The space-time structure of a teak wood specific gravity (SG) dataset was analyzed using a mixed-effects model. Spatial correlation increased in space, a phenomenon attributable to the maturation of apical meristems, while the temporal correlation of vascular meristems decreased over time. The decay of temporal correlation over time was attributed to the diminishing crown effect on the later formed wood further away from the pith, morphogen gradient, and probably changing microenvironmental conditions. The Kronecker product was used to collect spatiotemporal data on the intricate dynamic process of the evolution of the apical and lateral meristems. The results showed that height and relative radial distance (RRD) (i.e., the flow of time with wood formation) were statistically significant factors, with their interaction showing no significance. The results confirm the usefulness of using the space-time approach to elucidate the interaction between the apical and lateral meristems, two major inherent biological systems that control tree growth and wood formation dynamics. To understand the origins of patterns that vary both temporally and spatially in the tree, future work should describe the variation of SG within the tree due to increasing height (space) and diameter (age) as a matrix; then the correlation function can be modelled.
To test whether radial variation of wood specific gravity (WSG) is controlled by tree age or tree size in teak (Tectona grandis L.f) plantation trees, opposing different-length pith-to-bark strips which represents the differential lateral growth rate was compared using mixed-effects model which considers the heterogeneity of variances and dependency in the data to gain insight into the stochastic processes that govern the wood formation process. Various models were tested in devising an appropriate radial WSG model. Models that accounted for serial correlation in WSG data performed better than the simple structure that assumes zero correlation between measurements. The autoregressive plus random tree effect structure performed better in describing the radial variation pattern. The variability of the data related to random fluctuations during tree development and the wood formation process is modeled by the autoregressive parameter revealing the intrinsic complexity of wood formation. Since they cannot be attributed to observed factors, models should consider temporal or serial correlations when assessing wood quality. The results revealed that tree age is a decisive factor in controlling the WSG of wood, while tree size is statistically less important. Furthermore, the core wood production period varies with the growth rate. It is shown that the core wood area decreased with slow growth. Findings presented here appear to provide the first demonstration of radial variation in WSG with respect to growth rate and age for planted teak growing in Ghana.
Heartwood color is a complex trait that affects the economic and aesthetic value of the wood but is highly variable. How the color of the heartwood varies spatially and temporally is poorly understood. To illustrate how heartwood color varies within a tree, two opposite aspects of wood within the same tree, representing differential growth rate, were used to model the long-short axis system jointly. The color of the heartwood on the long and the short axis was considered to be two different traits. By jointly modeling the long and short axes, the correlation was examined between aspect (spatial) and contemporaneous correlations (within aspect). Spatial and temporal correlations and their interactions describe the indirect physiological, genetic, and environmental changes in wood formation with time and position in the trunk. Spatial correlations were consistently lower than temporal correlations but were positive and significant. Between the heartwood color parameters, b* showed a relatively higher spatial correlation. The results suggest that there is a spatial correlation in the long-short axis for all color parameters and in the two surfaces. Variations between aspects were not statistically significant for any color parameter. The bivariate mixed model method revealed hidden physics behind heartwood color formation. Models need to be developed to account for both spatial and temporal dependence in studies of wood property change.
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