Disentangling seasonal effects of environmental variability and population density on life‐history traits in a capital breeder
Bibliographic record
Abstract
High-latitude environments are characterised by strong seasonality, with resident animals experiencing a very short plant-growing season for reproduction and acquisition of resources before the long and demanding winter. These seasonal fluctuations in food availability are likely to affect density-dependent processes, with density-dependent food limitation expected to be stronger outside the plant-growing season. Density dependence and environmental variability can have both direct and indirect effects on reproductive success through their influence on important indicators of individual quality, such as body mass. Untangling the direct and indirect effects of environmental factors and population density on life-history tactics is fundamental for understanding population and food-web dynamics. We developed a mechanistic path model to quantify the relative importance of direct and indirect effects of density dependence and environmental variability on body mass and reproductive success of female reindeer. Long-term individual measurements before and after lactation allowed quantifying simultaneously the direct negative effect of reproductive success on autumn body mass and the strong positive seasonal covariation among the individuals' body mass measurements. This emphasises the co-occurrence of a cost of reproduction and individual heterogeneity. We show that the estimated impact of density dependence on female body mass at the end of the summer is only 25% of the impact of density dependence during winter, as failed reproduction induced by low spring body mass leads to a large compensatory growth over summer. While such a pattern is expected, our estimates provide a unique and robust quantification of its importance. This result challenges the use of body mass data of adult individuals harvested in autumn when studying temperate large herbivores, as density dependence during winter and compensatory adjustments of reproductive effort may not be revealed without considering body mass both before and after lactation.
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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 | 0.001 | 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".