Abstract Soil organic carbon (SOC) is a key driver of ecosystem functioning and may also contribute to climate change mitigation through the sequestration of carbon. Therefore, having an understanding of the key drivers of SOC may inform management changes that will improve ecosystem function and climate change mitigation. The selected study area is ranged from montane forests to tropical grasslands. Extensive soil sampling (0–0.15 m and 0.15–0.30 m) was undertaken across this region to inform our knowledge about key drivers of SOC at different spatial scales. Initially spatial modelling was carried out using spatial linear mixed modelling approach using a variety of environmental covariates. The model had a Lin’s concordance correlation coefficient value of 56–60%, and indicated that SOC was predominately influenced by vegetation type and elevation, although the sub-surface (0.15–0.30 m) SOC was influenced by slope and wetness index. Further, four spatial transects with 100 m sampling interval were extracted from the digital maps representing the study area and empirical mode decomposition (EMD) analysis was carried out to examine the scale specific variability of SOC stocks. The EMD, a mathematical analysis, separates dominant frequencies within a spatial/temporal series representing variability created by various underlying processes operating at different scales into a finite number of scale components or intrinsic mode functions (IMFs). Decomposition of SOC spatial series for the considered transects resulted up to 7 IMFs. The scale components with lower IMF numbers separated higher frequency oscillations, whereas higher IMF numbers separated lower frequency oscillations, which is the representative of smaller and larger scale processes, respectively. Spectral analysis was performed to identify the scales of IMFs and the correlation analysis was carried out with different environmental covariates to identify the dominant controlling factors at different depths. Majority of the large-scale variations (e.g. 2037–8149 m for IMF’s 6 for depth interval 0–0.15 m for transect 1–4) were attributed to the elevation and climatic factors controlling the forest type, while small-scale (e.g. 69–118 m for IMF’s 1 for depth interval 0–0.15 m for transect 1–4) variations were more attributed terrain derived attributes. Similar scales were identified for the depth 0.15–0.30 m. The scale-specific controlling factors at different locations and their relative controlling factors may help in selecting environmental covariates that enables us to model SOC more accurately rather than fitting one global model. The study provided firsthand information on baseline SOC stock values from a tropical forest ecosystem with six different vegetation types. The information revealed in this study will be useful in the conservation of tropical forests in the region and towards providing vital firsthand information to establishing a national carbon accounting system for land sector in the future.

中文翻译：

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确定热带生态系统中土壤有机碳的空间驱动因素和特定尺度变化：斯里兰卡 Knuckles 森林保护区的案例研究

摘要 土壤有机碳 (SOC) 是生态系统功能的关键驱动因素，也可能通过固碳来减缓气候变化。因此，了解 SOC 的关键驱动因素可以为改善生态系统功能和减缓气候变化的管理变化提供信息。选定的研究区域范围从山地森林到热带草原。在该地区进行了广泛的土壤采样（0-0.15 m 和 0.15-0.30 m），以告知我们关于不同空间尺度 SOC 关键驱动因素的知识。最初的空间建模是通过使用各种环境协变量的空间线性混合建模方法进行的。该模型的林氏一致性相关系数值为 56-60%，并表明 SOC 主要受植被类型和海拔的影响，尽管地下 (0.15-0.30 m) SOC 受坡度和湿度指数的影响。此外，从代表研究区域的数字地图中提取了四个采样间隔为 100 m 的空间断面，并进行了经验模式分解 (EMD) 分析以检查 SOC 储量的特定尺度变异性。EMD 是一种数学分析，它将空间/时间序列中的主要频率分离成有限数量的尺度分量或固有模式函数 (IMF)，这些频率表示由在不同尺度上运行的各种潜在过程产生的可变性。所考虑横断面的 SOC 空间序列分解导致多达 7 个 IMF。具有较低 IMF 数的尺度分量分离了较高频率的振荡，而较高的 IMF 数则分离了较低频率的振荡，这分别代表了较小和较大尺度的过程。进行光谱分析以确定IMFs的尺度，并用不同的环境协变量进行相关分析，以确定不同深度的主导控制因素。大多数大尺度变化（例如 IMF 6 的 2037-8149 m，横断面 0-0.15 m 的深度间隔）归因于控制森林类型的海拔和气候因素，而小尺度（例如 69- IMF 的 1 为 118 m，深度间隔为 0-0.15 m，断面 1-4) 变化更多地归因于地形衍生属性。为 0.15-0.30 m 的深度确定了类似的尺度。不同位置的特定尺度控制因素及其相对控制因素可能有助于选择环境协变量，使我们能够更准确地模拟 SOC，而不是拟合一个全局模型。该研究提供了来自具有六种不同植被类型的热带森林生态系统的基线 SOC 储量值的第一手信息。本研究揭示的信息将有助于保护该地区的热带森林，并为未来建立国家土地部门碳核算系统提供重要的第一手信息。该研究提供了来自具有六种不同植被类型的热带森林生态系统的基线 SOC 储量值的第一手信息。本研究揭示的信息将有助于保护该地区的热带森林，并为未来建立国家土地部门碳核算系统提供重要的第一手信息。该研究提供了来自具有六种不同植被类型的热带森林生态系统的基线 SOC 储量值的第一手信息。本研究揭示的信息将有助于保护该地区的热带森林，并为未来建立国家土地部门碳核算系统提供重要的第一手信息。