Quantifying relationships between traits and explicitly measured gradients of stress and disturbance in early successional plant communities
Why this work is in the frame
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Bibliographic record
Abstract
Questions: How can one explicitly quantify, and separately measure, stress and disturbance gradients? How do these gradients affect functional composition in early successional plant communities and to what extent? Can we accurately predict trait composition from knowledge of these gradients? Location: Southern Quebec, Canada. Methods: Using eight environmental variables measured in 48 early successional plant communities, we estimated stress and disturbance gradients through structural equation modelling. We then measured 10 functional traits on the most abundant species of these 48 communities and calculated their community-level mean and variance weighted by the relative abundance of each species. Finally, we related these community-weighted means and variances to the estimated stress and disturbance gradients using general linear models or generalized additive models. Results: We obtained a well-fitting measurement model of the stress and disturbance gradients existing in our sites. Of the 10 studied traits, only average plant reproductive height was strongly correlated with the stress (r2=0.464) and disturbance (r2=0.543) gradients. Leaf traits were not significantly related to either the stress or disturbance gradients. Conclusions: The well-fitting measurement model of the stress and disturbance gradients, combined with the generally weak trait–environment linkages, suggests that community assembly in these early successional plant communities is driven primarily by stochastic processes linked to the history of arrival of propagules and not to trait-based environmental filtering.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it