Thermal ecology of the federally endangered blunt-nosed leopard lizard (Gambelia sila)
Bibliographic record
Abstract
Abstract Recognizing how climate change will impact populations can aid in making decisions about approaches for conservation of endangered species. The blunt-nosed leopard lizard (Gambelia sila) is a federally endangered species that, despite protection, remains in extremely arid, hot areas and may be at risk of extirpation due to climate change. We collected data on the field-active body temperatures, preferred body temperatures and upper thermal tolerance of G. sila. We then described available thermal habitat using biophysical models, which allowed us to (i) describe patterns in lizard body temperatures, microhabitat temperatures and lizard microhabitat use; (ii) quantify the lizards’ thermoregulatory accuracy; (iii) calculate the number of hours they are currently thermally restricted in microhabitat use; (iv) project how the number of restricted hours will change in the future as ambient temperatures rise; and (v) assess the importance of giant kangaroo rat burrows and shade-providing shrubs in the current and projected future thermal ecology of G. sila. Lizards maintained fairly consistent daytime body temperatures over the course of the active season, and use of burrows and shrubs increased as the season progressed and ambient temperatures rose. During the hottest part of the year, lizards shuttled among kangaroo rat burrows, shrubs, and open habitat to maintain body temperatures below their upper thermal tolerance, but, occasionally, higher than their preferred body temperature range. Lizards are restricted from staying in the open habitat for 75% of daylight hours and are forced to seek refuge under shrubs or burrows to avoid surpassing their upper thermal threshold. After applying climatic projections of 1 and 2°C increases to 2018 ambient temperatures, G. sila will lose additional hours of activity time that could compound stressors faced by this population, potentially leading to extirpation.
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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 | 0.000 |
| 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".