Why wait? Three mechanisms selecting for environment‐dependent developmental delays
Bibliographic record
Abstract
Many species delay development unless particular environments or rare disturbance events occur. How can such a strategy be favoured over continued development? Typically, it is assumed that continued development (e.g. germination) is not advantageous in environments that have low juvenile/seedling survival (mechanism 1), either due to abiotic or competitive effects. However, it has not previously been shown how low early survival must be in order to favour environment-specific developmental delays for long-lived species. Using seed dormancy as an example of developmental delays, we identify a threshold level of seedling survival in 'bad' environments below which selection can favour germination that is limited to 'good' environments. This can be used to evaluate whether observed differences in seedling survival are sufficient to favour conditional germination. We also present mathematical models that demonstrate two other, often overlooked, mechanisms that can favour conditional germination in the absence of differences in seedling survival. Specifically, physiological trade-offs can make it difficult to have germination rates that are equally high in all environments (mechanism 2). We show that such trade-offs can either favour conditional germination or intermediate (mixed) strategies, depending on the trade-off shape. Finally, germination in every year increases the likelihood that some individuals are killed in population-scale disturbances before reproducing; it can thus be favourable to only germinate immediately after a disturbance (mechanism 3). We demonstrate how demographic data can be used to evaluate these selection pressures. By presenting these three mechanisms and the conditions that favour conditional germination in each case, we provide three hypotheses that can be tested as explanations for the evolution of environment-dependent developmental delays.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".