Ecological generalism and behavioural innovation in birds: technical intelligence or the simple incorporation of new foods?
Bibliographic record
Abstract
Generalist species are more successful than specialists in anthropogenically modified environments or in environments in which they have been introduced, but the nature of the link between generalism and establishment success is unclear. A higher feeding innovation rate has previously been reported in habitat generalist birds from North America. By allowing them to exploit new resources, this higher feeding innovation rate might explain the generalists' advantage. This result might be due to generalists being more likely to find new resources because they are exposed to more diverse environmental conditions. Alternatively, they might differ from specialists in other traits, in particular cognitive skills that might allow them to innovate more complex food searching and handling techniques. To test these hypotheses, we separated avian feeding innovations into a 'technical' (novel searching and handling behaviour) and a 'food type' (incorporation of a new food in a species' diet) category. Technical innovations, but not food type innovations, have previously been shown to correlate with avian brain size, suggesting they reflect cognitive ability. We used a world-wide data base of 2339 feeding innovations recorded in the literature, covering a total of 765 avian species and assessed the correlations between brain size and feeding innovation rates on one side and habitat and diet generalism on the other. Habitat generalism was positively related with food type innovation rate, but not technical innovation rate or brain size. This suggests that habitat generalist species are more likely to incorporate new food types in their diet because of higher chances to find new food resources in their environment, or of a higher opportunism, but not enhanced cognitive skills. In contrast, diet generalist species had higher food type and technical innovation rates, as well as larger brains, suggesting that cognitive skills might help species expand their diet breadth or that an increase in diet breadth might favour the evolution of enhanced cognitive abilities. Our results provide new insights into the nature of the generalists' advantage in the face of environmental changes, and suggest that dietary and habitat generalism are different, but convergent, routes to feeding flexibility and adaptation to changed 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".