Assessing the effects of changes in reproductive condition on the survival of anadromous Dolly Varden ( <i>Salvelinus malma</i> ) using Bayesian multistate capture–recapture modelling
Bibliographic record
Abstract
Understanding the interrelationship between the survival and reproductive states of salmonids is important for both management and conservation purposes; however, their complex life history introduces challenges. The survival probabilities of anadromous Dolly Varden ( Salvelinus malma) are influenced by their reproductive cycle, while predicting their reproductive state in successive years is difficult as it can vary based on an individual’s sex. We developed a Bayesian multi-state capture–recapture model that estimates reproductive state transition probabilities, survival probabilities, and the effect of sex on state transitions using the Cormack–Jolly–Seber model. We applied the model to Dolly Varden data collected from five river systems in the western Canadian Arctic. We demonstrate sex-specific state transition probability differences, with females exhibiting higher transition rates into reproductive states. Moreover, survival probabilities were influenced by both sex and reproductive status. Across all rivers, survival probabilities for both sexes decreased by approximately 50% while spawning compared to non-spawning. This study provides important insights into how reproduction and sex affect survival among stocks, which improves their assessment to ensure management and conservation goals.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".