Contrasted thermal preferences translate into divergences in habitat use and realized performance in two sympatric snakes
Bibliographic record
Abstract
Temperature influences ectotherm fitness by affecting physiological performance. Terrestrial reptiles behaviourally regulate their body temperature by selecting particular microhabitats or activity periods. In temperate climates, thermal constraints make precise thermoregulation costly. Theoretical models of thermoregulation predict that species in cool environments should exhibit lower optimal temperature for performance and lower thermal preferences to minimize thermoregulatory costs. Empirical data in support of this prediction remain equivocal because several species maintain high and constant body temperatures, even in cool environments. We studied two largely sympatric colubrid snakes, Hierophis viridiflavus and Zamenis longissimus that share numerous morphological and ecological similarities, but differ markedly in thermal preference. Our objective was to quantify their thermoregulatory strategies in the field to determine how thermal preferences translate in habitat use and performance gain. The thermophilic species, H. viridiflavus, selected open microhabitats, whereas Z. longissimus, which prefers cooler temperatures, used a greater diversity of microhabitats. The two species differed markedly in their exposure levels. Hierophis viridiflavus was constrained to shuttle between sun and shade to maintain preferred body temperatures rendering it very exposed, while covered microhabitats were usually thermally compatible with the requirements of Z. longissimus. High exposure was apparently counterbalanced by higher locomotor performances in H. viridiflavus. The divergence in thermal ecology between Z. longissimus and H. viridiflavus likely reflects different trade-offs between energy gain and predator avoidance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".