Key message: With a retrospective growth analysis over 20 years, we showed that silviculture (in particular thinning) ofCedrus atlantica(Manetti) could to some extent mitigate the effects of drought episodes. Severe reductions of density resulted in faster recovery of radial growth after severe drought episodes. Context: Climate change is expected to increase the frequency and the intensity of drought in southern Europe. Silviculture could help in mitigating the effects of water deficit on forests. Aim: The aim of this study is to assess the effect of thinning intensity on the climate-growth relationship of different crown classes in Cedrus atlantica (Manetti). Material and methods: A 20-year annual survey was conducted on plots managed under contrasted thinning regimes in a site of the southern French Alps. Using a linear mixed modeling approach, we evaluated the modulation of the growth response to annual climate by thinning (climate × thinning interaction effect) during the experiment. The effect of thinning on the growth sensitivity to annual climate and the effect of thinning on the growth response to pointer years were also quantified. Results: The highest intensity of thinning significantly changed the climate-growth relationship of the two most dominant crown classes. Heavy thinning reduced the impact of negative pointer years during a period of 4–5 years and improved the post-drought recovery for a decade. Heavily thinned plots and dominant crown classes had significantly higher relative growth sensitivities to annual climate over the studied period. Conclusions: Forest management has the potential to reduce the effect of water deficit on the growth of Atlas cedar stands. We provide keystones for future silvicultural guidelines, in the context of a larger introduction of C. atlantica in the French territory.
CITATION STYLE
Guillemot, J., Klein, E. K., Davi, H., & Courbet, F. (2015). The effects of thinning intensity and tree size on the growth response to annual climate in Cedrus atlantica: a linear mixed modeling approach. Annals of Forest Science, 72(5), 651–663. https://doi.org/10.1007/s13595-015-0464-y
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