Examining the movement patterns of the European common blue butterfly (Polyommatus icarus): A study on short-term and long-term movement of an invasive species
Bibliographic record
Abstract
Understanding how individuals interact with biotic and abiotic factors in their habitats, and how these interactions influence movement through landscapes is a key step in understanding dispersal events. The world is experiencing increased introduction of non-native species into new regions, however without an understanding of how species use the space they occupy, it is not possible to understand how dispersal events occur. We studied the short- and long-term movement patterns of a non-native butterfly species in Montreal, Canada: the European common blue butterfly, Polyommatus icarus, to determine whether the movement patterns of these butterflies over multiple days can be predicted by individual movement behaviours measured over short time periods. We asked what factors can predict short-term movements in females of this species, and whether these short-term movements can be used to predict long-term movements. We examined short-term movement by following individuals over short time periods and compared these movements to long-term movements observed via a mark-release-recapture study. In doing so, we found that flowering, host-plant species presence, increased vegetation height, and road edges can predict short-term movement, and that the short-term movement model produced can be used to accurately predict long-term movements when they are less than 100 m. These findings suggest that an understanding of ground cover characteristics is important in being able to predict the dispersal of most individuals, but that further work will be required to accurately predict long-distance dispersal events, which are what appear to be driving range expansion of P. icarus in North America.
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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 | 0.000 | 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".