Redd superimposition mediates the accuracy, precision, and significance of redd counts for cutthroat trout
Bibliographic record
Abstract
Redd counts are commonly applied to estimate spawning population size for salmonids and allow for broad spatial and temporal coverage in monitoring efforts. However, the utility of redd counts may be compromised by observation error, particularly with respect to superimposition, where later arriving spawners construct redds overlapping existing redds. Here, we provide a mechanistic evaluation of the effects of superimposition on the error structure and biological significance of redd count data for Yellowstone cutthroat trout ( Oncorhynchus clarkii bouvieri) spawning within tributaries to the Snake River, Wyoming. We used a Bayesian framework to parse observation error into distinct components and found low detection of redd clusters (i.e., areas of superimposition) was offset by overestimates of the number of redds per cluster, such that observed counts accurately reflected census redd abundance. However, a saturating relationship between redd counts and spawner abundance indicated that counts is best interpreted as effective reproductive effort rather than spawner abundance. Our results provide a mechanistic understanding of redd count data that can be used to assess their application and interpretation for monitoring.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".