The Elusive Minimum Viable Population Size for White Sturgeon
Bibliographic record
Abstract
Abstract Damming of large rivers in the U.S. Pacific Northwest and Canada has divided the historical population of white sturgeonAcipenser transmontanusinto more than 36 fragmented populations, few of which are thriving. We now face the challenge of managing these populations to avoid extirpation. Two goals of this study were to identify extinction thresholds related to small size and inadequate habitat for this species. The minimum viable population size (MVP) is the threshold size above which populations support recruitment and grow and below which populations fail to support recruitment and decline. We estimated a single, cross‐population MVP using data from multiple populations and quantile regression, which removed the effects of factors other than population size. Only two populations (those in the Bonneville and Dalles reservoirs on the Columbia River), both with significant increasing trends, were larger than our MVP estimate. We detected significant decreasing trends in two populations—those below Bonneville Dam and in the Kootenai River. To discover how site‐specific differences in river habitat influence MVP, we used a population viability analysis (PVA) model that incorporated Allee mechanisms. The PVA model identified a river segment length below which extinction was certain regardless of initial population size. Above this threshold, simulated populations in river segments that were longer or that provided more frequent recruitment opportunities were able to persist with smaller initial sizes. Two priorities emerged for white sturgeon: monitoring age structure and understanding the circumstances preventing recruitment to age 1. Our results ultimately guided us toward thresholds in rearing habitat and age structure that promise to develop into more useful conservation tools than MVP for this and similar long‐lived species.
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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 | 0.003 |
| 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 | 0.001 |
| 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".