Poor prey quality is compensated by higher provisioning effort in passerine birds
Bibliographic record
Abstract
In altricial avian species, nutrition can significantly impact nestling fitness by increasing their survival and recruitment chances after fledging. Therefore, the effort invested by parents towards provisioning nestlings is crucial and represents a critical link between habitat resources and reproductive success. Recent studies suggest that the provisioning rate has little or no effect on the nestling growth rate. However, these studies do not consider prey quality, which may force breeding pairs to adjust provisioning rates to account for variation in prey nutritional value. In this 8-year study using black-capped (Poecile atricapillus) and boreal (Poecile hudsonicus) chickadees, we hypothesized that provisioning rates would negatively correlate with prey quality (i.e., energy content) across years if parents adjust their effort to maintain nestling growth rates. The mean daily growth rate was consistent across years in both species. However, prey energy content differed among years, and our results showed that parents brought more food to the nest and fed at a higher rate in years of low prey quality. This compensatory effect likely explains the lack of relationship between provisioning rate and growth rate reported in this and other studies. Therefore, our data support the hypothesis that parents increase provisioning efforts to compensate for poor prey quality and maintain offspring growth rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".