The contribution of intraspecific trait variation to changes in functional community structure along a stress gradient
Bibliographic record
Abstract
Abstract Question We considered two possibilities related to the contribution of intraspecific trait variation (ITV) to changes in functional community structure along a stress gradient in tundra vegetation. First, ITV could contribute to the success of plant species across the stress gradient by promoting optimal trait values for each condition along the gradient; thus, ITV enhances convergence toward optimal traits. Second, ITV of only a few dominant species aligns with the optimal trait, and ITV of other species promotes trait diversification within communities. Location Salluit, Québec, Canada (62°12′N, 75°39′W). Methods Vegetation was surveyed under three different conditions (harsh, intermediate, and competitive) representing an environmental stress gradient. We assessed ITV across the gradient for four plant functional traits: leaf carbon (C) and nitrogen (N) contents, specific leaf area (SLA), and plant height. We assessed community‐weighted means (CWMs) and functional dispersion (FDis) for each of the four traits. These indices were calculated from mean trait values from all individuals across all habitats (mean‐CWM and mean‐FDis), and from each habitat (ITV‐CWM and ITV‐FDis). Results For the four traits, increasing trends (leaf N content, SLA, and plant height) and decreasing trends (leaf C content) in mean‐CWM along the stress gradient were maintained and pronounced for ITV‐CWM. Comparisons between ITV‐FDis and mean‐FDis suggested that, except for leaf C content, ITV tends to reduce and increase FDis values at the abiotically stressful and competitive ends of the environmental stress gradient, respectively. Conclusions ITV along the abiotic stress–competition gradient appears to promote community‐level changes towards optimal traits. In particular, ITV in three traits (with the exception of leaf C content) may contribute to equalization of optimal trait values, and to “passage through environmental filtering” in harsh habitats. However, when species deal with competition for richer resources, only some dominant species may show optimal trait values. Therefore, ITV may allow for trait diversification within communities under competitive rather than harsh abiotic conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".