Department of Forest Resources, University of Minnesota, Saint Paul, Minnesota 55108, USA.
Ecology. 2010 Apr;91(4):1225-36. doi: 10.1890/09-0430.1.
The importance of litter decomposition to carbon and nutrient cycling has motivated substantial research. Commonly, researchers fit a single-pool negative exponential model to data to estimate a decomposition rate (k). We review recent decomposition research, use data simulations, and analyze real data to show that this practice has several potential pitfalls. Specifically, two common decisions regarding model form (how to model initial mass) and data transformation (log-transformed vs. untransformed data) can lead to erroneous estimates of k. Allowing initial mass to differ from its true, measured value resulted in substantial over- or underestimation of k. Log-transforming data to estimate k using linear regression led to inaccurate estimates unless errors were lognormally distributed, while nonlinear regression of untransformed data accurately estimated k regardless of error structure. Therefore, we recommend fixing initial mass at the measured value and estimating k with nonlinear regression (untransformed data) unless errors are demonstrably lognormal. If data are log-transformed for linear regression, zero values should be treated as missing data; replacing zero values with an arbitrarily small value yielded poor k estimates. These recommendations will lead to more accurate k estimates and allow cross-study comparison of k values, increasing understanding of this important ecosystem process.
垃圾分解对碳和养分循环的重要性激发了大量的研究。通常，研究人员将单库负指数模型拟合到数据中，以估计分解速率（k）。我们回顾了最近的分解研究，使用数据模拟，并分析了真实数据，表明这种做法有几个潜在的陷阱。具体来说，关于模型形式（如何对初始质量进行建模）和数据转换（对数转换与非对数转换数据）的两个常见决策可能导致 k 的错误估计。允许初始质量与其真实的、测量的价值不同，会导致 k 的过高或过低估计。使用线性回归对数转换数据来估计 k 会导致不准确的估计，除非误差呈对数正态分布，而非线性回归非对数转换数据无论误差结构如何都能准确估计 k。因此，我们建议将初始质量固定在测量值，并使用非线性回归（非对数转换数据）估计 k，除非误差明显呈对数正态分布。如果数据对数转换用于线性回归，则应将零值视为缺失数据；用任意小的值替换零值会导致 k 的估计值很差。这些建议将导致更准确的 k 估计，并允许对 k 值进行跨研究比较，从而增加对这一重要生态系统过程的理解。
