Supraoptimal temperatures influence the range dynamics of a non‐native insect
Bibliographic record
Abstract
Abstract Aim To examine the relationship between the range dynamics of the non‐native species Lymantria dispar (L.) and supraoptimal temperatures during its larval and pupal period. Location West Virginia and Virginia, United States, North America. Methods We linked the annual frequency of supraoptimal temperatures during the larval and pupal period of L. dispar with annual changes in its range dynamics based upon a spatially robust 20‐year dataset. Correlation analyses were used to estimate the association between exposure time above the optimal temperature for L. dispar larval and pupal development, and the rate of invasion spread when adjusted for spatial autocorrelation. Results We documented L. dispar range expansion, stasis, and retraction across a fairly narrow latitudinal region. We also observed differences in the amount of exposure above the optimal temperature for L. dispar larval and pupal development across this region. Temperature regimes in the Coastal Plain and Piedmont regions of Virginia, where the L. dispar range has retracted or remained static, were warmer than those in the Appalachian Mountains of Virginia and West Virginia, where L. dispar has expanded its range. Our analyses at a smaller spatial scale confirmed a statistically negative association between exposure time above the optimal temperature for L. dispar larvae and pupae, and the rate of L. dispar invasion spread over the 20‐year period. Main conclusions The shifting, expansion and retraction of species distributional ranges holds critical implications to both invasion ecology and conservation biology. This work provides novel empirical evidence of the importance of supraoptimal temperatures on the range dynamics of a non‐native invasive insect with application to both non‐native and native species whose physiological processes are strongly regulated by temperature.
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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 | 0.001 |
| 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 | 0.001 | 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".