Herbarium specimens as tools to assess the impact of large herbivores on plant species
Bibliographic record
Abstract
Herbarium specimens can be used to reconstruct spatiotemporal changes in plant morphology caused by environmental pressures. The reliability of herbarium-derived data requires evaluation, because specimen collection is subject to biases. We used herbarium and field data to investigate the impact of large herbivore browsing on the size of a forb. White trillium (Trillium grandiflorum (Michaux) Salisbury) was studied because the impacts of white-tailed deer (Odocoileus virginianus Zimmermann) browsing on this species are well-known. A total of 692 herbarium specimens collected in Quebec (Canada) were used to evaluate leaf area. Leaf area values of herbarium specimens were compared with those of modern (2013, 2014) specimens collected in sites either with >8 deer per square kilometre or without deer. Flowering individuals in modern sites with deer had a significantly lower leaf area than herbarium specimens and modern specimens collected in sites without deer. The distribution of white trillium individuals in deer sites was also skewed towards plants with a smaller leaf area. Herbarium specimens may offer unique and inexpensive data, compared with methods traditionally used to assess the impacts of herbivores on plants, such as exclosures or clipping experiments. Unfortunately, this rich data source is seriously jeopardized by the downtrend in specimen collecting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".