Along For the Ride? Odocoileus virginianus as a potential driver for Alliaria petiolata invasion
Bibliographic record
Abstract
Garlic mustard (Alliaria petiolata) is a non-native understory herb native to Eurasia. It is considered invasive and has spread throughout North American forests, notably forming dense monocultures with little to no native plant biodiversity. Previous studies have hypothesized that white-tailed deer (Odocoileus virginianus) facilitate garlic mustard expansion; as deer populations grow beyond carrying capacity, selective herbivory and disturbance could allow garlic mustard to infiltrate ecosystems while avoiding direct competition. Deer exclosures were constructed at Katharine Ordway Natural History Study Area in 2018 to investigate how deer removal affects (i) garlic mustard growth and (ii) understory community composition, and (iii) how these effects differ across naturally occurring garlic mustard densities. Exclosures were placed in areas with high or low densities of garlic mustard and paired plots were selected inside and outside of the exclosures. Vegetation surveys were conducted in 2018, 2019, and 2022 to measure understory community response to deer removal across garlic mustard concentrations. Community composition, assessed by principal component analysis, changed significantly over time in exclosure areas; exclosed areas became more different over time, especially in high garlic mustard areas. Canadian wood nettle (Laportea canadensis) dominated two exclosures in high garlic mustard areas (>90% coverage) with little cover in the respective paired plots (<4%). Linear mixed models showed no significant effects of treatment for garlic mustard total cover, rosette cover, stem cover, or the number of stems within plots, although this could be confounded by its biennial phenology. Differences in the species represented in the five tallest plants in each plot were significant in 2020-2022 for high garlic mustard areas and for 2022 in low garlic mustard areas, supporting the hypothesis that treatment effects would be more significant in high garlic mustard areas. Overall, these results indicate that deer have a greater effect on community composition in high garlic mustard 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".