Multidimensional nutritional ecology and urban birds
Bibliographic record
Abstract
Abstract There is growing interest in the question of how urbanization affects the ecology of birds, across timescales from relatively short‐term physiological responses to long‐term evolutionary adaptation. The ability to gain the required nutrients in urban habitats is a key trait of successful urban birds. Foraging behavior, in itself, increasingly is recognized as a complex nutritional phenomenon, where the ratios, proportions, and amounts of macronutrients (protein, carbohydrate, and lipid) in foods, meals, and diets have been shown to exert a driving influence. Yet, despite the rising trend of urbanization, the importance of food quality and quantity in urban ecology, and the growing evidence demonstrating the pervasive and sometimes complex role of macronutrients in foraging behavior, the nutritional ecology of urban birds remains poorly understood. Here, we review the foraging behavior and role of macronutrients in the ecology of urban birds and demonstrate how incorporating a multidimensional approach to nutrition can provide new insights into their urban ecology. To that end, we demonstrate how a macronutrient‐based view can aid in understanding the relationships between natural, anthropogenic, and supplementary foods. We then provide an overview of multidimensional nutritional niche concepts that can be used to generate explanatory and predictive models for urban bird ecology. We conclude that multidimensional nutritional ecology provides an appropriate framework for understanding the roles that nutrition plays in the relationships between urban birds and their 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".