Rising global temperatures reduce soil microbial diversity over the long term
Bibliographic record
Abstract
Soil microbial diversity is crucial to sustaining ecosystem productivity and improving carbon sequestration. Global temperature continues to rise, but how climate warming affects microbial diversity and its capacity to sequester soil organic carbon (SOC) remains uncertain. Here, by conducting a global meta-analysis with 251 paired observations from 102 studies, we showed that, on average, warming reduced bacterial and fungal diversity (measured by richness and Shannon index) by 16.0 and 19.7%, respectively, and SOC by 18.1%. The negative responses of both soil bacterial and fungal diversity to warming became more pronounced with increasing warming magnitude, experimental duration, and decreasing soil nitrogen availability. Under the worst-case climate warming scenario (2010 to 2070, 3.4 increase in °C), soil bacterial diversity and fungal diversity are projected to reduce by 56% and 81%, respectively, over 60 y. Importantly, in addition to the direct impact of warming on SOC, warming-induced declines in microbial diversity also contributed to SOC losses. We highlight that prolonged warming could substantially reduce soil microbial diversity and decrease SOC sequestration, accelerating future warming and underscoring the urgent need for decisive actions to mitigate global climate change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".