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生态学杂志 ›› 2022, Vol. 41 ›› Issue (4): 813-821.doi: 10.13292/j.1000-4890.202203.003

• 技术与方法 • 上一篇    下一篇

土壤细菌多样性空间预测方法对比

徐爱爱1,2,刘杰1,2,王昌昆1,2,郭志英1,2,潘恺1,2,张芳芳1,2,潘贤章1,2*   

  1. 1土壤与农业可持续发展国家重点实验室, 中国科学院南京土壤研究所, 南京 210008;2中国科学院大学, 北京 100049)
  • 出版日期:2022-04-10 发布日期:2022-09-09

Comparison of different methods for spatial prediction of soil bacterial diversity.

XU Ai-ai1,2, LIU Jie1,2, WANG Chang-kun1,2, GUO Zhi-ying1,2, PAN Kai1,2, ZHANG Fang-fang1,2, PAN Xian-zhang1,2*   

  1. (1State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; 2University of Chinese Academy of Sciences, Beijing 100049, China).
  • Online:2022-04-10 Published:2022-09-09

摘要: 为探索适合土壤微生物多样性的空间预测方法,本文以内蒙古中部赤峰市周边为研究区域,采集35个草地土壤样品,利用高通量测序技术,结合从相关网站获取的气候、植被和高程相关数据集,采用多元线性回归(MLR)、普通克里格(OK)、回归克里格(RK)和经验贝叶斯克里格回归预测(EBKR)4种方法,对土壤细菌多样性进行空间预测,并比较其预测精度。结果表明:干旱指数、年平均降水和净光合作用是解释土壤细菌多样性变化的最佳环境变量组合;4种方法预测的细菌多样性空间分布总体趋势相似,均表现为东南高西北低,但涉及回归的3种方法可以更好地反映细菌多样性的局部变异特征;MLR、OK、RK和EBKR的留一交叉验证决定系数(R2)分别为0.408、0.439、0.476和0.638,ME分别为-0.065、0.033、0.017和-0.009,RMSE分别为5.23、5.04、4.95和4.05,表明OK的预测精度稍高于MLR,而整合了辅助环境变量的RK和EBKR的预测精度得到进一步提升,且由于EBKR克服了RK用单一半方差函数概括所有位置数据空间结构的局限性,其预测精度最高。综上可知,结合辅助环境变量并同时考虑空间结构局部差异的地统计学方法在土壤微生物多样性空间预测中展现出较大潜力。

关键词: 细菌多样性, 多元线性回归, 普通克里格, 回归克里格, 经验贝叶斯克里格回归预测

Abstract: To get a suitable method for spatial prediction of soil microbial diversity, multiple linear regression (MLR), ordinary kriging (OK), regression kriging (RK), and empirical Bayesian kriging regression (EBKR) methods were compared. A total of 35 soil samples were collected from grasslands in Chifeng, central Inner Mongolia. Soil bacterial diversity, measured by Faith’s phylogenetic diversity (PD) index, was examined by high-throughput sequencing. Auxiliary environmental data related to climate, vegetation and elevation were acquired from relevant websites as the inputs of the MLR, RK, and EBKR predictions. Leaveoneout crossvalidation, mean error (ME), and root mean square error (RMSE) were used to evaluate the prediction accuracy. Results showed that aridity index, mean annual precipitation, and net photosynthesis were the best combination of environmental variables explaining the variation of soil bacterial diversity. The spatial distribution patterns of the diversity predicted by the MLR, OK, RK, and EBKR methods were quite similar. The diversity in the southeast part of the study area was higher, whereas that in the northwest part of the study area was relatively lower. The three methods involving regression analyses could better describe the local variation of the diversity than the OK method. Furthermore, for the predictions of MLR, OK, RK, and EBKR, the coefficients of determination of leaveoneout crossvalidations were 0.408, 0.439, 0.476, and 0.638, the values of ME were -0.065, 0.033, 0.017, and -0.009, and the values of RMSE were 5.23, 5.04, 4.95, and 4.05, respectively. These results suggested that the OK method had a slightly higher prediction accuracy than the MLR method, while the RK and EBKR methods, which combined the auxiliary environmental variables, further improved the prediction accuracy. Specifically, the EBKR method better predicted the spatial distribution of the diversity because it could overcome the limitation of the RK method, which usually generalizes the spatial structure of all location data with a single semivariogram function. In conclusion, the geostatistical methods combining auxiliary environmental variables and considering local differences in spatial variability have great potential in predicting the spatial distribution of soil microbial diversity.

Key words: soil bacterial diversity, multiple linear regression, ordinary kriging, regression kriging, empirical Bayesian kriging regression prediction.