Individual variation in feeding morphology, not diet, can facilitate the success of generalist species in urban ecosystems
Bibliographic record
Abstract
Abstract Generalist species dominate urban ecosystems. The success of urban generalists is often related to a plastic diet and feeding traits that allow them to take advantage of a variety of food resources provided by humans in cities. The classification of a species as a generalist is commonly based on mean estimates of diet‐ and feeding‐related traits. However, there is increasing evidence that a generalist population can consist of individual specialists. In such cases, estimates based on mean can hide important individual variation that can explain trophic ecology and the success of urban dwellers. Here, we focus on guppies, Poecilia reticulata, a widespread alien fish species which has invaded both urban and non‐urban systems, to explore the effect of urbanization on individual diet and feeding morphology (cranium shape). Our results show that guppies in urban and non‐urban populations are not individual specialists, having a similar generalist diet despite the high population density. However, there is important individual variation in cranium shape which allow urban guppies to feed more efficiently on highly nutritious food. Our data suggest that individual variation in feeding efficiency can be a critical overlooked trait that facilitates the success of urban generalists.
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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 | 0.000 | 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".