Soil nitrogen and carbon heterogeneity in woodlands and grasslands: contrasts between temperate and tropical regions
Bibliographic record
Abstract
ABSTRACT Aim Soil resource heterogeneity is linked to several ecological processes including invasion of woody species into grasslands. Studies from the temperate zone have demonstrated greater soil heterogeneity beneath woody vegetation than beneath grasslands. Woody species have a more widespread and coarser root system than herbaceous species, and may have a competitive advantage in relatively heterogeneous soils. We tested the global generality of greater soil heterogeneity beneath woody vegetation. Location Global. Methods We used data from published literature for soil nitrogen and carbon heterogeneity from paired woodland and grassland sites around the world. Results Woodland and grassland soil heterogeneities from paired observations were strongly correlated. There was, however, significant geographical variability in the relationship. Soils were more heterogeneous in woodlands than grasslands in temperate areas, but the opposite was true for tropical habitats. Grassland soils were more heterogeneous at lower than higher latitudes. Woodland soil heterogeneity did not vary with latitude. Main conclusions The previously described high soil heterogeneity in woody vegetation compared to grasslands holds only for temperate regions. Consequently, the relationship between soil resource heterogeneity and vegetation type is dependent on the study region. Macroecological studies should test the generality of relationships between soil and vegetation at the global scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".