Direct and indirect effects of a dense understory on tree seedling recruitment in temperate forests: habitat-mediated predation versus competition
Bibliographic record
Abstract
In forests characterized by a dense woody and herbaceous understory layer, seedling recruitment is often directly suppressed via interspecific competition. Alternatively, these dense layers may indirectly lower tree recruitment by providing a haven for seed and seedling predators that prey on neighboring plant species. To simultaneously test for resource competition and indirect, habitat-mediated effects, we factorially manipulated understory plant cover (removed versus intact) and predation (exclosures versus controls) at three forested sites. We found that vegetation cover created privileged foraging areas that increased seed removal and seedling predation rates. Predator preference was directly related to seed size with larger seeded species including Prunus serotina Ehrh. and Fagus grandifolia Ehrh. removed more readily than smaller seeded species such as Fraxinus americana L. We found strong species-specific evidence for habitat-mediated indirect effects; establishment of P. serotina and Acer saccharum Marsh.was significantly lower under an intact hay-scented fern ( Dennstaedtia punctilobula (Michx.) T. Moore) canopy when small mammals were present. Competition also played a strong role; both P. serotina and Acer rubrum L. survival as well as A. rubrum emergence were reduced under a fern canopy with or without seed predators. The impact of habitat-mediated indirect effects and resource competition appear to vary predictably based upon predator preferences and differences in the timing of woody seed dispersal and germination relative to vegetation cover phenology. Overall, our results suggest that habitat-mediated indirect effects may be common and occur wherever vegetation provides the potential for creating privileged foraging areas.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".