Mapping monarch seasonal breeding patterns in Eastern North America to inform mowing strategies for roadsides and other rights-of-ways
Bibliographic record
Abstract
Abstract Monarch populations have declined precipitously over the past decades, largely due to the loss of their breeding host plant, milkweed. One mitigation strategy is to plant milkweed along rights-of-ways. However, many rights-of-ways undergo routine mowing, which can result in egg, caterpillar, and chrysalis mortality. To minimize this risk, it is critical to time mowing activity to avoid the peak breeding activity of monarchs. In this study we used community science data to define breeding patterns and timing of monarch breeding throughout the United States and Canada. We identified four breeding patterns: (1) year-round, (2) spring-only, (3) summer, and (4) disjunct breeding. Year-round and disjunct breeding were concentrated around the Gulf of Mexico, including Florida, and the southern United States, respectively. As expected, we found that monarch breeding was later with increased latitude but with some longitudinal variation; for summer breeding regions, breeding occurred earlier in the western portion of the study area relative to the east, but the end of breeding was later in the east relative to the west, resulting in breeding seasons of similar duration. Additionally, in the east, breeding occurred later along the Appalachian Mountains. We suggest adapting our findings into mowing practices that benefit monarchs while considering the life histories of milkweed and the broader insect community. Implications for insect conservation. Mowing and other maintenance activities in habitat where milkweed is present can be detrimental to monarch breeding. Our analysis provides guidance to minimize monarch mortality and loss of milkweed during peak breeding periods.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".