Developmental Thresholds and the Evolution of Reaction Norms for Age and Size at Life‐History Transitions
Bibliographic record
Abstract
It is quite common in studies of life-history plasticity to find a negative relationship between the age at which various life-history transitions occur and the growth conditions under which individuals develop. In particular, high growth typically results in earlier transitions, often at a larger size. Here, we use a relatively general optimization model for age and size at life-history transitions to argue that current life-history theory cannot adequately explain these results. Specifically, most such theory requires key assumptions that are unlikely to be generally met. This suggests that some important component of the biology of many organisms must be missing from many of the models in life-history theory. We suggest that this missing component might be the phenomenon of developmental thresholds. There are at least two different types of developmental thresholds possible, and we incorporate these into our general optimality model to demonstrate how they can cause a negative relationship between growth conditions and age at a transition. If developmental thresholds are common throughout taxa, then this might explain the empirical results. Our model formulation and analysis also formalizes the popular Wilbur-Collins hypothesis for age and size at metamorphosis in amphibians. The results demonstrate that optimal combinations of age and size, and the slope of the reaction norm connecting them, depend on the existence and type of threshold assumed. Our results also provide an evolutionary framework that can be used to view the data and many of the proximate submodels derived from the Wilbur-Collins hypothesis.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".