Identifying the spatial drivers and scale-specific variations of soil organic carbon in tropical ecosystems: A case study from Knuckles Forest Reserve in Sri Lanka

• Published in: Forest ecology and management 2020-10, Vol.474, p.118285, Article 118285
• Main Authors: Rajapaksha, R.P.S.K., Karunaratne, S.B., Biswas, A., Paul, K., Madawala, H.M.S.P., Gunathilake, S.K., Ratnayake, R.R.
• Format: Article
• Language: English
• Subjects: Digital soil mapping; Scale specific variation; Soil carbon sequestration; Soil carbon stocks; Tropical forests
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/j.foreco.2020.118285

•Detailed analysis of below ground carbon (soil organic carbon) drivers with different scales in tropical forest ecosystem.•Provided firsthand information on baseline soil organic carbon from a tropical forest ecosystem.•First ever, detailed large extent estimation of soil organic carbon across different vegetation types in Sri Lanka.•Provide baseline information for climate change models for future applications. 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