Soil organic carbon loss decreases biodiversity but stimulates multitrophic interactions that promote belowground metabolism
Bibliographic record
Abstract
Soil organic carbon (SOC) plays an essential role in mediating community structure and metabolic activities of belowground biota. Unraveling the evolution of belowground communities and their feedback mechanisms on SOC dynamics helps embed the ecology of soil microbiome into carbon cycling, which serves to improve biodiversity conservation and carbon management strategy under global change. Here, croplands with a SOC gradient were used to understand how belowground metabolisms and SOC decomposition were linked to the diversity, composition, and co-occurrence networks of belowground communities encompassing archaea, bacteria, fungi, protists, and invertebrates. As SOC decreased, the diversity of prokaryotes and eukaryotes also decreased, but their network complexity showed contrasting patterns: prokaryotes increased due to intensified niche overlap, while that of eukaryotes decreased possibly because of greater dispersal limitation owing to the breakdown of macroaggregates. Despite the decrease in biodiversity and SOC stocks, the belowground metabolic capacity was enhanced as indicated by increased enzyme activity and decreased enzymatic stoichiometric imbalance. This could, in turn, expedite carbon loss through respiration, particularly in the slow-cycling pool. The enhanced belowground metabolic capacity was dominantly driven by greater multitrophic network complexity and particularly negative (competitive and predator-prey) associations, which fostered the stability of the belowground metacommunity. Interestingly, soil abiotic conditions including pH, aeration, and nutrient stocks, exhibited a less significant role. Overall, this study reveals a greater need for soil C resources across multitrophic levels to maintain metabolic functionality as declining SOC results in biodiversity loss. Our researchers highlight the importance of integrating belowground biological processes into models of SOC turnover, to improve agroecosystem functioning and carbon management in face of intensifying anthropogenic land-use and climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".