Limited evidence of vertical fine‐root segregation in a subtropical forest
Bibliographic record
Abstract
Vertical root segregation and the resulting niche partitioning can be a key underpinning of species coexistence. This could result from substantial interspecific variations in root profiles and rooting plasticity in response to soil heterogeneity and neighbours, but they remain largely untested in forest communities. In a diverse forest in subtropical China, we randomly sampled > 4000 root samples from 625 0-30 cm soil profiles. Using morphological and DNA-based methods, we identified 109 woody plant species, determined the degree of vertical fine-root segregation, and examined rooting plasticity in response to soil heterogeneity and neighbour structure. We found no evidence of vertical fine-root segregation among cooccurring species. By contrast, root abundance of different species tended to be positively correlated within soil zones. Underlying these findings was a lack of interspecific variation in fine-root profiles with over 90% of species concentrated in the 0-10 cm soil zone with only one species dominating in the 10-20 cm soil zone. Root profiles exhibited low responsiveness to root neighbours but tended to be shallow in soils with low phosphorus and copper content. These findings suggest that if there is niche differentiation leading to coexistence in this diverse forest, it would be occurring by mechanisms other than vertical fine-root segregation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".