The allometry of coarse root biomass: log-transformed linear regression or nonlinear regression?
The allometry of coarse root biomass: log-transformed linear regression or nonlinear regression?
复制标题
粗根生物量的异速生长：对数变换线性回归还是非线性回归？
DOI：
10.1371/journal.pone.0077007
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Ma K
中科院分区：
文献类型：
--
作者：
Lai J;Yang B;Lin D;Kerkhoff AJ;Ma K
精确估算根系生物量对于了解森林碳储量和动态具有重要意义。传统上，生物量估计是基于茎直径和粗根生物量之间的异速生长比例关系，使用对数转换数据的线性回归（LR）计算。近年来，非线性回归（NLR）被认为是一种较好的尺度关系拟合方法。但是，尽管这一主张在理论和经验方面都受到了质疑，并且已经开发了统计方法来帮助在特定情况下在两种方法之间进行选择，但很少有研究审查错误应用NLR的后果。本文利用对华东地区3个地方优势树种159株树木的直接观测资料，对径根生物量异速生长的LR和NLR模型进行了比较。然后，我们对比模型预测估计站粗根生物量的基础上，从附近的24公顷古田山森林地块的普查数据，并通过测试模型的能力来预测已知的根生物量值测量多个热带物种在马来西亚的Pasoh森林保护区。基于模型误差分布的似然估计，以及外推预测的准确性，我们发现，对数转换数据的LR是上级NLR拟合直径根生物量缩放模型。更重要的是，不适当地使用NLR导致严重不准确的林分生物量估计，特别是对于以较小树木为主的林分。
Precise estimation of root biomass is important for understanding carbon stocks and dynamics in forests. Traditionally, biomass estimates are based on allometric scaling relationships between stem diameter and coarse root biomass calculated using linear regression (LR) on log-transformed data. Recently, it has been suggested that nonlinear regression (NLR) is a preferable fitting method for scaling relationships. But while this claim has been contested on both theoretical and empirical grounds, and statistical methods have been developed to aid in choosing between the two methods in particular cases, few studies have examined the ramifications of erroneously applying NLR. Here, we use direct measurements of 159 trees belonging to three locally dominant species in east China to compare the LR and NLR models of diameter-root biomass allometry. We then contrast model predictions by estimating stand coarse root biomass based on census data from the nearby 24-ha Gutianshan forest plot and by testing the ability of the models to predict known root biomass values measured on multiple tropical species at the Pasoh Forest Reserve in Malaysia. Based on likelihood estimates for model error distributions, as well as the accuracy of extrapolative predictions, we find that LR on log-transformed data is superior to NLR for fitting diameter-root biomass scaling models. More importantly, inappropriately using NLR leads to grossly inaccurate stand biomass estimates, especially for stands dominated by smaller trees.
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