Stem CO2 efflux (EA) significantly contributes to autotrophic and ecosystem respiration in tropical forests, but field methodologies often introduce biases and uncertainty. This study evaluates these biases and their impact on scaling EA at the stand-level. Diel and vertical patterns of EA were investigated, along with the accuracy of estimating stem surface area from allometric equations vs terrestrial light dection and ranging (LiDAR) scanning (TLS) in Maliau Basin Conservation Area, Sabah, Malaysian Borneo. Diel EA exhibited no uniform pattern due to inter-tree variability, but results suggest measuring EA before 15:00 h. EA was significantly higher on buttresses and above the first major branching point, but vertical variations in EA did not impact stand-level EA when stem surface area was accurately estimated. Allometric equations underestimated total stem surface area by c. 40% compared with TLS, but applying a site-specific correction factor yielded a similar stand-level EA and total stem surface area to TLS. This study provides guidance for measuring EA in the field and suggests that measuring at one time point and one height along the stem can produce accurate results if conducted using the correct time frame and if stem surface area is accurately estimated.
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.