INDIVIDUALLY VARIABLE ENERGY MANAGEMENT STRATEGIES IN RELATION TO ENERGETIC COSTS OF EGG PRODUCTION
Bibliographic record
Abstract
Marked interindividual variation in metabolic rate suggests considerable complexity in energy management strategies, but attempts to further our understanding of the relationship between resting metabolic rate (RMR), daily energy expenditure (DEE), and reproductive effort have been hampered by the complexity of studying this system in the field. Here, we describe energy management strategies in a captive-breeding system, using Zebra Finches (Taeniopygia guttata), to demonstrate the high level of complexity and interindividual variability in energy expenditure, food intake, locomotor activity, and reproductive effort. In particular, we investigated whether the increase in RMR associated with egg production is additive, resulting in higher DEE and a need for elevated food intake, or whether this cost is compensated by reduced expenditure in nonreproductive components of the energy budget. We found high levels of intra-individual variation in energy expenditure associated with egg production in female Zebra Finches, e.g., comparing nonbreeding stage with the one-egg stage, change in RMR varied from 4.0% and 41.3%, and change in DEE varied from -33.3% to +46.4%. This variation was systematically related to aspects of locomotor activity and reproductive effort. Females with the largest increase in RMR during egg production decreased locomotor activity the most but still had increased DEE at the one-egg stage, and females with high DEE at the one-egg stage produced larger clutches. Our study suggests that females minimize increases in DEE during egg production through behavioral energy reallocation (reduced locomotor activity) but that individuals differ in their use of this strategy, which, in turn, is related to the absolute level of reproductive investment. This suggests a very complex, individually variable system of energy management to meet the demands of egg production.
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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".