High environmental temperatures put nest excavation by ants on fast forward: they dig the same nests, faster
Bibliographic record
Abstract
Abstract Environmental temperature influences the physiology and the behaviour of ectothermic organisms, including ants. However, the complex collective behaviour exhibited by ant colonies means that it is difficult to predict how the effects of temperature translate to colony-level functioning and features, such as the form of their nests. This study aims to determine the effects of environmental temperature on nest excavation rate and on the morphology of excavated nests. To this end, we characterized the nest digging activity of the yellow meadow ant Lasius flavus confined to dig in a nearly two-dimensional experimental setup maintained at a constant temperature ranging from 15 to 30 degrees Celsius. Ants dug faster at higher temperature, with an increase of digging rate that reflected the temperature-induced increase of movement speed of individual ants. Nevertheless, the shape of excavated nests remained statistically unchanged across the full range of temperatures we tested. These results suggest that temperature accelerates all aspects of the excavation process uniformly, rather than selectively influencing specific components such as tunnel branching or elongation. The ability to produce a consistent overall nest structure, irrespective of the temperature conditions encountered at the time of digging, may provide adaptive benefits to the colony.
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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 | 0.000 |
| 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".