Estimates of Effective Number of Breeding Adults and Reproductive Success for White Sturgeon
Bibliographic record
Abstract
Abstract Accurate estimates of the number of adults contributing to offspring (Ns), effective breeding number (Nb), and individual adult contributions to recruitment are required for recovery planning for endangered White Sturgeon Acipenser transmontanus populations, many of which are suffering from prolonged periods of recruitment failure. We show that genetic techniques can be used to characterize important features of White Sturgeon reproductive ecology in large rivers where census data are extremely difficult to obtain. We used 12 microsatellite loci and likelihood‐based pedigree analysis to estimate Ns, Nb, number of kin groups (Nk), and individual reproductive success of White Sturgeon contributing to viable eggs and larvae collected in the upper Columbia River in each of 2 years. Estimated mean ±SD annual Ns was 121.5 ± 34.7, Nb was 86.5 ± 10.6, and Nk was 73.5 ± 17.3. Large variations in estimates of Ns, Nb, and Nk were observed between three spawning areas, in which one spawning site representing 61% of total adult spawning population. Variation in adult reproductive success was observed within and among sites. Estimated mean ±SD individual spawning duration was 1.9 ± 1.1 d, and number of mates per adult was 2.9 ± 2.5, which also varied spatially and temporally. Based on age of collected eggs and larvae, number of spawning days ranged from 5 to 19 d between years and among sites. Genetically derived estimates of Ns were lower but generally concordant with empirical estimates of available spawners (Nc), based on sex ratios and maturation staging of adults captured independently (Ns/Nc ratio = 0.683). Results increase our understanding of White Sturgeon reproductive ecology and recruitment and allow projections of cohort levels of genetic diversity. Similar data can be applied to recovery planning and aquaculture programs for this and other species of conservation concern. Received January 6, 2014; accepted May 28, 2014
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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 | 0.001 |
| 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) | 0.001 | 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".