The effect of habitat quality on foraging patterns, provisioning rate and nestling growth in Corsican Blue Tits <i>Parus caeruleus</i>
Bibliographic record
Abstract
Many bird species face seasonal and spatial variation in the availability of the specific food required to rear chicks. Caterpillar availability is often identified as the most important factor determining chick quality and breeding success in forest birds, such as tits Parus spp. It is assumed that parents play an important role in mediating the effect of environment on chick development. A reduction in prey availability should therefore result in increased foraging effort to maintain the amount of food required for optimal chick development. To investigate the capacity of adults to compensate for a reduction in food supply, we compared the foraging behaviour of Blue Tits Parus caeruleus breeding in rich and poor habitats in Corsica. We monitored the foraging effort of adults using radiotelemetry. We also identified and quantified prey items provided to nestlings by using a video camera mounted on the nest. We found that the mean travelling distance of adults was twice as great in the poor habitat as it was in the rich. Despite the marked difference in foraging distance, the proportion of optimal prey (caterpillars) in the diet of the chicks and the total biomass per hour per chick did not differ between the two habitats. We argue that relationships between habitat richness, offspring quality and breeding success cannot be understood adequately without quantifying parental effort.
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 | 0.001 |
| 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".