Habitat Use is Linked to Components of Fitness Through the Temperature-Dependence of Performance in Ratsnakes (<i>Elaphe Obsoleta</i>)
Bibliographic record
Abstract
For ectothermic reptiles, habitat selection is mechanistically linked to fitness through the temperature-dependence of performance. Many reptiles occupy thermally heterogeneous environments and regulate their body temperature through selective use of habitats within their environments, making reptiles ideal subjects to understand the fitness consequences of habitat use. Our goal here was to investigate the link between habitat selection, thermoregulation, and fitness by comparing the expected distribution of performance for real ratsnakes that thermoregulate through selective use of habitat with the performance of hypothetical snakes that are assumed to use habitats randomly. Thermal data for real snakes were obtained using temperature-sensitive radio-transmitters implanted in free-living snakes, whereas thermal data for hypothetical snakes were obtained by sampling environmental temperatures that a randomly moving snake would encounter. Thermal data were then transformed into performance using an experimentally derived equation relating performance (swimming speed) to temperature. Habitat selection allowed snakes to avoid lethal temperatures and resulted in an average improvement of 18% in locomotor performance. A more exact measure of the fitness improvement accrued through habitat selection will have to await data relating body temperature to ultimate measures of fitness and a deeper understanding of the contribution of different performances to fitness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".