Data from: Understanding the dominant controls on litter decomposition
收藏DataONE2015-11-11 更新2024-06-27 收录
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Litter decomposition is a biogeochemical process fundamental to element cycling within ecosystems, influencing plant productivity, species composition and carbon storage. Climate has long been considered the primary broad-scale control on litter decomposition rates, yet recent work suggests that plant litter traits may predominate. Both decomposition paradigms, however, rely on inferences from cross-biome litter decomposition studies that analyse site-level means. We re-analyse data from a classical cross-biome study to demonstrate that previous research may falsely inflate the regulatory role of climate on decomposition and mask the influence of unmeasured local-scale factors. Using the re-analysis as a platform, we advocate experimental designs of litter decomposition studies that involve high within-site replication, measurements of regulatory factors and processes at the same local spatial grain, analysis of individual observations and biome-scale gradients. Synthesis. We question the assumption that climate is the predominant regulator of decomposition rates at broad spatial scales. We propose a framework for a new generation of studies focused on factoring local-scale variation into the measurement and analysis of soil processes across broad scales. Such efforts may suggest a revised decomposition paradigm and ultimately improve confidence in the structure, parameter estimates and hence projections of biogeochemical models.
凋落物分解(Litter decomposition)是生态系统内元素循环的核心生物地球化学过程,对植物生产力、物种组成及碳储存均具有关键调控作用。长期以来,气候被视作调控凋落物分解速率的主要大尺度调控因子,但近期研究显示植物凋落物性状(plant litter traits)或占据主导地位。然而,这两种分解范式(decomposition paradigms)均依赖于跨生物群区凋落物分解研究中基于样地水平均值的推论。我们重新分析了一项经典跨生物群区研究的数据,结果表明此前的研究可能错误地夸大了气候对分解过程的调控作用,并掩盖了未被测量的局地尺度因子的影响。以此重新分析为研究基础,我们倡议凋落物分解研究采用如下实验设计:提升样地内重复采样水平,在相同局地空间粒度下对调控因子与过程开展测定,对单个观测值进行分析,并设置生物群区尺度梯度。
综合分析。我们对“气候是大空间尺度下分解速率的主要调控因子”这一主流假设提出质疑,并提出了新一代研究的框架:将局地尺度变异纳入大尺度土壤过程的测定与分析流程中。此类研究或可推动分解范式的修正,并最终提升生物地球化学模型的结构合理性、参数估计精度,进而提升其预测结果的可信度。
创建时间:
2015-11-11



