The successful invasion of the European earwig across North America reflects adaptations to thermal regimes but not mean temperatures
Bibliographic record
Abstract
ABSTRACT Understanding the mechanisms by which an introduced species adapt to newly encountered habitats is a major question in ecology. A key method to address this question is to collect data on introduced species that have successfully invaded a broad diversity of novel environments, and analyze how their life-history traits changed with these new constraints. Here, we present and analyze such a unique data set in the European earwig Forficula auricularia L, an insect that invaded North America during the last century. We conducted a common garden experiment, in which we measured 13 life-history traits in 4158 individuals from 19 populations across North America. Our results demonstrate that the successful invasion of this species came with changes in 10 of their life-history traits in response to thermal regimes (winter-summer and autumn-spring temperatures), but with no change in response to the overall mean temperatures of the invaded locations. Importantly, we show that some of these changes are by-products of novel thermal regimes, whereas others reflect adaptive strategies of females to these constraints. Overall, our findings reveal the importance of thermal regimes over mean temperatures in climate adaptation, and emphasize that studying adaptive capabilities is crucial to predict the limits of biological invasions.
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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".