Effectiveness of lobster fisheries management in New Zealand and Nova Scotia from multi-species and ecosystem perspectives
Bibliographic record
Abstract
In New Zealand and Nova Scotia, lobster (Jasus edwardsii and Homarus americanus, respectively) is the most valuable export fishery. Although stock assessments and indicators assist in evaluating lobster fisheries, ecosystem effects are largely unknown, hindering ecosystem-based fisheries management (EBFM). We employed ecosystem models for the Cook Strait, New Zealand and western Scotian Shelf, Nova Scotia, Canada, to evaluate trade-offs between catches and ecosystem impacts in lobster fisheries from single- and multi-species perspectives. We ran simulations to independently determine exploitation rates that produced maximum sustainable yield (MSY) for lobster, and for all fished groups. We then ran simulations using these MSY exploitation rates simultaneously, and simulations to maximize multi-species MSY (MMSY). Our results indicate that current lobster exploitation rates in both regions are greater than those producing MSY, and have significant ecosystem impacts. Simulating multi-species fisheries, in both systems the sum of single-species MSY for all fished groups was less than the sum of catches where exploitation rates were run simultaneously. Runs maximizing MMSY across the entire ecosystem increased exploitation rates on many fished groups, and produced even greater total catch—yet with much greater ecological costs—and in Nova Scotia, collapses of sharks, large predators, and lobster themselves. As fisheries management moves towards multi-species and ecosystem-based approaches, we suggest that MMSY targets should be treated similarly to MSY—not as a target, but a limit. Even then, careful evaluation is required before implementation to ensure that there are no undesirable economic or ecological consequences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".