Recent and rapid ecogeographical rule reversals in Northern Treeshrews
Bibliographic record
Abstract
Two of the most-studied ecogeographical rules describe patterns of body size variation within species. Bergmann's rule predicts that individuals have larger body sizes in colder climates (typically at higher latitudes), and the island rule predicts that island populations of small-bodied species average larger in size than their mainland counterparts (insular gigantism). These rules are rarely tested in conjunction or assessed across space and time simultaneously. We investigated these patterns in the Northern Treeshrew (Tupaia belangeri) using museum specimens collected across a wide spatial and temporal range. Contrary to Bergmann's rule, size increases with temperature in T. belangeri, a signal that is highly consistent across space and time. We also show that these rules are intertwined: Bergmann's rule is reversed on the mainland but holds on islands, and therefore the island rule is upheld at higher, but not lower, latitudes. Moreover, we demonstrate a rapid reversal of both rules over time. The mechanism behind these inversions remains unclear, though temperature and precipitation are significant predictors of body size. Ecogeographical rules rely on the assumption of a constant relationship between size and the factors driving its variation. Our results highlight the need to question this assumption and reevaluate these rules in the context of accelerating and uneven climate change.
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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 | 0.002 |
| 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 | 0.000 | 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".