THERMAL ECOLOGY OF BLACK RAT SNAKES (<i>ELAPHE OBSOLETA</i>) IN A THERMALLY CHALLENGING ENVIRONMENT
Bibliographic record
Abstract
A general model in thermal ecology predicts that ectotherms should stop thermoregulating when the costs outweigh the benefits. Support for this model comes from studies of warm-temperate species, but the extent to which the model can be extrapolated to species living in climatic extremes is unknown, because of the lack of information regarding the thermoregulatory behavior of such species. We tested the applicability of this cost–benefit model using data for black rat snakes (Elaphe obsoleta obsoleta) studied at the northern extreme of their range in Ontario. During 1997–1999, we used automated temperature-sensitive radiotelemetry to collect ∼150 000 body temperatures from 53 free-ranging rat snakes. Simultaneously, we used physical models of snakes to measure the environmental operative temperatures available to black rat snakes, and we determined their preferred body temperature range in a laboratory thermal gradient. The mostly forested habitats inhabited by rat snakes in Ontario were more thermally challenging than the habitats of other species studied to date. The preferred body temperature ranges of male, nongravid female, and gravid female black rat snakes were not significantly different (preferred body temperature averaged across all individuals, 28.1°C). However, free-ranging gravid females tended to maintain higher body temperatures in order to thermoregulate more effectively as well as exploit their thermal environment more than males and nongravid females. This difference was most pronounced during the day and prior to egg laying, and constituted the first documentation of such a phenomenon in an oviparous snake. Black rat snakes had indices of thermoregulation effectiveness similar to other species but tended to exploit opportunities for thermoregulation less. Overall, our data provided support that was at best ambiguous for the current cost–benefit thermoregulation model, suggesting that this model may generally be less applicable to species inhabiting climatic extremes. We propose that, for species in extreme climates, the costs associated with thermoconformity may be more important than previously recognized. We identified several problems associated with the index of thermoregulation effectiveness used by previous researchers, and we propose a mathematically simpler alternative that circumvents these problems. We also make recommendations regarding the future use of the various indices of thermoregulation developed in recent years.
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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 | 0.001 |
| 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".