Ecotypic variation in recruitment of reintroduced bighorn sheep: Implications for translocation
Bibliographic record
Abstract
ABSTRACT European settlement led to extirpation of native Audubon's bighorn sheep (formerly Ovis canadensis auduboni ) from North Dakota during the early 20th century. The North Dakota Game and Fish Department subsequently introduced California bighorn sheep (formerly O. c. californiana ) that were indigenous to the Williams Lake region of British Columbia, Canada, and Rocky Mountain bighorn sheep ( O. c. canadensis ) that were indigenous to the Sun River region of Montana. Although California bighorn sheep are no longer recognized as a distinct subspecies, they are smaller and adapted to a milder climate than either the native bighorn sheep of North Dakota or introduced bighorn sheep from Montana. Because reintroductions still play a key role in the management of bighorn sheep and because local adaptation may have substantial demographic consequences, we evaluated causes of variation in recruitment of bighorn sheep reintroduced in North Dakota. During 2006–2011, Montana stock recruited 0.54 juveniles/adult female ( n = 113), whereas British Columbia stock recruited 0.24 juveniles/adult female ( n = 562). Our most plausible mixed‐effects logistic regression model (53% of model weight) attributed variation in recruitment to differences between source populations (odds ratio = 4.5; 90% CI = 1.5, 15.3). Greater recruitment of Montana stock (fitted mean = 0.56 juveniles/adult female; 90% CI = 0.41, 0.70) contributed to a net gain in abundance ( r = 0.15), whereas abundance of British Columbia stock declined (fitted mean = 0.24 juveniles/adult female; 90% CI = 0.09, 0.41; r = − 0.04). Translocations have been the primary tool used to augment and restore populations of wild sheep but often have failed to achieve objectives. Our results show that ecotypic differences among source stocks may have long‐term implications for recruitment and demographic performance of reintroduced populations. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.
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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) | 0.000 | 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".