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    色季拉山西坡表层土壤有机碳的小尺度空间分布特征

    Spatial distribution characteristics at small scale of soil organic carbon in topsoil of the west slope in Sejila Mountain, western China

    • 摘要:
        目的  对小空间尺度上土壤有机碳(SOC)及其密度(SOCD)的空间分布进行研究,以期为大尺度下高寒土壤碳储量的精确估算提供理论支撑。
        方法  本研究以色季拉山西坡海拔4 200 ~ 4 400 m的苔草高寒草甸(CAM)、林芝杜鹃灌丛(RTS)和雪山杜鹃灌丛(RAS)为研究对象,采用10 m × 10 m规则格网采集表层(0 ~ 10 cm)土壤样品,借助GS+和ArcGIS软件以分析不同植被类型SOC和SOCD的空间结构性、分布格局及影响因素。
        结果  (1)研究区表层SOC平均值达100.97 g/kg,表现为RAS(146.45 g/kg) > CAM(95.60 g/kg) > RTS(60.43 g/kg),SOCD平均值达6.28 kg/m2,表现为CAM(7.34 kg/m2) > RAS(6.32 kg/m2) > RTS(4.80 kg/m2),均高于全国0 ~ 10 cm土壤水平(24.56 g/kg、1.21 kg/m2)。(2)除RAS外,该区域SOC、SOCD均具有强烈的空间自相关性,结构比为1.46% ~ 12.51%,表明结构性因素引起的空间变异为主。RAS的SOC、SOCD结构比达100%,空间依赖性较弱,随机因素引起的空间变异为主。SOC、SOCD空间自相关尺度在17.44 ~ 30.29 m之间,并且SOC > SOCD,CAM > RTS,这表明植被类型可能是影响表层SOC和SOCD空间变异及格局的主要因素。(3)克里格插值表明,CAM表层SOC、SOCD的高值斑块与高土壤含水率相对应,低值斑块与沟壑位置相对应,RTS的SOC、SOCD呈高低值斑块交错分布,与地面覆被物(灌丛、草本、裸地)的镶嵌性分布对应,表明土壤含水率、微地形、植被覆盖等是影响研究区表层SOC和SOCD空间分布的主要因素。(4)冗余分析表明,土壤含水率、土壤密度、pH、全氮是影响3种植被类型SOC、SOCD含量及空间异质性的关键要素,其次是机械组成、坡度,全磷的影响不明显。
        结论  色季拉山SOC含量比较丰富,小空间尺度下枯落物量、微地形、土壤性质(含水率、土壤密度等)的空间异质性显著影响SOC、SOCD的空间分布和预测。

       

      Abstract:
        Objective  Analyzing the spatial distribution of soil organic carbon (SOC) and its density (SOCD) at small spatial scales aims to provide theoretical support for accurate estimation of carbon storage in alpine soils at large scales.
        Method  Taking Carex alpine meadow (CAM), Rhododendron tanastylum shrub (RTS) and Rhododendron aganniphum shrub (RAS) lying in western slope of Sejila Mountain at altitudes of 4 200−4 400 meters as objects of research, and collecting soil samples at the depths of 0−10 cm with grid method, we analyzed spatial heterogeneity and influencing factors of SOC and SOCD.
        Result  (1) The content of SOC was rich with an average up to 100.97 g/kg in the study area, and content of SOC declined in the following rank order: RAS (146.45 g/kg) > CAM (95.60 g/kg) > RTS (60.43 g/kg), while SOCD declined in the following rank order: CAM (7.34 kg/m2) > RAS (6.32 kg/m2) > RTS (4.80 kg/m2). (2) SOC fitted well spherical model while SOCD fitted well exponential model in CAM, as well as SOC and SOCD of RTS both matched spherical model. SOC and SOCD of CAM and RTS both had strong spatial autocorrelation with nugget/sill ratio in 1.46%−12.51% range, suggesting that spatial variability of SOC and SOCD was mainly caused by structural factors. In RAS, SOC and SOCD conformed to linear model, spatial variation was caused by random factors. This indicated that vegetation types may be the main factors affecting the spatial distribution of SOC in topsoil. Ranges of SOC and SOCD in CAM were 30.29 and 20.31 m, respectively, which was larger than RTS (17.68 and 17.44 m), indicating that spatial variability scales of SOC and SOCD in CAM were larger than RTS. At a sampling interval of 10 m, pure nugget effect appeared in SOC and SOCD of RAS, it may be that the spatial variability scale was less than 10 m, so a smaller scale research of SOC and SOCD in RAS was needed. (3) Kriging interpolation showed that the horizontal distribution of SOC and SOCD in CAM was affected by the moisture content with high value areas in the eastern and southern edges of the sample plot, in attention, it may also be related to the existence of slope and gully. The continuity of the horizontal distribution of SOC and SOCD in the RTS area was poor, and the degree of fragmentation was high, which may be related to the mosaic distribution of ground cover (shrub, herb, bare land). (4) Redundancy analysis showed that soil water content, soil density, and pH were the key factors affecting spatial heterogeneity of SOC and SOCD in 3 vegetation types, mechanical composition and slope were the second. However, the effect of total phosphorus was not apparent in this study.
        Conclusion  SOC content of Sejila Mountain is relatively rich. The spatial heterogeneity of micro-topography, litter volume, and soil properties (water content, bulk density, etc.) at a small spatial scale significantly affect the spatial distribution and prediction of SOC and SOCD.

       

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