Conservation of migratory species at risk: Environmental conditions experienced from pre-gestation to parturition affect fall recruitment in migratory caribou
Bibliographic record
Abstract
Annual variations in environmental conditions can strongly affect vital rates such as survival and recruitment. These effects are likely to be exacerbated in highly seasonal environments and in species facing substantial energetic needs during specific seasons. For instance, pregnant migratory females must balance energy expenditure between long-distance travel and gestation. Little is known, however, on the environmental factors influencing recruitment in species exhibiting long-distance migrations. We aimed to fill this gap by evaluating the effect of environmental conditions from pre-gestation to weaning on fall recruitment of two migratory caribou (Rangifer tarandus) herds followed for over 35 years in northeastern Canada. Fall recruitment decreased with high precipitation experienced by adult females around estrus in previous fall (12% [95% confidence interval: 1-22]) and during gestation in winter (13% [3-21]), as well as with warm temperature (13% [3-23]) during gestation in winter and at calving (12% [2-21]). These environmental conditions experienced by both populations were positively correlated, suggesting these parameters could increase overall recruitment synchrony between nearby populations. We found no support for an effect of conditions experienced after birth, demonstrating that environmental conditions encountered from conception to calving are the strongest determinants of recruitment in migratory caribou. Our results highlight the importance of female condition throughout gestation with maternal allocation in fetal growth and newborn calf playing a major role in recruitment. Synchrony observed in recruitment between herds also underlines the importance of considering multiple populations facing similar environmental conditions in conservation strategies as they may exhibit simultaneous fluctuations.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".