Quantifying deer and turkey leaf litter disturbances in the eastern deciduous forest: have nontrophic effects of consumers been overlooked?
Bibliographic record
Abstract
Vertebrate herbivores and omnivores modify forest regeneration not only via direct and indirect trophic pathways, but also via nontrophic pathways such as litter and soil disturbances that may favor the regeneration of some species but not others. Deer are overabundant throughout vast portions of the eastern deciduous forest, and turkeys, once nearly extirpated, are now far more common; their foraging habits disturb litter over large areas, though this has rarely been evaluated. We quantified the size and frequency of litter disturbances created by both deer and turkeys. In addition, we tested the hypothesis that excluding vertebrates via fences would cause a reduction in the abundance and size of litter disturbances. We tested this hypothesis inside and outside six exclosures and adjacent control plots in an old-growth deciduous forest in Pennsylvania. Bare soil patches were ∼60% smaller and between 50% and 90% less abundant inside exclosures. The mean size of turkey litter disturbances was large (mean > 30 m2) and significantly greater than deer disturbances (p = 0.002), though turkey disturbances were less frequent. Our findings should apply broadly and are some of the first to demonstrate the extent of turkey disturbances. In addition, we demonstrate that exclosure studies reduce physical disturbances as well as browsing, both of which may synergistically act to cause changes in forest communities. We also caution that many deer fences exclude other vertebrates such as turkeys, which are important herbivores, seed predators, and major agents of disturbance. Consequently, we argue that studies that use fences to exclude deer should explicitly consider nontrophic indirect effects, particularly leaf disturbances and the potential impact of other large consumers as well (e.g., turkeys).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".