Abstract

Large-scale climate indices, such as the Atlantic Multidecadal Oscillation (AMO), the East-Atlantic pattern (EA), the North-Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO), are recognized as important drivers of tree growth and forest productivity in south-western Europe. However, it is still poorly understood whether species respond to these climatic influences differently. Do different pine tree species respond similarly to the main climatic patterns in south-western Europe? If not, will the species respond according to what should be expected from their biogeographical character? Here we analyse the paper of climatic patterns on tree growth in a mountainous Mediterranean environment where three contrasting pine species cohabit (Pinus nigra, P. pinaster and P. sylvestris). We first identified the climatic patterns that most influence temperature and precipitation variability during the coldest months of the year. Secondly, we evaluated the role of these climatic patterns as drivers of Basal Area Increments (BAI) using mixed-effect models separately for each of the three pines considered. A backward model selection was applied using AICc. Only two out of the four climatic patterns analysed were supported as potential drivers of temperature and precipitation variability in our study site. Specifically, EA correlated positively with temperatures and precipitation, whereas NAO correlated negatively with precipitation. Model selection supported both EA and NAO as important drivers of BAI in P. nigra and P. sylvestris, while P. pinaster was sensitive to EA. Our results highlight EA as the most influential climatic driver of tree growth in this area. Furthermore, and according to the biogeographical characters of the species studied, the models show how high temperatures influence P. pinaster the most, while P. sylvestris is the most sensitive species to precipitation variability. Our findings show the existence of clear species-specific differences in tree growth responses to climatic variability patterns.

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