Mortality of Monarch Butterflies (Danaus plexippus) at Two Highway Crossing “Hotspots” During Autumn Migration in Northeast Mexico
Bibliographic record
Abstract
The contribution to annual mortality of migrating monarch butterflies (Danaus plexippus) due to collisions with vehicles is poorly understood but likely significant. Recent estimates based on a study in Texas suggests that mortality during autumn migration may be of the order of 2 million per year or about 3% of the population. However, MaxEnt models used in that study are not well suited to quantifying mortality at hotspots where monarchs are concentrated by topography such as canyons when crossing highways. Potentially catastrophic mortality could occur at such sites if timing of migration and weather conditions conspire to force a large proportion of the migrating population across highways at low altitude. We investigated monarch mortality 15 October to 11 November, 2018 at two highway crossings in northeastern Mexico known for their frequent and extensive collisions (La Muralla and Santa Catarina). During a 15-19 day period of migration, we collected dead and injured monarchs along a series of 500 m roadside transects. We estimated a minimum total mortality during fall migration at just these sites of about 196,560 individuals. Monarchs exhibited a diurnal pattern of passage at Santa Catarina of peaks in late morning and late afternoon. Average vehicle speeds exceeded posted 60 km/h limits designed to protect monarchs, ranging from 75.1 to 99.6 km/h at La Muralla and 86.6 to 106.8 km/h at Santa Catarina. We recommend finer-scale documentation of migration pathways and an inventory of significant highway crossing hotspots for monarchs during fall migration in northeast Mexico. Mitigative measures could include better enforced vehicle speeds at least during the short period of migration, deflection structures to raise the height of crossing monarchs and/or manipulation of habitat to lower the potential for monarchs descending to roost near key crossing points.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".