Key message

A random sampling between 30 and 50 trees is sufficient for forest-level wood density estimates.

Abstract

Wood density (WD) is a key trait used to determine forest biomass and carbon stocks, but determining WD accurately is logistically demanding and expensive. These challenges also hamper comparisons across studies and different forest types, because sampling intensity within forests and within individual trees often vary across studies. We aimed to evaluate the relationship between WD and forest type using a standardized protocol and to simulate the number of samples required to obtain a representative estimation of WD of trees belonging to different tropical vegetation types representing an increasing order of aridity: rain forest, semideciduous forest, evergreen dry forest, savannah woodland, and seasonally deciduous forest. We measured WD at five vertical profiles along the trunks of 1,671 trees representing 349 species. Using bootstrapping analyses, we modeled WD as a function of the different combinations of samples extracted at the five sampling heights and evaluated the models with the best performance. The lowest and highest mean WD values were found in rain forest and seasonally deciduous forests, respectively, in line with the correspondingly low and high aridity of these habitats. Depending on forest type, sampling approximately 30–60 trees is sufficient for stabilizing the coefficient of variation in WD. Additionally, using samples collected at 25% and 50% height from the base along the vertical profile of each tree is adequate for WD estimations. These insights could be used to develop less destructive methodologies for wood density sampling, and, thus, help to reduce costs of carbon stock inventories in tropical forests.

中文翻译：

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有多少树木和样品足以估算不同热带森林中木材的比重？

关键信息

在30到50棵树之间进行随机抽样就足以估算森林级的木材密度。

抽象

木材密度（WD）是用于确定森林生物量和碳储量的关键特征，但准确地确定WD在逻辑上要求高且昂贵。这些挑战也妨碍了研究与不同森林类型之间的比较，因为森林和单个树木内的采样强度在研究之间常常不同。我们旨在使用标准化协议评估WD与森林类型之间的关系，并模拟获得代表代表干旱顺序的不同热带植被类型的树木的WD的代表性估计所需的样本数量，包括雨林，半落叶林，常绿的干旱森林，大草原林地和季节性落叶林。我们沿着代表349种树种的1,671棵树干的五个垂直剖面测量了WD。使用自举分析，我们将WD建模为在五个采样高度提取的样本的不同组合的函数，并评估了具有最佳性能的模型。在雨林和季节性落叶林中分别发现了最低和最高的平均WD值，这与这些生境的相应的低干旱和高干旱一致。根据森林类型的不同，采样大约30–60棵树木足以稳定WD的变异系数。另外，使用沿每棵树的垂直轮廓从底部以25％和50％的高度收集的样本足以进行WD估计。这些见解可用于开发破坏性较小的木材密度采样方法，从而帮助降低热带森林中碳库的成本。我们将WD建模为在五个采样高度处提取的样本的不同组合的函数，并评估了具有最佳性能的模型。在雨林和季节性落叶林中分别发现了最低和最高的平均WD值，这与这些生境的相应的低干旱和高干旱一致。根据森林类型的不同，采样大约30–60棵树木足以稳定WD的变异系数。另外，使用沿每棵树的垂直轮廓从底部以25％和50％的高度收集的样本足以进行WD估计。这些见解可用于开发破坏性较小的木材密度采样方法，从而帮助降低热带森林中碳库的成本。我们将WD建模为在五个采样高度处提取的样本的不同组合的函数，并评估了具有最佳性能的模型。在雨林和季节性落叶林中分别发现了最低和最高的平均WD值，这与这些生境的相应的低干旱和高干旱一致。根据森林类型的不同，采样大约30–60棵树木足以稳定WD的变异系数。另外，使用沿每棵树的垂直轮廓从底部以25％和50％的高度收集的样本足以进行WD估计。这些见解可用于开发破坏性较小的木材密度采样方法，从而帮助降低热带森林中碳库的成本。