Early‐life conditions determine the between‐individual heterogeneity in plasticity of calving date in reindeer
Bibliographic record
Abstract
Phenotypic plasticity has become a key concept to enhance our ability to understand the adaptive potential of species to track the pace of climate change by allowing a relatively rapid adjustment of life-history traits. Recently, population-level trends of an earlier timing of reproduction to climate change have been highlighted in many taxa, but only few studies have explicitly taken into consideration between-individual heterogeneity in phenotypic plasticity. Using a long-term data of a semi-domesticated reindeer (Rangifer tarandus) population, we demonstrated that females differed greatly in their mean calving date but only slightly in the magnitude of their plastic response to the amount of precipitation in April. We also showed that despite the absence of a population trend, females individually responded to the amount of precipitation in April by delaying their calving dates. Females' calving date under average climatic conditions was best predicted by their birthdate, by their physical condition in March-April-May before their first calving season and by their first calving date. The degree of their phenotypic plasticity was not dependent on any of the females' attributes early in life tested in this study. However, females who delayed their calving dates in response to a higher amount of precipitation in April slightly produced less calves over their reproductive life. These findings confirmed that early-life conditions of female reindeer can shape their phenotypic value during reproductive life, supporting the importance of maternal effects in shaping individuals' lifetime reproductive success. Whether females differed in the magnitude of their plastic response to climatic changes has received contrasted responses for various ungulate species. This calls for more research to enhance our understanding of the underlying mechanisms, leading to the complexity of plastic responses among populations to cope with current climate change.
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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 | 0.000 |
| 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".