Mountain Tai Forest Ecosystem Research Station of State Forestry and Grassland Administration, Forestry College, Shandong Agricultural University, Tai'an, Shandong, 271018, China.
National Engineering Research Center for Efficient Utilization of Soil and Fertilizer Resources, Shandong Agricultural University, Taian, Shandong, 271018, China.
J Environ Manage. 2022 Sep 1;317:115472. doi: 10.1016/j.jenvman.2022.115472. Epub 2022 Jun 3.
Soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP) are important indicators reflecting soil quality, and they can be used to effectively evaluate the effect of soil remediation. Many studies have evaluated the content of SOC, TN and TP in different ecosystems. However, after constructing protected forests for ecological restoration in the ecologically fragile coastal zone, the spatial distribution and influencing mechanism of SOC, TN and TP content is still uncertain. In this study, the spatial heterogeneity and influencing factors of SOC, TN and TP in surface (0-20 cm) soil were analyzed by traditional analysis and geostatistics. A total of 39 soil samples were collected under the coastal zone protected forest types including Quercus acutissima Carruth (QAC), Pinus thunbergii Parl (PTP), mixed PTP and QAC (QP) and Castanea mollissima BL (CMB) in the coastal zone protected forests in northern China. The results show that SOC, TN and TP content were defined as moderate variation, and they also show significant changes under different protected forest types (P < 0.05). The semivariance results indicate that SOC, TN and TP all exhibited strong spatial dependence class, with Range of 224 m, 229 m and 282 m respectively, which were more than the sampling scale of 200 m. The spatial prediction results showed that SOC, TN and TP content all appear in large areas of extremely low value in CMB, and its cross validation results showed that using vegetation and terrain factors as covariates in the spatial prediction of SOC, TN and TP can improve the prediction accuracy. The results of correlation analysis showed that the influencing factor for SOC and TN, and TP were NDVI and topographical changes, respectively. In general, vegetation and terrain factors as auxiliary factors can improved the accuracy of soil C-N-P spatial distribution prediction after afforestation in coastal zone.
土壤有机碳（SOC）、总氮（TN）和总磷（TP）是反映土壤质量的重要指标，可有效评估土壤修复效果。许多研究已经评估了不同生态系统中 SOC、TN 和 TP 的含量。然而，在生态脆弱的沿海地区构建保护林进行生态恢复后，SOC、TN 和 TP 含量的空间分布及其影响机制仍不确定。本研究采用传统分析和地统计学方法，分析了中国北方沿海地区保护林不同林型（麻栎、黑松、黑松与麻栎混交林、板栗）下 0-20cm 表层土壤中 SOC、TN 和 TP 的空间异质性及其影响因素。共采集了 39 个土壤样本。结果表明，SOC、TN 和 TP 含量属于中等变异性，且在不同保护林型下存在显著差异（P<0.05）。半方差结果表明，SOC、TN 和 TP 均表现出较强的空间相关性，范围分别为 224m、229m 和 282m，均大于 200m 的采样尺度。空间预测结果表明，板栗林 SOC、TN 和 TP 含量均呈现大面积极低值，交叉验证结果表明，将植被和地形因素作为协变量进行 SOC、TN 和 TP 的空间预测，可以提高预测精度。相关分析结果表明，SOC 和 TN 的影响因素分别为 NDVI 和地形变化，TP 的影响因素为地形变化。总之，在沿海地区造林后，将植被和地形因素作为辅助因素可以提高土壤 C-N-P 空间分布预测的准确性。
