Data from: Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

Accurately monitoring tropical forest carbon stocks is an outstanding challenge. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference in the coming years. However, this reference model shows a systematic bias for the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass dataset on 673 trees measured in five tropical countries (101 trees > 100 cm in diameter) and an original dataset of 130 forest plots (1 ha) from central Africa to quantify the error of biomass allometric models at the individual and plot levels when explicitly accounting or not accounting for crown mass variations. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥ 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Accounting for a crown mass proxy in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error from −23–16 to 0–10 %. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by accounting for a crown mass proxy for the largest trees in a stand, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

精准监测热带森林碳储量是一项极具挑战性的难题。以树木胸径、树高与木材密度作为预测变量的异速生长模型（allometric models），目前已成为多数热带森林碳研究的核心手段。尤为值得一提的是，一款泛热带生物量模型（pantropical biomass model）已被广泛应用近十年，其最新版本无疑将在未来数年成为该领域的参考基准。但该参考模型对最大体型的树木存在系统性偏差。由于大型树木是森林碳储量与动态变化的核心驱动因子，探明该偏差的成因与影响便成为重中之重。本研究整合了一套独特的树木生物量数据集：该数据集涵盖5个热带国家实测的673株树木（其中101株胸径超过100厘米），以及来自中非的130个1公顷森林样地的原始实测数据，用于量化在明确考虑与未考虑冠层生物量（crown mass）变化的两种情境下，生物量异速生长模型在单株树木与样地两个尺度上的误差。研究首先发现，树木冠层生物量占地上总生物量的比例存在极强的个体间变异，其范围为3%至88%。对于生物量小于10兆克（Mg）的树木，该比例的平均值稳定在34%左右；而当生物量超过该阈值后，该比例随树木生物量急剧上升，对于生物量≥45兆克的树木，该比例的平均值更是超过了50%。这一变化趋势与泛热带生物量模型估算值与实际树木生物量之间的渐进式偏差完全吻合。在新构建的模型中引入冠层生物量替代指标后，成功消除了大型树木（生物量>1兆克）的估算偏差，并将样地尺度的误差范围从-23%至16%压缩至0%至10%。由此可见，大型树木对冠层生物量不成比例的高分配量，或许正是近期泛热带参考模型出现偏差的原因所在。该偏差会在样地尺度上造成不容忽视却常被忽视的系统性误差，而通过对林分中的大型树木引入冠层生物量替代指标便可轻松修正这一问题，这意味着仅需极小的投入即可大幅提升森林碳储量估算的准确性。

搜集汇总

背景与挑战

背景概述

该数据集包含来自五个热带国家的673棵树木质量测量数据和130个森林样地数据，旨在改进热带森林生物量估算。研究发现，树冠质量占总生物量的比例在大型树木中显著增加，导致现有全热带模型存在系统性偏差；通过纳入树冠质量代理，新模型有效消除了这一偏差，并将样地级误差大幅降低，从而提高了碳储量估算的准确性。