No abstract

Wood density (WD) relates to important tree functions such as stem mechanics and resistance against pathogens. this functional trait can exhibit high intraindividual variability both radially and vertically. With the rise of LiDAR-based methodologies allowing nondestructive tree volume estimations, failing to account for WD variations related to tree function and biomass investment strategies may lead to large systematic bias in AGB estimations. Here, we use a unique destructive dataset from 822 trees belonging to 51 phylogenetically dispersed tree species harvested across forest types in Central Africa to determine vertical gradients in WD from the stump to the branch tips, how these gradients relate to regeneration guilds and their implications for AGB estimations. We find that decreasing WD from the tree base to the branch tips is characteristic of shade-tolerant species, while light-demanding and pioneer species exhibit stationary or increasing vertical trends. Across all species, the WD range is narrower in tree crowns than at the tree base, reflecting more similar physiological and mechanical constraints in the canopy. Vertical gradients in WD induce significant bias (10%) in AGB estimates when using database-derived speciesaverage WD data. However, the correlation between the vertical gradients and basal WD allows the derivation of general correction models. With the ongoing development of remote sensing products providing 3D information for entire trees and forest stands, our findings indicate promising ways to improve greenhouse gas accounting in tropical countries and advance our understanding of adaptive strategies allowing trees to grow and survive in dense rainforests. Terrestrial plants account for 83% of the living carbon on Earth 1 , of which tropical forests are estimated to account for close to half 2 , principally contained within woody plant parts. Tropical forests are therefore becoming a key element in international carbon trading schemes despite obvious difficulties in accurately estimating stocks

Understanding the dynamics of dominant tree species in tropical forests is important both for biodiversity and carbon-related issues. We focus on the Congo Basin (East of Kisangani) to investigate the respective roles of topographic/soil gradients and endogenous dynamics in shaping local variations in dominance. We used a dataset of 30 1-ha plots, in which all trees above 10 cm diameter at breast height (DBH) were censused. Soil samples were analyzed for standard pedologic variables and a digital elevation model permitted to infer topography and hydromorphy. We found that this forest is characterized by variations in the abundance of three dominant species: Petersianthus macrocarpus (P.Beauv.) Liben (PM), Gilbertiodendron dewevrei (De Wild.) J.Leonard (GD) and Julbernardia seretii (De Wild.) Troupin (JS). These variations occur independently of substratum or topography variations. At plot level, the local relative abundance never reached 50%, the threshold for monodominance proposed in the literature, but rather progressively increased to reach higher values for canopy trees (>60 cm DBH), where this threshold could be exceeded. We found no sign of shifting compositional dynamics, whereby the dominant species would switch between the canopy and the undergrowth. Our results, therefore, support the possibility of the existence of stable dominance states, induced by endogenous processes, such as biological positive feedbacks fostering monodominance. We also document a strong relation between monodominance level and alpha diversity, when giving more weight to abundant species which is expected (R² = 0.79) but also when giving more weight to rare species (R² = 0.37), showing that monodominance influences tree species richness patterns. Structural differences existed between groups, with the PM group having more (and on average smaller) stems and lighter wood on average, but paradoxically also higher biomass and basal area.

Tree height and crown area are important predictors of aboveground biomass but difficult to measure on the ground. Numerous allometric models have been established to predict tree height from diameter (H–D) and crown area from diameter (CA–D). A major challenge is to select the most precise and accurate allometric model among existing ones, depending on the species composition and forest type where the model is to be applied. To propose a principle to select tree H–D and tree CA–D allometric models, we build a method based on k-fold cross-validation using a large dataset spanning six forest types from central Africa. We then compared the errors and biases using 22 previously established H–D and CA–D allometric model forms via three inter-comparable scenarios: locally derived for the forest type vs. regional vs. pantropical; regional (encompassing the forest type) vs. pantropical; regional (not encompassing the forest type) vs. pantropical model. H–D allometries were more variable across forest types in central Africa than CA–D allometries: (i) forest type explained 6% of the variance in H–D allometry and 2% of the variance in CA–D allometry, while species explained 9% and 2% of the variance in H–D allometry and CA–D allometry, respectively; (ii) for H–D allometry, the six forest types resulted in five best-fit models whereas, for CA–D allometry, four models provided the best fit for the six forest types. We recommend using allometric models specific to the forest type, preferentially to regional ones. Regional models should in turn be preferred to pantropical allometric models.