Eastern Scotian Shelf Northern Shrimp (Pandanus borealis) stock assessment framework : model, indicators, and reference point development
Bibliographic record
Abstract
The Eastern Scotian Shelf (ESS) Northern Shrimp fishery has been ongoing since the early– 1980s, although its contemporary history began in 1991 with introduction of the Nordmøre grate that reduced bycatch and enabled fishery expansion. In support of the fishery, an ESS Northern Shrimp stock framework CSAS peer-review was held over two meetings: 1. model development was peer-reviewed at a first meeting held on October 29–31, 2024; and 2. new indicators for the traffic light approach (TLA) were examined in context of the new model, and options for a new limit reference point (LRP) based on modeled results, were peer-reviewed at a second meeting held on March 5–6, 2025. This research document describes development of a stock assessment model for the ESS Northern Shrimp fishery in Shrimp Fishing Areas (SFA) 13, 14, and 15, as no analytical models have previously been implemented in these areas. Aspects of species and stock’s biology, distribution, and stock structure are presented, along with a brief description of the history of stock assessment for the fishery, in order to contextualize new developments. Data sources include fishery-dependent data from logbooks, port samples and observer trips, and fishery independent data from the Fisheries and Oceans Canada (DFO)-Industry survey, which deploys both a main trawl and a specialized belly bag. Three models —simplified delay-difference (SDD), tow level model (TLM), and Spatially Explicit Assessment Model (SEAM) — are described, fit to available data, and results compared to each other. While the SDD was deemed inappropriate for the ESS Northern Shrimp stock, both the TLM and SEAM indicated that exploitation rates have been relatively consistent over time, although ESS Northern Shrimp productivity has declined consistently since 2005, with 2023 demonstrating a marked decline and departure from past productivity expectations. There is evidence that the fishery has impacted stock dynamics, as both models indicated that biomass tends to decrease with exploitation rates above 6%. Both SDD and SEAM indicated that the least biased 1-year projection method was the mean growth approach. While both models are adequate for assessing the ESS Northern Shrimp stock, SEAM outperformed TLM, especially in terms of recruitment. As such, it is recommended that SEAM be used to provide science advice for the ESS Northern Shrimp stock using the median growth approach for 1-year stock projections. In the terms of reference points and indicators, results showed that LRPs based on maximum sustainable yield (MSY) simulations are not currently appropriate for the stock. An LRP based on an historical proxy for the theoretical long-term equilibrium biomass, in the absence of fishing (𝐵𝐵0), is proposed instead. In addition, most of the legacy TLA indicators demonstrated clear utility in the context of yearly science advice, with only a few inadequate indicators (i.e., Age 2, Snow Crab, Cod Recruitment, Turbot Abundance) being replaced by new and more useful indicators (i.e., Shrimp Bycatch, Area Occupied, and Atlantic Cod, Turbot, and American Plaice). In conclusion, the SEAM model, LRP, and stock indicators outlined in this framework research document received consensus support from peer-reviewers and meeting participants for use moving forward to assess the ESS Northern Shrimp stock.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".