Data from: Growth and reproduction trade-offs can estimate previous reproductive history in alpine ungulates
Bibliographic record
Abstract
1. Life-history theory predicts energy allocation trade-offs between traits when resources are limited. If females reduce allocation to growth when they reproduce, annual growth could reveal past reproductive effort, which would be useful to assess population dynamics and harvest sustainability. The potential and accuracy of growth measures for predicting reproductive success have rarely been evaluated with individuals with known reproductive history. 2. We used long-term monitoring of annual growth and reproduction of marked female bighorn sheep and mountain goats, two species in which primiparity normally occurs well before growth completion, to evaluate growth vs. reproduction trade-offs and their potential for predicting reproductive history of young females using mixed models and 10-fold block cross-validation. 3. We documented a significant reduction in mass gain and horn growth in young reproducing females of both species. This trade-off was affected by individual differences in energy acquisition and allocation, because population density and previous allocation to growth affected the trade-off. We then parametrized models to predict individual reproductive history of young females based on the growth traits subjected to a reproductive trade-off. 4. The accuracy of predictive models ranged from 85.2% to 91.0% across species and traits, indicating that growth is a good predictor of reproductive history. This method is especially useful for population management of species with traits that form permanent visible yearly annuli because they retain a record of annual growth that allows retrospective estimation of reproductive history over multiple years. 5. Synthesis and applications. We show that because growth significantly decreased in years of allocation to reproduction, annual growth increments provide insights on reproductive history of young females. Population or temporal differences in reproduction of young females affect demographic rates and sustainable harvest. Growth measures of traits that form yearly annuli, such as teeth and horns, could be easily obtained at a low cost from animals harvested or found dead in multiple species. Thus, predictive models of reproductive history based on annual growth could assist conservation and management in a broad range of species.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".