Neonate mortality in mountain caribou: Patterns of predation during onset of a wolf reduction program
Bibliographic record
Abstract
Abstract Caribou ( Rangifer tarandus ) calf mortality during the neonatal period is commonly attributed to predation, particularly by gray wolves ( Canis lupus ). However, neonate mortality remains understudied in mountain caribou, despite increasing wolf reduction programs. We used an individual‐based movement method to infer parturition and neonate mortality from adult female telemetry data (78 individual‐years), supplemented with 3 years of camera trap data (89 cameras), to examine changes in neonate mortality rates, timing, and locations before (2012–2014) and after (2020–2021) wolf reduction, across 2 calving areas (one rugged, another gradual) for the Itcha‐Ilgachuz subpopulation in British Columbia, Canada. Given the likely difference in the timing of wolf–neonate overlap between calving areas, we hypothesized that wolf predation would be additive to other mortality sources typically affecting younger neonates (e.g., grizzly bear [ Ursus arctos ] predation) in the rugged area, but compensatory in the gradual area. Accordingly, we predicted that reducing wolves would increase survival and lower the average mortality age for neonates in the rugged area, with smaller gains in survival and minimal change in average mortality age in the gradual area. After wolf reduction, survival increased 41% in the rugged area but did not improve in the gradual area, resulting in no overall increase in survival at the subpopulation level. Average mortality age decreased in the rugged area from approximately 14 days to 8 days, coinciding with the peak in camera detections of grizzlies and wolverines ( Gulo gulo ), and remained at approximately 9 days in the gradual area. Mortalities before wolf reduction (i.e., those more likely caused by wolves) were more strongly associated with anthropogenic linear features and treed valley bottoms. Our findings highlight the value of considering habitat‐specific mechanisms influencing calf mortality and integrating indirect approaches to address knowledge gaps in prey–predator dynamics.
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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) | 0.001 | 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".