HABITAT USE AND DIET OF DESERT BIGHORN SHEEP (OVIS CANADENSIS MEXICANA) AND ENDEMIC MULE DEER (ODOCOILEUS HEMIONUS NELSONII) ON TIBURÓN ISLAND, MEXICO
Bibliographic record
Abstract
In 1975, wildlife managers reintroduced desert bighorn sheep (Ovis canadensis mexicana) to Tiburón Island, Mexico, where endemic mule deer (Odocoileus hemionus sheldoni) occur. We predicted that these species would use both mountain and plains habitats, but would have different diets, minimizing negative interspecific effects. By censusing pellet groups, we found that both ungulates occurred in both habitats, but bighorn sheep were more abundant in mountains and mule deer were more abundant in plains. Microhistological analyses determined that diets of both species consisted of the same 39 plant species, of which 13 plant species each composed ≥4% of the diet and together composed 70–80% of the total diet. Plants in low abundance represented 26–32% of diets in the mountains and 45–56% of diets in the plains. Overall diet overlap was 68% and overlap did not differ between mountains or plains, but did differ among seasons. Overlap in habitat agreed with predictions, but diet overlap was contrary to our predictions. Our results differ from studies on the mainland, which found overlap in habitat use, but not in diet. Overlap in resource use coupled with the rapid increase in abundance of bighorn sheep since their reintroduction to Tiburón may have an impact on mule deer. Additional research is needed to determine whether abundance of mule deer has changed in response to the reintroduction of bighorn sheep.
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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 | 0.000 |
| 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".