INFERRING BREEDING SUCCESS THROUGH RADIOTELEMETRY IN THE MARBLED MURRELET
Bibliographic record
Abstract
Demographic parameter estimates are essential for understanding population ecology and developing management plans for species of concern. We inferred measures of breeding success using radiotelemetry in the marbled murrelet (Brachyramphus marmoratus), a secretive, forest-nesting seabird, from 1998 to 2001 in Desolation Sound, British Columbia, Canada. Our estimates of mean annual nesting success and fecundity (0.19–0.23 female offspring/adult female/yr) are among the highest reported for the species. We suspect that our estimates are high compared to previous estimates in our study area (Manley 1999), primarily because of our higher success in areas inaccessible to ground-based human observers. We detail how behavior-based inferences of activity at different reproductive stages did not differ between confirmed nest sites and suspected nest sites that were physically inaccessible to us. We were able to accurately predict initiations of breeding and incubation success from the duration of adults' repeated daily shifts from the ocean to their inland nest sites. Chick-rearing success was accurately predicted by visitation rates of adults during provisioning. We discuss the assumptions and potential biases of our methods and their effects on our results. Our method may overestimate early breeding failure, but it likely provided unbiased fecundity estimates for our population. Accurately inferring breeding success through radiotelemetry is costly and labor intensive. However, radiotelemetry could provide crucial demographic information once thought impossible to obtain for secretive breeding species.
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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 | 0.001 | 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) | 0.000 | 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".