Quantifying the relationship linking the community‐weighted means of plant traits and soil fertility
Bibliographic record
Abstract
Abstract Is it possible to generalize relationships between certain plant traits and soil fertility? In particular, are there quantitative relationships between community‐weighted mean (CWM) trait values of leaf dry‐matter content (LDMC), specific leaf area (SLA), plant height, and Grime’s competitor‐stress tolerator‐ruderal (CSR) strategy scores and the generalized soil fertility, FG (i.e., the capacity of a soil to produce biomass when all nonsoil variables are held constant) that are generalizable across different species assemblages and geographical areas? We assessed FG in 21 sites in southern Quebec and 7 sites in southern France using a previously published method based on structural equation modeling. We then determined the CWM values of LDMC, SLA, plant height, and CSR scores in the 21 Quebec sites to obtain quantitative relationships between FG and these CWM traits. Using these relationships, we independently tested the generality of the trait–fertility relationships using data from French sites. The relationships between FG and the CWM traits were nonlinear, but displayed the expected qualitative trends as reported in the literature. In particular, the S score and CWM LDMC decreased with increasing soil fertility, and the R score and CWM SLA increased. CWM traits were more strongly correlated to measures of FG (r2 up to 0.48) than to measures of other soil characteristics (r2 up to 0.17 for nitrogen flux). Importantly, the independently tested French FG–trait relationships showed no significant deviations from these quantitative relationships. Further investigation is necessary to confirm if the same trend applies to other regions and or ecosystems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".