Assessing connectivity patterns among management units of the Newfoundland and Labrador shrimp population
Bibliographic record
Abstract
Abstract The Eastern Canadian northern shrimp population, representing one of the most important fisheries in the region, decreased dramatically since the mid‐2000s to a historical low in 2017, but changes were not spatially uniform. Applying a biophysical model within Newfoundland and Labrador (NL) management areas, we investigated connectivity processes during the long pelagic larval phase (2–3 months) of Pandalus borealis and key drivers of larval dispersal in different environmental conditions. We selected 3 years representative of contrasting North Atlantic Oscillation (NAO) phases to assess potential larval dispersal patterns of the northern shrimp population in NL, and hierarchically assessed the impact of the timing of release (yearly and daily), release location, and vertical migration behaviour on shrimp larval dispersal. Overall, we found that populations located on the northern Newfoundland and Labrador shelf supplied potential settlers to southern populations because of the dominant Labrador Current. Ocean circulation and current velocities during the NAO positive year differed from other years, generating contrasting settlement spatial patterns. Larval release location and vertical migration behaviour were the two most important influences on the strength of larval supply and settlement patterns. Inclusion of diel and ontogenic swimming behaviour increased settlement success of larvae released from inshore areas, regardless of study years. Our study improves understanding of northern shrimp stock‐recruitment relationships, their sensitivity to changing environmental conditions, and spatially non‐homogeneous population decline for bentho‐pelagic species with a long larval phase, which could potentially help improve management strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".