The global climate has increased by 0.6°C over the last century, with further increases predicted. Mountain ecosystems are expected to be sensitive to climatic change. European studies show an upward movement of plant species, which has been attributed to climate change. Given the lack of suitable historical surveys to investigate the potential changes in Scottish mountain vegetation due to climate change over the past century, so a different approach was taken:
Firstly, the potential for seed to move to higher altitudes under predicted climate change was investigated. Seed rain and the seedbanks were measured along several altitudinal transects in the Grampian Mountains (Cairngorms), Scotland. A vegetation survey was carried out.
Secondly, Open Top Chambers were used to simulate predicted effects of climate change, to test whether the balance between competition and facilitation would change in arctic/alpine plant communities. The target plants, Carex bigelowii and Alchemilla alpina, were transplanted into replicate environmental treatments, with and without neighbours. Measurements were taken of final above ground biomass and environmental variables.
No evidence was found for significant upward movements of seed. Calluna vulgaris was found in the seedbank at higher altitudes than the surface vegetation. However, this is unlikely to lead to Calluna colonising the alpine zone under predicted climate change. No significant changes in seedbanks were found. The results of the competition/facilitation experiment were inconclusive.
In conclusion, mountain vegetation zones in Scotland will not move as whole, but there may be some migrations of individual species. These migrations could have unexpected consequences and may be in directions different to expectation. Changes in zonation of communities are unlikely to be solely temperature driven, however current modelling approaches are not sensitive to other factors. There is a need for more field data on how climate change will affect alpine/arctic vegetation, before more reliable models can be developed.
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© Kim Harding 2005 - 2023
