Population Trends of Monarch Butterflies (Lepidoptera: Nymphalidae) Migrating From the Core of Canada’s Eastern Breeding Population
Bibliographic record
Abstract
Abstract The Monarch butterfly (Danaus plexippus) (Linnaeus 1758) is being considered for up-listing to Endangered under the Canadian federal Species-at-Risk Act due to population declines recorded throughout the annual life cycle. Understanding local population dynamics is therefore necessary to assess the effect of environmental and human induced stressors, and to establish a benchmark from which management success can be measured through time. Using fall count data collected along the Lake Erie shoreline, which captures migrants moving south from the core eastern breeding population in Canada, monarch abundance trends are quantified. Count data from three migration count sites and one roost site were analyzed following similar methods as Crewe and McCracken (2015) to make findings comparable. Two of these datasets are newly compiled and are analyzed here for the first time. Results suggest that during the past 10 yr, the number of migrating monarchs along the north shore of Lake Erie has been stable [mean: −3.05% per year, credibility interval (CI): −13.15, 9.97], which is consistent with changes being observed on the wintering grounds. Only migration counts collected between 1995 and 2018 at the Long Point sites demonstrated significant abundance declines (5.25% per year, CI: −8.60, −1.39), which is a similar results to previous analysis of this dataset. Opportunities for future research are discussed within the context of using monarch count data for future conservation efforts.
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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 | 0.001 | 0.002 |
| 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".