Spatial variation of soil respiration is linked to the forest structure and soil parameters in an old-growth mixed broadleaved-Korean pine forest in northeastern China

• Published in: Plant and soil Vol. 400; no. 1-2; pp. 263 - 274
• Main Authors: Shi, Baoku, Gao, Weifeng, Cai, Huiying, Jin, Guangze
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2016; Springer; Springer Nature B.V
• Subjects: Agrology; autocorrelation; Biomedical and Life Sciences; bulk density; Carbon cycle; Coniferous forests; Desert soils; Ecology; Environmental aspects; Forest dynamics; Forest ecosystems; Forest soils; forest stands; Forests; Geostatistics; Life Sciences; Mixed forests; Moisture content; Observations; Old growth forests; Pine; Pinus; Plant Physiology; Plant Sciences; Plant-soil relationships; Regular Article; Respiration; Seasons; Soil respiration; Soil Science & Conservation; soil structure; Soil temperature; Soil water; Soil water content; Spatial analysis; stand structure; stems; tree and stand measurements; Trees; variance; Water content; China; China, People's Rep; Geostatistics; Tree diameter; Old-growth forest; CO; efflux; Spatial variation; Ecosystem carbon cycling
• ISSN: 0032-079X; 1573-5036
• DOI: 10.1007/s11104-015-2730-z

AIMS: The aim of this study was to quantify and understand the driving factors of the spatial variation of soil respiration (R S) in an old-growth mixed broadleaved-Korean pine forest in northeastern China. METHODS: All woody stems ≥1 cm diameter at breast height (DBH) were measured in the 9 ha plot. Simultaneous measurements of R S, soil temperature (T S) and soil water content (W S) were conducted for 256 sampling points on a regular 20-m grid refined with 512 additional sampling points randomly placed within each of the 20-m blocks in May, July and September of 2014. RESULTS: The variogram analyses revealed 87–91 % of the sample variance was explained by autocorrelation over a range of 15 to 23 m during the observation periods. The R S were highly correlated among the measurements made in May, July and September. The model indicated that the W S, bulk density (BD) and maximum DBH for trees within 3 m (radius) of the measurement collars explained 46 % of the spatial variation in R S seasonally averaged across three observations. CONCLUSIONS: The spatial patterns of R S remained constant across the three measurement campaigns. The spatial variation in R S was primarily controlled by the W S and forest stand structure.