Physiological adjustments to high foraging effort negatively affect fecundity but not final reproductive output in captive zebra finches
Bibliographic record
Abstract
Foraging at elevated rates to provision offspring is thought to be an energetically costly activity and it has been suggested that there are physiological costs associated with the high workload involved. However, for the most part, evidence for costs of increased foraging and/or reproductive effort is weak. Furthermore, despite some experimental evidence demonstrating negative effects of increased foraging and parental effort, the physiological mechanisms underlying costs associated with high workload remain poorly understood. To examine how high workload affects haematology, oxidative stress and reproductive output, we experimentally manipulated foraging effort in captive zebra finches, Taeniopygia guttata, using a previously described technique, and allowed individuals to breed first in low foraging effort conditions and then in high foraging effort conditions. We found that birds upregulated haematocrit and haemoglobin concentration in response to training. Birds subjected to increased workload during reproduction had lower fecundity, although final reproductive output was not significantly different than that of controls. Offspring of parents subjected to high workload during reproduction also had higher oxidative stress when they were 90 days of age. Total antioxidant capacity and reactive oxygen metabolites of birds responded differently in the two breeding attempts, but we did detect an overall increase in oxidative stress in response to training in either attempt, which could explain the lower fecundity observed in birds subjected to increased workload during reproduction.
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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 | 0.000 | 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".