Impacts of cattle grazing on small-rodent communities: an experimental case study
Bibliographic record
Abstract
We used experimental cattle ungrazed and grazed sites to evaluate what impact different intensities of cattle grazing have on deer mice ( Peromyscus maniculatus (Wagner, 1845)) and meadow voles ( Microtus pennsylvanicus (Ord, 1815)). Live-trapping of these small rodents was conducted on paired treatment plots (grazed and ungrazed) at Sheep River Provincial Park in southwestern Alberta, Canada. Before grazing started, both rodent species were equally abundant in either grazed or ungrazed sites. Introduction of grazing resulted in strong but differing responses by both rodent species. Deer mice had higher population density (measured as individual animals trapped) in the grazed than in ungrazed plots, but their body mass was negatively correlated with increasing grazing pressure. Meadow voles were more heavily affected by grazing, as a strong avoidance of cattle-grazed plots was observed already at low grazing intensity. In addition, cattle grazing had noticeable effects, impacting the survival, sex and age ratios, and the ectoparasite prevalence of these two rodent populations. We conclude that there are diverse and different levels of impact of cattle grazing on those two rodents, leading to much more complex species interactions than previously thought. We suggest that the presence or absence, density, and body condition of small mammals could be used as a tool for ecosystem health assessment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".