Ask the locals: application of fishers ecological knowledge to characterize use of a previously collapsed spawning ground by Atlantic herring ( <i>Clupea harengus</i> )
Bibliographic record
Abstract
Reproduction is a key component of species life history, and understanding spawning habitat, location, and timing is essential for effective population monitoring, conservation, and management. When a spawning ground of a fish population collapses, often monitoring resources are directed away from the area, resulting in a knowledge gap. Fishers who operate on these spawning grounds have extensive local knowledge and can see changes occurring in species occupancy and spawning activity in real time. This study integrates fishers’ ecological knowledge and newly established scientific monitoring to characterize the spawning habitat, locations, and timing of southern Gulf of St. Lawrence spring-spawning Atlantic herring ( Clupea harengus) around the Magdalen Islands, a historically critical but collapsed spawning ground. Fisher interviews revealed key spawning locations and identified eelgrass and macroalgae as primary spawning substrates. Fishers reported that spawn timing has remained relatively stable, most commonly occurring between 15 April and 1 May. A confirmed spawning event in Havre Aubert on 21 April 2024 reinforced fisher observations and provided direct evidence that Atlantic herring continue to reproduce around the Islands. Overall, this study illuminates the value of integrating local ecological knowledge with scientific information to better understand the species ecology and inform conservation and management action.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".