Search

Aim Examining tree species-environment association can offer insight into the drivers of vegetation patterns and key information of practical relevance to forest management. Here, we aim to quantify the contribution of climate and soil gradients to variation in Central African tree species composition (abundance and occurrence). Location Tropical rain forests of southern and eastern Cameroon. Methods We established 82 1-ha permanent plots across seven localities and censused all trees >= 10 cm in diameter, representing a total of 37,733 trees and 455 species. In 60 of those plots, we measured 10 soil variables describing texture and nutrients levels and extracted 10 bioclimatic variables from global-gridded climate databases. We synthesized the main environmental gradients by conducting principal component analyses on climate and soil data, respectively. We performed unconstrained and constrained non-symmetric correspondence analyses to account for the individual and joint contributions of climate and soil on species abundance and occurrence. Results Climate and soil contributed similarly to variances of species abundance and occurrence (12-15% variance for climate vs 11-12% variance for soil). Climate influence mostly concerns some abundant species, while some of the less abundant species were mainly driven by soil. Fractions of species variances accounted for by climate and soil show strong correlation when assessed from species occurrence and abundance data. Conclusion Variation in occurrence and abundance of tropical forest trees can be partly shaped by both climate and soil gradients in Cameroon, which emphasizes the importance to jointly consider soil and climate in species distribution modeling. Less abundant species may express environmental influence differently than abundant species and convey complementary information about community assemblage. Though showing congruent patterns here, species abundance and occurrence reflect different interacting community processes and both should be examined to better understand vegetation patterns.

With the improvement of remote sensing techniques for forest inventory application such as terrestrial LiDAR, tree volume can now be measured directly, without resorting to allometric equations. However, wood specific gravity (WSG) remains a crucial factor for converting these precise volume measurements into unbiased biomass estimates. In addition to this WSG values obtained from samples collected at the base of the tree (WSG(Base)) or from global repositories such as Dryad (WSG(Dryad)) can be substantially biased relative to the overall tree value. Our aim was to assess and mitigate error propagation at tree and stand level using a pragmatic approach that could be generalized to National Forest Inventories or other carbon assessment efforts based on measured volumetric data. In the semi-deciduous forests of Eastern Cameroon, we destructively sampled 130 trees belonging to 15 species mostly represented by large trees (up to 45 Mg). We also used stand-level dendrometric parameters from 21 1-ha plots inventoried in the same area to propagate the tree-level bias at the plot level. A new descriptor, volume average-weighted WSG (WWSG) of the tree was computed by weighting the WSG of tree compartments by their relative volume prior to summing at tree level. As WWSG cannot be assessed non-destructively, linear models were adjusted to predict field WWSG and revealed that a combination of WSG(Dryad), diameter at breast height (DBH) and species stem morphology (S-m) were significant predictors explaining together 72% of WWSG variation. At tree level, estimating tree aboveground biomass using WSG(Base) and WSG(Dryad) yielded overestimations of 10% and 7% respectively whereas predicted WWSG only produced an underestimation of less than 1%. At stand-level, WSG(Base) and WSG(Dryad) gave an average simulated bias of 9% (S.D. = +/- 7) and 3% (S.D. = +/- 7) respectively whereas predicted WWSG reduced the bias by up to 0.1% (S.D. = +/- 8). We also observed that the stand-level bias obtained with WSG(Base) and WSG(Dryad) decreased with total plot size and plot area. The systematic bias induced by WSG(Base) and WSG(Dryad) for biomass estimations using measured volumes are clearly not negligible but yet generally overlooked. A simple corrective approach such as the one proposed with our predictive WWSG model is liable to improve the precision of remote sensing-based approaches for broader scale biomass estimations.