Distribution and environmental relationships of three species of wolffish <i>(Anarhichas</i> spp.<i>)</i> in the Gulf of St. Lawrence
Bibliographic record
Abstract
Abstract This study examines the spatial distribution of three species of wolffish in the Gulf of St. Lawrence on the basis of trawl surveys. A standardized method is proposed to assess species–habitat associations for the purpose of management and recovery of endangered marine species. Catch data (presence/absence in trawl sets) and landscape and environmental characteristics of the sea floor were aggregated using a common grid (100 km2 cells), and species–habitat relationships were explored using geospatial tools. Relative occurrence was lower and area of occupancy and concentration were much smaller for the northern wolffish (Anarhichas denticulatus) than for the spotted wolffish (Anarhichas minor), with the striped wolffish (Anarhichas lupus) being most widespread. Significant relationships were observed between values of the local spatial autocorrelation Gi* statistic and habitat descriptors for each of the three species. Hot spots for spotted and striped wolffish occurred in areas where a greater diversity of relief and habitats was found. They were associated with intermediate depths, coarse sediments and rock outcrops, and lower salinities and temperatures than for northern wolffish. Northern wolffish appeared to be associated mainly with the deep water sloped habitat bordering deep channels, whereas spotted and striped wolffish both concentrated most intensively into neighbouring deep water shelf habitats and relatively cold shallow to mid‐depth shelf habitats of the northern Gulf. The RDA analysis indicated a significant relationship between Gi* scores of the three species and environmental variables. The model explained 52% of the variability in the data; northern wolffish showed a distinct relationship compared with the two other species. The conservation of marine species and protection of their habitats pose a major challenge given the limited amount of information typically available on rare species and the scale and complexity of marine processes that affect those species. The broad‐scale approach presented here allows the provision of advice on important habitats on the basis of the best available knowledge. Copyright © 2013 Her Majesty the Queen in Right of Canada
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".