Analyses from stand to tree level allow disentangling the effects of age, size, origin and competition on tree growth sensitivity to climate in natural and afforested Scots pine forests

The sensitivity of tree growth to climate is conditioned by several variables, often intermingled, such as the origin of the forest (natural vs. artificial), tree age, tree size and tree-to-tree competition. The effect of these variables is usually inferred from average growth series obtained at the stand level, thus ignoring the differences at the individual tree level and their drivers. Our objective is to disentangle the effects of stand origin, age, size, competition and social status on the sensitivity of tree growth to climate, including the effects of extreme climatic events, such as droughts, on resistance and resilience, and also to show the advantages of using a tree-level approach. To this end, we compared four stands of Pinus sylvestris with contrasting characteristics: young afforested, young of natural origin, old afforested and old of natural origin. We analyzed differences in growth sensitivity to climate at both stand and tree levels to compare both approaches. Our results show the great complexity of the relationships of the variables considered with the sensitivity of growth to climate. All these variables are important and with strong interactions between them, which makes their effects not unidirectional and strongly dependent on the site conditions. While the stand approach hides these interactions, the tree-level approach makes it possible to analyze them in detail. In general, the sensitivity of growth to climate increases with age, diameter, afforested origin, decreasing competition and higher social status; growth resistance increases with age, decreasing diameter, afforested origin, decreasing competition and higher social status; and growth resilience increases with age, diameter, natural origin, decreasing competition and higher social status. We show the usefulness of the analysis of data at individual tree level, which, combined with the joint analysis at stand level, allows us to obtain more accurate and detailed information.

Horizonte 2020 de la Unión Europea -SUPERB H2020-101036849

Instituto de Ciencias Forestales ​(ICIFOR-INIA) del CSIC. España - IFN-2021

Ministerio de Ciencia e Innovación de España -TED2021-129770B-C21

Agencia Estatal de Investigación española - PID2020-119204RB-C22

Universidad de Sevilla - VII-PPITUS 2022