Data from: A scenario for the evolution of selective egg colouration: the roles of enemy-free space, camouflage, thermoregulation, and pigment limitation
Bibliographic record
Abstract
Behavioural plasticity can drive the evolution of new traits in animals. In oviparous species, plasticity in oviposition behaviour could promote the evolution of new egg traits by exposing them to different selective pressures in novel oviposition sites. Individual females of the predatory stink bug Podisus maculiventris are able to selectively colour their eggs depending on leaf side, laying lightly pigmented eggs on leaf undersides and more pigmented eggs, which are more resistant to ultraviolet (UV) radiation damage, on leaf tops. Here, we propose an evolutionary scenario for P. maculiventris egg pigmentation and its selective application. We experimentally tested the influence of several ecological factors that: (i) could have favoured a behavioural shift towards laying eggs on leaf tops and thus the evolution of a UV-protective egg pigment (i.e. exploitation of enemy-reduced space or a thermoregulatory benefit) and (ii) could have subsequently led to the evolution of selective pigment application (i.e. camouflage or costly pigment production). We found evidence that a higher predation pressure on leaf undersides could have caused a shift in oviposition effort towards leaf tops. We also found the first evidence of an insect egg pigment providing a thermoregulatory advantage. Our study contributes to an understanding of how plasticity in oviposition behaviour could shape the responses of organisms to ecological factors affecting their reproductive success, spurring the evolution of new morphological traits.
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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.202 | 0.087 |
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".