Guidelines for Defining Limit Reference Points for Pacific Salmon Stock Management Units
Bibliographic record
Abstract
Limit reference points, LRPs, define the stock status below which serious harm is expected to occur to a stock. LRPs are required for major fish stocks, or Stock Management Units (SMUs) that are prescribed by regulation under amendments to the Canadian Fisheries Act (2019). Pacific salmon are unique among marine fish stocks due to their high levels of intraspecific diversity which gives rise to a large range in data availability, considerations, and approaches for assessments and LRP development. In this paper, we identify six principles for developing LRPs for Pacific salmon that are adapted from principles used more broadly among marine species. One principle unique to Pacific salmon is that LRPs should be aligned with Canada’s Wild Salmon Policy (WSP) objective of preserving biodiversity of salmon at the scale of Conservation Units (CUs), which are nested within SMUs. We developed methods for calculating LRPs, and established guidelines on how to implement them including under which conditions they should or should not be applied. We propose that LRPs be identified from the proportion of CUs that have status above the Red zone for WSP status assessments, as a default approach. This provides some consistency with status assessments already produced under the WSP, and can inform management decisions for harvest, habitat and hatcheries that often occur at finer, CU scales. To supplement the default approach, we provide LRPs based on metrics of aggregate abundances for the entire SMU, which may be required for fisheries management purposes in some cases. These latter LRPs are derived to have a desired probability of all component CUs being above Red status given an assumed relationship between aggregate abundance and the probability that all CUs will be above Red status. We identify uncertainties associated with each approach, and describe how they can be applied across a range of data types, qualities and quantities. Analyses to support our development of guidelines has been informed by three cases studies: Interior Fraser Coho Salmon Oncorhynchus kisutch, West Coast Vancouver Island Chinook Salmon, O. tshawytscha, and Inside South Coast Chum Salmon, O. keta, excluding the Fraser River.
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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 | 0.045 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".