Atlantic Bluefin Tuna Habitat Utilisation and the Environmental Influences on Catch per Unit Effort in the Southern Gulf of St. Lawrence
Bibliographic record
Abstract
Commercial fisheries heavily exploit Atlantic bluefin tuna (Thunnus thynnus) and despite quota establishment and management under the auspices of the International Commission for the Conservation of Atlantic Tunas, the western population is estimated to have undergone little population growth. In contrast to other western bluefin-tuna fisheries, contemporary estimates of catch per unit effort (CPUE) in the southern Gulf of St. Lawrence are at record highs. This area contains the Cold Intermediate Layer (CIL), a striking feature in temperature with waters <3°C just 30-40m below the surface in September. Data from pop-up satellite archival tags deployed on bluefin tuna were examined to determine bluefin tuna habitat utilisation and diving behaviour the southern Gulf of St. Lawrence. The variation in oceanographic conditions of the bluefin tuna’s habitat and relationship between environmental variables and bluefin-tuna CPUE also were investigated using delta-lognormal modelling. There is considerable spatial and temporal variation of water-mass characteristics and the amount of available habitat in the southern Gulf of St. Lawrence (assuming a > 3°C thermal ambit) for bluefin tuna has been increasing. The percentage of the water column occupied by the CIL and presence of the CIL are significant variables in the delta-lognormal modelling of CPUE. The presence of the CIL is negatively correlated with the local occurrence of bluefin tuna (as indicated in the standardised CPUE), and also may impact the spatial extent of the fishery. Presenter Bio Angelia Vanderlaan is currently a Visiting Fellow at the St. Andrews Biological Station in New Brunswick, Canada, where she examines environmental influences on Atlantic bluefin tuna catch rates in the southern Gulf of St. Lawrence, as well as bluefin tuna habitat utilisation and diving behaviour. She completed her PhD in Biological Oceanography at Dalhousie University where her research focused on quantifying the risk to North Atlantic right whales from ocean-going vessels and fishing gear. This research was used in the design and justification for conservation initiatives to protect right whale from vessel strikes in Canadian waters. In recognition of the conservation efforts of her research, she was awarded the William T. Hornaday Conservation Award from the American Society of Mammalogists as well as a Canadian Whale Institute Award. She also has an MSc in Statistics from the University of Victoria and a BSc in Marine Biology and Statistics from Dalhousie University.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".