Consumer‐based limitations drive oak recruitment failure
Bibliographic record
Abstract
A number of North American oaks are experiencing recruitment failure, with explanatory hypotheses including a range of consumer- or resource-based limitations. Using a factorial experiment with transplanted seedlings, we demonstrate how direct and indirect consumer effects prevent recruitment by Quercus garryana, a keystone savanna species of northwestern North America. Foremost, intense herbivory by mostly exotic small mammals severely damaged or killed 100% of unprotected seedlings during winter. Many damaged seedlings survived but were 64% smaller in size and produced 75% fewer leaves. Herbivory by deer had no detectable impact despite being long hypothesized as a major contributor to oak decline. Indirectly, herbivory altered the outcome of moisture limitation and competition. Without small mammals, summer drought and a dense exotic grass cover associated with fire suppression significantly reduced growth but caused little mortality. With small mammals, both significantly increased mortality of herbivore-damaged seedlings. Herbivore damage also increased the likelihood of severe insect attack, possibly due to reduced investment in foliar defense by recovering seedlings. These results show that no single factor necessarily prevents seedling establishment by oaks, but that the combination of herbivory, undisturbed exotic grass swards, and summer drought creates an almost insurmountable barrier for recruitment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".