Behavioural flexibility predicts invasion success in birds introduced to New Zealand
Bibliographic record
Abstract
A fundamental question in ecology is whether there are evolutionary characteristics of species that make some better than others at invading new communities. In birds, nesting habits, sexually selected traits, migration, clutch size and body mass have been suggested as important variables, but behavioural flexibility is another obvious trait that has received little attention. Behavioural flexibility allows animals to respond more rapidly to environmental changes and can therefore be advantageous when invading novel habitats. Behavioural flexibility is linked to relative brain size and, for foraging, has been operationalised as the number of innovations per taxon reported in the short note sections of ornithology journals. Here, we use data on avian species introduced to New Zealand and test the link between forebrain size, feeding innovation frequency and invasion success. Relative brain size was, as expected, a significant predictor of introduction success, after removing the effect of introduction effort. Species with relatively larger brains tended to be better invaders than species with smaller ones. Introduction effort, migratory strategy and mode of juvenile development were also significant in the models. Pair‐wise comparisons of closely related species indicate that successful invaders also showed a higher frequency of foraging innovations in their region of origin. This study provides the first evidence in vertebrates of a general set of traits, behavioural flexibility, that can potentially favour invasion success.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".