Minimum viable population size for lake sturgeon (Acipenser fulvescens) using an individual-based model of demographics and genetics
Bibliographic record
Abstract
Population viability analysis is a useful tool to explore the relationship between extinction risk and population size, but often does not include genetic factors. Our objectives were to determine minimum viable population size (MVP) for lake sturgeon ( Acipenser fulvescens ) and examine how inbreeding depression may affect MVP. Our individual-based model incorporated inbreeding depression in two ways: individuals with inbreeding coefficients above a threshold experienced inbreeding depression (threshold), and individuals experienced inbreeding depression at a rate related to their inbreeding coefficient (gradual). Three mechanisms relating inbreeding to fitness were explored (young-of-the-year (YOY) viability, post-YOY viability, number of progeny). The criterion we used to determine MVP was a 5% chance of extinction over 250 years. The estimated MVP without inbreeding effects was 80 individuals. For some scenarios incorporating inbreeding, MVP did not change, but for others, MVP was substantially higher, reaching values up to 1800. Results demonstrate that extinction risk and MVP can be influenced by both demographic stochasticity and inbreeding depression. This research should inform management by determining MVP and how inbreeding, which is expected to accrue in remnant populations because of generations of low abundance, may affect MVP.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".