Linking hierarchical population models with habitat data improves assessment of data-limited salmon stocks
Bibliographic record
Abstract
ABSTRACT Objective Managing data-limited populations is a challenge to the sustainability of fisheries globally. Meta-analytic approaches, where insights from data-rich populations are drawn on to inform data-limited ones, along with the use of habitat-based information, have each been proposed as ways to overcome data-limited assessment challenges, but the two approaches have rarely been combined. Sockeye Salmon Oncorhynchus nerka spawn and rear in many remote coastal watersheds of British Columbia, Canada, challenging comprehensive population assessments. Estimating conservation and management reference points for such populations is particularly relevant given their importance to Indigenous and commercial fisheries. Most Sockeye Salmon have obligate lake-rearing as juveniles, and total abundance is typically limited by production in nursery lakes. Although methods have been developed to estimate population capacity based on the photosynthetic rate of nursery lakes and lake area or volume, they have not yet been widely incorporated into spawner–recruitment analyses. Methods We tested the value of combining these lake-based capacity estimates with various hierarchical structures in spawner–recruitment analyses to assess population status using a set of Bayesian spawner–recruitment models for 69 populations across coastal British Columbia, many of which were data limited. Results Our analysis revealed regional variation in the population productivity of Sockeye Salmon, with coastal populations exhibiting slightly lower mean productivity than those in interior watersheds. Hierarchical spawner–recruitment models with and without informative lake habitat-based priors greatly improved predictive ability across all populations. Conclusions These findings reveal opportunities to integrate spatial analyses of habitat characteristics with population models to inform the conservation and management of exploited species and their natal habitats, particularly where populations are data limited.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".