New tree height allometries derived from terrestrial laser scanning reveal substantial discrepancies with forest inventory methods in tropical rainforests.

Tree allometric models, essential for monitoring and predicting terrestrial carbon stocks, are traditionally built on global databases with forest inventory measurements of stem diameter (D) and tree height (H). However, these databases often combine H measurements obtained through various measurement methods, each with distinct error patterns, affecting the resulting H:D allometries. In recent decades, terrestrial laser scanning (TLS) has emerged as a widely accepted method for accurate, non-destructive tree structural measurements. This study used TLS data to evaluate the prediction accuracy of forest inventory-based H:D allometries and to develop more accurate pantropical allometries. We considered 19 tropical rainforest plots across four continents. Eleven plots had forest inventory and RIEGL VZ-400(i) TLS-based D and H data, allowing accuracy assessment of local forest inventory-based H:D allometries. Additionally, TLS-based data from 1951 trees from all 19 plots were used to create new pantropical H:D allometries for tropical rainforests. Our findings reveal that in most plots, forest inventory-based H:D allometries underestimated H compared with TLS-based allometries. For 30-metre-tall trees, these underestimations varied from -1.6 m (-5.3%) to -7.5 m (-25.4%). In the Malaysian plot with trees reaching up to 77 m in height, the underestimation was as much as -31.7 m (-41.3%). We propose a TLS-based pantropical H:D allometry, incorporating maximum climatological water deficit for site effects, with a mean uncertainty of 19.1% and a mean bias of -4.8%. While the mean uncertainty is roughly 2.3% greater than that of the Chave2014 model, this model demonstrates more consistent uncertainties across tree size and delivers less biased estimates of H (with a reduction of 8.23%). In summary, recognizing the errors in H measurements from forest inventory methods is vital, as they can propagate into the allometries they inform. This study underscores the potential of TLS for accurate H and D measurements in tropical rainforests, essential for refining tree allometries.

High photosynthetic capacity of Sahelian C3 and C4 plants.

The semi-arid ecosystems of the African Sahel play an important role in the global carbon cycle and are among the most sensitive ecosystems to future environmental pressures. Still, basic data of photosynthetic characteristics of Sahelian vegetation are very limited, preventing us to properly understand these ecosystems and to project their response to future global changes. Here, we aim to study and quantify key leaf traits, including photosynthetic parameters and leaf nutrients (Nleaf and Pleaf), of common C3 and C4 Sahelian plants (trees, lianas, and grasses) at the Dahra field site (Senegal). Dahra is a reference site for grazed semi-arid Sahelian savannah ecosystems in carbon cycle studies. Within the studied species, we found that photosynthetic parameters varied considerably between functional types. We also found significant relationships between and within photosynthetic parameters and leaf traits which mostly differed in their slopes from C3 to C4 plants. In agreement with the leaf economic spectrum, strong relationships (R2 = 0.71) were found between SLA and Nleaf whereby C3 and C4 plants showed very similar relationships. By comparing our data to a global dataset of plant traits, we show that measured Sahelian plants exhibit higher photosynthetic capacity (Asat) compared to the non-Sahelian vegetation, with values that are on average a fourfold of the global average. Moreover, Sahelian C3 plants showed photosynthetic nutrient use efficiencies that were on average roughly twice as high as global averages. We interpreted these results as the potential adaptation of Sahelian plants to short growing season lengths via an efficient nutrient allocation to optimize photosynthesis during this period. Our study provides robust estimates of key functional traits, but also traits relationships that will help to calibrate and validate vegetation models over this data-poor region.