Response of reindeer mating time to climatic variability
Bibliographic record
Abstract
BACKGROUND: The breeding time of many species has changed over the past 2-3 decades in response to climate change. Yet it is a key reproductive trait that affects individual's parturition time and reproductive success, and thereby population dynamics. In order to predict how climate change will affect species' viability, it is crucial to understand how species base their reproductive efforts on environmental cues. RESULTS: By using long-term datasets of mating behaviours and copulation dates recorded since 1996 on a semi-domesticated reindeer population, we showed that mating time occurred earlier in response to weather conditions at different key periods in their annual breeding cycle. The mating time occurred earlier following a reducing snow cover in early spring, colder minimum temperatures in the last 2 weeks of July and less precipitation in August-September. CONCLUSIONS: The mediated effect of a reduced snow cover in early spring on improving individuals' pre-rut body weight through a better availability of late winter food and reduced costs of locomotion on snow would explain that mating time has occurred earlier overtime. A lower level of insect harassment caused by colder maximum temperatures in July might have caused an advance in mating time. Less precipitation in August-September also caused the mating time to occur earlier, although the direct effects of the last two weather variables were not mediated through the pre-rut body weight of individuals. As such, the causal effects of weather conditions on seasonal timing of animals are still unclear and other mechanisms than just body weight might be involved (e.g. socio-biological factors). The plastic response of reindeer mating time to climatic variability, despite supplemental feeding occurring in late April, demonstrated that environmental factors may have a greater influence on reproductive outputs than previously thought in reindeer.
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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".