Evaluation of the relationship between soil properties and functional traits in three forest types in a subtropical mountain in Mexico
Bibliographic record
Abstract
Although it is well-established that soil nutrients determine leaf morpho-physiology and tree dimensions, linking soil physicochemical properties with functional community patterns under field conditions remains difficult. We examine how soil properties shape community-weighted mean (CWM) traits and species composition in pine, mixed, and broadleaf forests along a topographic gradient in the subtropical mountains of western Mexico. We related pH, organic matter, calcium, boron, and silt with stomatal density, stomatal conductance, leaf water potential (Ψ), specific leaf area (SLA), diameter at breast height (DBH), and tree height across three forest types and nine 1000 m 2 plots along the gradient. Pine forests, with distinct species composition, occupied stressful environments characterized by acidic soils and low organic matter. They adopted a conservative resources use strategy, with lower CWM-SLA and higher CWM-DBH, along with less negative CWM-Ψ, indicating reduced water stress compared to broadleaf and mixed forests. In contrast, broadleaf and mixed forests inhabit soils without nutrient limitations, showed a more acquisitive strategy, and high species turnover resulted in similar CWM values between them. This functional heterogeneity may be a key factor in the resilience of subtropical forest to disturbances.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".