Data from: Low temperatures impact species distributions of jumping spiders across a desert elevational cline
Bibliographic record
Abstract
Temperature is known to influence many aspects of organisms and is frequently linked to geographical species distributions. Despite the importance of a broad understanding of an animal’s thermal biology, few studies in- corporate more than one metric of thermal biology. Here we examined an elevational assemblage of Habronattus jumping spiders to measure different aspects of their thermal biology including thermal limits (CTmin , CTmax), thermal preference, V̇CO2 as proxy for metabolic rate, locomotor behavior and warming tolerance. We used these data to test whether thermal biology helped explain how species were distributed across elevation. Habronattus had high CTmax values, which did not differ among species across the elevational gradient. The highest-eleva- tion species had a lower CTmin than any other species. All species had a strong thermal preference around 37 °C. With respect to performance, one of the middle elevation species was significantly less temperature-sensitive in metabolic rate. Differences between species with respect to locomotion (jump distance) were likely driven by differences in mass, with no differences in thermal performance across elevation. We suggest that Habronattus distributions follow Brett’s Rule, a rule that predicts more geographical variation in cold tolerance than heat. Additionally, we suggest that physiological tolerances interact with biotic factors, particularly those related to courtship and mate choice to influence species distributions. Habronattus also had very high warming tolerance values (>20 °C, on average). Taken together, these data suggest that Habronattus are resilient in the face of cli- mate-change related shifts in temperature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".