Spatio-Temporal Distribution of Monarch Butterflies Along Their Migratory Route
Bibliographic record
Abstract
Efforts to conserve the migratory phenomenon of monarch butterflies in eastern North America have increased since a 2013-2014 monitoring report documenting a historical population low at the Monarch Butterfly Biosphere Reserve in Mexico. Surprisingly, there have been few systematic attempts to develop predictive models of monarch butterfly distributions along their migratory route between Mexico and the United States and Canada. Here we produced monthly habitat suitability models for monarch butterflies along their migratory route to identify potential areas for resting, feeding, and reproduction of the population. We compiled a point occurrence database of monarch butterflies for Mexico, USA, and Canada, including georeferenced records from GBIF, the Naturalista platform in Mexico, Correo Real initiative, and the Mexican governmental monitoring network for the monarch butterfly. We produced monthly habitat suitability models (HSM), using the R language and environment for statistical computing, abiotic (WorldClim), edaphic, and topographic variables. A total of 95 HSM were produced for each month. June to September, corresponding to the reproduction months in North America showed the highest geographic extent with suitable habitats; April, corresponding to the reproduction of the first post-migration generation, showed the smallest area. September, October and November, correspond to the movement of the monarch butterflies southwards, showed typical recognized distribution of the phenomenon and the overwintering months. December to February showed the smallest geographic extent in habitat suitability. Edaphic variables ranked high in importance in HSM for 11 of 12 months, indicating the relevance of vegetation and floral resources in the monarch butterfly migration route. Identifying such regions contribute to establish concrete conservation programs accordingly, as reduction of the use of pesticides and herbicides, decrease in the speed of cars in roads, and planting species with high nectary value, among others.. Our study provides a first predictive spatio-temporal approximation of the monarch butterfly migratory route annual cycle..
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".