Developing a Bayesian mark-recapture modelling framework to inform stock recovery and rebuilding strategies for long-lived, anadromous species
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The slow growth and late maturation of sturgeons make their populations highly susceptible to depletion from exploitation. Their anadromous life history also makes them vulnerable to modifications of the rivers in which they spawn and feed. These factors are responsible for the severe depletion of populations of most sturgeon species throughout their range. Mark-recapture methods provide a suitable tool for the assessment of population abundance and estimation of key population dynamics parameters and fishery-specific mortality rates, all of which are prerequisites for stock rebuilding and conservation. White sturgeon (Acipenser transmontanus) in the Fraser River, British Columbia supported a commercial fishery which saw peak catches of over 500 tonnes at the end of the nineteenth century; by the 1960s the commercial catch was in the range 4 to 25 tonnes annually. The federal Department of Fisheries and Oceans Canada (DFO) placed a moratorium on commercial harvest in 1994, since when catch-and-release sport fishing regulations have been in force. This paper describes a Bayesian mark-recapture model of PIT (passive integrated transponder) tags in the recreational fishery for Fraser River white sturgeon. The Bayesian approach allows incorporation of empirical estimates of the PIT tag reporting rates and probabilistic estimation of age-class specific mortality rates, catchability coefficients and seasonal movement rates; posterior distributions for these parameters can be used as inputs to an age-structured population dynamics model. This will ultimately be used within a decision analytic framework to facilitate stock rebuilding and develop area-based management tools for the conservation of Fraser River white sturgeon.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".