Can long‐term diapause/dormancy improve persistence in a positively autocorrelated environment?
Bibliographic record
Abstract
Abstract A temporal bet‐hedging strategy spreads demographic risk across multiple years via diapause/dormancy mechanisms. Diapause polymorphisms (i.e. individuals varying in the length of their diapause) are said to lead to greater persistence in white‐noise stochastic environments but to be less advantageous in temporally autocorrelated or ‘reddened’ environments because immediate future conditions are more predictable. Only diapause lasting for up to 2 years has been investigated, despite numerous species possessing longer diapause polymorphisms. We predicted that longer diapause polymorphisms (>2 years) would extend the advantage of a bet‐hedging strategy to times when reddened environments become unpredictable again. Using data from the Sinai Baton Blue butterfly, we modelled the effect of longer‐diapause strategies (>2 years) and varying degrees of temporal autocorrelation of environmental stochasticity. Both 3‐year and 8‐year diapause polymorphisms were more advantageous, characterised by greater stability or slower population decline than a simple one‐year diapause in white‐noise stochastic environments; while the longer 8‐year diapause polymorphism was more advantageous in reddened stochastic environments. This marks the first evidence that longer diapause polymorphisms are an evolutionary response to reddened stochastic environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".