Cold Temperatures Increase Cold Hardiness in the Next Generation Ophraella communa Beetles
Bibliographic record
Abstract
The leaf beetle, Ophraella communa, has been introduced to control the spread of the common ragweed, Ambrosia artemisiifolia, in China. We hypothesized that the beetle, to be able to track host-range expansion into colder climates, can phenotypically adapt to cold temperatures across generations. Therefore, we questioned whether parental experience of colder temperatures increases cold tolerance of the progeny. Specifically, we studied the demography, including development, fecundity, and survival, as well as physiological traits, including supercooling point (SCP), water content, and glycerol content of O. communa progeny whose parents were maintained at different temperature regimes. Overall, the entire immature stage decreased survival of about 0.2%-4.2% when parents experienced cold temperatures compared to control individuals obtained from parents raised at room temperature. However, intrinsic capacity for increase (r), net reproductive rate (R 0) and finite rate of increase (λ) of progeny O. communa were maximum when parents experienced cold temperatures. Glycerol contents of both female and male in progeny was significantly higher when maternal and paternal adults were cold acclimated as compared to other treatments. This resulted in the supercooling point of the progeny adults being significantly lower compared to beetles emerging from parents that experienced room temperatures. These results suggest that cold hardiness of O. communa can be promoted by cold acclimation in previous generation, and it might counter-balance reduced survival in the next generation, especially when insects are tracking their host-plants into colder climates.
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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 | 0.000 | 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) | 0.001 | 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".