Influence of prey abundance on northern spotted owl reproductive success in western Oregon
Bibliographic record
Abstract
The hypothesis that high temporal variability of northern spotted owl (Strix occidentalis caurina) reproductive success is a response to prey abundance remains largely untested. We evaluated this relationship in the Oregon Cascade Mountains. Despite similar biomass of northern flying squirrels (Glaucomys sabrinus) (169 ± 13.9 g/ha) and deer mice (Peromyscus maniculatus) (160 ± 18.8 g/ha), flying squirrels dominated the breeding season diet based on both biomass (49%) and numbers (40%). Abundance of flying squirrels and western red-backed voles (Clethrionomys californicus) was more variable spatially ([Formula: see text]38% of process variation) than temporally (15%–24%), whereas abundance of deer mice was more similar across stands (12% spatial variation) than among years (68% temporal variation). Spotted owl reproductive success was statistically associated only with the abundance of deer mice (number of young per territory: r2 = 0.68). However, deer mice comprised only 1.6 ± 0.5% of the biomass consumed. The low temporal variability of the dominant prey species provided evidence that simple prey relationship models were not likely to explain the highly synchronous and temporally dynamic patterns of spotted owl reproductive performance. Reproductive success was likely a result of the interaction of both weather and prey and the life history strategy of this long-lived owl.
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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 | 0.001 |
| 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".