A decade of irrigation transforms the soil microbiome of a semi‐arid pine forest
Bibliographic record
Abstract
The impact of climate change on the soil microbiome potentially alters the biogeochemical cycle of terrestrial ecosystems. In semi-arid environments, water availability is a major constraint on biogeochemical cycles due to the combination of high summer temperatures and low rainfall. Here, we explored how 10 years of irrigation of a water-limited pine forest in the central European Alps altered the soil microbiome and associated ecosystem functioning. A decade of irrigation stimulated tree growth, resulting in higher crown cover, larger yearly increments of tree biomass, increased litter fall and greater root biomass. Greater amounts of plant-derived inputs associated with increased primary production in the irrigated forest stands stimulated soil microbial activity coupled with pronounced shifts in the microbiome from largely oligotrophic to more copiotrophic lifestyles. Microbial groups benefitting from increased resource availabilities (litter, rhizodeposits) thrived under irrigation, leading to enhanced soil organic matter mineralization and carbon respired from irrigated soils. This unique long-term study provides new insights into the impact of precipitation changes on the soil microbiome and associated ecosystem functioning in a water-limited pine forest ecosystem and improves our understanding of the persistency of long-term soil carbon stocks in a changing climate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".