Plant Community Biomass Shifts in Response to Mowing and Fencing in Invaded Oak Meadows with Non‐Native Grasses and Abundant Ungulates
Bibliographic record
Abstract
Many herbaceous meadows are dominated by competitive non‐native grasses and subject to ungulate herbivory, ecological processes that shift the proportional biomass of plant groups in the community. Predicting the outcome of restoration is complicated because herbivory and competition can interact. We examined the relationship between herbivory by native black‐tailed deer and domestic sheep and dominance of non‐native grasses in Garry oak meadows, one of North America's most endangered habitat types. A 3‐year factorial experiment tested the effects of mowing and fencing on plant community biomass, categorized into eight groups by geographic origin (native/non‐native), growth form (annual/perennial), and plant type (forb/grass). To test if the rarity of native plant groups was related to herbivory, we estimated ungulate foraging preferences for each plant group. Mowing and fencing treatments interacted for annual and perennial non‐native grasses. Dominance was shifted from non‐native to native grasses only when both mowing and fencing were applied. Fencing increased the total biomass, whereas mowing had no overall effect; however, fencing alone did not affect any individual plant group. Mowing shifted dominance from grasses to forbs, although both native and non‐native forbs benefited from the increased light availability. We also noted that herbivore fecal pellet densities were greatest in the spring, which coincided with the peak season of their preferred plant group, native perennial forbs. Overall, applying both mowing and fencing was the most effective restoration treatment to increase native plant groups and biomass.
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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 | 0.000 |
| 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) | 0.000 | 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".