Mechanisms driving postfire abundance of a generalist mammal
Bibliographic record
Abstract
Changes in vertebrate abundance following disturbance are commonly attributed to shifts in food resources or predation pressure, but underlying mechanisms have rarely been tested. We examined four hypotheses for the commonly reported increase in abundance of deer mouse ( Peromyscus maniculatus (Wagner, 1845)) following forest fires: source–sink dynamics, decreased predation, increased food resources, and increased foraging efficiency. We found that reproduction of deer mouse was considerably higher in burned versus unburned forests and survival did not differ between habitats, indicating that burned forests were not sink habitats. Comparable survival also suggested that predation rates were similar between habitats. Increased reproduction in burned versus unburned forest suggested better resource conditions, but abundance of seeds and arthropods (the primary food resources for mice) either did not differ between habitats or were higher overall in unburned forest. Foraging experiments indicated that seed removal from depots was substantially higher in burned versus unburned forests after controlling for mouse density. Additionally, in both habitats, mice were captured more often in open microhabitats and the odds of individual insect removal increased with decreasing cover during certain sampling periods. Of the four hypotheses tested, greater foraging efficiency provided the best explanation for elevated populations of deer mouse. However, predation risk may have influenced foraging success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 teacher head, 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".