Giant mice on small islands: biogeographic and ecological differences contribute to gigantism in island populations
Bibliographic record
Abstract
Island populations of small land vertebrates frequently exhibit insular gigantism, presenting with larger body sizes in comparison to mainland counterparts. While insular gigantism has been observed globally, the effects of biogeographic and ecological factors on body size in island systems are not well understood. Here, we examine the biogeographic and ecological associations of insular gigantism. Deer mice ( Peromyscus maniculatus ) were live trapped and body mass measured on six of the Gulf Islands and the nearby mainland of British Columbia, Canada. Biogeographic measures of land area and island distance from the mainland and the ecological measure of predator species richness were used in piecewise structural equation modelling to identify associations with insular gigantism. We found evidence of insular gigantism in the Gulf Islands system, with island mice having a larger mean body mass than mainland populations. Land area was positively associated with predator species richness, and predator species richness had a strong negative effect on deer mouse body mass, resulting in the observed pattern of insular gigantism. The concurrent analysis of biogeographic and ecological factors contributes to a better understanding of the evolution of insular gigantism in small vertebrates and its juxtaposition to the phenomenon of insular dwarfism of large vertebrates.
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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 | 0.000 |
| 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".