Using fishers’ knowledge to determine the spatial extent of deep-water spawning of capelin (Mallotus villosus) in Newfoundland, Canada
Bibliographic record
Abstract
Introduction On the Newfoundland-Labrador Shelf, Canada, capelin ( Mallotus villosus ) is a key forage fish that migrates annually from offshore to spawn within coastal embayments. Although capelin are thought to primarily spawn on beaches in this region, they also spawn subtidally in deeper water (5–40 m), where their eggs remain throughout incubation. The spatial extent of subtidal (i.e. “deep-water”) spawning habitat in coastal Newfoundland is unknown and is a research priority for fishers and management. Methods We collaborated with capelin fishers to identify putative deep-water spawning sites as a first step in determining the contribution of deep-water spawning to capelin recruitment. Given limited fine-scale coastal bathymetry and seabed habitat type data, which impeded spatial modeling to determine suitable capelin spawning habitat, this science-industry research collaboration was key to addressing this knowledge gap. Results Through two years of multi-bay fisher interviews, 84% of interviewed fishers (56 interviewees) reported having observed deep-water spawning and identified a broad distribution of putative spawning sites throughout coastal Newfoundland. The majority of fishers indicated inter-annual variation in beach and deep-water spawning habitat use, and most interviewees linked this variation to temperature and capelin abundance. Further collaborations with fishers during boat-based surveys, we sampled 136 unique sites within 12 search areas in eastern Placentia Bay and 26 unique sites within six search areas in Bonavista Bay. Underwater video surveys combined with sediment sampling revealed seven previously undocumented deep-water spawning sites. Conclusion The deep-water spawning areas derived from these fisher interviews can now be used to build a time series for monitoring capelin spawning habitat use alongside citizen-based beach monitoring data, as a general capelin stock health indicator in a weight of evidence approach for the science advisory process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".