Are there links between responses of soil microbes and ecosystem functioning to elevated <scp>CO</scp><sub>2</sub>, N deposition and warming? A global perspective
Bibliographic record
Abstract
In recent years, there has been an increase in research to understand how global changes' impacts on soil biota translate into altered ecosystem functioning. However, results vary between global change effects, soil taxa, and ecosystem processes studied, and a synthesis of relationships is lacking. Therefore, here we initiate such a synthesis to assess whether the effect size of global change drivers (elevated CO2, N deposition, and warming) on soil microbial abundance is related with the effect size of these drivers on ecosystem functioning (plant biomass, soil C cycle, and soil N cycle) using meta-analysis and structural equation modeling. For N deposition and warming, the global change effect size on soil microbes was positively associated with the global change effect size on ecosystem functioning, and these relationships were consistent across taxa and ecosystem processes. However, for elevated CO2, such links were more taxon and ecosystem process specific. For example, fungal abundance responses to elevated CO2 were positively correlated with those of plant biomass but negatively with those of the N cycle. Our results go beyond previous assessments of the sensitivity of soil microbes and ecosystem processes to global change, and demonstrate the existence of general links between the responses of soil microbial abundance and ecosystem functioning. Further we identify critical areas for future research, specifically altered precipitation, soil fauna, soil community composition, and litter decomposition, that are need to better quantify the ecosystem consequences of global change impacts on soil biodiversity.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".