Phenotypic plasticity under desert environment constraints: mandible variation in the dwarf fat-tailed jerboa, <i>Pygeretmus pumilio</i> (Rodentia: Dipodidae)
Bibliographic record
Abstract
Arid areas have a comparatively narrow range of habitat types, with restricted variation in environmental parameters, leaving narrow boundaries for phenotypic variation to correlate with ecological variables. To test this presumption, we explored variation in size and shape of the mandible in the dwarf fat-tailed jerboa (Pygeretmus pumilio (Kerr, 1792)) under the constraints of a rigorous desert environment. Size varied significantly and predictably with geographic position and demonstrated a strong, nonlinear longitudinal pattern. Moreover, size was associated with several other climatic variables but not with soil properties or with proxies for primary productivity. Our results suggest that for rodents exposed to rapid and extreme changes, larger size may have multiple advantages, notably in maintaining euthermia during cold nights and efficient water metabolism under aridity stress, in accumulating fat reserves for hibernation, and in digging deeper burrows, better protected from surface extremes. Shape varied clinally along the longitudinal transect, and the pattern was affected more by temperature than by precipitation. We conclude that the success of dwarf fat-tailed jerboa in occupying an extensive geographic range relies on their ability to meet environmental heterogeneity through cohesive and diverse responses, including physiology, behaviour, life-history traits, and morphological plasticity.
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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 | 0.000 |
| 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 | 0.000 |
| 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".