Decline of migratory monarch butterflies during their fall migration in North America
Bibliographic record
Abstract
The migratory population of monarch butterflies in North America has significantly declined over the past few decades, leading to their classification as an endangered species in Canada.Scientists have identified two major groups of threats responsible for this decline: those affecting monarchs during migration and those affecting them during the breeding season.In this study, I investigated two threats potentially impacting monarchs during their fall migration: the range expansion of winterbreeding monarchs and roadkill during fall migration.Winter-breeding monarchs in North America do not migrate to overwintering areas and continue breeding during the winter in regions with a suitable climate and available non-native milkweed.These monarchs carry a higher burden of the Ophryocystis elektroscirrha parasite, which can increase the infection rate among the offspring of migratory monarchs that interact with them.In the first project in this thesis, I used species distribution modeling to estimate the current distribution of winter-breeding monarchs across North America and projected their potential future distribution based on different climate change scenarios.In the second project, using community science data and accounting for sampling bias, I mapped the distribution of migratory monarchs in late summer before they start their fall migration.Understanding the pre-migration distribution of monarchs was essential for my third project, where I estimated the average probability of roadkill for migratory monarchs starting their fall migration iii from any location across their distribution.Even under the most optimistic scenarios, my results suggest a 98% and 22% range expansion by 2100 for western and eastern winter-breeding monarchs, respectively.Additionally, my results show that the average estimate of roadkill probability for monarchs during fall migration is 99.5%.My research sheds light on the extent to which these two migration-related threats affect monarchs and helps decision-makers identify priority areas for stopping the spread of non-native milkweed.Furthermore, these findings assist in making informed decisions to recover the population of migratory monarchs by highlighting the high roadkill risk they face during migration.When I was accepted at Carleton University, it came after a very tough time in academia.But once I started this journey, I realized how different the environment and people could be.This positive experience has helped me become a better scientist and, hopefully, a better person.Being a graduate student is always stressful, even in the best environments.I want to first thank my small family: my wife, Sahebeh, and my little girl, Liyana.I am truly grateful for all the support you have given me throughout my research.I apologize for not being the ideal husband and father.Without your support and understanding
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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 | 0.000 | 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) | 0.001 | 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".