Nonlinearity, irreversibility and surprise - managing Atlantic cod under climate change
Bibliographic record
Abstract
Climate change is at the forefront of today’s global challenges with its potential to turn into a runaway process. Fishing pressure acts in concert and exacerbates the impacts of climate change. The North Atlantic Ocean is no exemption of the increasing anthropogenic stress with Atlantic cod, Gadus Morhua, one of its most prominent fish species, displaying the ocean’s state. Most Atlantic cod stocks have experienced high rates of fishing and biomass declines, leading to renovation of fishing regulations and the implementation of rebuilding strategies. Today, the cod stocks differ considerably in trends and commercial status with 8 stocks considered collapsed and 57 % of today’s landings supplied by one single stock, the North East Arctic cod. \nWhat drives the collapse and what drives the recovery of a stock? Elucidating drivers of Atlantic cod productivity at low abundance is inevitable for sustainably managing the species in its changing habitat. This thesis attempts a comprehensive study on climate change impacts by addressing rising ocean temperature (paper I-III), temperature variability (paper II), acidification (paper III) and uncertainty (of the biology and as risk in management under the precautionary approach [paper IV]). Individual and synergistic impacts of climate change are discussed with a particular focus on nonlinear dynamics, including the potential for Allee effects (paper I-III). Allee effects describe the decrease in per capita growth rate at small population size, which can hinder population recovery by reinforcing degradation. Such a shift in the underlying biology can be irreversible and demands proactive and precautionary management measures. \nApplication of precautionary measures to protect the environment and manage risks in situations of high uncertainty is a central tenet of the “precautionary approach”, a guiding principle in fisheries management. The poor state of various commercial fish stocks worldwide stands in contrast to the precautionary approach and suggests a subordinate role of science in fisheries management. In paper IV, Canada’s fisheries policy and advisory process is contrasted with the EU’s Common Fisheries Policy in regard to the precautionary approach and the role of science, in order to identify policy and institutional constraints that have hindered sustainable, precautionary management practices. \nDrawing from insights on climate change driven productivity changes (paper I-III) and the importance of a policy and institutional framework that acknowledges these (paper IV), this thesis ends with suggestions for scientifically informed, precautionary and sustainable fisheries management practices that can speed up recovery and allow for a vital fishery in the future.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".