Inexplicable Inefficiency of Avian Molt? Insights from an Opportunistically Breeding Arid-Zone Species, Lichenostomus penicillatus
Bibliographic record
Abstract
The majority of bird species studied to date have molt schedules that are not concurrent with other energy demanding life history stages, an outcome assumed to arise from energetic trade-offs. Empirical studies reveal that molt is one of the most energetically demanding and perplexingly inefficient growth processes measured. Furthermore, small birds, which have the highest mass-specific basal metabolic rates (BMR(m)), have the highest costs of molt per gram of feathers produced. However, many small passerines, including white-plumed honeyeaters (WPHE; Lichenostomus penicillatus), breed in response to resource availability at any time of year, and do so without interrupting their annual molt. We examined the energetic cost of molt in WPHE by quantifying weekly changes in minimum resting metabolic rate (RMR(min)) during a natural-molt period in 7 wild-caught birds. We also measured the energetic cost of feather replacement in a second group of WPHEs that we forced to replace an additional 25% of their plumage at the start of their natural molt period. Energy expenditure during natural molt revealed an energy conversion efficiency of just 6.9% (±0.57) close to values reported for similar-sized birds from more predictable north-temperate environments. Maximum increases in RMR(min) during the molt of WPHE, at 82% (±5.59) above individual pre-molt levels, were some of the highest yet reported. Yet RMR(min) maxima during molt were not coincident with the peak period of feather replacement in naturally molting or plucked birds. Given the tight relationship between molt efficiency and mass-specific metabolic rate in all species studied to date, regardless of life-history pattern (Efficiency (%) = 35.720 x 10(-0.494BMRm); r² = 0.944; p = or < 0.0001), there appears to be concomitant physiological costs entrained in the molt period that is not directly due to feather replacement. Despite these high total expenditures, the protracted molt period of WPHE significantly reduces these added costs on a daily basis.
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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".