Interspecific and intraspecific variation in leaf toughness of Arctic plants in relation to habitat and nutrient supply
Bibliographic record
Abstract
Leaf toughness is an important functional trait that confers resistance to herbivory and mechanical damage. We sought to determine how species composition, climate, seasonality, and nutrient availability influence leaf toughness in two types of tundra in northern Alaska. We measured leaf toughness as force to punch for 11 species of Arctic plants in tussock tundra and dry heath tundra at 17 sites distributed along a latitudinal gradient. Rubus chamaemorus L. and the graminoids occupied opposite ends of the leaf toughness spectrum, with Rubus chamaemorus requiring the least force to punch, whereas one of the graminoids, Eriophorum vaginatum L., required the most. Leaf toughness increased with mean summer temperature for Eriophorum vaginatum and Betula nana L., whereas it declined with warmer temperatures for the other species. Toughness of mature leaves of Eriophorum vaginatum did not vary through the growing season but declined significantly after senescence. Application of N and P fertilizer in an experimental site decreased leaf toughness in three species but had no effect on four others. Leaf toughness of four out of five species in dry heath was greater than for the same species in tussock tundra, but there was no difference in community-weighted mean toughness between tussock tundra and dry heath.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".