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Published on: July 24, 2018
Effects of different harvesting methods on soil bacterial diversity in Betula platyphylla secondary forest in Daxing
Kaitao Zhai1,2, Shengwei Liu1,2, Hao Zhang1,2
1College of Forestry, Inner Mongolia Agricultural University, Hohhot, China.
Introduction:
In the context of global climate change, optimizing forest management is essential for improving cold-temperate forest ecosystems. However, the pathways through which forest management influences ecological functions via soil and microbial communities remain unclear.
Methods:
This study examined how logging methods impact the soil properties and bacterial communities in the Daxing 'an Mountains, Inner Mongolia. Sample plots for five conditions were analyzed: secondary gradual cutting (S1 and S2), secondary gradual cutting with replanting (S3 and S4), clear cutting (ML and MB), and original forests (L1, L2, and L3).
Results:
Logging methods drove distinct soil ecosystem responses among plots, mediated by long-term disturbance legacies and successional processes. (1) ML had the highest pH, total nitrogen, total phosphorus, and capillary porosity. MB showed a favorable nutrient status, characterized by high total potassium. S1 and S4 demonstrated good organic matter and physical structure, while S2 and S3 showed nutrient imbalances or structural issues. Notably, L1-L3 displayed the poorest nutrient levels and physical properties. (2) All plots shared Proteobacteria, Acidobacteriota, and Actinobacteriota as core phyla, with specific enrichments: Verrucomicrobiota in ML; Chloroflexi in S3-S4; and Planctomycetota in L1-L3. Diversity was highest in L1 and lowest in L3, with high levels in ML, MB, and S3 and lower levels in S1, S2, and S4. Community composition was similar among S4, ML, and MB but distinct in other plots. Bacterial biomarkers were significantly associated with soil factors (p < 0.05): A21b_unclassified (S1), Granulicella (S2), Gemmataceae_unclassified (S3), AD3_unclassified (S4), KD4-96_unclassified (ML), RB41 (MB), and Vicinamibacterales_unclassified (L1). PICRUSt2 predictions may indicate potential increases in metabolism (S2, L1, and L3), environmental information processing (S3, L1, and L3), genetic information processing (S4 and ML), and cellular processes (MB and L3). (3) Soil factor analysis identified pH as the original community driver overall. Total nitrogen and soil organic matter dominated in cutting plots, while moisture content was key in forest plots.
Conclusions:
This study found that appropriate logging methods improve soil nutrient content and bacterial diversity by regulating soil pH, total nitrogen, and moisture. These results demonstrate the profound impact of forest management on core soil ecological processes.
