Effects of Gill‐Net Fishing on Marine Birds in a Biological Hotspot in the Northwest Atlantic
Bibliographic record
Abstract
Marine biological hotspots, or areas where high abundances of species overlap in space and time, are ecologically important areas because energy flow through marine food webs, a key ecosystem process, is maximized in these areas. I investigated whether top predators aggregated at persistent spawning sites of a key forage fish species, capelin (Mallotus villosus), on the NE coast of Newfoundland during July and August 2000-2003. By examining the distributional patterns of top predators through ship-based surveys at multiple spatial and temporal scales, I found that the biomasses of birds-dominated by Common Murres (Uria aalge)-and mammals-dominated by whale species-were concentrated along the coast, with a biological hotspot forming near two persistent spawning sites of capelin in all years. The formation of this hotspot was well defined in space and time from middle of July to middle of August, likely coinciding with the spawning chronology of capelin. Within this hotspot, there was a high spatial and temporal overlap of Common Murres and gill nets set to capture Atlantic cod (Gadus morhua). This resulted in breeding murres becoming entangled in gill nets while feeding on spawning capelin. Despite an acknowledged uncertainty of bycatch mortality, estimates for the larger regional-scale area (1936-4973 murres/year; 0.2-0.6% of the breeding population) underestimated mortality relative to estimates within the hotspot (3053-14054 murres/year; 0.4-1.7%). Although fishing effort for Atlantic cod has declined substantially since the groundfish moratorium in 1992, chronic, unnatural, and additive mortality through bycatch continues in coastal Newfoundland. Restricted use of gill nets within this and other biological hotspots during the capelin spawning period appears to be a straightforward application of the "ecological and biologically significant area" management framework in Canada's Oceans Act. This protection would minimize murre bycatch and maintain ecosystem integrity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".