Estimating survival of an anadromous salmonid using Bayesian hierarchical models applied to acoustic telemetry, biological, and environmental data
Bibliographic record
Abstract
Abstract Hierarchical modelling is frequently used to model ecological processes because of its ability to handle complex ecological phenomena by decomposing them into naturally explainable sub-models. Hierarchical Bayesian approaches have gained widespread use in health, social, and environmental sciences, including in the estimation of demo-graphic parameters such as survival. In this study, we combine Bayesian hierarchical models with acoustic telemetry data to estimate survival probabilities for high-latitude populations of an anadromous salmonid, the Arctic char (Salvelinus alpinus) , while in-corporating environmental and biological covariates to assess their impact on survival. The model we present here can also account for temporally varying detection probabilities due to changes to the acoustic receiver array design and seasonal variation in the detection probabilities related to environmental conditions (e.g., ice vs. no ice). As previously documented in this species, survival was high ( > 0.87) and we found that the covariates pertaining to sea ice coverage and Fulton’s condition factor impacted the survival probabilities. Contrary to our expectations, high-condition fish had lower survival rates. Survival was also considerably lower during the summer (open-water) compared to winter (ice-covered) seasons. While the biological explanations and implications of these findings require further exploration, they nonetheless demonstrate the utility of this approach. Specifically, we present a hierarchical Bayesian model that can consider environmental and biological covariates while accounting for varying detection probabilities, a major concern of acoustic telemetry studies. The model can be easily adapted for other taxa with similar life histories where mark recapture data are available and can be extended to include additional environmental (e.g., salinity) and biological parameters (e.g., sex).
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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 | 0.006 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".