| The black soil region in Northeast China is one of the four most famous black soil regions in the world.After long-term development,it has become one of China’s important grain production bases,serving as a cornerstone and stabilizer in ensuring the stability of China’s food security.However,due to long-term unreasonable farming practices,soil erosion,and global warming,the black soil is facing the dilemma of "quantity reduction and quality decline".Soil microorganisms are the most active components in the soil,which are closely related to the soil multi-functionalities.They participate in various ecological processes,such as soil structure formation,element cycling,and pollutant degradation.Meanwhile,they also have a complex coupling relationship with aboveground crops,and are closely related to agricultural production.Therefore,it is of great significance to investigate the distribution of soil microbial communities and their influencing factors in cultivated land,as well as the point-tosurface expansion method of microbial diversity.Herein,we conducted soil sampling in Youyi County of the Sanjiang Plain,and performed high-throughput sequencing and bioinformatics techniques to quantify bacterial diversity of cultivated soils.Combining with environmental factors such as soil,terrain,climate,and vegetation,we analyzed the composition,diversity,and distribution characteristics of soil bacterial communities,and elucidated their influencing factors.Furthermore,geostatistics and regression methods were used to construct a predictive model for bacterial diversity.Based on the selected best prediction model,the distribution map of bacterial diversity in cultivated land soil was generated covering the study area,revealing the continuous spatial distribution of soil bacterial diversity.The main results are listed below:(1)Distribution characteristics and its influencing factors of soil bacterial diversityThrough analyzing the 12 dominant phyla in the microbial community of cultivated land soil,it was found that their relative abundances were significant different between paddy fields and drylands.Acidobacteria,Actinobacteria,and Chloroflexi were the three dominant phyla showing the greatest differences between paddy fields and drylands.The relative abundances of Acidobacteria and Actinobacteria were significantly higher in drylands than in paddy fields(P<0.001),while the relative abundance of Chloroflexi was higher in paddy fields(P<0.001).Meanwhile,the PCo A analysis result showed that soil bacterial communities were distinct between paddy fields and drylands,and were clustered accordingly.Overall,soil bacterial community structure in cultivated land was primarily affected by soil p H and fertility factors,and was also significantly influenced by geographic distance.When considering paddy field or dryland separately,it was found that total phosphorus(TP)had no significant effect on bacterial community structure in paddy field,while TP and geographic distance had no significant effects in dryland either.Furthermore,there were significant differences in soil bacterial α diversity between paddy fields and drylands,with soil bacterial αdiversity significantly higher in paddy fields than in drylands.Soil p H,organic matter(SOM),total nitrogen(TN),and total potassium(TK)were the main factors influencing soil bacterial α diversity in cultivated land.However,when analyzed paddy fields or dryland separately,the influence of soil physicochemical properties on bacterial diversity was weakened.(2)Construction of a predictive model for soil bacterial diversity distributionBased on the study results of soil microbial diversity distribution characteristics in cultivated land at the county scale,we tried to construct the predictive models of soil bacterial diversity using stepwise regression(SR),ordinary kriging(OK),and empirical Bayesian kriging regression prediction(EBKR).In detail,soil bacterial α diversity was taken as the target variable,and the collected and collated raster datasets of soil,topography,vegetation,and land use in the study area were taken as variable factors.Through comparing the predictive accuracy,we discovered that the OK model had the lowest predictive accuracy,while the SR model had higher accuracy but lower than the EBKR model.Detailed analyses further revealed that the low prediction accuracy of the OK model might be resulted from the fact that this model was constructed without environmental factors,which had a significant impact on bacterial α diversity.Compared with the EBKR model,although the SR model considered the relationship between environmental variables and bacterial α diversity,it did not take geographic distance into account.The EBKR model,which combined regression and EBK interpolation methods,considered not only the influence of environmental variables but also the spatial autocorrelation of regression residuals,and had a higher prediction accuracy.(3)Spatial mapping of soil bacterial diversityBased on the existing grid datasets of soil,terrain,and vegetation,the optimal soil bacterial diversity distribution prediction model,EBKR,was used to predict the spatial distribution of soil bacterial α diversity and species richness at different classification levels in the study area.Combined with land use data,we had drawn the spatial distribution maps of soil bacterial α diversity and species richness with a spatial resolution of 90 meters for Youyi County.From the spatial distribution maps,it can be found that soil bacterial α diversity and species richness were high in the northeast and low in the southwest.In summary,in the Youyi County of the Sanjiang Plain in Northeast China,the main factors affecting the α diversity and community structure of soil bacteria in cultivated land were land use and soil physicochemical properties.The EBKR model predicted the distribution of soil bacterial α diversity in cultivated land with greater accuracy than SR,and OK,indicating that geostatistical methods combining environmental variables and spatial structures with local variation have significant advantages in predicting the spatial distribution of soil bacterial α diversity in cultivated land at the county scale.The research results of this study further enhance our understanding of the distribution characteristics and influencing factors of soil bacterial diversity in a typical county of black soil region in Northeast China,and provide a method for predicting the spatial distribution of soil bacterial diversity at the county scale,which may be helpful in the evaluation of cultivated land quality,rational use of soil resources,and sustainable management of black soil. |
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