fennoscandia and northern Russia, that human-animal impact, e.g. concentrated grazing and trampling by semi-domesticated reindeer herds have changed the vegetation at large by creating graminoid dominated green patches, which have persisted over the centuries in some places [Hagström 1750; Aronsson 1991; Grøn et al. 1999; Forbes et al. 2001; Tømmervik et al. 2010]. Pollen-based reconstructions of land-cover by using the landscape reconstruction algorithm have been produced for several years in the more populated areas of the world [Sugita 2007a; 2007b]. This is not the case in the Arctic tundra, partly because of the lack of pollen productivity estimates of the tundra vegetation, partly because the area is virtually uninhabited and hence not in a significant role when the focus is on land-use change and its role in the anthropogenic climate forcing. To be able to model the effects that the growing size of the reindeer herds has had on the vegetation since the beginning of the domestication, a set of pollen productivity estimates from the tundra will be needed. Current work aims to calculate pollen productivity estimates for the most common taxa in tundra, and use pollen records from tundra lakes to run landscape reconstruction algorithm to reconstruct changes in the regional vegetation over millennia. The research area is located south of the Yuribey River in the Central Yamal Peninsula, western Siberia, Russia. Vegetation in the area is grass and sedge dominated dwarf shrubs tundra, Betula nana and Salix sp. growing in the moist areas. A series of 46 surface pollen samples with the percentage cover of the vegetation estimated around them were collected during two summer field seasons in 2013 and 2014. The vegetation data for the distance weighted plant abundance will be combined from field observations and ground truthed very high ...
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