Applying the Declining Population Paradigm: Diagnosing Causes of Poor Reproduction in the Marbled Murrelet
Bibliographic record
Abstract
Abstract: We identified six approaches to diagnosing causes of population declines and illustrate the use of the most general one (“multiple competing hypotheses”) to determine which of three candidate limiting factors—food availability, nesting site availability, and nest predation—were responsible for the exceptionally poor reproduction of Marbled Murrelets ( Brachyramphus marmoratus ) in central California. We predicted how six attributes of murrelet demography, behavior, and physiology should be affected by the candidate limiting factors and tested predictions with field data collected over 2 years. The average proportion of breeders, as estimated with radiotelemetry, was low (0.31) and varied significantly between years: 0.11 in 2000 and 0.50 in 2001. Murrelets spent significantly more time foraging in 2000 than in 2001, suggesting that low food availability limited breeding in 2000. In 2001, 50% of radio‐marked murrelets nested and 67% of females were in breeding condition, suggesting that enough nest sites existed for much of the population to breed. However, rates of nest failure and nest predation were high (0.84 and 0.67–0.81, respectively) and few young were produced, even when a relatively high proportion of murrelets bred. Thus, we suggest that reproduction of Marbled Murrelets in central California is limited by food availability in some years and by nest predation in others, but apparently is not limited by availability of nesting sites. The multiple‐competing‐hypotheses approach provides a rigorous framework for identifying causes of population declines because it integrates multiple types of data sets and can incorporate elements of other commonly used approaches.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".