Using genetic pedigree reconstruction to estimate effective spawner abundance from redd surveys: an example involving Pacific lamprey (<i>Entosphenus tridentatus</i>)
Bibliographic record
Abstract
Redd surveys are a commonly used technique for indexing the abundance of sexually mature fish in streams; however, substantial effort is often required to link redd counts to actual spawner abundance. In this study, we describe how genetic pedigree reconstruction can be used to estimate effective spawner abundance in a stream reach, using Pacific lamprey (Entosphenus tridentatus) as an example. Lamprey embryos were sampled from redds within a 2.5 km reach of the Luckiamute River, Oregon, USA. Embryos were found in only 20 of the 48 redds sampled (suggesting 58% false redds); however, multiple sets of parents were detected in 44% of the true redds. Estimates from pedigree reconstruction suggested that there were 0.48 (95% CI: 0.29–0.88) effective spawners per redd and revealed that individual lamprey contributed gametes to a minimum of between one and six redds, and in one case, spawned in patches that were separated by over 800 m. Our findings demonstrate the utility of pedigree reconstruction techniques for both inferring spawning-ground behaviors and providing useful information for refining lamprey redd survey methodologies.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".