Interacting effects of urbanization and coastal gradients on ant thermal responses
Bibliographic record
Abstract
Urban-to-rural gradients intersect with other, often unmeasured, environmental gradients that may influence or even supersede species responses. Here we use coastal-to-interior and urban-to-rural gradients to investigate woodland ant response (physiological thermal tolerance, community structure and spring phenology) to two overlapping thermal gradients, the Great Lakes (Erie and Ontario) and the Buffalo, NY urban center (USA). Woodland ant physiological and behavioral responses, and community responses, shifted along the coastal-to-interior and urban-to-rural gradients, but they were generally best explained by lake effects (though urban ants tolerated higher temperatures than rural ants). The relatively colder spring temperatures in coastal areas (as compared with inland) corresponded with higher physiological cold tolerance in the ants, even though the coastal areas are annually warmer. The coastal spring temperatures also influenced ant phenology so that, in a warm year, the coastal ants began foraging considerably earlier than inland ants, likely due to their lower physiological cold tolerance. Ant community responses also shifted with proximity to the lakes and urban areas, but those changes appeared more linked with land use than climate. These results suggest that species responses to urbanization gradients may be influenced, or even superseded, by the impacts of proximate large water bodies. Our results suggested that spring coldness nearer the Great Lakes may select for cold tolerance in ants (despite that the coastal areas are relatively warmer annually), whereas urbanization selected for greater heat tolerance.
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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 | 0.001 |
| 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".