Species composition and habitat preferences of the nearshore fish fauna of Bonne Bay, Newfoundland
Bibliographic record
Abstract
Determining biodiversity baseline is the first step toward establishing species monitoring and conservation programs. In this study we report on a 10-year survey of the fish fauna of Bonne Bay, a fjord surrounded by the Gros Morne National Park on the west coast of Newfoundland, Canada. The objectives of our study were: 1) to determine the fish fauna of Bonne Bay using standardized sampling methods; 2) to gather information on the habitats of fishes of conservation concern; and 3) to provide baseline information on Bonne Bay as a potential candidate for a National Marine Conservation Area (NMCA). A survey of the fish fauna of the inner Bonne Bay was conducted each summer from 2002 to 2011 at multiple sites representing a range of fish habitats within the fjord. Sampling gears included two types of beach seine, gillnets with various mesh sizes and a bottom trawl. Species composition was statistically compared across sites within the fjord. We collected and identified 29 fish species from 17 families. Fish assemblages comprised anadromous, estuarine, and marine fish species, including a late-maturing type of winter skate ( Leucoraja ocellata ) that is rarely found in the adjacent waters of the northern Gulf of St. Lawrence. Similarity in species composition across sites reflected salinity, substrate composition, and presence of eelgrass ( Zostera marina ), but not the geographic distance between sites. Bonne Bay’s adjacency to a United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage Site, its diverse fish fauna that includes several species of conservation concern, its potential for education and enjoyment, and its stewardship by local people are suggestive of the future candidacy as a NMCA. The data presented here will help managers assess the potential of Bonne Bay as a National Marine Conservation Area.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".