Exploring trait–performance relationships of tree seedlings along experimentally manipulated light and water gradients
Bibliographic record
Abstract
A foundational assumption of trait-based ecology is that individual performances should be predicted by its functional traits. However, the trait-performance relationships reported in literature were typically weak, probably due to the ignorance of interactions between traits and environments, intraspecific trait variability, and hard traits (directly linked to physiological processes of interest). We conducted an experiment of planting 900 seedlings of six tree species separately (one seedling per pot) along experimentally manipulated light and water gradients, monitored their survival and growth, and measured their morphological, photosynthetic, and hydraulic traits. Most trait-performance relationships depended on the environments, either marginally changing (weak trait × environment interaction) or even reversing (strong trait × environment interaction) along light or water gradients in our experiment. Such trait × environment interactions were more likely to be detected in growth models using individual-level traits than models using species mean traits, but seedling growth was not better modeled with individual-level traits than species mean traits. Additionally, none of the hard traits (photosynthetic and hydraulic traits) were better predictors than soft traits (morphological traits) modeling seedling growth and survival along light and water gradients. Our study highlights the necessities of considering trait × environment interactions when predicting response of plants to changing environments. The benefits of using individual-level traits or hard traits to predict plant performance might be reduced or even canceled if their measurement errors are not well controlled.
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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 | 0.000 | 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 | 0.001 |
| 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".