Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Soil organic nitrogen (N) cycling processes constitute a bottleneck of soil N cycling, yet little is known about how tree species composition may influence these rates, and even less under changes in soil water availability such as those that are being induced by climate change. In this study, we used a 12‐year‐old tree biodiversity experiment in southwestern France to assess the interactive effects of soil water availability (half of the blocks seasonally irrigated to double precipitation) and tree species composition (monocultural vs. mixed plots of coniferous Pinus pinaster , and of broadleaf Betula pendula ). We measured gross protein depolymerisation rates using a novel high‐throughput isotope pool dilution method, along with soil microbial biomass carbon and N to calculate microbial biomass‐specific activities of soil organic N processes. Overall, high soil water availability led to a 42% increase in soil protein depolymerisation rates compared to the unirrigated plots, but we found no effect of species composition on these soil organic N cycling processes. When investigating the interactive effect of tree species mixing and soil water availability, the results suggest that mixing tree species had a negative effect on soil organic N cycling processes in the non‐irrigated blocks subject to dry summers, but that this effect tended to become positive at higher soil water availability in irrigated plots. These results put forth that soil water availability could influence potential tree species mixing effects on soil organic N cycling processes in dry conditions. Highlights Tree species (with different litter C:N ratios) had little effect on protein depolymerisation Increasing water availability via irrigation accelerated depolymerisation rates No interactive effect between tree species mixing and water availability, although trends emerged Positive trend of mixing under high water availability and negative trend under low water
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it