A Natural Experiment on the Impact of Overabundant Deer on Forest Invertebrates
Bibliographic record
Abstract
Abstract: In large parts of North America and Europe, deer overabundance threatens forest plant diversity. Few researchers have examined its effects on invertebrate assemblages. In a natural experiment on Haida Gwaii (British Columbia, Canada), where Sitka black‐tailed deer ( Odocoileus hemionus sitkensis ) were introduced, we compared islands with no deer, with deer for fewer than 20 years, and with deer for more than 50 years. We sampled invertebrates in three habitat categories: forest edge vegetation below the browse line, forest interior vegetation below the browse line, and forest interior litter. In forest edge vegetation, invertebrate abundance and species density decreased with increasing length of browsing history. In forest interior vegetation, decrease was significant only on islands with more than 50 years of browsing. Insect abundance in the vegetation decreased eightfold and species density sixfold on islands browsed for more than 50 years compared with islands without deer. Primary consumers were most affected. Invertebrates from the litter showed little or no variation related to browsing history. We attributed the difference between vegetation‐dwelling and litter‐dwelling invertebrates to differences in the effect of browsing on their habitat. In the layer below the browse line deer progressively removed the habitat. The extent of litter habitat was not affected, but its quality changed. We recommend more attention be given to the effect of overabundant ungulates on forest invertebrate conservation with a focus on edge and understory vegetation in addition to litter habitat.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".