Annual climate fluctuations can influence iguana growth and degrowth
Bibliographic record
Abstract
Abstract It has been proposed that some vertebrates can undergo a reduction in body size in response to harsh environmental conditions. However, there is a paucity of evidence regarding the reality of this phenomenon. The objective of this study was to examine the possible occurrence of degrowth in a population of Lesser Antillean iguanas and to investigate whether it might be linked to climatic variations. We used a database of 2871 wild iguanas from the islands of Saint-Barthélemy, Chancel, and Guadeloupe (French Antilles). Body size was assessed through measurements of the snout–vent length (SVL) and body mass. Morphological analyses revealed that 44.3% of iguanas showed a decrease in body size. The analyses also showed that individuals can undergo both growth and degrowth multiple times, with 31.5% exhibiting such fluctuations across years. To assess the robustness of these results, we used body mass measurement as a complementary proxy for body size measurement. As expected, we found a significant positive correlation between variations in SVL and body mass. This relationship was consistent across sexes. With respect to climatic variations, rainfall and temperature in the preceding year significantly influenced SVL variation. These results challenge the traditional view of continuous reptile growth and suggest an adaptive mechanism enabling individuals to cope with environmental stressors.
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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 | 0.001 |
| 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 | 0.000 |
| 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".