Regional variability of climate-growth relationships in Pinus cembra high elevation forests in the Alps.

1. The tree-ring growth response of stone pine (Pinus cembra L.) to climatic variability was studied in the Alps. The aims were (i) to assess tree-ring growth patterns at different spatial-temporal scales; (ii) to identify the climate parameters that explain most of the variability in radial growth at different time domains; and (iii) to study past and current trends in radial growth and climate-growth relationships at different locations. 2. High- and low-frequency stone pine chronologies were compiled for 30 treeline sites on the French and Italian Alps. We used gridded climate data computed from 200 years of instrumental records from an extensive Alpine network. Climate-growth relationships were computed with bootstrap correlation functions and their stationarity and consistency over time assessed with moving correlation. 3. No spatial patterns were detected in stone pine chronology statistics despite the regional clustering observed in tree-ring series and climate responses. This can be attributed to (i) local weather variability; (ii) different biophysical conditions caused by soil moisture, solar radiation, snowmelt dynamics and growing season length; and (iii) forest stand history and age structure, the expression of long-term land use and disturbances. 4. The exceptionally long-term climate records allowed significant stone pine growth response changes to be assessed at both annual and decadal time scales. Winter conditions and spring-summer temperatures mainly affected the growing season length, in addition to site carbon and water balance. Most of these limiting factors varied spatially and temporally along the latitudinal and longitudinal gradients in response to the corresponding changes in local conditions. 5. Our results show evidence of a clear response variability of Pinus cembra to climate limiting factors, at both spatial and temporal scale. Such knowledge extended to other species and regions will provide better estimates of the effect of climate variability on species distribution and dynamics within global change scenarios and more accurate past climate reconstruction and forest ecosystem modelling