Plasticity of cold hardiness in the eastern spruce budworm, <i>Choristoneura fumiferana</i>
Bibliographic record
Abstract
Abstract High latitude insect populations must cope with extreme conditions, particularly cold temperatures. Insects use a variety of cold hardiness mechanisms to withstand this temperature stress, and these can drive geographic distributions through overwintering mortality. The degree of cold hardiness can be altered by two evolved responses: phenotypic plasticity and local adaptation. Phenotypic plasticity can occur within or between generations (transgenerational plasticity; TGP), and local adaptation can evolve through directional selection in response to regional climatic differences. We used the eastern spruce budworm, Choristoneura fumiferana (Lepidoptera: Tortricidae) as a model to explore the role that variable winter temperatures play in inducing two aspects of plasticity in cold hardiness: TGP and local adaptation in phenotypic plasticity. This species is one of the most destructive boreal forest pests in North America, therefore accurately predicting overwintering survival is essential for effective management. While we found no evidence of TGP in cold hardiness, there was a long-term fitness cost to larvae that experienced repeated cold exposures. We also found evidence of local adaptation in both seasonal and short-term plasticity of cold hardiness. These findings provide evidence for the importance of phenotypic plasticity and local adaptation when modelling species distributions.
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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".