ContextWood specific gravity is a key element in tropical forest ecology. It integrates many aspects of tree mechanical properties and functioning and is an important predictor of tree biomass. Wood specific gravity varies widely among and within species and also within individual trees. Notably, contrasted patterns of radial variation of wood specific gravity have been demonstrated and related to regeneration guilds (light demanding vs. shade-bearing). However, although being repeatedly invoked as a potential source of error when estimating the biomass of trees, both intraspecific and radial variations remain little studied. In this study we characterized detailed pith-to-bark wood specific gravity profiles among contrasted species prominently contributing to the biomass of the forest, i.e., the dominant species, and we quantified the consequences of such variations on the biomass.MethodsRadial profiles of wood density at 8% moisture content were compiled for 14 dominant species in the Democratic Republic of Congo, adapting a unique 3D X-ray scanning technique at very high spatial resolution on core samples. Mean wood density estimates were validated by water displacement measurements. Wood density profiles were converted to wood specific gravity and linear mixed models were used to decompose the radial variance. Potential errors in biomass estimation were assessed by comparing the biomass estimated from the wood specific gravity measured from pith-to-bark profiles, from global repositories, and from partial information (outer wood or inner wood).ResultsWood specific gravity profiles from pith-to-bark presented positive, neutral and negative trends. Positive trends mainly characterized light-demanding species, increasing up to 1.8 g.cm-3 per meter for Piptadeniastrum africanum, and negative trends characterized shade-bearing species, decreasing up to 1 g.cm-3 per meter for Strombosia pustulata. The linear mixed model showed the greater part of wood specific gravity variance was explained by species only (45%) followed by a redundant part between species and regeneration guilds (36%). Despite substantial variation in wood specific gravity profiles among species and regeneration guilds, we found that values from the outer wood were strongly correlated to values from the whole profile, without any significant bias. In addition, we found that wood specific gravity from the DRYAD global repository may strongly differ depending on the species (up to 40% for Dialium pachyphyllum).Main ConclusionTherefore, when estimating forest biomass in specific sites, we recommend the systematic collection of outer wood samples on dominant species. This should prevent the main errors in biomass estimations resulting from wood specific gravity and allow for the collection of new information to explore the intraspecific variation of mechanical properties of trees.

The phenomenon of distinct, absent or indistinct growth rings is a highly variable feature used for wood identification and a wide range of tree-ring studies. Causes for its variability are not yet fully understood. There is also a lack of consensus within the scientific community about how distinct and indistinct tree rings should be defined and classified. We use a selection of 103 Central African rainforest trees to analyse the anatomy of growth-ring boundaries of 103 Central African rainforest species and assessed the influence of the climate, tree organ and leaf shedding behaviour on growth-ring distinctness and anatomy. We observed a high variability of tree-ring boundaries anatomy and distinctness within and among individuals and species. Although, for some semi-deciduous species, higher incidence of distinct growth rings appears to be related with a more pronounced seasonal climate, no general trends are observed for the assembly of studied species. Growth rings are variable within individuals depending on the considered organ: trunks tend to show more distinct rings than branches. Growth-ring distinctness is difficult to implement as a trait to measure tree performance when only based on abrupt changes in fibre size and cell wall thickness. From the potential growth-ring markers identified in the IAWA list of hardwood features, those applying to vessel and parenchyma density and distended rays appear to be more useful in tropical trees than abruptly flattened latewood fibres or abrupt changes in vessel diameter.

Wood density profiles reveal a tree’s life strategy and growth. Density profiles are, however, rarely defined in terms of tissue fractions for wood of tropical angiosperm trees. Here, we aim at linking these fractions to corresponding density profiles of tropical trees from the Congo Basin. Cores of 8 tree species were scanned with X-ray Computed Tomography to calculate density profiles. Then, cores were sanded and the outermost 3 cm were used to semi-automatically measure vessel lumen, parenchyma and fibre fractions using the Weka segmentation tool in ImageJ. Fibre wall and lumen widths were measured using a newly developed semi-automated method. An assessment of density variation in function of growth ring boundary detection is done. A mixed regression model estimated the relative contribution of each trait to the density, with a species effect on slope and intercept of the regression. Position-dependent correlations were made between the fractions and the corresponding wood density profile. On average, density profile variation mostly reflects variations in fibre lumen and wall fractions, but these are species- and position-dependent: on some positions, parenchyma and vessels have a more pronounced effect on density. The model linking density to traits explains 92% of the variation, with 65% of the density profile variation attributed to the three measured traits. The remaining 27% is explained by species as a random effect. There is a clear variation between trees and within trees that have implications for interpreting density profiles in angiosperm trees: the exact driving anatomical fraction behind every density value will depend on the position within the core. The underlying function of density will thus vary accordingly.