Abstract
Seasonal analyses of cambial cell production and day-by-day stem radial increment can help to elucidate how climate modulates wood formation in conifers. Intra-annual dynamics of wood formation were determined with microcores and dendrometers and related to climatic signals in Norway spruce (Picea abies (L.) Karst.). The seasonal dynamics of these processes were observed at two sites of different altitude, Savignano (650 m a.s.l.) and Lavazè (1800 m a.s.l.) in the Italian Alps. Seasonal dynamics of cambial activity were found to be site specifc, indicating that the phenology of cambial cell production is highly variable and plastic with altitude. There was a site-specifc trend in the number of cells in the wall thickening phase, with the maximum cell production in early July (DOY 186) at Savignano and in mid-July (DOY 200) at Lavazè. The formation of mature cells showed similar trends at the two sites, although different numbers of cells and timing of cell differentiation were visible in the model shapes; at the end of ring formation in 2010, the number of cells was four times higher at Savignano (106.5 cells) than at Lavazè (26.5 cells). At low altitudes, microcores and dendrometers described the radial growth patterns comparably, though the dendrometer function underlined the higher upper asymptote of maximum growth in comparison with the cell production function. In contrast, at high altitude, these functions exhibited different trends. The best model was obtained by ftting functions of the Gompertz model to the experimental data. By combining radial growth and cambial activity indices we defned a model system able to synchronize these processes. Processes of adaptation of the pattern of xylogenesis occurred, enabling P. abies to occupy sites with contrasting climatic conditions. The use of daily climatic variables in combination with plant functional traits obtained by sensors and/or destructive sampling could provide a suitable tool to better investigate the effect of disturbances on response strategies in trees and, consequently, contribute to improving our prediction of tree growth and species resilience based on climate scenarios. © 2016 The Author. Published by Oxford University Press.