Rocky rule: the idiosyncrasy of spatial and temporal size variation in mammals
Bibliographic record
Abstract
Abstract Mammals are predicted to vary in body size following Bergmann’s rule, with individuals found at higher latitudes in colder temperatures being larger in size compared to conspecifics occurring at lower latitudes in warmer temperatures. Body size is similarly expected to vary temporally, with a decrease in size through time due to recent climate warming. While Bergmann’s rule is well-supported in mammals, there is increasing evidence of exceptions to the rule. Here, we present patterns of size variation in 17 North American mammal species using five morphological traits (condylobasal skull length, skull width, maxillary toothrow length, body weight, and head-and-body length) to determine if size varies predictably for each species in space and time. We found little support for a widespread Bergmannian pattern for these species at a broad spatial scale (across North America) and a contemporary temporal scale (the past 120 years). The effects of latitude or year on each trait were highly variable with three types of responses: an increase, a decrease, or no change in size across space or through time. Spatial size trends were detected more often than temporal size trends, as the temperature range was significantly larger in space than through time. Body weight (the most variable trait) and head-and-body length were more likely to conform to Bergmann’s rule than craniodental measurements. We did not detect any changes in size variability with latitude, and our study species either increased or decreased in size variability over time. Our findings demonstrate that size variation in mammals is highly context-dependent. As such, caution is needed when using rules of body size variation to predict the future response of species to climate warning while valid in theory, it is likely too simplistic of an approach.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".