Predation risk and space use of a declining Dall sheep (Ovis dalli dalli) population
Bibliographic record
Abstract
The abundance of ungulate populations may fluctuate in response to several limiting factors, including climate, diseases, and predation. In the northern Richardson Mountains, Canada, Dall sheep (Ovis dalli dalli) have undergone a major decline in the past decades and predation by grizzly bears (Ursus arctos) and wolves (Canis lupus) was suspected as a leading cause. To better understand the relationship between these three species located in this rugged and remote ecosystem, we relied on a combination of indirect methods. We investigated the apparent role of predation on the Dall sheep population using spatial ecology and stable isotopes. We examined seasonal variation in predation risk, focusing on how it may affect Dall sheep habitat use and sexual segregation, and we evaluated the proportion of Dall sheep in the diet of both predators using stable isotopes. The movements of the three species were monitored by satellite telemetry. Dall sheep habitat use patterns were analyzed using topographical features, greenness index, land cover, and apparent predation risk. The diets of grizzly bears and wolves were examined using a Bayesian mixing model for carbon and nitrogen stable isotopes. We found that Dall sheep habitat use varied seasonally, with different patterns for ewes and rams. Exposure to grizzly bear risk was higher for rams during summer, while ewes were further exposed to wolf apparent predation risk during winter. The importance of safe habitats for ewes was reflected in space use patterns. Stable isotopes analyses suggested that the diet of grizzly bears was largely from animal sources, with mountain mammals comprising about one quarter. Wolves mostly fed on both aquatic browsers and mountain mammals. Diet variation between individual predators suggested that some individuals specialized on mountain mammals, likely including Dall sheep. We conclude that grizzly bear and wolf apparent predation risk are important in driving Dall sheep habitat use and play a role in sexual segregation. Overall, this study presents an innovative combination of indirect methods that could be applied elsewhere to better understand predator-prey dynamics in remote ecosystems.
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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 | 0.001 |
| 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 | 0.000 |
| 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".