Accounting for age: uncovering the nuanced drivers of mammal body-size responses to climate change
Bibliographic record
Abstract
Abstract Shifts in mean body size coinciding with environmental change are well documented across animal species and populations, serving as a widespread and complex indicator of climate-change response. In mammal research, identifying and disentangling the potential drivers of these trends (e.g., thermoregulation, resource availability) is hindered by treating adult size as fixed, ignoring morphological changes that occur throughout life in many species. However, observed population-level size trends may reflect underlying shifts in age structure (i.e., change in the proportion of older, potentially larger individuals in the population). Here, we assessed the role of age structure by explicitly evaluating age as a contributor to temporal variation in skull size (a proxy for body size) in 2 carnivorans, Canadian Lynx (Lynx canadensis) and American Marten (Martes americana). Using a series of linear and nonlinear models, we tested age in years (determined by cementum-layer analysis) as a predictor of skull size alongside other factors previously proposed to be important drivers of body-size trends, including population density for lynx and growing season conditions for martens. In both species, age was a significant predictor of skull size indicating a rapid year-to-year increase in young adult size that diminished in later adulthood. However, temporal shifts in age structure alone did not explain the observed changes in size over time, indicating that age structure acts in concert with other as-yet unidentified factors to drive body-size change. By explicitly evaluating the role of age, we can both refine models of temporal body-size trends and gain insights into size change as a signal of underlying demographic shifts—such as age-specific survivorship—providing a more holistic understanding of how mammals are responding to 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".