Identifying ontogenetic, environmental and individual components of forest tree growth

Abstract

† Background and Aims This study aimed to identify and characterize the ontogenetic, environmental and individual components of forest tree growth. In the proposed approach, the tree growth data typically correspond to the retrospective measurement of annual shoot characteristics (e.g. length) along the trunk. † Methods Dedicated statistical models (semi-Markov switching linear mixed models) were applied to data sets of Corsican pine and sessile oak. In the semi-Markov switching linear mixed models estimated from these data sets, the underlying semi-Markov chain represents both the succession of growth phases and their lengths, while the linear mixed models represent both the influence of climatic factors and the inter-individual heterogeneity within each growth phase. † Key Results On the basis of these integrative statistical models, it is shown that growth phases are not only defined by average growth level but also by growth fluctuation amplitudes in response to climatic factors and inter-individual heterogeneity and that the individual tree status within the population may change between phases. Species plasticity affected the response to climatic factors while tree origin, sampling strategy and silvicultural interventions impacted inter-individual heterogeneity. † Conclusions The transposition of the proposed integrative statistical modelling approach to cambial growth in relation to climatic factors and the study of the relationship between apical growth and cambial growth constitute the next steps in this research.

Growth-unit structure in trees: effects of branch category and position on Nothofagus nervosa, N. obliqua and their hybrids (Nothofagaceae)

“…All individuals chosen for this study had relatively unshaded upper branches and shaded lower branches, so that had light conditions ruled GU traits, within-individual variability in these traits would have been higher than it actually was. Thus, the results sustain the idea that microclimate modulates the variations in GU traits around mean values typical of each ontogenetic stage within the species' sequence, in compliance with previous studies (Suzuki 2003;Suzuki and Suzuki 2009;Fernández et al 2007;Chaubert-Pereira et al 2009;Coste et al 2009). Thomas and Winner (2002), Ishida et al (2005), Holdaway et al (2008 and Valladares and Niinemets (2008) underlined the relevance of axis differentiation for the acclimation of plants to contrasting conditions.…”

“…In addition, each of these axis categories would drift from an earlier ontogenetic stage at the time of their inception, to later ontogenetic stages as their development progresses over the years. Branches developed at high levels on the trunk would be expressing an earlier ontogenetic stage than the older, lower branches (see Chaubert-Pereira et al 2009). In the Nothofagus entities studied here, there seems to be an ontogenetic tendency towards a decreasing investment in support tissues in favour of the investment in leaf area along the development of each axis.…”

Markov and Semi‐Markov Switching Linear Mixed Models Used to Identify Forest Tree Growth Components

“…Complementary biological results concerning Corsican pine and sessile oak growth can be found in Chaubert‐Pereira et al (2009).…”

Identifying developmental phases in the Arabidopsis thaliana rosette using integrative segmentation models

“…Semi-Markov switching models for identifying developmental or growth phases in various plants species described at different scales Semi-Markov switching models are flexible models that were previously applied to identify and characterize developmental or growth phases in plants described at various scales. They were first used to identify growth phases in different forest tree species (Corsican pine, Scots pine, silver fir, Persian walnut, sessile oak) where the main stems were described retrospectively by annual shoots (Gu edon et al, 2007;Chaubert-Pereira et al, 2009;Taugourdeau et al, 2011Taugourdeau et al, , 2015. The observation models within Semi-Markov switching models were typically various distributions for modeling dimensions (annual shoot length) or counts (number of branches per tier for pines and silver fir, number of growth units for sessile oak).…”