Are innovative species ecological generalists? A test in North American birds
Bibliographic record
Abstract
Foraging innovation occurs when animals exploit novel food sources or invent new foraging techniques. Species vary widely in their rates of innovation, and these differences can be quantified using counts of novel behavior observed in the wild. One of the assumed benefits of innovativeness is that it allows species to exploit a wider variety of habitats and foods, enhancing survival when resources are in shortage or when individuals invade new environments. However, the relationship between innovation propensity and ecological generalism lacks firm empirical support. Moreover, innovativeness does not only imply benefits but may also lead to higher risks incurred in the wide array of habitats exploited. In this study, we test whether innovative species exploit a wider variety of habitats and food types as well as face the potential risk of more predators as a consequence of their ecological generalism. Using data for 193 North American bird species in a phylogenetically informed analysis, we find a significant positive relationship between innovation rate and habitat generalism, but not diet breadth. Although habitat generalism is also associated with exposure to a wider variety of predators, there is no direct relationship between innovation rate and predation. Our results suggest that although innovators use a wider variety of habitats, they are not necessarily diet generalists, challenging the classic view that feeding generalism is equivalent to feeding flexibility.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".