Linking fine-scale sub-arctic vegetation distribution in complex topography with surface-air-temperature modelled at 50-m resolution.

Recent studies have shown that the complexities of the surface features in mountainous terrain require a re-assessment of climate impacts at the local level. We explored the importance of surface-air-temperature based on a recently published 50-m-gridded dataset, versus soil variables for explaining vegetation distribution in Swedish Lapland using generalised linear models (GLMs). The results demonstrated that the current distribution of the birch forest and snowbed community strongly relied on the surface-air-temperature. However, temperature alone is a poor predictor of many plant communities (wetland, meadow). Because of diminishing sample representation with increasing altitude, the snowbed community was under-sampled at higher altitudes. This results in underestimation of the current distribution of the snowbed community around the mountain summits. The analysis suggests that caution is warranted when applying GLMs at the local level.