Developmental synchrony in multivoltine insects: generation separation versus smearing
Bibliographic record
Abstract
Abstract Many insect species undergo multiple generations each year. They are found across biomes that vary in their strength of seasonality and, depending on location and species, can display a wide range of population dynamics. Some species exhibit cycles with distinct generations (developmental synchrony/generation separation), some exhibit overlapping generations with multiple life stages present simultaneously (generation smearing), while others have intermediate dynamics with early season separation followed by late season smearing. There are two main hypotheses to explain these dynamics. The first is the ‘seasonal disturbance’ hypothesis where winter synchronizes the developmental clock among individuals, which causes transient generation separation early in the season that erodes through the summer. The second is the ‘temperature destabilization’ hypothesis where warm temperatures during the summer cause population dynamics to become unstable giving rise to single generation cycles. Both hypotheses are supported by detailed mathematical theory incorporating mechanisms that are likely to drive dynamics in nature. In this review, we synthesize the theory and propose a conceptual framework—where each mechanism may be seen as an independent axis shaping the developmental (a)synchrony—that allows us to predict dynamic patterns from insect life‐history characteristics. High fecundity, short adult life‐span and strong seasonality enhance synchrony, while developmental plasticity and environmental heterogeneity erode synchrony. We further review current mathematical and statistical tools to study multi‐generational dynamics and illustrate using case studies of multivoltine tortrix moths. By integrating two disparate bodies of theory, we articulate a deep connection among temperature, stability, developmental synchrony and inter‐generational dynamics of multivoltine insects that is missing in current literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".