Impacts of generalist mesopredators on the demography of small-mammal populations in fragmented landscapes
Bibliographic record
Abstract
A consequence of the reduction and subsequent fragmentation of native habitats has been the loss or severe reduction of specialist predator populations from these altered ecosystems, resulting in a “release” of generalist predators. Demographic aspects of small-rodent populations, especially predator-driven density cycles, have been extensively studied. However, the majority of studies examining predator–prey dynamics have been conducted in relatively undisturbed ecosystems, while more limited data are available for regions that have been greatly modified by human settlement. Using raccoons ( Procyon lotor (L., 1758)) and white-footed mice ( Peromyscus leucopus (Rafinesque, 1818)) as focal species, we used an experimental framework to evaluate the hypothesis that generalist mesopredators limit small-mammal abundance in landscapes that have been significantly altered by human land use. Both parametric and nonparametric analyses indicated that populations of white-footed mice exhibited a significant increase (32%) in density where raccoon abundance was reduced when compared with control populations. Our study highlights an important role that superabundant mesopredators can play in ecosystems through the limitation of secondary prey populations. This research suggests that further investigation of the trophic dynamics of agricultural ecosystems is critical if we are to elucidate the fundamental ecological mechanisms associated with the persistence of species in disturbed environments.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".