Cougar predation and population growth of sympatric mule deer and white-tailed deer
Bibliographic record
Abstract
Mule deer (Odocoileus hemionus) populations throughout the west appear to be declining, whereas white-tailed deer (Odocoileus virginianus) populations are increasing. We compared abundance, number of fetuses per female (maternity rate), recruitment, and cause-specific adult ([Formula: see text]1 year old) mortality rate for sympatric mule deer and white-tailed deer in south-central British Columbia to assess population growth for each species. White-tailed deer were three times more abundant (908 ± 152) than mule deer (336 ± 122) (mean ± 1 SE). Fetal rates of white-tailed deer (1.83) were similar to those of mule deer (1.78). There was no statistically significant difference in recruitment of white-tailed deer (56 fawns : 100 does) and mule deer (38 fawns : 100 does). The annual survival rate for adult white-tailed deer (SWT = 0.81) was significantly higher than that for mule deer (SMD = 0.72). The main cause of mortality in both populations was cougar predation. The lower mule deer survival rate could be directly linked to a higher predation rate (0.17) than for white-tailed deer (0.09). The finite growth rate (λ) was 0.88 for mule deer and 1.02 for white-tailed deer. The disparate survival and predation rates are consistent with the apparent-competition hypothesis.
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 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 | 0.001 | 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) | 0.001 | 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".