Seasonal patterns of mortality for boreal caribou (Rangifer tarandus caribou) in an intact environment
Bibliographic record
Abstract
Seasonality is an important component in shaping the dynamics that influence ecosystems, including mortality. Animals experience temporal variation in vulnerability to mortality due to interactions among environmental conditions, nutritional condition, age and life stages, and changes in their movements and behaviours as well as those of their predators. Consequently, mortalities may follow a temporal pattern that can provide insight into factors influencing the population trends or ecology of a species. We investigated patterns of mortality in the boreal ecotype of woodland caribou (Rangifer tarandus caribou) in the southern Northwest Territories, Canada. Survival data were collected from 423 adult female caribou tracked by radio collars, 172 of which died during the study from predation (106), non-predation (i.e., starvation), (15), harvest (11), accidents (3), or unknown causes (37). We used generalized additive mixed models to evaluate temporal patterns of mortality across the year. We found that probability of mortality followed a trimodal pattern with three peaks, one during pre-calving, one in mid-summer, and a smaller peak in late autumn, with a 6-fold difference in mortality risk between the lowest and highest periods of the year. Mortality risk was higher from late spring (pre-calving) to mid-summer than it was from late summer until the end of winter, despite decreasing for about 6 weeks post-calving. Increased encounter rates, as predicted by higher caribou movement rates, corresponded to the pre-calving and late autumn mortality peaks, but not the mid-summer mortality peak. The mid-summer mortality peak was better explained by caribou nutritional condition, as adult female caribou experience the greatest depletion of body reserves from spring to mid-summer. Predation mortalities followed the same temporal pattern as total mortalities, whereas non-predation mortalities (i.e., starvation) were clustered in the weeks between peak calving and mid-summer. Seasonal fluctuations in predator-prey encounter probabilities, energetic demands, and nutritional condition that result in caribou being more vulnerable to predation should be considered when evaluating pressures on this species.
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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 | 0.001 | 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) | 0.000 | 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 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".