Museum insights for conservation: unraveling the extinction factors in the Jambato Harlequin frog, <i>Atelopus ignescens</i>
Bibliographic record
Abstract
Natural history museums harbor invaluable resources for conserving endangered species by providing insights into the mechanism of historical population declines. We conducted data synthesis to better understand the extinction factors of the iconic Jambato Harlequin frog, Atelopus ignescens, which was widespread in the Ecuadorian Andes before 1985 but vanished in 1988. We synthesized historical data from natural history museums, the global biodiversity information facility, and mtDNA sequences to examine whether Batrachochytrium dendrobatidis ( Bd) fungus infection, climate change, and/or their interaction contributed to the rapid population decline. We found excessive rare alleles reflected in the negative Tajima's D estimated from the mtDNA samples from 1984, indicating a selective sweep or population bottleneck. Sex and geography showed stronger effects on adult body sizes than Bd epizootic timing. The body sizes of adult males formed a geographic cline. Species distribution modeling based on temperature and precipitation accurately predicted the occupancy of A. ignescens in 1960–69, which further projected a rapid decline in species distribution between 1970 and 2020. This investigation revealed strong climate effect and weak epizootics effect on A. ignescens extinction, and inspires future museum genomic studies to dissect the potential climatic maladaptation behind dramatic historical extinctions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".