Does the leaf economic spectrum hold within local species pools across varying environmental conditions?
Bibliographic record
Abstract
Summary Understanding patterns of trait variation across environmental variability is necessary for development of ecological predictions. The leaf economic spectrum ( LES ) has demonstrated global trade‐offs in leaf traits, but it is unclear whether such patterns are robust in local communities exposed to varying environments. We conducted separate greenhouse experiments to examine the effects of varying water‐table depth and nitrogen availability on leaf‐level trait values among a suite of co‐occurring wetland species. We then assessed the effects of species‐specific trait value responses on relationships predicted by LES and whether species responded similarly to variations in water‐table depth and nitrogen availability. We found that both water‐table depth and nitrogen availability had significant species by treatment interactions for specific leaf area, leaf nitrogen and photosynthetic rates, indicating species‐specific responses to environmental variability. The responses of individual traits to different treatment levels were relatively consistent across species, but multivariate responses were more variable. We found that apart from significant relationships between specific leaf area and photosynthetic rate under some treatments, there was little support for the relationships predicted by the LES . These results suggest that, before trait‐based ecology will be able to make progress towards using plant traits to predict responses of communities and ecosystems to changes in environmental drivers, considerable attention needs to be paid to the processes that control intraspecific trait variation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".