An evaluation of the opportunities and impediments in managing quota fisheries for biodiversity
Bibliographic record
Abstract
Emerging political, ecological, and social priorities support inclusion of biodiversity conservation on national research and management agendas.Meeting biodiversity conservation objectives, however, will be difficult for fishery management systems that traditionally rely on the single-species, single-population "stock concept".My dissertation examines four scientific and institutional challenges to broadening the scope of fisheries management to include controlling fishery impacts on biodiversity.First, despite broad recognition of its importance in ecosystems, there is no single definition of biodiversity that can be used in tactical fisheries management.I recommend extending single-species approaches to include diversity within populations across space as a first step toward biodiversity-based management.Second, many existing data collection programs are not structured to account for spatial diversity within fish populations.I use the case of Pacific herring (Clupea pallasi ) in the Strait of Georgia, British Columbia, to illustrate how a monitoring program designed to estimate biomass of a spatially structured population generates management vulnerabilities and opens the system to disputes over biodiversity conservation.Third, many fisheries management systems knowingly ignore spatial diversity in fish populations.The management implications may include a loss of biodiversity and over-fishing of certain components of the population.I developed a closed-loop simulation model based on the dynamics of British Columbia herring populations and iii fisheries to evaluate the consequences of single-species management of spatially diverse fish populations.I demonstrate that the impact of this approach can not be inferred from the characteristics of the population or the scale of management.Depending on the nature of the population and the fishery, well mixed populations may be more vulnerable to overfishing than spatially discrete populations.Fourth, reduced availability of funding for fisheries science may stifle innovation and reinforce the use of single-species approaches.I document shifts in Canadian science policy that have shifted the funding of public science in favor of oceans and ecosystems, and have required the fishing industry to offset cuts to fisheries science budgets.This funding model may restrict the nature and scope of fisheries research in 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".