Brain Size Predicts the Success of Mammal Species Introduced into Novel Environments
Bibliographic record
Abstract
Large brains, relative to body size, can confer advantages to individuals in the form of behavioral flexibility. Such enhanced behavioral flexibility is predicted to carry fitness benefits to individuals facing novel or altered environmental conditions, a theory known as the brain size-environmental change hypothesis. Here, we provide the first empirical link between brain size and survival in novel environments in mammals, the largest-brained animals on Earth. Using a global database documenting the outcome of more than 400 introduction events, we show that mammal species with larger brains, relative to their body mass, tend to be more successful than species with smaller brains at establishing themselves when introduced to novel environments, when both taxonomic and regional autocorrelations are accounted for. This finding is robust to the effect of other factors known to influence establishment success, including introduction effort and habitat generalism. Our results replicate similar findings in birds, increasing the generality of evidence for the idea that enlarged brains can provide a survival advantage in novel environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".