Modelling the effects of variation in growth, recruitment, and harvest on lake sturgeon population viability and recovery
Bibliographic record
Abstract
Abstract Regulatory agencies and fisheries managers tasked with implementing recovery plans for endangered species must frequently make decisions based on limited data, while also considering general uncertainty associated with prediction. Population viability analysis (PVA) is a modelling tool that is particularly useful for long‐lived species and can be applied even in the absence of robust estimates of life history parameters. As empirical data are accumulated over time, PVA inputs, model structure, and parameter estimates can be refined to increase model realism and accuracy. Since the first iterations of lake sturgeon (Acipenser fulvescens) PVA were run, approximately 15 years ago, based on conservative inputs for Canadian population units, important empirical data have accumulated from a variety of river systems that the species inhabits. Erratic recruitment patterns have been revealed, juvenile survival has been determined to be higher than initially believed, and growth rates have been found to vary by habitat (river) type. Exploiting an improved biological understanding of the species, somatic growth models derived from three well‐studied lake sturgeon populations were used to examine the effects of recruitment variability and incremental levels of adult harvest (mortality) on the probability of population recovery and risk of population decline. A total of 110 PVA scenarios (with 1,000 replicates per scenario) were run for each of the three somatic growth models (slow, medium, and fast). The PVA results suggest that the recovery potential for lake sturgeon populations may be higher than previous models, based on conservative inputs, have indicated. A few scenarios resulted in recovery after 100 years, and nearly half of the scenarios resulted in recovery after 250 years. Similarly, the probability of decline for small populations was most sensitive to adult harvest, with little change in the number of scenarios resulting in decline at the time points of 100, 250, and 500 years. A worked example highlights how the targeted monitoring of both juvenile and adult life stages can be used to evaluate the recovery potential of more than one population and prioritize management initiatives, such as harvest reduction and stocking.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".