A test of the competitive ability–cold tolerance trade‐off hypothesis in seasonally breeding beetles
Bibliographic record
Abstract
Abstract Closely related species that use similar resources often differ in their seasonal patterns of activity, but the factors that limit their distributions across seasons are unknown for most species. One hypothesis to explain seasonal variation in the distributions of species involves a trade‐off between competitive ability and cold tolerance, where tolerance to the cold compromises competitive ability in warmer (benign) temperatures, either at the level of the individual or population. We tested both individual‐level and population‐level mechanisms of this hypothesis in two co‐occurring species of temperate burying beetles (Silphidae: Nicrophorus sayi , N. orbicollis ) that differ in their seasonal patterns of activity. We measured cold tolerance, breeding activity as a function of temperature, and competitive ability as a function of temperature and season. Consistent with our hypothesis, the mid‐season N. orbicollis was less able to function at the cold temperatures that characterise early spring, when the early‐season N. sayi is most active. The larger beetle, however, always won one‐on‐one competitive trials at warm temperatures, regardless of species, inconsistent with an individual‐level trade‐off. N. orbicollis was usually larger and successful when competing for the same carrion later in the season, mostly because of its larger population size, consistent with a trade‐off between competitive ability, and cold tolerance acting at the population level. Our findings suggest that cold temperatures limit the mid‐season N. orbicollis from earlier spring emergence, while competitive pressure from the more abundant, larger N. orbicollis constrains the early‐season N. sayi from remaining active through the summer.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".