Can antiherbivory resistance explain the abundance of woody species in a Neotropical savanna?
Bibliographic record
Abstract
The disturbance resistance model states that, in nutrient-poor communities, species more resistant to herbivory should dominate over the more palatable ones. Here we postulated that the disturbance resistance model should explain the species abundances in a nutrient-poor Neotropical savanna community. If so, the highly resistant species should be the commonest, whereas the poorly resistant ones should be rare. In an area of 2500 m2of woodland cerrado, a type of savanna, we measured the abundance of all 61 species as the total basal area and 9 antiherbivore defence traits from 10 individuals of each species. We used multiple and simple linear regressions to test the relationships between abundance and each trait or total investment in defence. Abundance was negatively related to specific leaf area (R2 = 0.18, b = –0.87, P < 0.001), but not with the other traits nor with total defence. The relationship between specific leaf area and abundance showed that plant functional traits may influence species abundance and supported the idea that nonrandom and resource-mediated processes should prevail at a fine scale. Nevertheless, we did not find strong evidence that antiherbivory resistance can explain species abundance in resource-poor communities, in contrast to the prediction of the disturbance resistance model.
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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 | 0.002 |
| 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 | 0.001 |
| 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".