Bibliographic record
Abstract
This thesis provides the first direct comparison between – and integration of – community-based and science-based approaches to the establishment of marine protected areas (MPAs). MPAs are one potentially effective conservation tool, but are being established very slowly. My research shows that community involvement in placing MPAs can help meet many ecological goals, although biophysical data improve the conservation value of sitings. To assess the need for MPAs in British Columbia (BC), Canada, I mapped stressors resulting from human activities. This produced a powerful rationale for MPAs: very little of the ocean, and almost none of the continental shelf of BC, lies beyond the reach of human stressors. My work helps reconcile differing perspectives about the efficacy of community-based vs. science-based MPA selection. I explored and analyzed these approaches, separately and together, in two areas in BC. First, I generated a community-based plan for MPA placement through partnerships with two First Nations (indigenous peoples) in BC. They offered strong support for spatial protection measures, and individuals nominated overlapping areas. Second, I applied a decision support tool (Marxan) to determine MPA placement under scientific precepts. Conservation planning usually lacks detailed ecological information but the Marxan approach was robust to some missing data; in such cases, it was best to use available abiotic and biotic data to ensure that both habitats and species were represented. Third, I integrated community-based and science-based approaches, to find that they verified and complemented each other. Indeed, an integration of the two was preferred by participants and also achieved all conservation objectives. Finally, I took a novel and pragmatic approach to ocean zoning. I used spatial data for thirteen commercial fisheries on Canada’s west coast to select areas where fishing should be permitted, rather than prohibiting fishing under a MPA paradigm. The results revealed that small reductions in fisheries yields, if judiciously selected, could allow creation of large unfished areas that embraced diverse biophysical regions and habitat types. Such a pragmatic approach could achieve remarkable conservation gains.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".