Consequences of Hierarchical Allocation for the Evolution of Life‐History Traits
Bibliographic record
Abstract
Resource allocation within individuals may often be hierarchical, and this may have important effects on genetic correlations and on trait evolution. For example, organisms may divide energy between reproduction and somatic growth and then subdivide reproductive resources. Genetic variation in allocation to pathways early in such hierarchies (e.g., reproduction) can cause positive genetic correlations between traits that trade off (e.g., offspring size and number) because some individuals invest more resources in reproduction than others. We used quantitative-genetic models to explore the evolutionary implications of allocation hierarchies. Our results showed that when variation in allocation early in the hierarchy exceeds subsequent variation in allocation, genetic covariances and initial responses to selection do not reflect trade-offs occurring at later levels in the hierarchy. This general pattern was evident for many starting allocations and optima and for whether traits contributed multiplicatively or additively to fitness. Finally, artificial selection on a single trait revealed masked trade-offs when variation in early allocation was comparable to subsequent variation in allocation. This result confirms artificial selection as a powerful, but not foolproof, method of detecting trade-offs. Thus, allocation hierarchies can profoundly affect life-history evolution by causing traits to evolve in the opposite direction to that predicted by trade-offs.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".