Resource competition and apparent competition in declining mule deer (<i>Odocoileus</i> <i>hemionus</i>)
Bibliographic record
Abstract
Resource competition and apparent competition have both been suggested as the cause of mule deer (Odocoileus hemionus (Rafinesque, 1817)) decline concurrent with white-tailed deer (Odocoileus virginianus (Zimmerman, 1780)) increase. I tested for both hypotheses by conducting a “press” and “release” experiment in a mule deer, white-tailed deer, and cougar (Puma concolor (L., 1771)) community. If resource competition is causal, then predation should decrease, but other sources of mortality should increase following increased mortality of cougars and release of competing white-tailed deer. If apparent competition is causal, then predation should decrease and mule deer should increase following increased mortality of cougars and release of white-tailed deer. I accepted the apparent competition hypothesis because high mortality of female cougars and cougar population decline was associated with both white-tailed deer and mule deer population growth. Very high mortality of female cougars appeared to result in mule deer population recovery. However, high mortality of male cougars (with increased male immigration) preceding high female mortality appeared to result in sexually segregated prey-switching by females with cubs from abundant white-tailed deer to rare mule deer to avoid sexually selected infanticide. High mortality of resident male cougars may have precipitated the mule deer decline in the first place.
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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 | 0.001 | 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".