Indicators of habitat quality and the reproductive output of a forest songbird in small and large fragments
Bibliographic record
Abstract
The distribution of individuals among habitats that vary in quality (i.e. resource availability) may affect reproductive output at a population level. I compared indicators of habitat quality including the breeding experience ratios (inexperienced:experienced birds), turnover rates, pairing success, and densities of a forest songbird, the Eastern Yellow Robin Eopsaltria australis , in two small, food‐poor forest fragments, with those in two large, food‐rich fragments. I then evaluated the likelihood that the breeding experience ratio affected the reproductive output of populations. Inexperienced males occurred in small fragments eight times as often as in large fragments. Male turnover rates were 1.5 times higher in the small than large fragments, and 20% (10/50) of the male population were unpaired in the small fragments compared to 0% (0/25) in the large. None of these measures differed significantly for females. Experienced birds of both sexes produced almost all of the offspring compared with inexperienced birds. Despite these findings, reproductive output did not vary with fragment size for robins. Thus, while breeding experience clearly influenced the reproductive success of individuals, there were no obvious population consequences of having disproportionately more inexperienced males in the small fragments. I conclude that while male traits may be good indicators of resource levels within fragments (specifically, food availability), they may not be adequate predictors of population performance. Thus, the reproductive output of populations must be measured directly before conclusions concerning population performance can be made. Interestingly, breeding densities were not accurate indicators of either resource levels or population performance. Densities were two times higher in the smaller fragments, and I suggest that this result reflects problems with male dispersal among fragments caused by isolation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".