Abstract
The long-term persistence of extracellular DNA in soils is well-documented, yet its impacts on analyzing soil microbial abundance and diversity remain controversial. This primarily arises from our limited comprehension regarding the reliability of various methods for studying soil live microbiotas. In this study, we systematically compared and assessed commonly used methods for studying live soil microbial abundance and diversity, including alkaline buffer washing, propidium monoazide (PMA) treatment, DNase pre-digestion, and rRNA-based analysis, using soils collected from a wide range of locations across China. We found that the elimination of extracellular DNA substantially influenced the analysis of soil prokaryotic abundance, diversity, community profiles, and co-occurrence patterns, but not community assembly mechanisms. However, the effects varied considerably across different methods. DNase pre-digestion and PMA treatment led to significant decreases in prokaryotic abundance, while alkaline buffer washing and rRNA-based analysis had negligible effects. As for prokaryotic richness, DNase pre-digestion and rRNA-based analysis significantly decreased and increased it, respectively. Although 67.8% of amplicon sequence variants were shared, significant differences in their relative abundance were observed across various methods. While the removal of extracellular DNA simplified the co-occurrence network, it also enhanced its robustness. According to the assessment experiments, DNase pre-digestion showed the highest extracellular DNA removal efficiency and live prokaryotic characterizing accuracy. Concerns for other methods include low DNA removal efficiency, instability, and uncertainties in result explanation. This study suggests that soil live prokaryotic diversity and abundance characterized by different methods exhibit high variability, and DNase pre-digestion is recommended for characterizing soil live prokaryotic communities. These findings provide crucial information for optimizing soil microbiome research methodologies.