Landscape factors influencing roost site selection by monarch butterflies Danaus plexippus during fall migration in Ontario, Canada
Bibliographic record
Abstract
Worldwide, insect populations are declining, and the eastern migratory group of the monarch butterfly Danaus plexippus in North America has not escaped this fate. The conservation of this iconic species is an international priority but requires knowledge of how monarchs interact with the landscape during different stages of the annual cycle. To better understand habitat needs of monarchs departing their core breeding range in southern Ontario, Canada, we examined how various landscape features influenced roost site selection during fall migration—an instrumental resource link between the breeding and wintering grounds. Using dedicated fall migration surveys along the Great Lakes coastlines and a citizen science dataset collected across all of Ontario, we evaluated the relationship between roost site occupancy and 18 landscape variables using a boosted regression tree (BRT) modelling approach. Results suggest that a closer distance to the Great Lakes, increased goldenrod Solidago spp. cover, moderate forest cover, rural road cover, and urban land cover are all important to roosting site selection. Our research provides important insights into the habitat characteristics of stopover sites, which will help guide future investigations and conservation actions to preserve monarch butterflies and their migratory phenomenon.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".