Vegetation and Soil Types Affect Microbial Carbon Metabolism in the Black Soil Region of Northeast China
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| Publicado en: | Eurasian Soil Science vol. 58, no. 13 (Dec 2025), p. 201 |
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| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text |
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MARC
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| 001 | 3278658142 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1064-2293 | ||
| 022 | |a 1556-195X | ||
| 022 | |a 0038-5832 | ||
| 024 | 7 | |a 10.1134/S1064229325602884 |2 doi | |
| 035 | |a 3278658142 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 084 | |a 108320 |2 nlm | ||
| 100 | 1 | |a Chen, Yang |u College of Wildlife and Protected Area, Northeast Forestry University, Harbin, China (GRID:grid.412246.7) (ISNI:0000 0004 1789 9091) | |
| 245 | 1 | |a Vegetation and Soil Types Affect Microbial Carbon Metabolism in the Black Soil Region of Northeast China | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study explored the ecological functions and environmental effects of soil microorganisms in the black soil region by examining microbial carbon source metabolic capacity across four vegetation types (reed wetland, maize field, paddy field, aspen woodland) and three soil types (Histosols, Planosols, Gleysols) in the Naoli River Reserve, with soil samples collected at a depth of 0–20 cm. Using Biolog-ECO microplate technology, we assessed soil microbial carbon source metabolic activity, utilization patterns, and functional diversity. Structural equation modeling and random forest analysis were applied to explore the influence of environmental factors on microbial carbon metabolism. Results showed that microbial carbon metabolic activity was highest in maize fields and Histosols, exceeding that in paddy fields and Gleysols. Microorganisms preferred amino acids, polymers, and carboxylic acids over carbohydrates, amines, and phenolic acids. The Simpson index of microbial diversity was positively correlated with microbial biomass carbon and moisture content, while Chao1 and Ace indices were correlated with microbial biomass phosphorus. Key microbial phyla, such as Bacteroides and Monomonas, were closely related to carbon source utilization. The structural equation modeling indicated that microbial biomass carbon, microbial biomass nitrogen, and soil organic carbon were the main drivers of microbial carbon metabolism. The RF model identified i-erythritol as a key predictor of microbial carbon metabolism, and amines as the best predictor of average well color development (AWCD) changes. The McIntosh index was the most influential variable for AWCD variation. These findings provide a scientific basis for evaluating soil health and supporting sustainable black soil management. | |
| 651 | 4 | |a China | |
| 653 | |a Phenolic acids | ||
| 653 | |a Biogeochemistry | ||
| 653 | |a Phosphorus | ||
| 653 | |a Biomass | ||
| 653 | |a Metabolism | ||
| 653 | |a Rice fields | ||
| 653 | |a Vegetation | ||
| 653 | |a Water depth | ||
| 653 | |a Soil types | ||
| 653 | |a Soil microorganisms | ||
| 653 | |a Ecological function | ||
| 653 | |a Moisture content | ||
| 653 | |a Carbohydrates | ||
| 653 | |a Amino acids | ||
| 653 | |a Climate change | ||
| 653 | |a Soil | ||
| 653 | |a Carboxylic acids | ||
| 653 | |a Wetlands | ||
| 653 | |a Nitrogen | ||
| 653 | |a Soil management | ||
| 653 | |a Microorganisms | ||
| 653 | |a Precipitation | ||
| 653 | |a Greenhouse gases | ||
| 653 | |a Soil fertility | ||
| 653 | |a Environmental effects | ||
| 653 | |a Corn | ||
| 653 | |a Environmental factors | ||
| 653 | |a Woodlands | ||
| 653 | |a Plates | ||
| 653 | |a Organic carbon | ||
| 653 | |a Flowers & plants | ||
| 653 | |a Polymers | ||
| 653 | |a Modelling | ||
| 653 | |a Amines | ||
| 653 | |a Carbon sources | ||
| 653 | |a Decomposition | ||
| 653 | |a Ecosystems | ||
| 653 | |a Ecological effects | ||
| 653 | |a Water content | ||
| 653 | |a Carbon sequestration | ||
| 653 | |a River ecology | ||
| 653 | |a Carbon cycle | ||
| 653 | |a Structure-function relationships | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Zhang, Li |u College of Wildlife and Protected Area, Northeast Forestry University, Harbin, China (GRID:grid.412246.7) (ISNI:0000 0004 1789 9091) | |
| 700 | 1 | |a Meng, Siyan |u College of Wildlife and Protected Area, Northeast Forestry University, Harbin, China (GRID:grid.412246.7) (ISNI:0000 0004 1789 9091) | |
| 700 | 1 | |a Fan, Linlin |u College of Wildlife and Protected Area, Northeast Forestry University, Harbin, China (GRID:grid.412246.7) (ISNI:0000 0004 1789 9091) | |
| 700 | 1 | |a Wang, Guangxin |u Heilongjiang Naoli River National Nature Reserve Administration, Shuangyashan, China (GRID:grid.412246.7) | |
| 700 | 1 | |a Yu, Bing |u College of Wildlife and Protected Area, Northeast Forestry University, Harbin, China (GRID:grid.412246.7) (ISNI:0000 0004 1789 9091) | |
| 773 | 0 | |t Eurasian Soil Science |g vol. 58, no. 13 (Dec 2025), p. 201 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3278658142/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3278658142/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |