Assessment of Bias in Pan-Tropical Biomass Predictions
Assessment of Bias in Pan-Tropical Biomass Predictions
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DOI：
10.3389/ffgc.2020.00012
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发表时间：
2020-02-20
影响因子：
3.2
通讯作者：
Disney, Mathias
中科院分区：
文献类型：
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作者：
Burt, Andrew;Calders, Kim;Disney, Mathias
林地生物量（AGB）是森林的一个基本描述，用于生态和气候相关的研究。在树和林分尺度上，破坏性的，但直接测量的AGB被替换为异速生长模型的预测特征AGB之间的相关关系，预测变量，包括茎直径，树高和木材密度。这些模型通常通过线性回归从收获的校准数据构建。在这里，我们评估的系统误差，在测量，编译和建模的样本校准数据的过程中引入的AGB的样本外预测。各种传统的双变量和多变量模型构建从热带森林的开放获取数据。元数据分析，拟合诊断和交叉验证结果表明，几个模型的错误：主要是，不一致的测量误差预测变量之间的样本和样本外的数据。模拟表明，保守的不一致性，可以引入显着的偏差到树和林分规模的AGB预测。当树高和木材密度作为预测因子时，应修改模型以纠正偏差。最后，我们探讨了传统的异速生长的一个基本假设，即模型参数是独立的树的大小。也就是说，相同的模型可以提供一致的真实性的预测，无论大小类。当前校准数据集中的大多数观测结果来自较小的树，这意味着存在大小依赖性会使较大树的预测产生偏差。我们确定，检测到的大小依赖性的存在或不存在，目前防止模型规格错误和校准数据的不平衡。我们呼吁收集更多的收获数据，特别是代表性不足的大型树木。
Above-ground biomass (AGB) is an essential descriptor of forests, of use in ecological and climate-related research. At tree- and stand-scale, destructive but direct measurements of AGB are replaced with predictions from allometric models characterizing the correlational relationship between AGB, and predictor variables including stem diameter, tree height and wood density. These models are constructed from harvested calibration data, usually via linear regression. Here, we assess systematic error in out-of-sample predictions of AGB introduced during measurement, compilation and modeling of in-sample calibration data. Various conventional bivariate and multivariate models are constructed from open access data of tropical forests. Metadata analysis, fit diagnostics and cross-validation results suggest several model misspecifications: chiefly, unaccounted for inconsistent measurement error in predictor variables between in- and out-of-sample data. Simulations demonstrate conservative inconsistencies can introduce significant bias into tree- and stand-scale AGB predictions. When tree height and wood density are included as predictors, models should be modified to correct for bias. Finally, we explore a fundamental assumption of conventional allometry, that model parameters are independent of tree size. That is, the same model can provide predictions of consistent trueness irrespective of size-class. Most observations in current calibration datasets are from smaller trees, meaning the existence of a size dependency would bias predictions for larger trees. We determine that detecting the absence or presence of a size dependency is currently prevented by model misspecifications and calibration data imbalances. We call for the collection of additional harvest data, specifically under-represented larger trees.