Low reproductive success of great tits in the preferred habitat: A role of food availability
Bibliographic record
Abstract
Habitat-related variation has been revealed in the seasonal reproductive patterns of great tits inhabiting mosaics of deciduous woodland fragments and managed coniferous forests. The number and quality of offspring tend to be higher in the non-preferred coniferous habitat compared with the preferred deciduous habitat. We explored whether these patterns can be explained by variation in food abundance and/or parental provisioning ability. Frass fall seemed not to be a reliable measure of the seasonal dynamics of nestling feeding conditions, although it is widely used for this purpose. No habitat-related differences were found in the tendency of parents to increase provisioning frequency in response to increased hunger levels of nestlings, suggesting that provisioning frequency as such is not a limiting factor for nestling growth. Higher feeding rates in deciduous habitat were associated with lower proportions of high-quality food items among prey delivered to offspring. These findings confirm that relatively high nestling feeding rates in birds may reflect the low quality of available food rather than the quality of parental care or an abundance of food in the environment. The results also indicate that deciduous forest habitat, though preferred by tits, may sometimes provide poorer brood-rearing conditions than the non-preferred, coniferous habitat. We suggest that the great tit's preference for deciduous habitat, which presumably evolved in more southern regions, may be maladaptive in northern regions where deciduous woods are mainly young, secondary stands of alder and birch.
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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) | 0.001 | 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".