The complexity of trait–environment performance landscapes in a local subtropical forest
Bibliographic record
Abstract
That functional traits should affect individual performance and, in turn, determine fitness and population growth, is a foundational assumption of trait-based ecology. This assumption is, however, not supported by a strong empirical base. Here, we measured simultaneously two individual performance metrics (survival and growth), seven traits and 10 environmental properties for each of 3981 individuals of 205 species in a 50-ha stem-mapped subtropical forest. We then modelled survival/growth as a function of traits, environments and trait × environment interactions, and quantified their relative importance at both the species and individual levels. We found evidence of alternative functional designs and multiple performance peaks along environmental gradients, indicating the presence of complicated trait × environment interactions. However, such interactions were relatively unimportant in our site, which had relatively low environmental variations. Moreover, individual performance was not better predicted, and trait × environment interactions were not more likely detected, at the individual level than at the species level. Although the trait × environment interactions might be safely ignored in relatively homogeneous environments, we encourage future studies to test the interactive effects of traits and environments on individual performances and lifelong fitness at larger spatial scales or along experimentally manipulated environmental gradients.
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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 | 0.000 | 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".