Facultative nocturnal behaviour in snakes: experimental examination of why and how with Ratsnakes (<i>Elaphe obsoleta</i>) and Racers (<i>Coluber constrictor</i>)
Bibliographic record
Abstract
Diel activity patterns are often fixed within species such that most animals can be classified as diurnal, crepuscular, or nocturnal, and have sensory abilities that reflect when they are active. However, many snake species appear capable of switching between diurnal and nocturnal activity. Here, we evaluate the hypothesis that some species are constrained in their activity by the sensory cues used for foraging. We experimentally assessed differences between two sympatric snake species in their ability to alter diel activity patterns, to address why those snakes that switch do so (do thermal constraints force them to be active in otherwise nonpreferred conditions?), and to explore how sensory abilities to locate prey facilitate or constrain this shift. Ratsnakes (Elaphe obsoleta (Say in James, 1823)) were active when temperature was optimal, regardless of light level, suggesting their activity pattern is genuinely plastic. Consistent with our predictions, Ratsnakes successfully detected prey in low and high light using visual or chemical cues, and were most successful when cues were coupled. Racers (Coluber constrictor L., 1758) were almost exclusively diurnal, regardless of temperature, and became less active when daytime temperatures were suboptimal. The ability of Ratsnakes to shift activity may confer a foraging advantage and should buffer Ratsnakes and similarly flexible species from climate change, whereas climate change may pose a more serious threat to inflexible species such as Racers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".