Indirect Effects of Pandemic Deer Overabundance Inferred from Caterpillar‐Host Relations
Bibliographic record
Abstract
Externally feeding phytophagous insect larvae (i.e., caterpillars, here, larval Lepidoptera and sawflies, Hymenoptera: Symphyta) are important canopy herbivores and prey resources in temperate deciduous forests. However, composition of forest trees has changed dramatically in the eastern United States since 1900. In particular, browsing by high densities of white-tailed deer (Odocoileus virginianus) has resulted in forests dominated by browse-tolerant species, such as black cherry (Prunus serotina), and greatly reduced relative abundance of other tree species, notably pin cherry (Prunus pensylvanica) and birches (Betula spp.). To quantify effects of these changes on caterpillars, we sampled caterpillars from 960 branch tips of the 8 tree species that comprise 95% of trees in Allegheny hardwood forests: red maple (Acer rubrum), striped maple (Acer pensylvanicum), sugar maple (Acer saccharum), sweet birch (Betula lenta), yellow birch (Betula allegheniensis), American beech (Fagus grandifolia), black cherry, and pin cherry. We collected 547 caterpillar specimens that belonged to 66 Lepidoptera and 10 Hymenoptera species. Caterpillar density, species richness, and community composition differed significantly among tree species sampled. Pin cherry, nearly eliminated at high deer density, had the highest density and diversity of caterpillars. Pin cherry shared a common caterpillar community with black cherry, which was distinct from those of other tree hosts. As high deer density continues to replace diverse forests of cherries, maples, birches, and beech with monodominant stands of black cherry, up to 66% of caterpillar species may be eliminated. Hence, deer-induced changes in forest vegetation are likely to ricochet back up forest food webs and therefore negatively affect species that depend on caterpillars and moths for food and pollination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".