Amphibian breeding phenology trends under climate change: predicting the past to forecast the future
Bibliographic record
Abstract
Global climate warming is predicted to hasten the onset of spring breeding by anuran amphibians in seasonal environments. Previous data had indicated that the breeding phenology of a population of Fowler's Toads (Anaxyrus fowleri) at their northern range limit had been progressively later in spring, contrary to generally observed trends in other species. Although these animals are known to respond to environmental temperature and the lunar cycle to commence breeding, the timing of breeding should also be influenced by the onset of overwintering animals' prior upward movement through the soil column from beneath the frost line as winter becomes spring. I used recorded weather data to identify four factors of temperature, rainfall and snowfall in late winter and early spring that correlated with the toads' eventual date of emergence aboveground. Estimated dates of spring emergence of the toads calculated using a predictive model based on these factors, as well as the illumination of the moon, were highly correlated with observed dates of emergence over 24 consecutive years. Using the model to estimate of past dates of spring breeding (i.e. retrodiction) indicated that even three decades of data were insufficient to discern any appreciable phenological trend in these toads. However, by employing weather data dating back to 1876, I detected a significant trend over 140 years towards earlier spring emergence by the toads by less than half a day/decade, while, over the same period of time, average annual air temperature and annual precipitation had both increased. Changes in the springtime breeding phenology for late-breeding species, such as Fowler's Toads, therefore may conform to expectations of earlier breeding under global warming. Improved understanding of the environmental cues that bring organisms out of winter dormancy will enable better interpretation of long-term phenological trends.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".